system

Abstract

We provide a system to enable user-optimized coaching. [Solution] A system comprising: means for having the user input authentication information and sending it to a server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for having the user input consultation content as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the most suitable coach type from multiple coach types based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type, and send and display it to the terminal; means for collecting feedback from the user and sending it to the server; and means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional coaching systems, the support for users is unified, and it is difficult to provide appropriate coaching according to individual needs. Also, many systems cannot fully utilize the past behaviors and feedback of users, resulting in the problem that the quality of coaching does not improve. Furthermore, there is a problem that the interface for users to receive specific instructions and support for consultation content is insufficient, leading to low user satisfaction.

Means for Solving the Problems

[0005] This invention provides a system that enables smooth user authentication and input of consultation content by comprising means for having the user input authentication information and sending it to a server, means for the server to compare the authentication information with a database and send the authentication result to a terminal, and means for having the user input consultation content as text or voice data and sending it to the server. Furthermore, by comprising means for the server to analyze the consultation content, select the most suitable coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal, the system can provide coaching that is best suited to individual needs. In addition, by comprising means for generating an appropriate response using a Natural Language Processing engine based on the coach type selected by the server and sending it to the terminal, and means for displaying the response to the user on the terminal, the system can provide specific and appropriate coaching in real time. Furthermore, by comprising means for collecting user feedback and sending it to the server, and means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching, the system can provide a system that can continuously improve the quality of coaching.

[0006] "User authentication" is a procedure to verify that a user is a legitimate user when accessing a system.

[0007] A "terminal" is a device (e.g., computer, smartphone, tablet) used by a user to access a system and input information.

[0008] A "server" is a central computer system that handles data processing for the entire system, database management, and user requests.

[0009] A "database" is a system for systematically storing data such as user information, consultation details, and feedback.

[0010] "Authentication information" refers to the ID, password, or other identifying information that a user provides when accessing the system.

[0011] A "Natural Language Processing engine" is an algorithm and software that analyzes text and audio data entered by a user and generates appropriate responses.

[0012] "Consultation content" refers to questions, problems, or requests that users input into the system via text or voice.

[0013] "Coach types" refer to four coaching styles based on the Path-Goal theory: directive, supportive, participatory, and achievement-oriented.

[0014] "Feedback" refers to users providing an evaluation of whether the coaching system's responses were helpful.

[0015] "Improving coaching accuracy" refers to efforts to improve the system's algorithms and response quality based on collected user feedback. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

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

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

[0019] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[0038] 1. User Authentication:

[0039] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby completing the user authentication process.

[0040] 2. Enter your consultation details:

[0041] The terminal provides an interface for the user to input their consultation details. The user inputs their consultation details via text chat or voice input, and the terminal sends the content to the server. The server passes the received consultation details to a Natural Language Processing (NLP) engine, which then analyzes the text.

[0042] 3. Selecting a coach type:

[0043] The server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type using a coach type selection algorithm. The server then presents the four selected coach types (directive, supportive, participatory, and achievement-oriented) to the user's terminal.

[0044] The user selects their preferred coach type from the options presented, and the process is completed when they send their selection from their device to the server.

[0045] 4. Consultation support:

[0046] The server uses an NLP engine to analyze the consultation content in order to generate an appropriate response based on the selected coach type. For example, in the case of an directive coach, specific steps and action plans will be presented. The server sends the generated response to the terminal, which then displays the response to the user.

[0047] 5. Gathering feedback:

[0048] The device provides an interface for collecting user feedback. Users input feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0049] Specific example:

[0050] 1. The user logs into the system:

[0051] The device prompts the user to enter their ID and password.

[0052] The user enters "user123" and "password," and the terminal sends this information to the server.

[0053] The server compares the authentication information with the database and notifies the terminal of successful authentication.

[0054] The device displays a message to the user indicating successful authentication.

[0055] 2. The user enters the details of their inquiry:

[0056] In the chat interface displayed on the device, the user typed, "I don't know how to proceed with the new project."

[0057] The terminal sends this text to the server.

[0058] The server uses an NLP engine to analyze the consultation content.

[0059] 3. Selecting a coach type:

[0060] The server recommends "directive coaching" based on past history.

[0061] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[0062] The device displays the coach type to the user.

[0063] The user selects "directive coaching," and the device sends that information to the server.

[0064] 4. Consultation support:

[0065] The server generates responses from the instructional coach.

[0066] It generates specific answers such as, "For the new project, first do A, and then do B."

[0067] The server sends the generated response to the terminal.

[0068] The device displays the answer to the user.

[0069] 5. Gathering feedback:

[0070] The device displays the message, "Was this answer helpful?"

[0071] The user enters "Yes" as feedback.

[0072] The device sends feedback to the server.

[0073] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0074] In this way, specific processes are carried out at each step, enabling effective coaching tailored to the user.

[0075] The following describes the processing flow.

[0076] Step 1:

[0077] The device prompts the user to enter authentication information. Specifically, the device displays an ID and password input form, and the user enters this information.

[0078] Step 2:

[0079] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[0080] Step 3:

[0081] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[0082] Step 4:

[0083] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[0084] Step 5:

[0085] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[0086] Step 6:

[0087] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[0088] Step 7:

[0089] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[0090] Step 8:

[0091] The server passes the received consultation content to the NLP engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[0092] Step 9:

[0093] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[0094] Step 10:

[0095] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[0096] Step 11:

[0097] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[0098] Step 12:

[0099] The user selects their preferred coach type, and the device sends this selection to the server.

[0100] Step 13:

[0101] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation.

[0102] Step 14:

[0103] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[0104] Step 15:

[0105] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[0106] Step 16:

[0107] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[0108] (Example 1)

[0109] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0110] In modern coaching systems, there is a challenge in providing the optimal coaching method based on the individual needs and past consultation history of each user. Furthermore, there are insufficient means to effectively collect and analyze user feedback and utilize it to improve the accuracy of future coaching. Given this situation, a new system is needed to realize coaching optimized for each user.

[0111] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0112] In this invention, the server includes means for receiving authentication information from the user and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for receiving consultation content from the user as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using the Natural Language Processing engine of a generation AI model based on the selected coach type and send it to the terminal; means for collecting feedback from the user and sending it to the server; and means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching. This enables the provision of individually optimized coaching to the user and continuous improvement of accuracy.

[0113] "Authentication information" refers to information such as IDs and passwords that users provide to access a system.

[0114] A "server" is a computer system that receives requests from users via a network and processes and stores data.

[0115] A "database" is a structured information system for efficiently storing, managing, and retrieving data in digital format.

[0116] A "terminal" is a computing device used by a user to interface with a system. Specifically, this includes personal computers and smartphones.

[0117] "Consultation content" refers to the problems, questions, and requests for advice that users provide to the system.

[0118] A "Natural Language Processing engine" is an artificial intelligence technology that analyzes natural language and understands its meaning and context.

[0119] "Coaching style" refers to the approach style used when providing problem-solving or guidance methods to users. Specifically, it includes directive, supportive, participatory, and achievement-oriented styles.

[0120] A "generative AI model" is a pre-trained artificial intelligence model that has the ability to generate and analyze data for a specific task.

[0121] "Feedback" refers to information that includes evaluations and suggestions for improvement regarding the answers and information provided by users.

[0122] A "prompt statement" is an instruction or question that is entered to operate a generative AI model.

[0123] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[0124] First, in order for a user to access the system, the user must enter authentication information using a terminal. This authentication information includes a user ID and password, which the terminal sends to the server. The server verifies this authentication information against a database and sends the authentication result back to the terminal. The terminal then displays the result to the user.

[0125] Next, the user enters their consultation details using the system. The terminal offers the user the option of using either a chat interface or voice input. Once the user enters their consultation details, the terminal sends them to the server. The server then passes the received consultation details to the Natural Language Processing engine of the AI ​​model, which analyzes the text.

[0126] Once the analysis is complete, the server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type. There are four coach types: directive, supportive, participatory, and achievement-oriented. The selected coach type is presented on the terminal, and the user chooses their preferred coach type. The terminal then sends this selection information to the server.

[0127] Next, the server uses the Natural Language Processing engine of its generative AI model to generate an appropriate response based on the selected coach type. For example, if a user asks, "I don't know how to proceed with a new project," a directive coach would provide specific steps such as, "For a new project, first do A, and then do B." The server sends the generated response to the device, which then displays the response to the user.

[0128] Furthermore, users can provide feedback on the provided answers. The device displays a feedback collection interface and sends the user's feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0129] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0130] "The user is seeking advice on how to proceed with a new project. They have selected a directive coach. Please provide specific steps and an action plan."

[0131] This system allows us to provide personalized coaching to each user and continuously improve the system based on the results.

[0132] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0133] Step 1:

[0134] The user enters their authentication information. This information includes the user ID and password.

[0135] Step 2:

[0136] The terminal receives the user's authentication information and sends it to the server. The entered data is encrypted and transmitted to the server.

[0137] Step 3:

[0138] The server compares the received authentication information with the database. It checks if it matches the user information stored in the database and generates a result. The matching result is output as authentication success or failure.

[0139] Step 4:

[0140] The server sends the authentication result to the terminal. If authentication is successful, user information is also sent.

[0141] Step 5:

[0142] The terminal receives the authentication result from the server and displays the result to the user. If successful, it displays "Login successful"; if unsuccessful, it displays "Login failed".

[0143] Step 6:

[0144] The terminal provides an interface for the user to input their consultation details. This interface can be either a chat box or voice input.

[0145] Step 7:

[0146] The user enters the details of their inquiry. For example, they might enter a specific problem such as "I don't know how to proceed with a new project" into the text box.

[0147] Step 8:

[0148] The terminal receives the entered consultation content and sends it to the server. Text-based content is sent as is, while voice input is converted to text before transmission.

[0149] Step 9:

[0150] The server passes the received consultation content to the NLP engine for analysis. Through this analysis, the meaning and context of the user's consultation content are understood and stored as internal data.

[0151] Step 10:

[0152] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary data to understand the user's background.

[0153] Step 11:

[0154] The server selects the optimal coach type. Based on the acquired profile information and past consultation history, it runs an algorithm to determine the most suitable coach type from four options: directive, supportive, participatory, and achievement-oriented.

[0155] Step 12:

[0156] The server sends the selected coach type to the terminal. This information includes four coach types to present to the user.

[0157] Step 13:

[0158] The device displays four coaching types to the user. It provides an interface that allows the user to select one.

[0159] Step 14:

[0160] The user selects their preferred coach type. Once the selection is complete, press the "Confirm" button.

[0161] Step 15:

[0162] The terminal sends the user's selection results to the server. Data based on the user's selection is transmitted to the server.

[0163] Step 16:

[0164] The server uses an NLP engine to generate appropriate responses based on the selected coach type. For example, in the case of an instructional coach, specific steps and action plans will be created.

[0165] Step 17:

[0166] The server sends the generated response to the terminal. The response is sent in text format.

[0167] Step 18:

[0168] The device displays the received response to the user. Specific steps and action plans are displayed in the chat box. For example, "For new projects, first do A, and then do B."

[0169] Step 19:

[0170] The device displays a feedback collection interface to the user. For example, it might display a question such as, "Was this answer helpful?"

[0171] Step 20:

[0172] Users provide feedback. For example, they can select "yes" or "no," or enter specific comments.

[0173] Step 21:

[0174] The device sends feedback to the server. User feedback data is sent to the server.

[0175] Step 22:

[0176] The server records the feedback it receives in a database. This feedback data is saved to improve the accuracy of future coaching.

[0177] (Application Example 1)

[0178] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0179] This invention relates to a system that responds quickly and accurately to user inquiries. Conventional methods have been time-consuming and labor-intensive in dealing with security system problems and questions. Furthermore, the process of generating appropriate answers based on user inquiries sometimes failed to adequately consider the user's individual circumstances and past inquiry history. To solve these problems, this invention provides a system that encompasses everything from user authentication to coach type selection, answer generation, and feedback collection.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0181] In this invention, the server includes means for prompting the user to input authentication information and transmitting it; means for comparing the authentication information with a database and transmitting the authentication result to the terminal; and means for prompting the user to input consultation content as text or voice data and transmitting it. This makes it possible for users to easily and quickly consult about security systems. Furthermore, the server includes means for analyzing the consultation content, selecting and presenting the optimal coach type based on the user's profile and past consultation history; means for generating and transmitting an appropriate response using a natural language processing engine based on the selected coach type; means for displaying the response to the user on the terminal; means for collecting and transmitting feedback from the user; means for recording the received feedback in a database and using it to improve the accuracy of future coaching; and means for generating a response to the inquiry content on the server and displaying the response result on the terminal when the consultation content is transmitted. This enables a quick and accurate response to the consultation content and can increase user satisfaction.

[0182] User authentication is the process of verifying the identity of a user accessing a system and confirming that the user has legitimate authority.

[0183] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[0184] A "server" is a central processing unit that processes information sent by users, performs necessary database matching and analysis, and provides the results.

[0185] "Coaching style" refers to a method of providing advice and solutions to a user's concerns using one of the following styles: directive, supportive, participatory, or achievement-oriented.

[0186] A "natural language processing engine" is an artificial intelligence technology that analyzes user input (text or speech), understands its meaning and intent, and generates appropriate responses.

[0187] "Feedback" refers to evaluations and opinions that users give regarding the answers and services they receive, and these are used to improve the system.

[0188] A "database" is a system that stores data, such as user profile information and past consultation history, in a searchable and retrieval format.

[0189] A "terminal" is a device (such as a smartphone, tablet, or personal computer) used by a user to input information and receive results.

[0190] "Consultation content" refers to the questions or problems that users enter into the system.

[0191] "Answer generation" is the process by which a system derives appropriate solutions or advice in response to a user's question or problem.

[0192] This invention relates to a system that includes a series of processes, from user authentication to analysis of consultation content, selection of coach type, generation and display of appropriate answers, collection of feedback, and system improvement based on that feedback. To realize this system, the following programs, hardware, and software are used.

[0193] The server compares the authentication information entered by the user against a database to perform user authentication. This verifies that the user is legitimate, and the server then sends the authentication result to the terminal.

[0194] The terminal provides an interface that allows the user to input their consultation details. The text or voice data entered by the user is sent from the terminal to the server. The server uses a natural language processing engine (e.g., BERT, SpaCy) to analyze the received consultation details.

[0195] The server selects the most suitable coach type based on the user's profile and past consultation history. The selected coach type is then chosen from directive, supportive, participatory, and achievement-oriented, and presented on the user's device. The user can then choose the appropriate coach type from the options presented.

[0196] Next, the server generates an appropriate response based on the selected coach type. Using a natural language processing engine, it constructs specific steps and advice for the user's consultation. The generated response is sent to the terminal and displayed to the user.

[0197] The device collects user feedback and sends it to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0198] As a concrete example, consider the following prompt message:

[0199] 1. User Authentication:

[0200] The prompt message to send to the authentication server after entering the user ID is: "User ID: security_user123, Password: securepassword"

[0201] If authentication is successful: "Authentication successful, welcome."

[0202] 2. Input and analysis of consultation details:

[0203] User prompt to enter their question: "My security camera is not transmitting video. What should I do?"

[0204] The server analyzes the inquiry and generates an appropriate response: "Access the security camera settings screen and check the network connection. Then, restart the camera."

[0205] In this way, the system of the present invention can respond quickly and accurately to user inquiries. This improves user satisfaction and streamlines system troubleshooting.

[0206] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0207] Step 1:

[0208] User Authentication

[0209] Input: Enter your User ID and password into the terminal.

[0210] Operation: The terminal sends the authentication information entered by the user to the server. The server compares the authentication information with the database and generates an authentication result.

[0211] Output: The authentication result (success or failure) is sent to the terminal, and the terminal displays the authentication result to the user.

[0212] Step 2:

[0213] Enter your consultation details

[0214] Input: The user enters their inquiry details as text or voice data into the chat interface.

[0215] Operation: The terminal sends the entered consultation content to the server. The server passes the received data to the natural language processing engine.

[0216] Output: Analyzed consultation content. The NLP engine converts the text into structured data and returns it to the server.

[0217] Step 3:

[0218] Choosing a Coach Type

[0219] Input: Analyzed consultation content, user profile information, past consultation history.

[0220] Operation: The server uses a coach type selection algorithm to select an appropriate coach type (directive, supportive, participatory, achievement-oriented) and presents it to the terminal.

[0221] Output: Selection result for the appropriate coach type.

[0222] Step 4:

[0223] Choosing a Coach Type

[0224] Input: User selection of coach type.

[0225] Operation: The terminal sends the coach type selected by the user to the server. The server prepares the following actions based on the selected coach type.

[0226] Output: Selected coach type.

[0227] Step 5:

[0228] Answer generation

[0229] Input: Selected coach type, analyzed consultation content.

[0230] Operation: The server uses a natural language processing engine to generate appropriate responses based on the selected coach type. Specifically, it provides specific instructions for directive coaches, advice for supportive coaches, suggestions for collaborative work for participatory coaches, and goal setting for achievement-oriented coaches.

[0231] Output: Generated answer.

[0232] Step 6:

[0233] Display the answer

[0234] Input: Generated response sent from the server.

[0235] Operation: The device displays the received response to the user. Specific steps and advice are displayed in text format.

[0236] Output: The answer displayed to the user.

[0237] Step 7:

[0238] Gathering feedback

[0239] Input: User feedback (whether the answer was helpful, specific comments, etc.).

[0240] Operation: The terminal collects feedback from the user and sends it to the server. The server records the feedback in a database and analyzes it to improve the accuracy of future coaching.

[0241] Output: Feedback recorded in the database.

[0242] Through the steps outlined above, a system is created that responds quickly and accurately to user inquiries. This system includes user authentication, analysis of the inquiry content, selection of the optimal coach type, generation and display of appropriate answers, and collection and analysis of feedback.

[0243] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0244] This invention is a coaching system that combines an emotion engine that recognizes the user's emotions. This system is implemented by the user, terminal, and server performing the following procedures and roles.

[0245] 1. User Authentication:

[0246] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby authenticating the user.

[0247] 2. Enter your consultation details:

[0248] The terminal displays an interface that prompts the user to input their consultation details. The user inputs the consultation details as text or voice data, and the terminal sends it to the server. The server passes the received data to the NLP engine and emotion engine, which analyze the text and emotions.

[0249] 3. Selecting a coach type:

[0250] The server retrieves the user's profile information and past consultation history from the database and uses a coach type selection algorithm to choose the most suitable coach type from four options (directive, supportive, participatory, and achievement-oriented). The user's emotional information, as recognized by the emotion engine, is also taken into consideration during this process.

[0251] The server displays the selected coach types to the terminal, and the user selects their preferred coach type. The process is completed when the terminal sends the selection result to the server.

[0252] 4. Consultation support:

[0253] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation. For example, in the case of an directive coach, specific steps and action plans will be presented. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine. The server sends the generated response to the terminal, which then displays the response to the user.

[0254] 5. Gathering feedback:

[0255] The device displays an interface for collecting user feedback. The user enters feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0256] Specific example:

[0257] 1. The user logs into the system:

[0258] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[0259] The device sends this information to the server.

[0260] The server checks against the database and notifies the terminal of successful authentication.

[0261] The device displays a message to the user indicating successful authentication.

[0262] 2. The user enters the details of their inquiry:

[0263] In the chat interface displayed on the device, the user types, "I don't know how to proceed with the new project."

[0264] The terminal sends this text to the server.

[0265] The server uses an NLP engine and an emotion engine to analyze the content and emotions. The emotion engine detects "anxiety."

[0266] 3. Selecting a coach type:

[0267] The server recommended a "supportive coach" based on past history.

[0268] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[0269] The device displays the coach type to the user.

[0270] The user selects "supportive coaching," and the device sends that information to the server.

[0271] 4. Consultation support:

[0272] The server generates a supportive coach response, such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," taking into account the results of the emotion engine.

[0273] The server sends the generated response to the terminal.

[0274] The device displays the answer to the user.

[0275] 5. Gathering feedback:

[0276] The device displays the message, "Was this answer helpful?"

[0277] The user enters "Yes" as feedback.

[0278] The device sends feedback to the server.

[0279] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0280] In this way, specific processing is carried out at each step, enabling effective coaching tailored to the user. Furthermore, the emotion engine takes into account the user's emotional state, allowing for even more personalized support.

[0281] The following describes the processing flow.

[0282] Step 1:

[0283] The terminal prompts the user to enter authentication information. Specifically, the terminal displays input forms for the ID and password, and the user enters this information.

[0284] Step 2:

[0285] The terminal sends the authentication information entered by the user to the server. The authentication information is sent using a secure communication protocol.

[0286] Step 3:

[0287] The server compares the received authentication information with the database. The server compares the user information stored in the database with the sent authentication information to check for a match.

[0288] Step 4:

[0289] The server sends the verification result to the terminal. If the authentication is successful, a success message is sent; if it fails, an error message is sent.

[0290] Step 5:

[0291] The terminal displays the authentication result to the user. If the authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[0292] Step 6:

[0293] The terminal displays an interface that prompts the user to enter consultation content. This includes a text chat input field and a voice input button, etc.

[0294] Step 7:

[0295] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[0296] Step 8:

[0297] The server passes the received consultation content to an NLP (Natural Language Processing) engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[0298] Step 9:

[0299] The server simultaneously uses an emotion engine to analyze the user's emotions from the consultation content and voice data. The emotion engine identifies the user's emotions from factors such as the tone of voice and the wording used in the text.

[0300] Step 10:

[0301] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[0302] Step 11:

[0303] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented). In this process, the user's emotional information, as recognized by the emotion engine, is also taken into consideration.

[0304] Step 12:

[0305] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[0306] Step 13:

[0307] The user selects the coach type they desire, and the terminal sends the selection result to the server.

[0308] Step 14:

[0309] Based on the selected coach type, the server uses the NLP engine to generate an appropriate response to the consultation content. The response is adjusted considering the user's emotions (e.g., stress, anxiety, joy) recognized by the emotion engine.

[0310] Step 15:

[0311] The server sends the generated response to the terminal. The terminal displays the response to the consultation content to the user.

[0312] Step 16:

[0313] The terminal displays an interface to collect feedback from the user. The user inputs the feedback, and the terminal sends it to the server.

[0314] Step 17:

[0315] The server records the received feedback in the database. The collected feedback is used to improve future coaching accuracy.

[0316] Specific example:

[0317] 1. Steps 1 to 5: The user logs in to the system

[0318] The terminal displays an input form for the ID and password, and the user enters "user123" and "password".

[0319] The terminal sends this information to the server. The server checks it against the database and notifies the terminal of successful authentication. The terminal displays the successful authentication to the user.

[0320] 2. Steps 6 to 7: The user enters the consultation details.

[0321] In the chat interface on the device, the user types "I don't know how to proceed with the new project," and the device sends the text data to the server.

[0322] 3. Steps 8 to 9: Analysis of the consultation content and emotions

[0323] The server uses an NLP engine to analyze the text and extract key keywords and phrases. Simultaneously, an emotion engine detects "anxiety."

[0324] 4. Steps 10 to 11: Selecting a Coach Type

[0325] The server retrieves the user's profile information and past consultation history and recommends a "supportive coach." It also takes into account the user's "anxiety" as analyzed by an emotion engine.

[0326] 5. Steps 12 to 13: Coach type presentation and selection

[0327] The server sends four coach types to the terminal. The terminal displays the coach types to the user, who then selects "Supportive Coach" and sends it to the server.

[0328] 6. Steps 14 to 15: Consultation

[0329] The server generates a supportive coach response. It generates specific responses such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," and adjusts them while taking into account the anxieties recognized by the emotion engine. The server sends the generated response to the device, and the device displays the response to the user.

[0330] 7. Steps 16 to 17: Gathering Feedback

[0331] The device displays "Was this answer helpful?", and the user types "Yes".

[0332] The device sends feedback to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0335] In modern society, there is a need for coaching systems that can effectively address the problems and stresses individuals face. Traditional systems provide uniform advice without considering the user's emotional state, resulting in a lack of appropriate support tailored to individual needs and emotions. Furthermore, there are insufficient mechanisms to effectively incorporate user feedback and improve the system's accuracy. As a result, the effectiveness of coaching for users decreases, and their satisfaction levels decline.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for the user to input consultation content as text or voice data and send it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and means for the server to analyze the user's emotions using an emotion recognition engine and reflect it in the selection of the coach type and the generation of the response. This enables effective coaching tailored to the user's individual needs and emotional state.

[0338] "Authentication information" refers to information such as the ID and password that a user enters to log in to a system.

[0339] A "server" is a computer system that processes requests from users, performs authentication, analyzes data, generates responses, and sends them.

[0340] A "terminal" is a device that a user uses to access the system, enter authentication information, input consultation details, view answers, and provide feedback.

[0341] A "database" is a data storage system that stores and manages user authentication information, profile information, consultation history, feedback, and other data.

[0342] A "Natural Language Processing engine" is software that analyzes text data entered by the user and understands its context and meaning.

[0343] An "emotion recognition engine" is software that identifies and analyzes a user's emotional state based on their input data and consultation content.

[0344] "Coaching style" refers to the style of coaching provided in response to a consultation, and includes four types: directive, supportive, participatory, and achievement-oriented.

[0345] "Feedback" refers to the evaluations and opinions that users provide regarding the system's responses.

[0346] The "coach type selection algorithm" is a computational method for selecting the optimal coach type based on the user's profile information, past consultation history, and emotional state.

[0347] This invention is a coaching system that combines an emotion recognition engine to recognize the user's emotions. This system is implemented through the specific roles of the user, terminal, and server. The details of the system's program processing are described below.

[0348] Hardware and software to be used

[0349] The server is responsible for the main processing of the coaching system and houses the database, Natural Language Processing engine (NLP engine), and emotion recognition engine.

[0350] A terminal is a device that a user uses to access the system, and examples include PCs, smartphones, and tablets.

[0351] The database is used to store user authentication information, profile information, consultation history, and feedback.

[0352] An NLP engine is software that analyzes text data entered by a user to understand its context and meaning.

[0353] An emotion recognition engine is software that identifies and analyzes an emotional state from user input data and consultation content.

[0354] Specific example of processing

[0355] 1. User Authentication

[0356] The device displays a login screen and prompts you to enter your ID and password (e.g., enter "user123" and "password").

[0357] The device sends the entered authentication information to the server.

[0358] The server compares the authentication information with the database and sends the authentication result to the terminal.

[0359] The device displays the authentication result to the user (e.g., displays a "Authentication successful" message).

[0360] 2. Enter the details of your consultation.

[0361] The terminal displays an interface that prompts the user to input their inquiry details. Text or voice input is available.

[0362] The user enters the details of their inquiry (e.g., "I don't know how to proceed with a new project").

[0363] The terminal sends the entered consultation details to the server.

[0364] The server uses an NLP engine and an emotion recognition engine to analyze text and emotions (e.g., detects that the emotion is "anxiety").

[0365] 3. Selecting a Coach Type

[0366] The server retrieves the user's profile information and past consultation history from the database.

[0367] The server executes a coach type selection algorithm and chooses the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[0368] The server sends the selection results to the terminal and presents them to the user (e.g., "Supportive Coach" is recommended).

[0369] The user selects a coaching type, and the device sends the result to the server.

[0370] 4. Consultation support

[0371] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation (e.g., "First, consult with the team to gather their opinions, and then we'll make a plan").

[0372] The server adjusts its response, taking into account the results of the emotion recognition engine.

[0373] The server sends the generated response to the terminal.

[0374] The device displays the answer to the user.

[0375] 5. Gathering feedback

[0376] The device displays an interface for collecting user feedback (e.g., "Was this answer helpful?").

[0377] The user enters feedback (e.g., answers "Yes").

[0378] The device sends feedback to the server.

[0379] The server records the feedback in a database, which is then used to improve the accuracy of future coaching sessions.

[0380] Example of a prompt

[0381] "I don't know how to proceed with the new project."

[0382] "Could you tell me about your role as a team leader?"

[0383] As described above, a coaching system incorporating an emotion recognition engine can provide personalized responses by considering the user's consultation content and emotional state. Furthermore, user feedback can be recorded in a database and used to improve the accuracy of future coaching.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1: User Authentication

[0386] Input: The user enters their ID and password into the terminal ("user123" and "password").

[0387] Operation: The terminal displays the login screen and sends the user's input to the server.

[0388] Data processing: The server executes an SQL query to compare the authentication information received with the database.

[0389] Output: The server generates an authentication result (success or failure) and sends it to the terminal.

[0390] Specific action: The device displays the authentication result to the user (e.g., displays the message "Authentication successful").

[0391] Step 2: Enter your consultation details

[0392] Input: The user enters their question as text or voice data into their device (e.g., "I don't know how to proceed with the new project").

[0393] Operation: The terminal displays the user's inquiry details on an input screen, and after the user completes the input, it sends it to the server.

[0394] Data Processing: The server passes the received consultation content to the NLP engine and emotion recognition engine, which analyze the text and emotions. The NLP engine performs contextual analysis, and the emotion recognition engine detects emotions such as "anxiety."

[0395] Output: Generates analysis results (contextual information and emotional state) and stores them internally for use in the next step of processing.

[0396] Specific operation: The server uses an NLP engine and an emotion recognition engine to analyze the consultation content and emotional state.

[0397] Step 3: Selecting a Coach Type

[0398] Input: The server retrieves the user's profile information, past consultation history, and sentiment analysis results from the database.

[0399] Operation: The server executes a coach type selection algorithm and selects the optimal coach type (e.g., "Supportive").

[0400] Data processing: The server calculates the optimal coach type based on the data above. Emotional information is also taken into consideration.

[0401] Output: Generate selection results and send them to the terminal.

[0402] Specific operation: The server sends the result of selecting a coach type to the terminal, and the terminal displays the selection result to the user (e.g., presents "Supportive Coach").

[0403] Step 4: Consultation

[0404] Input: The user selects one of the coach types presented on the device.

[0405] Operation: The terminal sends the user's selection to the server.

[0406] Data Processing: The server uses an NLP engine based on the selected coach type to generate appropriate answers to the user's questions (e.g., "To move forward with a new project, first consult with the team to gather opinions, and then create a plan").

[0407] Output: Sends the generated response to the device.

[0408] Specific operation: The server adjusts the response based on the results of the emotion recognition engine and sends it to the terminal, which then displays the response to the user.

[0409] Step 5: Gathering Feedback

[0410] Input: The user enters their feedback on the feedback input screen displayed on their device (e.g., enters "Yes").

[0411] Operation: The device sends user feedback to the server.

[0412] Data processing: The server stores the received feedback in a database and analyzes it to improve the accuracy of future coaching.

[0413] Output: The feedback is saved to the database and used as data for future improvements.

[0414] Specific operation: The server records feedback and uses it as new training data as needed.

[0415] (Application Example 2)

[0416] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0417] In today's diverse work environments, workers frequently encounter new ways of operating machinery and tools, which can easily lead to stress and anxiety. Furthermore, traditional coaching systems often provide uniform instruction without considering the user's emotional state, making effective support difficult, especially in fatigued and stressful work environments. Therefore, there is a need for a system that recognizes the user's emotions in real time, dynamically selects the appropriate coach type based on that information, and provides personalized coaching.

[0418] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0419] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for having the user input consultation content as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the optimal coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and further, means for combining this with an emotion recognition engine that recognizes the user's emotions in real time, dynamically adjusting the coach type based on the user's emotional state, and providing an appropriate response. This enables personalized coaching based on a user profile that includes emotional state.

[0420] "User" refers to an individual or entity that uses this system.

[0421] "Authentication information" refers to data used to verify a user's identity, and includes things like IDs and passwords.

[0422] A "server" is a computer system that processes user requests and compares them with a database.

[0423] A "database" is a system that structures and stores important data, such as authentication information and consultation history.

[0424] A "device" is a device that the user directly operates, and includes personal computers and smartphones.

[0425] A "Natural Language Processing engine" is software that analyzes input text data and generates appropriate information.

[0426] An "emotion recognition engine" is software used to recognize a user's emotional state.

[0427] "Coaching style" refers to a specific coaching style and is classified into four types: directive, supportive, participatory, and achievement-oriented.

[0428] "Feedback" refers to the opinions and evaluations that users provide regarding the systems and services they offer.

[0429] "Personalized coaching" refers to individualized instruction tailored to each user's specific circumstances and emotions.

[0430] "Text data" refers to character information entered by the user.

[0431] "Voice data" refers to voice information entered by the user.

[0432] "Emotional state" refers to the type of emotion the user is experiencing, and includes anxiety, joy, anger, and so on.

[0433] To implement this invention, the following system configuration and processing procedure should be used.

[0434] The server provides an interface for receiving user authentication information. The user enters authentication information such as ID and password through the terminal and sends it to the server. The server compares the received authentication information with the database and sends the authentication result back to the terminal. This requires a REST API and a database. Specifically, it is common to use the requests library for sending and receiving data, and MySQL® or PostgreSQL for database management.

[0435] After successful authentication, the user uses their device to input their consultation details as text or voice data and sends it to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. The NLP engine uses generative AI models such as the GPT-3® model, and the emotion recognition engine uses the Emotion API, among others. For example, if a prompt such as "I don't know how to proceed with the new project" is entered as text input, the server analyzes this to understand the user's intent and emotions.

[0436] Next, the server selects the optimal coach type based on the user's profile information and past consultation history, and further considers their emotional state based on the analysis results. The selected coach types (directive, supportive, participatory, achievement-oriented) are presented on the terminal for the user to choose from. During this process, the coach type selection algorithm operates dynamically, and the results of the emotion recognition engine are also reflected.

[0437] Based on the coach type selected by the user, the server uses an NLP engine to generate appropriate answers to the consultation. The answers are then adjusted to take into account the user's emotional state (e.g., anxiety, joy, anger). For example, a specific answer might be generated such as, "To move forward with a new project, first consult with your team to gather their opinions, and then create a plan. Also, if you have any concerns about the plan, it's a good idea to seek feedback as needed."

[0438] The server then sends the generated response to the terminal, which displays the response to the user. The user can provide feedback on this response, which is then sent back to the server and recorded in the database. This feedback is used to improve the accuracy of future coaching.

[0439] As a concrete example of this system, if a worker who doesn't know how to operate a new machine inputs "I don't know how to operate the new machine," the system recognizes this emotion as "anxiety" and recommends a supportive coach. The coach provides specific steps and points to note to alleviate the user's anxiety.

[0440] Examples of prompt statements:

[0441] "I don't know how to proceed with the new project. What should I do?"

[0442] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0443] Step 1:

[0444] The user enters authentication information into the terminal. This consists of a user ID and password, and is sent from the terminal to the server. The server receives this information and verifies it against its database. This data processing includes comparing the authentication information to generate an accurate authentication result. The authentication result is then sent back from the server to the terminal, which displays it to the user.

[0445] Step 2:

[0446] The user inputs their consultation details as text or audio data using a terminal. This data is sent from the terminal to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. This data processing includes text analysis and emotion analysis to recognize the user's intentions and emotional state. The analysis results are stored on the server.

[0447] Step 3:

[0448] The server retrieves the user's profile information and past consultation history from the database. Furthermore, considering the analysis results obtained in step 2, it selects the optimal coach type (directive, supportive, participatory, achievement-oriented). A coach type selection algorithm is used for the selection, and dynamic emotion recognition is also reflected. The selected coach type is sent from the server to the terminal, which then presents it to the user.

[0449] Step 4:

[0450] The user selects their preferred coach type on their device and sends this information to the server. The server uses an NLP engine to generate an appropriate response based on the selected coach type. The results of the emotion recognition engine are also incorporated, and the response is refined accordingly. The generated response is sent from the server to the device, which then displays it to the user.

[0451] Step 5:

[0452] The user provides feedback on the answer. This feedback is sent from the terminal to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching. The content of the feedback is reflected in the system's algorithm and processed to make it more useful for future coaching sessions.

[0453] Thus, the invented system handles a series of steps, starting with user authentication, analyzing the consultation content, selecting a coach type, generating appropriate answers, and collecting feedback. The data processing of inputs and outputs at each step is a key element in improving the system's accuracy and user experience. For example, if the prompt "I don't know how to proceed with a new project" is entered, advice tailored to the user's feelings and wishes will be generated.

[0454] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0455] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0456] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0457] [Second Embodiment]

[0458] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0459] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0460] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0461] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0462] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0463] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0464] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0465] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0466] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0467] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0468] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0469] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0470] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[0471] 1. User Authentication:

[0472] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby completing the user authentication process.

[0473] 2. Enter your consultation details:

[0474] The terminal provides an interface for the user to input their consultation details. The user inputs their consultation details via text chat or voice input, and the terminal sends the content to the server. The server passes the received consultation details to a Natural Language Processing (NLP) engine, which then analyzes the text.

[0475] 3. Selecting a coach type:

[0476] The server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type using a coach type selection algorithm. The server then presents the four selected coach types (directive, supportive, participatory, and achievement-oriented) to the user's terminal.

[0477] The user selects their preferred coach type from the options presented, and the process is completed when they send their selection from their device to the server.

[0478] 4. Consultation support:

[0479] The server uses an NLP engine to analyze the consultation content in order to generate an appropriate response based on the selected coach type. For example, in the case of an directive coach, specific steps and action plans will be presented. The server sends the generated response to the terminal, which then displays the response to the user.

[0480] 5. Gathering feedback:

[0481] The device provides an interface for collecting user feedback. Users input feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0482] Specific example:

[0483] 1. The user logs into the system:

[0484] The device prompts the user to enter their ID and password.

[0485] The user enters "user123" and "password," and the terminal sends this information to the server.

[0486] The server compares the authentication information with the database and notifies the terminal of successful authentication.

[0487] The device displays a message to the user indicating successful authentication.

[0488] 2. The user enters the details of their inquiry:

[0489] In the chat interface displayed on the device, the user typed, "I don't know how to proceed with the new project."

[0490] The terminal sends this text to the server.

[0491] The server uses an NLP engine to analyze the consultation content.

[0492] 3. Selecting a coach type:

[0493] The server recommends "directive coaching" based on past history.

[0494] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[0495] The device displays the coach type to the user.

[0496] The user selects "directive coaching," and the device sends that information to the server.

[0497] 4. Consultation support:

[0498] The server generates responses from the instructional coach.

[0499] It generates specific answers such as, "For the new project, first do A, and then do B."

[0500] The server sends the generated response to the terminal.

[0501] The device displays the answer to the user.

[0502] 5. Gathering feedback:

[0503] The device displays the message, "Was this answer helpful?"

[0504] The user enters "Yes" as feedback.

[0505] The device sends feedback to the server.

[0506] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0507] In this way, specific processes are carried out at each step, enabling effective coaching tailored to the user.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The device prompts the user to enter authentication information. Specifically, the device displays an ID and password input form, and the user enters this information.

[0511] Step 2:

[0512] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[0513] Step 3:

[0514] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[0515] Step 4:

[0516] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[0517] Step 5:

[0518] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[0519] Step 6:

[0520] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[0521] Step 7:

[0522] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[0523] Step 8:

[0524] The server passes the received consultation content to the NLP engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[0525] Step 9:

[0526] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[0527] Step 10:

[0528] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[0529] Step 11:

[0530] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[0531] Step 12:

[0532] The user selects their preferred coach type, and the device sends this selection to the server.

[0533] Step 13:

[0534] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation.

[0535] Step 14:

[0536] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[0537] Step 15:

[0538] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[0539] Step 16:

[0540] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[0541] (Example 1)

[0542] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0543] In modern coaching systems, there is a challenge in providing the optimal coaching method based on the individual needs and past consultation history of each user. Furthermore, there are insufficient means to effectively collect and analyze user feedback and utilize it to improve the accuracy of future coaching. Given this situation, a new system is needed to realize coaching optimized for each user.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0545] In this invention, the server includes means for receiving authentication information from the user and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for receiving consultation content from the user as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using the Natural Language Processing engine of a generation AI model based on the selected coach type and send it to the terminal; means for collecting feedback from the user and sending it to the server; and means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching. This enables the provision of individually optimized coaching to the user and continuous improvement of accuracy.

[0546] "Authentication information" refers to information such as IDs and passwords that users provide to access a system.

[0547] A "server" is a computer system that receives requests from users via a network and processes and stores data.

[0548] A "database" is a structured information system for efficiently storing, managing, and retrieving data in digital format.

[0549] A "terminal" is a computing device used by a user to interface with a system. Specifically, this includes personal computers and smartphones.

[0550] "Consultation content" refers to the problems, questions, and requests for advice that users provide to the system.

[0551] A "Natural Language Processing engine" is an artificial intelligence technology that analyzes natural language and understands its meaning and context.

[0552] "Coaching style" refers to the approach style used when providing problem-solving or guidance methods to users. Specifically, it includes directive, supportive, participatory, and achievement-oriented styles.

[0553] A "generative AI model" is a pre-trained artificial intelligence model that has the ability to generate and analyze data for a specific task.

[0554] "Feedback" refers to information that includes evaluations and suggestions for improvement regarding the answers and information provided by users.

[0555] A "prompt statement" is an instruction or question that is entered to operate a generative AI model.

[0556] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[0557] First, in order for a user to access the system, the user must enter authentication information using a terminal. This authentication information includes a user ID and password, which the terminal sends to the server. The server verifies this authentication information against a database and sends the authentication result back to the terminal. The terminal then displays the result to the user.

[0558] Next, the user enters their consultation details using the system. The terminal offers the user the option of using either a chat interface or voice input. Once the user enters their consultation details, the terminal sends them to the server. The server then passes the received consultation details to the Natural Language Processing engine of the AI ​​model, which analyzes the text.

[0559] Once the analysis is complete, the server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type. There are four coach types: directive, supportive, participatory, and achievement-oriented. The selected coach type is presented on the terminal, and the user chooses their preferred coach type. The terminal then sends this selection information to the server.

[0560] Next, the server uses the Natural Language Processing engine of its generative AI model to generate an appropriate response based on the selected coach type. For example, if a user asks, "I don't know how to proceed with a new project," a directive coach would provide specific steps such as, "For a new project, first do A, and then do B." The server sends the generated response to the device, which then displays the response to the user.

[0561] Furthermore, users can provide feedback on the provided answers. The device displays a feedback collection interface and sends the user's feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0562] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0563] "The user is seeking advice on how to proceed with a new project. They have selected a directive coach. Please provide specific steps and an action plan."

[0564] This system allows us to provide personalized coaching to each user and continuously improve the system based on the results.

[0565] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0566] Step 1:

[0567] The user enters their authentication information. This information includes the user ID and password.

[0568] Step 2:

[0569] The terminal receives the user's authentication information and sends it to the server. The entered data is encrypted and transmitted to the server.

[0570] Step 3:

[0571] The server compares the received authentication information with the database. It checks if it matches the user information stored in the database and generates a result. The matching result is output as authentication success or failure.

[0572] Step 4:

[0573] The server sends the authentication result to the terminal. If authentication is successful, user information is also sent.

[0574] Step 5:

[0575] The terminal receives the authentication result from the server and displays the result to the user. If successful, it displays "Login successful"; if unsuccessful, it displays "Login failed".

[0576] Step 6:

[0577] The terminal provides an interface for the user to input their consultation details. This interface can be either a chat box or voice input.

[0578] Step 7:

[0579] The user enters the details of their inquiry. For example, they might enter a specific problem such as "I don't know how to proceed with a new project" into the text box.

[0580] Step 8:

[0581] The terminal receives the entered consultation content and sends it to the server. Text-based content is sent as is, while voice input is converted to text before transmission.

[0582] Step 9:

[0583] The server passes the received consultation content to the NLP engine for analysis. Through this analysis, the meaning and context of the user's consultation content are understood and stored as internal data.

[0584] Step 10:

[0585] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary data to understand the user's background.

[0586] Step 11:

[0587] The server selects the optimal coach type. Based on the acquired profile information and past consultation history, it runs an algorithm to determine the most suitable coach type from four options: directive, supportive, participatory, and achievement-oriented.

[0588] Step 12:

[0589] The server sends the selected coach type to the terminal. This information includes four coach types to present to the user.

[0590] Step 13:

[0591] The device displays four coaching types to the user. It provides an interface that allows the user to select one.

[0592] Step 14:

[0593] The user selects their preferred coach type. Once the selection is complete, press the "Confirm" button.

[0594] Step 15:

[0595] The terminal sends the user's selection results to the server. Data based on the user's selection is transmitted to the server.

[0596] Step 16:

[0597] The server uses an NLP engine to generate appropriate responses based on the selected coach type. For example, in the case of an instructional coach, specific steps and action plans will be created.

[0598] Step 17:

[0599] The server sends the generated response to the terminal. The response is sent in text format.

[0600] Step 18:

[0601] The device displays the received response to the user. Specific steps and action plans are displayed in the chat box. For example, "For new projects, first do A, and then do B."

[0602] Step 19:

[0603] The device displays a feedback collection interface to the user. For example, it might display a question such as, "Was this answer helpful?"

[0604] Step 20:

[0605] Users provide feedback. For example, they can select "yes" or "no," or enter specific comments.

[0606] Step 21:

[0607] The device sends feedback to the server. User feedback data is sent to the server.

[0608] Step 22:

[0609] The server records the feedback it receives in a database. This feedback data is saved to improve the accuracy of future coaching.

[0610] (Application Example 1)

[0611] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0612] This invention relates to a system that responds quickly and accurately to user inquiries. Conventional methods have been time-consuming and labor-intensive in dealing with security system problems and questions. Furthermore, the process of generating appropriate answers based on user inquiries sometimes failed to adequately consider the user's individual circumstances and past inquiry history. To solve these problems, this invention provides a system that encompasses everything from user authentication to coach type selection, answer generation, and feedback collection.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0614] In this invention, the server includes means for prompting the user to input authentication information and transmitting it; means for comparing the authentication information with a database and transmitting the authentication result to the terminal; and means for prompting the user to input consultation content as text or voice data and transmitting it. This makes it possible for users to easily and quickly consult about security systems. Furthermore, the server includes means for analyzing the consultation content, selecting and presenting the optimal coach type based on the user's profile and past consultation history; means for generating and transmitting an appropriate response using a natural language processing engine based on the selected coach type; means for displaying the response to the user on the terminal; means for collecting and transmitting feedback from the user; means for recording the received feedback in a database and using it to improve the accuracy of future coaching; and means for generating a response to the inquiry content on the server and displaying the response result on the terminal when the consultation content is transmitted. This enables a quick and accurate response to the consultation content and can increase user satisfaction.

[0615] User authentication is the process of verifying the identity of a user accessing a system and confirming that the user has legitimate authority.

[0616] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[0617] A "server" is a central processing unit that processes information sent by users, performs necessary database matching and analysis, and provides the results.

[0618] "Coaching style" refers to a method of providing advice and solutions to a user's concerns using one of the following styles: directive, supportive, participatory, or achievement-oriented.

[0619] A "natural language processing engine" is an artificial intelligence technology that analyzes user input (text or speech), understands its meaning and intent, and generates appropriate responses.

[0620] "Feedback" refers to evaluations and opinions that users give regarding the answers and services they receive, and these are used to improve the system.

[0621] A "database" is a system that stores data, such as user profile information and past consultation history, in a searchable and retrieval format.

[0622] A "terminal" is a device (such as a smartphone, tablet, or personal computer) used by a user to input information and receive results.

[0623] "Consultation content" refers to the questions or problems that users enter into the system.

[0624] "Answer generation" is the process by which a system derives appropriate solutions or advice in response to a user's question or problem.

[0625] This invention relates to a system that includes a series of processes, from user authentication to analysis of consultation content, selection of coach type, generation and display of appropriate answers, collection of feedback, and system improvement based on that feedback. To realize this system, the following programs, hardware, and software are used.

[0626] The server compares the authentication information entered by the user against a database to perform user authentication. This verifies that the user is legitimate, and the server then sends the authentication result to the terminal.

[0627] The terminal provides an interface that allows the user to input their consultation details. The text or voice data entered by the user is sent from the terminal to the server. The server uses a natural language processing engine (e.g., BERT, SpaCy) to analyze the received consultation details.

[0628] The server selects the most suitable coach type based on the user's profile and past consultation history. The selected coach type is then chosen from directive, supportive, participatory, and achievement-oriented, and presented on the user's device. The user can then choose the appropriate coach type from the options presented.

[0629] Next, the server generates an appropriate response based on the selected coach type. Using a natural language processing engine, it constructs specific steps and advice for the user's consultation. The generated response is sent to the terminal and displayed to the user.

[0630] The device collects user feedback and sends it to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0631] As a concrete example, consider the following prompt message:

[0632] 1. User Authentication:

[0633] The prompt message to send to the authentication server after entering the user ID is: "User ID: security_user123, Password: securepassword"

[0634] If authentication is successful: "Authentication successful, welcome."

[0635] 2. Input and analysis of consultation details:

[0636] User prompt to enter their question: "My security camera is not transmitting video. What should I do?"

[0637] The server analyzes the inquiry and generates an appropriate response: "Access the security camera settings screen and check the network connection. Then, restart the camera."

[0638] In this way, the system of the present invention can respond quickly and accurately to user inquiries. This improves user satisfaction and streamlines system troubleshooting.

[0639] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0640] Step 1:

[0641] User Authentication

[0642] Input: Enter your User ID and password into the terminal.

[0643] Operation: The terminal sends the authentication information entered by the user to the server. The server compares the authentication information with the database and generates an authentication result.

[0644] Output: The authentication result (success or failure) is sent to the terminal, and the terminal displays the authentication result to the user.

[0645] Step 2:

[0646] Enter your consultation details

[0647] Input: The user enters their inquiry details as text or voice data into the chat interface.

[0648] Operation: The terminal sends the entered consultation content to the server. The server passes the received data to the natural language processing engine.

[0649] Output: Analyzed consultation content. The NLP engine converts the text into structured data and returns it to the server.

[0650] Step 3:

[0651] Choosing a Coach Type

[0652] Input: Analyzed consultation content, user profile information, past consultation history.

[0653] Operation: The server uses a coach type selection algorithm to select an appropriate coach type (directive, supportive, participatory, achievement-oriented) and presents it to the terminal.

[0654] Output: Selection result for the appropriate coach type.

[0655] Step 4:

[0656] Choosing a Coach Type

[0657] Input: User selection of coach type.

[0658] Operation: The terminal sends the coach type selected by the user to the server. The server prepares the following actions based on the selected coach type.

[0659] Output: Selected coach type.

[0660] Step 5:

[0661] Answer generation

[0662] Input: Selected coach type, analyzed consultation content.

[0663] Operation: The server uses a natural language processing engine to generate appropriate responses based on the selected coach type. Specifically, it provides specific instructions for directive coaches, advice for supportive coaches, suggestions for collaborative work for participatory coaches, and goal setting for achievement-oriented coaches.

[0664] Output: Generated answer.

[0665] Step 6:

[0666] Display the answer

[0667] Input: Generated response sent from the server.

[0668] Operation: The device displays the received response to the user. Specific steps and advice are displayed in text format.

[0669] Output: The answer displayed to the user.

[0670] Step 7:

[0671] Gathering feedback

[0672] Input: User feedback (whether the answer was helpful, specific comments, etc.).

[0673] Operation: The terminal collects feedback from the user and sends it to the server. The server records the feedback in a database and analyzes it to improve the accuracy of future coaching.

[0674] Output: Feedback recorded in the database.

[0675] Through the steps outlined above, a system is created that responds quickly and accurately to user inquiries. This system includes user authentication, analysis of the inquiry content, selection of the optimal coach type, generation and display of appropriate answers, and collection and analysis of feedback.

[0676] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0677] This invention is a coaching system that combines an emotion engine that recognizes the user's emotions. This system is implemented by the user, terminal, and server performing the following procedures and roles.

[0678] 1. User Authentication:

[0679] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby authenticating the user.

[0680] 2. Enter your consultation details:

[0681] The terminal displays an interface that prompts the user to input their consultation details. The user inputs the consultation details as text or voice data, and the terminal sends it to the server. The server passes the received data to the NLP engine and emotion engine, which analyze the text and emotions.

[0682] 3. Selecting a coach type:

[0683] The server retrieves the user's profile information and past consultation history from the database and uses a coach type selection algorithm to choose the most suitable coach type from four options (directive, supportive, participatory, and achievement-oriented). The user's emotional information, as recognized by the emotion engine, is also taken into consideration during this process.

[0684] The server displays the selected coach types to the terminal, and the user selects their preferred coach type. The process is completed when the terminal sends the selection result to the server.

[0685] 4. Consultation support:

[0686] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation. For example, in the case of an directive coach, specific steps and action plans will be presented. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine. The server sends the generated response to the terminal, which then displays the response to the user.

[0687] 5. Gathering feedback:

[0688] The device displays an interface for collecting user feedback. The user enters feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0689] Specific example:

[0690] 1. The user logs into the system:

[0691] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[0692] The device sends this information to the server.

[0693] The server checks against the database and notifies the terminal of successful authentication.

[0694] The device displays a message to the user indicating successful authentication.

[0695] 2. The user enters the details of their inquiry:

[0696] In the chat interface displayed on the device, the user types, "I don't know how to proceed with the new project."

[0697] The terminal sends this text to the server.

[0698] The server uses an NLP engine and an emotion engine to analyze the content and emotions. The emotion engine detects "anxiety."

[0699] 3. Selecting a coach type:

[0700] The server recommended a "supportive coach" based on past history.

[0701] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[0702] The device displays the coach type to the user.

[0703] The user selects "supportive coaching," and the device sends that information to the server.

[0704] 4. Consultation support:

[0705] The server generates a supportive coach response, such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," taking into account the results of the emotion engine.

[0706] The server sends the generated response to the terminal.

[0707] The device displays the answer to the user.

[0708] 5. Gathering feedback:

[0709] The device displays the message, "Was this answer helpful?"

[0710] The user enters "Yes" as feedback.

[0711] The device sends feedback to the server.

[0712] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0713] In this way, specific processing is carried out at each step, enabling effective coaching tailored to the user. Furthermore, the emotion engine takes into account the user's emotional state, allowing for even more personalized support.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The device prompts the user to enter authentication information. Specifically, the device displays an input form for an ID and password, and the user enters this information.

[0717] Step 2:

[0718] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[0719] Step 3:

[0720] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[0721] Step 4:

[0722] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[0723] Step 5:

[0724] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[0725] Step 6:

[0726] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[0727] Step 7:

[0728] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[0729] Step 8:

[0730] The server passes the received consultation content to an NLP (Natural Language Processing) engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[0731] Step 9:

[0732] The server simultaneously uses an emotion engine to analyze the user's emotions from the consultation content and voice data. The emotion engine identifies the user's emotions from factors such as the tone of voice and the wording used in the text.

[0733] Step 10:

[0734] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[0735] Step 11:

[0736] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented). In this process, the user's emotional information, as recognized by the emotion engine, is also taken into consideration.

[0737] Step 12:

[0738] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[0739] Step 13:

[0740] The user selects their preferred coach type, and the device sends this selection to the server.

[0741] Step 14:

[0742] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine.

[0743] Step 15:

[0744] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[0745] Step 16:

[0746] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[0747] Step 17:

[0748] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[0749] Specific example:

[0750] 1. Steps 1 through 5: The user logs into the system.

[0751] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[0752] The device sends this information to the server. The server compares it with the database and notifies the device that authentication was successful. The device then displays an authentication success message to the user.

[0753] 2. Steps 6 to 7: The user enters the consultation details.

[0754] In the chat interface on the device, the user types "I don't know how to proceed with the new project," and the device sends the text data to the server.

[0755] 3. Steps 8 to 9: Analysis of the consultation content and emotions

[0756] The server uses an NLP engine to analyze the text and extract key keywords and phrases. Simultaneously, an emotion engine detects "anxiety."

[0757] 4. Steps 10 to 11: Selecting a Coach Type

[0758] The server retrieves the user's profile information and past consultation history and recommends a "supportive coach." It also takes into account the user's "anxiety" as analyzed by an emotion engine.

[0759] 5. Steps 12 to 13: Coach type presentation and selection

[0760] The server sends four coach types to the terminal. The terminal displays the coach types to the user, who then selects "Supportive Coach" and sends it to the server.

[0761] 6. Steps 14 to 15: Consultation

[0762] The server generates a supportive coach response. It generates specific responses such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," and adjusts them while taking into account the anxieties recognized by the emotion engine. The server sends the generated response to the device, and the device displays the response to the user.

[0763] 7. Steps 16 to 17: Gathering Feedback

[0764] The device displays "Was this answer helpful?", and the user types "Yes".

[0765] The device sends feedback to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0766] (Example 2)

[0767] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0768] In modern society, there is a need for coaching systems that can effectively address the problems and stresses individuals face. Traditional systems provide uniform advice without considering the user's emotional state, resulting in a lack of appropriate support tailored to individual needs and emotions. Furthermore, there are insufficient mechanisms to effectively incorporate user feedback and improve the system's accuracy. As a result, the effectiveness of coaching for users decreases, and their satisfaction levels decline.

[0769] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0770] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for the user to input consultation content as text or voice data and send it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and means for the server to analyze the user's emotions using an emotion recognition engine and reflect it in the selection of the coach type and the generation of the response. This enables effective coaching tailored to the user's individual needs and emotional state.

[0771] "Authentication information" refers to information such as the ID and password that a user enters to log in to a system.

[0772] A "server" is a computer system that processes requests from users, performs authentication, analyzes data, generates responses, and sends them.

[0773] A "terminal" is a device that a user uses to access the system, enter authentication information, input consultation details, view answers, and provide feedback.

[0774] A "database" is a data storage system that stores and manages user authentication information, profile information, consultation history, feedback, and other data.

[0775] A "Natural Language Processing engine" is software that analyzes text data entered by the user and understands its context and meaning.

[0776] An "emotion recognition engine" is software that identifies and analyzes a user's emotional state based on their input data and consultation content.

[0777] "Coaching style" refers to the style of coaching provided in response to a consultation, and includes four types: directive, supportive, participatory, and achievement-oriented.

[0778] "Feedback" refers to the evaluations and opinions that users provide regarding the system's responses.

[0779] The "coach type selection algorithm" is a computational method for selecting the optimal coach type based on the user's profile information, past consultation history, and emotional state.

[0780] This invention is a coaching system that combines an emotion recognition engine to recognize the user's emotions. This system is implemented through the specific roles of the user, terminal, and server. The details of the system's program processing are described below.

[0781] Hardware and software to be used

[0782] The server is responsible for the main processing of the coaching system and houses the database, Natural Language Processing engine (NLP engine), and emotion recognition engine.

[0783] A terminal is a device that a user uses to access the system, and examples include PCs, smartphones, and tablets.

[0784] The database is used to store user authentication information, profile information, consultation history, and feedback.

[0785] An NLP engine is software that analyzes text data entered by a user to understand its context and meaning.

[0786] An emotion recognition engine is software that identifies and analyzes an emotional state from user input data and consultation content.

[0787] Specific example of processing

[0788] 1. User Authentication

[0789] The device displays a login screen and prompts you to enter your ID and password (e.g., enter "user123" and "password").

[0790] The device sends the entered authentication information to the server.

[0791] The server compares the authentication information with the database and sends the authentication result to the terminal.

[0792] The device displays the authentication result to the user (e.g., displays a "Authentication successful" message).

[0793] 2. Enter the details of your consultation.

[0794] The terminal displays an interface that prompts the user to input their inquiry details. Text or voice input is available.

[0795] The user enters the details of their inquiry (e.g., "I don't know how to proceed with a new project").

[0796] The terminal sends the entered consultation details to the server.

[0797] The server uses an NLP engine and an emotion recognition engine to analyze text and emotions (e.g., detects that the emotion is "anxiety").

[0798] 3. Selecting a Coach Type

[0799] The server retrieves the user's profile information and past consultation history from the database.

[0800] The server executes a coach type selection algorithm and chooses the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[0801] The server sends the selection results to the terminal and presents them to the user (e.g., "Supportive Coach" is recommended).

[0802] The user selects a coaching type, and the device sends the result to the server.

[0803] 4. Consultation support

[0804] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation (e.g., "First, consult with the team to gather their opinions, and then we'll make a plan").

[0805] The server adjusts its response, taking into account the results of the emotion recognition engine.

[0806] The server sends the generated response to the terminal.

[0807] The device displays the answer to the user.

[0808] 5. Gathering feedback

[0809] The device displays an interface for collecting user feedback (e.g., "Was this answer helpful?").

[0810] The user enters feedback (e.g., answers "Yes").

[0811] The device sends feedback to the server.

[0812] The server records the feedback in a database, which is then used to improve the accuracy of future coaching sessions.

[0813] Example of a prompt

[0814] "I don't know how to proceed with the new project."

[0815] "Could you tell me about your role as a team leader?"

[0816] As described above, a coaching system incorporating an emotion recognition engine can provide personalized responses by considering the user's consultation content and emotional state. Furthermore, user feedback can be recorded in a database and used to improve the accuracy of future coaching.

[0817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0818] Step 1: User Authentication

[0819] Input: The user enters their ID and password into the terminal ("user123" and "password").

[0820] Operation: The terminal displays the login screen and sends the user's input to the server.

[0821] Data processing: The server executes an SQL query to compare the authentication information received with the database.

[0822] Output: The server generates an authentication result (success or failure) and sends it to the terminal.

[0823] Specific action: The device displays the authentication result to the user (e.g., displays the message "Authentication successful").

[0824] Step 2: Enter your consultation details

[0825] Input: The user enters their question as text or voice data into their device (e.g., "I don't know how to proceed with the new project").

[0826] Operation: The terminal displays the user's inquiry details on an input screen, and after the user completes the input, it sends it to the server.

[0827] Data Processing: The server passes the received consultation content to the NLP engine and emotion recognition engine, which analyze the text and emotions. The NLP engine performs contextual analysis, and the emotion recognition engine detects emotions such as "anxiety."

[0828] Output: Generates analysis results (contextual information and emotional state) and stores them internally for use in the next step of processing.

[0829] Specific operation: The server uses an NLP engine and an emotion recognition engine to analyze the consultation content and emotional state.

[0830] Step 3: Selecting a Coach Type

[0831] Input: The server retrieves the user's profile information, past consultation history, and sentiment analysis results from the database.

[0832] Operation: The server executes a coach type selection algorithm and selects the optimal coach type (e.g., "Supportive").

[0833] Data processing: The server calculates the optimal coach type based on the data above. Emotional information is also taken into consideration.

[0834] Output: Generate selection results and send them to the terminal.

[0835] Specific operation: The server sends the result of selecting a coach type to the terminal, and the terminal displays the selection result to the user (e.g., presents "Supportive Coach").

[0836] Step 4: Consultation

[0837] Input: The user selects one of the coach types presented on the device.

[0838] Operation: The terminal sends the user's selection to the server.

[0839] Data Processing: The server uses an NLP engine based on the selected coach type to generate appropriate answers to the user's questions (e.g., "To move forward with a new project, first consult with the team to gather their opinions, and then create a plan").

[0840] Output: Sends the generated response to the device.

[0841] Specific operation: The server adjusts the response based on the results of the emotion recognition engine and sends it to the terminal, which then displays the response to the user.

[0842] Step 5: Gathering Feedback

[0843] Input: The user enters their feedback on the feedback input screen displayed on their device (e.g., enters "Yes").

[0844] Operation: The device sends user feedback to the server.

[0845] Data processing: The server stores the received feedback in a database and analyzes it to improve the accuracy of future coaching.

[0846] Output: The feedback is saved to the database and used as data for future improvements.

[0847] Specific operation: The server records feedback and uses it as new training data as needed.

[0848] (Application Example 2)

[0849] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0850] In today's diverse work environments, workers frequently encounter new ways of operating machinery and tools, which can easily lead to stress and anxiety. Furthermore, traditional coaching systems often provide uniform instruction without considering the user's emotional state, making effective support difficult, especially in fatigued and stressful work environments. Therefore, there is a need for a system that recognizes the user's emotions in real time, dynamically selects the appropriate coach type based on that information, and provides personalized coaching.

[0851] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0852] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for having the user input consultation content as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the optimal coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and further, means for combining this with an emotion recognition engine that recognizes the user's emotions in real time, dynamically adjusting the coach type based on the user's emotional state, and providing an appropriate response. This enables personalized coaching based on a user profile that includes emotional state.

[0853] "User" refers to an individual or entity that uses this system.

[0854] "Authentication information" refers to data used to verify a user's identity, and includes things like IDs and passwords.

[0855] A "server" is a computer system that processes user requests and compares them with a database.

[0856] A "database" is a system that structures and stores important data, such as authentication information and consultation history.

[0857] A "device" is a device that the user directly operates, and includes personal computers and smartphones.

[0858] A "Natural Language Processing engine" is software that analyzes input text data and generates appropriate information.

[0859] An "emotion recognition engine" is software designed to recognize a user's emotional state.

[0860] "Coaching style" refers to a specific coaching style and is classified into four types: directive, supportive, participatory, and achievement-oriented.

[0861] "Feedback" refers to the opinions and evaluations that users provide regarding the systems and services they offer.

[0862] "Personalized coaching" refers to individualized instruction tailored to each user's specific situation and emotions.

[0863] "Text data" refers to character information entered by the user.

[0864] "Voice data" refers to voice information entered by the user.

[0865] "Emotional state" refers to the type of emotion the user is experiencing, and includes anxiety, joy, anger, and so on.

[0866] To implement this invention, the following system configuration and processing procedure should be used.

[0867] The server provides an interface for receiving user authentication information. The user enters authentication information, such as their ID and password, through their terminal and sends it to the server. The server then compares the received authentication information with a database and returns the authentication result to the terminal. This requires a REST API and a database. Specifically, it is common to use libraries such as `requests` for data transmission and `MySQL` or PostgreSQL for database management.

[0868] After successful authentication, the user uses their device to input their consultation details as text or voice data and sends it to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. The NLP engine uses generative AI models such as the GPT-3 model, and the emotion recognition engine uses the Emotion API, among others. For example, if a prompt such as "I don't know how to proceed with the new project" is entered as text input, the server analyzes this to understand the user's intent and emotions.

[0869] Next, the server selects the optimal coach type based on the user's profile information and past consultation history, and further considers their emotional state based on the analysis results. The selected coach types (directive, supportive, participatory, achievement-oriented) are presented on the terminal for the user to choose from. During this process, the coach type selection algorithm operates dynamically, and the results of the emotion recognition engine are also reflected.

[0870] Based on the coach type selected by the user, the server uses an NLP engine to generate appropriate answers to the consultation. The answers are then adjusted to take into account the user's emotional state (e.g., anxiety, joy, anger). For example, a specific answer might be generated such as, "To move forward with a new project, first consult with your team to gather their opinions, and then create a plan. Also, if you have any concerns about the plan, it's a good idea to seek feedback as needed."

[0871] The server then sends the generated response to the terminal, which displays the response to the user. The user can provide feedback on this response, which is then sent back to the server and recorded in the database. This feedback is used to improve the accuracy of future coaching.

[0872] As a concrete example of this system, if a worker who doesn't know how to operate a new machine inputs "I don't know how to operate the new machine," the system recognizes this emotion as "anxiety" and recommends a supportive coach. The coach provides specific steps and points to note to alleviate the user's anxiety.

[0873] Examples of prompt statements:

[0874] "I don't know how to proceed with the new project. What should I do?"

[0875] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0876] Step 1:

[0877] The user enters authentication information into the terminal. This consists of a user ID and password, and is sent from the terminal to the server. The server receives this information and verifies it against its database. This data processing includes comparing the authentication information to generate an accurate authentication result. The authentication result is then sent back from the server to the terminal, which displays it to the user.

[0878] Step 2:

[0879] The user inputs their consultation details as text or audio data using a terminal. This data is sent from the terminal to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. This data processing includes text analysis and emotion analysis to recognize the user's intentions and emotional state. The analysis results are stored on the server.

[0880] Step 3:

[0881] The server retrieves the user's profile information and past consultation history from the database. Furthermore, considering the analysis results obtained in step 2, it selects the optimal coach type (directive, supportive, participatory, achievement-oriented). A coach type selection algorithm is used for the selection, and dynamic emotion recognition is also reflected. The selected coach type is sent from the server to the terminal, which then presents it to the user.

[0882] Step 4:

[0883] The user selects their preferred coach type on their device and sends this information to the server. The server uses an NLP engine to generate an appropriate response based on the selected coach type. The results of the emotion recognition engine are also incorporated, and the response is refined accordingly. The generated response is sent from the server to the device, which then displays it to the user.

[0884] Step 5:

[0885] The user provides feedback on the answer. This feedback is sent from the terminal to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching. The content of the feedback is reflected in the system's algorithm and processed to make it more useful for future coaching sessions.

[0886] Thus, the invented system handles a series of steps, starting with user authentication, analyzing the consultation content, selecting a coach type, generating appropriate answers, and collecting feedback. The data processing of inputs and outputs at each step is a key element in improving the system's accuracy and user experience. For example, if the prompt "I don't know how to proceed with a new project" is entered, advice tailored to the user's feelings and wishes will be generated.

[0887] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0888] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0889] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0890] [Third Embodiment]

[0891] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0892] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0893] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0894] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0895] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0896] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0897] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0898] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0899] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0900] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0901] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0902] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0903] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[0904] 1. User Authentication:

[0905] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby completing the user authentication process.

[0906] 2. Enter your consultation details:

[0907] The terminal provides an interface for the user to input their consultation details. The user inputs their consultation details via text chat or voice input, and the terminal sends the content to the server. The server passes the received consultation details to a Natural Language Processing (NLP) engine, which then analyzes the text.

[0908] 3. Selecting a coach type:

[0909] The server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type using a coach type selection algorithm. The server then presents the four selected coach types (directive, supportive, participatory, and achievement-oriented) to the user's terminal.

[0910] The user selects their preferred coach type from the options presented, and the process is completed when they send their selection from their device to the server.

[0911] 4. Consultation support:

[0912] The server uses an NLP engine to analyze the consultation content in order to generate an appropriate response based on the selected coach type. For example, in the case of an directive coach, specific steps and action plans will be presented. The server sends the generated response to the terminal, which then displays the response to the user.

[0913] 5. Gathering feedback:

[0914] The device provides an interface for collecting user feedback. Users input feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0915] Specific example:

[0916] 1. The user logs into the system:

[0917] The device prompts the user to enter their ID and password.

[0918] The user enters "user123" and "password," and the terminal sends this information to the server.

[0919] The server compares the authentication information with the database and notifies the terminal of successful authentication.

[0920] The device displays a message to the user indicating successful authentication.

[0921] 2. The user enters the details of their inquiry:

[0922] In the chat interface displayed on the device, the user typed, "I don't know how to proceed with the new project."

[0923] The terminal sends this text to the server.

[0924] The server uses an NLP engine to analyze the consultation content.

[0925] 3. Selecting a coach type:

[0926] The server recommends "directive coaching" based on past history.

[0927] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[0928] The device displays the coach type to the user.

[0929] The user selects "directive coaching," and the device sends that information to the server.

[0930] 4. Consultation support:

[0931] The server generates responses from the instructional coach.

[0932] It generates specific answers such as, "For the new project, first do A, and then do B."

[0933] The server sends the generated response to the terminal.

[0934] The device displays the answer to the user.

[0935] 5. Gathering feedback:

[0936] The device displays the message, "Was this answer helpful?"

[0937] The user enters "Yes" as feedback.

[0938] The device sends feedback to the server.

[0939] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[0940] In this way, specific processes are carried out at each step, enabling effective coaching tailored to the user.

[0941] The following describes the processing flow.

[0942] Step 1:

[0943] The device prompts the user to enter authentication information. Specifically, the device displays an ID and password input form, and the user enters this information.

[0944] Step 2:

[0945] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[0946] Step 3:

[0947] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[0948] Step 4:

[0949] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[0950] Step 5:

[0951] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[0952] Step 6:

[0953] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[0954] Step 7:

[0955] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[0956] Step 8:

[0957] The server passes the received consultation content to the NLP engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[0958] Step 9:

[0959] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[0960] Step 10:

[0961] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[0962] Step 11:

[0963] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[0964] Step 12:

[0965] The user selects their preferred coach type, and the device sends this selection to the server.

[0966] Step 13:

[0967] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation.

[0968] Step 14:

[0969] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[0970] Step 15:

[0971] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[0972] Step 16:

[0973] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[0974] (Example 1)

[0975] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0976] In modern coaching systems, there is a challenge in providing the optimal coaching method based on the individual needs and past consultation history of each user. Furthermore, there are insufficient means to effectively collect and analyze user feedback and utilize it to improve the accuracy of future coaching. Given this situation, a new system is needed to realize coaching optimized for each user.

[0977] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0978] In this invention, the server includes means for receiving authentication information from the user and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for receiving consultation content from the user as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using the Natural Language Processing engine of a generation AI model based on the selected coach type and send it to the terminal; means for collecting feedback from the user and sending it to the server; and means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching. This enables the provision of individually optimized coaching to the user and continuous improvement of accuracy.

[0979] "Authentication information" refers to information such as IDs and passwords that users provide to access a system.

[0980] A "server" is a computer system that receives requests from users via a network and processes and stores data.

[0981] A "database" is a structured information system for efficiently storing, managing, and retrieving data in digital format.

[0982] A "terminal" is a computing device used by a user to interface with a system. Specifically, this includes personal computers and smartphones.

[0983] "Consultation content" refers to the problems, questions, and requests for advice that users provide to the system.

[0984] A "Natural Language Processing engine" is an artificial intelligence technology that analyzes natural language and understands its meaning and context.

[0985] "Coaching style" refers to the approach style used when providing problem-solving or guidance methods to users. Specifically, it includes directive, supportive, participatory, and achievement-oriented styles.

[0986] A "generative AI model" is a pre-trained artificial intelligence model that has the ability to generate and analyze data for a specific task.

[0987] "Feedback" refers to information that includes evaluations and suggestions for improvement regarding the answers and information provided by users.

[0988] A "prompt statement" is an instruction or question that is entered to operate a generative AI model.

[0989] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[0990] First, in order for a user to access the system, the user must enter authentication information using a terminal. This authentication information includes a user ID and password, which the terminal sends to the server. The server verifies this authentication information against a database and sends the authentication result back to the terminal. The terminal then displays the result to the user.

[0991] Next, the user enters their consultation details using the system. The terminal offers the user the option of using either a chat interface or voice input. Once the user enters their consultation details, the terminal sends them to the server. The server then passes the received consultation details to the Natural Language Processing engine of the AI ​​model, which analyzes the text.

[0992] Once the analysis is complete, the server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type. There are four coach types: directive, supportive, participatory, and achievement-oriented. The selected coach type is presented on the terminal, and the user chooses their preferred coach type. The terminal then sends this selection information to the server.

[0993] Next, the server uses the Natural Language Processing engine of its generative AI model to generate an appropriate response based on the selected coach type. For example, if a user asks, "I don't know how to proceed with a new project," a directive coach would provide specific steps such as, "For a new project, first do A, and then do B." The server sends the generated response to the device, which then displays the response to the user.

[0994] Furthermore, users can provide feedback on the provided answers. The device displays a feedback collection interface and sends the user's feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[0995] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[0996] "The user is seeking advice on how to proceed with a new project. They have selected a directive coach. Please provide specific steps and an action plan."

[0997] This system allows us to provide personalized coaching to each user and continuously improve the system based on the results.

[0998] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0999] Step 1:

[1000] The user enters their authentication information. This information includes the user ID and password.

[1001] Step 2:

[1002] The terminal receives the user's authentication information and sends it to the server. The entered data is encrypted and transmitted to the server.

[1003] Step 3:

[1004] The server compares the received authentication information with the database. It checks if it matches the user information stored in the database and generates a result. The matching result is output as authentication success or failure.

[1005] Step 4:

[1006] The server sends the authentication result to the terminal. If authentication is successful, user information is also sent.

[1007] Step 5:

[1008] The terminal receives the authentication result from the server and displays the result to the user. If successful, it displays "Login successful"; if unsuccessful, it displays "Login failed".

[1009] Step 6:

[1010] The terminal provides an interface for the user to input their consultation details. This interface can be either a chat box or voice input.

[1011] Step 7:

[1012] The user enters the details of their inquiry. For example, they might enter a specific problem such as "I don't know how to proceed with a new project" into the text box.

[1013] Step 8:

[1014] The terminal receives the entered consultation content and sends it to the server. Text-based content is sent as is, while voice input is converted to text before transmission.

[1015] Step 9:

[1016] The server passes the received consultation content to the NLP engine for analysis. Through this analysis, the meaning and context of the user's consultation content are understood and stored as internal data.

[1017] Step 10:

[1018] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary data to understand the user's background.

[1019] Step 11:

[1020] The server selects the optimal coach type. Based on the acquired profile information and past consultation history, it runs an algorithm to determine the most suitable coach type from four options: directive, supportive, participatory, and achievement-oriented.

[1021] Step 12:

[1022] The server sends the selected coach type to the terminal. This information includes four coach types to present to the user.

[1023] Step 13:

[1024] The device displays four coaching types to the user. It provides an interface that allows the user to select one.

[1025] Step 14:

[1026] The user selects their preferred coach type. Once the selection is complete, press the "Confirm" button.

[1027] Step 15:

[1028] The terminal sends the user's selection results to the server. Data based on the user's selection is transmitted to the server.

[1029] Step 16:

[1030] The server uses an NLP engine to generate appropriate responses based on the selected coach type. For example, in the case of an instructional coach, specific steps and action plans will be created.

[1031] Step 17:

[1032] The server sends the generated response to the terminal. The response is sent in text format.

[1033] Step 18:

[1034] The device displays the received response to the user. Specific steps and action plans are displayed in the chat box. For example, "For new projects, first do A, and then do B."

[1035] Step 19:

[1036] The device displays a feedback collection interface to the user. For example, it might display a question such as, "Was this answer helpful?"

[1037] Step 20:

[1038] Users provide feedback. For example, they can select "yes" or "no," or enter specific comments.

[1039] Step 21:

[1040] The device sends feedback to the server. User feedback data is sent to the server.

[1041] Step 22:

[1042] The server records the feedback it receives in a database. This feedback data is saved to improve the accuracy of future coaching.

[1043] (Application Example 1)

[1044] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1045] This invention relates to a system that responds quickly and accurately to user inquiries. Conventional methods have been time-consuming and labor-intensive in dealing with security system problems and questions. Furthermore, the process of generating appropriate answers based on user inquiries sometimes failed to adequately consider the user's individual circumstances and past inquiry history. To solve these problems, this invention provides a system that encompasses everything from user authentication to coach type selection, answer generation, and feedback collection.

[1046] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1047] In this invention, the server includes means for prompting the user to input authentication information and transmitting it; means for comparing the authentication information with a database and transmitting the authentication result to the terminal; and means for prompting the user to input consultation content as text or voice data and transmitting it. This makes it possible for users to easily and quickly consult about security systems. Furthermore, the server includes means for analyzing the consultation content, selecting and presenting the optimal coach type based on the user's profile and past consultation history; means for generating and transmitting an appropriate response using a natural language processing engine based on the selected coach type; means for displaying the response to the user on the terminal; means for collecting and transmitting feedback from the user; means for recording the received feedback in a database and using it to improve the accuracy of future coaching; and means for generating a response to the inquiry content on the server and displaying the response result on the terminal when the consultation content is transmitted. This enables a quick and accurate response to the consultation content and can increase user satisfaction.

[1048] User authentication is the process of verifying the identity of a user accessing a system and confirming that the user has legitimate authority.

[1049] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[1050] A "server" is a central processing unit that processes information sent by users, performs necessary database matching and analysis, and provides the results.

[1051] "Coaching style" refers to a method of providing advice and solutions to a user's concerns using one of the following styles: directive, supportive, participatory, or achievement-oriented.

[1052] A "natural language processing engine" is an artificial intelligence technology that analyzes user input (text or speech), understands its meaning and intent, and generates appropriate responses.

[1053] "Feedback" refers to evaluations and opinions that users give regarding the answers and services they receive, and these are used to improve the system.

[1054] A "database" is a system that stores data, such as user profile information and past consultation history, in a searchable and retrieval format.

[1055] A "terminal" is a device (such as a smartphone, tablet, or personal computer) used by a user to input information and receive results.

[1056] "Consultation content" refers to the questions or problems that users enter into the system.

[1057] "Answer generation" is the process by which a system derives appropriate solutions or advice in response to a user's question or problem.

[1058] This invention relates to a system that includes a series of processes, from user authentication to analysis of consultation content, selection of coach type, generation and display of appropriate answers, collection of feedback, and system improvement based on that feedback. To realize this system, the following programs, hardware, and software are used.

[1059] The server compares the authentication information entered by the user against a database to perform user authentication. This verifies that the user is legitimate, and the server then sends the authentication result to the terminal.

[1060] The terminal provides an interface that allows the user to input their consultation details. The text or voice data entered by the user is sent from the terminal to the server. The server uses a natural language processing engine (e.g., BERT, SpaCy) to analyze the received consultation details.

[1061] The server selects the most suitable coach type based on the user's profile and past consultation history. The selected coach type is then chosen from directive, supportive, participatory, and achievement-oriented, and presented on the user's device. The user can then choose the appropriate coach type from the options presented.

[1062] Next, the server generates an appropriate response based on the selected coach type. Using a natural language processing engine, it constructs specific steps and advice for the user's consultation. The generated response is sent to the terminal and displayed to the user.

[1063] The device collects user feedback and sends it to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1064] As a concrete example, consider the following prompt message:

[1065] 1. User Authentication:

[1066] The prompt message to send to the authentication server after entering the user ID is: "User ID: security_user123, Password: securepassword"

[1067] If authentication is successful: "Authentication successful, welcome."

[1068] 2. Input and analysis of consultation details:

[1069] User prompt to enter their question: "My security camera is not transmitting video. What should I do?"

[1070] The server analyzes the inquiry and generates an appropriate response: "Access the security camera settings screen and check the network connection. Then, restart the camera."

[1071] In this way, the system of the present invention can respond quickly and accurately to user inquiries. This improves user satisfaction and streamlines system troubleshooting.

[1072] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1073] Step 1:

[1074] User Authentication

[1075] Input: Enter your User ID and password into the terminal.

[1076] Operation: The terminal sends the authentication information entered by the user to the server. The server compares the authentication information with the database and generates an authentication result.

[1077] Output: The authentication result (success or failure) is sent to the terminal, and the terminal displays the authentication result to the user.

[1078] Step 2:

[1079] Enter your consultation details

[1080] Input: The user enters their inquiry details as text or voice data into the chat interface.

[1081] Operation: The terminal sends the entered consultation content to the server. The server passes the received data to the natural language processing engine.

[1082] Output: Analyzed consultation content. The NLP engine converts the text into structured data and returns it to the server.

[1083] Step 3:

[1084] Choosing a Coach Type

[1085] Input: Analyzed consultation content, user profile information, past consultation history.

[1086] Operation: The server uses a coach type selection algorithm to select an appropriate coach type (directive, supportive, participatory, achievement-oriented) and presents it to the terminal.

[1087] Output: Selection result for the appropriate coach type.

[1088] Step 4:

[1089] Choosing a Coach Type

[1090] Input: User selection of coach type.

[1091] Operation: The terminal sends the coach type selected by the user to the server. The server prepares the following actions based on the selected coach type.

[1092] Output: Selected coach type.

[1093] Step 5:

[1094] Answer generation

[1095] Input: Selected coach type, analyzed consultation content.

[1096] Operation: The server uses a natural language processing engine to generate appropriate responses based on the selected coach type. Specifically, it provides specific instructions for directive coaches, advice for supportive coaches, suggestions for collaborative work for participatory coaches, and goal setting for achievement-oriented coaches.

[1097] Output: Generated answer.

[1098] Step 6:

[1099] Display the answer

[1100] Input: Generated response sent from the server.

[1101] Operation: The device displays the received response to the user. Specific steps and advice are displayed in text format.

[1102] Output: The answer displayed to the user.

[1103] Step 7:

[1104] Gathering feedback

[1105] Input: User feedback (whether the answer was helpful, specific comments, etc.).

[1106] Operation: The terminal collects feedback from the user and sends it to the server. The server records the feedback in a database and analyzes it to improve the accuracy of future coaching.

[1107] Output: Feedback recorded in the database.

[1108] Through the steps outlined above, a system is created that responds quickly and accurately to user inquiries. This system includes user authentication, analysis of the inquiry content, selection of the optimal coach type, generation and display of appropriate answers, and collection and analysis of feedback.

[1109] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1110] This invention is a coaching system that combines an emotion engine that recognizes the user's emotions. This system is implemented by the user, terminal, and server performing the following procedures and roles.

[1111] 1. User Authentication:

[1112] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby authenticating the user.

[1113] 2. Enter your consultation details:

[1114] The terminal displays an interface that prompts the user to input their consultation details. The user inputs the consultation details as text or voice data, and the terminal sends it to the server. The server passes the received data to the NLP engine and emotion engine, which analyze the text and emotions.

[1115] 3. Selecting a coach type:

[1116] The server retrieves the user's profile information and past consultation history from the database and uses a coach type selection algorithm to choose the most suitable coach type from four options (directive, supportive, participatory, and achievement-oriented). The user's emotional information, as recognized by the emotion engine, is also taken into consideration during this process.

[1117] The server displays the selected coach types to the terminal, and the user selects their preferred coach type. The process is completed when the terminal sends the selection result to the server.

[1118] 4. Consultation support:

[1119] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation. For example, in the case of an directive coach, specific steps and action plans will be presented. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine. The server sends the generated response to the terminal, which then displays the response to the user.

[1120] 5. Gathering feedback:

[1121] The device displays an interface for collecting user feedback. The user enters feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[1122] Specific example:

[1123] 1. The user logs into the system:

[1124] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[1125] The device sends this information to the server.

[1126] The server checks against the database and notifies the terminal of successful authentication.

[1127] The device displays a message to the user indicating successful authentication.

[1128] 2. The user enters the details of their inquiry:

[1129] In the chat interface displayed on the device, the user types, "I don't know how to proceed with the new project."

[1130] The terminal sends this text to the server.

[1131] The server uses an NLP engine and an emotion engine to analyze the content and emotions. The emotion engine detects "anxiety."

[1132] 3. Selecting a coach type:

[1133] The server recommended a "supportive coach" based on past history.

[1134] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[1135] The device displays the coach type to the user.

[1136] The user selects "supportive coaching," and the device sends that information to the server.

[1137] 4. Consultation support:

[1138] The server generates a supportive coach response, such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," taking into account the results of the emotion engine.

[1139] The server sends the generated response to the terminal.

[1140] The device displays the answer to the user.

[1141] 5. Gathering feedback:

[1142] The device displays the message, "Was this answer helpful?"

[1143] The user enters "Yes" as feedback.

[1144] The device sends feedback to the server.

[1145] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1146] In this way, specific processing is carried out at each step, enabling effective coaching tailored to the user. Furthermore, the emotion engine takes into account the user's emotional state, allowing for even more personalized support.

[1147] The following describes the processing flow.

[1148] Step 1:

[1149] The device prompts the user to enter authentication information. Specifically, the device displays an input form for an ID and password, and the user enters this information.

[1150] Step 2:

[1151] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[1152] Step 3:

[1153] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[1154] Step 4:

[1155] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[1156] Step 5:

[1157] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[1158] Step 6:

[1159] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[1160] Step 7:

[1161] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[1162] Step 8:

[1163] The server passes the received consultation content to an NLP (Natural Language Processing) engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[1164] Step 9:

[1165] The server simultaneously uses an emotion engine to analyze the user's emotions from the consultation content and voice data. The emotion engine identifies the user's emotions from factors such as the tone of voice and the wording used in the text.

[1166] Step 10:

[1167] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[1168] Step 11:

[1169] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented). In this process, the user's emotional information, as recognized by the emotion engine, is also taken into consideration.

[1170] Step 12:

[1171] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[1172] Step 13:

[1173] The user selects their preferred coach type, and the device sends this selection to the server.

[1174] Step 14:

[1175] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine.

[1176] Step 15:

[1177] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[1178] Step 16:

[1179] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[1180] Step 17:

[1181] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[1182] Specific example:

[1183] 1. Steps 1 through 5: The user logs into the system.

[1184] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[1185] The device sends this information to the server. The server compares it with the database and notifies the device that authentication was successful. The device then displays an authentication success message to the user.

[1186] 2. Steps 6 to 7: The user enters the consultation details.

[1187] In the chat interface on the device, the user types "I don't know how to proceed with the new project," and the device sends the text data to the server.

[1188] 3. Steps 8 to 9: Analysis of the consultation content and emotions

[1189] The server uses an NLP engine to analyze the text and extract key keywords and phrases. Simultaneously, an emotion engine detects "anxiety."

[1190] 4. Steps 10 to 11: Selecting a Coach Type

[1191] The server retrieves the user's profile information and past consultation history and recommends a "supportive coach." It also takes into account the user's "anxiety" as analyzed by an emotion engine.

[1192] 5. Steps 12 to 13: Coach type presentation and selection

[1193] The server sends four coach types to the terminal. The terminal displays the coach types to the user, who then selects "Supportive Coach" and sends it to the server.

[1194] 6. Steps 14 to 15: Consultation

[1195] The server generates a supportive coach response. It generates specific responses such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," and adjusts them while taking into account the anxieties recognized by the emotion engine. The server sends the generated response to the device, and the device displays the response to the user.

[1196] 7. Steps 16 to 17: Gathering Feedback

[1197] The device displays "Was this answer helpful?", and the user types "Yes".

[1198] The device sends feedback to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1199] (Example 2)

[1200] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1201] In modern society, there is a need for coaching systems that can effectively address the problems and stresses individuals face. Traditional systems provide uniform advice without considering the user's emotional state, resulting in a lack of appropriate support tailored to individual needs and emotions. Furthermore, there are insufficient mechanisms to effectively incorporate user feedback and improve the system's accuracy. As a result, the effectiveness of coaching for users decreases, and their satisfaction levels decline.

[1202] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1203] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for the user to input consultation content as text or voice data and send it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and means for the server to analyze the user's emotions using an emotion recognition engine and reflect it in the selection of the coach type and the generation of the response. This enables effective coaching tailored to the user's individual needs and emotional state.

[1204] "Authentication information" refers to information such as the ID and password that a user enters to log in to a system.

[1205] A "server" is a computer system that processes requests from users, performs authentication, analyzes data, generates responses, and sends them.

[1206] A "terminal" is a device that a user uses to access the system, enter authentication information, input consultation details, view answers, and provide feedback.

[1207] A "database" is a data storage system that stores and manages user authentication information, profile information, consultation history, feedback, and other data.

[1208] A "Natural Language Processing engine" is software that analyzes text data entered by the user and understands its context and meaning.

[1209] An "emotion recognition engine" is software that identifies and analyzes a user's emotional state based on their input data and consultation content.

[1210] "Coaching style" refers to the style of coaching provided in response to a consultation, and includes four types: directive, supportive, participatory, and achievement-oriented.

[1211] "Feedback" refers to the evaluations and opinions that users provide regarding the system's responses.

[1212] The "coach type selection algorithm" is a computational method for selecting the optimal coach type based on the user's profile information, past consultation history, and emotional state.

[1213] This invention is a coaching system that combines an emotion recognition engine to recognize the user's emotions. This system is implemented through the specific roles of the user, terminal, and server. The details of the system's program processing are described below.

[1214] Hardware and software to be used

[1215] The server is responsible for the main processing of the coaching system and houses the database, Natural Language Processing engine (NLP engine), and emotion recognition engine.

[1216] A terminal is a device that a user uses to access the system, and examples include PCs, smartphones, and tablets.

[1217] The database is used to store user authentication information, profile information, consultation history, and feedback.

[1218] An NLP engine is software that analyzes text data entered by a user to understand its context and meaning.

[1219] An emotion recognition engine is software that identifies and analyzes an emotional state from user input data and consultation content.

[1220] Specific example of processing

[1221] 1. User Authentication

[1222] The device displays a login screen and prompts you to enter your ID and password (e.g., enter "user123" and "password").

[1223] The device sends the entered authentication information to the server.

[1224] The server compares the authentication information with the database and sends the authentication result to the terminal.

[1225] The device displays the authentication result to the user (e.g., displays a "Authentication successful" message).

[1226] 2. Enter the details of your consultation.

[1227] The terminal displays an interface that prompts the user to input their inquiry details. Text or voice input is available.

[1228] The user enters the details of their inquiry (e.g., "I don't know how to proceed with a new project").

[1229] The terminal sends the entered consultation details to the server.

[1230] The server uses an NLP engine and an emotion recognition engine to analyze text and emotions (e.g., detects that the emotion is "anxiety").

[1231] 3. Selecting a Coach Type

[1232] The server retrieves the user's profile information and past consultation history from the database.

[1233] The server executes a coach type selection algorithm and chooses the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[1234] The server sends the selection results to the terminal and presents them to the user (e.g., "Supportive Coach" is recommended).

[1235] The user selects a coaching type, and the device sends the result to the server.

[1236] 4. Consultation support

[1237] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation (e.g., "First, consult with the team to gather their opinions, and then we'll make a plan").

[1238] The server adjusts its response, taking into account the results of the emotion recognition engine.

[1239] The server sends the generated response to the terminal.

[1240] The device displays the answer to the user.

[1241] 5. Gathering feedback

[1242] The device displays an interface for collecting user feedback (e.g., "Was this answer helpful?").

[1243] The user enters feedback (e.g., answers "Yes").

[1244] The device sends feedback to the server.

[1245] The server records the feedback in a database, which is then used to improve the accuracy of future coaching sessions.

[1246] Example of a prompt

[1247] "I don't know how to proceed with the new project."

[1248] "Could you tell me about your role as a team leader?"

[1249] As described above, a coaching system incorporating an emotion recognition engine can provide personalized responses by considering the user's consultation content and emotional state. Furthermore, user feedback can be recorded in a database and used to improve the accuracy of future coaching.

[1250] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1251] Step 1: User Authentication

[1252] Input: The user enters their ID and password into the terminal ("user123" and "password").

[1253] Operation: The terminal displays the login screen and sends the user's input to the server.

[1254] Data processing: The server executes an SQL query to compare the authentication information received with the database.

[1255] Output: The server generates an authentication result (success or failure) and sends it to the terminal.

[1256] Specific action: The device displays the authentication result to the user (e.g., displays the message "Authentication successful").

[1257] Step 2: Enter your consultation details

[1258] Input: The user enters their question as text or voice data into their device (e.g., "I don't know how to proceed with the new project").

[1259] Operation: The terminal displays the user's inquiry details on an input screen, and after the user completes the input, it sends it to the server.

[1260] Data Processing: The server passes the received consultation content to the NLP engine and emotion recognition engine, which analyze the text and emotions. The NLP engine performs contextual analysis, and the emotion recognition engine detects emotions such as "anxiety."

[1261] Output: Generates analysis results (contextual information and emotional state) and stores them internally for use in the next step of processing.

[1262] Specific operation: The server uses an NLP engine and an emotion recognition engine to analyze the consultation content and emotional state.

[1263] Step 3: Selecting a Coach Type

[1264] Input: The server retrieves the user's profile information, past consultation history, and sentiment analysis results from the database.

[1265] Operation: The server executes a coach type selection algorithm and selects the optimal coach type (e.g., "Supportive").

[1266] Data processing: The server calculates the optimal coach type based on the data above. Emotional information is also taken into consideration.

[1267] Output: Generate selection results and send them to the terminal.

[1268] Specific operation: The server sends the result of selecting a coach type to the terminal, and the terminal displays the selection result to the user (e.g., presents "Supportive Coach").

[1269] Step 4: Consultation

[1270] Input: The user selects one of the coach types presented on the device.

[1271] Operation: The terminal sends the user's selection to the server.

[1272] Data Processing: The server uses an NLP engine based on the selected coach type to generate appropriate answers to the user's questions (e.g., "To move forward with a new project, first consult with the team to gather their opinions, and then create a plan").

[1273] Output: Sends the generated response to the device.

[1274] Specific operation: The server adjusts the response based on the results of the emotion recognition engine and sends it to the terminal, which then displays the response to the user.

[1275] Step 5: Gathering Feedback

[1276] Input: The user enters their feedback on the feedback input screen displayed on their device (e.g., enters "Yes").

[1277] Operation: The device sends user feedback to the server.

[1278] Data processing: The server stores the received feedback in a database and analyzes it to improve the accuracy of future coaching.

[1279] Output: The feedback is saved to the database and used as data for future improvements.

[1280] Specific operation: The server records feedback and uses it as new training data as needed.

[1281] (Application Example 2)

[1282] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1283] In today's diverse work environments, workers frequently encounter new ways of operating machinery and tools, which can easily lead to stress and anxiety. Furthermore, traditional coaching systems often provide uniform instruction without considering the user's emotional state, making effective support difficult, especially in fatigued and stressful work environments. Therefore, there is a need for a system that recognizes the user's emotions in real time, dynamically selects the appropriate coach type based on that information, and provides personalized coaching.

[1284] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1285] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for having the user input consultation content as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the optimal coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and further, means for combining this with an emotion recognition engine that recognizes the user's emotions in real time, dynamically adjusting the coach type based on the user's emotional state, and providing an appropriate response. This enables personalized coaching based on a user profile that includes emotional state.

[1286] "User" refers to an individual or entity that uses this system.

[1287] "Authentication information" refers to data used to verify a user's identity, and includes things like IDs and passwords.

[1288] A "server" is a computer system that processes user requests and compares them with a database.

[1289] A "database" is a system that structures and stores important data, such as authentication information and consultation history.

[1290] A "device" is a device that the user directly operates, and includes personal computers and smartphones.

[1291] A "Natural Language Processing engine" is software that analyzes input text data and generates appropriate information.

[1292] An "emotion recognition engine" is software designed to recognize a user's emotional state.

[1293] "Coaching style" refers to a specific coaching style and is classified into four types: directive, supportive, participatory, and achievement-oriented.

[1294] "Feedback" refers to the opinions and evaluations that users provide regarding the systems and services they offer.

[1295] "Personalized coaching" refers to individualized instruction tailored to each user's specific situation and emotions.

[1296] "Text data" refers to character information entered by the user.

[1297] "Voice data" refers to voice information entered by the user.

[1298] "Emotional state" refers to the type of emotion the user is experiencing, and includes anxiety, joy, anger, and so on.

[1299] To implement this invention, the following system configuration and processing procedure should be used.

[1300] The server provides an interface for receiving user authentication information. The user enters authentication information, such as their ID and password, through their terminal and sends it to the server. The server then compares the received authentication information with a database and returns the authentication result to the terminal. This requires a REST API and a database. Specifically, it is common to use libraries such as `requests` for data transmission and `MySQL` or PostgreSQL for database management.

[1301] After successful authentication, the user uses their device to input their consultation details as text or voice data and sends it to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. The NLP engine uses generative AI models such as the GPT-3 model, and the emotion recognition engine uses the Emotion API, among others. For example, if a prompt such as "I don't know how to proceed with the new project" is entered as text input, the server analyzes this to understand the user's intent and emotions.

[1302] Next, the server selects the optimal coach type based on the user's profile information and past consultation history, and further considers their emotional state based on the analysis results. The selected coach types (directive, supportive, participatory, achievement-oriented) are presented on the terminal for the user to choose from. During this process, the coach type selection algorithm operates dynamically, and the results of the emotion recognition engine are also reflected.

[1303] Based on the coach type selected by the user, the server uses an NLP engine to generate appropriate answers to the consultation. The answers are then adjusted to take into account the user's emotional state (e.g., anxiety, joy, anger). For example, a specific answer might be generated such as, "To move forward with a new project, first consult with your team to gather their opinions, and then create a plan. Also, if you have any concerns about the plan, it's a good idea to seek feedback as needed."

[1304] The server then sends the generated response to the terminal, which displays the response to the user. The user can provide feedback on this response, which is then sent back to the server and recorded in the database. This feedback is used to improve the accuracy of future coaching.

[1305] As a concrete example of this system, if a worker who doesn't know how to operate a new machine inputs "I don't know how to operate the new machine," the system recognizes this emotion as "anxiety" and recommends a supportive coach. The coach provides specific steps and points to note to alleviate the user's anxiety.

[1306] Examples of prompt statements:

[1307] "I don't know how to proceed with the new project. What should I do?"

[1308] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1309] Step 1:

[1310] The user enters authentication information into the terminal. This consists of a user ID and password, and is sent from the terminal to the server. The server receives this information and verifies it against its database. This data processing includes comparing the authentication information to generate an accurate authentication result. The authentication result is then sent back from the server to the terminal, which displays it to the user.

[1311] Step 2:

[1312] The user inputs their consultation details as text or audio data using a terminal. This data is sent from the terminal to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. This data processing includes text analysis and emotion analysis to recognize the user's intentions and emotional state. The analysis results are stored on the server.

[1313] Step 3:

[1314] The server retrieves the user's profile information and past consultation history from the database. Furthermore, considering the analysis results obtained in step 2, it selects the optimal coach type (directive, supportive, participatory, achievement-oriented). A coach type selection algorithm is used for the selection, and dynamic emotion recognition is also reflected. The selected coach type is sent from the server to the terminal, which then presents it to the user.

[1315] Step 4:

[1316] The user selects their preferred coach type on their device and sends this information to the server. The server uses an NLP engine to generate an appropriate response based on the selected coach type. The results of the emotion recognition engine are also incorporated, and the response is refined accordingly. The generated response is sent from the server to the device, which then displays it to the user.

[1317] Step 5:

[1318] The user provides feedback on the answer. This feedback is sent from the terminal to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching. The content of the feedback is reflected in the system's algorithm and processed to make it more useful for future coaching sessions.

[1319] Thus, the invented system handles a series of steps, starting with user authentication, analyzing the consultation content, selecting a coach type, generating appropriate answers, and collecting feedback. The data processing of inputs and outputs at each step is a key element in improving the system's accuracy and user experience. For example, if the prompt "I don't know how to proceed with a new project" is entered, advice tailored to the user's feelings and wishes will be generated.

[1320] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1321] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1322] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1323] [Fourth Embodiment]

[1324] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1325] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1326] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1327] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1328] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1329] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1330] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1331] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1332] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1333] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1334] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1335] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1336] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1337] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[1338] 1. User Authentication:

[1339] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby completing the user authentication process.

[1340] 2. Enter your consultation details:

[1341] The terminal provides an interface for the user to input their consultation details. The user inputs their consultation details via text chat or voice input, and the terminal sends the content to the server. The server passes the received consultation details to a Natural Language Processing (NLP) engine, which then analyzes the text.

[1342] 3. Selecting a coach type:

[1343] The server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type using a coach type selection algorithm. The server then presents the four selected coach types (directive, supportive, participatory, and achievement-oriented) to the user's terminal.

[1344] The user selects their preferred coach type from the options presented, and the process is completed when they send their selection from their device to the server.

[1345] 4. Consultation support:

[1346] The server uses an NLP engine to analyze the consultation content in order to generate an appropriate response based on the selected coach type. For example, in the case of an directive coach, specific steps and action plans will be presented. The server sends the generated response to the terminal, which then displays the response to the user.

[1347] 5. Gathering feedback:

[1348] The device provides an interface for collecting user feedback. Users input feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[1349] Specific example:

[1350] 1. The user logs into the system:

[1351] The device prompts the user to enter their ID and password.

[1352] The user enters "user123" and "password," and the terminal sends this information to the server.

[1353] The server compares the authentication information with the database and notifies the terminal of successful authentication.

[1354] The device displays a message to the user indicating successful authentication.

[1355] 2. The user enters the details of their inquiry:

[1356] In the chat interface displayed on the device, the user typed, "I don't know how to proceed with the new project."

[1357] The terminal sends this text to the server.

[1358] The server uses an NLP engine to analyze the consultation content.

[1359] 3. Selecting a coach type:

[1360] The server recommends "directive coaching" based on past history.

[1361] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[1362] The device displays the coach type to the user.

[1363] The user selects "directive coaching," and the device sends that information to the server.

[1364] 4. Consultation support:

[1365] The server generates responses from the instructional coach.

[1366] It generates specific answers such as, "For the new project, first do A, and then do B."

[1367] The server sends the generated response to the terminal.

[1368] The device displays the answer to the user.

[1369] 5. Gathering feedback:

[1370] The device displays the message, "Was this answer helpful?"

[1371] The user enters "Yes" as feedback.

[1372] The device sends feedback to the server.

[1373] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1374] In this way, specific processes are carried out at each step, enabling effective coaching tailored to the user.

[1375] The following describes the processing flow.

[1376] Step 1:

[1377] The device prompts the user to enter authentication information. Specifically, the device displays an ID and password input form, and the user enters this information.

[1378] Step 2:

[1379] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[1380] Step 3:

[1381] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[1382] Step 4:

[1383] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[1384] Step 5:

[1385] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[1386] Step 6:

[1387] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[1388] Step 7:

[1389] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[1390] Step 8:

[1391] The server passes the received consultation content to the NLP engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[1392] Step 9:

[1393] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[1394] Step 10:

[1395] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[1396] Step 11:

[1397] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[1398] Step 12:

[1399] The user selects their preferred coach type, and the device sends this selection to the server.

[1400] Step 13:

[1401] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation.

[1402] Step 14:

[1403] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[1404] Step 15:

[1405] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[1406] Step 16:

[1407] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[1408] (Example 1)

[1409] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1410] In modern coaching systems, there is a challenge in providing the optimal coaching method based on the individual needs and past consultation history of each user. Furthermore, there are insufficient means to effectively collect and analyze user feedback and utilize it to improve the accuracy of future coaching. Given this situation, a new system is needed to realize coaching optimized for each user.

[1411] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1412] In this invention, the server includes means for receiving authentication information from the user and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for receiving consultation content from the user as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using the Natural Language Processing engine of a generation AI model based on the selected coach type and send it to the terminal; means for collecting feedback from the user and sending it to the server; and means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching. This enables the provision of individually optimized coaching to the user and continuous improvement of accuracy.

[1413] "Authentication information" refers to information such as IDs and passwords that users provide to access a system.

[1414] A "server" is a computer system that receives requests from users via a network and processes and stores data.

[1415] A "database" is a structured information system for efficiently storing, managing, and retrieving data in digital format.

[1416] A "terminal" is a computing device used by a user to interface with a system. Specifically, this includes personal computers and smartphones.

[1417] "Consultation content" refers to the problems, questions, and requests for advice that users provide to the system.

[1418] A "Natural Language Processing engine" is an artificial intelligence technology that analyzes natural language and understands its meaning and context.

[1419] "Coaching style" refers to the approach style used when providing problem-solving or guidance methods to users. Specifically, it includes directive, supportive, participatory, and achievement-oriented styles.

[1420] A "generative AI model" is a pre-trained artificial intelligence model that has the ability to generate and analyze data for a specific task.

[1421] "Feedback" refers to information that includes evaluations and suggestions for improvement regarding the answers and information provided by users.

[1422] A "prompt statement" is an instruction or question that is entered to operate a generative AI model.

[1423] In order to implement this invention, the user, terminal, and server must each perform the following procedures and roles.

[1424] First, in order for a user to access the system, the user must enter authentication information using a terminal. This authentication information includes a user ID and password, which the terminal sends to the server. The server verifies this authentication information against a database and sends the authentication result back to the terminal. The terminal then displays the result to the user.

[1425] Next, the user enters their consultation details using the system. The terminal offers the user the option of using either a chat interface or voice input. Once the user enters their consultation details, the terminal sends them to the server. The server then passes the received consultation details to the Natural Language Processing engine of the AI ​​model, which analyzes the text.

[1426] Once the analysis is complete, the server retrieves the user's profile information and past consultation history from the database and selects the most suitable coach type. There are four coach types: directive, supportive, participatory, and achievement-oriented. The selected coach type is presented on the terminal, and the user chooses their preferred coach type. The terminal then sends this selection information to the server.

[1427] Next, the server uses the Natural Language Processing engine of its generative AI model to generate an appropriate response based on the selected coach type. For example, if a user asks, "I don't know how to proceed with a new project," a directive coach would provide specific steps such as, "For a new project, first do A, and then do B." The server sends the generated response to the device, which then displays the response to the user.

[1428] Furthermore, users can provide feedback on the provided answers. The device displays a feedback collection interface and sends the user's feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[1429] As a concrete example, consider inputting the following prompt sentence into the AI ​​model:

[1430] "The user is seeking advice on how to proceed with a new project. They have selected a directive coach. Please provide specific steps and an action plan."

[1431] This system allows us to provide personalized coaching to each user and continuously improve the system based on the results.

[1432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1433] Step 1:

[1434] The user enters their authentication information. This information includes the user ID and password.

[1435] Step 2:

[1436] The terminal receives the user's authentication information and sends it to the server. The entered data is encrypted and transmitted to the server.

[1437] Step 3:

[1438] The server compares the received authentication information with the database. It checks if it matches the user information stored in the database and generates a result. The matching result is output as authentication success or failure.

[1439] Step 4:

[1440] The server sends the authentication result to the terminal. If authentication is successful, user information is also sent.

[1441] Step 5:

[1442] The terminal receives the authentication result from the server and displays the result to the user. If successful, it displays "Login successful"; if unsuccessful, it displays "Login failed".

[1443] Step 6:

[1444] The terminal provides an interface for the user to input their consultation details. This interface can be either a chat box or voice input.

[1445] Step 7:

[1446] The user enters the details of their inquiry. For example, they might enter a specific problem such as "I don't know how to proceed with a new project" into the text box.

[1447] Step 8:

[1448] The terminal receives the entered consultation content and sends it to the server. Text-based content is sent as is, while voice input is converted to text before transmission.

[1449] Step 9:

[1450] The server passes the received consultation content to the NLP engine for analysis. Through this analysis, the meaning and context of the user's consultation content are understood and stored as internal data.

[1451] Step 10:

[1452] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary data to understand the user's background.

[1453] Step 11:

[1454] The server selects the optimal coach type. Based on the acquired profile information and past consultation history, it runs an algorithm to determine the most suitable coach type from four options: directive, supportive, participatory, and achievement-oriented.

[1455] Step 12:

[1456] The server sends the selected coach type to the terminal. This information includes four coach types to present to the user.

[1457] Step 13:

[1458] The device displays four coaching types to the user. It provides an interface that allows the user to select one.

[1459] Step 14:

[1460] The user selects their preferred coach type. Once the selection is complete, press the "Confirm" button.

[1461] Step 15:

[1462] The terminal sends the user's selection results to the server. Data based on the user's selection is transmitted to the server.

[1463] Step 16:

[1464] The server uses an NLP engine to generate appropriate responses based on the selected coach type. For example, in the case of an instructional coach, specific steps and action plans will be created.

[1465] Step 17:

[1466] The server sends the generated response to the terminal. The response is sent in text format.

[1467] Step 18:

[1468] The device displays the received response to the user. Specific steps and action plans are displayed in the chat box. For example, "For new projects, first do A, and then do B."

[1469] Step 19:

[1470] The device displays a feedback collection interface to the user. For example, it might display a question such as, "Was this answer helpful?"

[1471] Step 20:

[1472] Users provide feedback. For example, they can select "yes" or "no," or enter specific comments.

[1473] Step 21:

[1474] The device sends feedback to the server. User feedback data is sent to the server.

[1475] Step 22:

[1476] The server records the feedback it receives in a database. This feedback data is saved to improve the accuracy of future coaching.

[1477] (Application Example 1)

[1478] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1479] This invention relates to a system that responds quickly and accurately to user inquiries. Conventional methods have been time-consuming and labor-intensive in dealing with security system problems and questions. Furthermore, the process of generating appropriate answers based on user inquiries sometimes failed to adequately consider the user's individual circumstances and past inquiry history. To solve these problems, this invention provides a system that encompasses everything from user authentication to coach type selection, answer generation, and feedback collection.

[1480] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1481] In this invention, the server includes means for prompting the user to input authentication information and transmitting it; means for comparing the authentication information with a database and transmitting the authentication result to the terminal; and means for prompting the user to input consultation content as text or voice data and transmitting it. This makes it possible for users to easily and quickly consult about security systems. Furthermore, the server includes means for analyzing the consultation content, selecting and presenting the optimal coach type based on the user's profile and past consultation history; means for generating and transmitting an appropriate response using a natural language processing engine based on the selected coach type; means for displaying the response to the user on the terminal; means for collecting and transmitting feedback from the user; means for recording the received feedback in a database and using it to improve the accuracy of future coaching; and means for generating a response to the inquiry content on the server and displaying the response result on the terminal when the consultation content is transmitted. This enables a quick and accurate response to the consultation content and can increase user satisfaction.

[1482] User authentication is the process of verifying the identity of a user accessing a system and confirming that the user has legitimate authority.

[1483] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[1484] A "server" is a central processing unit that processes information sent by users, performs necessary database matching and analysis, and provides the results.

[1485] "Coaching style" refers to a method of providing advice and solutions to a user's concerns using one of the following styles: directive, supportive, participatory, or achievement-oriented.

[1486] A "natural language processing engine" is an artificial intelligence technology that analyzes user input (text or speech), understands its meaning and intent, and generates appropriate responses.

[1487] "Feedback" refers to evaluations and opinions that users give regarding the answers and services they receive, and these are used to improve the system.

[1488] A "database" is a system that stores data, such as user profile information and past consultation history, in a searchable and retrieval format.

[1489] A "terminal" is a device (such as a smartphone, tablet, or personal computer) used by a user to input information and receive results.

[1490] "Consultation content" refers to the questions or problems that users enter into the system.

[1491] "Answer generation" is the process by which a system derives appropriate solutions or advice in response to a user's question or problem.

[1492] This invention relates to a system that includes a series of processes, from user authentication to analysis of consultation content, selection of coach type, generation and display of appropriate answers, collection of feedback, and system improvement based on that feedback. To realize this system, the following programs, hardware, and software are used.

[1493] The server compares the authentication information entered by the user against a database to perform user authentication. This verifies that the user is legitimate, and the server then sends the authentication result to the terminal.

[1494] The terminal provides an interface that allows the user to input their consultation details. The text or voice data entered by the user is sent from the terminal to the server. The server uses a natural language processing engine (e.g., BERT, SpaCy) to analyze the received consultation details.

[1495] The server selects the most suitable coach type based on the user's profile and past consultation history. The selected coach type is then chosen from directive, supportive, participatory, and achievement-oriented, and presented on the user's device. The user can then choose the appropriate coach type from the options presented.

[1496] Next, the server generates an appropriate response based on the selected coach type. Using a natural language processing engine, it constructs specific steps and advice for the user's consultation. The generated response is sent to the terminal and displayed to the user.

[1497] The device collects user feedback and sends it to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1498] As a concrete example, consider the following prompt message:

[1499] 1. User Authentication:

[1500] The prompt message to send to the authentication server after entering the user ID is: "User ID: security_user123, Password: securepassword"

[1501] If authentication is successful: "Authentication successful, welcome."

[1502] 2. Input and analysis of consultation details:

[1503] User prompt to enter their question: "My security camera is not transmitting video. What should I do?"

[1504] The server analyzes the inquiry and generates an appropriate response: "Access the security camera settings screen and check the network connection. Then, restart the camera."

[1505] In this way, the system of the present invention can respond quickly and accurately to user inquiries. This improves user satisfaction and streamlines system troubleshooting.

[1506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1507] Step 1:

[1508] User Authentication

[1509] Input: Enter your User ID and password into the terminal.

[1510] Operation: The terminal sends the authentication information entered by the user to the server. The server compares the authentication information with the database and generates an authentication result.

[1511] Output: The authentication result (success or failure) is sent to the terminal, and the terminal displays the authentication result to the user.

[1512] Step 2:

[1513] Enter your consultation details

[1514] Input: The user enters their inquiry details as text or voice data into the chat interface.

[1515] Operation: The terminal sends the entered consultation content to the server. The server passes the received data to the natural language processing engine.

[1516] Output: Analyzed consultation content. The NLP engine converts the text into structured data and returns it to the server.

[1517] Step 3:

[1518] Choosing a Coach Type

[1519] Input: Analyzed consultation content, user profile information, past consultation history.

[1520] Operation: The server uses a coach type selection algorithm to select an appropriate coach type (directive, supportive, participatory, achievement-oriented) and presents it to the terminal.

[1521] Output: Selection result for the appropriate coach type.

[1522] Step 4:

[1523] Choosing a Coach Type

[1524] Input: User selection of coach type.

[1525] Operation: The terminal sends the coach type selected by the user to the server. The server prepares the following actions based on the selected coach type.

[1526] Output: Selected coach type.

[1527] Step 5:

[1528] Answer generation

[1529] Input: Selected coach type, analyzed consultation content.

[1530] Operation: The server uses a natural language processing engine to generate appropriate responses based on the selected coach type. Specifically, it provides specific instructions for directive coaches, advice for supportive coaches, suggestions for collaborative work for participatory coaches, and goal setting for achievement-oriented coaches.

[1531] Output: Generated answer.

[1532] Step 6:

[1533] Display the answer

[1534] Input: Generated response sent from the server.

[1535] Operation: The device displays the received response to the user. Specific steps and advice are displayed in text format.

[1536] Output: The answer displayed to the user.

[1537] Step 7:

[1538] Gathering feedback

[1539] Input: User feedback (whether the answer was helpful, specific comments, etc.).

[1540] Operation: The terminal collects feedback from the user and sends it to the server. The server records the feedback in a database and analyzes it to improve the accuracy of future coaching.

[1541] Output: Feedback recorded in the database.

[1542] Through the steps outlined above, a system is created that responds quickly and accurately to user inquiries. This system includes user authentication, analysis of the inquiry content, selection of the optimal coach type, generation and display of appropriate answers, and collection and analysis of feedback.

[1543] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1544] This invention is a coaching system that combines an emotion engine that recognizes the user's emotions. This system is implemented by the user, terminal, and server performing the following procedures and roles.

[1545] 1. User Authentication:

[1546] The terminal prompts the user to enter authentication information, which is then sent to the server. The server compares the received authentication information with its database and sends the authentication result to the terminal. The terminal then displays the authentication result to the user, thereby authenticating the user.

[1547] 2. Enter your consultation details:

[1548] The terminal displays an interface that prompts the user to input their consultation details. The user inputs the consultation details as text or voice data, and the terminal sends it to the server. The server passes the received data to the NLP engine and emotion engine, which analyze the text and emotions.

[1549] 3. Selecting a coach type:

[1550] The server retrieves the user's profile information and past consultation history from the database and uses a coach type selection algorithm to choose the most suitable coach type from four options (directive, supportive, participatory, and achievement-oriented). The user's emotional information, as recognized by the emotion engine, is also taken into consideration during this process.

[1551] The server displays the selected coach types to the terminal, and the user selects their preferred coach type. The process is completed when the terminal sends the selection result to the server.

[1552] 4. Consultation support:

[1553] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation. For example, in the case of an directive coach, specific steps and action plans will be presented. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine. The server sends the generated response to the terminal, which then displays the response to the user.

[1554] 5. Gathering feedback:

[1555] The device displays an interface for collecting user feedback. The user enters feedback on the answers, and the device sends that feedback to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching.

[1556] Specific example:

[1557] 1. The user logs into the system:

[1558] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[1559] The device sends this information to the server.

[1560] The server checks against the database and notifies the terminal of successful authentication.

[1561] The device displays a message to the user indicating successful authentication.

[1562] 2. The user enters the details of their inquiry:

[1563] In the chat interface displayed on the device, the user types, "I don't know how to proceed with the new project."

[1564] The terminal sends this text to the server.

[1565] The server uses an NLP engine and an emotion engine to analyze the content and emotions. The emotion engine detects "anxiety."

[1566] 3. Selecting a coach type:

[1567] The server recommended a "supportive coach" based on past history.

[1568] The server sends four coaching types (directive, supportive, participatory, and achievement-oriented) to the device.

[1569] The device displays the coach type to the user.

[1570] The user selects "supportive coaching," and the device sends that information to the server.

[1571] 4. Consultation support:

[1572] The server generates a supportive coach response, such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," taking into account the results of the emotion engine.

[1573] The server sends the generated response to the terminal.

[1574] The device displays the answer to the user.

[1575] 5. Gathering feedback:

[1576] The device displays the message, "Was this answer helpful?"

[1577] The user enters "Yes" as feedback.

[1578] The device sends feedback to the server.

[1579] The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1580] In this way, specific processing is carried out at each step, enabling effective coaching tailored to the user. Furthermore, the emotion engine takes into account the user's emotional state, allowing for even more personalized support.

[1581] The following describes the processing flow.

[1582] Step 1:

[1583] The device prompts the user to enter authentication information. Specifically, the device displays an input form for an ID and password, and the user enters this information.

[1584] Step 2:

[1585] The device sends the authentication information entered by the user to the server. The authentication information is transmitted using a secure communication protocol.

[1586] Step 3:

[1587] The server compares the received authentication information with the database. The server compares the user information stored in the database with the transmitted authentication information to check if they match.

[1588] Step 4:

[1589] The server sends the verification result to the terminal. If authentication is successful, a success message is sent; if it fails, an error message is sent.

[1590] Step 5:

[1591] The device displays the authentication result to the user. If authentication is successful, a button to proceed to the next interface is displayed; if it fails, a message prompting re-entry is displayed.

[1592] Step 6:

[1593] The device displays an interface that allows the user to input their inquiry details. This includes a text chat input field and a voice input button.

[1594] Step 7:

[1595] The user enters their inquiry as text or audio data, and the device sends it to the server. Text data is sent as is, while audio data is sent as an audio file.

[1596] Step 8:

[1597] The server passes the received consultation content to an NLP (Natural Language Processing) engine, which analyzes the text. The NLP engine analyzes the text and extracts key keywords and phrases.

[1598] Step 9:

[1599] The server simultaneously uses an emotion engine to analyze the user's emotions from the consultation content and voice data. The emotion engine identifies the user's emotions from factors such as the tone of voice and the wording used in the text.

[1600] Step 10:

[1601] The server retrieves the user's profile information and past consultation history from the database. This provides the necessary information to select the most suitable coach type for each individual's situation.

[1602] Step 11:

[1603] The server uses a coach type selection algorithm to choose the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented). In this process, the user's emotional information, as recognized by the emotion engine, is also taken into consideration.

[1604] Step 12:

[1605] The server presents the terminal with four selected coaching types. The terminal then displays the coaching type options to the user.

[1606] Step 13:

[1607] The user selects their preferred coach type, and the device sends this selection to the server.

[1608] Step 14:

[1609] Based on the selected coach type, the server uses an NLP engine to generate appropriate responses to the consultation. The response is also adjusted by considering the user's emotions (e.g., stress, anxiety, joy) as recognized by the emotion engine.

[1610] Step 15:

[1611] The server sends the generated response to the terminal. The terminal displays the response to the inquiry to the user.

[1612] Step 16:

[1613] The device displays an interface for collecting user feedback. The user enters their feedback, and the device sends it to the server.

[1614] Step 17:

[1615] The server records the feedback it receives in a database. The collected feedback is used to improve the accuracy of future coaching.

[1616] Specific example:

[1617] 1. Steps 1 through 5: The user logs into the system.

[1618] The device displays an input form for the ID and password, and the user enters "user123" and "password".

[1619] The device sends this information to the server. The server compares it with the database and notifies the device that authentication was successful. The device then displays an authentication success message to the user.

[1620] 2. Steps 6 to 7: The user enters the consultation details.

[1621] In the chat interface on the device, the user types "I don't know how to proceed with the new project," and the device sends the text data to the server.

[1622] 3. Steps 8 to 9: Analysis of the consultation content and emotions

[1623] The server uses an NLP engine to analyze the text and extract key keywords and phrases. Simultaneously, an emotion engine detects "anxiety."

[1624] 4. Steps 10 to 11: Selecting a Coach Type

[1625] The server retrieves the user's profile information and past consultation history and recommends a "supportive coach." It also takes into account the user's "anxiety" as analyzed by an emotion engine.

[1626] 5. Steps 12 to 13: Coach type presentation and selection

[1627] The server sends four coach types to the terminal. The terminal displays the coach types to the user, who then selects "Supportive Coach" and sends it to the server.

[1628] 6. Steps 14 to 15: Consultation

[1629] The server generates a supportive coach response. It generates specific responses such as, "To move forward with a new project, first consult with your team and gather their opinions, then create a plan. Also, if you have any concerns about the plan, it's a good idea to ask for feedback as needed," and adjusts them while taking into account the anxieties recognized by the emotion engine. The server sends the generated response to the device, and the device displays the response to the user.

[1630] 7. Steps 16 to 17: Gathering Feedback

[1631] The device displays "Was this answer helpful?", and the user types "Yes".

[1632] The device sends feedback to the server. The server records the feedback in a database and uses it to improve the accuracy of future coaching.

[1633] (Example 2)

[1634] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1635] In modern society, there is a need for coaching systems that can effectively address the problems and stresses individuals face. Traditional systems provide uniform advice without considering the user's emotional state, resulting in a lack of appropriate support tailored to individual needs and emotions. Furthermore, there are insufficient mechanisms to effectively incorporate user feedback and improve the system's accuracy. As a result, the effectiveness of coaching for users decreases, and their satisfaction levels decline.

[1636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1637] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for the user to input consultation content as text or voice data and send it to the server; means for the server to analyze the consultation content, select the most suitable coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and means for the server to analyze the user's emotions using an emotion recognition engine and reflect it in the selection of the coach type and the generation of the response. This enables effective coaching tailored to the user's individual needs and emotional state.

[1638] "Authentication information" refers to information such as the ID and password that a user enters to log in to a system.

[1639] A "server" is a computer system that processes requests from users, performs authentication, analyzes data, generates responses, and sends them.

[1640] A "terminal" is a device that a user uses to access the system, enter authentication information, input consultation details, view answers, and provide feedback.

[1641] A "database" is a data storage system that stores and manages user authentication information, profile information, consultation history, feedback, and other data.

[1642] A "Natural Language Processing engine" is software that analyzes text data entered by the user and understands its context and meaning.

[1643] An "emotion recognition engine" is software that identifies and analyzes a user's emotional state based on their input data and consultation content.

[1644] "Coaching style" refers to the style of coaching provided in response to a consultation, and includes four types: directive, supportive, participatory, and achievement-oriented.

[1645] "Feedback" refers to the evaluations and opinions that users provide regarding the system's responses.

[1646] The "coach type selection algorithm" is a computational method for selecting the optimal coach type based on the user's profile information, past consultation history, and emotional state.

[1647] This invention is a coaching system that combines an emotion recognition engine to recognize the user's emotions. This system is implemented through the specific roles of the user, terminal, and server. The details of the system's program processing are described below.

[1648] Hardware and software to be used

[1649] The server is responsible for the main processing of the coaching system and houses the database, Natural Language Processing engine (NLP engine), and emotion recognition engine.

[1650] A terminal is a device that a user uses to access the system, and examples include PCs, smartphones, and tablets.

[1651] The database is used to store user authentication information, profile information, consultation history, and feedback.

[1652] An NLP engine is software that analyzes text data entered by a user to understand its context and meaning.

[1653] An emotion recognition engine is software that identifies and analyzes an emotional state from user input data and consultation content.

[1654] Specific example of processing

[1655] 1. User Authentication

[1656] The device displays a login screen and prompts you to enter your ID and password (e.g., enter "user123" and "password").

[1657] The device sends the entered authentication information to the server.

[1658] The server compares the authentication information with the database and sends the authentication result to the terminal.

[1659] The device displays the authentication result to the user (e.g., displays a "Authentication successful" message).

[1660] 2. Enter the details of your consultation.

[1661] The terminal displays an interface that prompts the user to input their inquiry details. Text or voice input is available.

[1662] The user enters the details of their inquiry (e.g., "I don't know how to proceed with a new project").

[1663] The terminal sends the entered consultation details to the server.

[1664] The server uses an NLP engine and an emotion recognition engine to analyze text and emotions (e.g., detects that the emotion is "anxiety").

[1665] 3. Selecting a Coach Type

[1666] The server retrieves the user's profile information and past consultation history from the database.

[1667] The server executes a coach type selection algorithm and chooses the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented).

[1668] The server sends the selection results to the terminal and presents them to the user (e.g., "Supportive Coach" is recommended).

[1669] The user selects a coaching type, and the device sends the result to the server.

[1670] 4. Consultation support

[1671] The server uses an NLP engine based on the selected coach type to generate an appropriate response to the consultation (e.g., "First, consult with the team to gather their opinions, and then we'll make a plan").

[1672] The server adjusts its response, taking into account the results of the emotion recognition engine.

[1673] The server sends the generated response to the terminal.

[1674] The device displays the answer to the user.

[1675] 5. Gathering feedback

[1676] The device displays an interface for collecting user feedback (e.g., "Was this answer helpful?").

[1677] The user enters feedback (e.g., answers "Yes").

[1678] The device sends feedback to the server.

[1679] The server records the feedback in a database, which is then used to improve the accuracy of future coaching sessions.

[1680] Example of a prompt

[1681] "I don't know how to proceed with the new project."

[1682] "Could you tell me about your role as a team leader?"

[1683] As described above, a coaching system incorporating an emotion recognition engine can provide personalized responses by considering the user's consultation content and emotional state. Furthermore, user feedback can be recorded in a database and used to improve the accuracy of future coaching.

[1684] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1685] Step 1: User Authentication

[1686] Input: The user enters their ID and password into the terminal ("user123" and "password").

[1687] Operation: The terminal displays the login screen and sends the user's input to the server.

[1688] Data processing: The server executes an SQL query to compare the authentication information received with the database.

[1689] Output: The server generates an authentication result (success or failure) and sends it to the terminal.

[1690] Specific action: The device displays the authentication result to the user (e.g., displays the message "Authentication successful").

[1691] Step 2: Enter your consultation details

[1692] Input: The user enters their question as text or voice data into their device (e.g., "I don't know how to proceed with the new project").

[1693] Operation: The terminal displays the user's inquiry details on an input screen, and after the user completes the input, it sends it to the server.

[1694] Data Processing: The server passes the received consultation content to the NLP engine and emotion recognition engine, which analyze the text and emotions. The NLP engine performs contextual analysis, and the emotion recognition engine detects emotions such as "anxiety."

[1695] Output: Generates analysis results (contextual information and emotional state) and stores them internally for use in the next step of processing.

[1696] Specific operation: The server uses an NLP engine and an emotion recognition engine to analyze the consultation content and emotional state.

[1697] Step 3: Selecting a Coach Type

[1698] Input: The server retrieves the user's profile information, past consultation history, and sentiment analysis results from the database.

[1699] Operation: The server executes a coach type selection algorithm and selects the optimal coach type (e.g., "Supportive").

[1700] Data processing: The server calculates the optimal coach type based on the data above. Emotional information is also taken into consideration.

[1701] Output: Generate selection results and send them to the terminal.

[1702] Specific operation: The server sends the result of selecting a coach type to the terminal, and the terminal displays the selection result to the user (e.g., presents "Supportive Coach").

[1703] Step 4: Consultation

[1704] Input: The user selects one of the coach types presented on the device.

[1705] Operation: The terminal sends the user's selection to the server.

[1706] Data Processing: The server uses an NLP engine based on the selected coach type to generate appropriate answers to the user's questions (e.g., "To move forward with a new project, first consult with the team to gather their opinions, and then create a plan").

[1707] Output: Sends the generated response to the device.

[1708] Specific operation: The server adjusts the response based on the results of the emotion recognition engine and sends it to the terminal, which then displays the response to the user.

[1709] Step 5: Gathering Feedback

[1710] Input: The user enters their feedback on the feedback input screen displayed on their device (e.g., enters "Yes").

[1711] Operation: The device sends user feedback to the server.

[1712] Data processing: The server stores the received feedback in a database and analyzes it to improve the accuracy of future coaching.

[1713] Output: The feedback is saved to the database and used as data for future improvements.

[1714] Specific operation: The server records feedback and uses it as new training data as needed.

[1715] (Application Example 2)

[1716] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1717] In today's diverse work environments, workers frequently encounter new ways of operating machinery and tools, which can easily lead to stress and anxiety. Furthermore, traditional coaching systems often provide uniform instruction without considering the user's emotional state, making effective support difficult, especially in fatigued and stressful work environments. Therefore, there is a need for a system that recognizes the user's emotions in real time, dynamically selects the appropriate coach type based on that information, and provides personalized coaching.

[1718] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1719] In this invention, the server includes means for having the user input authentication information and sending it to the server; means for the server to compare the authentication information with a database and send the authentication result to the terminal; means for having the user input consultation content as text or voice data and sending it to the server; means for the server to analyze the consultation content, select the optimal coach type from four coach types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and present it to the terminal; means for the server to generate an appropriate response using a Natural Language Processing engine based on the selected coach type and send it to the terminal; means for the terminal to display the response to the user; means for collecting feedback from the user and sending it to the server; means for the server to record the received feedback in a database and use it to improve the accuracy of future coaching; and further, means for combining this with an emotion recognition engine that recognizes the user's emotions in real time, dynamically adjusting the coach type based on the user's emotional state, and providing an appropriate response. This enables personalized coaching based on a user profile that includes emotional state.

[1720] "User" refers to an individual or entity that uses this system.

[1721] "Authentication information" refers to data used to verify a user's identity, and includes things like IDs and passwords.

[1722] A "server" is a computer system that processes user requests and compares them with a database.

[1723] A "database" is a system that structures and stores important data, such as authentication information and consultation history.

[1724] A "device" is a device that the user directly operates, and includes personal computers and smartphones.

[1725] A "Natural Language Processing engine" is software that analyzes input text data and generates appropriate information.

[1726] An "emotion recognition engine" is software designed to recognize a user's emotional state.

[1727] "Coaching style" refers to a specific coaching style and is classified into four types: directive, supportive, participatory, and achievement-oriented.

[1728] "Feedback" refers to the opinions and evaluations that users provide regarding the systems and services they offer.

[1729] "Personalized coaching" refers to individualized instruction tailored to each user's specific situation and emotions.

[1730] "Text data" refers to character information entered by the user.

[1731] "Voice data" refers to voice information entered by the user.

[1732] "Emotional state" refers to the type of emotion the user is experiencing, and includes anxiety, joy, anger, and so on.

[1733] To implement this invention, the following system configuration and processing procedure should be used.

[1734] The server provides an interface for receiving user authentication information. The user enters authentication information, such as their ID and password, through their terminal and sends it to the server. The server then compares the received authentication information with a database and returns the authentication result to the terminal. This requires a REST API and a database. Specifically, it is common to use libraries such as `requests` for data transmission and `MySQL` or PostgreSQL for database management.

[1735] After successful authentication, the user uses their device to input their consultation details as text or voice data and sends it to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. The NLP engine uses generative AI models such as the GPT-3 model, and the emotion recognition engine uses the Emotion API, among others. For example, if a prompt such as "I don't know how to proceed with the new project" is entered as text input, the server analyzes this to understand the user's intent and emotions.

[1736] Next, the server selects the optimal coach type based on the user's profile information and past consultation history, and further considers their emotional state based on the analysis results. The selected coach types (directive, supportive, participatory, achievement-oriented) are presented on the terminal for the user to choose from. During this process, the coach type selection algorithm operates dynamically, and the results of the emotion recognition engine are also reflected.

[1737] Based on the coach type selected by the user, the server uses an NLP engine to generate appropriate answers to the consultation. The answers are then adjusted to take into account the user's emotional state (e.g., anxiety, joy, anger). For example, a specific answer might be generated such as, "To move forward with a new project, first consult with your team to gather their opinions, and then create a plan. Also, if you have any concerns about the plan, it's a good idea to seek feedback as needed."

[1738] The server then sends the generated response to the terminal, which displays the response to the user. The user can provide feedback on this response, which is then sent back to the server and recorded in the database. This feedback is used to improve the accuracy of future coaching.

[1739] As a concrete example of this system, if a worker who doesn't know how to operate a new machine inputs "I don't know how to operate the new machine," the system recognizes this emotion as "anxiety" and recommends a supportive coach. The coach provides specific steps and points to note to alleviate the user's anxiety.

[1740] Examples of prompt statements:

[1741] "I don't know how to proceed with the new project. What should I do?"

[1742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1743] Step 1:

[1744] The user enters authentication information into the terminal. This consists of a user ID and password, and is sent from the terminal to the server. The server receives this information and verifies it against its database. This data processing includes comparing the authentication information to generate an accurate authentication result. The authentication result is then sent back from the server to the terminal, which displays it to the user.

[1745] Step 2:

[1746] The user inputs their consultation details as text or audio data using a terminal. This data is sent from the terminal to the server. The server receives this data and passes it to a Natural Language Processing (NLP) engine and an emotion recognition engine for analysis. This data processing includes text analysis and emotion analysis to recognize the user's intentions and emotional state. The analysis results are stored on the server.

[1747] Step 3:

[1748] The server retrieves the user's profile information and past consultation history from the database. Furthermore, considering the analysis results obtained in step 2, it selects the optimal coach type (directive, supportive, participatory, achievement-oriented). A coach type selection algorithm is used for the selection, and dynamic emotion recognition is also reflected. The selected coach type is sent from the server to the terminal, which then presents it to the user.

[1749] Step 4:

[1750] The user selects their preferred coach type on their device and sends this information to the server. The server uses an NLP engine to generate an appropriate response based on the selected coach type. The results of the emotion recognition engine are also incorporated, and the response is refined accordingly. The generated response is sent from the server to the device, which then displays it to the user.

[1751] Step 5:

[1752] The user provides feedback on the answer. This feedback is sent from the terminal to the server. The server records the received feedback in a database and uses it to improve the accuracy of future coaching. The content of the feedback is reflected in the system's algorithm and processed to make it more useful for future coaching sessions.

[1753] Thus, the invented system handles a series of steps, starting with user authentication, analyzing the consultation content, selecting a coach type, generating appropriate answers, and collecting feedback. The data processing of inputs and outputs at each step is a key element in improving the system's accuracy and user experience. For example, if the prompt "I don't know how to proceed with a new project" is entered, advice tailored to the user's feelings and wishes will be generated.

[1754] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1755] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1756] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1757] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1758] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1759] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1760] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1761] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1762] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1763] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1764] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1765] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1766] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1767] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1768] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1769] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1770] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1771] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1772] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1773] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1774] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1775] The following is further disclosed regarding the embodiments described above.

[1776] (Claim 1)

[1777] A means of having the user enter authentication information and sending it to the server,

[1778] A means for the server to compare the authentication information with a database and send the authentication result to the terminal,

[1779] A means of having the user input their consultation details as text or audio data and sending it to the server,

[1780] The server analyzes the consultation content, selects the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and presents it to the terminal.

[1781] A means for generating an appropriate response using a Natural Language Processing engine based on the coach type selected on the server and sending it to the terminal,

[1782] A means of displaying the answer to the user on the device,

[1783] A means of collecting user feedback and sending it to the server,

[1784] A method for recording feedback received by the server in a database and using it to improve the accuracy of future coaching,

[1785] A system that includes this.

[1786] (Claim 2)

[1787] The system according to claim 1, wherein responses to user inquiries are generated based on four coaching types: directive, supportive, participatory, and achievement-oriented.

[1788] (Claim 3)

[1789] The system according to claim 1, wherein the server analyzes the user's inquiry using a Natural Language Processing engine and generates an appropriate response.

[1790] "Example 1"

[1791] (Claim 1)

[1792] A means of having the user enter authentication information and sending it to the server,

[1793] A means for the server to compare the authentication information with a database and send the authentication result to the terminal,

[1794] A means of having the user input their consultation details as text or audio data and sending it to the server,

[1795] The server analyzes the consultation content, selects the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and presents it to the terminal.

[1796] A means for generating an appropriate response using the Natural Language Processing engine of a generative AI model based on the coach type selected on the server, and sending it to the terminal,

[1797] A means of displaying the answer to the user on the device,

[1798] A means of collecting user feedback and sending it to the server,

[1799] A method for recording feedback received by the server in a database and using it to improve the accuracy of future coaching,

[1800] A system that includes this.

[1801] (Claim 2)

[1802] The system according to claim 1, wherein responses to user inquiries are generated based on four coaching types: directive, supportive, participatory, and achievement-oriented.

[1803] (Claim 3)

[1804] The system according to claim 1, wherein the server analyzes the user's inquiry using the Natural Language Processing engine of the generated AI model and generates an appropriate response.

[1805] "Application Example 1"

[1806] (Claim 1)

[1807] A means of having the user enter authentication information and then transmitting it,

[1808] A means of verifying authentication information against a database and sending the authentication result to the terminal,

[1809] A means of having the user input their consultation details as text or audio data and then transmitting it,

[1810] A method for analyzing the consultation content, selecting and presenting the most suitable coach type from multiple coach types based on the user's profile and past consultation history,

[1811] A means for generating and sending appropriate responses using a natural language processing engine based on the selected coach type,

[1812] A means of displaying the answer to the user on the device,

[1813] A means of collecting and sending user feedback,

[1814] A means of recording the received feedback in a database and using it to improve the accuracy of future coaching,

[1815] When submitting a consultation request, a response to the inquiry is generated on the server and the response is displayed on the terminal.

[1816] A system that includes this.

[1817] (Claim 2)

[1818] The system according to claim 1, wherein responses to user inquiries are generated based on each coaching type: directive, supportive, participatory, and achievement-oriented.

[1819] (Claim 3)

[1820] The system according to claim 1, wherein the server analyzes the user's inquiry using a natural language processing engine and generates an appropriate response.

[1821] "Example 2 of combining an emotion engine"

[1822] (Claim 1)

[1823] A means of having the user enter authentication information and sending it to the server,

[1824] A means for the server to compare the authentication information with a database and send the authentication result to the terminal,

[1825] A means of having the user input their consultation details as text or audio data and sending it to the server,

[1826] The server analyzes the consultation content, selects the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and presents it to the terminal.

[1827] A means for generating an appropriate response using a Natural Language Processing engine based on the coach type selected on the server and sending it to the terminal,

[1828] A means of displaying the answer to the user on the device,

[1829] A means of collecting user feedback and sending it to the server,

[1830] A method for recording feedback received by the server in a database and using it to improve the accuracy of future coaching,

[1831] A means by which the server uses an emotion recognition engine to analyze the user's emotions and reflect this in the selection of the coach type and the generation of responses,

[1832] A system that includes this.

[1833] (Claim 2)

[1834] The system according to claim 1, wherein responses to user inquiries are generated based on four coaching types—directive, supportive, participatory, and achievement-oriented—taking into account the user's emotional perception results.

[1835] (Claim 3)

[1836] The system according to claim 1, wherein the server analyzes the user's consultation content using an emotion recognition engine and a Natural Language Processing engine and generates an appropriate response.

[1837] "Application example 2 when combining with an emotional engine"

[1838] (Claim 1)

[1839] A means of having the user enter authentication information and sending it to the server,

[1840] A means for the server to compare the authentication information with a database and send the authentication result to the terminal,

[1841] A means of having the user input their consultation details as text or audio data and sending it to the server,

[1842] The server analyzes the consultation content, selects the most suitable coach type from four types (directive, supportive, participatory, and achievement-oriented) based on the user's profile and past consultation history, and presents it to the terminal.

[1843] A means for generating an appropriate response using a Natural Language Processing engine based on the coach type selected on the server and sending it to the terminal,

[1844] A means of displaying the answer to the user on the device,

[1845] A means of collecting user feedback and sending it to the server,

[1846] A method for recording feedback received by the server in a database and using it to improve the accuracy of future coaching,

[1847] Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions in real time, and by dynamically adjusting the coach type based on the user's emotional state to provide appropriate answers,

[1848] A system that includes this.

[1849] (Claim 2)

[1850] The system according to claim 1, wherein the response to the user's consultation is generated based on four coaching types: directive, supportive, participatory, and achievement-oriented, and is further adjusted to take into account the user's emotional state.

[1851] (Claim 3)

[1852] The system according to claim 1, wherein the server analyzes the user's consultation content using a Natural Language Processing engine and generates an appropriate response in cooperation with an emotion recognition engine. [Explanation of Symbols]

[1853] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of having the user enter authentication information and sending it to the server, A means for the server to compare the authentication information with a database and send the authentication result to the terminal, A means of having the user input their consultation details as text or audio data and sending it to the server, A means for analyzing the consultation content on a server, selecting the most suitable coach type from multiple coach types based on the user's profile and past consultation history, and presenting it to the terminal, A means for generating an appropriate response using a Natural Language Processing engine based on the coach type selected on the server and sending it to the terminal, A means of displaying the answer to the user on the device, A means of collecting user feedback and sending it to the server, A method for recording feedback received by the server in a database and using it to improve the accuracy of future coaching, A system that includes this.

2. The system according to claim 1, wherein responses to user inquiries are generated based on four coaching types: directive, supportive, participatory, and achievement-oriented.

3. The system according to claim 1, wherein the server analyzes the user's inquiry using a Natural Language Processing engine and generates an appropriate response.

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