System

Abstract

A system is provided.SOLUTION: A system comprising: means for generating, using a generative AI model, a response for a customer service role play based on a scenario selected by a user; means for sending information entered by the user to a server and receiving a response from the server; and means for displaying the response received from the server and prompting the user for new input.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a problem that staff working in the customer service industry have limited opportunities to improve their skills so that they can smoothly handle customer service when new devices or new services are launched. It is particularly difficult for new staff and busy managers to hone practical customer service skills on the job. There is also a lack of effective means to quickly improve response capabilities to the latest systems and services. This presents a problem that could lead to a decline in the quality of customer service and an impact on customer satisfaction. [Means for solving the problem]

[0005] The present invention provides a system that enables realistic customer service role-playing using a generative AI model. Specifically, the system includes a means for the AI ​​model to generate responses based on a scenario selected by the user, a means for sending the user's input information to a server and receiving the responses, and a means for displaying the received responses and prompting the user for new input. Furthermore, the system includes a means for generating feedback based on the generated responses and providing it to the user, and a means for analyzing the result data of the customer service role-playing and providing feedback to help the user improve their skills. This allows users to participate in practical customer service role-playing at any time using a device. This system allows staff to improve their customer service skills regardless of location, such as at their own store or at home, thereby improving the quality of customer service and increasing customer satisfaction.

[0006] A "generative AI model" is an artificial intelligence model that generates natural language responses based on user input.

[0007] A "scenario" is like a script that contains details of specific situations or scenes that are anticipated in a customer service role-play.

[0008] "Customer service role-play" is a training method that simulates actual customer service situations and allows users to improve their skills by interacting with virtual customers.

[0009] "User input information" refers to text and voice data that the user provides to the avatar or system during the customer service role-play.

[0010] The "server" is a central management system that processes user requests and generates responses using generative AI models.

[0011] A "response" is an automatic reply message generated by a generative AI model in response to user input.

[0012] "Feedback" refers to evaluations and information on areas for improvement provided based on the results of customer service role-plays and the user's behavior.

[0013] "Result data" refers to the records of all interactions between the user and the system during the customer service role-play, as well as the analysis results.

[0014] A "device" is an electronic terminal (e.g., a PC, tablet, smartphone, etc.) that a user uses to perform customer service role-playing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0036] The present invention relates to a customer service role-playing system that uses a generative AI model. This system allows staff working in the customer service industry to effectively practice customer service when new devices or new services are launched. The following describes in detail the embodiments of the present invention.

[0037] System Overview

[0038] This system consists of four main components: a generative AI model, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user and provides it to the user via the terminal. It also provides feedback based on the results of the role-play, helping the user improve their customer service skills.

[0039] Program processing flow

[0040] 1. Booting the system and logging in

[0041] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[0042] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[0043] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[0044] 2. Start customer service role-play

[0045] User: Select the scenario you want to practice on the scenario selection screen.

[0046] Terminal: Sends the selection information to the server.

[0047] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0048] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0049] 3. Customer service practice

[0050] User: Enters customer service details for the avatar.

[0051] Terminal: Sends the entered information to the server.

[0052] Server: Receives input, passes it to the generative AI model (ChatGPT) to generate a response, and returns the generated response to the device.

[0053] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0054] 4. Customer Service Evaluation and Feedback

[0055] Terminal: The user declares the end of the role-play and sends an end request and result data to the server.

[0056] Server: Receives the end request and result data, analyzes the data, extracts reputation points, generates feedback based on the reputation points, and returns it to the device.

[0057] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[0058] Specific examples

[0059] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[0060] 1. Log in and select a scenario

[0061] The user logs in to the app and selects "New Smartphone Plan Description."

[0062] 2. Scenario Generation

[0063] The server generates the scenario and avatar details and sends them to the device.

[0064] 3. Roleplay begins

[0065] The user begins explaining the new plan to the avatar.

[0066] User input is sent to the server, and a generative AI model generates the avatar's response.

[0067] 4. Response display and feedback

[0068] The server's response is displayed on the terminal, and the user can continue the conversation by looking at the avatar's reaction.

[0069] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[0070] Through this concrete example, it can be seen that this system can function as an effective tool for practically improving customer service skills, particularly for strengthening the ability to respond to frequently changing new devices and services.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[0074] Step 2:

[0075] Terminal: Sends the entered user ID and password to the server.

[0076] Step 3:

[0077] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[0078] Step 4:

[0079] Server: Returns the authentication result (success or failure) to the terminal.

[0080] Step 5:

[0081] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen, and if authentication fails, displays an error message.

[0082] Step 6:

[0083] User: Select the scenario you want to practice on the scenario selection screen.

[0084] Step 7:

[0085] Terminal: Sends the selection information to the server.

[0086] Step 8:

[0087] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0088] Step 9:

[0089] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0090] Step 10:

[0091] User: Enters customer service details for the avatar.

[0092] Step 11:

[0093] Terminal: Sends the entered information to the server.

[0094] Step 12:

[0095] Server: Receives input, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0096] Step 13:

[0097] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0098] Step 14:

[0099] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[0100] Step 15:

[0101] Terminal: Repeats the process of sending each user input to the server, receiving a response from the server, and displaying it on the avatar.

[0102] Step 16:

[0103] User: Declare the end of the roleplay.

[0104] Step 17:

[0105] Terminal: Sends the end request and role-play result data to the server.

[0106] Step 18:

[0107] Server: Receives the end request and the result data, analyzes the result data to extract evaluation points, and generates feedback.

[0108] Step 19:

[0109] Server: Returns the generated feedback to the device.

[0110] Step 20:

[0111] Terminal: Displays received feedback to the user.

[0112] Step 21:

[0113] Users: Review feedback and understand areas for improvement in customer service skills.

[0114] Through the above steps, this system allows users to effectively improve the customer service skills required when starting to use new terminals or services.

[0115] Example 1

[0116] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0117] Conventional customer service training systems are limited to fixed scenarios and responses, making it difficult to acquire the flexibility and real-time response skills required in actual customer service situations. Also, it takes time for users to receive feedback on their practice results, making it difficult to immediately improve their skills.

[0118] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0119] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for starting the system and authenticating the user, and means for analyzing the generated responses and result data and generating feedback for the user. This allows the user to receive immediate feedback while flexibly practicing customer service in real time.

[0120] A "generative AI model" is an algorithm or program that automatically generates customer service role-play responses based on a scenario selected by the user.

[0121] A "user" is an individual who uses this system to practice customer service.

[0122] A "server" is a central processing unit that accepts requests from users, generates responses using generative AI models, and analyzes the resulting data to provide feedback.

[0123] A "terminal" is an electronic device such as a computer or smartphone that is used by a user to operate.

[0124] A "scenario" is a specific situation or theme selected by the user for customer service practice.

[0125] A "response" is the dialogue content generated by a generative AI model in response to user input.

[0126] An "authentication token" is a digital key that indicates that a user has been successfully authenticated.

[0127] "Feedback" refers to evaluations and advice provided to help users improve their skills based on the results of customer service role-playing.

[0128] "Result data" refers to all data generated during the customer service role-play.

[0129] "Evaluation points" are evaluation items related to the user's skills that are extracted by analyzing the result data.

[0130] The present invention relates to a customer service role-play system that utilizes a generative AI model. Hereinafter, an embodiment of the present invention will be specifically described.

[0131] The main components of the system include a server, a terminal, a user, and a generative AI model. These systems work together to allow users to role-play customer service.

[0132] First, the user starts the system from a device on which a dedicated app is installed and logs in. The login screen displays a form for entering a user ID and password. After entering the correct authentication information, the device sends it to the server. The server acts as a central processing unit and accesses a database (e.g., MySQL) to verify the user information. If authentication is successful at this stage, the server generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, the server returns an error message.

[0133] After successful authentication, the user selects a scenario to practice on the scenario selection screen. For example, they can select the scenario "Explanation of a new smartphone plan." The device sends this selection information to the server. The server receives the information and performs initial setup using a generative AI model (e.g., OpenAI's GPT-3). Specifically, it generates details of the scenario and avatar information and returns it to the device.

[0134] The user confirms the scenario start screen and begins role-playing. The user inputs customer service details for the avatar. For example, the user inputs an explanation such as "This plan includes unlimited data." The device sends this input to the server. The server passes the input to a generative AI model, which generates an appropriate response. The generated response might be something like "That's great. Can you tell me about the price?" The server returns the response to the device, which displays it to the user. The user confirms the avatar's response and makes the next input.

[0135] When the role-playing is finished, the user declares that they are finished. The device sends the role-playing result data along with an end request to the server. The server analyzes the result data, extracts evaluation points, and generates feedback. Evaluation points include, for example, "The customer service content was clear" and "Questions were asked at the appropriate time." The server returns the generated feedback to the device, and the user can review it and use it to help them practice their next customer service encounter.

[0136] This system allows users to practice customer service flexibly in real time while receiving immediate feedback.

[0137] Examples of prompts include:

[0138] 1. User type: "This new phone has unlimited data."

[0139] 2. The generative AI model responds: “That’s great! Can you tell me the price?”

[0140] This invention helps users improve their customer service skills by providing a flexible, real-time customer service practice environment using a generative AI model.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1: Booting and logging in

[0143] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and clicks the "Login" button.

[0144] Input: User ID, Password

[0145] Output: Authentication request

[0146] Terminal: Sends the user's input information to the server as an authentication request.

[0147] Server: Receives the authentication request and accesses the database to verify the user information. This part uses the MySQL database. If verification is successful, it generates an authentication token and a list of available scenarios and returns them to the terminal. If verification fails, it returns an error message.

[0148] Input: Authentication Request

[0149] Output: Authentication result (authentication token, scenario list or error message)

[0150] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication fails, displays an error message and prompts the user to re-enter information.

[0151] Input: Authentication result

[0152] Output: Scenario selection screen or error message

[0153] Step 2: Select a scenario

[0154] User: After successful login, the scenario selection screen will be displayed. The user will select the scenario they want to practice (for example, "Explanation of a new smartphone plan").

[0155] Input: Select scenario

[0156] Output: Scenario selection information

[0157] Terminal: Sends the user's selections to the server.

[0158] Input: Scenario selection information

[0159] Output: Server request

[0160] Server: Receives scenario selection information and performs initial setup in the generative AI model. Specifically, it generates scenario details and avatar information and returns this to the device.

[0161] Input: Scenario selection information

[0162] Output: Scenario details, avatar information

[0163] Terminal: Displays the scenario start screen and allows the user to begin role-playing.

[0164] Input: Scenario details, avatar information

[0165] Output: Scenario start screen

[0166] Step 3: Begin the customer service role-play

[0167] User: When the scenario start screen appears, enter the customer service details for the avatar.

[0168] Input: Customer service details

[0169] Output: User input information

[0170] Terminal: Sends user input to the server.

[0171] Input: User-entered information

[0172] Output: Server request

[0173] Server: Passes input content to the generative AI model to generate a response, which is then returned to the device.

[0174] Input: User-entered information

[0175] Output: The generated response

[0176] Terminal: Displays responses received from the server and simulates an avatar stating the response.

[0177] Input: Generated response

[0178] Output: Avatar response display

[0179] Step 4: Proceed with customer service practice

[0180] User: Look at the avatar's response displayed on the terminal and enter the next customer service request. Continue the conversation.

[0181] Input: New customer service content

[0182] Output: The next input from the user

[0183] Terminal: Sends the next input contents of the user to the server one by one.

[0184] Input: The following user-entered information:

[0185] Output: Server request

[0186] Server: Using a generative AI model, it generates appropriate responses to the user's input and returns them to the device. The responses are stored and used for evaluation at the end of the role-play.

[0187] Input: The following user-entered information:

[0188] Output: The generated response

[0189] Terminal: Displays the response received from the server as an avatar, allowing the user to continue role-playing.

[0190] Input: Generated response

[0191] Output: Avatar response display

[0192] Step 5: Customer evaluation and feedback

[0193] User: Click the End Roleplay button to declare the end.

[0194] Input: Termination declaration

[0195] Output: Finished request

[0196] Terminal: Sends the role-play result data along with an end request to the server.

[0197] Input: End request, result data

[0198] Output: Server request

[0199] Server: Receives the end request and the result data, analyzes the data, extracts evaluation points based on the user's response and the avatar's reaction, and generates feedback.

[0200] Input: Result data

[0201] Output: Evaluation points, feedback

[0202] Terminal: Receives the generated feedback and displays it to the user.

[0203] Input: Feedback

[0204] Output: User feedback display, confirmation

[0205] In this way, the user, terminal, and server work together at each step to smoothly progress the customer service role-play.

[0206] (Application example 1)

[0207] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0208] Conventional customer service training systems have difficulty simulating real-time customer service situations, making it difficult to adequately train staff to respond quickly, especially when new products or services are introduced. Another problem is that they are unable to obtain specific feedback, making it difficult to effectively identify areas for improvement to improve skills. This invention has been proposed to solve these problems.

[0209] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0210] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for practicing dialogue with a virtual customer using a smartphone or head-mounted display, and means for saving and evaluating data on the results of the role-playing, thereby enabling advanced customer service practice with real-time and specific feedback.

[0211] A "generative AI model" is a type of artificial intelligence that automatically generates responses based on scenarios selected by the user.

[0212] A "scenario" is a set of specific situations and conditions for the user to practice in customer service role-playing.

[0213] "Customer service role-play" refers to practice and simulations that simulate actual customer service situations.

[0214] A "response" is a reply or reaction generated by a generative AI model based on information entered by a user.

[0215] A "server" is a computer system that receives information entered by a user, generates a response using a generative AI model, and transmits the result to a terminal.

[0216] A "terminal" is a device that allows a user to use the customer service role-play system, and includes a smartphone or a head-mounted display.

[0217] A "smartphone" is a type of mobile phone that has the ability to connect to the Internet and run applications.

[0218] A "head-mounted display" is a display device that allows a user to experience a virtual reality environment by wearing it.

[0219] A "virtual customer" is a fictitious customer simulated by AI in a customer service role-play.

[0220] "Feedback" refers to evaluations and areas for improvement provided to users based on the results of customer service role-playing.

[0221] "Role-play result data" is recorded data of the user's actions and responses collected during the customer service role-play.

[0222] The "evaluation means" is a mechanism for analyzing the result data of the role-play and evaluating the user's performance.

[0223] The present invention relates to a customer service role-play system using a generative AI model. Specific embodiments for carrying out the present invention will be described below.

[0224] System Overview

[0225] This system consists of three main components: a server, a terminal, and a user. The terminal is a smartphone or a head-mounted display, which the user uses to role-play customer service. The server uses a generative AI model to generate responses based on the scenario selected by the user and provides them to the user.

[0226] Hardware / Software

[0227] Hardware:

[0228] Smartphone (iOS / Android)

[0229] Head-mounted displays (e.g., Oculus Quest 2)

[0230] software:

[0231] Generative AI models (e.g., OpenAI's ChatGPT)

[0232] Authentication services (e.g. Firebase Auth)

[0233] Data analysis tools (e.g., Amazon Sagemaker)

[0234] Data storage (e.g. AWS S3)

[0235] Request processing (e.g. AWS Lambda)

[0236] Response generation (e.g., Google Dialogflow)

[0237] Natural language explanation of the process

[0238] 1. Boot the system and log in:

[0239] The user launches a dedicated app from their device, enters their ID and password, and sends an authentication request to the server.

[0240] The server receives the authentication request and verifies the user information using Firebase Auth. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[0241] The terminal receives the authentication result, and if the authentication is successful, displays a scenario selection screen.

[0242] 2. Select a scenario and begin roleplaying:

[0243] The user selects the scenario they want to practice on the scenario selection screen.

[0244] The terminal transmits scenario selection information to the server.

[0245] The server processes the selected scenario information using Google Dialogflow, generates details of the scenario and avatar using a generative AI model (ChatGPT), and sends them to the device.

[0246] The terminal displays the scenario start screen and prepares for the user to begin role-playing.

[0247] 3. Customer service practice:

[0248] The user explains products to virtual customers (avatars) and responds to their inquiries.

[0249] The terminal sends the user's input to the server.

[0250] The server receives the input, and the generative AI model (ChatGPT) generates a response and returns it to the device.

[0251] The terminal displays the response from the server, and the user continues the dialogue by looking at the avatar's response.

[0252] 4. Customer Service Evaluation and Feedback:

[0253] The user declares the end of the role-play and sends the result data to the server.

[0254] The server receives the end request and the result data and analyzes the data using Amazon Sagemaker.

[0255] The server generates feedback based on the analyzed evaluation points and returns it to the terminal.

[0256] The device displays the received feedback to the user, allowing them to see areas for improvement in their customer service skills.

[0257] Adding specific examples

[0258] Example: When selecting the scenario "Explaining the features of a new product"

[0259] 1. Log in and select a scenario:

[0260] The user logs in to the app and selects "New Product Features."

[0261] Example prompt: "Describe the features of your new smartphone."

[0262] 2. Scenario generation:

[0263] The server uses the AI ​​model to generate customer question scenarios and avatar details, which are then sent to the device.

[0264] Example prompt: "A customer asks, 'How good is the camera on this phone?'"

[0265] 3. Roleplay begins:

[0266] The user explains the features of a new product to the avatar.

[0267] Example prompt: "This smartphone is equipped with a 12MP camera and is excellent at taking night shots."

[0268] 4. Response display and feedback:

[0269] The server's response is displayed on the terminal, and the user can continue the conversation by watching the avatar's reaction.

[0270] After the role-play is completed, specific feedback based on the analysis results will be provided.

[0271] Example feedback: "The product description was detailed, but it could be improved to capture customers' interest."

[0272] This system will improve customer service capabilities in actual customer service situations, particularly strengthening the ability to respond quickly and effectively when introducing new products or services.

[0273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0274] Step 1:

[0275] The user launches the dedicated app from their device and logs in by entering their ID and password. The data entered is the user ID and password. The device sends this data to the server. The server verifies the user information using Firebase Auth, and if authentication is successful, generates an authentication token and a list of available scenarios and returns them to the device. The output is the authentication token and list of scenarios.

[0276] Step 2:

[0277] The user selects the scenario they want to practice on the scenario selection screen. The user's selection information is entered into the device and sent to the server. The input is the selected scenario information. The server processes the scenario information using Google Dialogflow, generates scenario details and an avatar using a generative AI model (ChatGPT), and sends them to the device. The output is the scenario details and avatar information.

[0278] Step 3:

[0279] The user views the scenario start screen and begins role-playing. The user explains products to virtual customers (avatars) and responds to their inquiries. The device sends the user's input to the server. The input is the user's dialogue. The server receives the input, passes it to a generative AI model (ChatGPT), and generates a response. The generated response is returned to the device. The output is the avatar's response.

[0280] Step 4:

[0281] The terminal displays the avatar's response received from the server on the screen. The user can continue the dialogue by looking at the avatar's response. The input is the response data from the server, and the output is the response message displayed on the terminal.

[0282] Step 5:

[0283] The user declares the end of the role-play and sends an end request and result data from the device to the server. The input is the role-play result data. The server receives the end request, analyzes the data using Amazon Sagemaker, and extracts evaluation points. The output is the analysis result.

[0284] Step 6:

[0285] The server generates feedback based on the evaluation points and returns it to the device. The input is the analysis result. The feedback includes a detailed evaluation of the user's performance and suggestions for improvement. The output is a feedback message.

[0286] Step 7:

[0287] The terminal displays the received feedback to the user. The user checks the feedback and understands areas for improvement in their customer service skills. The input is the feedback message from the server, and the output is the feedback content displayed to the user.

[0288] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0289] The present invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when new devices or new services are launched. Furthermore, by recognizing the user's emotions and providing appropriate responses and feedback accordingly, more practical training becomes possible. The following describes in detail the embodiments of the present invention.

[0290] System Overview

[0291] This system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user, and the emotion engine recognizes the user's emotional state and sends that information to the server. The server then provides responses and feedback according to the user's emotions.

[0292] Program processing flow

[0293] 1. Booting the system and logging in

[0294] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[0295] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[0296] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[0297] 2. Start customer service role-play

[0298] User: Select the scenario you want to practice on the scenario selection screen.

[0299] Terminal: Sends the selection information to the server.

[0300] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0301] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0302] 3. Customer service practice and emotion recognition

[0303] User: Enters customer service details for the avatar.

[0304] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[0305] Terminal: Sends input content and emotional information to the server.

[0306] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0307] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0308] 4. Customer Service Evaluation and Feedback

[0309] Terminal: The user declares the end of the role-play and sends an end request, result data, and emotion information to the server.

[0310] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[0311] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[0312] Specific examples

[0313] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[0314] 1. Log in and select a scenario

[0315] The user logs in to the app and selects "New Smartphone Plan Description."

[0316] 2. Scenario Generation

[0317] The server generates the scenario and avatar details and sends them to the device.

[0318] 3. Roleplay begins

[0319] The user begins explaining the new plan to the avatar.

[0320] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[0321] User input and emotional information is sent to the server, and a generative AI model generates the avatar's response.

[0322] 4. Display of responses and emotional responses

[0323] The server's response is displayed on the terminal, and the user can check the avatar's reaction and input an appropriate response depending on their feelings, such as tension or confusion.

[0324] The emotion engine continuously monitors the user's emotions and adjusts the feedback as emotions change.

[0325] 5. Termination and Evaluation

[0326] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[0327] Feedback that reflects emotional information also helps users improve their emotional response.

[0328] Through this concrete example, it can be seen that this system aims to practically improve customer service skills, and in particular functions as an effective tool for strengthening the ability to respond to users' emotions.

[0329] The processing flow will be explained below.

[0330] Step 1:

[0331] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[0332] Step 2:

[0333] Terminal: Sends the entered user ID and password to the server.

[0334] Step 3:

[0335] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[0336] Step 4:

[0337] Server: Returns the authentication result (success or failure) to the terminal.

[0338] Step 5:

[0339] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[0340] Step 6:

[0341] User: Select the scenario you want to practice on the scenario selection screen.

[0342] Step 7:

[0343] Terminal: Sends the selection information to the server.

[0344] Step 8:

[0345] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0346] Step 9:

[0347] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0348] Step 10:

[0349] User: Enters customer service details for the avatar.

[0350] Step 11:

[0351] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[0352] Step 12:

[0353] Terminal: Sends input content and emotional information to the server.

[0354] Step 13:

[0355] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0356] Step 14:

[0357] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0358] Step 15:

[0359] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[0360] Step 16:

[0361] Emotion Engine: Continuously monitors the user's emotional state and sends the information to the server whenever a change is detected.

[0362] Step 17:

[0363] Terminal & Server: For each user input, the terminal sends the input and emotional information to the server, and the server generates a response and returns it to the terminal, repeating the process.

[0364] Step 18:

[0365] User: Declare the end of the roleplay.

[0366] Step 19:

[0367] Terminal: Sends an end request, role-play result data, and emotional information to the server.

[0368] Step 20:

[0369] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, and generates feedback based on the evaluation points.

[0370] Step 21:

[0371] Server: Returns the generated feedback to the device.

[0372] Step 22:

[0373] Terminal: Displays received feedback to the user.

[0374] Step 23:

[0375] Users: Review feedback and understand areas for improvement in customer service skills.

[0376] Specific examples

[0377] For example, if a user selects the scenario "Explanation of a new smartphone plan" and practices, the specific flow will be as follows.

[0378] 1. Scenario Selection:

[0379] The user logs in to the app and selects "New Smartphone Plan Description."

[0380] 2. Scenario and avatar initial settings:

[0381] The server generates the scenario and avatar details and sends them to the device.

[0382] 3. Begin the role-play:

[0383] The user enters a description for the new plan into the avatar.

[0384] The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize their emotional state.

[0385] 4. Generate and display the response:

[0386] The device sends the input content and emotional information to the server.

[0387] The server generates a response using a generative AI model and returns it to the device.

[0388] The device displays the response on the avatar and prompts the user for new input.

[0389] 5. Emotional Response:

[0390] The emotion engine continuously monitors changes in the user's emotions and transmits them to the server.

[0391] The server adjusts the response based on the emotional information and returns it to the device.

[0392] 6. End of role play:

[0393] The user ends the role-play and sends an end request, result data, and emotion information to the server.

[0394] 7. Feedback Generation and Display:

[0395] The server analyzes the result data and emotional information to generate feedback.

[0396] The device displays feedback to the user.

[0397] Through this specific example, we can see that this system is an effective tool that not only effectively improves the customer service skills required when users start using new devices or services, but also strengthens their ability to respond to users' emotions.

[0398] Example 2

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

[0400] Conventional customer service role-playing systems are unable to fully recognize the user's emotional state and have difficulty reflecting the recognition results in feedback. This limits the effectiveness of customer service practice, and training to improve the ability to respond to emotions is particularly insufficient. Furthermore, because responses are not generated based on real-time emotion recognition, there is a problem of a gap between the actual customer service situation and the system.

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

[0402] In this invention, the server includes: means for generating a response for a customer service role-play based on a scenario selected by a user using a generative AI model; means for transmitting the user's input information and the user's emotional state to the server and receiving a response from the server; means for displaying the response received from the server and prompting the user for new input; means for analyzing the user's input information, facial expressions, voice, etc., and recognizing the user's emotional state; means for evaluating the results of the customer service based on predetermined evaluation criteria; and means for generating and providing feedback to the user. This enables the system to recognize the user's emotional state in real time and to perform practical customer service role-playing based on that information. Furthermore, by incorporating emotional information into the feedback, more effective improvement of customer service skills can be expected.

[0403] A "generative AI model" is an artificial intelligence model that generates responses based on a scenario selected by the user, and is a technology that uses natural language processing to provide appropriate responses in real time.

[0404] "User" refers to an individual or an employee of an organization who wishes to improve their customer service skills by using the customer service role-playing system.

[0405] A "server" is a computer system that receives information sent by a user, generates a response using a generative AI model, and provides feedback.

[0406] A "terminal" is a device operated by a user, and is a device for accessing the customer service role-play system, inputting information, and receiving responses and feedback from the server.

[0407] An "emotion engine" is a software or hardware technology that analyzes a user's input information, facial expressions, voice, etc., and recognizes the user's emotional state.

[0408] "Feedback" refers to information and advice provided to evaluate the results of the customer service role-play and to help the user improve their skills.

[0409] A "scenario" is a pre-set situation or story used in customer service role-playing, and serves as a simulation of when the user actually serves customers.

[0410] A "response" is the dialogue content that a generative AI model generates in response to user input, and is an appropriate reply based on the content entered by the user.

[0411] The "evaluation criteria" are a set of indicators and rules for evaluating the results of the user's customer service role-play, and feedback to the user is constructed based on these.

[0412] This invention relates to a customer service role-play system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when launching new devices or services. It also enables more practical training by recognizing users' emotions and providing appropriate responses and feedback accordingly.

[0413] The system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user.

[0414] Generative AI models, such as GPT-4, are used, which are specialized for natural language processing. These models generate appropriate responses based on the scenario selected by the user.

[0415] The emotion engine is a technology that recognizes a user's emotional state by analyzing their facial expressions, voice, and input information. This emotion engine works in conjunction with input devices such as cameras and microphones to analyze emotional data in real time.

[0416] The server collects and analyzes this data and generates responses using a generative AI model. Furthermore, the server provides feedback based on predefined evaluation criteria. This feedback reflects the user's emotional information, resulting in a personalized evaluation.

[0417] The terminal is a device operated by the user, and provides an interface for accessing the customer service role-play system. The terminal transmits the user's input information and emotional data to the server, and displays responses and feedback from the server.

[0418] The user selects a scenario and role-plays with the avatar. An example scenario is "Explanation of new smartphone plans."

[0419] The actual operation process is shown below.

[0420] 1. Boot the system and log in:

[0421] The user launches the dedicated app from their device and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server. The server receives the authentication request and checks the user information against the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[0422] 2. Start the customer service role play:

[0423] The user selects the scenario they want to practice on the scenario selection screen. For example, they select "Explanation of a new smartphone plan." The selection information is sent to the server, which receives the scenario selection information, performs initial settings in the generative AI model, generates the scenario and avatar details, and returns them to the device.

[0424] 3. Customer service practice and emotion recognition:

[0425] The user inputs customer service details into the avatar, asking questions such as "How much does the new plan cost?" The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize the emotional state. The device sends this data to the server, which passes it to a generative AI model to generate a response. The generated response is returned to the device and displayed on the avatar.

[0426] 4. Customer Service Evaluation and Feedback:

[0427] After completing the role-play, the user sends an end request to the server. The server receives the end request, the result data, and the emotion information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and returns it to the device. The device displays the feedback to the user, who can review it and understand where they need to improve their customer service skills.

[0428] Examples of prompt statements

[0429] For example, if the user selects the "New Smartphone Plan Explained" scenario, the prompt text might look like this:

[0430] You are a salesperson explaining a new smartphone plan. Based on the following scenario, provide the information the avatar requests.

[0431] Scenario: New smartphone plan explained

[0432] Avatar asks:

[0433] What are the features of the new plan?

[0434] What are the monthly costs?

[0435] What is the contract period and cancellation fee?

[0436] Your response:

[0437] Explain in detail the plan's features, pricing, contract length, and cancellation fees.

[0438] The system allows customer service staff to undergo real-time training that takes emotion recognition into account through realistic scenarios, allowing users to practice and improve their skills in situations that are closer to real-life customer service situations.

[0439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] Booting and logging in

[0442] Device: The user launches the dedicated app and displays the login screen. They enter their user ID and password and tap the "Login" button.

[0443] Input: User ID and password.

[0444] Output: Sends an authentication request to the server.

[0445] What happens: The app makes an HTTP request and sends the user's authentication information to the server.

[0446] Step 2:

[0447] User Authentication

[0448] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message.

[0449] Input: User ID and password.

[0450] Output: Authentication token and a list of scenarios, or an error message.

[0451] Specific behavior: Executes a database query to verify user information. If authentication is successful, issues an authentication token using a token generation algorithm and sends it as an HTTP response.

[0452] Step 3:

[0453] Receiving authentication results

[0454] Device: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication is unsuccessful, displays an error message.

[0455] Input: Authentication token and scenario list, or error message.

[0456] Output: Scenario selection screen or error message displayed.

[0457] Specific operation: Analyze the response content, and if authentication is successful, update the UI and display the scenario selection screen.

[0458] Step 4:

[0459] Scenario Selection

[0460] User: Select the scenario you want to practice on the scenario selection screen. For example, "Explanation of a new smartphone plan."

[0461] Input: The scenario selected by the user.

[0462] Output: Scenario selection information is sent from the device to the server.

[0463] Specific operation: The selected scenario is sent to the server as an HTTP request.

[0464] Step 5:

[0465] Scenario Generation

[0466] Server: Receives scenario selection information, performs initial setup using a generative AI model (e.g., GPT-4), generates scenario and avatar details, and returns them to the device.

[0467] Input: User selected scenario information.

[0468] Output: Scenario details and avatar information.

[0469] Specific operation: Calls the generative AI model, generates a story and detailed avatar information based on the selected scenario, and returns it as a response.

[0470] Step 6:

[0471] Scenario Display

[0472] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0473] Input: Scenario details and avatar information from the server.

[0474] Output: Display of the scenario start screen.

[0475] Specific behavior: Displays the received scenario details and avatar information, and enables the roleplay start button.

[0476] Step 7:

[0477] Start of customer service practice

[0478] User: Enters customer service information into the avatar. For example, asking, "How much does the new plan cost?"

[0479] Input: The question or input the user makes.

[0480] Output: Sending input from the terminal to the server.

[0481] Specific behavior: The user enters text into the input field and presses the submit button. This input is sent to the server as is.

[0482] Step 8:

[0483] emotion recognition

[0484] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[0485] Input: Data such as user text input, facial expressions, and voice.

[0486] Output: Parsed emotional state data.

[0487] Specific operation: Analyzes data collected from cameras and microphones in real time and tags emotional states.

[0488] Step 9:

[0489] Data transmission

[0490] Terminal: Sends input content and emotional information to the server.

[0491] Input: User text input and emotional state data.

[0492] Output: Sending input and emotion data to the server.

[0493] Specific operation: The text input and analyzed emotion data are sent together to the server.

[0494] Step 10:

[0495] Response Generation

[0496] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0497] Input: User text input and emotional state data.

[0498] Output: The generated response.

[0499] Specific operation: Calls a generative AI model, generates a response based on the input content and emotional data, and returns it as a response.

[0500] Step 11:

[0501] Response Display

[0502] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0503] Input: The generated response from the server.

[0504] Output: Avatar displaying the response.

[0505] Specific operation: The avatar speaks the received response text and displays the corresponding facial expression.

[0506] Step 12:

[0507] Preparing for customer service evaluation

[0508] User: Declare the end of the role-play and send an end request, result data, and emotion information to the server via the terminal.

[0509] Input: Exit button click, result data, emotion information.

[0510] Output: Sending the end request, result data, and emotion information.

[0511] Specific operation: When the user presses the finish button, all data is sent to the server.

[0512] Step 13:

[0513] Evaluation and feedback generation

[0514] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[0515] Input: End request, result data, emotion information.

[0516] Output: Feedback.

[0517] Specific operation: Analyze the collected data, generate feedback based on predetermined evaluation criteria, and return it to the device as a response.

[0518] Step 14:

[0519] Feedback Display

[0520] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[0521] Input: Feedback from the server.

[0522] Output: Display feedback.

[0523] What this does: Updates the feedback screen to show detailed comments and scores to the user.

[0524] This allows users to receive practical training based on customer service scenarios and improve their skills through real-time feedback based on emotion recognition.

[0525] (Application example 2)

[0526] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0527] Conventional customer service role-playing systems are unable to provide feedback based on the user's emotional state, making it difficult to effectively provide practical training necessary to improve customer service skills. This is particularly true when training is required for new products or services, as it is difficult to develop appropriate emotional responses.

[0528] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a response for a customer service role-play based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user and the user's emotional state analyzed by an emotion engine to the server and receiving a response from the server, means for displaying the response received from the server and prompting the user to enter new input, and means for providing feedback according to the user's emotions using the emotion engine. This allows the user to receive real-time responses and feedback based on their emotions, enabling more practical customer service training.

[0529] A "generative AI model" is an artificial intelligence technology that automatically generates responses based on a scenario selected by the user.

[0530] An "emotion engine" is a technology that analyzes a user's emotional state from input information, facial expressions, voice, etc.

[0531] The "server" is a computer system that receives the user's input information and emotional state, generates an appropriate response using a generative AI model, and sends it to the device.

[0532] A "terminal" is a device through which a user inputs information through an interface and displays responses sent by a server.

[0533] "Customer service role-play" is a scenario-based simulation that allows participants to experience customer service work and practice skills.

[0534] "Feedback" refers to information such as evaluations and suggestions for improvement provided based on the user's behavior and emotional state.

[0535] A "response" is a dialogue or instruction generated by a generative AI model and provided to the user via the device.

[0536] A "scenario" is an item that defines a specific customer service situation selected by the user, and is the basis on which role-playing progresses based on that situation.

[0537] This invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows users working in the customer service industry to effectively practice customer service when launching a new product or service. Specific embodiments for implementing this invention are described below.

[0538] System configuration

[0539] The system mainly consists of the following components:

[0540] 1. Server

[0541] 2. Terminal

[0542] 3. Generative AI Models

[0543] 4. Emotion Engine

[0544] 5. Users

[0545] Hardware and Software

[0546] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest)

[0547] Software: Python-based local server, generative AI model, emotion engine

[0548] Processing flow explanation

[0549] First, the user launches the dedicated application on the device and enters their authentication information on the login screen. The server verifies the information, and if authentication is successful, the scenario selection screen is displayed on the device.

[0550] When a user selects a scenario they want to practice, that information is sent to the server, and the generative AI model performs initial setup based on the selected scenario. The server then generates information about the specified scenario and avatar details and sends them to the device.

[0551] When a user begins a customer service role-play, they input information into the avatar. The emotion engine analyzes the user's emotional data, such as facial expressions and tone of voice, and sends that information to the server. The server then analyzes this data using a generative AI model and generates an appropriate response. The response is sent to the device, which displays it to the user through the avatar. The user considers their next input while looking at the avatar's response.

[0552] Once the training is complete, the server analyzes the role-play results and emotional data to generate feedback for the user. The feedback is displayed on the device as advice based on the user's strengths and areas for improvement, as well as their emotions. This allows the user to specifically understand where they need to improve their customer service skills.

[0553] Specific examples of processing

[0554] For example, if a user selects the scenario "Explanation of new smartphone plans," the following prompt sentence is used: This prompt sentence allows the system to generate instructions to appropriately proceed with the customer service role-play.

[0555] Example prompt sentence:

[0556] "Simulate a plan explanation for a new smartphone."

[0557] In this scenario, a user practices explaining a complex plan to an avatar. The emotion engine analyzes the user's emotions, such as nervousness or confusion, in real time, and the server generates feedback based on that. For example, if the user is confused, the generative AI model generates a response such as, "Please let me know if there's anything you don't understand, and I'll explain it in more detail."

[0558] This system allows users to improve their practical customer service skills while receiving real-time support based on their emotions.

[0559] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0560] Step 1:

[0561] The user launches a dedicated application from their device and enters their user ID and password on the login screen. The device sends the entered authentication information to the server. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. The device receives the authentication results and displays a scenario selection screen.

[0562] Step 2:

[0563] The user selects the scenario they want to practice on the scenario selection screen and sends the selection information from the device to the server. The server receives the scenario selection information, performs initial settings in the generative AI model, and generates the scenario and avatar details. This information is sent to the device, which then displays the scenario start screen.

[0564] Step 3:

[0565] The user begins the customer service role-play on the scenario start screen on the device and inputs the customer service details to the avatar. The emotion engine analyzes the user's input information, facial expressions, and voice to recognize the user's emotional state. The device sends the input customer service details and emotional information to the server. The server analyzes the received information and passes it to the generative AI model to generate a response. The generated response is sent back to the device, which then displays the avatar's response to the user.

[0566] Step 4:

[0567] The user confirms the avatar's response and continues to input their customer service needs. The emotion engine continues to analyze the user's emotional state and transmits the data to the server. The server continuously passes the user's input and emotional information to the generative AI model, which generates new responses and transmits them to the device. This cycle repeats until the user declares the end of the role-play.

[0568] Step 5:

[0569] When the user declares the end of the role-play, the device sends an end request, result data, and emotional information to the server. The server receives the end request, result data, and emotional information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and sends the feedback information to the device. The device displays the received feedback to the user, allowing the user to understand areas for improvement in their customer service skills and emotional response.

[0570] In this way, a system that provides real-time, practical customer service training is realized through how the user, device, server, emotion engine, and generative AI model work together at each processing step.

[0571] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0572] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0574] [Second embodiment]

[0575] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0576] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0577] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0578] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0579] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0580] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0581] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0582] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0583] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0584] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0585] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0586] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0587] The present invention relates to a customer service role-playing system that uses a generative AI model. This system allows staff working in the customer service industry to effectively practice customer service when new devices or new services are launched. The following describes in detail the embodiments of the present invention.

[0588] System Overview

[0589] This system consists of four main components: a generative AI model, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user and provides it to the user via the terminal. It also provides feedback based on the results of the role-play, helping the user improve their customer service skills.

[0590] Program processing flow

[0591] 1. Booting the system and logging in

[0592] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[0593] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[0594] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[0595] 2. Start customer service role-play

[0596] User: Select the scenario you want to practice on the scenario selection screen.

[0597] Terminal: Sends the selection information to the server.

[0598] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0599] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0600] 3. Customer service practice

[0601] User: Enters customer service details for the avatar.

[0602] Terminal: Sends the entered information to the server.

[0603] Server: Receives input, passes it to the generative AI model (ChatGPT) to generate a response, and returns the generated response to the device.

[0604] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0605] 4. Customer Service Evaluation and Feedback

[0606] Terminal: The user declares the end of the role-play and sends an end request and result data to the server.

[0607] Server: Receives the end request and result data, analyzes the data, extracts reputation points, generates feedback based on the reputation points, and returns it to the device.

[0608] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[0609] Specific examples

[0610] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[0611] 1. Log in and select a scenario

[0612] The user logs in to the app and selects "New Smartphone Plan Description."

[0613] 2. Scenario Generation

[0614] The server generates the scenario and avatar details and sends them to the device.

[0615] 3. Roleplay begins

[0616] The user begins explaining the new plan to the avatar.

[0617] User input is sent to the server, and a generative AI model generates the avatar's response.

[0618] 4. Response display and feedback

[0619] The server's response is displayed on the terminal, and the user can continue the conversation by looking at the avatar's reaction.

[0620] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[0621] Through this concrete example, it can be seen that this system can function as an effective tool for practically improving customer service skills, particularly for strengthening the ability to respond to frequently changing new devices and services.

[0622] The processing flow will be explained below.

[0623] Step 1:

[0624] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[0625] Step 2:

[0626] Terminal: Sends the entered user ID and password to the server.

[0627] Step 3:

[0628] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[0629] Step 4:

[0630] Server: Returns the authentication result (success or failure) to the terminal.

[0631] Step 5:

[0632] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen, and if authentication fails, displays an error message.

[0633] Step 6:

[0634] User: Select the scenario you want to practice on the scenario selection screen.

[0635] Step 7:

[0636] Terminal: Sends the selection information to the server.

[0637] Step 8:

[0638] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0639] Step 9:

[0640] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0641] Step 10:

[0642] User: Enters customer service details for the avatar.

[0643] Step 11:

[0644] Terminal: Sends the entered information to the server.

[0645] Step 12:

[0646] Server: Receives input, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0647] Step 13:

[0648] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0649] Step 14:

[0650] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[0651] Step 15:

[0652] Terminal: Repeats the process of sending each user input to the server, receiving a response from the server, and displaying it on the avatar.

[0653] Step 16:

[0654] User: Declare the end of the roleplay.

[0655] Step 17:

[0656] Terminal: Sends the end request and role-play result data to the server.

[0657] Step 18:

[0658] Server: Receives the end request and the result data, analyzes the result data to extract evaluation points, and generates feedback.

[0659] Step 19:

[0660] Server: Returns the generated feedback to the device.

[0661] Step 20:

[0662] Terminal: Displays received feedback to the user.

[0663] Step 21:

[0664] Users: Review feedback and understand areas for improvement in customer service skills.

[0665] Through the above steps, this system allows users to effectively improve the customer service skills required when starting to use new terminals or services.

[0666] Example 1

[0667] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] Conventional customer service training systems are limited to fixed scenarios and responses, making it difficult to acquire the flexibility and real-time response skills required in actual customer service situations. Also, it takes time for users to receive feedback on their practice results, making it difficult to immediately improve their skills.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0670] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for starting the system and authenticating the user, and means for analyzing the generated responses and result data and generating feedback for the user. This allows the user to receive immediate feedback while flexibly practicing customer service in real time.

[0671] A "generative AI model" is an algorithm or program that automatically generates customer service role-play responses based on a scenario selected by the user.

[0672] A "user" is an individual who uses this system to practice customer service.

[0673] A "server" is a central processing unit that accepts requests from users, generates responses using generative AI models, and analyzes the resulting data to provide feedback.

[0674] A "terminal" is an electronic device such as a computer or smartphone that is used by a user to operate.

[0675] A "scenario" is a specific situation or theme selected by the user for customer service practice.

[0676] A "response" is the dialogue content generated by a generative AI model in response to user input.

[0677] An "authentication token" is a digital key that indicates that a user has been successfully authenticated.

[0678] "Feedback" refers to evaluations and advice provided to help users improve their skills based on the results of customer service role-playing.

[0679] "Result data" refers to all data generated during the customer service role-play.

[0680] "Evaluation points" are evaluation items related to the user's skills that are extracted by analyzing the result data.

[0681] The present invention relates to a customer service role-play system that utilizes a generative AI model. Hereinafter, an embodiment of the present invention will be specifically described.

[0682] The main components of the system include a server, a terminal, a user, and a generative AI model. These systems work together to allow users to role-play customer service.

[0683] First, the user starts the system from a device on which a dedicated app is installed and logs in. The login screen displays a form for entering a user ID and password. After entering the correct authentication information, the device sends it to the server. The server acts as a central processing unit and accesses a database (e.g., MySQL) to verify the user information. If authentication is successful at this stage, the server generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, the server returns an error message.

[0684] After successful authentication, the user selects a scenario to practice on the scenario selection screen. For example, they can select the scenario "Explanation of a new smartphone plan." The device sends this selection information to the server. The server receives the information and performs initial setup using a generative AI model (e.g., OpenAI's GPT-3). Specifically, it generates details of the scenario and avatar information and returns it to the device.

[0685] The user confirms the scenario start screen and begins role-playing. The user inputs customer service details for the avatar. For example, the user inputs an explanation such as "This plan includes unlimited data." The device sends this input to the server. The server passes the input to a generative AI model, which generates an appropriate response. The generated response might be something like "That's great. Can you tell me about the price?" The server returns the response to the device, which displays it to the user. The user confirms the avatar's response and makes the next input.

[0686] When the role-playing is finished, the user declares that they are finished. The device sends the role-playing result data along with an end request to the server. The server analyzes the result data, extracts evaluation points, and generates feedback. Evaluation points include, for example, "The customer service content was clear" and "Questions were asked at the appropriate time." The server returns the generated feedback to the device, and the user can review it and use it to help them practice their next customer service encounter.

[0687] This system allows users to practice customer service flexibly in real time while receiving immediate feedback.

[0688] Examples of prompts include:

[0689] 1. User type: "This new phone has unlimited data."

[0690] 2. The generative AI model responds: “That’s great! Can you tell me the price?”

[0691] This invention helps users improve their customer service skills by providing a flexible, real-time customer service practice environment using a generative AI model.

[0692] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0693] Step 1: Booting and logging in

[0694] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and clicks the "Login" button.

[0695] Input: User ID, Password

[0696] Output: Authentication request

[0697] Terminal: Sends the user's input information to the server as an authentication request.

[0698] Server: Receives the authentication request and accesses the database to verify the user information. This part uses the MySQL database. If verification is successful, it generates an authentication token and a list of available scenarios and returns them to the terminal. If verification fails, it returns an error message.

[0699] Input: Authentication Request

[0700] Output: Authentication result (authentication token, scenario list or error message)

[0701] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication fails, displays an error message and prompts the user to re-enter information.

[0702] Input: Authentication result

[0703] Output: Scenario selection screen or error message

[0704] Step 2: Select a scenario

[0705] User: After successful login, the scenario selection screen will be displayed. The user will select the scenario they want to practice (for example, "Explanation of a new smartphone plan").

[0706] Input: Select scenario

[0707] Output: Scenario selection information

[0708] Terminal: Sends the user's selections to the server.

[0709] Input: Scenario selection information

[0710] Output: Server request

[0711] Server: Receives scenario selection information and performs initial setup in the generative AI model. Specifically, it generates scenario details and avatar information and returns this to the device.

[0712] Input: Scenario selection information

[0713] Output: Scenario details, avatar information

[0714] Terminal: Displays the scenario start screen and allows the user to begin role-playing.

[0715] Input: Scenario details, avatar information

[0716] Output: Scenario start screen

[0717] Step 3: Begin the customer service role-play

[0718] User: When the scenario start screen appears, enter the customer service details for the avatar.

[0719] Input: Customer service details

[0720] Output: User input information

[0721] Terminal: Sends user input to the server.

[0722] Input: User-entered information

[0723] Output: Server request

[0724] Server: Passes input content to the generative AI model to generate a response, which is then returned to the device.

[0725] Input: User-entered information

[0726] Output: The generated response

[0727] Terminal: Displays responses received from the server and simulates an avatar stating the response.

[0728] Input: Generated response

[0729] Output: Avatar response display

[0730] Step 4: Proceed with customer service practice

[0731] User: Look at the avatar's response displayed on the terminal and enter the next customer service request. Continue the conversation.

[0732] Input: New customer service content

[0733] Output: The next input from the user

[0734] Terminal: Sends the next input contents of the user to the server one by one.

[0735] Input: The following user-entered information:

[0736] Output: Server request

[0737] Server: Using a generative AI model, it generates appropriate responses to the user's input and returns them to the device. The responses are stored and used for evaluation at the end of the role-play.

[0738] Input: The following user-entered information:

[0739] Output: The generated response

[0740] Terminal: Displays the response received from the server as an avatar, allowing the user to continue role-playing.

[0741] Input: Generated response

[0742] Output: Avatar response display

[0743] Step 5: Customer evaluation and feedback

[0744] User: Click the End Roleplay button to declare the end.

[0745] Input: Termination declaration

[0746] Output: Finished request

[0747] Terminal: Sends the role-play result data along with an end request to the server.

[0748] Input: End request, result data

[0749] Output: Server request

[0750] Server: Receives the end request and the result data, analyzes the data, extracts evaluation points based on the user's response and the avatar's reaction, and generates feedback.

[0751] Input: Result data

[0752] Output: Evaluation points, feedback

[0753] Terminal: Receives the generated feedback and displays it to the user.

[0754] Input: Feedback

[0755] Output: User feedback display, confirmation

[0756] In this way, the user, terminal, and server work together at each step to smoothly progress the customer service role-play.

[0757] (Application example 1)

[0758] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0759] Conventional customer service training systems have difficulty simulating real-time customer service situations, making it difficult to adequately train staff to respond quickly, especially when new products or services are introduced. Another problem is that they are unable to obtain specific feedback, making it difficult to effectively identify areas for improvement to improve skills. This invention has been proposed to solve these problems.

[0760] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0761] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for practicing dialogue with a virtual customer using a smartphone or head-mounted display, and means for saving and evaluating data on the results of the role-playing, thereby enabling advanced customer service practice with real-time and specific feedback.

[0762] A "generative AI model" is a type of artificial intelligence that automatically generates responses based on scenarios selected by the user.

[0763] A "scenario" is a set of specific situations and conditions for the user to practice in customer service role-playing.

[0764] "Customer service role-play" refers to practice and simulations that simulate actual customer service situations.

[0765] A "response" is a reply or reaction generated by a generative AI model based on information entered by a user.

[0766] A "server" is a computer system that receives information entered by a user, generates a response using a generative AI model, and transmits the result to a terminal.

[0767] A "terminal" is a device that allows a user to use the customer service role-play system, and includes a smartphone or a head-mounted display.

[0768] A "smartphone" is a type of mobile phone that has the ability to connect to the Internet and run applications.

[0769] A "head-mounted display" is a display device that allows a user to experience a virtual reality environment by wearing it.

[0770] A "virtual customer" is a fictitious customer simulated by AI in a customer service role-play.

[0771] "Feedback" refers to evaluations and areas for improvement provided to users based on the results of customer service role-playing.

[0772] "Role-play result data" is recorded data of the user's actions and responses collected during the customer service role-play.

[0773] The "evaluation means" is a mechanism for analyzing the result data of the role-play and evaluating the user's performance.

[0774] The present invention relates to a customer service role-play system using a generative AI model. Specific embodiments for carrying out the present invention will be described below.

[0775] System Overview

[0776] This system consists of three main components: a server, a terminal, and a user. The terminal is a smartphone or a head-mounted display, which the user uses to role-play customer service. The server uses a generative AI model to generate responses based on the scenario selected by the user and provides them to the user.

[0777] Hardware / Software

[0778] Hardware:

[0779] Smartphone (iOS / Android)

[0780] Head-mounted displays (e.g., Oculus Quest 2)

[0781] software:

[0782] Generative AI models (e.g., OpenAI's ChatGPT)

[0783] Authentication services (e.g. Firebase Auth)

[0784] Data analysis tools (e.g., Amazon Sagemaker)

[0785] Data storage (e.g. AWS S3)

[0786] Request processing (e.g. AWS Lambda)

[0787] Response generation (e.g., Google Dialogflow)

[0788] Natural language explanation of the process

[0789] 1. Boot the system and log in:

[0790] The user launches a dedicated app from their device, enters their ID and password, and sends an authentication request to the server.

[0791] The server receives the authentication request and verifies the user information using Firebase Auth. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[0792] The terminal receives the authentication result, and if the authentication is successful, displays a scenario selection screen.

[0793] 2. Select a scenario and begin roleplaying:

[0794] The user selects the scenario they want to practice on the scenario selection screen.

[0795] The terminal transmits scenario selection information to the server.

[0796] The server processes the selected scenario information using Google Dialogflow, generates details of the scenario and avatar using a generative AI model (ChatGPT), and sends them to the device.

[0797] The terminal displays the scenario start screen and prepares for the user to begin role-playing.

[0798] 3. Customer service practice:

[0799] The user explains products to virtual customers (avatars) and responds to their inquiries.

[0800] The terminal sends the user's input to the server.

[0801] The server receives the input, and the generative AI model (ChatGPT) generates a response and returns it to the device.

[0802] The terminal displays the response from the server, and the user continues the dialogue by looking at the avatar's response.

[0803] 4. Customer Service Evaluation and Feedback:

[0804] The user declares the end of the role-play and sends the result data to the server.

[0805] The server receives the end request and the result data and analyzes the data using Amazon Sagemaker.

[0806] The server generates feedback based on the analyzed evaluation points and returns it to the terminal.

[0807] The device displays the received feedback to the user, allowing them to see areas for improvement in their customer service skills.

[0808] Adding specific examples

[0809] Example: When selecting the scenario "Explaining the features of a new product"

[0810] 1. Log in and select a scenario:

[0811] The user logs in to the app and selects "New Product Features."

[0812] Example prompt: "Describe the features of your new smartphone."

[0813] 2. Scenario generation:

[0814] The server uses the AI ​​model to generate customer question scenarios and avatar details, which are then sent to the device.

[0815] Example prompt: "A customer asks, 'How good is the camera on this phone?'"

[0816] 3. Roleplay begins:

[0817] The user explains the features of a new product to the avatar.

[0818] Example prompt: "This smartphone is equipped with a 12MP camera and is excellent at taking night shots."

[0819] 4. Response display and feedback:

[0820] The server's response is displayed on the terminal, and the user can continue the conversation by watching the avatar's reaction.

[0821] After the role-play is completed, specific feedback based on the analysis results will be provided.

[0822] Example feedback: "The product description was detailed, but it could be improved to capture customers' interest."

[0823] This system will improve customer service capabilities in actual customer service situations, particularly strengthening the ability to respond quickly and effectively when introducing new products or services.

[0824] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0825] Step 1:

[0826] The user launches the dedicated app from their device and logs in by entering their ID and password. The data entered is the user ID and password. The device sends this data to the server. The server verifies the user information using Firebase Auth, and if authentication is successful, generates an authentication token and a list of available scenarios and returns them to the device. The output is the authentication token and list of scenarios.

[0827] Step 2:

[0828] The user selects the scenario they want to practice on the scenario selection screen. The user's selection information is entered into the device and sent to the server. The input is the selected scenario information. The server processes the scenario information using Google Dialogflow, generates scenario details and an avatar using a generative AI model (ChatGPT), and sends them to the device. The output is the scenario details and avatar information.

[0829] Step 3:

[0830] The user views the scenario start screen and begins role-playing. The user explains products to virtual customers (avatars) and responds to their inquiries. The device sends the user's input to the server. The input is the user's dialogue. The server receives the input, passes it to a generative AI model (ChatGPT), and generates a response. The generated response is returned to the device. The output is the avatar's response.

[0831] Step 4:

[0832] The terminal displays the avatar's response received from the server on the screen. The user can continue the dialogue by looking at the avatar's response. The input is the response data from the server, and the output is the response message displayed on the terminal.

[0833] Step 5:

[0834] The user declares the end of the role-play and sends an end request and result data from the device to the server. The input is the role-play result data. The server receives the end request, analyzes the data using Amazon Sagemaker, and extracts evaluation points. The output is the analysis result.

[0835] Step 6:

[0836] The server generates feedback based on the evaluation points and returns it to the device. The input is the analysis result. The feedback includes a detailed evaluation of the user's performance and suggestions for improvement. The output is a feedback message.

[0837] Step 7:

[0838] The terminal displays the received feedback to the user. The user checks the feedback and understands areas for improvement in their customer service skills. The input is the feedback message from the server, and the output is the feedback content displayed to the user.

[0839] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0840] The present invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when new devices or new services are launched. Furthermore, by recognizing the user's emotions and providing appropriate responses and feedback accordingly, more practical training becomes possible. The following describes in detail the embodiments of the present invention.

[0841] System Overview

[0842] This system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user, and the emotion engine recognizes the user's emotional state and sends that information to the server. The server then provides responses and feedback according to the user's emotions.

[0843] Program processing flow

[0844] 1. Booting the system and logging in

[0845] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[0846] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[0847] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[0848] 2. Start customer service role-play

[0849] User: Select the scenario you want to practice on the scenario selection screen.

[0850] Terminal: Sends the selection information to the server.

[0851] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0852] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0853] 3. Customer service practice and emotion recognition

[0854] User: Enters customer service details for the avatar.

[0855] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[0856] Terminal: Sends input content and emotional information to the server.

[0857] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0858] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0859] 4. Customer Service Evaluation and Feedback

[0860] Terminal: The user declares the end of the role-play and sends an end request, result data, and emotion information to the server.

[0861] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[0862] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[0863] Specific examples

[0864] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[0865] 1. Log in and select a scenario

[0866] The user logs in to the app and selects "New Smartphone Plan Description."

[0867] 2. Scenario Generation

[0868] The server generates the scenario and avatar details and sends them to the device.

[0869] 3. Roleplay begins

[0870] The user begins explaining the new plan to the avatar.

[0871] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[0872] User input and emotional information is sent to the server, and a generative AI model generates the avatar's response.

[0873] 4. Display of responses and emotional responses

[0874] The server's response is displayed on the terminal, and the user can check the avatar's reaction and input an appropriate response depending on their feelings, such as tension or confusion.

[0875] The emotion engine continuously monitors the user's emotions and adjusts the feedback as emotions change.

[0876] 5. Termination and Evaluation

[0877] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[0878] Feedback that reflects emotional information also helps users improve their emotional response.

[0879] Through this concrete example, it can be seen that this system aims to practically improve customer service skills, and in particular functions as an effective tool for strengthening the ability to respond to users' emotions.

[0880] The processing flow will be explained below.

[0881] Step 1:

[0882] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[0883] Step 2:

[0884] Terminal: Sends the entered user ID and password to the server.

[0885] Step 3:

[0886] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[0887] Step 4:

[0888] Server: Returns the authentication result (success or failure) to the terminal.

[0889] Step 5:

[0890] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[0891] Step 6:

[0892] User: Select the scenario you want to practice on the scenario selection screen.

[0893] Step 7:

[0894] Terminal: Sends the selection information to the server.

[0895] Step 8:

[0896] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[0897] Step 9:

[0898] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[0899] Step 10:

[0900] User: Enters customer service details for the avatar.

[0901] Step 11:

[0902] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[0903] Step 12:

[0904] Terminal: Sends input content and emotional information to the server.

[0905] Step 13:

[0906] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[0907] Step 14:

[0908] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[0909] Step 15:

[0910] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[0911] Step 16:

[0912] Emotion Engine: Continuously monitors the user's emotional state and sends the information to the server whenever a change is detected.

[0913] Step 17:

[0914] Terminal & Server: For each user input, the terminal sends the input and emotional information to the server, and the server generates a response and returns it to the terminal, repeating the process.

[0915] Step 18:

[0916] User: Declare the end of the roleplay.

[0917] Step 19:

[0918] Terminal: Sends an end request, role-play result data, and emotional information to the server.

[0919] Step 20:

[0920] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, and generates feedback based on the evaluation points.

[0921] Step 21:

[0922] Server: Returns the generated feedback to the device.

[0923] Step 22:

[0924] Terminal: Displays received feedback to the user.

[0925] Step 23:

[0926] Users: Review feedback and understand areas for improvement in customer service skills.

[0927] Specific examples

[0928] For example, if a user selects the scenario "Explanation of a new smartphone plan" and practices, the specific flow will be as follows.

[0929] 1. Scenario Selection:

[0930] The user logs in to the app and selects "New Smartphone Plan Description."

[0931] 2. Scenario and avatar initial settings:

[0932] The server generates the scenario and avatar details and sends them to the device.

[0933] 3. Begin the role-play:

[0934] The user enters a description for the new plan into the avatar.

[0935] The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize their emotional state.

[0936] 4. Generate and display the response:

[0937] The device sends the input content and emotional information to the server.

[0938] The server generates a response using a generative AI model and returns it to the device.

[0939] The device displays the response on the avatar and prompts the user for new input.

[0940] 5. Emotional Response:

[0941] The emotion engine continuously monitors changes in the user's emotions and transmits them to the server.

[0942] The server adjusts the response based on the emotional information and returns it to the device.

[0943] 6. End of role play:

[0944] The user ends the role-play and sends an end request, result data, and emotion information to the server.

[0945] 7. Feedback Generation and Display:

[0946] The server analyzes the result data and emotional information to generate feedback.

[0947] The device displays feedback to the user.

[0948] Through this specific example, we can see that this system is an effective tool that not only effectively improves the customer service skills required when users start using new devices or services, but also strengthens their ability to respond to users' emotions.

[0949] Example 2

[0950] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0951] Conventional customer service role-playing systems are unable to fully recognize the user's emotional state and have difficulty reflecting the recognition results in feedback. This limits the effectiveness of customer service practice, and training to improve the ability to respond to emotions is particularly insufficient. Furthermore, because responses are not generated based on real-time emotion recognition, there is a problem of a gap between the actual customer service situation and the system.

[0952] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0953] In this invention, the server includes: means for generating a response for a customer service role-play based on a scenario selected by a user using a generative AI model; means for transmitting the user's input information and the user's emotional state to the server and receiving a response from the server; means for displaying the response received from the server and prompting the user for new input; means for analyzing the user's input information, facial expressions, voice, etc., and recognizing the user's emotional state; means for evaluating the results of the customer service based on predetermined evaluation criteria; and means for generating and providing feedback to the user. This enables the system to recognize the user's emotional state in real time and to perform practical customer service role-playing based on that information. Furthermore, by incorporating emotional information into the feedback, more effective improvement of customer service skills can be expected.

[0954] A "generative AI model" is an artificial intelligence model that generates responses based on a scenario selected by the user, and is a technology that uses natural language processing to provide appropriate responses in real time.

[0955] "User" refers to an individual or an employee of an organization who wishes to improve their customer service skills by using the customer service role-playing system.

[0956] A "server" is a computer system that receives information sent by a user, generates a response using a generative AI model, and provides feedback.

[0957] A "terminal" is a device operated by a user, and is a device for accessing the customer service role-play system, inputting information, and receiving responses and feedback from the server.

[0958] An "emotion engine" is a software or hardware technology that analyzes a user's input information, facial expressions, voice, etc., and recognizes the user's emotional state.

[0959] "Feedback" refers to information and advice provided to evaluate the results of the customer service role-play and to help the user improve their skills.

[0960] A "scenario" is a pre-set situation or story used in customer service role-playing, and serves as a simulation of when the user actually serves customers.

[0961] A "response" is the dialogue content that a generative AI model generates in response to user input, and is an appropriate reply based on the content entered by the user.

[0962] The "evaluation criteria" are a set of indicators and rules for evaluating the results of the user's customer service role-play, and feedback to the user is constructed based on these.

[0963] This invention relates to a customer service role-play system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when launching new devices or services. It also enables more practical training by recognizing users' emotions and providing appropriate responses and feedback accordingly.

[0964] The system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user.

[0965] Generative AI models, such as GPT-4, are used, which are specialized for natural language processing. These models generate appropriate responses based on the scenario selected by the user.

[0966] The emotion engine is a technology that recognizes a user's emotional state by analyzing their facial expressions, voice, and input information. This emotion engine works in conjunction with input devices such as cameras and microphones to analyze emotional data in real time.

[0967] The server collects and analyzes this data and generates responses using a generative AI model. Furthermore, the server provides feedback based on predefined evaluation criteria. This feedback reflects the user's emotional information, resulting in a personalized evaluation.

[0968] The terminal is a device operated by the user, and provides an interface for accessing the customer service role-play system. The terminal transmits the user's input information and emotional data to the server, and displays responses and feedback from the server.

[0969] The user selects a scenario and role-plays with the avatar. An example scenario is "Explanation of new smartphone plans."

[0970] The actual operation process is shown below.

[0971] 1. Boot the system and log in:

[0972] The user launches the dedicated app from their device and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server. The server receives the authentication request and checks the user information against the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[0973] 2. Start the customer service role play:

[0974] The user selects the scenario they want to practice on the scenario selection screen. For example, they select "Explanation of a new smartphone plan." The selection information is sent to the server, which receives the scenario selection information, performs initial settings in the generative AI model, generates the scenario and avatar details, and returns them to the device.

[0975] 3. Customer service practice and emotion recognition:

[0976] The user inputs customer service details into the avatar, asking questions such as "How much does the new plan cost?" The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize the emotional state. The device sends this data to the server, which passes it to a generative AI model to generate a response. The generated response is returned to the device and displayed on the avatar.

[0977] 4. Customer Service Evaluation and Feedback:

[0978] After completing the role-play, the user sends an end request to the server. The server receives the end request, the result data, and the emotion information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and returns it to the device. The device displays the feedback to the user, who can review it and understand where they need to improve their customer service skills.

[0979] Examples of prompt statements

[0980] For example, if the user selects the "New Smartphone Plan Explained" scenario, the prompt text might look like this:

[0981] You are a salesperson explaining a new smartphone plan. Based on the following scenario, provide the information the avatar requests.

[0982] Scenario: New smartphone plan explained

[0983] Avatar asks:

[0984] What are the features of the new plan?

[0985] What are the monthly costs?

[0986] What is the contract period and cancellation fee?

[0987] Your response:

[0988] Explain in detail the plan's features, pricing, contract length, and cancellation fees.

[0989] The system allows customer service staff to undergo real-time training that takes emotion recognition into account through realistic scenarios, allowing users to practice and improve their skills in situations that are closer to real-life customer service situations.

[0990] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0991] Step 1:

[0992] Booting and logging in

[0993] Device: The user launches the dedicated app and displays the login screen. They enter their user ID and password and tap the "Login" button.

[0994] Input: User ID and password.

[0995] Output: Sends an authentication request to the server.

[0996] What happens: The app makes an HTTP request and sends the user's authentication information to the server.

[0997] Step 2:

[0998] User Authentication

[0999] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message.

[1000] Input: User ID and password.

[1001] Output: Authentication token and a list of scenarios, or an error message.

[1002] Specific behavior: Executes a database query to verify user information. If authentication is successful, issues an authentication token using a token generation algorithm and sends it as an HTTP response.

[1003] Step 3:

[1004] Receiving authentication results

[1005] Device: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication is unsuccessful, displays an error message.

[1006] Input: Authentication token and scenario list, or error message.

[1007] Output: Scenario selection screen or error message displayed.

[1008] Specific operation: Analyze the response content, and if authentication is successful, update the UI and display the scenario selection screen.

[1009] Step 4:

[1010] Scenario Selection

[1011] User: Select the scenario you want to practice on the scenario selection screen. For example, "Explanation of a new smartphone plan."

[1012] Input: The scenario selected by the user.

[1013] Output: Scenario selection information is sent from the device to the server.

[1014] Specific operation: The selected scenario is sent to the server as an HTTP request.

[1015] Step 5:

[1016] Scenario Generation

[1017] Server: Receives scenario selection information, performs initial setup using a generative AI model (e.g., GPT-4), generates scenario and avatar details, and returns them to the device.

[1018] Input: User selected scenario information.

[1019] Output: Scenario details and avatar information.

[1020] Specific operation: Calls the generative AI model, generates a story and detailed avatar information based on the selected scenario, and returns it as a response.

[1021] Step 6:

[1022] Scenario Display

[1023] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1024] Input: Scenario details and avatar information from the server.

[1025] Output: Display of the scenario start screen.

[1026] Specific behavior: Displays the received scenario details and avatar information, and enables the roleplay start button.

[1027] Step 7:

[1028] Start of customer service practice

[1029] User: Enters customer service information into the avatar. For example, asking, "How much does the new plan cost?"

[1030] Input: The question or input the user makes.

[1031] Output: Sending input from the terminal to the server.

[1032] Specific behavior: The user enters text into the input field and presses the submit button. This input is sent to the server as is.

[1033] Step 8:

[1034] emotion recognition

[1035] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[1036] Input: Data such as user text input, facial expressions, and voice.

[1037] Output: Parsed emotional state data.

[1038] Specific operation: Analyzes data collected from cameras and microphones in real time and tags emotional states.

[1039] Step 9:

[1040] Data transmission

[1041] Terminal: Sends input content and emotional information to the server.

[1042] Input: User text input and emotional state data.

[1043] Output: Sending input and emotion data to the server.

[1044] Specific operation: The text input and analyzed emotion data are sent together to the server.

[1045] Step 10:

[1046] Response Generation

[1047] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1048] Input: User text input and emotional state data.

[1049] Output: The generated response.

[1050] Specific operation: Calls a generative AI model, generates a response based on the input content and emotional data, and returns it as a response.

[1051] Step 11:

[1052] Response Display

[1053] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1054] Input: The generated response from the server.

[1055] Output: Avatar displaying the response.

[1056] Specific operation: The avatar speaks the received response text and displays the corresponding facial expression.

[1057] Step 12:

[1058] Preparing for customer service evaluation

[1059] User: Declare the end of the role-play and send an end request, result data, and emotion information to the server via the terminal.

[1060] Input: Exit button click, result data, emotion information.

[1061] Output: Sending the end request, result data, and emotion information.

[1062] Specific operation: When the user presses the finish button, all data is sent to the server.

[1063] Step 13:

[1064] Evaluation and feedback generation

[1065] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[1066] Input: End request, result data, emotion information.

[1067] Output: Feedback.

[1068] Specific operation: Analyze the collected data, generate feedback based on predetermined evaluation criteria, and return it to the device as a response.

[1069] Step 14:

[1070] Feedback Display

[1071] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[1072] Input: Feedback from the server.

[1073] Output: Display feedback.

[1074] What this does: Updates the feedback screen to show detailed comments and scores to the user.

[1075] This allows users to receive practical training based on customer service scenarios and improve their skills through real-time feedback based on emotion recognition.

[1076] (Application example 2)

[1077] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1078] Conventional customer service role-playing systems are unable to provide feedback based on the user's emotional state, making it difficult to effectively provide practical training necessary to improve customer service skills. This is particularly true when training is required for new products or services, as it is difficult to develop appropriate emotional responses.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a response for a customer service role-play based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user and the user's emotional state analyzed by an emotion engine to the server and receiving a response from the server, means for displaying the response received from the server and prompting the user to enter new input, and means for providing feedback according to the user's emotions using the emotion engine. This allows the user to receive real-time responses and feedback based on their emotions, enabling more practical customer service training.

[1080] A "generative AI model" is an artificial intelligence technology that automatically generates responses based on a scenario selected by the user.

[1081] An "emotion engine" is a technology that analyzes a user's emotional state from input information, facial expressions, voice, etc.

[1082] The "server" is a computer system that receives the user's input information and emotional state, generates an appropriate response using a generative AI model, and sends it to the device.

[1083] A "terminal" is a device through which a user inputs information through an interface and displays responses sent by a server.

[1084] "Customer service role-play" is a scenario-based simulation that allows participants to experience customer service work and practice skills.

[1085] "Feedback" refers to information such as evaluations and suggestions for improvement provided based on the user's behavior and emotional state.

[1086] A "response" is a dialogue or instruction generated by a generative AI model and provided to the user via the device.

[1087] A "scenario" is an item that defines a specific customer service situation selected by the user, and is the basis on which role-playing progresses based on that situation.

[1088] This invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows users working in the customer service industry to effectively practice customer service when launching a new product or service. Specific embodiments for implementing this invention are described below.

[1089] System configuration

[1090] The system mainly consists of the following components:

[1091] 1. Server

[1092] 2. Terminal

[1093] 3. Generative AI Models

[1094] 4. Emotion Engine

[1095] 5. Users

[1096] Hardware and Software

[1097] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest)

[1098] Software: Python-based local server, generative AI model, emotion engine

[1099] Processing flow explanation

[1100] First, the user launches the dedicated application on the device and enters their authentication information on the login screen. The server verifies the information, and if authentication is successful, the scenario selection screen is displayed on the device.

[1101] When a user selects a scenario they want to practice, that information is sent to the server, and the generative AI model performs initial setup based on the selected scenario. The server then generates information about the specified scenario and avatar details and sends them to the device.

[1102] When a user begins a customer service role-play, they input information into the avatar. The emotion engine analyzes the user's emotional data, such as facial expressions and tone of voice, and sends that information to the server. The server then analyzes this data using a generative AI model and generates an appropriate response. The response is sent to the device, which displays it to the user through the avatar. The user considers their next input while looking at the avatar's response.

[1103] Once the training is complete, the server analyzes the role-play results and emotional data to generate feedback for the user. The feedback is displayed on the device as advice based on the user's strengths and areas for improvement, as well as their emotions. This allows the user to specifically understand where they need to improve their customer service skills.

[1104] Specific examples of processing

[1105] For example, if a user selects the scenario "Explanation of new smartphone plans," the following prompt sentence is used: This prompt sentence allows the system to generate instructions to appropriately proceed with the customer service role-play.

[1106] Example prompt sentence:

[1107] "Simulate a plan explanation for a new smartphone."

[1108] In this scenario, a user practices explaining a complex plan to an avatar. The emotion engine analyzes the user's emotions, such as nervousness or confusion, in real time, and the server generates feedback based on that. For example, if the user is confused, the generative AI model generates a response such as, "Please let me know if there's anything you don't understand, and I'll explain it in more detail."

[1109] This system allows users to improve their practical customer service skills while receiving real-time support based on their emotions.

[1110] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1111] Step 1:

[1112] The user launches a dedicated application from their device and enters their user ID and password on the login screen. The device sends the entered authentication information to the server. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. The device receives the authentication results and displays a scenario selection screen.

[1113] Step 2:

[1114] The user selects the scenario they want to practice on the scenario selection screen and sends the selection information from the device to the server. The server receives the scenario selection information, performs initial settings in the generative AI model, and generates the scenario and avatar details. This information is sent to the device, which then displays the scenario start screen.

[1115] Step 3:

[1116] The user begins the customer service role-play on the scenario start screen on the device and inputs the customer service details to the avatar. The emotion engine analyzes the user's input information, facial expressions, and voice to recognize the user's emotional state. The device sends the input customer service details and emotional information to the server. The server analyzes the received information and passes it to the generative AI model to generate a response. The generated response is sent back to the device, which then displays the avatar's response to the user.

[1117] Step 4:

[1118] The user confirms the avatar's response and continues to input their customer service needs. The emotion engine continues to analyze the user's emotional state and transmits the data to the server. The server continuously passes the user's input and emotional information to the generative AI model, which generates new responses and transmits them to the device. This cycle repeats until the user declares the end of the role-play.

[1119] Step 5:

[1120] When the user declares the end of the role-play, the device sends an end request, result data, and emotional information to the server. The server receives the end request, result data, and emotional information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and sends the feedback information to the device. The device displays the received feedback to the user, allowing the user to understand areas for improvement in their customer service skills and emotional response.

[1121] In this way, a system that provides real-time, practical customer service training is realized through how the user, device, server, emotion engine, and generative AI model work together at each processing step.

[1122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1124] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1125] [Third embodiment]

[1126] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1130] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1134] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1136] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1137] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1138] The present invention relates to a customer service role-playing system that uses a generative AI model. This system allows staff working in the customer service industry to effectively practice customer service when new devices or new services are launched. The following describes in detail the embodiments of the present invention.

[1139] System Overview

[1140] This system consists of four main components: a generative AI model, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user and provides it to the user via the terminal. It also provides feedback based on the results of the role-play, helping the user improve their customer service skills.

[1141] Program processing flow

[1142] 1. Booting the system and logging in

[1143] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[1144] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[1145] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[1146] 2. Start customer service role-play

[1147] User: Select the scenario you want to practice on the scenario selection screen.

[1148] Terminal: Sends the selection information to the server.

[1149] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1150] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1151] 3. Customer service practice

[1152] User: Enters customer service details for the avatar.

[1153] Terminal: Sends the entered information to the server.

[1154] Server: Receives input, passes it to the generative AI model (ChatGPT) to generate a response, and returns the generated response to the device.

[1155] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1156] 4. Customer Service Evaluation and Feedback

[1157] Terminal: The user declares the end of the role-play and sends an end request and result data to the server.

[1158] Server: Receives the end request and result data, analyzes the data, extracts reputation points, generates feedback based on the reputation points, and returns it to the device.

[1159] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[1160] Specific examples

[1161] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[1162] 1. Log in and select a scenario

[1163] The user logs in to the app and selects "New Smartphone Plan Description."

[1164] 2. Scenario Generation

[1165] The server generates the scenario and avatar details and sends them to the device.

[1166] 3. Roleplay begins

[1167] The user begins explaining the new plan to the avatar.

[1168] User input is sent to the server, and a generative AI model generates the avatar's response.

[1169] 4. Response display and feedback

[1170] The server's response is displayed on the terminal, and the user can continue the conversation by looking at the avatar's reaction.

[1171] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[1172] Through this concrete example, it can be seen that this system can function as an effective tool for practically improving customer service skills, particularly for strengthening the ability to respond to frequently changing new devices and services.

[1173] The processing flow will be explained below.

[1174] Step 1:

[1175] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[1176] Step 2:

[1177] Terminal: Sends the entered user ID and password to the server.

[1178] Step 3:

[1179] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[1180] Step 4:

[1181] Server: Returns the authentication result (success or failure) to the terminal.

[1182] Step 5:

[1183] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen, and if authentication fails, displays an error message.

[1184] Step 6:

[1185] User: Select the scenario you want to practice on the scenario selection screen.

[1186] Step 7:

[1187] Terminal: Sends the selection information to the server.

[1188] Step 8:

[1189] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1190] Step 9:

[1191] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1192] Step 10:

[1193] User: Enters customer service details for the avatar.

[1194] Step 11:

[1195] Terminal: Sends the entered information to the server.

[1196] Step 12:

[1197] Server: Receives input, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1198] Step 13:

[1199] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1200] Step 14:

[1201] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[1202] Step 15:

[1203] Terminal: Repeats the process of sending each user input to the server, receiving a response from the server, and displaying it on the avatar.

[1204] Step 16:

[1205] User: Declare the end of the roleplay.

[1206] Step 17:

[1207] Terminal: Sends the end request and role-play result data to the server.

[1208] Step 18:

[1209] Server: Receives the end request and the result data, analyzes the result data to extract evaluation points, and generates feedback.

[1210] Step 19:

[1211] Server: Returns the generated feedback to the device.

[1212] Step 20:

[1213] Terminal: Displays received feedback to the user.

[1214] Step 21:

[1215] Users: Review feedback and understand areas for improvement in customer service skills.

[1216] Through the above steps, this system allows users to effectively improve the customer service skills required when starting to use new terminals or services.

[1217] Example 1

[1218] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1219] Conventional customer service training systems are limited to fixed scenarios and responses, making it difficult to acquire the flexibility and real-time response skills required in actual customer service situations. Also, it takes time for users to receive feedback on their practice results, making it difficult to immediately improve their skills.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1221] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for starting the system and authenticating the user, and means for analyzing the generated responses and result data and generating feedback for the user. This allows the user to receive immediate feedback while flexibly practicing customer service in real time.

[1222] A "generative AI model" is an algorithm or program that automatically generates customer service role-play responses based on a scenario selected by the user.

[1223] A "user" is an individual who uses this system to practice customer service.

[1224] A "server" is a central processing unit that accepts requests from users, generates responses using generative AI models, and analyzes the resulting data to provide feedback.

[1225] A "terminal" is an electronic device such as a computer or smartphone that is used by a user to operate.

[1226] A "scenario" is a specific situation or theme selected by the user for customer service practice.

[1227] A "response" is the dialogue content generated by a generative AI model in response to user input.

[1228] An "authentication token" is a digital key that indicates that a user has been successfully authenticated.

[1229] "Feedback" refers to evaluations and advice provided to help users improve their skills based on the results of customer service role-playing.

[1230] "Result data" refers to all data generated during the customer service role-play.

[1231] "Evaluation points" are evaluation items related to the user's skills that are extracted by analyzing the result data.

[1232] The present invention relates to a customer service role-play system that utilizes a generative AI model. Hereinafter, an embodiment of the present invention will be specifically described.

[1233] The main components of the system include a server, a terminal, a user, and a generative AI model. These systems work together to allow users to role-play customer service.

[1234] First, the user starts the system from a device on which a dedicated app is installed and logs in. The login screen displays a form for entering a user ID and password. After entering the correct authentication information, the device sends it to the server. The server acts as a central processing unit and accesses a database (e.g., MySQL) to verify the user information. If authentication is successful at this stage, the server generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, the server returns an error message.

[1235] After successful authentication, the user selects a scenario to practice on the scenario selection screen. For example, they can select the scenario "Explanation of a new smartphone plan." The device sends this selection information to the server. The server receives the information and performs initial setup using a generative AI model (e.g., OpenAI's GPT-3). Specifically, it generates details of the scenario and avatar information and returns it to the device.

[1236] The user confirms the scenario start screen and begins role-playing. The user inputs customer service details for the avatar. For example, the user inputs an explanation such as "This plan includes unlimited data." The device sends this input to the server. The server passes the input to a generative AI model, which generates an appropriate response. The generated response might be something like "That's great. Can you tell me about the price?" The server returns the response to the device, which displays it to the user. The user confirms the avatar's response and makes the next input.

[1237] When the role-playing is finished, the user declares that they are finished. The device sends the role-playing result data along with an end request to the server. The server analyzes the result data, extracts evaluation points, and generates feedback. Evaluation points include, for example, "The customer service content was clear" and "Questions were asked at the appropriate time." The server returns the generated feedback to the device, and the user can review it and use it to help them practice their next customer service encounter.

[1238] This system allows users to practice customer service flexibly in real time while receiving immediate feedback.

[1239] Examples of prompts include:

[1240] 1. User type: "This new phone has unlimited data."

[1241] 2. The generative AI model responds: “That’s great! Can you tell me the price?”

[1242] This invention helps users improve their customer service skills by providing a flexible, real-time customer service practice environment using a generative AI model.

[1243] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1244] Step 1: Booting and logging in

[1245] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and clicks the "Login" button.

[1246] Input: User ID, Password

[1247] Output: Authentication request

[1248] Terminal: Sends the user's input information to the server as an authentication request.

[1249] Server: Receives the authentication request and accesses the database to verify the user information. This part uses the MySQL database. If verification is successful, it generates an authentication token and a list of available scenarios and returns them to the terminal. If verification fails, it returns an error message.

[1250] Input: Authentication Request

[1251] Output: Authentication result (authentication token, scenario list or error message)

[1252] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication fails, displays an error message and prompts the user to re-enter information.

[1253] Input: Authentication result

[1254] Output: Scenario selection screen or error message

[1255] Step 2: Select a scenario

[1256] User: After successful login, the scenario selection screen will be displayed. The user will select the scenario they want to practice (for example, "Explanation of a new smartphone plan").

[1257] Input: Select scenario

[1258] Output: Scenario selection information

[1259] Terminal: Sends the user's selections to the server.

[1260] Input: Scenario selection information

[1261] Output: Server request

[1262] Server: Receives scenario selection information and performs initial setup in the generative AI model. Specifically, it generates scenario details and avatar information and returns this to the device.

[1263] Input: Scenario selection information

[1264] Output: Scenario details, avatar information

[1265] Terminal: Displays the scenario start screen and allows the user to begin role-playing.

[1266] Input: Scenario details, avatar information

[1267] Output: Scenario start screen

[1268] Step 3: Begin the customer service role-play

[1269] User: When the scenario start screen appears, enter the customer service details for the avatar.

[1270] Input: Customer service details

[1271] Output: User input information

[1272] Terminal: Sends user input to the server.

[1273] Input: User-entered information

[1274] Output: Server request

[1275] Server: Passes input content to the generative AI model to generate a response, which is then returned to the device.

[1276] Input: User-entered information

[1277] Output: The generated response

[1278] Terminal: Displays responses received from the server and simulates an avatar stating the response.

[1279] Input: Generated response

[1280] Output: Avatar response display

[1281] Step 4: Proceed with customer service practice

[1282] User: Look at the avatar's response displayed on the terminal and enter the next customer service request. Continue the conversation.

[1283] Input: New customer service content

[1284] Output: The next input from the user

[1285] Terminal: Sends the next input contents of the user to the server one by one.

[1286] Input: The following user-entered information:

[1287] Output: Server request

[1288] Server: Using a generative AI model, it generates appropriate responses to the user's input and returns them to the device. The responses are stored and used for evaluation at the end of the role-play.

[1289] Input: The following user-entered information:

[1290] Output: The generated response

[1291] Terminal: Displays the response received from the server as an avatar, allowing the user to continue role-playing.

[1292] Input: Generated response

[1293] Output: Avatar response display

[1294] Step 5: Customer evaluation and feedback

[1295] User: Click the End Roleplay button to declare the end.

[1296] Input: Termination declaration

[1297] Output: Finished request

[1298] Terminal: Sends the role-play result data along with an end request to the server.

[1299] Input: End request, result data

[1300] Output: Server request

[1301] Server: Receives the end request and the result data, analyzes the data, extracts evaluation points based on the user's response and the avatar's reaction, and generates feedback.

[1302] Input: Result data

[1303] Output: Evaluation points, feedback

[1304] Terminal: Receives the generated feedback and displays it to the user.

[1305] Input: Feedback

[1306] Output: User feedback display, confirmation

[1307] In this way, the user, terminal, and server work together at each step to smoothly progress the customer service role-play.

[1308] (Application example 1)

[1309] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1310] Conventional customer service training systems have difficulty simulating real-time customer service situations, making it difficult to adequately train staff to respond quickly, especially when new products or services are introduced. Another problem is that they are unable to obtain specific feedback, making it difficult to effectively identify areas for improvement to improve skills. This invention has been proposed to solve these problems.

[1311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1312] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for practicing dialogue with a virtual customer using a smartphone or head-mounted display, and means for saving and evaluating data on the results of the role-playing, thereby enabling advanced customer service practice with real-time and specific feedback.

[1313] A "generative AI model" is a type of artificial intelligence that automatically generates responses based on scenarios selected by the user.

[1314] A "scenario" is a set of specific situations and conditions for the user to practice in customer service role-playing.

[1315] "Customer service role-play" refers to practice and simulations that simulate actual customer service situations.

[1316] A "response" is a reply or reaction generated by a generative AI model based on information entered by a user.

[1317] A "server" is a computer system that receives information entered by a user, generates a response using a generative AI model, and transmits the result to a terminal.

[1318] A "terminal" is a device that allows a user to use the customer service role-play system, and includes a smartphone or a head-mounted display.

[1319] A "smartphone" is a type of mobile phone that has the ability to connect to the Internet and run applications.

[1320] A "head-mounted display" is a display device that allows a user to experience a virtual reality environment by wearing it.

[1321] A "virtual customer" is a fictitious customer simulated by AI in a customer service role-play.

[1322] "Feedback" refers to evaluations and areas for improvement provided to users based on the results of customer service role-playing.

[1323] "Role-play result data" is recorded data of the user's actions and responses collected during the customer service role-play.

[1324] The "evaluation means" is a mechanism for analyzing the result data of the role-play and evaluating the user's performance.

[1325] The present invention relates to a customer service role-play system using a generative AI model. Specific embodiments for carrying out the present invention will be described below.

[1326] System Overview

[1327] This system consists of three main components: a server, a terminal, and a user. The terminal is a smartphone or a head-mounted display, which the user uses to role-play customer service. The server uses a generative AI model to generate responses based on the scenario selected by the user and provides them to the user.

[1328] Hardware / Software

[1329] Hardware:

[1330] Smartphone (iOS / Android)

[1331] Head-mounted displays (e.g., Oculus Quest 2)

[1332] software:

[1333] Generative AI models (e.g., OpenAI's ChatGPT)

[1334] Authentication services (e.g. Firebase Auth)

[1335] Data analysis tools (e.g., Amazon Sagemaker)

[1336] Data storage (e.g. AWS S3)

[1337] Request processing (e.g. AWS Lambda)

[1338] Response generation (e.g., Google Dialogflow)

[1339] Natural language explanation of the process

[1340] 1. Boot the system and log in:

[1341] The user launches a dedicated app from their device, enters their ID and password, and sends an authentication request to the server.

[1342] The server receives the authentication request and verifies the user information using Firebase Auth. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[1343] The terminal receives the authentication result, and if the authentication is successful, displays a scenario selection screen.

[1344] 2. Select a scenario and begin roleplaying:

[1345] The user selects the scenario they want to practice on the scenario selection screen.

[1346] The terminal transmits scenario selection information to the server.

[1347] The server processes the selected scenario information using Google Dialogflow, generates details of the scenario and avatar using a generative AI model (ChatGPT), and sends them to the device.

[1348] The terminal displays the scenario start screen and prepares for the user to begin role-playing.

[1349] 3. Customer service practice:

[1350] The user explains products to virtual customers (avatars) and responds to their inquiries.

[1351] The terminal sends the user's input to the server.

[1352] The server receives the input, and the generative AI model (ChatGPT) generates a response and returns it to the device.

[1353] The terminal displays the response from the server, and the user continues the dialogue by looking at the avatar's response.

[1354] 4. Customer Service Evaluation and Feedback:

[1355] The user declares the end of the role-play and sends the result data to the server.

[1356] The server receives the end request and the result data and analyzes the data using Amazon Sagemaker.

[1357] The server generates feedback based on the analyzed evaluation points and returns it to the terminal.

[1358] The device displays the received feedback to the user, allowing them to see areas for improvement in their customer service skills.

[1359] Adding specific examples

[1360] Example: When selecting the scenario "Explaining the features of a new product"

[1361] 1. Log in and select a scenario:

[1362] The user logs in to the app and selects "New Product Features."

[1363] Example prompt: "Describe the features of your new smartphone."

[1364] 2. Scenario generation:

[1365] The server uses the AI ​​model to generate customer question scenarios and avatar details, which are then sent to the device.

[1366] Example prompt: "A customer asks, 'How good is the camera on this phone?'"

[1367] 3. Roleplay begins:

[1368] The user explains the features of a new product to the avatar.

[1369] Example prompt: "This smartphone is equipped with a 12MP camera and is excellent at taking night shots."

[1370] 4. Response display and feedback:

[1371] The server's response is displayed on the terminal, and the user can continue the conversation by watching the avatar's reaction.

[1372] After the role-play is completed, specific feedback based on the analysis results will be provided.

[1373] Example feedback: "The product description was detailed, but it could be improved to capture customers' interest."

[1374] This system will improve customer service capabilities in actual customer service situations, particularly strengthening the ability to respond quickly and effectively when introducing new products or services.

[1375] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1376] Step 1:

[1377] The user launches the dedicated app from their device and logs in by entering their ID and password. The data entered is the user ID and password. The device sends this data to the server. The server verifies the user information using Firebase Auth, and if authentication is successful, generates an authentication token and a list of available scenarios and returns them to the device. The output is the authentication token and list of scenarios.

[1378] Step 2:

[1379] The user selects the scenario they want to practice on the scenario selection screen. The user's selection information is entered into the device and sent to the server. The input is the selected scenario information. The server processes the scenario information using Google Dialogflow, generates scenario details and an avatar using a generative AI model (ChatGPT), and sends them to the device. The output is the scenario details and avatar information.

[1380] Step 3:

[1381] The user views the scenario start screen and begins role-playing. The user explains products to virtual customers (avatars) and responds to their inquiries. The device sends the user's input to the server. The input is the user's dialogue. The server receives the input, passes it to a generative AI model (ChatGPT), and generates a response. The generated response is returned to the device. The output is the avatar's response.

[1382] Step 4:

[1383] The terminal displays the avatar's response received from the server on the screen. The user can continue the dialogue by looking at the avatar's response. The input is the response data from the server, and the output is the response message displayed on the terminal.

[1384] Step 5:

[1385] The user declares the end of the role-play and sends an end request and result data from the device to the server. The input is the role-play result data. The server receives the end request, analyzes the data using Amazon Sagemaker, and extracts evaluation points. The output is the analysis result.

[1386] Step 6:

[1387] The server generates feedback based on the evaluation points and returns it to the device. The input is the analysis result. The feedback includes a detailed evaluation of the user's performance and suggestions for improvement. The output is a feedback message.

[1388] Step 7:

[1389] The terminal displays the received feedback to the user. The user checks the feedback and understands areas for improvement in their customer service skills. The input is the feedback message from the server, and the output is the feedback content displayed to the user.

[1390] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1391] The present invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when new devices or new services are launched. Furthermore, by recognizing the user's emotions and providing appropriate responses and feedback accordingly, more practical training becomes possible. The following describes in detail the embodiments of the present invention.

[1392] System Overview

[1393] This system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user, and the emotion engine recognizes the user's emotional state and sends that information to the server. The server then provides responses and feedback according to the user's emotions.

[1394] Program processing flow

[1395] 1. Booting the system and logging in

[1396] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[1397] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[1398] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[1399] 2. Start customer service role-play

[1400] User: Select the scenario you want to practice on the scenario selection screen.

[1401] Terminal: Sends the selection information to the server.

[1402] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1403] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1404] 3. Customer service practice and emotion recognition

[1405] User: Enters customer service details for the avatar.

[1406] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[1407] Terminal: Sends input content and emotional information to the server.

[1408] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1409] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1410] 4. Customer Service Evaluation and Feedback

[1411] Terminal: The user declares the end of the role-play and sends an end request, result data, and emotion information to the server.

[1412] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[1413] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[1414] Specific examples

[1415] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[1416] 1. Log in and select a scenario

[1417] The user logs in to the app and selects "New Smartphone Plan Description."

[1418] 2. Scenario Generation

[1419] The server generates the scenario and avatar details and sends them to the device.

[1420] 3. Roleplay begins

[1421] The user begins explaining the new plan to the avatar.

[1422] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[1423] User input and emotional information is sent to the server, and a generative AI model generates the avatar's response.

[1424] 4. Display of responses and emotional responses

[1425] The server's response is displayed on the terminal, and the user can check the avatar's reaction and input an appropriate response depending on their feelings, such as tension or confusion.

[1426] The emotion engine continuously monitors the user's emotions and adjusts the feedback as emotions change.

[1427] 5. Termination and Evaluation

[1428] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[1429] Feedback that reflects emotional information also helps users improve their emotional response.

[1430] Through this concrete example, it can be seen that this system aims to practically improve customer service skills, and in particular functions as an effective tool for strengthening the ability to respond to users' emotions.

[1431] The processing flow will be explained below.

[1432] Step 1:

[1433] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[1434] Step 2:

[1435] Terminal: Sends the entered user ID and password to the server.

[1436] Step 3:

[1437] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[1438] Step 4:

[1439] Server: Returns the authentication result (success or failure) to the terminal.

[1440] Step 5:

[1441] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[1442] Step 6:

[1443] User: Select the scenario you want to practice on the scenario selection screen.

[1444] Step 7:

[1445] Terminal: Sends the selection information to the server.

[1446] Step 8:

[1447] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1448] Step 9:

[1449] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1450] Step 10:

[1451] User: Enters customer service details for the avatar.

[1452] Step 11:

[1453] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[1454] Step 12:

[1455] Terminal: Sends input content and emotional information to the server.

[1456] Step 13:

[1457] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1458] Step 14:

[1459] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1460] Step 15:

[1461] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[1462] Step 16:

[1463] Emotion Engine: Continuously monitors the user's emotional state and sends the information to the server whenever a change is detected.

[1464] Step 17:

[1465] Terminal & Server: For each user input, the terminal sends the input and emotional information to the server, and the server generates a response and returns it to the terminal, repeating the process.

[1466] Step 18:

[1467] User: Declare the end of the roleplay.

[1468] Step 19:

[1469] Terminal: Sends an end request, role-play result data, and emotional information to the server.

[1470] Step 20:

[1471] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, and generates feedback based on the evaluation points.

[1472] Step 21:

[1473] Server: Returns the generated feedback to the device.

[1474] Step 22:

[1475] Terminal: Displays received feedback to the user.

[1476] Step 23:

[1477] Users: Review feedback and understand areas for improvement in customer service skills.

[1478] Specific examples

[1479] For example, if a user selects the scenario "Explanation of a new smartphone plan" and practices, the specific flow will be as follows.

[1480] 1. Scenario Selection:

[1481] The user logs in to the app and selects "New Smartphone Plan Description."

[1482] 2. Scenario and avatar initial settings:

[1483] The server generates the scenario and avatar details and sends them to the device.

[1484] 3. Begin the role-play:

[1485] The user enters a description for the new plan into the avatar.

[1486] The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize their emotional state.

[1487] 4. Generate and display the response:

[1488] The device sends the input content and emotional information to the server.

[1489] The server generates a response using a generative AI model and returns it to the device.

[1490] The device displays the response on the avatar and prompts the user for new input.

[1491] 5. Emotional Response:

[1492] The emotion engine continuously monitors changes in the user's emotions and transmits them to the server.

[1493] The server adjusts the response based on the emotional information and returns it to the device.

[1494] 6. End of role play:

[1495] The user ends the role-play and sends an end request, result data, and emotion information to the server.

[1496] 7. Feedback Generation and Display:

[1497] The server analyzes the result data and emotional information to generate feedback.

[1498] The device displays feedback to the user.

[1499] Through this specific example, we can see that this system is an effective tool that not only effectively improves the customer service skills required when users start using new devices or services, but also strengthens their ability to respond to users' emotions.

[1500] Example 2

[1501] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1502] Conventional customer service role-playing systems are unable to fully recognize the user's emotional state and have difficulty reflecting the recognition results in feedback. This limits the effectiveness of customer service practice, and training to improve the ability to respond to emotions is particularly insufficient. Furthermore, because responses are not generated based on real-time emotion recognition, there is a problem of a gap between the actual customer service situation and the system.

[1503] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1504] In this invention, the server includes: means for generating a response for a customer service role-play based on a scenario selected by a user using a generative AI model; means for transmitting the user's input information and the user's emotional state to the server and receiving a response from the server; means for displaying the response received from the server and prompting the user for new input; means for analyzing the user's input information, facial expressions, voice, etc., and recognizing the user's emotional state; means for evaluating the results of the customer service based on predetermined evaluation criteria; and means for generating and providing feedback to the user. This enables the system to recognize the user's emotional state in real time and to perform practical customer service role-playing based on that information. Furthermore, by incorporating emotional information into the feedback, more effective improvement of customer service skills can be expected.

[1505] A "generative AI model" is an artificial intelligence model that generates responses based on a scenario selected by the user, and is a technology that uses natural language processing to provide appropriate responses in real time.

[1506] "User" refers to an individual or an employee of an organization who wishes to improve their customer service skills by using the customer service role-playing system.

[1507] A "server" is a computer system that receives information sent by a user, generates a response using a generative AI model, and provides feedback.

[1508] A "terminal" is a device operated by a user, and is a device for accessing the customer service role-play system, inputting information, and receiving responses and feedback from the server.

[1509] An "emotion engine" is a software or hardware technology that analyzes a user's input information, facial expressions, voice, etc., and recognizes the user's emotional state.

[1510] "Feedback" refers to information and advice provided to evaluate the results of the customer service role-play and to help the user improve their skills.

[1511] A "scenario" is a pre-set situation or story used in customer service role-playing, and serves as a simulation of when the user actually serves customers.

[1512] A "response" is the dialogue content that a generative AI model generates in response to user input, and is an appropriate reply based on the content entered by the user.

[1513] The "evaluation criteria" are a set of indicators and rules for evaluating the results of the user's customer service role-play, and feedback to the user is constructed based on these.

[1514] This invention relates to a customer service role-play system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when launching new devices or services. It also enables more practical training by recognizing users' emotions and providing appropriate responses and feedback accordingly.

[1515] The system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user.

[1516] Generative AI models, such as GPT-4, are used, which are specialized for natural language processing. These models generate appropriate responses based on the scenario selected by the user.

[1517] The emotion engine is a technology that recognizes a user's emotional state by analyzing their facial expressions, voice, and input information. This emotion engine works in conjunction with input devices such as cameras and microphones to analyze emotional data in real time.

[1518] The server collects and analyzes this data and generates responses using a generative AI model. Furthermore, the server provides feedback based on predefined evaluation criteria. This feedback reflects the user's emotional information, resulting in a personalized evaluation.

[1519] The terminal is a device operated by the user, and provides an interface for accessing the customer service role-play system. The terminal transmits the user's input information and emotional data to the server, and displays responses and feedback from the server.

[1520] The user selects a scenario and role-plays with the avatar. An example scenario is "Explanation of new smartphone plans."

[1521] The actual operation process is shown below.

[1522] 1. Boot the system and log in:

[1523] The user launches the dedicated app from their device and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server. The server receives the authentication request and checks the user information against the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[1524] 2. Start the customer service role play:

[1525] The user selects the scenario they want to practice on the scenario selection screen. For example, they select "Explanation of a new smartphone plan." The selection information is sent to the server, which receives the scenario selection information, performs initial settings in the generative AI model, generates the scenario and avatar details, and returns them to the device.

[1526] 3. Customer service practice and emotion recognition:

[1527] The user inputs customer service details into the avatar, asking questions such as "How much does the new plan cost?" The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize the emotional state. The device sends this data to the server, which passes it to a generative AI model to generate a response. The generated response is returned to the device and displayed on the avatar.

[1528] 4. Customer Service Evaluation and Feedback:

[1529] After completing the role-play, the user sends an end request to the server. The server receives the end request, the result data, and the emotion information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and returns it to the device. The device displays the feedback to the user, who can review it and understand where they need to improve their customer service skills.

[1530] Examples of prompt statements

[1531] For example, if the user selects the "New Smartphone Plan Explained" scenario, the prompt text might look like this:

[1532] You are a salesperson explaining a new smartphone plan. Based on the following scenario, provide the information the avatar requests.

[1533] Scenario: New smartphone plan explained

[1534] Avatar asks:

[1535] What are the features of the new plan?

[1536] What are the monthly costs?

[1537] What is the contract period and cancellation fee?

[1538] Your response:

[1539] Explain in detail the plan's features, pricing, contract length, and cancellation fees.

[1540] The system allows customer service staff to undergo real-time training that takes emotion recognition into account through realistic scenarios, allowing users to practice and improve their skills in situations that are closer to real-life customer service situations.

[1541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1542] Step 1:

[1543] Booting and logging in

[1544] Device: The user launches the dedicated app and displays the login screen. They enter their user ID and password and tap the "Login" button.

[1545] Input: User ID and password.

[1546] Output: Sends an authentication request to the server.

[1547] What happens: The app makes an HTTP request and sends the user's authentication information to the server.

[1548] Step 2:

[1549] User Authentication

[1550] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message.

[1551] Input: User ID and password.

[1552] Output: Authentication token and a list of scenarios, or an error message.

[1553] Specific behavior: Executes a database query to verify user information. If authentication is successful, issues an authentication token using a token generation algorithm and sends it as an HTTP response.

[1554] Step 3:

[1555] Receiving authentication results

[1556] Device: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication is unsuccessful, displays an error message.

[1557] Input: Authentication token and scenario list, or error message.

[1558] Output: Scenario selection screen or error message displayed.

[1559] Specific operation: Analyze the response content, and if authentication is successful, update the UI and display the scenario selection screen.

[1560] Step 4:

[1561] Scenario Selection

[1562] User: Select the scenario you want to practice on the scenario selection screen. For example, "Explanation of a new smartphone plan."

[1563] Input: The scenario selected by the user.

[1564] Output: Scenario selection information is sent from the device to the server.

[1565] Specific operation: The selected scenario is sent to the server as an HTTP request.

[1566] Step 5:

[1567] Scenario Generation

[1568] Server: Receives scenario selection information, performs initial setup using a generative AI model (e.g., GPT-4), generates scenario and avatar details, and returns them to the device.

[1569] Input: User selected scenario information.

[1570] Output: Scenario details and avatar information.

[1571] Specific operation: Calls the generative AI model, generates a story and detailed avatar information based on the selected scenario, and returns it as a response.

[1572] Step 6:

[1573] Scenario Display

[1574] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1575] Input: Scenario details and avatar information from the server.

[1576] Output: Display of the scenario start screen.

[1577] Specific behavior: Displays the received scenario details and avatar information, and enables the roleplay start button.

[1578] Step 7:

[1579] Start of customer service practice

[1580] User: Enters customer service information into the avatar. For example, asking, "How much does the new plan cost?"

[1581] Input: The question or input the user makes.

[1582] Output: Sending input from the terminal to the server.

[1583] Specific behavior: The user enters text into the input field and presses the submit button. This input is sent to the server as is.

[1584] Step 8:

[1585] emotion recognition

[1586] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[1587] Input: Data such as user text input, facial expressions, and voice.

[1588] Output: Parsed emotional state data.

[1589] Specific operation: Analyzes data collected from cameras and microphones in real time and tags emotional states.

[1590] Step 9:

[1591] Data transmission

[1592] Terminal: Sends input content and emotional information to the server.

[1593] Input: User text input and emotional state data.

[1594] Output: Sending input and emotion data to the server.

[1595] Specific operation: The text input and analyzed emotion data are sent together to the server.

[1596] Step 10:

[1597] Response Generation

[1598] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1599] Input: User text input and emotional state data.

[1600] Output: The generated response.

[1601] Specific operation: Calls a generative AI model, generates a response based on the input content and emotional data, and returns it as a response.

[1602] Step 11:

[1603] Response Display

[1604] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1605] Input: The generated response from the server.

[1606] Output: Avatar displaying the response.

[1607] Specific operation: The avatar speaks the received response text and displays the corresponding facial expression.

[1608] Step 12:

[1609] Preparing for customer service evaluation

[1610] User: Declare the end of the role-play and send an end request, result data, and emotion information to the server via the terminal.

[1611] Input: Exit button click, result data, emotion information.

[1612] Output: Sending the end request, result data, and emotion information.

[1613] Specific operation: When the user presses the finish button, all data is sent to the server.

[1614] Step 13:

[1615] Evaluation and feedback generation

[1616] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[1617] Input: End request, result data, emotion information.

[1618] Output: Feedback.

[1619] Specific operation: Analyze the collected data, generate feedback based on predetermined evaluation criteria, and return it to the device as a response.

[1620] Step 14:

[1621] Feedback Display

[1622] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[1623] Input: Feedback from the server.

[1624] Output: Display feedback.

[1625] What this does: Updates the feedback screen to show detailed comments and scores to the user.

[1626] This allows users to receive practical training based on customer service scenarios and improve their skills through real-time feedback based on emotion recognition.

[1627] (Application example 2)

[1628] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1629] Conventional customer service role-playing systems are unable to provide feedback based on the user's emotional state, making it difficult to effectively provide practical training necessary to improve customer service skills. This is particularly true when training is required for new products or services, as it is difficult to develop appropriate emotional responses.

[1630] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a response for a customer service role-play based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user and the user's emotional state analyzed by an emotion engine to the server and receiving a response from the server, means for displaying the response received from the server and prompting the user to enter new input, and means for providing feedback according to the user's emotions using the emotion engine. This allows the user to receive real-time responses and feedback based on their emotions, enabling more practical customer service training.

[1631] A "generative AI model" is an artificial intelligence technology that automatically generates responses based on a scenario selected by the user.

[1632] An "emotion engine" is a technology that analyzes a user's emotional state from input information, facial expressions, voice, etc.

[1633] The "server" is a computer system that receives the user's input information and emotional state, generates an appropriate response using a generative AI model, and sends it to the device.

[1634] A "terminal" is a device through which a user inputs information through an interface and displays responses sent by a server.

[1635] "Customer service role-play" is a scenario-based simulation that allows participants to experience customer service work and practice skills.

[1636] "Feedback" refers to information such as evaluations and suggestions for improvement provided based on the user's behavior and emotional state.

[1637] A "response" is a dialogue or instruction generated by a generative AI model and provided to the user via the device.

[1638] A "scenario" is an item that defines a specific customer service situation selected by the user, and is the basis on which role-playing progresses based on that situation.

[1639] This invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows users working in the customer service industry to effectively practice customer service when launching a new product or service. Specific embodiments for implementing this invention are described below.

[1640] System configuration

[1641] The system mainly consists of the following components:

[1642] 1. Server

[1643] 2. Terminal

[1644] 3. Generative AI Models

[1645] 4. Emotion Engine

[1646] 5. Users

[1647] Hardware and Software

[1648] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest)

[1649] Software: Python-based local server, generative AI model, emotion engine

[1650] Processing flow explanation

[1651] First, the user launches the dedicated application on the device and enters their authentication information on the login screen. The server verifies the information, and if authentication is successful, the scenario selection screen is displayed on the device.

[1652] When a user selects a scenario they want to practice, that information is sent to the server, and the generative AI model performs initial setup based on the selected scenario. The server then generates information about the specified scenario and avatar details and sends them to the device.

[1653] When a user begins a customer service role-play, they input information into the avatar. The emotion engine analyzes the user's emotional data, such as facial expressions and tone of voice, and sends that information to the server. The server then analyzes this data using a generative AI model and generates an appropriate response. The response is sent to the device, which displays it to the user through the avatar. The user considers their next input while looking at the avatar's response.

[1654] Once the training is complete, the server analyzes the role-play results and emotional data to generate feedback for the user. The feedback is displayed on the device as advice based on the user's strengths and areas for improvement, as well as their emotions. This allows the user to specifically understand where they need to improve their customer service skills.

[1655] Specific examples of processing

[1656] For example, if a user selects the scenario "Explanation of new smartphone plans," the following prompt sentence is used: This prompt sentence allows the system to generate instructions to appropriately proceed with the customer service role-play.

[1657] Example prompt sentence:

[1658] "Simulate a plan explanation for a new smartphone."

[1659] In this scenario, a user practices explaining a complex plan to an avatar. The emotion engine analyzes the user's emotions, such as nervousness or confusion, in real time, and the server generates feedback based on that. For example, if the user is confused, the generative AI model generates a response such as, "Please let me know if there's anything you don't understand, and I'll explain it in more detail."

[1660] This system allows users to improve their practical customer service skills while receiving real-time support based on their emotions.

[1661] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1662] Step 1:

[1663] The user launches a dedicated application from their device and enters their user ID and password on the login screen. The device sends the entered authentication information to the server. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. The device receives the authentication results and displays a scenario selection screen.

[1664] Step 2:

[1665] The user selects the scenario they want to practice on the scenario selection screen and sends the selection information from the device to the server. The server receives the scenario selection information, performs initial settings in the generative AI model, and generates the scenario and avatar details. This information is sent to the device, which then displays the scenario start screen.

[1666] Step 3:

[1667] The user begins the customer service role-play on the scenario start screen on the device and inputs the customer service details to the avatar. The emotion engine analyzes the user's input information, facial expressions, and voice to recognize the user's emotional state. The device sends the input customer service details and emotional information to the server. The server analyzes the received information and passes it to the generative AI model to generate a response. The generated response is sent back to the device, which then displays the avatar's response to the user.

[1668] Step 4:

[1669] The user confirms the avatar's response and continues to input their customer service needs. The emotion engine continues to analyze the user's emotional state and transmits the data to the server. The server continuously passes the user's input and emotional information to the generative AI model, which generates new responses and transmits them to the device. This cycle repeats until the user declares the end of the role-play.

[1670] Step 5:

[1671] When the user declares the end of the role-play, the device sends an end request, result data, and emotional information to the server. The server receives the end request, result data, and emotional information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and sends the feedback information to the device. The device displays the received feedback to the user, allowing the user to understand areas for improvement in their customer service skills and emotional response.

[1672] In this way, a system that provides real-time, practical customer service training is realized through how the user, device, server, emotion engine, and generative AI model work together at each processing step.

[1673] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1674] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1675] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1676] [Fourth embodiment]

[1677] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1678] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1679] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1680] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1681] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1682] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1683] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1684] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1685] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1686] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1687] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1688] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1689] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1690] The present invention relates to a customer service role-playing system that uses a generative AI model. This system allows staff working in the customer service industry to effectively practice customer service when new devices or new services are launched. The following describes in detail the embodiments of the present invention.

[1691] System Overview

[1692] This system consists of four main components: a generative AI model, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user and provides it to the user via the terminal. It also provides feedback based on the results of the role-play, helping the user improve their customer service skills.

[1693] Program processing flow

[1694] 1. Booting the system and logging in

[1695] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[1696] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[1697] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[1698] 2. Start customer service role-play

[1699] User: Select the scenario you want to practice on the scenario selection screen.

[1700] Terminal: Sends the selection information to the server.

[1701] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1702] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1703] 3. Customer service practice

[1704] User: Enters customer service details for the avatar.

[1705] Terminal: Sends the entered information to the server.

[1706] Server: Receives input, passes it to the generative AI model (ChatGPT) to generate a response, and returns the generated response to the device.

[1707] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1708] 4. Customer Service Evaluation and Feedback

[1709] Terminal: The user declares the end of the role-play and sends an end request and result data to the server.

[1710] Server: Receives the end request and result data, analyzes the data, extracts reputation points, generates feedback based on the reputation points, and returns it to the device.

[1711] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[1712] Specific examples

[1713] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[1714] 1. Log in and select a scenario

[1715] The user logs in to the app and selects "New Smartphone Plan Description."

[1716] 2. Scenario Generation

[1717] The server generates the scenario and avatar details and sends them to the device.

[1718] 3. Roleplay begins

[1719] The user begins explaining the new plan to the avatar.

[1720] User input is sent to the server, and a generative AI model generates the avatar's response.

[1721] 4. Response display and feedback

[1722] The server's response is displayed on the terminal, and the user can continue the conversation by looking at the avatar's reaction.

[1723] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[1724] Through this concrete example, it can be seen that this system can function as an effective tool for practically improving customer service skills, particularly for strengthening the ability to respond to frequently changing new devices and services.

[1725] The processing flow will be explained below.

[1726] Step 1:

[1727] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[1728] Step 2:

[1729] Terminal: Sends the entered user ID and password to the server.

[1730] Step 3:

[1731] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[1732] Step 4:

[1733] Server: Returns the authentication result (success or failure) to the terminal.

[1734] Step 5:

[1735] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen, and if authentication fails, displays an error message.

[1736] Step 6:

[1737] User: Select the scenario you want to practice on the scenario selection screen.

[1738] Step 7:

[1739] Terminal: Sends the selection information to the server.

[1740] Step 8:

[1741] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1742] Step 9:

[1743] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1744] Step 10:

[1745] User: Enters customer service details for the avatar.

[1746] Step 11:

[1747] Terminal: Sends the entered information to the server.

[1748] Step 12:

[1749] Server: Receives input, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1750] Step 13:

[1751] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1752] Step 14:

[1753] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[1754] Step 15:

[1755] Terminal: Repeats the process of sending each user input to the server, receiving a response from the server, and displaying it on the avatar.

[1756] Step 16:

[1757] User: Declare the end of the roleplay.

[1758] Step 17:

[1759] Terminal: Sends the end request and role-play result data to the server.

[1760] Step 18:

[1761] Server: Receives the end request and the result data, analyzes the result data to extract evaluation points, and generates feedback.

[1762] Step 19:

[1763] Server: Returns the generated feedback to the device.

[1764] Step 20:

[1765] Terminal: Displays received feedback to the user.

[1766] Step 21:

[1767] Users: Review feedback and understand areas for improvement in customer service skills.

[1768] Through the above steps, this system allows users to effectively improve the customer service skills required when starting to use new terminals or services.

[1769] Example 1

[1770] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1771] Conventional customer service training systems are limited to fixed scenarios and responses, making it difficult to acquire the flexibility and real-time response skills required in actual customer service situations. Also, it takes time for users to receive feedback on their practice results, making it difficult to immediately improve their skills.

[1772] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1773] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for starting the system and authenticating the user, and means for analyzing the generated responses and result data and generating feedback for the user. This allows the user to receive immediate feedback while flexibly practicing customer service in real time.

[1774] A "generative AI model" is an algorithm or program that automatically generates customer service role-play responses based on a scenario selected by the user.

[1775] A "user" is an individual who uses this system to practice customer service.

[1776] A "server" is a central processing unit that accepts requests from users, generates responses using generative AI models, and analyzes the resulting data to provide feedback.

[1777] A "terminal" is an electronic device such as a computer or smartphone that is used by a user to operate.

[1778] A "scenario" is a specific situation or theme selected by the user for customer service practice.

[1779] A "response" is the dialogue content generated by a generative AI model in response to user input.

[1780] An "authentication token" is a digital key that indicates that a user has been successfully authenticated.

[1781] "Feedback" refers to evaluations and advice provided to help users improve their skills based on the results of customer service role-playing.

[1782] "Result data" refers to all data generated during the customer service role-play.

[1783] "Evaluation points" are evaluation items related to the user's skills that are extracted by analyzing the result data.

[1784] The present invention relates to a customer service role-play system that utilizes a generative AI model. Hereinafter, an embodiment of the present invention will be specifically described.

[1785] The main components of the system include a server, a terminal, a user, and a generative AI model. These systems work together to allow users to role-play customer service.

[1786] First, the user starts the system from a device on which a dedicated app is installed and logs in. The login screen displays a form for entering a user ID and password. After entering the correct authentication information, the device sends it to the server. The server acts as a central processing unit and accesses a database (e.g., MySQL) to verify the user information. If authentication is successful at this stage, the server generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, the server returns an error message.

[1787] After successful authentication, the user selects a scenario to practice on the scenario selection screen. For example, they can select the scenario "Explanation of a new smartphone plan." The device sends this selection information to the server. The server receives the information and performs initial setup using a generative AI model (e.g., OpenAI's GPT-3). Specifically, it generates details of the scenario and avatar information and returns it to the device.

[1788] The user confirms the scenario start screen and begins role-playing. The user inputs customer service details for the avatar. For example, the user inputs an explanation such as "This plan includes unlimited data." The device sends this input to the server. The server passes the input to a generative AI model, which generates an appropriate response. The generated response might be something like "That's great. Can you tell me about the price?" The server returns the response to the device, which displays it to the user. The user confirms the avatar's response and makes the next input.

[1789] When the role-playing is finished, the user declares that they are finished. The device sends the role-playing result data along with an end request to the server. The server analyzes the result data, extracts evaluation points, and generates feedback. Evaluation points include, for example, "The customer service content was clear" and "Questions were asked at the appropriate time." The server returns the generated feedback to the device, and the user can review it and use it to help them practice their next customer service encounter.

[1790] This system allows users to practice customer service flexibly in real time while receiving immediate feedback.

[1791] Examples of prompts include:

[1792] 1. User type: "This new phone has unlimited data."

[1793] 2. The generative AI model responds: “That’s great! Can you tell me the price?”

[1794] This invention helps users improve their customer service skills by providing a flexible, real-time customer service practice environment using a generative AI model.

[1795] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1796] Step 1: Booting and logging in

[1797] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and clicks the "Login" button.

[1798] Input: User ID, Password

[1799] Output: Authentication request

[1800] Terminal: Sends the user's input information to the server as an authentication request.

[1801] Server: Receives the authentication request and accesses the database to verify the user information. This part uses the MySQL database. If verification is successful, it generates an authentication token and a list of available scenarios and returns them to the terminal. If verification fails, it returns an error message.

[1802] Input: Authentication Request

[1803] Output: Authentication result (authentication token, scenario list or error message)

[1804] Terminal: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication fails, displays an error message and prompts the user to re-enter information.

[1805] Input: Authentication result

[1806] Output: Scenario selection screen or error message

[1807] Step 2: Select a scenario

[1808] User: After successful login, the scenario selection screen will be displayed. The user will select the scenario they want to practice (for example, "Explanation of a new smartphone plan").

[1809] Input: Select scenario

[1810] Output: Scenario selection information

[1811] Terminal: Sends the user's selections to the server.

[1812] Input: Scenario selection information

[1813] Output: Server request

[1814] Server: Receives scenario selection information and performs initial setup in the generative AI model. Specifically, it generates scenario details and avatar information and returns this to the device.

[1815] Input: Scenario selection information

[1816] Output: Scenario details, avatar information

[1817] Terminal: Displays the scenario start screen and allows the user to begin role-playing.

[1818] Input: Scenario details, avatar information

[1819] Output: Scenario start screen

[1820] Step 3: Begin the customer service role-play

[1821] User: When the scenario start screen appears, enter the customer service details for the avatar.

[1822] Input: Customer service details

[1823] Output: User input information

[1824] Terminal: Sends user input to the server.

[1825] Input: User-entered information

[1826] Output: Server request

[1827] Server: Passes input content to the generative AI model to generate a response, which is then returned to the device.

[1828] Input: User-entered information

[1829] Output: The generated response

[1830] Terminal: Displays responses received from the server and simulates an avatar stating the response.

[1831] Input: Generated response

[1832] Output: Avatar response display

[1833] Step 4: Proceed with customer service practice

[1834] User: Look at the avatar's response displayed on the terminal and enter the next customer service request. Continue the conversation.

[1835] Input: New customer service content

[1836] Output: The next input from the user

[1837] Terminal: Sends the next input contents of the user to the server one by one.

[1838] Input: The following user-entered information:

[1839] Output: Server request

[1840] Server: Using a generative AI model, it generates appropriate responses to the user's input and returns them to the device. The responses are stored and used for evaluation at the end of the role-play.

[1841] Input: The following user-entered information:

[1842] Output: The generated response

[1843] Terminal: Displays the response received from the server as an avatar, allowing the user to continue role-playing.

[1844] Input: Generated response

[1845] Output: Avatar response display

[1846] Step 5: Customer evaluation and feedback

[1847] User: Click the End Roleplay button to declare the end.

[1848] Input: Termination declaration

[1849] Output: Finished request

[1850] Terminal: Sends the role-play result data along with an end request to the server.

[1851] Input: End request, result data

[1852] Output: Server request

[1853] Server: Receives the end request and the result data, analyzes the data, extracts evaluation points based on the user's response and the avatar's reaction, and generates feedback.

[1854] Input: Result data

[1855] Output: Evaluation points, feedback

[1856] Terminal: Receives the generated feedback and displays it to the user.

[1857] Input: Feedback

[1858] Output: User feedback display, confirmation

[1859] In this way, the user, terminal, and server work together at each step to smoothly progress the customer service role-play.

[1860] (Application example 1)

[1861] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1862] Conventional customer service training systems have difficulty simulating real-time customer service situations, making it difficult to adequately train staff to respond quickly, especially when new products or services are introduced. Another problem is that they are unable to obtain specific feedback, making it difficult to effectively identify areas for improvement to improve skills. This invention has been proposed to solve these problems.

[1863] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1864] In this invention, the server includes means for generating responses for customer service role-playing based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user to the server and receiving responses from the server, means for displaying the responses received from the server and prompting the user for new input, means for practicing dialogue with a virtual customer using a smartphone or head-mounted display, and means for saving and evaluating data on the results of the role-playing, thereby enabling advanced customer service practice with real-time and specific feedback.

[1865] A "generative AI model" is a type of artificial intelligence that automatically generates responses based on scenarios selected by the user.

[1866] A "scenario" is a set of specific situations and conditions for the user to practice in customer service role-playing.

[1867] "Customer service role-play" refers to practice and simulations that simulate actual customer service situations.

[1868] A "response" is a reply or reaction generated by a generative AI model based on information entered by a user.

[1869] A "server" is a computer system that receives information entered by a user, generates a response using a generative AI model, and transmits the result to a terminal.

[1870] A "terminal" is a device that allows a user to use the customer service role-play system, and includes a smartphone or a head-mounted display.

[1871] A "smartphone" is a type of mobile phone that has the ability to connect to the Internet and run applications.

[1872] A "head-mounted display" is a display device that allows a user to experience a virtual reality environment by wearing it.

[1873] A "virtual customer" is a fictitious customer simulated by AI in a customer service role-play.

[1874] "Feedback" refers to evaluations and areas for improvement provided to users based on the results of customer service role-playing.

[1875] "Role-play result data" is recorded data of the user's actions and responses collected during the customer service role-play.

[1876] The "evaluation means" is a mechanism for analyzing the result data of the role-play and evaluating the user's performance.

[1877] The present invention relates to a customer service role-play system using a generative AI model. Specific embodiments for carrying out the present invention will be described below.

[1878] System Overview

[1879] This system consists of three main components: a server, a terminal, and a user. The terminal is a smartphone or a head-mounted display, which the user uses to role-play customer service. The server uses a generative AI model to generate responses based on the scenario selected by the user and provides them to the user.

[1880] Hardware / Software

[1881] Hardware:

[1882] Smartphone (iOS / Android)

[1883] Head-mounted displays (e.g., Oculus Quest 2)

[1884] software:

[1885] Generative AI models (e.g., OpenAI's ChatGPT)

[1886] Authentication services (e.g. Firebase Auth)

[1887] Data analysis tools (e.g., Amazon Sagemaker)

[1888] Data storage (e.g. AWS S3)

[1889] Request processing (e.g. AWS Lambda)

[1890] Response generation (e.g., Google Dialogflow)

[1891] Natural language explanation of the process

[1892] 1. Boot the system and log in:

[1893] The user launches a dedicated app from their device, enters their ID and password, and sends an authentication request to the server.

[1894] The server receives the authentication request and verifies the user information using Firebase Auth. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[1895] The terminal receives the authentication result, and if the authentication is successful, displays a scenario selection screen.

[1896] 2. Select a scenario and begin roleplaying:

[1897] The user selects the scenario they want to practice on the scenario selection screen.

[1898] The terminal transmits scenario selection information to the server.

[1899] The server processes the selected scenario information using Google Dialogflow, generates details of the scenario and avatar using a generative AI model (ChatGPT), and sends them to the device.

[1900] The terminal displays the scenario start screen and prepares for the user to begin role-playing.

[1901] 3. Customer service practice:

[1902] The user explains products to virtual customers (avatars) and responds to their inquiries.

[1903] The terminal sends the user's input to the server.

[1904] The server receives the input, and the generative AI model (ChatGPT) generates a response and returns it to the device.

[1905] The terminal displays the response from the server, and the user continues the dialogue by looking at the avatar's response.

[1906] 4. Customer Service Evaluation and Feedback:

[1907] The user declares the end of the role-play and sends the result data to the server.

[1908] The server receives the end request and the result data and analyzes the data using Amazon Sagemaker.

[1909] The server generates feedback based on the analyzed evaluation points and returns it to the terminal.

[1910] The device displays the received feedback to the user, allowing them to see areas for improvement in their customer service skills.

[1911] Adding specific examples

[1912] Example: When selecting the scenario "Explaining the features of a new product"

[1913] 1. Log in and select a scenario:

[1914] The user logs in to the app and selects "New Product Features."

[1915] Example prompt: "Describe the features of your new smartphone."

[1916] 2. Scenario generation:

[1917] The server uses the AI ​​model to generate customer question scenarios and avatar details, which are then sent to the device.

[1918] Example prompt: "A customer asks, 'How good is the camera on this phone?'"

[1919] 3. Roleplay begins:

[1920] The user explains the features of a new product to the avatar.

[1921] Example prompt: "This smartphone is equipped with a 12MP camera and is excellent at taking night shots."

[1922] 4. Response display and feedback:

[1923] The server's response is displayed on the terminal, and the user can continue the conversation by watching the avatar's reaction.

[1924] After the role-play is completed, specific feedback based on the analysis results will be provided.

[1925] Example feedback: "The product description was detailed, but it could be improved to capture customers' interest."

[1926] This system will improve customer service capabilities in actual customer service situations, particularly strengthening the ability to respond quickly and effectively when introducing new products or services.

[1927] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1928] Step 1:

[1929] The user launches the dedicated app from their device and logs in by entering their ID and password. The data entered is the user ID and password. The device sends this data to the server. The server verifies the user information using Firebase Auth, and if authentication is successful, generates an authentication token and a list of available scenarios and returns them to the device. The output is the authentication token and list of scenarios.

[1930] Step 2:

[1931] The user selects the scenario they want to practice on the scenario selection screen. The user's selection information is entered into the device and sent to the server. The input is the selected scenario information. The server processes the scenario information using Google Dialogflow, generates scenario details and an avatar using a generative AI model (ChatGPT), and sends them to the device. The output is the scenario details and avatar information.

[1932] Step 3:

[1933] The user views the scenario start screen and begins role-playing. The user explains products to virtual customers (avatars) and responds to their inquiries. The device sends the user's input to the server. The input is the user's dialogue. The server receives the input, passes it to a generative AI model (ChatGPT), and generates a response. The generated response is returned to the device. The output is the avatar's response.

[1934] Step 4:

[1935] The terminal displays the avatar's response received from the server on the screen. The user can continue the dialogue by looking at the avatar's response. The input is the response data from the server, and the output is the response message displayed on the terminal.

[1936] Step 5:

[1937] The user declares the end of the role-play and sends an end request and result data from the device to the server. The input is the role-play result data. The server receives the end request, analyzes the data using Amazon Sagemaker, and extracts evaluation points. The output is the analysis result.

[1938] Step 6:

[1939] The server generates feedback based on the evaluation points and returns it to the device. The input is the analysis result. The feedback includes a detailed evaluation of the user's performance and suggestions for improvement. The output is a feedback message.

[1940] Step 7:

[1941] The terminal displays the received feedback to the user. The user checks the feedback and understands areas for improvement in their customer service skills. The input is the feedback message from the server, and the output is the feedback content displayed to the user.

[1942] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1943] The present invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when new devices or new services are launched. Furthermore, by recognizing the user's emotions and providing appropriate responses and feedback accordingly, more practical training becomes possible. The following describes in detail the embodiments of the present invention.

[1944] System Overview

[1945] This system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user. The generative AI model generates a response based on a scenario selected by the user, and the emotion engine recognizes the user's emotional state and sends that information to the server. The server then provides responses and feedback according to the user's emotions.

[1946] Program processing flow

[1947] 1. Booting the system and logging in

[1948] Device: The user launches the dedicated app from the device, and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server.

[1949] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message to the device.

[1950] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[1951] 2. Start customer service role-play

[1952] User: Select the scenario you want to practice on the scenario selection screen.

[1953] Terminal: Sends the selection information to the server.

[1954] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[1955] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[1956] 3. Customer service practice and emotion recognition

[1957] User: Enters customer service details for the avatar.

[1958] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[1959] Terminal: Sends input content and emotional information to the server.

[1960] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[1961] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[1962] 4. Customer Service Evaluation and Feedback

[1963] Terminal: The user declares the end of the role-play and sends an end request, result data, and emotion information to the server.

[1964] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[1965] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[1966] Specific examples

[1967] For example, if a user selects the scenario "New Smartphone Plan Explained," the following process is performed:

[1968] 1. Log in and select a scenario

[1969] The user logs in to the app and selects "New Smartphone Plan Description."

[1970] 2. Scenario Generation

[1971] The server generates the scenario and avatar details and sends them to the device.

[1972] 3. Roleplay begins

[1973] The user begins explaining the new plan to the avatar.

[1974] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[1975] User input and emotional information is sent to the server, and a generative AI model generates the avatar's response.

[1976] 4. Display of responses and emotional responses

[1977] The server's response is displayed on the terminal, and the user can check the avatar's reaction and input an appropriate response depending on their feelings, such as tension or confusion.

[1978] The emotion engine continuously monitors the user's emotions and adjusts the feedback as emotions change.

[1979] 5. Termination and Evaluation

[1980] After the role-play is completed, the feedback generated by the server is displayed on the terminal for the user to confirm.

[1981] Feedback that reflects emotional information also helps users improve their emotional response.

[1982] Through this concrete example, it can be seen that this system aims to practically improve customer service skills, and in particular functions as an effective tool for strengthening the ability to respond to users' emotions.

[1983] The processing flow will be explained below.

[1984] Step 1:

[1985] User: Starts the application from the terminal and displays the login screen. Enters the user ID and password.

[1986] Step 2:

[1987] Terminal: Sends the entered user ID and password to the server.

[1988] Step 3:

[1989] Server: Receives the login request and checks the user information against the database. If authentication is successful, generates an authentication token and a list of available scenarios. If authentication fails, generates an error message.

[1990] Step 4:

[1991] Server: Returns the authentication result (success or failure) to the terminal.

[1992] Step 5:

[1993] Terminal: Receives the authentication result, and displays the scenario selection screen if authentication is successful. If authentication fails, displays an error message.

[1994] Step 6:

[1995] User: Select the scenario you want to practice on the scenario selection screen.

[1996] Step 7:

[1997] Terminal: Sends the selection information to the server.

[1998] Step 8:

[1999] Server: Receives scenario selection information, performs initial setup using the generative AI model, generates scenario and avatar details, and returns them to the device.

[2000] Step 9:

[2001] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[2002] Step 10:

[2003] User: Enters customer service details for the avatar.

[2004] Step 11:

[2005] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[2006] Step 12:

[2007] Terminal: Sends input content and emotional information to the server.

[2008] Step 13:

[2009] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[2010] Step 14:

[2011] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[2012] Step 15:

[2013] User: Continue roleplaying and continue typing appropriate responses to the avatar's responses.

[2014] Step 16:

[2015] Emotion Engine: Continuously monitors the user's emotional state and sends the information to the server whenever a change is detected.

[2016] Step 17:

[2017] Terminal & Server: For each user input, the terminal sends the input and emotional information to the server, and the server generates a response and returns it to the terminal, repeating the process.

[2018] Step 18:

[2019] User: Declare the end of the roleplay.

[2020] Step 19:

[2021] Terminal: Sends an end request, role-play result data, and emotional information to the server.

[2022] Step 20:

[2023] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, and generates feedback based on the evaluation points.

[2024] Step 21:

[2025] Server: Returns the generated feedback to the device.

[2026] Step 22:

[2027] Terminal: Displays received feedback to the user.

[2028] Step 23:

[2029] Users: Review feedback and understand areas for improvement in customer service skills.

[2030] Specific examples

[2031] For example, if a user selects the scenario "Explanation of a new smartphone plan" and practices, the specific flow will be as follows.

[2032] 1. Scenario Selection:

[2033] The user logs in to the app and selects "New Smartphone Plan Description."

[2034] 2. Scenario and avatar initial settings:

[2035] The server generates the scenario and avatar details and sends them to the device.

[2036] 3. Begin the role-play:

[2037] The user enters a description for the new plan into the avatar.

[2038] The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize their emotional state.

[2039] 4. Generate and display the response:

[2040] The device sends the input content and emotional information to the server.

[2041] The server generates a response using a generative AI model and returns it to the device.

[2042] The device displays the response on the avatar and prompts the user for new input.

[2043] 5. Emotional Response:

[2044] The emotion engine continuously monitors changes in the user's emotions and transmits them to the server.

[2045] The server adjusts the response based on the emotional information and returns it to the device.

[2046] 6. End of role play:

[2047] The user ends the role-play and sends an end request, result data, and emotion information to the server.

[2048] 7. Feedback Generation and Display:

[2049] The server analyzes the result data and emotional information to generate feedback.

[2050] The device displays feedback to the user.

[2051] Through this specific example, we can see that this system is an effective tool that not only effectively improves the customer service skills required when users start using new devices or services, but also strengthens their ability to respond to users' emotions.

[2052] Example 2

[2053] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2054] Conventional customer service role-playing systems are unable to fully recognize the user's emotional state and have difficulty reflecting the recognition results in feedback. This limits the effectiveness of customer service practice, and training to improve the ability to respond to emotions is particularly insufficient. Furthermore, because responses are not generated based on real-time emotion recognition, there is a problem of a gap between the actual customer service situation and the system.

[2055] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2056] In this invention, the server includes: means for generating a response for a customer service role-play based on a scenario selected by a user using a generative AI model; means for transmitting the user's input information and the user's emotional state to the server and receiving a response from the server; means for displaying the response received from the server and prompting the user for new input; means for analyzing the user's input information, facial expressions, voice, etc., and recognizing the user's emotional state; means for evaluating the results of the customer service based on predetermined evaluation criteria; and means for generating and providing feedback to the user. This enables the system to recognize the user's emotional state in real time and to perform practical customer service role-playing based on that information. Furthermore, by incorporating emotional information into the feedback, more effective improvement of customer service skills can be expected.

[2057] A "generative AI model" is an artificial intelligence model that generates responses based on a scenario selected by the user, and is a technology that uses natural language processing to provide appropriate responses in real time.

[2058] "User" refers to an individual or an employee of an organization who wishes to improve their customer service skills by using the customer service role-playing system.

[2059] A "server" is a computer system that receives information sent by a user, generates a response using a generative AI model, and provides feedback.

[2060] A "terminal" is a device operated by a user, and is a device for accessing the customer service role-play system, inputting information, and receiving responses and feedback from the server.

[2061] An "emotion engine" is a software or hardware technology that analyzes a user's input information, facial expressions, voice, etc., and recognizes the user's emotional state.

[2062] "Feedback" refers to information and advice provided to evaluate the results of the customer service role-play and to help the user improve their skills.

[2063] A "scenario" is a pre-set situation or story used in customer service role-playing, and serves as a simulation of when the user actually serves customers.

[2064] A "response" is the dialogue content that a generative AI model generates in response to user input, and is an appropriate reply based on the content entered by the user.

[2065] The "evaluation criteria" are a set of indicators and rules for evaluating the results of the user's customer service role-play, and feedback to the user is constructed based on these.

[2066] This invention relates to a customer service role-play system that uses a generative AI model and an emotion engine. This system allows customer service staff to effectively practice customer service when launching new devices or services. It also enables more practical training by recognizing users' emotions and providing appropriate responses and feedback accordingly.

[2067] The system consists of five main components: a generative AI model, an emotion engine, a server, a terminal, and a user.

[2068] Generative AI models, such as GPT-4, are used, which are specialized for natural language processing. These models generate appropriate responses based on the scenario selected by the user.

[2069] The emotion engine is a technology that recognizes a user's emotional state by analyzing their facial expressions, voice, and input information. This emotion engine works in conjunction with input devices such as cameras and microphones to analyze emotional data in real time.

[2070] The server collects and analyzes this data and generates responses using a generative AI model. Furthermore, the server provides feedback based on predefined evaluation criteria. This feedback reflects the user's emotional information, resulting in a personalized evaluation.

[2071] The terminal is a device operated by the user, and provides an interface for accessing the customer service role-play system. The terminal transmits the user's input information and emotional data to the server, and displays responses and feedback from the server.

[2072] The user selects a scenario and role-plays with the avatar. An example scenario is "Explanation of new smartphone plans."

[2073] The actual operation process is shown below.

[2074] 1. Boot the system and log in:

[2075] The user launches the dedicated app from their device and the login screen is displayed. The user enters their user ID and password and sends an authentication request to the server. The server receives the authentication request and checks the user information against the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device.

[2076] 2. Start the customer service role play:

[2077] The user selects the scenario they want to practice on the scenario selection screen. For example, they select "Explanation of a new smartphone plan." The selection information is sent to the server, which receives the scenario selection information, performs initial settings in the generative AI model, generates the scenario and avatar details, and returns them to the device.

[2078] 3. Customer service practice and emotion recognition:

[2079] The user inputs customer service details into the avatar, asking questions such as "How much does the new plan cost?" The emotion engine analyzes the user's input information, facial expressions, voice, etc. to recognize the emotional state. The device sends this data to the server, which passes it to a generative AI model to generate a response. The generated response is returned to the device and displayed on the avatar.

[2080] 4. Customer Service Evaluation and Feedback:

[2081] After completing the role-play, the user sends an end request to the server. The server receives the end request, the result data, and the emotion information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and returns it to the device. The device displays the feedback to the user, who can review it and understand where they need to improve their customer service skills.

[2082] Examples of prompt statements

[2083] For example, if the user selects the "New Smartphone Plan Explained" scenario, the prompt text might look like this:

[2084] You are a salesperson explaining a new smartphone plan. Based on the following scenario, provide the information the avatar requests.

[2085] Scenario: New smartphone plan explained

[2086] Avatar asks:

[2087] What are the features of the new plan?

[2088] What are the monthly costs?

[2089] What is the contract period and cancellation fee?

[2090] Your response:

[2091] Explain in detail the plan's features, pricing, contract length, and cancellation fees.

[2092] The system allows customer service staff to undergo real-time training that takes emotion recognition into account through realistic scenarios, allowing users to practice and improve their skills in situations that are closer to real-life customer service situations.

[2093] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2094] Step 1:

[2095] Booting and logging in

[2096] Device: The user launches the dedicated app and displays the login screen. They enter their user ID and password and tap the "Login" button.

[2097] Input: User ID and password.

[2098] Output: Sends an authentication request to the server.

[2099] What happens: The app makes an HTTP request and sends the user's authentication information to the server.

[2100] Step 2:

[2101] User Authentication

[2102] Server: Receives an authentication request and checks the user information in the database. If authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. If authentication fails, it returns an error message.

[2103] Input: User ID and password.

[2104] Output: Authentication token and a list of scenarios, or an error message.

[2105] Specific behavior: Executes a database query to verify user information. If authentication is successful, issues an authentication token using a token generation algorithm and sends it as an HTTP response.

[2106] Step 3:

[2107] Receiving authentication results

[2108] Device: Receives the authentication result, and if authentication is successful, displays the scenario selection screen. If authentication is unsuccessful, displays an error message.

[2109] Input: Authentication token and scenario list, or error message.

[2110] Output: Scenario selection screen or error message displayed.

[2111] Specific operation: Analyze the response content, and if authentication is successful, update the UI and display the scenario selection screen.

[2112] Step 4:

[2113] Scenario Selection

[2114] User: Select the scenario you want to practice on the scenario selection screen. For example, "Explanation of a new smartphone plan."

[2115] Input: The scenario selected by the user.

[2116] Output: Scenario selection information is sent from the device to the server.

[2117] Specific operation: The selected scenario is sent to the server as an HTTP request.

[2118] Step 5:

[2119] Scenario Generation

[2120] Server: Receives scenario selection information, performs initial setup using a generative AI model (e.g., GPT-4), generates scenario and avatar details, and returns them to the device.

[2121] Input: User selected scenario information.

[2122] Output: Scenario details and avatar information.

[2123] Specific operation: Calls the generative AI model, generates a story and detailed avatar information based on the selected scenario, and returns it as a response.

[2124] Step 6:

[2125] Scenario Display

[2126] Terminal: Displays the scenario start screen and prepares the user to begin role-playing.

[2127] Input: Scenario details and avatar information from the server.

[2128] Output: Display of the scenario start screen.

[2129] Specific behavior: Displays the received scenario details and avatar information, and enables the roleplay start button.

[2130] Step 7:

[2131] Start of customer service practice

[2132] User: Enters customer service information into the avatar. For example, asking, "How much does the new plan cost?"

[2133] Input: The question or input the user makes.

[2134] Output: Sending input from the terminal to the server.

[2135] Specific behavior: The user enters text into the input field and presses the submit button. This input is sent to the server as is.

[2136] Step 8:

[2137] emotion recognition

[2138] Emotion engine: Analyzes the user's input information, facial expressions, voice, etc. to recognize the user's emotional state.

[2139] Input: Data such as user text input, facial expressions, and voice.

[2140] Output: Parsed emotional state data.

[2141] Specific operation: Analyzes data collected from cameras and microphones in real time and tags emotional states.

[2142] Step 9:

[2143] Data transmission

[2144] Terminal: Sends input content and emotional information to the server.

[2145] Input: User text input and emotional state data.

[2146] Output: Sending input and emotion data to the server.

[2147] Specific operation: The text input and analyzed emotion data are sent together to the server.

[2148] Step 10:

[2149] Response Generation

[2150] Server: Receives input content and emotion information, passes it to the generative AI model to generate a response, and returns the generated response to the device.

[2151] Input: User text input and emotional state data.

[2152] Output: The generated response.

[2153] Specific operation: Calls a generative AI model, generates a response based on the input content and emotional data, and returns it as a response.

[2154] Step 11:

[2155] Response Display

[2156] Terminal: The response received from the server is displayed on the avatar. The user can confirm the avatar's response and continue inputting.

[2157] Input: The generated response from the server.

[2158] Output: Avatar displaying the response.

[2159] Specific operation: The avatar speaks the received response text and displays the corresponding facial expression.

[2160] Step 12:

[2161] Preparing for customer service evaluation

[2162] User: Declare the end of the role-play and send an end request, result data, and emotion information to the server via the terminal.

[2163] Input: Exit button click, result data, emotion information.

[2164] Output: Sending the end request, result data, and emotion information.

[2165] Specific operation: When the user presses the finish button, all data is sent to the server.

[2166] Step 13:

[2167] Evaluation and feedback generation

[2168] Server: Receives the end request, result data, and emotion information, analyzes them, extracts evaluation points, generates feedback based on the evaluation points, and returns it to the device.

[2169] Input: End request, result data, emotion information.

[2170] Output: Feedback.

[2171] Specific operation: Analyze the collected data, generate feedback based on predetermined evaluation criteria, and return it to the device as a response.

[2172] Step 14:

[2173] Feedback Display

[2174] Terminal: The received feedback is displayed to the user, who can then review the feedback and understand how to improve their customer service skills.

[2175] Input: Feedback from the server.

[2176] Output: Display feedback.

[2177] What this does: Updates the feedback screen to show detailed comments and scores to the user.

[2178] This allows users to receive practical training based on customer service scenarios and improve their skills through real-time feedback based on emotion recognition.

[2179] (Application example 2)

[2180] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2181] Conventional customer service role-playing systems are unable to provide feedback based on the user's emotional state, making it difficult to effectively provide practical training necessary to improve customer service skills. This is particularly true when training is required for new products or services, as it is difficult to develop appropriate emotional responses.

[2182] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a response for a customer service role-play based on a scenario selected by the user using a generative AI model, means for transmitting information entered by the user and the user's emotional state analyzed by an emotion engine to the server and receiving a response from the server, means for displaying the response received from the server and prompting the user to enter new input, and means for providing feedback according to the user's emotions using the emotion engine. This allows the user to receive real-time responses and feedback based on their emotions, enabling more practical customer service training.

[2183] A "generative AI model" is an artificial intelligence technology that automatically generates responses based on a scenario selected by the user.

[2184] An "emotion engine" is a technology that analyzes a user's emotional state from input information, facial expressions, voice, etc.

[2185] The "server" is a computer system that receives the user's input information and emotional state, generates an appropriate response using a generative AI model, and sends it to the device.

[2186] A "terminal" is a device through which a user inputs information through an interface and displays responses sent by a server.

[2187] "Customer service role-play" is a scenario-based simulation that allows participants to experience customer service work and practice skills.

[2188] "Feedback" refers to information such as evaluations and suggestions for improvement provided based on the user's behavior and emotional state.

[2189] A "response" is a dialogue or instruction generated by a generative AI model and provided to the user via the device.

[2190] A "scenario" is an item that defines a specific customer service situation selected by the user, and is the basis on which role-playing progresses based on that situation.

[2191] This invention relates to a customer service role-playing system that uses a generative AI model and an emotion engine. This system allows users working in the customer service industry to effectively practice customer service when launching a new product or service. Specific embodiments for implementing this invention are described below.

[2192] System configuration

[2193] The system mainly consists of the following components:

[2194] 1. Server

[2195] 2. Terminal

[2196] 3. Generative AI Models

[2197] 4. Emotion Engine

[2198] 5. Users

[2199] Hardware and Software

[2200] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest)

[2201] Software: Python-based local server, generative AI model, emotion engine

[2202] Processing flow explanation

[2203] First, the user launches the dedicated application on the device and enters their authentication information on the login screen. The server verifies the information, and if authentication is successful, the scenario selection screen is displayed on the device.

[2204] When a user selects a scenario they want to practice, that information is sent to the server, and the generative AI model performs initial setup based on the selected scenario. The server then generates information about the specified scenario and avatar details and sends them to the device.

[2205] When a user begins a customer service role-play, they input information into the avatar. The emotion engine analyzes the user's emotional data, such as facial expressions and tone of voice, and sends that information to the server. The server then analyzes this data using a generative AI model and generates an appropriate response. The response is sent to the device, which displays it to the user through the avatar. The user considers their next input while looking at the avatar's response.

[2206] Once the training is complete, the server analyzes the role-play results and emotional data to generate feedback for the user. The feedback is displayed on the device as advice based on the user's strengths and areas for improvement, as well as their emotions. This allows the user to specifically understand where they need to improve their customer service skills.

[2207] Specific examples of processing

[2208] For example, if a user selects the scenario "Explanation of new smartphone plans," the following prompt sentence is used: This prompt sentence allows the system to generate instructions to appropriately proceed with the customer service role-play.

[2209] Example prompt sentence:

[2210] "Simulate a plan explanation for a new smartphone."

[2211] In this scenario, a user practices explaining a complex plan to an avatar. The emotion engine analyzes the user's emotions, such as nervousness or confusion, in real time, and the server generates feedback based on that. For example, if the user is confused, the generative AI model generates a response such as, "Please let me know if there's anything you don't understand, and I'll explain it in more detail."

[2212] This system allows users to improve their practical customer service skills while receiving real-time support based on their emotions.

[2213] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2214] Step 1:

[2215] The user launches a dedicated application from their device and enters their user ID and password on the login screen. The device sends the entered authentication information to the server. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and a list of available scenarios and returns them to the device. The device receives the authentication results and displays a scenario selection screen.

[2216] Step 2:

[2217] The user selects the scenario they want to practice on the scenario selection screen and sends the selection information from the device to the server. The server receives the scenario selection information, performs initial settings in the generative AI model, and generates the scenario and avatar details. This information is sent to the device, which then displays the scenario start screen.

[2218] Step 3:

[2219] The user begins the customer service role-play on the scenario start screen on the device and inputs the customer service details to the avatar. The emotion engine analyzes the user's input information, facial expressions, and voice to recognize the user's emotional state. The device sends the input customer service details and emotional information to the server. The server analyzes the received information and passes it to the generative AI model to generate a response. The generated response is sent back to the device, which then displays the avatar's response to the user.

[2220] Step 4:

[2221] The user confirms the avatar's response and continues to input their customer service needs. The emotion engine continues to analyze the user's emotional state and transmits the data to the server. The server continuously passes the user's input and emotional information to the generative AI model, which generates new responses and transmits them to the device. This cycle repeats until the user declares the end of the role-play.

[2222] Step 5:

[2223] When the user declares the end of the role-play, the device sends an end request, result data, and emotional information to the server. The server receives the end request, result data, and emotional information, analyzes them, and extracts evaluation points. It generates feedback based on the evaluation points and sends the feedback information to the device. The device displays the received feedback to the user, allowing the user to understand areas for improvement in their customer service skills and emotional response.

[2224] In this way, a system that provides real-time, practical customer service training is realized through how the user, device, server, emotion engine, and generative AI model work together at each processing step.

[2225] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2226] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2227] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2228] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2229] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2230] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2231] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2232] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2233] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2234] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2235] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2236] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2237] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2238] 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.

[2239] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the stora...

Claims

1. A means for generating a customer service role-play response based on a scenario selected by a user using a generative AI model; means for transmitting information input by a user to a server and receiving a response from the server; a means for displaying the response received from the server and prompting the user for new input; A system including:

2. 10. The system of claim 1, further comprising means for generating and providing feedback to the user based on the generated response.

3. The system according to claim 1 , further comprising means for analyzing result data of the customer service role-play and generating feedback to support the user in improving their skills.

4. 10. The system of claim 1, further comprising means for enabling a user to participate in a customer service role-play using an accessible device.

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