system

Abstract

We provide the system. [Solution] A means for receiving and authenticating login information from the user, Means for selecting a training scenario, Based on the selected training scenario, a means to initiate a conversation with the character playing the role of a customer, A means for analyzing user statements and generating appropriate responses, Means for displaying or playing the generated response to the user, A means of evaluating user interactions and providing feedback, Means for storing conversation data and evaluation results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is to provide an effective and efficient training means for the problem that mass retailers cannot spend enough time on the education of new recruits. Specifically, it is to enable new recruits to acquire practical skills through an experience close to conversations with actual customers.

Means for Solving the Problems

[0005] The present invention solves the above problems with a system that includes means for receiving and authenticating login information from a user, means for selecting a training scenario, means for initiating a conversation with a customer character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response audibly to the user, means for evaluating the user's response and providing feedback, and means for storing conversation data and evaluation results.

[0006] "User" refers to a new crew member who is receiving training using the system.

[0007] "Login information" refers to identification information such as IDs and passwords necessary for user authentication.

[0008] "Authentication" refers to the process of verifying a user's legitimacy based on their login information and granting them access to the system.

[0009] A "training scenario" refers to a conversational simulation designed based on specific situations or customer requests.

[0010] A "customer character" refers to a virtual customer who interacts with the user within the system.

[0011] "Analyzing speech" refers to the process of analyzing the voice or text entered by the user to understand its intent and meaning.

[0012] "Generating a response" means creating appropriate answers or reactions based on the analyzed user statements.

[0013] "Feedback" refers to information that provides evaluations and advice regarding user interactions.

[0014] "Conversation data" refers to information that records all the dialogue exchanged between the user and the customer character.

[0015] "Evaluation results" refers to information including scores and comments for each item of evaluation of the user's response content.

Brief Description of Drawings

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

Embodiments 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 terms used in the following description will be explained.

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

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[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] This invention is a system for efficiently training new crew members and is implemented using users, terminals, and a server. The following describes in detail how this invention can be specifically implemented.

[0038] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0039] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[0040] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[0041] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which uses natural language processing to analyze the user's statements. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and the virtual customer that closely resembles a real customer service scenario.

[0042] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[0043] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[0044] Specific example

[0045] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0046] In this way, this system allows new crew members to gain experience that closely resembles actual customer interactions. This enables them to acquire practical skills effectively in a short period of time, thereby improving the quality of customer service in mass retail stores.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user enters their user ID and password on the device's login screen.

[0050] Step 2:

[0051] The terminal sends the entered authentication information to the server.

[0052] Step 3:

[0053] The server checks the received authentication information against the database and authenticates the user.

[0054] Step 4:

[0055] The server sends the authentication result back to the terminal.

[0056] Step 5:

[0057] The device displays the main menu screen to the user along with a message indicating successful authentication.

[0058] Step 6:

[0059] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[0060] Step 7:

[0061] The server sends the data for the selected training scenario to the terminal.

[0062] Step 8:

[0063] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[0064] Step 9:

[0065] The user clicks the "Start Scenario" button.

[0066] Step 10:

[0067] The device displays or plays an initial message from the customer character based on the scenario data.

[0068] Step 11:

[0069] The user responds to questions and comments from a character representing a customer using voice or text.

[0070] Step 12:

[0071] The terminal sends the user's response to the server.

[0072] Step 13:

[0073] The server uses natural language processing to analyze the user's statements.

[0074] Step 14:

[0075] The server generates an appropriate response based on the information it has analyzed.

[0076] Step 15:

[0077] The server sends the generated response to the terminal.

[0078] Step 16:

[0079] The device displays or plays the received response to the user.

[0080] Step 17:

[0081] The user responds again, either by voice or text, in response to the customer character's response.

[0082] Step 18:

[0083] The server and terminal continue their interaction by repeating steps 11 through 16.

[0084] Step 19:

[0085] The server detects that the scenario has ended.

[0086] Step 20:

[0087] The server evaluates the user's response and generates an evaluation result.

[0088] Step 21:

[0089] The server generates feedback based on the evaluation results.

[0090] Step 22:

[0091] The server sends the generated feedback message to the terminal.

[0092] Step 23:

[0093] The device displays a feedback message to the user.

[0094] Step 24:

[0095] The server saves all conversation data and evaluation results to a database.

[0096] Step 25:

[0097] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[0098] (Example 1)

[0099] 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."

[0100] Traditional new employee training systems had limited opportunities for simulating actual customer interactions, making it difficult for new crew members to effectively acquire practical skills in a short period. Furthermore, the lack of sufficient feedback when evaluating user responses meant that it was unclear to individual users what areas they needed to improve.

[0101] 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.

[0102] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a virtual person based on the selected training scenario; means for analyzing the user's statements using natural language processing and generating an appropriate response; means for displaying or playing the generated response to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for providing a user interface; and means for generating responses using a generative AI model. This enables new crew members to effectively acquire practical skills in a short period of time through scenarios that closely resemble actual customer interactions. Furthermore, by providing specific feedback after each session, users can more easily identify areas for improvement.

[0103] "Login information" refers to information such as the user ID and password that a user enters when accessing the system.

[0104] "Authentication means" refers to a mechanism by which a server verifies the user's identity by comparing login information sent by the user with a database.

[0105] A "training scenario" is a series of simulation situations set up for the purpose of training in specific job functions.

[0106] A "virtual character" refers to a computer-generated character or agent that interacts with the user within a system.

[0107] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0108] "Means of generating responses" refer to technologies and algorithms used to create appropriate responses based on user statements.

[0109] "Means of displaying or playing back" refers to a mechanism for providing the generated response to the user visually or audibly.

[0110] A "means for evaluating the content of interactions" refers to a system for evaluating the content of user conversations and responses based on various criteria.

[0111] A "means of providing feedback" refers to a system for communicating areas for improvement and evaluations to users based on the evaluation results.

[0112] "Conversation data" refers to the record of all interactions that take place between the user and the virtual character.

[0113] "Evaluation results" refer to the detailed evaluation obtained when assessing the content of the interaction.

[0114] "Means of preservation" refers to a system for recording conversation data and evaluation results in a database or similar format so that they can be referenced later.

[0115] A "user interface" refers to the screens and means of operation that allow a user to directly interact with a system.

[0116] A "generative AI model" is an artificial intelligence model used to create appropriate responses based on user statements.

[0117] This invention provides a system for efficiently training new crew members and is implemented using a user, a terminal, and a server. First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. Database management systems such as MySQL® or PostgreSQL are used for authentication.

[0118] Upon successful authentication, the server loads the user's profile data and displays the main menu on the terminal. Web technologies such as HTML, CSS, and JavaScript are used to display the main menu. The user selects a training scenario from the main menu.

[0119] When a user selects a training scenario, the server sends the selected scenario data to the device. This data is often sent in JSON format. Based on the scenario data, the device displays or plays an initial message from a virtual character representing the customer. A Text-to-Speech (TTS) engine is used for audio playback, such as Google Cloud Text-to-Speech.

[0120] The user responds to questions and comments from a virtual character using voice or text. The terminal sends the user's response to the server, which analyzes the user's utterance using natural language processing (NLP). Google Cloud Natural Language API and IBM Watson® are used for natural language processing. Based on the analysis results, the server generates an appropriate response using a generative AI model. An example of a generative AI model is OpenAI®'s GPT-3®.

[0121] The generated response is sent from the server to the terminal, which then displays or plays the response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[0122] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. Based on the evaluation, the server generates a feedback message and sends it to the terminal. This feedback message is displayed on the terminal.

[0123] Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and progress.

[0124] For example, consider a scenario where a user selects "consultation on purchasing a TV." A virtual character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models in our store," and sends it to the terminal. The user confirms this and continues with more detailed questions. This system allows users to efficiently acquire practical skills based on practice scenarios.

[0125] Examples of prompt statements include the following:

[0126] User query: "I'm looking for the latest 4K TVs."

[0127] Generate appropriate response for a customer query about 4K TVs. Provide information about recommended models and features.

[0128] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0129] Step 1:

[0130] To access the system, the user opens a web browser using a PC, tablet, or smartphone. The user enters the system's URL and reaches the login screen. Here, the user enters their user ID and password and clicks the "Login" button. The entered user ID and password are then sent from the device to the server.

[0131] Step 2:

[0132] The server receives login information sent from the terminal. The server uses a database management system (e.g., MySQL or PostgreSQL) to verify the received user ID and password. If authentication is successful, the user's profile data is loaded and sent to the terminal. The input is the user ID and password, and the output is the authentication result and profile data.

[0133] Step 3:

[0134] The terminal receives profile data sent from the server. The terminal uses HTML, CSS, and JavaScript to generate the main menu and display it to the user. The output is the main menu screen.

[0135] Step 4:

[0136] The user selects a training scenario from the main menu. For example, they might choose the scenario "Consulting about purchasing a TV." The user's input is then based on the selected scenario.

[0137] Step 5:

[0138] The server sends the corresponding data to the terminal in JSON format based on the training scenario selected by the user. The output is the data for the selected scenario.

[0139] Step 6:

[0140] The terminal analyzes the received scenario data and displays or plays an initial message from the virtual character. A Text-to-Speech (TTS) engine (e.g., Google Cloud Text-to-Speech) is used for audio playback. User input is the initial message, and output is either displayed or spoken.

[0141] Step 7:

[0142] The user responds to questions and comments from a virtual character using voice or text. The user's responses are sent from the terminal to the server. The input is the user's response.

[0143] Step 8:

[0144] The server analyzes user responses sent from the terminal using natural language processing (NLP). Specifically, it uses Google Cloud Natural Language API or IBM Watson. The input for the analysis is the user's response, and the output is the analysis result.

[0145] Step 9:

[0146] Based on the analysis results, the server generates an appropriate response using a generative AI model (e.g., OpenAI GPT-3). The generated response is sent to the terminal. The input is the analysis result, and the output is the generated response.

[0147] Step 10:

[0148] The terminal receives responses sent from the server and displays or plays them aloud to the user. For example, if the user responds, "I'm looking for the latest 4K TV," the terminal will display, "You're interested in 4K TVs. We recommend our latest models." The output is either visual or audible.

[0149] Step 11:

[0150] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The input for the evaluation is the user's interaction, and the output is the evaluation result.

[0151] Step 12:

[0152] The server generates a feedback message based on the evaluation results and sends it to the terminal. The terminal displays the feedback message to the user. The input is the evaluation result, and the output is the feedback message.

[0153] Step 13:

[0154] The server saves all session conversation data and evaluation results to a database. This saved data is for users to access later. The input is the conversation data and evaluation results, and the output is saving to the database.

[0155] (Application Example 1)

[0156] 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."

[0157] Traditional training systems for new crew members struggled to provide an environment that closely resembled actual customer service scenarios. Furthermore, the lack of quality and realism in virtual environments made it difficult to maximize user learning effectiveness. In particular, the insufficient analysis of user utterances and generation of appropriate responses using natural language processing technology posed challenges in acquiring practical skills.

[0158] 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.

[0159] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a customer character based on the selected training scenario within a virtual environment; means for analyzing the user's statements and generating appropriate responses; means for displaying or playing the generated responses audibly to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for the user to perform customer service simulations in a virtual environment using a virtual reality display device; and means for transmitting user input via a terminal connected to the virtual reality display device and analyzing it on the server. This enables the user to effectively acquire more practical skills in a short period of time through dialogue in a virtual environment that closely resembles actual customer service scenarios.

[0160] A "user" is a person who accesses the system, enters login information, and executes training scenarios.

[0161] "Login information" refers to authentication information, including user IDs and passwords, used to authenticate users.

[0162] A "training scenario" is scenario data that allows users to simulate conversations with customer-facing characters in a virtual environment and learn how to provide customer service.

[0163] A "virtual environment" is a computer-generated simulation environment into which users can immerse themselves, enabling the execution of customer service scenarios.

[0164] A "customer character" is a virtual character that the user interacts with during a customer service simulation.

[0165] "Speech analysis" is an information processing technique that uses natural language processing technology to analyze user-inputted voice and text data and generate appropriate responses.

[0166] "Response generation" is the process by which a server provides an appropriate response to a user based on its analysis of the user's speech.

[0167] "Feedback" refers to information that evaluates the appropriateness, speed, and politeness of a user's response, and then provides that evaluation back to the user.

[0168] "Conversation data" refers to a record of the dialogue exchanged between the user and a character representing a customer.

[0169] A "virtual reality display device" is a device that provides a virtual environment to a user visually, and includes, for example, a head-mounted display.

[0170] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and run training scenarios.

[0171] "Natural language processing" is a technology that allows computers to analyze, understand, and generate human language.

[0172] This invention is a system for efficiently training new crew members, and is implemented using users, terminals, and a server. The system aims to enable users to acquire practical skills in a short period of time through customer service simulations in a virtual environment.

[0173] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server, which authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0174] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario starts in the virtual environment.

[0175] The terminal is connected to a virtual reality display device that allows the user to immerse themselves in a virtual environment. Within the virtual environment, a character representing a customer appears and displays or plays an initial message. This character engages in dialogue that mirrors actual customer service scenarios, such as searching for products or asking specific questions.

[0176] The user responds to questions and comments from a character representing a customer using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing technology. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response aloud to the user.

[0177] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness. The server evaluates each criterion, generates a feedback message, and sends it to the user's terminal. Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[0178] (Specific example)

[0179] For example, if a user selects the "TV purchase consultation" scenario, the customer character will display or play a voice message in the virtual environment saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models from several manufacturers," and sends it to the terminal. The user confirms this and continues with further questions.

[0180] In this way, this system provides a mechanism that allows new crew members to gain experience that is similar to actual customer interactions. This is expected to improve the quality of customer service in mass retail stores.

[0181] Example of a prompt:

[0182] User: "Hello, what kind of product are you looking for today?"

[0183] Customer: "I'm looking for the latest 4K TV."

[0184] User: "So, you're looking for the latest 4K TV. Do you have any specific manufacturer or feature preferences?"

[0185] Customer: "I'd like something with a large screen and good sound quality."

[0186] User: "The screen size is large and the sound quality is good. We recommend the latest models from several manufacturers. Shall I explain the features of each?"

[0187] ---------

[0188] The above is a detailed description of the embodiments for carrying out the invention.

[0189] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0190] Step 1:

[0191] The user accesses the system using a terminal and enters their user ID and password on the login screen. The entered data (user ID and password) is sent from the terminal to the server. The server verifies this authentication information against a database to authenticate the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0192] Step 2:

[0193] The user selects a training scenario from the main menu. The selected scenario information is sent from the terminal to the server. The server loads the scenario data and sends it back to the terminal. The terminal displays a button to start the scenario to the user.

[0194] Step 3:

[0195] When the user clicks the "Start Scenario" button, the scenario begins. Using a virtual reality display device, a character representing the customer displays or plays an initial message in the virtual environment, such as "Hello, what kind of product are you looking for today?" The user responds to this message.

[0196] Step 4:

[0197] The user's response (voice or text) is processed on the terminal and sent to the server. The server analyzes the user's utterance using natural language processing. Specifically, it tokenizes the text data and performs semantic and contextual analysis. As a result of the analysis, the user's intent and requests are extracted.

[0198] Step 5:

[0199] The server generates an appropriate response based on the analysis results. A generative AI model is used for this process. The generated response is sent to the terminal, which then displays or plays it aloud for the user. For example, a response such as, "You're interested in 4K TVs, aren't you? We recommend the latest models from several manufacturers," might be generated.

[0200] Step 6:

[0201] The dialogue continues between the user and the customer character. Steps 4 and 5 are repeated for each response. The user continues to ask detailed questions, and the server generates corresponding responses.

[0202] Step 7:

[0203] Once the scenario is complete, the server evaluates all conversation data. Criteria such as appropriateness, speed, and politeness are used for evaluation. Based on each criterion, the server generates an evaluation score and feedback message. This evaluation data and feedback message are sent to the terminal.

[0204] Step 8:

[0205] The terminal displays feedback messages to the user. The server saves all session conversation data and evaluation results to a database. Users can later refer to this data to check their areas for improvement and progress.

[0206] 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.

[0207] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[0208] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0209] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[0210] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[0211] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing. Furthermore, an emotion engine recognizes emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[0212] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[0213] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[0214] Specific example

[0215] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0216] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user enters their user ID and password on the device's login screen.

[0220] Step 2:

[0221] The terminal sends the entered authentication information to the server.

[0222] Step 3:

[0223] The server checks the received authentication information against the database and authenticates the user.

[0224] Step 4:

[0225] The server sends the authentication result back to the terminal.

[0226] Step 5:

[0227] The device displays the main menu screen to the user along with a message indicating successful authentication.

[0228] Step 6:

[0229] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[0230] Step 7:

[0231] The server sends the data for the selected training scenario to the terminal.

[0232] Step 8:

[0233] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[0234] Step 9:

[0235] The user clicks the "Start Scenario" button.

[0236] Step 10:

[0237] The device displays or plays an initial message from the customer character based on the scenario data.

[0238] Step 11:

[0239] The user responds to questions and comments from a character representing a customer using voice or text.

[0240] Step 12:

[0241] The terminal sends the user's response to the server.

[0242] Step 13:

[0243] The server uses natural language processing to analyze the user's statements.

[0244] Step 14:

[0245] An emotion engine built into the server recognizes emotions from the user's statements and adds that emotional information to the analysis results.

[0246] Step 15:

[0247] The server generates an appropriate response based on the user's statements and perceived emotions.

[0248] Step 16:

[0249] The server sends the generated response to the terminal.

[0250] Step 17:

[0251] The device displays or plays the received response to the user.

[0252] Step 18:

[0253] The user responds again, either by voice or text, in response to the customer character's response.

[0254] Step 19:

[0255] The server and terminal continue their interaction by repeating steps 11 through 17.

[0256] Step 20:

[0257] The server detects that the scenario has ended.

[0258] Step 20:

[0259] The server evaluates the user's response. Evaluation criteria include appropriateness, speed, and politeness of the response, as well as the user's emotional perception.

[0260] Step 21:

[0261] The server generates feedback based on the evaluation results.

[0262] Step 22:

[0263] The server sends the generated feedback message to the terminal.

[0264] Step 23:

[0265] The device displays a feedback message to the user.

[0266] Step 24:

[0267] The server saves all conversation data and evaluation results to a database.

[0268] Step 25:

[0269] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[0270] (Example 2)

[0271] 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".

[0272] In a system designed to efficiently train new crew members, it is necessary to recognize user emotions and respond accordingly, thereby simulating actual customer service and improving their communication skills. Furthermore, by appropriately evaluating user interactions and providing feedback, the system is required to enable effective acquisition of practical skills in a short period of time.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating login information from a user, means for selecting a training scenario, means for initiating a conversation with a virtual character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response audibly to the user, emotion recognition means for recognizing emotions from the user's statements and generating a response based on those emotions, means for evaluating the user's response and providing feedback, and means for storing conversation data and evaluation results. This makes it possible for new crew members to experience conversations that are close to actual customer service and effectively improve their response skills and emotion recognition abilities.

[0274] A "user" refers to a person who uses the system to receive training.

[0275] "Login information" refers to information used to authenticate a user to the system, such as a user ID and password.

[0276] "Authentication means" refers to a means that has the function of verifying the entered login information against a database and authenticating the user.

[0277] A "training scenario" refers to the scenes and content of the simulation training that new crew members perform using the system.

[0278] The "means for selecting a scenario" refers to a means having a function for a user to select a desired scenario from a plurality of training scenarios.

[0279] The "virtual character" refers to a computer-generated person who serves as a customer in simulation training.

[0280] The "means for starting a conversation" refers to a means having a function for starting a conversation with a virtual character based on the selected training scenario.

[0281] The "means for analyzing a user's utterance" refers to a means having a function for analyzing the voice or text input by the user using natural language processing technology.

[0282] The "means for generating an appropriate response" refers to a means having a function for the system to automatically generate a response based on the analyzed user's utterance.

[0283] The "emotion recognition means" refers to a means having a function for recognizing an emotion from a user's utterance and generating a response based thereon.

[0284] The "means for displaying or playing back a response in voice" refers to a means having a function for providing the generated response to the user visually or aurally.

[0285] The "means for evaluating a user's response content" refers to a means having a function for evaluating based on the appropriateness, promptness, politeness, and emotion recognition of the response made by the user.

[0286] The "means for providing feedback" refers to a means having a function for conveying points for improvement and good points to the user based on the evaluation result.

[0287] The "means for saving conversation data and evaluation results" refers to a means having a function for saving the content of the conversation conducted within the system and its evaluation result in a database.

[0288] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion recognition engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[0289] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0290] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal and displays a button on the terminal to start the scenario. When the user clicks this button, the scenario begins.

[0291] The terminal displays or plays an initial message from a virtual customer character based on scenario data. For example, it simulates actual customer service, such as a customer searching for a product or asking a specific question. The user responds to the questions and comments from the virtual customer character using voice or text. The terminal sends the user's responses to the server.

[0292] The server analyzes the user's utterances using natural language processing. Additionally, an emotion recognition engine identifies emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real-world customer service scenario.

[0293] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user. The server also saves all session conversation data and evaluation results to a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[0294] Specific example

[0295] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion recognition engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0296] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[0297] Examples of prompts for generative AI models

[0298] "We have a training system for new crew members. In this system, you will progress through a scenario with a virtual customer character. Please explain in natural language the process of analyzing the user's statements and providing appropriate responses."

[0299] The flow of the specific process in Example 2 will be described using FIG. 13.

[0300] Step 1: User Login

[0301] Input: User ID, password

[0302] Output: Authentication success or failure, user profile data

[0303] Operation:

[0304] The user accesses the system using the terminal and enters the user ID and password on the login screen. The terminal sends the entered login information to the server. The server performs user authentication by comparing with the database. If the authentication is successful, the server loads the user's profile data and displays the main menu on the terminal. The user accesses the login screen from a dedicated app or web browser using a smartphone or PC. When the input is completed and the "Login" button is pressed, the information is transferred to the server.

[0305] Step 2: Selection of Training Scenario

[0306] Input: User's scenario selection information

[0307] Output: Selected scenario data

[0308] Operation:

[0309] The user selects a training scenario from the main menu. The terminal sends the selected scenario data to the server. The server provides the sent scenario to the terminal and displays an instruction button to start the scenario. The user selects and clicks on a training scenario from options such as "TV purchase consultation". The selection information is sent from the terminal to the server, and the appropriate scenario data is distributed to the terminal.

[0310] Step 3: Display of Initial Screen of Scenario

[0311] Input: Scenario data

[0312] Output: Display of initial message or playback of audio

[0313] Operation:

[0314] Based on the scenario data, the terminal displays or plays an initial message from a virtual customer character. It might display a message such as, "Hello, what product are you looking for today?" or the character might begin speaking aloud.

[0315] Step 4: User response input

[0316] Input: User voice or text response

[0317] Output: User response data

[0318] Operation:

[0319] The user responds to questions and comments from a virtual customer character using voice or text. The terminal transcribes the user's responses into text and sends it to the server. The user provides specific responses, such as "I'm looking for the latest 4K TV," using voice or keyboard input.

[0320] Step 5: Sending and analyzing responses, sentiment recognition

[0321] Input: User response data

[0322] Output: Appropriate response and recognized emotional information

[0323] Operation:

[0324] The terminal sends the user's response to the server. The server analyzes the user's utterance using natural language processing. Additionally, an emotion recognition engine recognizes the emotion from the user's utterance. The server uses NLP (Natural Language Processing) algorithms to analyze the content and emotion. For example, from the response "I'm looking for the latest 4K TV," it extracts the emotions "interest" and "curiosity."

[0325] Step 6: Generating and displaying the appropriate response

[0326] Input: Analyzed speech data and sentiment information

[0327] Output: Appropriate response data

[0328] Operation:

[0329] The server generates an appropriate response based on the recognized emotion information and sends it to the terminal. The terminal displays or plays the generated response to the user. For example, the server generates a response such as, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," which is sent to the terminal and displayed or played aloud.

[0330] Step 7: Session End and Evaluation

[0331] Input: User interaction data

[0332] Output: Evaluation results and feedback messages

[0333] Operation:

[0334] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, politeness, and emotion recognition results. The server evaluates each item, generates a feedback message, and sends it to the terminal. After the session ends, the server scores the user's interaction based on criteria such as "appropriateness of interaction," "speed of response," and "politeness," and displays the results as a feedback message.

[0335] Step 8: Saving and referencing past training data

[0336] Input: Conversation data and evaluation result data

[0337] Output: Data to be saved to the database and for future reference.

[0338] Operation:

[0339] The server stores all session conversation data and evaluation results in a database. Users can later refer to past training content and evaluations to check their areas for improvement and progress. If a user logs in later and wants to view their training history, they can view past evaluation results and session content. For example, evaluation results can be displayed in a graph to visually check progress.

[0340] (Application Example 2)

[0341] 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".

[0342] Traditional crew training systems differed from actual customer interactions in that they lacked real-time feedback and emotional recognition. As a result, new crew members struggled to effectively acquire practical skills on the job. Furthermore, traditional systems often delayed detailed evaluations and feedback on user interactions, making immediate improvement difficult.

[0343] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving and authenticating authentication information from the user, means for selecting a training scenario, means for initiating a dialogue with a customer character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response to the user, means for evaluating the user's response and providing feedback, means for storing conversation data and evaluation results, means for receiving questions and comments from a virtual customer using smart glasses and responding in voice or text, and means including an emotion engine that provides the generated feedback to the user in real time. This makes it possible for new crew members to efficiently acquire practical skills through simulations that closely resemble real-world problems.

[0344] "Means of receiving and authenticating authentication information from users" refers to the process or function of sending authentication information such as user ID and password to a server to verify the user's identity.

[0345] "Means for selecting a training scenario" refers to the interface or function that allows a user to select a specific scenario from among multiple training scenarios.

[0346] "Means of initiating a dialogue with a customer character based on a selected training scenario" refers to a function that allows the user to initiate a dialogue with a virtual character representing a customer based on a scenario selected by the user.

[0347] "Means for analyzing user statements and generating appropriate responses" refers to a process or function that analyzes user statements using natural language processing technology and generates appropriate responses in response.

[0348] "Means for displaying or playing the generated response to the user" refers to a function for displaying the generated response to the user as text or playing it as audio.

[0349] "Means of evaluating user interactions and providing feedback" refers to processes and functions that evaluate user interactions based on various evaluation criteria and provide corresponding feedback to the user.

[0350] "Means for saving conversation data and evaluation results" refers to a function for saving conversation data and evaluation results during training to a storage device such as a database.

[0351] "A means of receiving questions and comments from virtual customers using smart glasses and responding to them in voice or text" refers to a function that receives questions and comments from virtual customers via smart glasses and responds to them in voice or text.

[0352] "Means including an emotion engine that provides generated feedback to the user in real time" refers to a function that includes an emotion analysis engine to analyze emotions from the user's statements and actions and provide appropriate feedback in real time.

[0353] This invention provides a system for efficiently training new crew members, and in particular, uses smart glasses to perform real-time customer interaction simulations while providing appropriate feedback using an emotion engine. Specific embodiments of this system are described below.

[0354] This system allows users to receive questions and comments from virtual customers by wearing smart glasses, and respond to them in voice and text. It includes functions to receive and authenticate user authentication information, select training scenarios, initiate conversations with virtual customers based on the scenarios, analyze user statements and generate appropriate responses, present the generated responses to the user, evaluate the interaction and provide feedback, and save conversation data and evaluation results.

[0355] Hardware and software used

[0356] Smart glasses: Worn by the user, they display messages and play audio from virtual customers and receive voice input from the user.

[0357] Server: Manages user authentication information, generates training scenarios and responses, and manages evaluations. It uses an open-source database system to store conversation data and evaluation results.

[0358] Emotion Engine: An engine that analyzes user emotions from their statements and provides appropriate feedback in real time. It utilizes natural language processing technology (e.g., OpenAI's API).

[0359] Details of data processing and handling

[0360] 1. User Authentication:

[0361] The user logs into the system via smart glasses and enters their ID and password.

[0362] The server receives the entered information, compares it with the database, and authenticates the user.

[0363] Upon successful authentication, the user's profile information is loaded and displayed on the smart glasses.

[0364] 2. Training Scenario Selection:

[0365] The user selects any training scenario through the smart glasses interface.

[0366] The server sends the selected scenario data to the smart glasses and instructs them to start the scenario.

[0367] 3. Scenario Execution:

[0368] A character playing the role of a virtual customer displays or plays messages on smart glasses based on a scenario.

[0369] The user responds to the virtual customer's message using voice. The smart glasses then use speech recognition technology to convert this response into text.

[0370] 4. Analysis and response generation:

[0371] The server receives the user's response in text format and analyzes it using natural language processing techniques.

[0372] The emotion engine analyzes the user's responses to determine their emotions and generates an appropriate response based on that analysis.

[0373] The generated response is presented to the user via smart glasses.

[0374] 5. Evaluation and Feedback:

[0375] The server evaluates the user's interaction and generates feedback, including sentiment analysis results.

[0376] Feedback is displayed in real time on the smart glasses, allowing users to immediately understand areas for improvement.

[0377] 6. Data storage:

[0378] The server stores all conversation data and evaluation results in a database.

[0379] Users can later refer to their past training content and evaluation results, providing support for self-improvement.

[0380] Examples of specific cases and prompt statements

[0381] As a specific training scenario, the case of "consulting about purchasing a television" would be as follows:

[0382] Virtual customer: "Hello, I'm looking for the latest 4K TV."

[0383] User: "Which TV manufacturer would you recommend?"

[0384] Examples of prompts in this conversation are as follows:

[0385] Customer message: Hi, I'm looking for the latest 4K TV.

[0386] User's response: Which TV manufacturer would you recommend?

[0387] Please show your emotions and respond appropriately.

[0388] In this way, the system is designed to allow new crew members to efficiently acquire practical skills through simulations that closely resemble actual customer interactions. This is expected to enable effective training in a short period of time and improve the quality of customer service.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user puts on smart glasses and logs into the system. The smart glasses accept the user ID and password. This input is sent from the device to the server. The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server loads the user's profile information and displays the main menu on the device.

[0392] Step 2:

[0393] The user selects a training scenario through the smart glasses interface. This scenario selection is sent from the device to the server. The server retrieves the selected scenario data and sends it back to the device. The device then displays a button to initiate the scenario.

[0394] Step 3:

[0395] The user clicks the "Start Scenario" button. The device displays an initial message from the virtual customer character. This message is generated by the device based on the scenario data. The user reviews the virtual customer's message and responds with voice.

[0396] Step 4:

[0397] The terminal receives the user's voice input and converts it into text using a transcription engine. The converted text is sent from the terminal to the server. The server analyzes the user's speech using natural language processing technology. Based on this analysis, the server generates an appropriate response.

[0398] Step 5:

[0399] The server sends the generated response to the terminal. The terminal displays or plays the response message aloud to the user. The user reviews this response and asks additional questions or makes comments as needed. This process is repeated until the simulation scenario is complete.

[0400] Step 6:

[0401] The server evaluates the user's response after each interaction session. The evaluation criteria are based on appropriateness, speed, and politeness. In addition, an emotion engine analyzes the user's utterances to determine their emotions and generates additional feedback based on that analysis.

[0402] Step 7:

[0403] The server sends the generated evaluation results and feedback to the terminal. The terminal displays this to the user in real time. The user uses this feedback to improve their response.

[0404] Step 8:

[0405] The server stores all conversation data and evaluation results in a database. This allows users to refer to past training content and evaluation results later. The server can also analyze progress and suggest appropriate next training scenarios.

[0406] Each of these steps allows users to receive real-time feedback based on sentiment analysis, enabling them to effectively improve their customer service skills.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Second Embodiment]

[0411] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention is a system for efficiently training new crew members and is implemented using users, terminals, and a server. The following describes in detail how this invention can be specifically implemented.

[0424] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0425] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[0426] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[0427] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which uses natural language processing to analyze the user's statements. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and the virtual customer that closely resembles a real customer service scenario.

[0428] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[0429] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[0430] Specific example

[0431] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0432] In this way, this system allows new crew members to gain experience that closely resembles actual customer interactions. This enables them to acquire practical skills effectively in a short period of time, thereby improving the quality of customer service in mass retail stores.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The user enters their user ID and password on the device's login screen.

[0436] Step 2:

[0437] The terminal sends the entered authentication information to the server.

[0438] Step 3:

[0439] The server checks the received authentication information against the database and authenticates the user.

[0440] Step 4:

[0441] The server sends the authentication result back to the terminal.

[0442] Step 5:

[0443] The device displays the main menu screen to the user along with a message indicating successful authentication.

[0444] Step 6:

[0445] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[0446] Step 7:

[0447] The server sends the data for the selected training scenario to the terminal.

[0448] Step 8:

[0449] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[0450] Step 9:

[0451] The user clicks the "Start Scenario" button.

[0452] Step 10:

[0453] The device displays or plays an initial message from the customer character based on the scenario data.

[0454] Step 11:

[0455] The user responds to questions and comments from a character representing a customer using voice or text.

[0456] Step 12:

[0457] The terminal sends the user's response to the server.

[0458] Step 13:

[0459] The server uses natural language processing to analyze the user's statements.

[0460] Step 14:

[0461] The server generates an appropriate response based on the information it has analyzed.

[0462] Step 15:

[0463] The server sends the generated response to the terminal.

[0464] Step 16:

[0465] The device displays or plays the received response to the user.

[0466] Step 17:

[0467] The user responds again, either by voice or text, in response to the customer character's response.

[0468] Step 18:

[0469] The server and terminal continue their interaction by repeating steps 11 through 16.

[0470] Step 19:

[0471] The server detects that the scenario has ended.

[0472] Step 20:

[0473] The server evaluates the user's response and generates an evaluation result.

[0474] Step 21:

[0475] The server generates feedback based on the evaluation results.

[0476] Step 22:

[0477] The server sends the generated feedback message to the terminal.

[0478] Step 23:

[0479] The device displays a feedback message to the user.

[0480] Step 24:

[0481] The server saves all conversation data and evaluation results to a database.

[0482] Step 25:

[0483] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[0484] (Example 1)

[0485] 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."

[0486] Traditional new employee training systems had limited opportunities for simulating actual customer interactions, making it difficult for new crew members to effectively acquire practical skills in a short period. Furthermore, the lack of sufficient feedback when evaluating user responses meant that it was unclear to individual users what areas they needed to improve.

[0487] 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.

[0488] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a virtual person based on the selected training scenario; means for analyzing the user's statements using natural language processing and generating an appropriate response; means for displaying or playing the generated response to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for providing a user interface; and means for generating responses using a generative AI model. This enables new crew members to effectively acquire practical skills in a short period of time through scenarios that closely resemble actual customer interactions. Furthermore, by providing specific feedback after each session, users can more easily identify areas for improvement.

[0489] "Login information" refers to information such as the user ID and password that a user enters when accessing the system.

[0490] "Authentication means" refers to a mechanism by which a server verifies the user's identity by comparing login information sent by the user with a database.

[0491] A "training scenario" is a series of simulation situations set up for the purpose of training in specific job functions.

[0492] A "virtual character" refers to a computer-generated character or agent that interacts with the user within a system.

[0493] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0494] "Means of generating responses" refer to technologies and algorithms used to create appropriate responses based on user statements.

[0495] "Means of displaying or playing back" refers to a mechanism for providing the generated response to the user visually or audibly.

[0496] A "means for evaluating the content of interactions" refers to a system for evaluating the content of user conversations and responses based on various criteria.

[0497] A "means of providing feedback" refers to a system for communicating areas for improvement and evaluations to users based on the evaluation results.

[0498] "Conversation data" refers to the record of all interactions that take place between the user and the virtual character.

[0499] "Evaluation results" refer to the detailed evaluation obtained when assessing the content of the interaction.

[0500] "Means of preservation" refers to a system for recording conversation data and evaluation results in a database or similar format so that they can be referenced later.

[0501] A "user interface" refers to the screens and means of operation that allow a user to directly interact with a system.

[0502] A "generative AI model" is an artificial intelligence model used to create appropriate responses based on user statements.

[0503] This invention provides a system for efficiently training new crew members and is implemented using a user, a terminal, and a server. First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. Database management systems such as MySQL or PostgreSQL are used for authentication.

[0504] Upon successful authentication, the server loads the user's profile data and displays the main menu on the terminal. Web technologies such as HTML, CSS, and JavaScript are used to display the main menu. The user selects a training scenario from the main menu.

[0505] When a user selects a training scenario, the server sends the selected scenario data to the device. This data is often sent in JSON format. Based on the scenario data, the device displays or plays an initial message from a virtual character representing the customer. A Text-to-Speech (TTS) engine is used for audio playback, such as Google Cloud Text-to-Speech.

[0506] The user responds to questions and comments from a virtual character using voice or text. The terminal sends the user's response to the server, which analyzes the user's utterance using natural language processing (NLP). Google Cloud Natural Language API and IBM Watson are used for NLP. Based on the analysis results, the server generates an appropriate response using a generative AI model. An example of a generative AI model is OpenAI's GPT-3.

[0507] The generated response is sent from the server to the terminal, which then displays or plays the response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[0508] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. Based on the evaluation, the server generates a feedback message and sends it to the terminal. This feedback message is displayed on the terminal.

[0509] Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and progress.

[0510] For example, consider a scenario where a user selects "consultation on purchasing a TV." A virtual character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models in our store," and sends it to the terminal. The user confirms this and continues with more detailed questions. This system allows users to efficiently acquire practical skills based on practice scenarios.

[0511] Examples of prompt statements include the following:

[0512] User query: "I'm looking for the latest 4K TVs."

[0513] Generate appropriate response for a customer query about 4K TVs. Provide information about recommended models and features.

[0514] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0515] Step 1:

[0516] To access the system, the user opens a web browser using a PC, tablet, or smartphone. The user enters the system's URL and reaches the login screen. Here, the user enters their user ID and password and clicks the "Login" button. The entered user ID and password are then sent from the device to the server.

[0517] Step 2:

[0518] The server receives login information sent from the terminal. The server uses a database management system (e.g., MySQL or PostgreSQL) to verify the received user ID and password. If authentication is successful, the user's profile data is loaded and sent to the terminal. The input is the user ID and password, and the output is the authentication result and profile data.

[0519] Step 3:

[0520] The terminal receives profile data sent from the server. The terminal uses HTML, CSS, and JavaScript to generate the main menu and display it to the user. The output is the main menu screen.

[0521] Step 4:

[0522] The user selects a training scenario from the main menu. For example, they might choose the scenario "Consulting about purchasing a TV." The user's input is then based on the selected scenario.

[0523] Step 5:

[0524] The server sends the corresponding data to the terminal in JSON format based on the training scenario selected by the user. The output is the data for the selected scenario.

[0525] Step 6:

[0526] The terminal analyzes the received scenario data and displays or plays an initial message from the virtual character. A Text-to-Speech (TTS) engine (e.g., Google Cloud Text-to-Speech) is used for audio playback. User input is the initial message, and output is either displayed or spoken.

[0527] Step 7:

[0528] The user responds to questions and comments from a virtual character using voice or text. The user's responses are sent from the terminal to the server. The input is the user's response.

[0529] Step 8:

[0530] The server analyzes user responses sent from the terminal using natural language processing (NLP). Specifically, it uses Google Cloud Natural Language API or IBM Watson. The input for the analysis is the user's response, and the output is the analysis result.

[0531] Step 9:

[0532] Based on the analysis results, the server generates an appropriate response using a generative AI model (e.g., OpenAI GPT-3). The generated response is sent to the terminal. The input is the analysis result, and the output is the generated response.

[0533] Step 10:

[0534] The terminal receives responses sent from the server and displays or plays them aloud to the user. For example, if the user responds, "I'm looking for the latest 4K TV," the terminal will display, "You're interested in 4K TVs. We recommend our latest models." The output is either visual or audible.

[0535] Step 11:

[0536] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The input for the evaluation is the user's interaction, and the output is the evaluation result.

[0537] Step 12:

[0538] The server generates a feedback message based on the evaluation results and sends it to the terminal. The terminal displays the feedback message to the user. The input is the evaluation result, and the output is the feedback message.

[0539] Step 13:

[0540] The server saves all session conversation data and evaluation results to a database. This saved data is for users to access later. The input is the conversation data and evaluation results, and the output is saving to the database.

[0541] (Application Example 1)

[0542] 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."

[0543] Traditional training systems for new crew members struggled to provide an environment that closely resembled actual customer service scenarios. Furthermore, the lack of quality and realism in virtual environments made it difficult to maximize user learning effectiveness. In particular, the insufficient analysis of user utterances and generation of appropriate responses using natural language processing technology posed challenges in acquiring practical skills.

[0544] 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.

[0545] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a customer character based on the selected training scenario within a virtual environment; means for analyzing the user's statements and generating appropriate responses; means for displaying or playing the generated responses audibly to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for the user to perform customer service simulations in a virtual environment using a virtual reality display device; and means for transmitting user input via a terminal connected to the virtual reality display device and analyzing it on the server. This enables the user to effectively acquire more practical skills in a short period of time through dialogue in a virtual environment that closely resembles actual customer service scenarios.

[0546] A "user" is a person who accesses the system, enters login information, and executes training scenarios.

[0547] "Login information" refers to authentication information, including user IDs and passwords, used to authenticate users.

[0548] A "training scenario" is scenario data that allows users to simulate conversations with customer-facing characters in a virtual environment and learn how to provide customer service.

[0549] A "virtual environment" is a computer-generated simulation environment into which users can immerse themselves, enabling the execution of customer service scenarios.

[0550] A "customer character" is a virtual character that the user interacts with during a customer service simulation.

[0551] "Speech analysis" is an information processing technique that uses natural language processing technology to analyze user-inputted voice and text data and generate appropriate responses.

[0552] "Response generation" is the process by which a server provides an appropriate response to a user based on its analysis of the user's speech.

[0553] "Feedback" refers to information that evaluates the appropriateness, speed, and politeness of a user's response, and then provides that evaluation back to the user.

[0554] "Conversation data" refers to a record of the dialogue exchanged between the user and a character representing a customer.

[0555] A "virtual reality display device" is a device that provides a virtual environment to a user visually, and includes, for example, a head-mounted display.

[0556] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and run training scenarios.

[0557] "Natural language processing" is a technology that allows computers to analyze, understand, and generate human language.

[0558] This invention is a system for efficiently training new crew members, and is implemented using users, terminals, and a server. The system aims to enable users to acquire practical skills in a short period of time through customer service simulations in a virtual environment.

[0559] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server, which authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0560] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario starts in the virtual environment.

[0561] The terminal is connected to a virtual reality display device that allows the user to immerse themselves in a virtual environment. Within the virtual environment, a character representing a customer appears and displays or plays an initial message. This character engages in dialogue that mirrors actual customer service scenarios, such as searching for products or asking specific questions.

[0562] The user responds to questions and comments from a character representing a customer using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing technology. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response aloud to the user.

[0563] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness. The server evaluates each criterion, generates a feedback message, and sends it to the user's terminal. Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[0564] (Specific example)

[0565] For example, if a user selects the "TV purchase consultation" scenario, the customer character will display or play a voice message in the virtual environment saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models from several manufacturers," and sends it to the terminal. The user confirms this and continues with further questions.

[0566] In this way, this system provides a mechanism that allows new crew members to gain experience that is similar to actual customer interactions. This is expected to improve the quality of customer service in mass retail stores.

[0567] Example of a prompt:

[0568] User: "Hello, what kind of product are you looking for today?"

[0569] Customer: "I'm looking for the latest 4K TV."

[0570] User: "So, you're looking for the latest 4K TV. Do you have any specific manufacturer or feature preferences?"

[0571] Customer: "I'd like something with a large screen and good sound quality."

[0572] User: "The screen size is large and the sound quality is good. We recommend the latest models from several manufacturers. Shall I explain the features of each?"

[0573] ---------

[0574] The above is a detailed description of the embodiments for carrying out the invention.

[0575] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0576] Step 1:

[0577] The user accesses the system using a terminal and enters their user ID and password on the login screen. The entered data (user ID and password) is sent from the terminal to the server. The server verifies this authentication information against a database to authenticate the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0578] Step 2:

[0579] The user selects a training scenario from the main menu. The selected scenario information is sent from the terminal to the server. The server loads the scenario data and sends it back to the terminal. The terminal displays a button to start the scenario to the user.

[0580] Step 3:

[0581] When the user clicks the "Start Scenario" button, the scenario begins. Using a virtual reality display device, a character representing the customer displays or plays an initial message in the virtual environment, such as "Hello, what kind of product are you looking for today?" The user responds to this message.

[0582] Step 4:

[0583] The user's response (voice or text) is processed on the terminal and sent to the server. The server analyzes the user's utterance using natural language processing. Specifically, it tokenizes the text data and performs semantic and contextual analysis. As a result of the analysis, the user's intent and requests are extracted.

[0584] Step 5:

[0585] The server generates an appropriate response based on the analysis results. A generative AI model is used for this process. The generated response is sent to the terminal, which then displays or plays it aloud for the user. For example, a response such as, "You're interested in 4K TVs, aren't you? We recommend the latest models from several manufacturers," might be generated.

[0586] Step 6:

[0587] The dialogue continues between the user and the customer character. Steps 4 and 5 are repeated for each response. The user continues to ask detailed questions, and the server generates corresponding responses.

[0588] Step 7:

[0589] Once the scenario is complete, the server evaluates all conversation data. Criteria such as appropriateness, speed, and politeness are used for evaluation. Based on each criterion, the server generates an evaluation score and feedback message. This evaluation data and feedback message are sent to the terminal.

[0590] Step 8:

[0591] The terminal displays feedback messages to the user. The server saves all session conversation data and evaluation results to a database. Users can later refer to this data to check their areas for improvement and progress.

[0592] 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.

[0593] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[0594] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0595] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[0596] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[0597] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing. Furthermore, an emotion engine recognizes emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[0598] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[0599] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[0600] Specific example

[0601] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0602] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user enters their user ID and password on the device's login screen.

[0606] Step 2:

[0607] The terminal sends the entered authentication information to the server.

[0608] Step 3:

[0609] The server checks the received authentication information against the database and authenticates the user.

[0610] Step 4:

[0611] The server sends the authentication result back to the terminal.

[0612] Step 5:

[0613] The device displays the main menu screen to the user along with a message indicating successful authentication.

[0614] Step 6:

[0615] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[0616] Step 7:

[0617] The server sends the data for the selected training scenario to the terminal.

[0618] Step 8:

[0619] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[0620] Step 9:

[0621] The user clicks the "Start Scenario" button.

[0622] Step 10:

[0623] The device displays or plays an initial message from the customer character based on the scenario data.

[0624] Step 11:

[0625] The user responds to questions and comments from a character representing a customer using voice or text.

[0626] Step 12:

[0627] The terminal sends the user's response to the server.

[0628] Step 13:

[0629] The server uses natural language processing to analyze the user's statements.

[0630] Step 14:

[0631] An emotion engine built into the server recognizes emotions from the user's statements and adds that emotional information to the analysis results.

[0632] Step 15:

[0633] The server generates an appropriate response based on the user's statements and perceived emotions.

[0634] Step 16:

[0635] The server sends the generated response to the terminal.

[0636] Step 17:

[0637] The device displays or plays the received response to the user.

[0638] Step 18:

[0639] The user responds again, either by voice or text, in response to the customer character's response.

[0640] Step 19:

[0641] The server and terminal continue their interaction by repeating steps 11 through 17.

[0642] Step 20:

[0643] The server detects that the scenario has ended.

[0644] Step 20:

[0645] The server evaluates the user's response. Evaluation criteria include appropriateness, speed, and politeness of the response, as well as the user's emotional perception.

[0646] Step 21:

[0647] The server generates feedback based on the evaluation results.

[0648] Step 22:

[0649] The server sends the generated feedback message to the terminal.

[0650] Step 23:

[0651] The device displays a feedback message to the user.

[0652] Step 24:

[0653] The server saves all conversation data and evaluation results to a database.

[0654] Step 25:

[0655] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[0656] (Example 2)

[0657] 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".

[0658] In a system designed to efficiently train new crew members, it is necessary to recognize user emotions and respond accordingly, thereby simulating actual customer service and improving their communication skills. Furthermore, by appropriately evaluating user interactions and providing feedback, the system is required to enable effective acquisition of practical skills in a short period of time.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating login information from a user, means for selecting a training scenario, means for initiating a conversation with a virtual character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response audibly to the user, emotion recognition means for recognizing emotions from the user's statements and generating a response based on those emotions, means for evaluating the user's response and providing feedback, and means for storing conversation data and evaluation results. This makes it possible for new crew members to experience conversations that are close to actual customer service and effectively improve their response skills and emotion recognition abilities.

[0660] A "user" refers to a person who uses the system to receive training.

[0661] "Login information" refers to information used to authenticate a user to the system, such as a user ID and password.

[0662] "Authentication means" refers to a means that has the function of verifying the entered login information against a database and authenticating the user.

[0663] A "training scenario" refers to the scenes and content of the simulation training that new crew members perform using the system.

[0664] "Means for selecting a scenario" refers to a means that allows a user to select their preferred scenario from multiple training scenarios.

[0665] A "virtual character" refers to a computer-generated person who acts as a customer in the simulation training.

[0666] "Means of initiating a conversation" refers to means that have the functionality to start a conversation with a virtual character based on the selected training scenario.

[0667] "Means for analyzing user speech" refers to means that have the function of analyzing the voice or text input by the user using natural language processing technology.

[0668] "Means for generating appropriate responses" refers to means that have the function of automatically generating responses based on the analyzed user statements.

[0669] "Emotion recognition means" refers to means that have the function of recognizing emotions from a user's statements and generating a response based on those emotions.

[0670] "Means for displaying or playing a response audibly" refers to means that have the function of providing the generated response to the user visually or audibly.

[0671] "Means for evaluating user responses" refers to means that have the function of evaluating the appropriateness, speed, politeness, and emotional recognition of the responses provided by the user.

[0672] "Means of providing feedback" refers to means that have a function to communicate to users, based on evaluation results, areas for improvement and positive aspects.

[0673] "Means for saving conversation data and evaluation results" refers to means that have the function of saving the content of conversations that took place within the system and their evaluation results to a database.

[0674] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion recognition engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[0675] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0676] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal and displays a button on the terminal to start the scenario. When the user clicks this button, the scenario begins.

[0677] The terminal displays or plays an initial message from a virtual customer character based on scenario data. For example, it simulates actual customer service, such as a customer searching for a product or asking a specific question. The user responds to the questions and comments from the virtual customer character using voice or text. The terminal sends the user's responses to the server.

[0678] The server analyzes the user's utterances using natural language processing. Additionally, an emotion recognition engine identifies emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real-world customer service scenario.

[0679] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user. The server also saves all session conversation data and evaluation results to a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[0680] Specific example

[0681] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion recognition engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0682] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[0683] Examples of prompts for generative AI models

[0684] "We have a training system for new crew members. In this system, you will progress through a scenario with a virtual customer character. Please explain in natural language the process of analyzing the user's statements and providing appropriate responses."

[0685] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0686] Step 1: User Login

[0687] Input: User ID, Password

[0688] Output: Authentication success or failure, user profile data

[0689] Operation:

[0690] Users access the system using a device and enter their user ID and password on the login screen. The device sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the device. Users access the login screen from a dedicated app or web browser using a smartphone or PC. Once the input is complete and the "Login" button is pressed, the information is transferred to the server.

[0691] Step 2: Selecting a Training Scenario

[0692] Input: User scenario selection information

[0693] Output: Selected scenario data

[0694] Operation:

[0695] The user selects a training scenario from the main menu. The terminal sends the selected scenario data to the server. The server provides the transmitted scenario to the terminal and displays a button to start the scenario. The user selects and clicks a training scenario from options such as "Consult about purchasing a TV." This selection information is sent from the terminal to the server, and the appropriate scenario data is delivered to the terminal.

[0696] Step 3: Display the initial screen of the scenario.

[0697] Input: Scenario data

[0698] Output: Display of initial message or playback of audio

[0699] Operation:

[0700] Based on the scenario data, the terminal displays or plays an initial message from a virtual customer character. It might display a message such as, "Hello, what product are you looking for today?" or the character might begin speaking aloud.

[0701] Step 4: User response input

[0702] Input: User voice or text response

[0703] Output: User response data

[0704] Operation:

[0705] The user responds to questions and comments from a virtual customer character using voice or text. The terminal transcribes the user's responses into text and sends it to the server. The user provides specific responses, such as "I'm looking for the latest 4K TV," using voice or keyboard input.

[0706] Step 5: Sending and analyzing responses, sentiment recognition

[0707] Input: User response data

[0708] Output: Appropriate response and recognized emotional information

[0709] Operation:

[0710] The terminal sends the user's response to the server. The server analyzes the user's utterance using natural language processing. Additionally, an emotion recognition engine recognizes the emotion from the user's utterance. The server uses NLP (Natural Language Processing) algorithms to analyze the content and emotion. For example, from the response "I'm looking for the latest 4K TV," it extracts the emotions "interest" and "curiosity."

[0711] Step 6: Generating and displaying the appropriate response

[0712] Input: Analyzed speech data and sentiment information

[0713] Output: Appropriate response data

[0714] Operation:

[0715] The server generates an appropriate response based on the recognized emotion information and sends it to the terminal. The terminal displays or plays the generated response to the user. For example, the server generates a response such as, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," which is sent to the terminal and displayed or played aloud.

[0716] Step 7: Session End and Evaluation

[0717] Input: User interaction data

[0718] Output: Evaluation results and feedback messages

[0719] Operation:

[0720] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, politeness, and emotion recognition results. The server evaluates each item, generates a feedback message, and sends it to the terminal. After the session ends, the server scores the user's interaction based on criteria such as "appropriateness of interaction," "speed of response," and "politeness," and displays the results as a feedback message.

[0721] Step 8: Saving and referencing past training data

[0722] Input: Conversation data and evaluation result data

[0723] Output: Data to be saved to the database and for future reference.

[0724] Operation:

[0725] The server stores all session conversation data and evaluation results in a database. Users can later refer to past training content and evaluations to check their areas for improvement and progress. If a user logs in later and wants to view their training history, they can view past evaluation results and session content. For example, evaluation results can be displayed in a graph to visually check progress.

[0726] (Application Example 2)

[0727] 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."

[0728] Traditional crew training systems differed from actual customer interactions in that they lacked real-time feedback and emotional recognition. As a result, new crew members struggled to effectively acquire practical skills on the job. Furthermore, traditional systems often delayed detailed evaluations and feedback on user interactions, making immediate improvement difficult.

[0729] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving and authenticating authentication information from the user, means for selecting a training scenario, means for initiating a dialogue with a customer character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response to the user, means for evaluating the user's response and providing feedback, means for storing conversation data and evaluation results, means for receiving questions and comments from a virtual customer using smart glasses and responding in voice or text, and means including an emotion engine that provides the generated feedback to the user in real time. This makes it possible for new crew members to efficiently acquire practical skills through simulations that closely resemble real-world problems.

[0730] "Means of receiving and authenticating authentication information from users" refers to the process or function of sending authentication information such as user ID and password to a server to verify the user's identity.

[0731] "Means for selecting a training scenario" refers to the interface or function that allows a user to select a specific scenario from among multiple training scenarios.

[0732] "Means of initiating a dialogue with a customer character based on a selected training scenario" refers to a function that allows the user to initiate a dialogue with a virtual character representing a customer based on a scenario selected by the user.

[0733] "Means for analyzing user statements and generating appropriate responses" refers to a process or function that analyzes user statements using natural language processing technology and generates appropriate responses in response.

[0734] "Means for displaying or playing the generated response to the user" refers to a function for displaying the generated response to the user as text or playing it as audio.

[0735] "Means of evaluating user interactions and providing feedback" refers to processes and functions that evaluate user interactions based on various evaluation criteria and provide corresponding feedback to the user.

[0736] "Means for saving conversation data and evaluation results" refers to a function for saving conversation data and evaluation results during training to a storage device such as a database.

[0737] "A means of receiving questions and comments from virtual customers using smart glasses and responding to them in voice or text" refers to a function that receives questions and comments from virtual customers via smart glasses and responds to them in voice or text.

[0738] "Means including an emotion engine that provides generated feedback to the user in real time" refers to a function that includes an emotion analysis engine to analyze emotions from the user's statements and actions and provide appropriate feedback in real time.

[0739] This invention provides a system for efficiently training new crew members, and in particular, uses smart glasses to perform real-time customer interaction simulations while providing appropriate feedback using an emotion engine. Specific embodiments of this system are described below.

[0740] This system allows users to receive questions and comments from virtual customers by wearing smart glasses, and respond to them in voice and text. It includes functions to receive and authenticate user authentication information, select training scenarios, initiate conversations with virtual customers based on the scenarios, analyze user statements and generate appropriate responses, present the generated responses to the user, evaluate the interaction and provide feedback, and save conversation data and evaluation results.

[0741] Hardware and software used

[0742] Smart glasses: Worn by the user, they display messages and play audio from virtual customers and receive voice input from the user.

[0743] Server: Manages user authentication information, generates training scenarios and responses, and manages evaluations. It uses an open-source database system to store conversation data and evaluation results.

[0744] Emotion Engine: An engine that analyzes user emotions from their statements and provides appropriate feedback in real time. It utilizes natural language processing technology (e.g., OpenAI's API).

[0745] Details of data processing and handling

[0746] 1. User Authentication:

[0747] The user logs into the system via smart glasses and enters their ID and password.

[0748] The server receives the entered information, compares it with the database, and authenticates the user.

[0749] Upon successful authentication, the user's profile information is loaded and displayed on the smart glasses.

[0750] 2. Training Scenario Selection:

[0751] The user selects any training scenario through the smart glasses interface.

[0752] The server sends the selected scenario data to the smart glasses and instructs them to start the scenario.

[0753] 3. Scenario Execution:

[0754] A character playing the role of a virtual customer displays or plays messages on smart glasses based on a scenario.

[0755] The user responds to the virtual customer's message using voice. The smart glasses then use speech recognition technology to convert this response into text.

[0756] 4. Analysis and response generation:

[0757] The server receives the user's response in text format and analyzes it using natural language processing techniques.

[0758] The emotion engine analyzes the user's responses to determine their emotions and generates an appropriate response based on that analysis.

[0759] The generated response is presented to the user via smart glasses.

[0760] 5. Evaluation and Feedback:

[0761] The server evaluates the user's interaction and generates feedback, including sentiment analysis results.

[0762] Feedback is displayed in real time on the smart glasses, allowing users to immediately understand areas for improvement.

[0763] 6. Data storage:

[0764] The server stores all conversation data and evaluation results in a database.

[0765] Users can later refer to their past training content and evaluation results, providing support for self-improvement.

[0766] Examples of specific cases and prompt statements

[0767] As a specific training scenario, the case of "consulting about purchasing a television" would be as follows:

[0768] Virtual customer: "Hello, I'm looking for the latest 4K TV."

[0769] User: "Which TV manufacturer would you recommend?"

[0770] Examples of prompts in this conversation are as follows:

[0771] Customer message: Hi, I'm looking for the latest 4K TV.

[0772] User's response: Which TV manufacturer would you recommend?

[0773] Please show your emotions and respond appropriately.

[0774] In this way, the system is designed to allow new crew members to efficiently acquire practical skills through simulations that closely resemble actual customer interactions. This is expected to enable effective training in a short period of time and improve the quality of customer service.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The user puts on smart glasses and logs into the system. The smart glasses accept the user ID and password. This input is sent from the device to the server. The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server loads the user's profile information and displays the main menu on the device.

[0778] Step 2:

[0779] The user selects a training scenario through the smart glasses interface. This scenario selection is sent from the device to the server. The server retrieves the selected scenario data and sends it back to the device. The device then displays a button to initiate the scenario.

[0780] Step 3:

[0781] The user clicks the "Start Scenario" button. The device displays an initial message from the virtual customer character. This message is generated by the device based on the scenario data. The user reviews the virtual customer's message and responds with voice.

[0782] Step 4:

[0783] The terminal receives the user's voice input and converts it into text using a transcription engine. The converted text is sent from the terminal to the server. The server analyzes the user's speech using natural language processing technology. Based on this analysis, the server generates an appropriate response.

[0784] Step 5:

[0785] The server sends the generated response to the terminal. The terminal displays or plays the response message aloud to the user. The user reviews this response and asks additional questions or makes comments as needed. This process is repeated until the simulation scenario is complete.

[0786] Step 6:

[0787] The server evaluates the user's response after each interaction session. The evaluation criteria are based on appropriateness, speed, and politeness. In addition, an emotion engine analyzes the user's utterances to determine their emotions and generates additional feedback based on that analysis.

[0788] Step 7:

[0789] The server sends the generated evaluation results and feedback to the terminal. The terminal displays this to the user in real time. The user uses this feedback to improve their response.

[0790] Step 8:

[0791] The server stores all conversation data and evaluation results in a database. This allows users to refer to past training content and evaluation results later. The server can also analyze progress and suggest appropriate next training scenarios.

[0792] Each of these steps allows users to receive real-time feedback based on sentiment analysis, enabling them to effectively improve their customer service skills.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] [Third Embodiment]

[0797] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0798] 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.

[0799] 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).

[0800] 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.

[0801] 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.

[0802] 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).

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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".

[0809] This invention is a system for efficiently training new crew members and is implemented using users, terminals, and a server. The following describes in detail how this invention can be specifically implemented.

[0810] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0811] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[0812] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[0813] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which uses natural language processing to analyze the user's statements. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and the virtual customer that closely resembles a real customer service scenario.

[0814] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[0815] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[0816] Specific example

[0817] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0818] In this way, this system allows new crew members to gain experience that closely resembles actual customer interactions. This enables them to acquire practical skills effectively in a short period of time, thereby improving the quality of customer service in mass retail stores.

[0819] The following describes the processing flow.

[0820] Step 1:

[0821] The user enters their user ID and password on the device's login screen.

[0822] Step 2:

[0823] The terminal sends the entered authentication information to the server.

[0824] Step 3:

[0825] The server checks the received authentication information against the database and authenticates the user.

[0826] Step 4:

[0827] The server sends the authentication result back to the terminal.

[0828] Step 5:

[0829] The device displays the main menu screen to the user along with a message indicating successful authentication.

[0830] Step 6:

[0831] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[0832] Step 7:

[0833] The server sends the data for the selected training scenario to the terminal.

[0834] Step 8:

[0835] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[0836] Step 9:

[0837] The user clicks the "Start Scenario" button.

[0838] Step 10:

[0839] The device displays or plays an initial message from the customer character based on the scenario data.

[0840] Step 11:

[0841] The user responds to questions and comments from a character representing a customer using voice or text.

[0842] Step 12:

[0843] The terminal sends the user's response to the server.

[0844] Step 13:

[0845] The server uses natural language processing to analyze the user's statements.

[0846] Step 14:

[0847] The server generates an appropriate response based on the information it has analyzed.

[0848] Step 15:

[0849] The server sends the generated response to the terminal.

[0850] Step 16:

[0851] The device displays or plays the received response to the user.

[0852] Step 17:

[0853] The user responds again, either by voice or text, in response to the customer character's response.

[0854] Step 18:

[0855] The server and terminal continue their interaction by repeating steps 11 through 16.

[0856] Step 19:

[0857] The server detects that the scenario has ended.

[0858] Step 20:

[0859] The server evaluates the user's response and generates an evaluation result.

[0860] Step 21:

[0861] The server generates feedback based on the evaluation results.

[0862] Step 22:

[0863] The server sends the generated feedback message to the terminal.

[0864] Step 23:

[0865] The device displays a feedback message to the user.

[0866] Step 24:

[0867] The server saves all conversation data and evaluation results to a database.

[0868] Step 25:

[0869] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[0870] (Example 1)

[0871] 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."

[0872] Traditional new employee training systems had limited opportunities for simulating actual customer interactions, making it difficult for new crew members to effectively acquire practical skills in a short period. Furthermore, the lack of sufficient feedback when evaluating user responses meant that it was unclear to individual users what areas they needed to improve.

[0873] 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.

[0874] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a virtual person based on the selected training scenario; means for analyzing the user's statements using natural language processing and generating an appropriate response; means for displaying or playing the generated response to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for providing a user interface; and means for generating responses using a generative AI model. This enables new crew members to effectively acquire practical skills in a short period of time through scenarios that closely resemble actual customer interactions. Furthermore, by providing specific feedback after each session, users can more easily identify areas for improvement.

[0875] "Login information" refers to information such as the user ID and password that a user enters when accessing the system.

[0876] "Authentication means" refers to a mechanism by which a server verifies the user's identity by comparing login information sent by the user with a database.

[0877] A "training scenario" is a series of simulation situations set up for the purpose of training in specific job functions.

[0878] A "virtual character" refers to a computer-generated character or agent that interacts with the user within a system.

[0879] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0880] "Means of generating responses" refer to technologies and algorithms used to create appropriate responses based on user statements.

[0881] "Means of displaying or playing back" refers to a mechanism for providing the generated response to the user visually or audibly.

[0882] A "means for evaluating the content of interactions" refers to a system for evaluating the content of user conversations and responses based on various criteria.

[0883] A "means of providing feedback" refers to a system for communicating areas for improvement and evaluations to users based on the evaluation results.

[0884] "Conversation data" refers to the record of all interactions that take place between the user and the virtual character.

[0885] "Evaluation results" refer to the detailed evaluation obtained when assessing the content of the interaction.

[0886] "Means of preservation" refers to a system for recording conversation data and evaluation results in a database or similar format so that they can be referenced later.

[0887] A "user interface" refers to the screens and means of operation that allow a user to directly interact with a system.

[0888] A "generative AI model" is an artificial intelligence model used to create appropriate responses based on user statements.

[0889] This invention provides a system for efficiently training new crew members and is implemented using a user, a terminal, and a server. First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. Database management systems such as MySQL or PostgreSQL are used for authentication.

[0890] Upon successful authentication, the server loads the user's profile data and displays the main menu on the terminal. Web technologies such as HTML, CSS, and JavaScript are used to display the main menu. The user selects a training scenario from the main menu.

[0891] When a user selects a training scenario, the server sends the selected scenario data to the device. This data is often sent in JSON format. Based on the scenario data, the device displays or plays an initial message from a virtual character representing the customer. A Text-to-Speech (TTS) engine is used for audio playback, such as Google Cloud Text-to-Speech.

[0892] The user responds to questions and comments from a virtual character using voice or text. The terminal sends the user's response to the server, which analyzes the user's utterance using natural language processing (NLP). Google Cloud Natural Language API and IBM Watson are used for NLP. Based on the analysis results, the server generates an appropriate response using a generative AI model. An example of a generative AI model is OpenAI's GPT-3.

[0893] The generated response is sent from the server to the terminal, which then displays or plays the response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[0894] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. Based on the evaluation, the server generates a feedback message and sends it to the terminal. This feedback message is displayed on the terminal.

[0895] Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and progress.

[0896] For example, consider a scenario where a user selects "consultation on purchasing a TV." A virtual character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models in our store," and sends it to the terminal. The user confirms this and continues with more detailed questions. This system allows users to efficiently acquire practical skills based on practice scenarios.

[0897] Examples of prompt statements include the following:

[0898] User query: "I'm looking for the latest 4K TVs."

[0899] Generate appropriate response for a customer query about 4K TVs. Provide information about recommended models and features.

[0900] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0901] Step 1:

[0902] To access the system, the user opens a web browser using a PC, tablet, or smartphone. The user enters the system's URL and reaches the login screen. Here, the user enters their user ID and password and clicks the "Login" button. The entered user ID and password are then sent from the device to the server.

[0903] Step 2:

[0904] The server receives login information sent from the terminal. The server uses a database management system (e.g., MySQL or PostgreSQL) to verify the received user ID and password. If authentication is successful, the user's profile data is loaded and sent to the terminal. The input is the user ID and password, and the output is the authentication result and profile data.

[0905] Step 3:

[0906] The terminal receives profile data sent from the server. The terminal uses HTML, CSS, and JavaScript to generate the main menu and display it to the user. The output is the main menu screen.

[0907] Step 4:

[0908] The user selects a training scenario from the main menu. For example, they might choose the scenario "Consulting about purchasing a TV." The user's input is then based on the selected scenario.

[0909] Step 5:

[0910] The server sends the corresponding data to the terminal in JSON format based on the training scenario selected by the user. The output is the data for the selected scenario.

[0911] Step 6:

[0912] The terminal analyzes the received scenario data and displays or plays an initial message from the virtual character. A Text-to-Speech (TTS) engine (e.g., Google Cloud Text-to-Speech) is used for audio playback. User input is the initial message, and output is either displayed or spoken.

[0913] Step 7:

[0914] The user responds to questions and comments from a virtual character using voice or text. The user's responses are sent from the terminal to the server. The input is the user's response.

[0915] Step 8:

[0916] The server analyzes user responses sent from the terminal using natural language processing (NLP). Specifically, it uses Google Cloud Natural Language API or IBM Watson. The input for the analysis is the user's response, and the output is the analysis result.

[0917] Step 9:

[0918] Based on the analysis results, the server generates an appropriate response using a generative AI model (e.g., OpenAI GPT-3). The generated response is sent to the terminal. The input is the analysis result, and the output is the generated response.

[0919] Step 10:

[0920] The terminal receives responses sent from the server and displays or plays them aloud to the user. For example, if the user responds, "I'm looking for the latest 4K TV," the terminal will display, "You're interested in 4K TVs. We recommend our latest models." The output is either visual or audible.

[0921] Step 11:

[0922] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The input for the evaluation is the user's interaction, and the output is the evaluation result.

[0923] Step 12:

[0924] The server generates a feedback message based on the evaluation results and sends it to the terminal. The terminal displays the feedback message to the user. The input is the evaluation result, and the output is the feedback message.

[0925] Step 13:

[0926] The server saves all session conversation data and evaluation results to a database. This saved data is for users to access later. The input is the conversation data and evaluation results, and the output is saving to the database.

[0927] (Application Example 1)

[0928] 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."

[0929] Traditional training systems for new crew members struggled to provide an environment that closely resembled actual customer service scenarios. Furthermore, the lack of quality and realism in virtual environments made it difficult to maximize user learning effectiveness. In particular, the insufficient analysis of user utterances and generation of appropriate responses using natural language processing technology posed challenges in acquiring practical skills.

[0930] 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.

[0931] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a customer character based on the selected training scenario within a virtual environment; means for analyzing the user's statements and generating appropriate responses; means for displaying or playing the generated responses audibly to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for the user to perform customer service simulations in a virtual environment using a virtual reality display device; and means for transmitting user input via a terminal connected to the virtual reality display device and analyzing it on the server. This enables the user to effectively acquire more practical skills in a short period of time through dialogue in a virtual environment that closely resembles actual customer service scenarios.

[0932] A "user" is a person who accesses the system, enters login information, and executes training scenarios.

[0933] "Login information" refers to authentication information, including user IDs and passwords, used to authenticate users.

[0934] A "training scenario" is scenario data that allows users to simulate conversations with customer-facing characters in a virtual environment and learn how to provide customer service.

[0935] A "virtual environment" is a computer-generated simulation environment into which users can immerse themselves, enabling the execution of customer service scenarios.

[0936] A "customer character" is a virtual character that the user interacts with during a customer service simulation.

[0937] "Speech analysis" is an information processing technique that uses natural language processing technology to analyze user-inputted voice and text data and generate appropriate responses.

[0938] "Response generation" is the process by which a server provides an appropriate response to a user based on its analysis of the user's speech.

[0939] "Feedback" refers to information that evaluates the appropriateness, speed, and politeness of a user's response, and then provides that evaluation back to the user.

[0940] "Conversation data" refers to a record of the dialogue exchanged between the user and a character representing a customer.

[0941] A "virtual reality display device" is a device that provides a virtual environment to a user visually, and includes, for example, a head-mounted display.

[0942] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and run training scenarios.

[0943] "Natural language processing" is a technology that allows computers to analyze, understand, and generate human language.

[0944] This invention is a system for efficiently training new crew members, and is implemented using users, terminals, and a server. The system aims to enable users to acquire practical skills in a short period of time through customer service simulations in a virtual environment.

[0945] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server, which authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0946] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario starts in the virtual environment.

[0947] The terminal is connected to a virtual reality display device that allows the user to immerse themselves in a virtual environment. Within the virtual environment, a character representing a customer appears and displays or plays an initial message. This character engages in dialogue that mirrors actual customer service scenarios, such as searching for products or asking specific questions.

[0948] The user responds to questions and comments from a character representing a customer using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing technology. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response aloud to the user.

[0949] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness. The server evaluates each criterion, generates a feedback message, and sends it to the user's terminal. Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[0950] (Specific example)

[0951] For example, if a user selects the "TV purchase consultation" scenario, the customer character will display or play a voice message in the virtual environment saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models from several manufacturers," and sends it to the terminal. The user confirms this and continues with further questions.

[0952] In this way, this system provides a mechanism that allows new crew members to gain experience that is similar to actual customer interactions. This is expected to improve the quality of customer service in mass retail stores.

[0953] Example of a prompt:

[0954] User: "Hello, what kind of product are you looking for today?"

[0955] Customer: "I'm looking for the latest 4K TV."

[0956] User: "So, you're looking for the latest 4K TV. Do you have any specific manufacturer or feature preferences?"

[0957] Customer: "I'd like something with a large screen and good sound quality."

[0958] User: "The screen size is large and the sound quality is good. We recommend the latest models from several manufacturers. Shall I explain the features of each?"

[0959] ---------

[0960] The above is a detailed description of the embodiments for carrying out the invention.

[0961] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0962] Step 1:

[0963] The user accesses the system using a terminal and enters their user ID and password on the login screen. The entered data (user ID and password) is sent from the terminal to the server. The server verifies this authentication information against a database to authenticate the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0964] Step 2:

[0965] The user selects a training scenario from the main menu. The selected scenario information is sent from the terminal to the server. The server loads the scenario data and sends it back to the terminal. The terminal displays a button to start the scenario to the user.

[0966] Step 3:

[0967] When the user clicks the "Start Scenario" button, the scenario begins. Using a virtual reality display device, a character representing the customer displays or plays an initial message in the virtual environment, such as "Hello, what kind of product are you looking for today?" The user responds to this message.

[0968] Step 4:

[0969] The user's response (voice or text) is processed on the terminal and sent to the server. The server analyzes the user's utterance using natural language processing. Specifically, it tokenizes the text data and performs semantic and contextual analysis. As a result of the analysis, the user's intent and requests are extracted.

[0970] Step 5:

[0971] The server generates an appropriate response based on the analysis results. A generative AI model is used for this process. The generated response is sent to the terminal, which then displays or plays it aloud for the user. For example, a response such as, "You're interested in 4K TVs, aren't you? We recommend the latest models from several manufacturers," might be generated.

[0972] Step 6:

[0973] The dialogue continues between the user and the customer character. Steps 4 and 5 are repeated for each response. The user continues to ask detailed questions, and the server generates corresponding responses.

[0974] Step 7:

[0975] Once the scenario is complete, the server evaluates all conversation data. Criteria such as appropriateness, speed, and politeness are used for evaluation. Based on each criterion, the server generates an evaluation score and feedback message. This evaluation data and feedback message are sent to the terminal.

[0976] Step 8:

[0977] The terminal displays feedback messages to the user. The server saves all session conversation data and evaluation results to a database. Users can later refer to this data to check their areas for improvement and progress.

[0978] 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.

[0979] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[0980] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[0981] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[0982] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[0983] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing. Furthermore, an emotion engine recognizes emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[0984] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[0985] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[0986] Specific example

[0987] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[0988] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[0989] The following describes the processing flow.

[0990] Step 1:

[0991] The user enters their user ID and password on the device's login screen.

[0992] Step 2:

[0993] The terminal sends the entered authentication information to the server.

[0994] Step 3:

[0995] The server checks the received authentication information against the database and authenticates the user.

[0996] Step 4:

[0997] The server sends the authentication result back to the terminal.

[0998] Step 5:

[0999] The device displays the main menu screen to the user along with a message indicating successful authentication.

[1000] Step 6:

[1001] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[1002] Step 7:

[1003] The server sends the data for the selected training scenario to the terminal.

[1004] Step 8:

[1005] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[1006] Step 9:

[1007] The user clicks the "Start Scenario" button.

[1008] Step 10:

[1009] The device displays or plays an initial message from the customer character based on the scenario data.

[1010] Step 11:

[1011] The user responds to questions and comments from a character representing a customer using voice or text.

[1012] Step 12:

[1013] The terminal sends the user's response to the server.

[1014] Step 13:

[1015] The server uses natural language processing to analyze the user's statements.

[1016] Step 14:

[1017] An emotion engine built into the server recognizes emotions from the user's statements and adds that emotional information to the analysis results.

[1018] Step 15:

[1019] The server generates an appropriate response based on the user's statements and perceived emotions.

[1020] Step 16:

[1021] The server sends the generated response to the terminal.

[1022] Step 17:

[1023] The device displays or plays the received response to the user.

[1024] Step 18:

[1025] The user responds again, either by voice or text, in response to the customer character's response.

[1026] Step 19:

[1027] The server and terminal continue their interaction by repeating steps 11 through 17.

[1028] Step 20:

[1029] The server detects that the scenario has ended.

[1030] Step 20:

[1031] The server evaluates the user's response. Evaluation criteria include appropriateness, speed, and politeness of the response, as well as the user's emotional perception.

[1032] Step 21:

[1033] The server generates feedback based on the evaluation results.

[1034] Step 22:

[1035] The server sends the generated feedback message to the terminal.

[1036] Step 23:

[1037] The device displays a feedback message to the user.

[1038] Step 24:

[1039] The server saves all conversation data and evaluation results to a database.

[1040] Step 25:

[1041] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[1042] (Example 2)

[1043] 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."

[1044] In a system designed to efficiently train new crew members, it is necessary to recognize user emotions and respond accordingly, thereby simulating actual customer service and improving their communication skills. Furthermore, by appropriately evaluating user interactions and providing feedback, the system is required to enable effective acquisition of practical skills in a short period of time.

[1045] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating login information from a user, means for selecting a training scenario, means for initiating a conversation with a virtual character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response audibly to the user, emotion recognition means for recognizing emotions from the user's statements and generating a response based on those emotions, means for evaluating the user's response and providing feedback, and means for storing conversation data and evaluation results. This makes it possible for new crew members to experience conversations that are close to actual customer service and effectively improve their response skills and emotion recognition abilities.

[1046] A "user" refers to a person who uses the system to receive training.

[1047] "Login information" refers to information used to authenticate a user to the system, such as a user ID and password.

[1048] "Authentication means" refers to a means that has the function of verifying the entered login information against a database and authenticating the user.

[1049] A "training scenario" refers to the scenes and content of the simulation training that new crew members perform using the system.

[1050] "Means for selecting a scenario" refers to a means that allows a user to select their preferred scenario from multiple training scenarios.

[1051] A "virtual character" refers to a computer-generated person who acts as a customer in the simulation training.

[1052] "Means of initiating a conversation" refers to means that have the functionality to start a conversation with a virtual character based on the selected training scenario.

[1053] "Means for analyzing user speech" refers to means that have the function of analyzing the voice or text input by the user using natural language processing technology.

[1054] "Means for generating appropriate responses" refers to means that have the function of automatically generating responses based on the analyzed user statements.

[1055] "Emotion recognition means" refers to means that have the function of recognizing emotions from a user's statements and generating a response based on those emotions.

[1056] "Means for displaying or playing a response audibly" refers to means that have the function of providing the generated response to the user visually or audibly.

[1057] "Means for evaluating user responses" refers to means that have the function of evaluating the appropriateness, speed, politeness, and emotional recognition of the responses provided by the user.

[1058] "Means of providing feedback" refers to means that have a function to communicate to users, based on evaluation results, areas for improvement and positive aspects.

[1059] "Means for saving conversation data and evaluation results" refers to means that have the function of saving the content of conversations that took place within the system and their evaluation results to a database.

[1060] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion recognition engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[1061] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[1062] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal and displays a button on the terminal to start the scenario. When the user clicks this button, the scenario begins.

[1063] The terminal displays or plays an initial message from a virtual customer character based on scenario data. For example, it simulates actual customer service, such as a customer searching for a product or asking a specific question. The user responds to the questions and comments from the virtual customer character using voice or text. The terminal sends the user's responses to the server.

[1064] The server analyzes the user's utterances using natural language processing. Additionally, an emotion recognition engine identifies emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real-world customer service scenario.

[1065] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user. The server also saves all session conversation data and evaluation results to a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[1066] Specific example

[1067] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion recognition engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[1068] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[1069] Examples of prompts for generative AI models

[1070] "We have a training system for new crew members. In this system, you will progress through a scenario with a virtual customer character. Please explain in natural language the process of analyzing the user's statements and providing appropriate responses."

[1071] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1072] Step 1: User Login

[1073] Input: User ID, Password

[1074] Output: Authentication success or failure, user profile data

[1075] Operation:

[1076] Users access the system using a device and enter their user ID and password on the login screen. The device sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the device. Users access the login screen from a dedicated app or web browser using a smartphone or PC. Once the input is complete and the "Login" button is pressed, the information is transferred to the server.

[1077] Step 2: Selecting a Training Scenario

[1078] Input: User scenario selection information

[1079] Output: Selected scenario data

[1080] Operation:

[1081] The user selects a training scenario from the main menu. The terminal sends the selected scenario data to the server. The server provides the transmitted scenario to the terminal and displays a button to start the scenario. The user selects and clicks a training scenario from options such as "Consult about purchasing a TV." This selection information is sent from the terminal to the server, and the appropriate scenario data is delivered to the terminal.

[1082] Step 3: Display the initial screen of the scenario.

[1083] Input: Scenario data

[1084] Output: Display of initial message or playback of audio

[1085] Operation:

[1086] Based on the scenario data, the terminal displays or plays an initial message from a virtual customer character. It might display a message such as, "Hello, what product are you looking for today?" or the character might begin speaking aloud.

[1087] Step 4: User response input

[1088] Input: User voice or text response

[1089] Output: User response data

[1090] Operation:

[1091] The user responds to questions and comments from a virtual customer character using voice or text. The terminal transcribes the user's responses into text and sends it to the server. The user provides specific responses, such as "I'm looking for the latest 4K TV," using voice or keyboard input.

[1092] Step 5: Sending and analyzing responses, sentiment recognition

[1093] Input: User response data

[1094] Output: Appropriate response and recognized emotional information

[1095] Operation:

[1096] The terminal sends the user's response to the server. The server analyzes the user's utterance using natural language processing. Additionally, an emotion recognition engine recognizes the emotion from the user's utterance. The server uses NLP (Natural Language Processing) algorithms to analyze the content and emotion. For example, from the response "I'm looking for the latest 4K TV," it extracts the emotions "interest" and "curiosity."

[1097] Step 6: Generating and displaying the appropriate response

[1098] Input: Analyzed speech data and sentiment information

[1099] Output: Appropriate response data

[1100] Operation:

[1101] The server generates an appropriate response based on the recognized emotion information and sends it to the terminal. The terminal displays or plays the generated response to the user. For example, the server generates a response such as, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," which is sent to the terminal and displayed or played aloud.

[1102] Step 7: Session End and Evaluation

[1103] Input: User interaction data

[1104] Output: Evaluation results and feedback messages

[1105] Operation:

[1106] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, politeness, and emotion recognition results. The server evaluates each item, generates a feedback message, and sends it to the terminal. After the session ends, the server scores the user's interaction based on criteria such as "appropriateness of interaction," "speed of response," and "politeness," and displays the results as a feedback message.

[1107] Step 8: Saving and referencing past training data

[1108] Input: Conversation data and evaluation result data

[1109] Output: Data to be saved to the database and for future reference.

[1110] Operation:

[1111] The server stores all session conversation data and evaluation results in a database. Users can later refer to past training content and evaluations to check their areas for improvement and progress. If a user logs in later and wants to view their training history, they can view past evaluation results and session content. For example, evaluation results can be displayed in a graph to visually check progress.

[1112] (Application Example 2)

[1113] 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."

[1114] Traditional crew training systems differed from actual customer interactions in that they lacked real-time feedback and emotional recognition. As a result, new crew members struggled to effectively acquire practical skills on the job. Furthermore, traditional systems often delayed detailed evaluations and feedback on user interactions, making immediate improvement difficult.

[1115] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving and authenticating authentication information from the user, means for selecting a training scenario, means for initiating a dialogue with a customer character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response to the user, means for evaluating the user's response and providing feedback, means for storing conversation data and evaluation results, means for receiving questions and comments from a virtual customer using smart glasses and responding in voice or text, and means including an emotion engine that provides the generated feedback to the user in real time. This makes it possible for new crew members to efficiently acquire practical skills through simulations that closely resemble real-world problems.

[1116] "Means of receiving and authenticating authentication information from users" refers to the process or function of sending authentication information such as user ID and password to a server to verify the user's identity.

[1117] "Means for selecting a training scenario" refers to the interface or function that allows a user to select a specific scenario from among multiple training scenarios.

[1118] "Means of initiating a dialogue with a customer character based on a selected training scenario" refers to a function that allows the user to initiate a dialogue with a virtual character representing a customer based on a scenario selected by the user.

[1119] "Means for analyzing user statements and generating appropriate responses" refers to a process or function that analyzes user statements using natural language processing technology and generates appropriate responses in response.

[1120] "Means for displaying or playing the generated response to the user" refers to a function for displaying the generated response to the user as text or playing it as audio.

[1121] "Means of evaluating user interactions and providing feedback" refers to processes and functions that evaluate user interactions based on various evaluation criteria and provide corresponding feedback to the user.

[1122] "Means for saving conversation data and evaluation results" refers to a function for saving conversation data and evaluation results during training to a storage device such as a database.

[1123] "A means of receiving questions and comments from virtual customers using smart glasses and responding to them in voice or text" refers to a function that receives questions and comments from virtual customers via smart glasses and responds to them in voice or text.

[1124] "Means including an emotion engine that provides generated feedback to the user in real time" refers to a function that includes an emotion analysis engine to analyze emotions from the user's statements and actions and provide appropriate feedback in real time.

[1125] This invention provides a system for efficiently training new crew members, and in particular, uses smart glasses to perform real-time customer interaction simulations while providing appropriate feedback using an emotion engine. Specific embodiments of this system are described below.

[1126] This system allows users to receive questions and comments from virtual customers by wearing smart glasses, and respond to them in voice and text. It includes functions to receive and authenticate user authentication information, select training scenarios, initiate conversations with virtual customers based on the scenarios, analyze user statements and generate appropriate responses, present the generated responses to the user, evaluate the interaction and provide feedback, and save conversation data and evaluation results.

[1127] Hardware and software used

[1128] Smart glasses: Worn by the user, they display messages and play audio from virtual customers and receive voice input from the user.

[1129] Server: Manages user authentication information, generates training scenarios and responses, and manages evaluations. It uses an open-source database system to store conversation data and evaluation results.

[1130] Emotion Engine: An engine that analyzes user emotions from their statements and provides appropriate feedback in real time. It utilizes natural language processing technology (e.g., OpenAI's API).

[1131] Details of data processing and handling

[1132] 1. User Authentication:

[1133] The user logs into the system via smart glasses and enters their ID and password.

[1134] The server receives the entered information, compares it with the database, and authenticates the user.

[1135] Upon successful authentication, the user's profile information is loaded and displayed on the smart glasses.

[1136] 2. Training Scenario Selection:

[1137] The user selects any training scenario through the smart glasses interface.

[1138] The server sends the selected scenario data to the smart glasses and instructs them to start the scenario.

[1139] 3. Scenario Execution:

[1140] A character playing the role of a virtual customer displays or plays messages on smart glasses based on a scenario.

[1141] The user responds to the virtual customer's message using voice. The smart glasses then use speech recognition technology to convert this response into text.

[1142] 4. Analysis and response generation:

[1143] The server receives the user's response in text format and analyzes it using natural language processing techniques.

[1144] The emotion engine analyzes the user's responses to determine their emotions and generates an appropriate response based on that analysis.

[1145] The generated response is presented to the user via smart glasses.

[1146] 5. Evaluation and Feedback:

[1147] The server evaluates the user's interaction and generates feedback, including sentiment analysis results.

[1148] Feedback is displayed in real time on the smart glasses, allowing users to immediately understand areas for improvement.

[1149] 6. Data storage:

[1150] The server stores all conversation data and evaluation results in a database.

[1151] Users can later refer to their past training content and evaluation results, providing support for self-improvement.

[1152] Examples of specific cases and prompt statements

[1153] As a specific training scenario, the case of "consulting about purchasing a television" would be as follows:

[1154] Virtual customer: "Hello, I'm looking for the latest 4K TV."

[1155] User: "Which TV manufacturer would you recommend?"

[1156] Examples of prompts in this conversation are as follows:

[1157] Customer message: Hi, I'm looking for the latest 4K TV.

[1158] User's response: Which TV manufacturer would you recommend?

[1159] Please show your emotions and respond appropriately.

[1160] In this way, the system is designed to allow new crew members to efficiently acquire practical skills through simulations that closely resemble actual customer interactions. This is expected to enable effective training in a short period of time and improve the quality of customer service.

[1161] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1162] Step 1:

[1163] The user puts on smart glasses and logs into the system. The smart glasses accept the user ID and password. This input is sent from the device to the server. The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server loads the user's profile information and displays the main menu on the device.

[1164] Step 2:

[1165] The user selects a training scenario through the smart glasses interface. This scenario selection is sent from the device to the server. The server retrieves the selected scenario data and sends it back to the device. The device then displays a button to initiate the scenario.

[1166] Step 3:

[1167] The user clicks the "Start Scenario" button. The device displays an initial message from the virtual customer character. This message is generated by the device based on the scenario data. The user reviews the virtual customer's message and responds with voice.

[1168] Step 4:

[1169] The terminal receives the user's voice input and converts it into text using a transcription engine. The converted text is sent from the terminal to the server. The server analyzes the user's speech using natural language processing technology. Based on this analysis, the server generates an appropriate response.

[1170] Step 5:

[1171] The server sends the generated response to the terminal. The terminal displays or plays the response message aloud to the user. The user reviews this response and asks additional questions or makes comments as needed. This process is repeated until the simulation scenario is complete.

[1172] Step 6:

[1173] The server evaluates the user's response after each interaction session. The evaluation criteria are based on appropriateness, speed, and politeness. In addition, an emotion engine analyzes the user's utterances to determine their emotions and generates additional feedback based on that analysis.

[1174] Step 7:

[1175] The server sends the generated evaluation results and feedback to the terminal. The terminal displays this to the user in real time. The user uses this feedback to improve their response.

[1176] Step 8:

[1177] The server stores all conversation data and evaluation results in a database. This allows users to refer to past training content and evaluation results later. The server can also analyze progress and suggest appropriate next training scenarios.

[1178] Each of these steps allows users to receive real-time feedback based on sentiment analysis, enabling them to effectively improve their customer service skills.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] [Fourth Embodiment]

[1183] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1184] 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.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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).

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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".

[1196] This invention is a system for efficiently training new crew members and is implemented using users, terminals, and a server. The following describes in detail how this invention can be specifically implemented.

[1197] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[1198] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[1199] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[1200] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which uses natural language processing to analyze the user's statements. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and the virtual customer that closely resembles a real customer service scenario.

[1201] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[1202] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[1203] Specific example

[1204] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[1205] In this way, this system allows new crew members to gain experience that closely resembles actual customer interactions. This enables them to acquire practical skills effectively in a short period of time, thereby improving the quality of customer service in mass retail stores.

[1206] The following describes the processing flow.

[1207] Step 1:

[1208] The user enters their user ID and password on the device's login screen.

[1209] Step 2:

[1210] The terminal sends the entered authentication information to the server.

[1211] Step 3:

[1212] The server checks the received authentication information against the database and authenticates the user.

[1213] Step 4:

[1214] The server sends the authentication result back to the terminal.

[1215] Step 5:

[1216] The device displays the main menu screen to the user along with a message indicating successful authentication.

[1217] Step 6:

[1218] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[1219] Step 7:

[1220] The server sends the data for the selected training scenario to the terminal.

[1221] Step 8:

[1222] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[1223] Step 9:

[1224] The user clicks the "Start Scenario" button.

[1225] Step 10:

[1226] The device displays or plays an initial message from the customer character based on the scenario data.

[1227] Step 11:

[1228] The user responds to questions and comments from a character representing a customer using voice or text.

[1229] Step 12:

[1230] The terminal sends the user's response to the server.

[1231] Step 13:

[1232] The server uses natural language processing to analyze the user's statements.

[1233] Step 14:

[1234] The server generates an appropriate response based on the information it has analyzed.

[1235] Step 15:

[1236] The server sends the generated response to the terminal.

[1237] Step 16:

[1238] The device displays or plays the received response to the user.

[1239] Step 17:

[1240] The user responds again, either by voice or text, in response to the customer character's response.

[1241] Step 18:

[1242] The server and terminal continue their interaction by repeating steps 11 through 16.

[1243] Step 19:

[1244] The server detects that the scenario has ended.

[1245] Step 20:

[1246] The server evaluates the user's response and generates an evaluation result.

[1247] Step 21:

[1248] The server generates feedback based on the evaluation results.

[1249] Step 22:

[1250] The server sends the generated feedback message to the terminal.

[1251] Step 23:

[1252] The device displays a feedback message to the user.

[1253] Step 24:

[1254] The server saves all conversation data and evaluation results to a database.

[1255] Step 25:

[1256] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[1257] (Example 1)

[1258] 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".

[1259] Traditional new employee training systems had limited opportunities for simulating actual customer interactions, making it difficult for new crew members to effectively acquire practical skills in a short period. Furthermore, the lack of sufficient feedback when evaluating user responses meant that it was unclear to individual users what areas they needed to improve.

[1260] 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.

[1261] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a virtual person based on the selected training scenario; means for analyzing the user's statements using natural language processing and generating an appropriate response; means for displaying or playing the generated response to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for providing a user interface; and means for generating responses using a generative AI model. This enables new crew members to effectively acquire practical skills in a short period of time through scenarios that closely resemble actual customer interactions. Furthermore, by providing specific feedback after each session, users can more easily identify areas for improvement.

[1262] "Login information" refers to information such as the user ID and password that a user enters when accessing the system.

[1263] "Authentication means" refers to a mechanism by which a server verifies the user's identity by comparing login information sent by the user with a database.

[1264] A "training scenario" is a series of simulation situations set up for the purpose of training in specific job functions.

[1265] A "virtual character" refers to a computer-generated character or agent that interacts with the user within a system.

[1266] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[1267] "Means of generating responses" refer to technologies and algorithms used to create appropriate responses based on user statements.

[1268] "Means of displaying or playing back" refers to a mechanism for providing the generated response to the user visually or audibly.

[1269] A "means for evaluating the content of interactions" refers to a system for evaluating the content of user conversations and responses based on various criteria.

[1270] A "means of providing feedback" refers to a system for communicating areas for improvement and evaluations to users based on the evaluation results.

[1271] "Conversation data" refers to the record of all interactions that take place between the user and the virtual character.

[1272] "Evaluation results" refer to the detailed evaluation obtained when assessing the content of the interaction.

[1273] "Means of preservation" refers to a system for recording conversation data and evaluation results in a database or similar format so that they can be referenced later.

[1274] A "user interface" refers to the screens and means of operation that allow a user to directly interact with a system.

[1275] A "generative AI model" is an artificial intelligence model used to create appropriate responses based on user statements.

[1276] This invention provides a system for efficiently training new crew members and is implemented using a user, a terminal, and a server. First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. Database management systems such as MySQL or PostgreSQL are used for authentication.

[1277] Upon successful authentication, the server loads the user's profile data and displays the main menu on the terminal. Web technologies such as HTML, CSS, and JavaScript are used to display the main menu. The user selects a training scenario from the main menu.

[1278] When a user selects a training scenario, the server sends the selected scenario data to the device. This data is often sent in JSON format. Based on the scenario data, the device displays or plays an initial message from a virtual character representing the customer. A Text-to-Speech (TTS) engine is used for audio playback, such as Google Cloud Text-to-Speech.

[1279] The user responds to questions and comments from a virtual character using voice or text. The terminal sends the user's response to the server, which analyzes the user's utterance using natural language processing (NLP). Google Cloud Natural Language API and IBM Watson are used for NLP. Based on the analysis results, the server generates an appropriate response using a generative AI model. An example of a generative AI model is OpenAI's GPT-3.

[1280] The generated response is sent from the server to the terminal, which then displays or plays the response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[1281] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. Based on the evaluation, the server generates a feedback message and sends it to the terminal. This feedback message is displayed on the terminal.

[1282] Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and progress.

[1283] For example, consider a scenario where a user selects "consultation on purchasing a TV." A virtual character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models in our store," and sends it to the terminal. The user confirms this and continues with more detailed questions. This system allows users to efficiently acquire practical skills based on practice scenarios.

[1284] Examples of prompt statements include the following:

[1285] User query: "I'm looking for the latest 4K TVs."

[1286] Generate appropriate response for a customer query about 4K TVs. Provide information about recommended models and features.

[1287] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1288] Step 1:

[1289] To access the system, the user opens a web browser using a PC, tablet, or smartphone. The user enters the system's URL and reaches the login screen. Here, the user enters their user ID and password and clicks the "Login" button. The entered user ID and password are then sent from the device to the server.

[1290] Step 2:

[1291] The server receives login information sent from the terminal. The server uses a database management system (e.g., MySQL or PostgreSQL) to verify the received user ID and password. If authentication is successful, the user's profile data is loaded and sent to the terminal. The input is the user ID and password, and the output is the authentication result and profile data.

[1292] Step 3:

[1293] The terminal receives profile data sent from the server. The terminal uses HTML, CSS, and JavaScript to generate the main menu and display it to the user. The output is the main menu screen.

[1294] Step 4:

[1295] The user selects a training scenario from the main menu. For example, they might choose the scenario "Consulting about purchasing a TV." The user's input is then based on the selected scenario.

[1296] Step 5:

[1297] The server sends the corresponding data to the terminal in JSON format based on the training scenario selected by the user. The output is the data for the selected scenario.

[1298] Step 6:

[1299] The terminal analyzes the received scenario data and displays or plays an initial message from the virtual character. A Text-to-Speech (TTS) engine (e.g., Google Cloud Text-to-Speech) is used for audio playback. User input is the initial message, and output is either displayed or spoken.

[1300] Step 7:

[1301] The user responds to questions and comments from a virtual character using voice or text. The user's responses are sent from the terminal to the server. The input is the user's response.

[1302] Step 8:

[1303] The server analyzes user responses sent from the terminal using natural language processing (NLP). Specifically, it uses Google Cloud Natural Language API or IBM Watson. The input for the analysis is the user's response, and the output is the analysis result.

[1304] Step 9:

[1305] Based on the analysis results, the server generates an appropriate response using a generative AI model (e.g., OpenAI GPT-3). The generated response is sent to the terminal. The input is the analysis result, and the output is the generated response.

[1306] Step 10:

[1307] The terminal receives responses sent from the server and displays or plays them aloud to the user. For example, if the user responds, "I'm looking for the latest 4K TV," the terminal will display, "You're interested in 4K TVs. We recommend our latest models." The output is either visual or audible.

[1308] Step 11:

[1309] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction. The input for the evaluation is the user's interaction, and the output is the evaluation result.

[1310] Step 12:

[1311] The server generates a feedback message based on the evaluation results and sends it to the terminal. The terminal displays the feedback message to the user. The input is the evaluation result, and the output is the feedback message.

[1312] Step 13:

[1313] The server saves all session conversation data and evaluation results to a database. This saved data is for users to access later. The input is the conversation data and evaluation results, and the output is saving to the database.

[1314] (Application Example 1)

[1315] 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".

[1316] Traditional training systems for new crew members struggled to provide an environment that closely resembled actual customer service scenarios. Furthermore, the lack of quality and realism in virtual environments made it difficult to maximize user learning effectiveness. In particular, the insufficient analysis of user utterances and generation of appropriate responses using natural language processing technology posed challenges in acquiring practical skills.

[1317] 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.

[1318] In this invention, the server includes means for receiving and authenticating login information from a user; means for selecting a training scenario; means for initiating a conversation with a customer character based on the selected training scenario within a virtual environment; means for analyzing the user's statements and generating appropriate responses; means for displaying or playing the generated responses audibly to the user; means for evaluating the user's responses and providing feedback; means for storing conversation data and evaluation results; means for the user to perform customer service simulations in a virtual environment using a virtual reality display device; and means for transmitting user input via a terminal connected to the virtual reality display device and analyzing it on the server. This enables the user to effectively acquire more practical skills in a short period of time through dialogue in a virtual environment that closely resembles actual customer service scenarios.

[1319] A "user" is a person who accesses the system, enters login information, and executes training scenarios.

[1320] "Login information" refers to authentication information, including user IDs and passwords, used to authenticate users.

[1321] A "training scenario" is scenario data that allows users to simulate conversations with customer-facing characters in a virtual environment and learn how to provide customer service.

[1322] A "virtual environment" is a computer-generated simulation environment into which users can immerse themselves, enabling the execution of customer service scenarios.

[1323] A "customer character" is a virtual character that the user interacts with during a customer service simulation.

[1324] "Speech analysis" is an information processing technique that uses natural language processing technology to analyze user-inputted voice and text data and generate appropriate responses.

[1325] "Response generation" is the process by which a server provides an appropriate response to a user based on its analysis of the user's speech.

[1326] "Feedback" refers to information that evaluates the appropriateness, speed, and politeness of a user's response, and then provides that evaluation back to the user.

[1327] "Conversation data" refers to a record of the dialogue exchanged between the user and a character representing a customer.

[1328] A "virtual reality display device" is a device that provides a virtual environment to a user visually, and includes, for example, a head-mounted display.

[1329] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and run training scenarios.

[1330] "Natural language processing" is a technology that allows computers to analyze, understand, and generate human language.

[1331] This invention is a system for efficiently training new crew members, and is implemented using users, terminals, and a server. The system aims to enable users to acquire practical skills in a short period of time through customer service simulations in a virtual environment.

[1332] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server, which authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[1333] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario starts in the virtual environment.

[1334] The terminal is connected to a virtual reality display device that allows the user to immerse themselves in a virtual environment. Within the virtual environment, a character representing a customer appears and displays or plays an initial message. This character engages in dialogue that mirrors actual customer service scenarios, such as searching for products or asking specific questions.

[1335] The user responds to questions and comments from a character representing a customer using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing technology. Based on the analysis, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response aloud to the user.

[1336] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness. The server evaluates each criterion, generates a feedback message, and sends it to the user's terminal. Furthermore, the server stores all session conversation data and evaluation results in a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[1337] (Specific example)

[1338] For example, if a user selects the "TV purchase consultation" scenario, the customer character will display or play a voice message in the virtual environment saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which analyzes it. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest models from several manufacturers," and sends it to the terminal. The user confirms this and continues with further questions.

[1339] In this way, this system provides a mechanism that allows new crew members to gain experience that is similar to actual customer interactions. This is expected to improve the quality of customer service in mass retail stores.

[1340] Example of a prompt:

[1341] User: "Hello, what kind of product are you looking for today?"

[1342] Customer: "I'm looking for the latest 4K TV."

[1343] User: "So, you're looking for the latest 4K TV. Do you have any specific manufacturer or feature preferences?"

[1344] Customer: "I'd like something with a large screen and good sound quality."

[1345] User: "The screen size is large and the sound quality is good. We recommend the latest models from several manufacturers. Shall I explain the features of each?"

[1346] ---------

[1347] The above is a detailed description of the embodiments for carrying out the invention.

[1348] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1349] Step 1:

[1350] The user accesses the system using a terminal and enters their user ID and password on the login screen. The entered data (user ID and password) is sent from the terminal to the server. The server verifies this authentication information against a database to authenticate the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[1351] Step 2:

[1352] The user selects a training scenario from the main menu. The selected scenario information is sent from the terminal to the server. The server loads the scenario data and sends it back to the terminal. The terminal displays a button to start the scenario to the user.

[1353] Step 3:

[1354] When the user clicks the "Start Scenario" button, the scenario begins. Using a virtual reality display device, a character representing the customer displays or plays an initial message in the virtual environment, such as "Hello, what kind of product are you looking for today?" The user responds to this message.

[1355] Step 4:

[1356] The user's response (voice or text) is processed on the terminal and sent to the server. The server analyzes the user's utterance using natural language processing. Specifically, it tokenizes the text data and performs semantic and contextual analysis. As a result of the analysis, the user's intent and requests are extracted.

[1357] Step 5:

[1358] The server generates an appropriate response based on the analysis results. A generative AI model is used for this process. The generated response is sent to the terminal, which then displays or plays it aloud for the user. For example, a response such as, "You're interested in 4K TVs, aren't you? We recommend the latest models from several manufacturers," might be generated.

[1359] Step 6:

[1360] The dialogue continues between the user and the customer character. Steps 4 and 5 are repeated for each response. The user continues to ask detailed questions, and the server generates corresponding responses.

[1361] Step 7:

[1362] Once the scenario is complete, the server evaluates all conversation data. Criteria such as appropriateness, speed, and politeness are used for evaluation. Based on each criterion, the server generates an evaluation score and feedback message. This evaluation data and feedback message are sent to the terminal.

[1363] Step 8:

[1364] The terminal displays feedback messages to the user. The server saves all session conversation data and evaluation results to a database. Users can later refer to this data to check their areas for improvement and progress.

[1365] 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.

[1366] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[1367] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[1368] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal, and a button to start the scenario appears on the terminal. When the user clicks this button, the scenario begins.

[1369] The terminal displays or plays an initial message from a character representing a customer, based on scenario data. This simulates actual customer service interactions, such as when a customer is searching for products or asking specific questions.

[1370] The user responds to questions and comments from a customer character using voice or text. The terminal sends the user's responses to the server, which analyzes the user's statements using natural language processing. Furthermore, an emotion engine recognizes emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal displays or plays this response aloud to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real customer service scenario.

[1371] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user.

[1372] Furthermore, the server saves conversation data and evaluation results for all sessions to a database. This allows users to refer to past training content and evaluations later, enabling them to check their areas for improvement and their progress.

[1373] Specific example

[1374] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[1375] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[1376] The following describes the processing flow.

[1377] Step 1:

[1378] The user enters their user ID and password on the device's login screen.

[1379] Step 2:

[1380] The terminal sends the entered authentication information to the server.

[1381] Step 3:

[1382] The server checks the received authentication information against the database and authenticates the user.

[1383] Step 4:

[1384] The server sends the authentication result back to the terminal.

[1385] Step 5:

[1386] The device displays the main menu screen to the user along with a message indicating successful authentication.

[1387] Step 6:

[1388] The user displays a list of training scenarios from the main menu screen and selects their desired scenario.

[1389] Step 7:

[1390] The server sends the data for the selected training scenario to the terminal.

[1391] Step 8:

[1392] The device displays a button to the user instructing them to start the scenario, based on the received scenario data.

[1393] Step 9:

[1394] The user clicks the "Start Scenario" button.

[1395] Step 10:

[1396] The device displays or plays an initial message from the customer character based on the scenario data.

[1397] Step 11:

[1398] The user responds to questions and comments from a character representing a customer using voice or text.

[1399] Step 12:

[1400] The terminal sends the user's response to the server.

[1401] Step 13:

[1402] The server uses natural language processing to analyze the user's statements.

[1403] Step 14:

[1404] An emotion engine built into the server recognizes emotions from the user's statements and adds that emotional information to the analysis results.

[1405] Step 15:

[1406] The server generates an appropriate response based on the user's statements and perceived emotions.

[1407] Step 16:

[1408] The server sends the generated response to the terminal.

[1409] Step 17:

[1410] The device displays or plays the received response to the user.

[1411] Step 18:

[1412] The user responds again, either by voice or text, in response to the customer character's response.

[1413] Step 19:

[1414] The server and terminal continue their interaction by repeating steps 11 through 17.

[1415] Step 20:

[1416] The server detects that the scenario has ended.

[1417] Step 20:

[1418] The server evaluates the user's response. Evaluation criteria include appropriateness, speed, and politeness of the response, as well as the user's emotional perception.

[1419] Step 21:

[1420] The server generates feedback based on the evaluation results.

[1421] Step 22:

[1422] The server sends the generated feedback message to the terminal.

[1423] Step 23:

[1424] The device displays a feedback message to the user.

[1425] Step 24:

[1426] The server saves all conversation data and evaluation results to a database.

[1427] Step 25:

[1428] The device displays a session end message and prompts the user to take the next action (such as retraining or logging out).

[1429] (Example 2)

[1430] 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".

[1431] In a system designed to efficiently train new crew members, it is necessary to recognize user emotions and respond accordingly, thereby simulating actual customer service and improving their communication skills. Furthermore, by appropriately evaluating user interactions and providing feedback, the system is required to enable effective acquisition of practical skills in a short period of time.

[1432] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating login information from a user, means for selecting a training scenario, means for initiating a conversation with a virtual character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response audibly to the user, emotion recognition means for recognizing emotions from the user's statements and generating a response based on those emotions, means for evaluating the user's response and providing feedback, and means for storing conversation data and evaluation results. This makes it possible for new crew members to experience conversations that are close to actual customer service and effectively improve their response skills and emotion recognition abilities.

[1433] A "user" refers to a person who uses the system to receive training.

[1434] "Login information" refers to information used to authenticate a user to the system, such as a user ID and password.

[1435] "Authentication means" refers to a means that has the function of verifying the entered login information against a database and authenticating the user.

[1436] A "training scenario" refers to the scenes and content of the simulation training that new crew members perform using the system.

[1437] "Means for selecting a scenario" refers to a means that allows a user to select their preferred scenario from multiple training scenarios.

[1438] A "virtual character" refers to a computer-generated person who acts as a customer in the simulation training.

[1439] "Means of initiating a conversation" refers to means that have the functionality to start a conversation with a virtual character based on the selected training scenario.

[1440] "Means for analyzing user speech" refers to means that have the function of analyzing the voice or text input by the user using natural language processing technology.

[1441] "Means for generating appropriate responses" refers to means that have the function of automatically generating responses based on the analyzed user statements.

[1442] "Emotion recognition means" refers to means that have the function of recognizing emotions from a user's statements and generating a response based on those emotions.

[1443] "Means for displaying or playing a response audibly" refers to means that have the function of providing the generated response to the user visually or audibly.

[1444] "Means for evaluating user responses" refers to means that have the function of evaluating the appropriateness, speed, politeness, and emotional recognition of the responses provided by the user.

[1445] "Means of providing feedback" refers to means that have a function to communicate to users, based on evaluation results, areas for improvement and positive aspects.

[1446] "Means for saving conversation data and evaluation results" refers to means that have the function of saving the content of conversations that took place within the system and their evaluation results to a database.

[1447] This invention is a system for efficiently training new crew members, and in particular, it has a configuration that incorporates an emotion recognition engine for recognizing user emotions and responding accordingly. It is implemented using a user, a terminal, and a server.

[1448] First, the user accesses the system using a terminal and enters their user ID and password on the login screen. The terminal sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the terminal.

[1449] The user selects a training scenario from the main menu. The server sends the selected scenario data to the terminal and displays a button on the terminal to start the scenario. When the user clicks this button, the scenario begins.

[1450] The terminal displays or plays an initial message from a virtual customer character based on scenario data. For example, it simulates actual customer service, such as a customer searching for a product or asking a specific question. The user responds to the questions and comments from the virtual customer character using voice or text. The terminal sends the user's responses to the server.

[1451] The server analyzes the user's utterances using natural language processing. Additionally, an emotion recognition engine identifies emotions from the user's statements. Based on this recognized emotion information, the server generates an appropriate response and sends it to the terminal. The terminal then displays or plays this response audibly to the user. This allows for a dialogue between the user and a virtual customer that closely resembles a real-world customer service scenario.

[1452] Once a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, and politeness of the interaction, as well as the user's emotional perception. The server evaluates each criterion and generates a feedback message. This feedback message is sent to the terminal and displayed to the user. The server also saves all session conversation data and evaluation results to a database. This allows users to later refer to past training content and evaluations to identify areas for improvement and track their progress.

[1453] Specific example

[1454] For example, consider a scenario where the user chooses to "consult about buying a TV." The customer character displays or plays a voice message on the terminal saying, "Hello, what kind of product are you looking for today?" The user responds, "I'm looking for the latest 4K TV." The terminal sends this response to the server, which performs analysis. The emotion recognition engine also recognizes positive emotions such as interest from the user's statement. As a result, the server generates a response saying, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," and sends it to the terminal. The user confirms this and continues with more detailed questions.

[1455] Thus, this system not only allows new crew members to gain experience simulating actual customer interactions, but also supports the improvement of their customer service skills and emotional recognition abilities. As a result, they can acquire practical skills effectively in a short period of time, improving the quality of customer service in mass retail stores.

[1456] Examples of prompts for generative AI models

[1457] "We have a training system for new crew members. In this system, you will progress through a scenario with a virtual customer character. Please explain in natural language the process of analyzing the user's statements and providing appropriate responses."

[1458] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1459] Step 1: User Login

[1460] Input: User ID, Password

[1461] Output: Authentication success or failure, user profile data

[1462] Operation:

[1463] Users access the system using a device and enter their user ID and password on the login screen. The device sends the entered login information to the server. The server authenticates the user by comparing it with the database. If authentication is successful, the server loads the user's profile data and displays the main menu on the device. Users access the login screen from a dedicated app or web browser using a smartphone or PC. Once the input is complete and the "Login" button is pressed, the information is transferred to the server.

[1464] Step 2: Selecting a Training Scenario

[1465] Input: User scenario selection information

[1466] Output: Selected scenario data

[1467] Operation:

[1468] The user selects a training scenario from the main menu. The terminal sends the selected scenario data to the server. The server provides the transmitted scenario to the terminal and displays a button to start the scenario. The user selects and clicks a training scenario from options such as "Consult about purchasing a TV." This selection information is sent from the terminal to the server, and the appropriate scenario data is delivered to the terminal.

[1469] Step 3: Display the initial screen of the scenario.

[1470] Input: Scenario data

[1471] Output: Display of initial message or playback of audio

[1472] Operation:

[1473] Based on the scenario data, the terminal displays or plays an initial message from a virtual customer character. It might display a message such as, "Hello, what product are you looking for today?" or the character might begin speaking aloud.

[1474] Step 4: User response input

[1475] Input: User voice or text response

[1476] Output: User response data

[1477] Operation:

[1478] The user responds to questions and comments from a virtual customer character using voice or text. The terminal transcribes the user's responses into text and sends it to the server. The user provides specific responses, such as "I'm looking for the latest 4K TV," using voice or keyboard input.

[1479] Step 5: Sending and analyzing responses, sentiment recognition

[1480] Input: User response data

[1481] Output: Appropriate response and recognized emotional information

[1482] Operation:

[1483] The terminal sends the user's response to the server. The server analyzes the user's utterance using natural language processing. Additionally, an emotion recognition engine recognizes the emotion from the user's utterance. The server uses NLP (Natural Language Processing) algorithms to analyze the content and emotion. For example, from the response "I'm looking for the latest 4K TV," it extracts the emotions "interest" and "curiosity."

[1484] Step 6: Generating and displaying the appropriate response

[1485] Input: Analyzed speech data and sentiment information

[1486] Output: Appropriate response data

[1487] Operation:

[1488] The server generates an appropriate response based on the recognized emotion information and sends it to the terminal. The terminal displays or plays the generated response to the user. For example, the server generates a response such as, "You're interested in 4K TVs. We recommend the latest model from manufacturer XX," which is sent to the terminal and displayed or played aloud.

[1489] Step 7: Session End and Evaluation

[1490] Input: User interaction data

[1491] Output: Evaluation results and feedback messages

[1492] Operation:

[1493] When a session ends, the server evaluates the user's interaction. Evaluation criteria include appropriateness, speed, politeness, and emotion recognition results. The server evaluates each item, generates a feedback message, and sends it to the terminal. After the session ends, the server scores the user's interaction based on criteria such as "appropriateness of interaction," "speed of response," and "politeness," and displays the results as a feedback message.

[1494] Step 8: Saving and referencing past training data

[1495] Input: Conversation data and evaluation result data

[1496] Output: Data to be saved to the database and for future reference.

[1497] Operation:

[1498] The server stores all session conversation data and evaluation results in a database. Users can later refer to past training content and evaluations to check their areas for improvement and progress. If a user logs in later and wants to view their training history, they can view past evaluation results and session content. For example, evaluation results can be displayed in a graph to visually check progress.

[1499] (Application Example 2)

[1500] 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".

[1501] Traditional crew training systems differed from actual customer interactions in that they lacked real-time feedback and emotional recognition. As a result, new crew members struggled to effectively acquire practical skills on the job. Furthermore, traditional systems often delayed detailed evaluations and feedback on user interactions, making immediate improvement difficult.

[1502] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving and authenticating authentication information from the user, means for selecting a training scenario, means for initiating a dialogue with a customer character based on the selected training scenario, means for analyzing the user's statements and generating an appropriate response, means for displaying or playing the generated response to the user, means for evaluating the user's response and providing feedback, means for storing conversation data and evaluation results, means for receiving questions and comments from a virtual customer using smart glasses and responding in voice or text, and means including an emotion engine that provides the generated feedback to the user in real time. This makes it possible for new crew members to efficiently acquire practical skills through simulations that closely resemble real-world problems.

[1503] "Means of receiving and authenticating authentication information from users" refers to the process or function of sending authentication information such as user ID and password to a server to verify the user's identity.

[1504] "Means for selecting a training scenario" refers to the interface or function that allows a user to select a specific scenario from among multiple training scenarios.

[1505] "Means of initiating a dialogue with a customer character based on a selected training scenario" refers to a function that allows the user to initiate a dialogue with a virtual character representing a customer based on a scenario selected by the user.

[1506] "Means for analyzing user statements and generating appropriate responses" refers to a process or function that analyzes user statements using natural language processing technology and generates appropriate responses in response.

[1507] "Means for displaying or playing the generated response to the user" refers to a function for displaying the generated response to the user as text or playing it as audio.

[1508] "Means of evaluating user interactions and providing feedback" refers to processes and functions that evaluate user interactions based on various evaluation criteria and provide corresponding feedback to the user.

[1509] "Means for saving conversation data and evaluation results" refers to a function for saving conversation data and evaluation results during training to a storage device such as a database.

[1510] "A means of receiving questions and comments from virtual customers using smart glasses and responding to them in voice or text" refers to a function that receives questions and comments from virtual customers via smart glasses and responds to them in voice or text.

[1511] "Means including an emotion engine that provides generated feedback to the user in real time" refers to a function that includes an emotion analysis engine to analyze emotions from the user's statements and actions and provide appropriate feedback in real time.

[1512] This invention provides a system for efficiently training new crew members, and in particular, uses smart glasses to perform real-time customer interaction simulations while providing appropriate feedback using an emotion engine. Specific embodiments of this system are described below.

[1513] This system allows users to receive questions and comments from virtual customers by wearing smart glasses, and respond to them in voice and text. It includes functions to receive and authenticate user authentication information, select training scenarios, initiate conversations with virtual customers based on the scenarios, analyze user statements and generate appropriate responses, present the generated responses to the user, evaluate the interaction and provide feedback, and save conversation data and evaluation results.

[1514] Hardware and software used

[1515] Smart glasses: Worn by the user, they display messages and play audio from virtual customers and receive voice input from the user.

[1516] Server: Manages user authentication information, generates training scenarios and responses, and manages evaluations. It uses an open-source database system to store conversation data and evaluation results.

[1517] Emotion Engine: An engine that analyzes user emotions from their statements and provides appropriate feedback in real time. It utilizes natural language processing technology (e.g., OpenAI's API).

[1518] Details of data processing and handling

[1519] 1. User Authentication:

[1520] The user logs into the system via smart glasses and enters their ID and password.

[1521] The server receives the entered information, compares it with the database, and authenticates the user.

[1522] Upon successful authentication, the user's profile information is loaded and displayed on the smart glasses.

[1523] 2. Training Scenario Selection:

[1524] The user selects any training scenario through the smart glasses interface.

[1525] The server sends the selected scenario data to the smart glasses and instructs them to start the scenario.

[1526] 3. Scenario Execution:

[1527] A character playing the role of a virtual customer displays or plays messages on smart glasses based on a scenario.

[1528] The user responds to the virtual customer's message using voice. The smart glasses then use speech recognition technology to convert this response into text.

[1529] 4. Analysis and response generation:

[1530] The server receives the user's response in text format and analyzes it using natural language processing techniques.

[1531] The emotion engine analyzes the user's responses to determine their emotions and generates an appropriate response based on that analysis.

[1532] The generated response is presented to the user via smart glasses.

[1533] 5. Evaluation and Feedback:

[1534] The server evaluates the user's interaction and generates feedback, including sentiment analysis results.

[1535] Feedback is displayed in real time on the smart glasses, allowing users to immediately understand areas for improvement.

[1536] 6. Data storage:

[1537] The server stores all conversation data and evaluation results in a database.

[1538] Users can later refer to their past training content and evaluation results, providing support for self-improvement.

[1539] Examples of specific cases and prompt statements

[1540] As a specific training scenario, the case of "consulting about purchasing a television" would be as follows:

[1541] Virtual customer: "Hello, I'm looking for the latest 4K TV."

[1542] User: "Which TV manufacturer would you recommend?"

[1543] Examples of prompts in this conversation are as follows:

[1544] Customer message: Hi, I'm looking for the latest 4K TV.

[1545] User's response: Which TV manufacturer would you recommend?

[1546] Please show your emotions and respond appropriately.

[1547] In this way, the system is designed to allow new crew members to efficiently acquire practical skills through simulations that closely resemble actual customer interactions. This is expected to enable effective training in a short period of time and improve the quality of customer service.

[1548] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1549] Step 1:

[1550] The user puts on smart glasses and logs into the system. The smart glasses accept the user ID and password. This input is sent from the device to the server. The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server loads the user's profile information and displays the main menu on the device.

[1551] Step 2:

[1552] The user selects a training scenario through the smart glasses interface. This scenario selection is sent from the device to the server. The server retrieves the selected scenario data and sends it back to the device. The device then displays a button to initiate the scenario.

[1553] Step 3:

[1554] The user clicks the "Start Scenario" button. The device displays an initial message from the virtual customer character. This message is generated by the device based on the scenario data. The user reviews the virtual customer's message and responds with voice.

[1555] Step 4:

[1556] The terminal receives the user's voice input and converts it into text using a transcription engine. The converted text is sent from the terminal to the server. The server analyzes the user's speech using natural language processing technology. Based on this analysis, the server generates an appropriate response.

[1557] Step 5:

[1558] The server sends the generated response to the terminal. The terminal displays or plays the response message aloud to the user. The user reviews this response and asks additional questions or makes comments as needed. This process is repeated until the simulation scenario is complete.

[1559] Step 6:

[1560] The server evaluates the user's response after each interaction session. The evaluation criteria are based on appropriateness, speed, and politeness. In addition, an emotion engine analyzes the user's utterances to determine their emotions and generates additional feedback based on that analysis.

[1561] Step 7:

[1562] The server sends the generated evaluation results and feedback to the terminal. The terminal displays this to the user in real time. The user uses this feedback to improve their response.

[1563] Step 8:

[1564] The server stores all conversation data and evaluation results in a database. This allows users to refer to past training content and evaluation results later. The server can also analyze progress and suggest appropriate next training scenarios.

[1565] Each of these steps allows users to receive real-time feedback based on sentiment analysis, enabling them to effectively improve their customer service skills.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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."

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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 to be incorporated by reference.

[1587] The following is further disclosed regarding the embodiments described above.

[1588] (Claim 1)

[1589] A means of receiving and authenticating login information from a user,

[1590] Means for selecting a training scenario,

[1591] Based on the selected training scenario, a means to initiate a conversation with the character playing the role of a customer,

[1592] A means for analyzing user statements and generating appropriate responses,

[1593] Means for displaying or playing the generated response to the user,

[1594] A means of evaluating user interactions and providing feedback,

[1595] Means for storing conversation data and evaluation results,

[1596] A system that includes this.

[1597] (Claim 2)

[1598] The system according to claim 1, comprising means for analyzing a user's utterance using natural language processing and generating an appropriate response.

[1599] (Claim 3)

[1600] The system according to claim 1, which has means for evaluating the content of a response based on appropriateness, speed, and politeness.

[1601] "Example 1"

[1602] (Claim 1)

[1603] A means of receiving and authenticating login information from a user,

[1604] Means for selecting a training scenario,

[1605] Based on the selected training scenario, a means of initiating a conversation with a virtual character,

[1606] A means for analyzing user statements using natural language processing and generating appropriate responses,

[1607] Means for displaying or playing the generated response to the user,

[1608] A means of evaluating user interactions and providing feedback,

[1609] Means for storing conversation data and evaluation results,

[1610] Means for providing a user interface,

[1611] A means for generating a response using a generative AI model,

[1612] A system that includes this.

[1613] (Claim 2)

[1614] The system according to claim 1, comprising means for analyzing a user's utterance using natural language processing and generating an appropriate response.

[1615] (Claim 3)

[1616] The system according to claim 1, which has means for evaluating the content of a response based on appropriateness, speed, and politeness.

[1617] "Application Example 1"

[1618] (Claim 1)

[1619] A means of receiving and authenticating login information from a user,

[1620] Means for selecting a training scenario,

[1621] A means of initiating a conversation with a character representing a customer, based on a training scenario selected within a virtual environment,

[1622] A means for analyzing user statements and generating appropriate responses,

[1623] Means for displaying or playing the generated response to the user,

[1624] A means of evaluating user interactions and providing feedback,

[1625] Means for storing conversation data and evaluation results,

[1626] A means for a user to perform customer service simulations in a virtual environment using a virtual reality display device,

[1627] A means of transmitting user input via a terminal connected to a virtual reality display device and analyzing it on a server,

[1628] A system that includes this.

[1629] (Claim 2)

[1630] The system according to claim 1, comprising means for analyzing a user's utterance using natural language processing and generating an appropriate response.

[1631] (Claim 3)

[1632] The system according to claim 1, which has means for evaluating the content of a response based on appropriateness, speed, and politeness.

[1633] "Example 2 of combining an emotion engine"

[1634] (Claim 1)

[1635] A means of receiving and authenticating login information from a user,

[1636] Means for selecting a training scenario,

[1637] Based on the selected training scenario, a means of initiating a conversation with a virtual character,

[1638] A means for analyzing user statements and generating appropriate responses,

[1639] Means for displaying or playing the generated response to the user,

[1640] An emotion recognition means that recognizes emotions from user statements and generates responses based on those emotions,

[1641] A means of evaluating user interactions and providing feedback,

[1642] Means for storing conversation data and evaluation results,

[1643] A system that includes this.

[1644] (Claim 2)

[1645] The system according to claim 1, comprising means for analyzing a user's utterance using natural language processing and generating an appropriate response, and means for emotion recognition.

[1646] (Claim 3)

[1647] The system according to claim 1, which has means for evaluating the content of a response based on appropriateness, speed, politeness, and the result of the user's emotional recognition.

[1648] "Application example 2 when combining with an emotional engine"

[1649] (Claim 1)

[1650] A means of receiving and authenticating authentication information from the user,

[1651] Means for selecting a training scenario,

[1652] Based on the selected training scenario, a means to initiate a dialogue with the character playing the role of a customer,

[1653] A means of analyzing user statements and generating appropriate responses,

[1654] A means of displaying or playing the generated response to the user,

[1655] A means of evaluating user interactions and providing feedback,

[1656] Means for storing conversation data and evaluation results,

[1657] A method of receiving questions and comments from virtual customers using smart glasses and responding with voice or text,

[1658] A means including an emotion engine that provides generated feedback to the user in real time,

[1659] A system that includes this.

[1660] (Claim 2)

[1661] The system according to claim 1, comprising means for analyzing a user's statement using natural language processing and generating an appropriate response.

[1662] (Claim 3)

[1663] The system according to claim 1, which has means for evaluating the content of a response based on appropriateness, speed, and politeness. [Explanation of Symbols]

[1664] 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 receiving and authenticating login information from a user, Means for selecting a training scenario, Based on the selected training scenario, a means to initiate a conversation with the character playing the role of a customer, A means for analyzing user statements and generating appropriate responses, A means for displaying or playing the generated response to the user, A means of evaluating user interactions and providing feedback, Means for storing conversation data and evaluation results, A system that includes this.

2. The system according to claim 1, comprising means for analyzing a user's utterance using natural language processing and generating an appropriate response.

3. The system according to claim 1, which has means for evaluating the content of a response based on appropriateness, speed, and politeness.

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