system

Abstract

We provide the system. [Solution] A means of displaying a virtual character using a generative model that interacts with the user via a communication terminal, Means for recording the content of the aforementioned dialogue, A means for analyzing the aforementioned recorded content and generating feedback for the user, means for displaying the aforementioned feedback on the communication terminal, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the customer service industry, new or inexperienced employees require a lot of time to acquire practical customer service skills. Also, the burden on educators is large, and it is difficult to secure sufficient training time. Furthermore, the feedback is not based on individual cases and tends to be uniform guidance, which is an issue.

Means for Solving the Problems

[0006] A "communication terminal" is a device used by users to interact with virtual characters through a generative model, and includes smartphones, tablets, and other similar devices.

[0007] A "generative model" is an artificial intelligence technology used to create virtual characters for interaction with users, and is designed to generate appropriate responses based on user input.

[0008] A "virtual character" is a character displayed on a communication terminal using a generative model, designed to interact with the user.

[0009] "Dialogue content" refers to data from conversations and question-and-answer sessions between the user and the virtual character.

[0010] "Means of recording" refers to the technologies and functions for saving the content of a conversation as digital data.

[0011] "Means of analysis" refers to the process of analyzing the content of recorded conversations to identify the quality of user responses and any issues.

[0012] "Feedback" refers to information about specific areas for improvement and success points for users, generated based on the results of the analysis. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system that allows a user to interact with a virtual character using a generative model via a communication terminal. This system provides a means for users to effectively learn and improve their customer service skills.

[0035] The program for this system begins with installing an application on the communication terminal. This application includes a login screen for the user, and is accessible to each user individually. Within the application, the user selects a customer service scenario, and based on that, a virtual character created using a generative model is displayed on the terminal, and the interaction begins.

[0036] The virtual character is configured to match the scenario selected by the user and engages in real-time voice or text-based conversations. This interaction is designed to allow users to simulate situations that occur in various customer service scenarios. For example, if a new employee selects the "handling a complaint" scenario, the virtual character will appear as a dissatisfied customer, allowing the user to practice how to handle such a situation.

[0037] All conversations are recorded and sent to the server after completion. The server analyzes the recorded content and generates feedback to identify the quality of the user's responses, issues, and successes. This feedback is compared with specific performance metrics and data from other users to specifically highlight areas for improvement and strengths for the user. The generated feedback is sent to the communication terminal, which the user can receive and use to improve themselves.

[0038] As a concrete example, if a user chooses the scenario of "recommending products," a virtual character appears as a new customer and asks, "I don't know which product is good, so could you recommend something?" The user then accurately explains the product's features, benefits, price range, etc., and makes a suggestion to encourage a purchase. This entire conversation is recorded, analyzed, and provided as feedback, allowing the user to objectively evaluate and improve their customer service skills.

[0039] This allows users to efficiently and quantitatively improve their customer service skills in any situation, such as during breaks at work or at home.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches the application on their communication device and enters their authentication information on the login screen. The device then sends the information entered by the user to the server.

[0043] Step 2:

[0044] The server verifies the received authentication information, and if it is correct, sends data to the user's device to display the home screen.

[0045] Step 3:

[0046] The user selects a customer service scenario they want to practice from the home screen. Based on this selection, the device sends the scenario information to the server as a request.

[0047] Step 4:

[0048] The server uses a generative model to create data for generating a virtual character based on the scenario selected by the user, and then sends it back to the terminal.

[0049] Step 5:

[0050] The terminal displays a virtual character on the screen based on the received data and prepares to begin interacting with the user.

[0051] Step 6:

[0052] Users interact with virtual characters and practice responding to various customer service situations. These interactions are conducted via voice or text.

[0053] Step 7:

[0054] The terminal continuously records all of the conversations that take place in real time, and when the practice session ends, it sends that data to the server.

[0055] Step 8:

[0056] The server analyzes the received dialogue data, evaluates the quality of the user's responses, and generates feedback that identifies specific success stories and areas for improvement.

[0057] Step 9:

[0058] The server sends the generated feedback to the terminal and displays specific advice and points for improvement to the user.

[0059] Step 10:

[0060] Users can review feedback, plan actions to improve their skills, and then take action.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Traditional training methods for improving customer service skills rely on accumulating practical experience, which is inefficient. Furthermore, the lack of objective criteria for self-assessment makes it difficult to measure one's own improvement in customer service skills. Additionally, the inability to self-evaluate through comparison with other customers makes it difficult to develop competitive skills.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for displaying a virtual object using a generated data model that interacts with a user via a communication device, means for recording information about the interaction, and means for analyzing the recorded information and generating evaluation information for the user. This enables users to efficiently and quantitatively evaluate their own customer service skills and improve their skills competitively through comparison with other users.

[0066] A "communication device" is an electronic device used to send and receive data over a network.

[0067] A "generative data model" is a program or algorithm that uses AI to generate responses or information that are appropriate to language, context, or situation.

[0068] A "virtual object" is an artificial entity that is generated in digital form, visually represented on a computer screen, and capable of interacting with the user.

[0069] "Dialogue information" refers to data that includes the content of communication that took place between the user and the virtual object.

[0070] "Recorded information" refers to data that preserves the content and process of a dialogue and makes it available for later analysis.

[0071] "Evaluation information" refers to information that is analyzed based on recorded data and presented as feedback on the quality of user behavior and responses.

[0072] A "prompt" is a guideline in the form of instructions or questions that are input into a data generation model, and it is an important element in determining the model's output.

[0073] To implement this invention, the user first installs a dedicated application on their communication terminal. This application runs on a mobile device or personal computer and provides a user interface. The user can access the system by logging in and begin operations using their individually configured account.

[0074] The terminal generates and displays virtual objects using a generated data model based on the customer service scenario selected by the user. These virtual objects are generated in real time using AI technology and can mimic conversations that are in line with the scenario. If the user selects a scenario such as "handling a complaint" or "recommending products," the virtual objects will play the appropriate role.

[0075] During a conversation, the device records all conversational information and sends it to the server. This recorded information is received by the server and analyzed by a generating AI model. The server generates evaluation information based on the user's responses and the progress of the conversation. This evaluation information is displayed on the device as feedback for the user to self-assess and improve their skills.

[0076] For example, if the user chooses a scenario where they "recommend products," the virtual object, acting as a new customer, will ask questions such as "Which product do you recommend?" The user then explains the product's features and benefits, and makes a suggestion to encourage purchase. This entire dialogue process is recorded and evaluated later.

[0077] An example of a prompt message would be, "In a scenario suggesting recommended products, please appear as a new customer and present a situation where you are having trouble choosing a product." This instruction is then input into the AI ​​generation model. This mechanism generates more realistic dialogue and contributes to improving the user's skills.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user installs a dedicated application on their communication device and enters their authentication information on the login screen. This allows the user to access their account and prepares them to proceed to the next step. The input is the user's authentication information, and the output is the status of successful login to the application.

[0081] Step 2:

[0082] The user selects one of several customer service scenarios available within the application. Information about the selected scenario is entered, and this information is used to configure the virtual object. The output is the information of the selected scenario.

[0083] Step 3:

[0084] The device uses a generative AI model to generate virtual objects according to the scenario selected by the user. Here, prompts based on the selected scenario are input to the generative AI model. The model performs data calculations to generate an appropriate response, and the virtual object is displayed on the screen as output.

[0085] Step 4:

[0086] When a virtual object appears on the screen, real-time interaction with the user begins. User input is sent to the terminal as interaction information, and the virtual object operates based on this. The output is the content of the ongoing interaction with the user.

[0087] Step 5:

[0088] The terminal records the content of the conversation and sends it to the server as conversation information. The input is various conversation logs, and the output is the completion of the transfer to the server.

[0089] Step 6:

[0090] The server analyzes the received dialogue information and quantitatively evaluates the user's response using a generated AI model. The input is the dialogue information, and the generated evaluation information is output.

[0091] Step 7:

[0092] The server sends the generated evaluation information to the user's terminal. The terminal displays this feedback to the user. The input is the evaluation information, and the output is the feedback screen display. This feedback allows the user to evaluate their customer service skills and use it to improve them.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] When staff in physical stores try to improve their customer service skills through practical experience, they often rely on real-world situations. However, gaining sufficient experience requires many interactions with actual customers, which takes a lot of time and opportunity. Furthermore, learning from mistakes through interactions with real customers carries risks. For these reasons, there is a need to provide training in an environment that closely resembles real-world situations.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with the user via a communication device, means for recording information about the interaction, means for analyzing the recorded information and generating an evaluation of the user, and means for displaying the virtual person using a visual device and simulating the user's actions in the real world. This enables the user to safely and efficiently improve their customer service skills.

[0098] A "communication device" is an electronic device used to send and receive data using a network.

[0099] A "user" is a person who operates and interacts with this system.

[0100] A "generative model" is an algorithm that uses AI technology to generate dialogue between virtual characters.

[0101] A "virtual character" is a human-like character generated by a computer and represented on screen or through sound.

[0102] "Dialogue information" refers to data about the communication between the user and the virtual character.

[0103] "Recorded information" refers to data that preserves the content of a conversation.

[0104] "Evaluation" refers to data that analyzes the user's performance based on recorded conversation information and provides areas for improvement.

[0105] "Visual devices" are devices that provide information to users visually, and include smart glasses and head-mounted displays.

[0106] A "simulated experience" is an environment that virtually recreates real-world situations, allowing users to learn practical skills.

[0107] To implement this invention, a communication device, a vision device, and software applying a generative model are required. At the heart of the system is a generative AI model for speech recognition and natural language processing. This model is built using a machine learning framework such as TENSORFLOW® and runs on a Flask server.

[0108] The server receives user input transmitted from the communication device. The input is provided in voice or text format, and in the case of voice input, it is converted to text by speech recognition. This input data is analyzed by a generative AI model to generate appropriate dialogue content for the virtual character. In this process, the generative model is given instructions as prompts, such as "Customer service scenario: Complaint handling. Begin responding to an inquiry about a damaged product."

[0109] The generated dialogue is transmitted to a visual device via a communication device, and a virtual character is represented visually or audibly on the visual device. Smart glasses or head-mounted displays are used as the visual device. This system allows users to learn customer service skills required in real-world stores through simulated experiences.

[0110] For example, if a user selects a scenario where they "recommend products," a virtual character appears as a new customer requesting product information, and the user provides details accordingly. All interactions are recorded, sent to a server, analyzed, and then feedback is generated for the user. This feedback is presented as specific areas for improvement to help the user enhance their skills.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The terminal receives the user's login information as input and performs user authentication. If correct authentication information is provided, a list of available customer service scenarios for the user is displayed. This allows the user to select the appropriate scenario.

[0114] Step 2:

[0115] The user selects a specific customer service scenario from the displayed scenarios. The terminal sends this selection information as input to the server. Based on this information, the server generates prompt text for the AI ​​model and prepares dialogue content for a virtual character appropriate to the scenario.

[0116] Step 3:

[0117] The server uses a generative AI model to generate a virtual character dialogue based on a selected scenario. The prompt "Customer service scenario: Complaint handling." is given to the model as input, and this information is used to construct the virtual character's response. This dialogue is then sent to the terminal as output.

[0118] Step 4:

[0119] The terminal displays the received dialogue content on a visual device and presents a virtual person to the user via voice or text. The user receives this and provides input to respond. This input becomes data for the user to attempt ideal customer service.

[0120] Step 5:

[0121] The server receives user responses as input and records the interaction. The recorded information is used later for analysis. The server analyzes the input data and identifies perspectives for generating a user performance evaluation.

[0122] Step 6:

[0123] The server generates feedback based on recorded conversations. This feedback includes specific improvement suggestions and success stories. The analysis results are presented to support the user's skill improvement.

[0124] Step 7:

[0125] The device presents the generated feedback to the user. The user can use this information to learn independently and, if necessary, select a different scenario to re-experience the simulation.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention incorporates an emotion engine into a system in which a user interacts with a virtual character using a generative model on a communication terminal. By doing so, the system recognizes the user's emotions and provides more appropriate responses. This system enables the practice of more natural and effective customer service scenarios.

[0128] The system configuration is as follows: First, the user launches the application on a communication terminal, logs in, and then begins customer service practice. The user selects a scenario they wish to practice, and a corresponding virtual character is displayed on the terminal. The virtual character responds to the user's input in real time using a generative model.

[0129] This system also incorporates an emotion engine that can recognize emotions from the user's tone of voice, facial expressions, and textual expressions. For example, if the user appears confused or anxious, the emotion engine analyzes this and communicates it to the virtual character. The virtual character then adjusts its response based on the recognized emotional information, providing the user with more appropriate advice and support.

[0130] All user dialogue and emotional data are recorded. Upon completion of the practice session, the recorded data is sent to a server for analysis. The server evaluates the quality of the dialogue and generates feedback based on the data, including emotional changes. This feedback includes specific advice indicating how the user responded to the scenario and which parts were particularly effective or need improvement.

[0131] For example, in a "customer complaint handling" scenario, if the virtual character expresses dissatisfaction, the user might offer an apology in a somewhat anxious voice. The emotion engine would detect this anxiety and instruct the virtual character to respond calmly. This allows the user to learn how their emotions affect others and how that impacts the flow of the conversation.

[0132] In summary, this system allows users to practice more practical and situation-appropriate customer service, and provides a means to offer sophisticated feedback that even takes into account the user's emotions.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user launches the application on their communication terminal and enters their login information. The terminal then sends the entered information to the server to request authentication.

[0136] Step 2:

[0137] The server verifies the login information, and if authentication is successful, it sends data to provide the user with a home screen appropriate for their device.

[0138] Step 3:

[0139] The user selects their desired customer service scenario from the home screen. The device then sends a request to the server based on the selected scenario.

[0140] Step 4:

[0141] The server prepares data for generating a virtual character that matches the selected scenario and provides this data to the terminal.

[0142] Step 5:

[0143] The terminal uses the received data to display a virtual character and sets up the user to begin interacting with it. At this time, the emotion engine is also initialized and ready to recognize the user's emotions.

[0144] Step 6:

[0145] While the user interacts with the virtual character, the emotion engine analyzes the user's tone of voice, words, and facial expressions, and sends the identified emotion data to the virtual character.

[0146] Step 7:

[0147] The device dynamically adjusts the virtual character's responses based on data from the emotion engine, providing appropriate responses that match the user's emotions.

[0148] Step 8:

[0149] The device records dialogue and emotional data in real time and sends this data to the server at the end of the practice session.

[0150] Step 9:

[0151] The server analyzes the transmitted data and generates feedback that takes into account the quality of the interaction and the user's emotional changes. This feedback includes specific areas for improvement and examples of success.

[0152] Step 10:

[0153] The server sends the generated feedback to the terminal and displays it to the user. The user can then use this feedback to improve their customer service skills.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] Conventional virtual character dialogue systems have struggled to provide appropriate responses that fully consider the user's emotions. Furthermore, there were limitations in accurately generating specific feedback to improve the quality of the dialogue. This resulted in challenges for users in effectively learning and practicing.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes means for displaying a virtual character using a generative model that exchanges information with a user via a communication device, means for detecting the user's emotions when the information exchange is performed in real time, and means for recording the content of the information exchange and the detected emotion data. This makes it possible to provide appropriate responses in real time according to the user's emotions and improve the quality of information exchange.

[0159] A "communication device" is a device used for exchanging information between a user and a virtual character. It displays the response of the generative model and accepts input from the user.

[0160] A "generative model" is an artificial intelligence technique used by a virtual character to generate natural and appropriate responses to user input, and includes algorithms capable of handling a variety of scenarios.

[0161] A "virtual character" is a character that engages in dialogue using a generative model and serves as an entity with which users can exchange information.

[0162] "Information exchange" refers to all interactions and data exchanges that take place between virtual characters and users, including communication conducted through text and voice.

[0163] "Emotional data" refers to data that indicates the user's emotional state, and is based on information extracted from voice tone and facial expressions.

[0164] "Real-time" refers to the temporal characteristic of responding to or processing user input immediately, meaning that it requires high responsiveness and fast processing.

[0165] "Guidance" refers to educational or corrective advice or directional information provided to users, and is a type of feedback generated based on dialogue and emotions.

[0166] This invention is a system that enables users to interact with virtual characters using a communication device, providing more natural and adaptive responses. The communication device used is a common mobile communication device such as a smartphone or tablet. This device incorporates software for implementing a generative AI model and hardware for emotion recognition.

[0167] The communication device's functionality is activated when the user logs in through the application and initiates a conversation. A generative AI model analyzes text or voice input from the user and generates a corresponding response from a virtual character. Natural language processing technology is used as the generative AI model for response generation, and it incorporates a deep learning algorithm.

[0168] Furthermore, the communication device incorporates an emotion engine that analyzes the user's voice tone and facial expression data in real time and records emotional data. This emotional data is reflected in the virtual character's responses to enhance the naturalness of the responses. For example, if the user shows anxiety, the virtual character will respond calmly and adjust to provide a sense of reassurance.

[0169] After the interaction, the communication device sends the recorded interaction data and emotional data to the server. The server analyzes this data and generates feedback for each user. This feedback includes specific advice and suggestions for improvement to help the user improve their interaction skills. The generated feedback is returned to the communication device and displayed to the user.

[0170] As a concrete example, in a "customer complaint handling" scenario, a virtual character expresses dissatisfaction, and the user apologizes. If the user's voice indicates anxiety, the emotion engine recognizes this and instructs the virtual character to respond calmly.

[0171] An example of a prompt might be, "I want to practice how to respond to customer complaints." Based on this prompt, the communication device selects the most appropriate dialogue scenario and provides a flow of dialogue using a virtual character.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user launches the application using a communication device and enters login information. This login information is used for authentication by the communication device, allowing the user to access the application's main screen. Specific actions include filling out the login form and clicking the submit button.

[0175] Step 2:

[0176] The user selects a dialogue scenario. Based on this input, the communication device collects the data necessary to generate a virtual character that matches the selected scenario and provides it to the generation AI model. The generation AI model analyzes this data and generates the initial dialogue content of the virtual character in accordance with the selected scenario.

[0177] Step 3:

[0178] The terminal displays the generated virtual character and begins interacting with the user. When the user provides input (text or voice), the terminal captures it and passes it to the generating AI model. The generating AI model analyzes this input and generates the optimal response. Specific actions include processing voice input through the user's microphone and sending text chat messages.

[0179] Step 4:

[0180] The device incorporates an emotion engine that acquires emotional data by analyzing the user's voice tone or facial expressions. The user's voice tone and facial expressions are used as input, and the emotion engine performs analysis based on this. The results of this analysis are reflected in the virtual character's response, and the device adjusts the response before presenting it to the user.

[0181] Step 5:

[0182] After the conversation ends or after a certain period of time has elapsed, the terminal sends the recorded conversation data and emotional data to the server. The server receives the data and analyzes its quality and content. This analysis includes tracking emotional changes and evaluating the overall conversation using data processing algorithms.

[0183] Step 6:

[0184] The server generates feedback for the user based on the analysis results. This feedback includes specific advice to help improve the user's conversational skills. The generated feedback is sent from the server to the terminal and displayed to the user. Importantly, detailed feedback is provided based on the previously analyzed sentiment data.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] In many brick-and-mortar stores, improving the customer service skills of staff is crucial, but traditional training methods have limitations in providing training that includes realistic situational responses and emotional recognition. In particular, there is a lack of effective training methods to cultivate the ability to appropriately recognize emotions and adjust responses during conversations, thus creating a need for more practical methods to improve customer service skills that are tailored to individual circumstances.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with a user via a communication device, means for causing the virtual person to recognize the user's emotions and adjust its response, and means for recording the emotion recognition and response. This makes it possible for store employees to effectively improve their customer service skills in realistic situations.

[0190] A "communication device" is a device used to send and receive information via digital signals.

[0191] A "user" is an individual or group that uses the system to interact with a virtual character.

[0192] A "generative model" is an algorithm that uses machine learning techniques to automatically generate natural-sounding responses.

[0193] A "virtual character" is a digital character created using a generative model to interact with users.

[0194] "Emotion recognition" is the process of analyzing a user's emotional state from voice, facial expressions, and text data.

[0195] "Adjusting responses" means dynamically changing the content and tone of a virtual character's responses based on recognized emotional data.

[0196] "Recording" refers to the act of saving the content of a conversation and data on the user's emotions.

[0197] "Feedback" refers to rating information generated to return the results of the dialogue and areas for improvement to the user.

[0198] "Analyzing" is a method of extracting meaning and trends using collected data.

[0199] "Practice in a physical store" refers to training activities in which store employees simulate an actual sales environment to hone their customer service skills.

[0200] This invention can be specifically implemented as a training system to improve the customer service skills of store employees. The server executes a generative model via a communication device and displays a virtual character. The virtual character interacts with the user and dynamically adjusts its response based on emotional input from the user (voice, facial expressions, text). Software technology that analyzes voice and facial expressions is used for emotion recognition.

[0201] The server also records the content and emotional data of this interaction. This includes the ability to save the user's voice tone and facial expression changes as data. The recorded data is then used to generate feedback for the user. This feedback includes an evaluation of the quality of the interaction and areas for improvement.

[0202] The terminal can be implemented as part of this system using smart glasses or head-mounted displays such as Microsoft HoloLens®. This allows users to simulate actual store environments.

[0203] As a concrete example, a flower shop employee can hone their skills in handling customer complaints through dialogue with virtual customers. In this process, the system analyzes the user's emotions, such as anxiety and confusion, and provides appropriate feedback. For example, if the user shows anxiety, the system will provide feedback emphasizing the importance of responding calmly.

[0204] An example of a prompt to the generating AI model would be: "The current situation is a severe customer complaint. Please tell us what the appropriate response would be if the user is showing signs of anxiety." In this way, the system helps improve customer service skills to be more practical and personalized through emotion recognition and appropriate responses.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user activates the device and logs into the customer service training application. The information entered here is the user's login information, which the device sends to the authentication server for authentication. Upon successful authentication, the user gains access to the main menu.

[0208] Step 2:

[0209] The user selects the scenario they want to train. The input here is an instruction for scenario selection, and the device sends this selection information to the server. The server uses a generative AI model to generate a virtual character based on the selected scenario and sends it to the device.

[0210] Step 3:

[0211] A virtual character is generated and displayed on the device. The user begins interacting with the virtual character, providing input via voice and text. The input data also includes the user's facial expressions. The device sends this data to an emotion recognition engine.

[0212] Step 4:

[0213] The server uses an emotion recognition engine to analyze the user's voice and facial expression data to identify their emotional state. The input is the user's voice and facial expression data, and the output is the recognized emotion information.

[0214] Step 5:

[0215] The server uses a generated AI model to adjust the virtual character's response based on the recognized emotion. The input here is emotion information, and the output is the adjusted response. The server returns this response to the terminal.

[0216] Step 6:

[0217] The device displays a pre-arranged response from a virtual character to the user. The user can continue the conversation and, if necessary, select other scenarios.

[0218] Step 7:

[0219] After all conversations have ended, the terminal records the conversation content and emotional data and sends it to the server.

[0220] Step 8:

[0221] The server analyzes the received data and generates feedback. Input data includes dialogue content and sentiment data, while output is specific feedback. This feedback includes the user's strengths and areas for improvement.

[0222] Step 9:

[0223] The server sends feedback to the terminal, which then displays it to the user. The user can then review the feedback and use it to improve future training sessions.

[0224] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0225] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0226] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0230] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0231] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0232] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0233] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0234] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0235] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0236] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0237] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0238] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0239] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0240] This invention relates to a system that allows a user to interact with a virtual character using a generative model via a communication terminal. This system provides a means for users to effectively learn and improve their customer service skills.

[0241] The program for this system begins with installing an application on the communication terminal. This application includes a login screen for the user, and is accessible to each user individually. Within the application, the user selects a customer service scenario, and based on that, a virtual character created using a generative model is displayed on the terminal, and the interaction begins.

[0242] The virtual character is configured to match the scenario selected by the user and engages in real-time voice or text-based conversations. This interaction is designed to allow users to simulate situations that occur in various customer service scenarios. For example, if a new employee selects the "handling a complaint" scenario, the virtual character will appear as a dissatisfied customer, allowing the user to practice how to handle such a situation.

[0243] All conversations are recorded and sent to the server after completion. The server analyzes the recorded content and generates feedback to identify the quality of the user's responses, issues, and successes. This feedback is compared with specific performance metrics and data from other users to specifically highlight areas for improvement and strengths for the user. The generated feedback is sent to the communication terminal, which the user can receive and use to improve themselves.

[0244] As a concrete example, if a user chooses the scenario of "recommending products," a virtual character appears as a new customer and asks, "I don't know which product is good, so could you recommend something?" The user then accurately explains the product's features, benefits, price range, etc., and makes a suggestion to encourage a purchase. This entire conversation is recorded, analyzed, and provided as feedback, allowing the user to objectively evaluate and improve their customer service skills.

[0245] This allows users to efficiently and quantitatively improve their customer service skills in any situation, such as during breaks at work or at home.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The user launches the application on their communication device and enters their authentication information on the login screen. The device then sends the information entered by the user to the server.

[0249] Step 2:

[0250] The server verifies the received authentication information, and if it is correct, sends data to the user's device to display the home screen.

[0251] Step 3:

[0252] The user selects a customer service scenario they want to practice from the home screen. Based on this selection, the device sends the scenario information to the server as a request.

[0253] Step 4:

[0254] The server uses a generative model to create data for generating a virtual character based on the scenario selected by the user, and then sends it back to the terminal.

[0255] Step 5:

[0256] The terminal displays a virtual character on the screen based on the received data and prepares to begin interacting with the user.

[0257] Step 6:

[0258] Users interact with virtual characters and practice responding to various customer service situations. These interactions are conducted via voice or text.

[0259] Step 7:

[0260] The terminal continuously records all of the conversations that take place in real time, and when the practice session ends, it sends that data to the server.

[0261] Step 8:

[0262] The server analyzes the received dialogue data, evaluates the quality of the user's responses, and generates feedback that identifies specific success stories and areas for improvement.

[0263] Step 9:

[0264] The server sends the generated feedback to the terminal and displays specific advice and points for improvement to the user.

[0265] Step 10:

[0266] Users can review feedback, plan actions to improve their skills, and then take action.

[0267] (Example 1)

[0268] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] Traditional training methods for improving customer service skills rely on accumulating practical experience, which is inefficient. Furthermore, the lack of objective criteria for self-assessment makes it difficult to measure one's own improvement in customer service skills. Additionally, the inability to self-evaluate through comparison with other customers makes it difficult to develop competitive skills.

[0270] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0271] In this invention, the server includes means for displaying a virtual object using a generated data model that interacts with a user via a communication device, means for recording information about the interaction, and means for analyzing the recorded information and generating evaluation information for the user. This enables users to efficiently and quantitatively evaluate their own customer service skills and improve their skills competitively through comparison with other users.

[0272] A "communication device" is an electronic device used to send and receive data over a network.

[0273] A "generative data model" is a program or algorithm that uses AI to generate responses or information that are appropriate to language, context, or situation.

[0274] A "virtual object" is an artificial entity that is generated in digital form, visually represented on a computer screen, and capable of interacting with the user.

[0275] "Dialogue information" refers to data that includes the content of communication that took place between the user and the virtual object.

[0276] "Recorded information" refers to data that preserves the content and process of a dialogue and makes it available for later analysis.

[0277] "Evaluation information" refers to information that is analyzed based on recorded data and presented as feedback on the quality of user behavior and responses.

[0278] A "prompt" is a guideline in the form of instructions or questions that are input into a data generation model, and it is an important element in determining the model's output.

[0279] To implement this invention, the user first installs a dedicated application on their communication terminal. This application runs on a mobile device or personal computer and provides a user interface. The user can access the system by logging in and begin operations using their individually configured account.

[0280] Based on the customer service scenario selected by the user, the terminal utilizes the generated data model to generate and display virtual objects. The virtual objects are generated in real time using AI technology and can mimic conversations according to the scenario. When the user selects scenarios such as "claim handling" or "proposing recommended products", the virtual objects play corresponding roles.

[0281] During the conversation, the terminal records all conversation information and sends it to the server. This recorded information is received by the server and analyzed by the generated AI model. The server generates evaluation information based on the user's response and the progress of the conversation. This evaluation information is displayed on the terminal as feedback for the user to conduct self-evaluation and improve skills.

[0282] As a specific example, when the user selects the scenario of "proposing recommended products", the virtual object acts as a new customer and asks questions such as "Which products are recommended?" Here, the user will explain the features and advantages of the products and make proposals to encourage purchases. This series of conversation processes is recorded and evaluated later.

[0283] As an example of the prompt text, instructions such as "In the scenario of proposing recommended products, appear as a new customer role and present a situation where you are having trouble choosing products." are input into the generated AI model. This is a mechanism that generates more realistic conversation content and contributes to the improvement of the user's skills.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user installs a dedicated application on the communication terminal and enters authentication information on the login screen. Thereby, the user can access their account and is ready to proceed to the next operation. The input is the user's authentication information, and the output is the completion status of logging in to the application.

[0287] Step 2:

[0288] The user selects one from multiple customer service scenarios available within the application. Information of the selected scenario is input, and based on this information, virtual objects are set up. The output is the selected scenario information.

[0289] Step 3:

[0290] The terminal uses a generation AI model according to the scenario selected by the user to generate virtual objects. Here, a prompt based on the selected scenario is input into the generation AI model. Through data operations by the model, corresponding responses are generated, and virtual objects are displayed on the screen as output.

[0291] Step 4:

[0292] When the virtual object is displayed on the screen, real-time interaction with the user is started. Input from the user is sent to the terminal as interaction information, and based on this, the virtual object operates. The output is the continuous interaction content with the user.

[0293] Step 5:

[0294] The terminal records the content of the interaction and transmits it to the server as interaction information. This input is various interaction logs, and the output is the completion of transfer to the server.

[0295] Step 6:

[0296] The server analyzes the received interaction information and quantitatively evaluates the user's response using the generation AI model. The input is the interaction information, and the generated evaluation information is output.

[0297] Step 7:

[0298] The server sends the generated evaluation information to the user's terminal. The terminal displays this feedback to the user. The input is the evaluation information, and the output is the feedback screen display. This feedback allows the user to evaluate their customer service skills and use it to improve them.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] When staff in physical stores try to improve their customer service skills through practical experience, they often rely on real-world situations. However, gaining sufficient experience requires many interactions with actual customers, which takes a lot of time and opportunity. Furthermore, learning from mistakes through interactions with real customers carries risks. For these reasons, there is a need to provide training in an environment that closely resembles real-world situations.

[0302] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with the user via a communication device, means for recording information about the interaction, means for analyzing the recorded information and generating an evaluation of the user, and means for displaying the virtual person using a visual device and simulating the user's actions in the real world. This enables the user to safely and efficiently improve their customer service skills.

[0304] A "communication device" is an electronic device used to send and receive data using a network.

[0305] A "user" is a person who operates and interacts with this system.

[0306] The "generation model" is an algorithm that uses AI technology to generate conversations of virtual characters.

[0307] A "virtual character" is a character that is generated by a computer and is represented on a screen or by voice, resembling a human.

[0308] "Conversation information" refers to data related to the communication between the user and the virtual character.

[0309] "Recorded information" refers to data that stores the content of the conversation.

[0310] "Evaluation" refers to data that analyzes the user's performance based on the recorded conversation information and provides points for improvement.

[0311] A "visual device" is a device for visually providing information to the user, including smart glasses, head-mounted displays, etc.

[0312] "Simulated experience" is an environment that virtually reproduces real-world situations and enables users to learn practical skills.

[0313] To implement this invention, a communication device, a visual device, and software applying the generation model are required. At the center of the system, there is a generation AI model for speech recognition and natural language processing. This model is constructed using a machine learning framework such as TensorFlow and operates on a Flask server.

[0314] The server receives the user's input sent from the communication device. The input is provided in voice or text format, and in the case of voice input, it is converted into text by speech recognition. These input data are analyzed by the generation AI model, and appropriate conversation content for the virtual character is generated. In this process, an instruction such as "Customer service scenario: Claim handling. Please start handling inquiries about damaged products." is given to the generation model as a prompt sentence.

[0315] The generated dialogue is transmitted to a visual device via a communication device, and a virtual character is represented visually or audibly on the visual device. Smart glasses or head-mounted displays are used as the visual device. This system allows users to learn customer service skills required in real-world stores through simulated experiences.

[0316] For example, if a user selects a scenario where they "recommend products," a virtual character appears as a new customer requesting product information, and the user provides details accordingly. All interactions are recorded, sent to a server, analyzed, and then feedback is generated for the user. This feedback is presented as specific areas for improvement to help the user enhance their skills.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The terminal receives the user's login information as input and performs user authentication. If correct authentication information is provided, a list of available customer service scenarios for the user is displayed. This allows the user to select the appropriate scenario.

[0320] Step 2:

[0321] The user selects a specific customer service scenario from the displayed scenarios. The terminal sends this selection information as input to the server. Based on this information, the server generates prompt text for the AI ​​model and prepares dialogue content for a virtual character appropriate to the scenario.

[0322] Step 3:

[0323] The server uses a generative AI model to generate a virtual character dialogue based on a selected scenario. The prompt "Customer service scenario: Complaint handling." is given to the model as input, and this information is used to construct the virtual character's response. This dialogue is then sent to the terminal as output.

[0324] Step 4:

[0325] The terminal displays the received dialogue content on a visual device and presents a virtual person to the user via voice or text. The user receives this and provides input to respond. This input becomes data for the user to attempt ideal customer service.

[0326] Step 5:

[0327] The server receives user responses as input and records the interaction. The recorded information is used later for analysis. The server analyzes the input data and identifies perspectives for generating a user performance evaluation.

[0328] Step 6:

[0329] The server generates feedback based on recorded conversations. This feedback includes specific improvement suggestions and success stories. The analysis results are presented to support the user's skill improvement.

[0330] Step 7:

[0331] The device presents the generated feedback to the user. The user can use this information to learn independently and, if necessary, select a different scenario to re-experience the simulation.

[0332] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0333] This invention incorporates an emotion engine into a system in which a user interacts with a virtual character using a generative model on a communication terminal. By doing so, the system recognizes the user's emotions and provides more appropriate responses. This system enables the practice of more natural and effective customer service scenarios.

[0334] The system configuration is as follows: First, the user launches the application on a communication terminal, logs in, and then begins customer service practice. The user selects a scenario they wish to practice, and a corresponding virtual character is displayed on the terminal. The virtual character responds to the user's input in real time using a generative model.

[0335] This system also incorporates an emotion engine that can recognize emotions from the user's tone of voice, facial expressions, and textual expressions. For example, if the user appears confused or anxious, the emotion engine analyzes this and communicates it to the virtual character. The virtual character then adjusts its response based on the recognized emotional information, providing the user with more appropriate advice and support.

[0336] All user dialogue and emotional data are recorded. Upon completion of the practice session, the recorded data is sent to a server for analysis. The server evaluates the quality of the dialogue and generates feedback based on the data, including emotional changes. This feedback includes specific advice indicating how the user responded to the scenario and which parts were particularly effective or need improvement.

[0337] For example, in a "customer complaint handling" scenario, if the virtual character expresses dissatisfaction, the user might offer an apology in a somewhat anxious voice. The emotion engine would detect this anxiety and instruct the virtual character to respond calmly. This allows the user to learn how their emotions affect others and how that impacts the flow of the conversation.

[0338] In summary, this system allows users to practice more practical and situation-appropriate customer service, and provides a means to offer sophisticated feedback that even takes into account the user's emotions.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The user launches the application on their communication terminal and enters their login information. The terminal then sends the entered information to the server to request authentication.

[0342] Step 2:

[0343] The server verifies the login information, and if authentication is successful, it sends data to provide the user with a home screen appropriate for their device.

[0344] Step 3:

[0345] The user selects their desired customer service scenario from the home screen. The device then sends a request to the server based on the selected scenario.

[0346] Step 4:

[0347] The server prepares data for generating a virtual character that matches the selected scenario and provides this data to the terminal.

[0348] Step 5:

[0349] The terminal uses the received data to display a virtual character and sets up the user to begin interacting with it. At this time, the emotion engine is also initialized and ready to recognize the user's emotions.

[0350] Step 6:

[0351] While the user interacts with the virtual character, the emotion engine analyzes the user's tone of voice, words, and facial expressions, and sends the identified emotion data to the virtual character.

[0352] Step 7:

[0353] The device dynamically adjusts the virtual character's responses based on data from the emotion engine, providing appropriate responses that match the user's emotions.

[0354] Step 8:

[0355] The device records dialogue and emotional data in real time and sends this data to the server at the end of the practice session.

[0356] Step 9:

[0357] The server analyzes the transmitted data and generates feedback that takes into account the quality of the interaction and the user's emotional changes. This feedback includes specific areas for improvement and examples of success.

[0358] Step 10:

[0359] The server sends the generated feedback to the terminal and displays it to the user. The user can then use this feedback to improve their customer service skills.

[0360] (Example 2)

[0361] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0362] Conventional virtual character dialogue systems have struggled to provide appropriate responses that fully consider the user's emotions. Furthermore, there were limitations in accurately generating specific feedback to improve the quality of the dialogue. This resulted in challenges for users in effectively learning and practicing.

[0363] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0364] In this invention, the server includes means for displaying a virtual character using a generative model that exchanges information with a user via a communication device, means for detecting the user's emotions when the information exchange is performed in real time, and means for recording the content of the information exchange and the detected emotion data. This makes it possible to provide appropriate responses in real time according to the user's emotions and improve the quality of information exchange.

[0365] A "communication device" is a device used for exchanging information between a user and a virtual character. It displays the response of the generative model and accepts input from the user.

[0366] A "generative model" is an artificial intelligence technique used by a virtual character to generate natural and appropriate responses to user input, and includes algorithms capable of handling a variety of scenarios.

[0367] A "virtual character" is a character that engages in dialogue using a generative model and serves as an entity with which users can exchange information.

[0368] "Information exchange" refers to all interactions and data exchanges that take place between virtual characters and users, including communication conducted through text and voice.

[0369] "Emotional data" refers to data that indicates the user's emotional state, and is based on information extracted from voice tone and facial expressions.

[0370] "Real-time" refers to the temporal characteristic of responding to or processing user input immediately, meaning that it requires high responsiveness and fast processing.

[0371] "Guidance" refers to educational or corrective advice or directional information provided to users, and is a type of feedback generated based on dialogue and emotions.

[0372] This invention is a system that enables users to interact with virtual characters using a communication device, providing more natural and adaptive responses. The communication device used is a common mobile communication device such as a smartphone or tablet. This device incorporates software for implementing a generative AI model and hardware for emotion recognition.

[0373] The communication device's functionality is activated when the user logs in through the application and initiates a conversation. A generative AI model analyzes text or voice input from the user and generates a corresponding response from a virtual character. Natural language processing technology is used as the generative AI model for response generation, and it incorporates a deep learning algorithm.

[0374] Furthermore, the communication device incorporates an emotion engine that analyzes the user's voice tone and facial expression data in real time and records emotional data. This emotional data is reflected in the virtual character's responses to enhance the naturalness of the responses. For example, if the user shows anxiety, the virtual character will respond calmly and adjust to provide a sense of reassurance.

[0375] After the interaction, the communication device sends the recorded interaction data and emotional data to the server. The server analyzes this data and generates feedback for each user. This feedback includes specific advice and suggestions for improvement to help the user improve their interaction skills. The generated feedback is returned to the communication device and displayed to the user.

[0376] As a concrete example, in a "customer complaint handling" scenario, a virtual character expresses dissatisfaction, and the user apologizes. If the user's voice indicates anxiety, the emotion engine recognizes this and instructs the virtual character to respond calmly.

[0377] An example of a prompt might be, "I want to practice how to respond to customer complaints." Based on this prompt, the communication device selects the most appropriate dialogue scenario and provides a flow of dialogue using a virtual character.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The user launches the application using a communication device and enters login information. This login information is used for authentication by the communication device, allowing the user to access the application's main screen. Specific actions include filling out the login form and clicking the submit button.

[0381] Step 2:

[0382] The user selects a dialogue scenario. Based on this input, the communication device collects the data necessary to generate a virtual character that matches the selected scenario and provides it to the generation AI model. The generation AI model analyzes this data and generates the initial dialogue content of the virtual character in accordance with the selected scenario.

[0383] Step 3:

[0384] The terminal displays the generated virtual character and begins interacting with the user. When the user provides input (text or voice), the terminal captures it and passes it to the generating AI model. The generating AI model analyzes this input and generates the optimal response. Specific actions include processing voice input through the user's microphone and sending text chat messages.

[0385] Step 4:

[0386] The device incorporates an emotion engine that acquires emotional data by analyzing the user's voice tone or facial expressions. The user's voice tone and facial expressions are used as input, and the emotion engine performs analysis based on this. The results of this analysis are reflected in the virtual character's response, and the device adjusts the response before presenting it to the user.

[0387] Step 5:

[0388] After the conversation ends or after a certain period of time has elapsed, the terminal sends the recorded conversation data and emotional data to the server. The server receives the data and analyzes its quality and content. This analysis includes tracking emotional changes and evaluating the overall conversation using data processing algorithms.

[0389] Step 6:

[0390] The server generates feedback for the user based on the analysis results. This feedback includes specific advice to help improve the user's conversational skills. The generated feedback is sent from the server to the terminal and displayed to the user. Importantly, detailed feedback is provided based on the previously analyzed sentiment data.

[0391] (Application Example 2)

[0392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0393] In many brick-and-mortar stores, improving the customer service skills of staff is crucial, but traditional training methods have limitations in providing training that includes realistic situational responses and emotional recognition. In particular, there is a lack of effective training methods to cultivate the ability to appropriately recognize emotions and adjust responses during conversations, thus creating a need for more practical methods to improve customer service skills that are tailored to individual circumstances.

[0394] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0395] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with a user via a communication device, means for causing the virtual person to recognize the user's emotions and adjust its response, and means for recording the emotion recognition and response. This makes it possible for store employees to effectively improve their customer service skills in realistic situations.

[0396] A "communication device" is a device used to send and receive information via digital signals.

[0397] A "user" is an individual or group that uses the system to interact with a virtual character.

[0398] A "generative model" is an algorithm that uses machine learning techniques to automatically generate natural-sounding responses.

[0399] A "virtual character" is a digital character created using a generative model to interact with users.

[0400] "Emotion recognition" is the process of analyzing a user's emotional state from voice, facial expressions, and text data.

[0401] "Adjusting responses" means dynamically changing the content and tone of a virtual character's responses based on recognized emotional data.

[0402] "Recording" refers to the act of saving the content of a conversation and data on the user's emotions.

[0403] "Feedback" refers to rating information generated to return the results of the dialogue and areas for improvement to the user.

[0404] "Analyzing" is a method of extracting meaning and trends using collected data.

[0405] "Practice in a physical store" refers to training activities in which store employees simulate an actual sales environment to hone their customer service skills.

[0406] This invention can be specifically implemented as a training system to improve the customer service skills of store employees. The server executes a generative model via a communication device and displays a virtual character. The virtual character interacts with the user and dynamically adjusts its response based on emotional input from the user (voice, facial expressions, text). Software technology that analyzes voice and facial expressions is used for emotion recognition.

[0407] The server also records the content and emotional data of this interaction. This includes the ability to save the user's voice tone and facial expression changes as data. The recorded data is then used to generate feedback for the user. This feedback includes an evaluation of the quality of the interaction and areas for improvement.

[0408] The terminal can be implemented as part of this system using smart glasses or head-mounted displays such as Microsoft HoloLens. This allows users to simulate a real store environment.

[0409] As a concrete example, a flower shop employee can hone their skills in handling customer complaints through dialogue with virtual customers. In this process, the system analyzes the user's emotions, such as anxiety and confusion, and provides appropriate feedback. For example, if the user shows anxiety, the system will provide feedback emphasizing the importance of responding calmly.

[0410] An example of a prompt to the generating AI model would be: "The current situation is a severe customer complaint. Please tell us what the appropriate response would be if the user is showing signs of anxiety." In this way, the system helps improve customer service skills to be more practical and personalized through emotion recognition and appropriate responses.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The user activates the device and logs into the customer service training application. The information entered here is the user's login information, which the device sends to the authentication server for authentication. Upon successful authentication, the user gains access to the main menu.

[0414] Step 2:

[0415] The user selects the scenario they want to train. The input here is an instruction for scenario selection, and the device sends this selection information to the server. The server uses a generative AI model to generate a virtual character based on the selected scenario and sends it to the device.

[0416] Step 3:

[0417] A virtual character is generated and displayed on the device. The user begins interacting with the virtual character, providing input via voice and text. The input data also includes the user's facial expressions. The device sends this data to an emotion recognition engine.

[0418] Step 4:

[0419] The server uses an emotion recognition engine to analyze the user's voice and facial expression data to identify their emotional state. The input is the user's voice and facial expression data, and the output is the recognized emotion information.

[0420] Step 5:

[0421] The server uses a generated AI model to adjust the virtual character's response based on the recognized emotion. The input here is emotion information, and the output is the adjusted response. The server returns this response to the terminal.

[0422] Step 6:

[0423] The device displays a pre-arranged response from a virtual character to the user. The user can continue the conversation and, if necessary, select other scenarios.

[0424] Step 7:

[0425] After all conversations have ended, the terminal records the conversation content and emotional data and sends it to the server.

[0426] Step 8:

[0427] The server analyzes the received data and generates feedback. Input data includes dialogue content and sentiment data, while output is specific feedback. This feedback includes the user's strengths and areas for improvement.

[0428] Step 9:

[0429] The server sends feedback to the terminal, which then displays it to the user. The user can then review the feedback and use it to improve future training sessions.

[0430] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0431] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0436] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0437] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0438] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0439] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0440] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0441] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0442] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0443] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0444] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0445] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0446] This invention relates to a system that allows a user to interact with a virtual character using a generative model via a communication terminal. This system provides a means for users to effectively learn and improve their customer service skills.

[0447] The program for this system begins with installing an application on the communication terminal. This application includes a login screen for the user, and is accessible to each user individually. Within the application, the user selects a customer service scenario, and based on that, a virtual character created using a generative model is displayed on the terminal, and the interaction begins.

[0448] The virtual character is configured to match the scenario selected by the user and engages in real-time voice or text-based conversations. This interaction is designed to allow users to simulate situations that occur in various customer service scenarios. For example, if a new employee selects the "handling a complaint" scenario, the virtual character will appear as a dissatisfied customer, allowing the user to practice how to handle such a situation.

[0449] All conversations are recorded and sent to the server after completion. The server analyzes the recorded content and generates feedback to identify the quality of the user's responses, issues, and successes. This feedback is compared with specific performance metrics and data from other users to specifically highlight areas for improvement and strengths for the user. The generated feedback is sent to the communication terminal, which the user can receive and use to improve themselves.

[0450] As a concrete example, if a user chooses the scenario of "recommending products," a virtual character appears as a new customer and asks, "I don't know which product is good, so could you recommend something?" The user then accurately explains the product's features, benefits, price range, etc., and makes a suggestion to encourage a purchase. This entire conversation is recorded, analyzed, and provided as feedback, allowing the user to objectively evaluate and improve their customer service skills.

[0451] This allows users to efficiently and quantitatively improve their customer service skills in any situation, such as during breaks at work or at home.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The user launches the application on their communication device and enters their authentication information on the login screen. The device then sends the information entered by the user to the server.

[0455] Step 2:

[0456] The server verifies the received authentication information, and if it is correct, sends data to the user's device to display the home screen.

[0457] Step 3:

[0458] The user selects a customer service scenario they want to practice from the home screen. Based on this selection, the device sends the scenario information to the server as a request.

[0459] Step 4:

[0460] The server uses a generative model to create data for generating a virtual character based on the scenario selected by the user, and then sends it back to the terminal.

[0461] Step 5:

[0462] The terminal displays a virtual character on the screen based on the received data and prepares to begin interacting with the user.

[0463] Step 6:

[0464] Users interact with virtual characters and practice responding to various customer service situations. These interactions are conducted via voice or text.

[0465] Step 7:

[0466] The terminal continuously records all of the conversations that take place in real time, and when the practice session ends, it sends that data to the server.

[0467] Step 8:

[0468] The server analyzes the received dialogue data, evaluates the quality of the user's responses, and generates feedback that identifies specific success stories and areas for improvement.

[0469] Step 9:

[0470] The server sends the generated feedback to the terminal and displays specific advice and points for improvement to the user.

[0471] Step 10:

[0472] Users can review feedback, plan actions to improve their skills, and then take action.

[0473] (Example 1)

[0474] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0475] Traditional training methods for improving customer service skills rely on accumulating practical experience, which is inefficient. Furthermore, the lack of objective criteria for self-assessment makes it difficult to measure one's own improvement in customer service skills. Additionally, the inability to self-evaluate through comparison with other customers makes it difficult to develop competitive skills.

[0476] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0477] In this invention, the server includes means for displaying a virtual object using a generated data model that interacts with a user via a communication device, means for recording information about the interaction, and means for analyzing the recorded information and generating evaluation information for the user. This enables users to efficiently and quantitatively evaluate their own customer service skills and improve their skills competitively through comparison with other users.

[0478] A "communication device" is an electronic device used to send and receive data over a network.

[0479] A "generative data model" is a program or algorithm that uses AI to generate responses or information that are appropriate to language, context, or situation.

[0480] A "virtual object" is an artificial entity that is generated in digital form, visually represented on a computer screen, and capable of interacting with the user.

[0481] "Dialogue information" refers to data that includes the content of communication that took place between the user and the virtual object.

[0482] "Recorded information" refers to data that preserves the content and process of a dialogue and makes it available for later analysis.

[0483] "Evaluation information" refers to information that is analyzed based on recorded data and presented as feedback on the quality of user behavior and responses.

[0484] A "prompt" is a guideline in the form of instructions or questions that are input into a data generation model, and it is an important element in determining the model's output.

[0485] To implement this invention, the user first installs a dedicated application on their communication terminal. This application runs on a mobile device or personal computer and provides a user interface. The user can access the system by logging in and begin operations using their individually configured account.

[0486] The terminal generates and displays virtual objects using a generated data model based on the customer service scenario selected by the user. These virtual objects are generated in real time using AI technology and can mimic conversations that are in line with the scenario. If the user selects a scenario such as "handling a complaint" or "recommending products," the virtual objects will play the appropriate role.

[0487] During a conversation, the device records all conversational information and sends it to the server. This recorded information is received by the server and analyzed by a generating AI model. The server generates evaluation information based on the user's responses and the progress of the conversation. This evaluation information is displayed on the device as feedback for the user to self-assess and improve their skills.

[0488] For example, if the user chooses a scenario where they "recommend products," the virtual object, acting as a new customer, will ask questions such as "Which product do you recommend?" The user then explains the product's features and benefits, and makes a suggestion to encourage purchase. This entire dialogue process is recorded and evaluated later.

[0489] An example of a prompt message would be, "In a scenario suggesting recommended products, please appear as a new customer and present a situation where you are having trouble choosing a product." This instruction is then input into the AI ​​generation model. This mechanism generates more realistic dialogue and contributes to improving the user's skills.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The user installs a dedicated application on their communication device and enters their authentication information on the login screen. This allows the user to access their account and prepares them to proceed to the next step. The input is the user's authentication information, and the output is the status of successful login to the application.

[0493] Step 2:

[0494] The user selects one of several customer service scenarios available within the application. Information about the selected scenario is entered, and this information is used to configure the virtual object. The output is the information of the selected scenario.

[0495] Step 3:

[0496] The device uses a generative AI model to generate virtual objects according to the scenario selected by the user. Here, prompts based on the selected scenario are input to the generative AI model. The model performs data calculations to generate an appropriate response, and the virtual object is displayed on the screen as output.

[0497] Step 4:

[0498] When a virtual object appears on the screen, real-time interaction with the user begins. User input is sent to the terminal as interaction information, and the virtual object operates based on this. The output is the content of the ongoing interaction with the user.

[0499] Step 5:

[0500] The terminal records the content of the conversation and sends it to the server as conversation information. The input is various conversation logs, and the output is the completion of the transfer to the server.

[0501] Step 6:

[0502] The server analyzes the received dialogue information and quantitatively evaluates the user's response using a generated AI model. The input is the dialogue information, and the generated evaluation information is output.

[0503] Step 7:

[0504] The server sends the generated evaluation information to the user's terminal. The terminal displays this feedback to the user. The input is the evaluation information, and the output is the feedback screen display. This feedback allows the user to evaluate their customer service skills and use it to improve them.

[0505] (Application Example 1)

[0506] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0507] When staff in physical stores try to improve their customer service skills through practical experience, they often rely on real-world situations. However, gaining sufficient experience requires many interactions with actual customers, which takes a lot of time and opportunity. Furthermore, learning from mistakes through interactions with real customers carries risks. For these reasons, there is a need to provide training in an environment that closely resembles real-world situations.

[0508] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0509] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with the user via a communication device, means for recording information about the interaction, means for analyzing the recorded information and generating an evaluation of the user, and means for displaying the virtual person using a visual device and simulating the user's actions in the real world. This enables the user to safely and efficiently improve their customer service skills.

[0510] A "communication device" is an electronic device used to send and receive data using a network.

[0511] A "user" is a person who operates and interacts with this system.

[0512] A "generative model" is an algorithm that uses AI technology to generate dialogue between virtual characters.

[0513] A "virtual character" is a human-like character generated by a computer and represented on screen or through sound.

[0514] "Dialogue information" refers to data about the communication between the user and the virtual character.

[0515] "Recorded information" refers to data that preserves the content of a conversation.

[0516] "Evaluation" refers to data that analyzes the user's performance based on recorded conversation information and provides areas for improvement.

[0517] "Visual devices" are devices that provide information to users visually, and include smart glasses and head-mounted displays.

[0518] A "simulated experience" is an environment that virtually recreates real-world situations, allowing users to learn practical skills.

[0519] To implement this invention, a communication device, a vision device, and software applying a generative model are required. At the heart of the system is a generative AI model for speech recognition and natural language processing. This model is built using a machine learning framework such as TensorFlow and runs on a Flask server.

[0520] The server receives user input transmitted from the communication device. The input is provided in voice or text format, and in the case of voice input, it is converted to text by speech recognition. This input data is analyzed by a generative AI model to generate appropriate dialogue content for the virtual character. In this process, the generative model is given instructions as prompts, such as "Customer service scenario: Complaint handling. Begin responding to an inquiry about a damaged product."

[0521] The generated dialogue is transmitted to a visual device via a communication device, and a virtual character is represented visually or audibly on the visual device. Smart glasses or head-mounted displays are used as the visual device. This system allows users to learn customer service skills required in real-world stores through simulated experiences.

[0522] For example, if a user selects a scenario where they "recommend products," a virtual character appears as a new customer requesting product information, and the user provides details accordingly. All interactions are recorded, sent to a server, analyzed, and then feedback is generated for the user. This feedback is presented as specific areas for improvement to help the user enhance their skills.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The terminal receives the user's login information as input and performs user authentication. If correct authentication information is provided, a list of available customer service scenarios for the user is displayed. This allows the user to select the appropriate scenario.

[0526] Step 2:

[0527] The user selects a specific customer service scenario from the displayed scenarios. The terminal sends this selection information as input to the server. Based on this information, the server generates prompt text for the AI ​​model and prepares dialogue content for a virtual character appropriate to the scenario.

[0528] Step 3:

[0529] The server uses a generative AI model to generate a virtual character dialogue based on a selected scenario. The prompt "Customer service scenario: Complaint handling." is given to the model as input, and this information is used to construct the virtual character's response. This dialogue is then sent to the terminal as output.

[0530] Step 4:

[0531] The terminal displays the received dialogue content on a visual device and presents a virtual person to the user via voice or text. The user receives this and provides input to respond. This input becomes data for the user to attempt ideal customer service.

[0532] Step 5:

[0533] The server receives user responses as input and records the interaction. The recorded information is used later for analysis. The server analyzes the input data and identifies perspectives for generating a user performance evaluation.

[0534] Step 6:

[0535] The server generates feedback based on recorded conversations. This feedback includes specific improvement suggestions and success stories. The analysis results are presented to support the user's skill improvement.

[0536] Step 7:

[0537] The device presents the generated feedback to the user. The user can use this information to learn independently and, if necessary, select a different scenario to re-experience the simulation.

[0538] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0539] This invention incorporates an emotion engine into a system in which a user interacts with a virtual character using a generative model on a communication terminal. By doing so, the system recognizes the user's emotions and provides more appropriate responses. This system enables the practice of more natural and effective customer service scenarios.

[0540] The system configuration is as follows: First, the user launches the application on a communication terminal, logs in, and then begins customer service practice. The user selects a scenario they wish to practice, and a corresponding virtual character is displayed on the terminal. The virtual character responds to the user's input in real time using a generative model.

[0541] This system also incorporates an emotion engine that can recognize emotions from the user's tone of voice, facial expressions, and textual expressions. For example, if the user appears confused or anxious, the emotion engine analyzes this and communicates it to the virtual character. The virtual character then adjusts its response based on the recognized emotional information, providing the user with more appropriate advice and support.

[0542] All user dialogue and emotional data are recorded. Upon completion of the practice session, the recorded data is sent to a server for analysis. The server evaluates the quality of the dialogue and generates feedback based on the data, including emotional changes. This feedback includes specific advice indicating how the user responded to the scenario and which parts were particularly effective or need improvement.

[0543] For example, in a "customer complaint handling" scenario, if the virtual character expresses dissatisfaction, the user might offer an apology in a somewhat anxious voice. The emotion engine would detect this anxiety and instruct the virtual character to respond calmly. This allows the user to learn how their emotions affect others and how that impacts the flow of the conversation.

[0544] In summary, this system allows users to practice more practical and situation-appropriate customer service, and provides a means to offer sophisticated feedback that even takes into account the user's emotions.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The user launches the application on their communication terminal and enters their login information. The terminal then sends the entered information to the server to request authentication.

[0548] Step 2:

[0549] The server verifies the login information, and if authentication is successful, it sends data to provide the user with a home screen appropriate for their device.

[0550] Step 3:

[0551] The user selects their desired customer service scenario from the home screen. The device then sends a request to the server based on the selected scenario.

[0552] Step 4:

[0553] The server prepares data for generating a virtual character that matches the selected scenario and provides this data to the terminal.

[0554] Step 5:

[0555] The terminal uses the received data to display a virtual character and sets up the user to begin interacting with it. At this time, the emotion engine is also initialized and ready to recognize the user's emotions.

[0556] Step 6:

[0557] While the user interacts with the virtual character, the emotion engine analyzes the user's tone of voice, words, and facial expressions, and sends the identified emotion data to the virtual character.

[0558] Step 7:

[0559] The device dynamically adjusts the virtual character's responses based on data from the emotion engine, providing appropriate responses that match the user's emotions.

[0560] Step 8:

[0561] The device records dialogue and emotional data in real time and sends this data to the server at the end of the practice session.

[0562] Step 9:

[0563] The server analyzes the transmitted data and generates feedback that takes into account the quality of the interaction and the user's emotional changes. This feedback includes specific areas for improvement and examples of success.

[0564] Step 10:

[0565] The server sends the generated feedback to the terminal and displays it to the user. The user can then use this feedback to improve their customer service skills.

[0566] (Example 2)

[0567] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0568] Conventional virtual character dialogue systems have struggled to provide appropriate responses that fully consider the user's emotions. Furthermore, there were limitations in accurately generating specific feedback to improve the quality of the dialogue. This resulted in challenges for users in effectively learning and practicing.

[0569] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0570] In this invention, the server includes means for displaying a virtual character using a generative model that exchanges information with a user via a communication device, means for detecting the user's emotions when the information exchange is performed in real time, and means for recording the content of the information exchange and the detected emotion data. This makes it possible to provide appropriate responses in real time according to the user's emotions and improve the quality of information exchange.

[0571] A "communication device" is a device used for exchanging information between a user and a virtual character. It displays the response of the generative model and accepts input from the user.

[0572] A "generative model" is an artificial intelligence technique used by a virtual character to generate natural and appropriate responses to user input, and includes algorithms capable of handling a variety of scenarios.

[0573] A "virtual character" is a character that engages in dialogue using a generative model and serves as an entity with which users can exchange information.

[0574] "Information exchange" refers to all interactions and data exchanges that take place between virtual characters and users, including communication conducted through text and voice.

[0575] "Emotional data" refers to data that indicates the user's emotional state, and is based on information extracted from voice tone and facial expressions.

[0576] "Real-time" refers to the temporal characteristic of responding to or processing user input immediately, meaning that it requires high responsiveness and fast processing.

[0577] "Guidance" refers to educational or corrective advice or directional information provided to users, and is a type of feedback generated based on dialogue and emotions.

[0578] This invention is a system that enables users to interact with virtual characters using a communication device, providing more natural and adaptive responses. The communication device used is a common mobile communication device such as a smartphone or tablet. This device incorporates software for implementing a generative AI model and hardware for emotion recognition.

[0579] The communication device's functionality is activated when the user logs in through the application and initiates a conversation. A generative AI model analyzes text or voice input from the user and generates a corresponding response from a virtual character. Natural language processing technology is used as the generative AI model for response generation, and it incorporates a deep learning algorithm.

[0580] Furthermore, the communication device incorporates an emotion engine that analyzes the user's voice tone and facial expression data in real time and records emotional data. This emotional data is reflected in the virtual character's responses to enhance the naturalness of the responses. For example, if the user shows anxiety, the virtual character will respond calmly and adjust to provide a sense of reassurance.

[0581] After the interaction, the communication device sends the recorded interaction data and emotional data to the server. The server analyzes this data and generates feedback for each user. This feedback includes specific advice and suggestions for improvement to help the user improve their interaction skills. The generated feedback is returned to the communication device and displayed to the user.

[0582] As a concrete example, in a "customer complaint handling" scenario, a virtual character expresses dissatisfaction, and the user apologizes. If the user's voice indicates anxiety, the emotion engine recognizes this and instructs the virtual character to respond calmly.

[0583] An example of a prompt might be, "I want to practice how to respond to customer complaints." Based on this prompt, the communication device selects the most appropriate dialogue scenario and provides a flow of dialogue using a virtual character.

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Step 1:

[0586] The user launches the application using a communication device and enters login information. This login information is used for authentication by the communication device, allowing the user to access the application's main screen. Specific actions include filling out the login form and clicking the submit button.

[0587] Step 2:

[0588] The user selects a dialogue scenario. Based on this input, the communication device collects the data necessary to generate a virtual character that matches the selected scenario and provides it to the generation AI model. The generation AI model analyzes this data and generates the initial dialogue content of the virtual character in accordance with the selected scenario.

[0589] Step 3:

[0590] The terminal displays the generated virtual character and begins interacting with the user. When the user provides input (text or voice), the terminal captures it and passes it to the generating AI model. The generating AI model analyzes this input and generates the optimal response. Specific actions include processing voice input through the user's microphone and sending text chat messages.

[0591] Step 4:

[0592] The device incorporates an emotion engine that acquires emotional data by analyzing the user's voice tone or facial expressions. The user's voice tone and facial expressions are used as input, and the emotion engine performs analysis based on this. The results of this analysis are reflected in the virtual character's response, and the device adjusts the response before presenting it to the user.

[0593] Step 5:

[0594] After the conversation ends or after a certain period of time has elapsed, the terminal sends the recorded conversation data and emotional data to the server. The server receives the data and analyzes its quality and content. This analysis includes tracking emotional changes and evaluating the overall conversation using data processing algorithms.

[0595] Step 6:

[0596] The server generates feedback for the user based on the analysis results. This feedback includes specific advice to help improve the user's conversational skills. The generated feedback is sent from the server to the terminal and displayed to the user. Importantly, detailed feedback is provided based on the previously analyzed sentiment data.

[0597] (Application Example 2)

[0598] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0599] In many brick-and-mortar stores, improving the customer service skills of staff is crucial, but traditional training methods have limitations in providing training that includes realistic situational responses and emotional recognition. In particular, there is a lack of effective training methods to cultivate the ability to appropriately recognize emotions and adjust responses during conversations, thus creating a need for more practical methods to improve customer service skills that are tailored to individual circumstances.

[0600] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0601] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with a user via a communication device, means for causing the virtual person to recognize the user's emotions and adjust its response, and means for recording the emotion recognition and response. This makes it possible for store employees to effectively improve their customer service skills in realistic situations.

[0602] A "communication device" is a device used to send and receive information via digital signals.

[0603] A "user" is an individual or group that uses the system to interact with a virtual character.

[0604] A "generative model" is an algorithm that uses machine learning techniques to automatically generate natural-sounding responses.

[0605] A "virtual character" is a digital character created using a generative model to interact with users.

[0606] "Emotion recognition" is the process of analyzing a user's emotional state from voice, facial expressions, and text data.

[0607] "Adjusting responses" means dynamically changing the content and tone of a virtual character's responses based on recognized emotional data.

[0608] "Recording" refers to the act of saving the content of a conversation and data on the user's emotions.

[0609] "Feedback" refers to rating information generated to return the results of the dialogue and areas for improvement to the user.

[0610] "Analyzing" is a method of extracting meaning and trends using collected data.

[0611] "Practice in a physical store" refers to training activities in which store employees simulate an actual sales environment to hone their customer service skills.

[0612] This invention can be specifically implemented as a training system to improve the customer service skills of store employees. The server executes a generative model via a communication device and displays a virtual character. The virtual character interacts with the user and dynamically adjusts its response based on emotional input from the user (voice, facial expressions, text). Software technology that analyzes voice and facial expressions is used for emotion recognition.

[0613] The server also records the content and emotional data of this interaction. This includes the ability to save the user's voice tone and facial expression changes as data. The recorded data is then used to generate feedback for the user. This feedback includes an evaluation of the quality of the interaction and areas for improvement.

[0614] The terminal can be implemented as part of this system using smart glasses or head-mounted displays such as Microsoft HoloLens. This allows users to simulate a real store environment.

[0615] As a concrete example, a flower shop employee can hone their skills in handling customer complaints through dialogue with virtual customers. In this process, the system analyzes the user's emotions, such as anxiety and confusion, and provides appropriate feedback. For example, if the user shows anxiety, the system will provide feedback emphasizing the importance of responding calmly.

[0616] An example of a prompt to the generating AI model would be: "The current situation is a severe customer complaint. Please tell us what the appropriate response would be if the user is showing signs of anxiety." In this way, the system helps improve customer service skills to be more practical and personalized through emotion recognition and appropriate responses.

[0617] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0618] Step 1:

[0619] The user activates the device and logs into the customer service training application. The information entered here is the user's login information, which the device sends to the authentication server for authentication. Upon successful authentication, the user gains access to the main menu.

[0620] Step 2:

[0621] The user selects the scenario they want to train. The input here is an instruction for scenario selection, and the device sends this selection information to the server. The server uses a generative AI model to generate a virtual character based on the selected scenario and sends it to the device.

[0622] Step 3:

[0623] A virtual character is generated and displayed on the device. The user begins interacting with the virtual character, providing input via voice and text. The input data also includes the user's facial expressions. The device sends this data to an emotion recognition engine.

[0624] Step 4:

[0625] The server uses an emotion recognition engine to analyze the user's voice and facial expression data to identify their emotional state. The input is the user's voice and facial expression data, and the output is the recognized emotion information.

[0626] Step 5:

[0627] The server uses a generated AI model to adjust the virtual character's response based on the recognized emotion. The input here is emotion information, and the output is the adjusted response. The server returns this response to the terminal.

[0628] Step 6:

[0629] The device displays a pre-arranged response from a virtual character to the user. The user can continue the conversation and, if necessary, select other scenarios.

[0630] Step 7:

[0631] After all conversations have ended, the terminal records the conversation content and emotional data and sends it to the server.

[0632] Step 8:

[0633] The server analyzes the received data and generates feedback. Input data includes dialogue content and sentiment data, while output is specific feedback. This feedback includes the user's strengths and areas for improvement.

[0634] Step 9:

[0635] The server sends feedback to the terminal, which then displays it to the user. The user can then review the feedback and use it to improve future training sessions.

[0636] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0637] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0638] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0642] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0643] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0644] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0645] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0646] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0647] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0648] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0649] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0650] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0651] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0652] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] This invention relates to a system that allows a user to interact with a virtual character using a generative model via a communication terminal. This system provides a means for users to effectively learn and improve their customer service skills.

[0654] The program for this system begins with installing an application on the communication terminal. This application includes a login screen for the user, and is accessible to each user individually. Within the application, the user selects a customer service scenario, and based on that, a virtual character created using a generative model is displayed on the terminal, and the interaction begins.

[0655] The virtual character is configured to match the scenario selected by the user and engages in real-time voice or text-based conversations. This interaction is designed to allow users to simulate situations that occur in various customer service scenarios. For example, if a new employee selects the "handling a complaint" scenario, the virtual character will appear as a dissatisfied customer, allowing the user to practice how to handle such a situation.

[0656] All conversations are recorded and sent to the server after completion. The server analyzes the recorded content and generates feedback to identify the quality of the user's responses, issues, and successes. This feedback is compared with specific performance metrics and data from other users to specifically highlight areas for improvement and strengths for the user. The generated feedback is sent to the communication terminal, which the user can receive and use to improve themselves.

[0657] As a concrete example, if a user chooses the scenario of "recommending products," a virtual character appears as a new customer and asks, "I don't know which product is good, so could you recommend something?" The user then accurately explains the product's features, benefits, price range, etc., and makes a suggestion to encourage a purchase. This entire conversation is recorded, analyzed, and provided as feedback, allowing the user to objectively evaluate and improve their customer service skills.

[0658] This allows users to efficiently and quantitatively improve their customer service skills in any situation, such as during breaks at work or at home.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The user launches the application on their communication device and enters their authentication information on the login screen. The device then sends the information entered by the user to the server.

[0662] Step 2:

[0663] The server verifies the received authentication information, and if it is correct, sends data to the user's device to display the home screen.

[0664] Step 3:

[0665] The user selects a customer service scenario they want to practice from the home screen. Based on this selection, the device sends the scenario information to the server as a request.

[0666] Step 4:

[0667] The server uses a generative model to create data for generating a virtual character based on the scenario selected by the user, and then sends it back to the terminal.

[0668] Step 5:

[0669] The terminal displays a virtual character on the screen based on the received data and prepares to begin interacting with the user.

[0670] Step 6:

[0671] Users interact with virtual characters and practice responding to various customer service situations. These interactions are conducted via voice or text.

[0672] Step 7:

[0673] The terminal continuously records all of the conversations that take place in real time, and when the practice session ends, it sends that data to the server.

[0674] Step 8:

[0675] The server analyzes the received dialogue data, evaluates the quality of the user's responses, and generates feedback that identifies specific success stories and areas for improvement.

[0676] Step 9:

[0677] The server sends the generated feedback to the terminal and displays specific advice and points for improvement to the user.

[0678] Step 10:

[0679] Users can review feedback, plan actions to improve their skills, and then take action.

[0680] (Example 1)

[0681] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] Traditional training methods for improving customer service skills rely on accumulating practical experience, which is inefficient. Furthermore, the lack of objective criteria for self-assessment makes it difficult to measure one's own improvement in customer service skills. Additionally, the inability to self-evaluate through comparison with other customers makes it difficult to develop competitive skills.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0684] In this invention, the server includes means for displaying a virtual object using a generated data model that interacts with a user via a communication device, means for recording information about the interaction, and means for analyzing the recorded information and generating evaluation information for the user. This enables users to efficiently and quantitatively evaluate their own customer service skills and improve their skills competitively through comparison with other users.

[0685] A "communication device" is an electronic device used to send and receive data over a network.

[0686] A "generative data model" is a program or algorithm that uses AI to generate responses or information that are appropriate to language, context, or situation.

[0687] A "virtual object" is an artificial entity that is generated in digital form, visually represented on a computer screen, and capable of interacting with the user.

[0688] "Dialogue information" refers to data that includes the content of communication that took place between the user and the virtual object.

[0689] "Recorded information" refers to data that preserves the content and process of a dialogue and makes it available for later analysis.

[0690] "Evaluation information" refers to information that is analyzed based on recorded data and presented as feedback on the quality of user behavior and responses.

[0691] A "prompt" is a guideline in the form of instructions or questions that are input into a data generation model, and it is an important element in determining the model's output.

[0692] To implement this invention, the user first installs a dedicated application on their communication terminal. This application runs on a mobile device or personal computer and provides a user interface. The user can access the system by logging in and begin operations using their individually configured account.

[0693] The terminal generates and displays virtual objects using a generated data model based on the customer service scenario selected by the user. These virtual objects are generated in real time using AI technology and can mimic conversations that are in line with the scenario. If the user selects a scenario such as "handling a complaint" or "recommending products," the virtual objects will play the appropriate role.

[0694] During a conversation, the device records all conversational information and sends it to the server. This recorded information is received by the server and analyzed by a generating AI model. The server generates evaluation information based on the user's responses and the progress of the conversation. This evaluation information is displayed on the device as feedback for the user to self-assess and improve their skills.

[0695] For example, if the user chooses a scenario where they "recommend products," the virtual object, acting as a new customer, will ask questions such as "Which product do you recommend?" The user then explains the product's features and benefits, and makes a suggestion to encourage purchase. This entire dialogue process is recorded and evaluated later.

[0696] An example of a prompt message would be, "In a scenario suggesting recommended products, please appear as a new customer and present a situation where you are having trouble choosing a product." This instruction is then input into the AI ​​generation model. This mechanism generates more realistic dialogue and contributes to improving the user's skills.

[0697] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0698] Step 1:

[0699] The user installs a dedicated application on their communication device and enters their authentication information on the login screen. This allows the user to access their account and prepares them to proceed to the next step. The input is the user's authentication information, and the output is the status of successful login to the application.

[0700] Step 2:

[0701] The user selects one of several customer service scenarios available within the application. Information about the selected scenario is entered, and this information is used to configure the virtual object. The output is the information of the selected scenario.

[0702] Step 3:

[0703] The device uses a generative AI model to generate virtual objects according to the scenario selected by the user. Here, prompts based on the selected scenario are input to the generative AI model. The model performs data calculations to generate an appropriate response, and the virtual object is displayed on the screen as output.

[0704] Step 4:

[0705] When a virtual object appears on the screen, real-time interaction with the user begins. User input is sent to the terminal as interaction information, and the virtual object operates based on this. The output is the content of the ongoing interaction with the user.

[0706] Step 5:

[0707] The terminal records the content of the conversation and sends it to the server as conversation information. The input is various conversation logs, and the output is the completion of the transfer to the server.

[0708] Step 6:

[0709] The server analyzes the received dialogue information and quantitatively evaluates the user's response using a generated AI model. The input is the dialogue information, and the generated evaluation information is output.

[0710] Step 7:

[0711] The server sends the generated evaluation information to the user's terminal. The terminal displays this feedback to the user. The input is the evaluation information, and the output is the feedback screen display. This feedback allows the user to evaluate their customer service skills and use it to improve them.

[0712] (Application Example 1)

[0713] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] When staff in physical stores try to improve their customer service skills through practical experience, they often rely on real-world situations. However, gaining sufficient experience requires many interactions with actual customers, which takes a lot of time and opportunity. Furthermore, learning from mistakes through interactions with real customers carries risks. For these reasons, there is a need to provide training in an environment that closely resembles real-world situations.

[0715] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0716] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with the user via a communication device, means for recording information about the interaction, means for analyzing the recorded information and generating an evaluation of the user, and means for displaying the virtual person using a visual device and simulating the user's actions in the real world. This enables the user to safely and efficiently improve their customer service skills.

[0717] A "communication device" is an electronic device used to send and receive data using a network.

[0718] A "user" is a person who operates and interacts with this system.

[0719] A "generative model" is an algorithm that uses AI technology to generate dialogue between virtual characters.

[0720] A "virtual character" is a human-like character generated by a computer and represented on screen or through sound.

[0721] "Dialogue information" refers to data about the communication between the user and the virtual character.

[0722] "Recorded information" refers to data that preserves the content of a conversation.

[0723] "Evaluation" refers to data that analyzes the user's performance based on recorded conversation information and provides areas for improvement.

[0724] "Visual devices" are devices that provide information to users visually, and include smart glasses and head-mounted displays.

[0725] A "simulated experience" is an environment that virtually recreates real-world situations, allowing users to learn practical skills.

[0726] To implement this invention, a communication device, a vision device, and software applying a generative model are required. At the heart of the system is a generative AI model for speech recognition and natural language processing. This model is built using a machine learning framework such as TensorFlow and runs on a Flask server.

[0727] The server receives user input transmitted from the communication device. The input is provided in voice or text format, and in the case of voice input, it is converted to text by speech recognition. This input data is analyzed by a generative AI model to generate appropriate dialogue content for the virtual character. In this process, the generative model is given instructions as prompts, such as "Customer service scenario: Complaint handling. Begin responding to an inquiry about a damaged product."

[0728] The generated dialogue is transmitted to a visual device via a communication device, and a virtual character is represented visually or audibly on the visual device. Smart glasses or head-mounted displays are used as the visual device. This system allows users to learn customer service skills required in real-world stores through simulated experiences.

[0729] For example, if a user selects a scenario where they "recommend products," a virtual character appears as a new customer requesting product information, and the user provides details accordingly. All interactions are recorded, sent to a server, analyzed, and then feedback is generated for the user. This feedback is presented as specific areas for improvement to help the user enhance their skills.

[0730] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0731] Step 1:

[0732] The terminal receives the user's login information as input and performs user authentication. If correct authentication information is provided, a list of available customer service scenarios for the user is displayed. This allows the user to select the appropriate scenario.

[0733] Step 2:

[0734] The user selects a specific customer service scenario from the displayed scenarios. The terminal sends this selection information as input to the server. Based on this information, the server generates prompt text for the AI ​​model and prepares dialogue content for a virtual character appropriate to the scenario.

[0735] Step 3:

[0736] The server uses a generative AI model to generate a virtual character dialogue based on a selected scenario. The prompt "Customer service scenario: Complaint handling." is given to the model as input, and this information is used to construct the virtual character's response. This dialogue is then sent to the terminal as output.

[0737] Step 4:

[0738] The terminal displays the received dialogue content on a visual device and presents a virtual person to the user via voice or text. The user receives this and provides input to respond. This input becomes data for the user to attempt ideal customer service.

[0739] Step 5:

[0740] The server receives user responses as input and records the interaction. The recorded information is used later for analysis. The server analyzes the input data and identifies perspectives for generating a user performance evaluation.

[0741] Step 6:

[0742] The server generates feedback based on recorded conversations. This feedback includes specific improvement suggestions and success stories. The analysis results are presented to support the user's skill improvement.

[0743] Step 7:

[0744] The device presents the generated feedback to the user. The user can use this information to learn independently and, if necessary, select a different scenario to re-experience the simulation.

[0745] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0746] This invention incorporates an emotion engine into a system in which a user interacts with a virtual character using a generative model on a communication terminal. By doing so, the system recognizes the user's emotions and provides more appropriate responses. This system enables the practice of more natural and effective customer service scenarios.

[0747] The system configuration is as follows: First, the user launches the application on a communication terminal, logs in, and then begins customer service practice. The user selects a scenario they wish to practice, and a corresponding virtual character is displayed on the terminal. The virtual character responds to the user's input in real time using a generative model.

[0748] This system also incorporates an emotion engine that can recognize emotions from the user's tone of voice, facial expressions, and textual expressions. For example, if the user appears confused or anxious, the emotion engine analyzes this and communicates it to the virtual character. The virtual character then adjusts its response based on the recognized emotional information, providing the user with more appropriate advice and support.

[0749] All user dialogue and emotional data are recorded. Upon completion of the practice session, the recorded data is sent to a server for analysis. The server evaluates the quality of the dialogue and generates feedback based on the data, including emotional changes. This feedback includes specific advice indicating how the user responded to the scenario and which parts were particularly effective or need improvement.

[0750] For example, in a "customer complaint handling" scenario, if the virtual character expresses dissatisfaction, the user might offer an apology in a somewhat anxious voice. The emotion engine would detect this anxiety and instruct the virtual character to respond calmly. This allows the user to learn how their emotions affect others and how that impacts the flow of the conversation.

[0751] In summary, this system allows users to practice more practical and situation-appropriate customer service, and provides a means to offer sophisticated feedback that even takes into account the user's emotions.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] The user launches the application on their communication terminal and enters their login information. The terminal then sends the entered information to the server to request authentication.

[0755] Step 2:

[0756] The server verifies the login information, and if authentication is successful, it sends data to provide the user with a home screen appropriate for their device.

[0757] Step 3:

[0758] The user selects their desired customer service scenario from the home screen. The device then sends a request to the server based on the selected scenario.

[0759] Step 4:

[0760] The server prepares data for generating a virtual character that matches the selected scenario and provides this data to the terminal.

[0761] Step 5:

[0762] The terminal uses the received data to display a virtual character and sets up the user to begin interacting with it. At this time, the emotion engine is also initialized and ready to recognize the user's emotions.

[0763] Step 6:

[0764] While the user interacts with the virtual character, the emotion engine analyzes the user's tone of voice, words, and facial expressions, and sends the identified emotion data to the virtual character.

[0765] Step 7:

[0766] The device dynamically adjusts the virtual character's responses based on data from the emotion engine, providing appropriate responses that match the user's emotions.

[0767] Step 8:

[0768] The device records dialogue and emotional data in real time and sends this data to the server at the end of the practice session.

[0769] Step 9:

[0770] The server analyzes the transmitted data and generates feedback that takes into account the quality of the interaction and the user's emotional changes. This feedback includes specific areas for improvement and examples of success.

[0771] Step 10:

[0772] The server sends the generated feedback to the terminal and displays it to the user. The user can then use this feedback to improve their customer service skills.

[0773] (Example 2)

[0774] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0775] Conventional virtual character dialogue systems have struggled to provide appropriate responses that fully consider the user's emotions. Furthermore, there were limitations in accurately generating specific feedback to improve the quality of the dialogue. This resulted in challenges for users in effectively learning and practicing.

[0776] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0777] In this invention, the server includes means for displaying a virtual character using a generative model that exchanges information with a user via a communication device, means for detecting the user's emotions when the information exchange is performed in real time, and means for recording the content of the information exchange and the detected emotion data. This makes it possible to provide appropriate responses in real time according to the user's emotions and improve the quality of information exchange.

[0778] A "communication device" is a device used for exchanging information between a user and a virtual character. It displays the response of the generative model and accepts input from the user.

[0779] A "generative model" is an artificial intelligence technique used by a virtual character to generate natural and appropriate responses to user input, and includes algorithms capable of handling a variety of scenarios.

[0780] A "virtual character" is a character that engages in dialogue using a generative model and serves as an entity with which users can exchange information.

[0781] "Information exchange" refers to all interactions and data exchanges that take place between virtual characters and users, including communication conducted through text and voice.

[0782] "Emotional data" refers to data that indicates the user's emotional state, and is based on information extracted from voice tone and facial expressions.

[0783] "Real-time" refers to the temporal characteristic of responding to or processing user input immediately, meaning that it requires high responsiveness and fast processing.

[0784] "Guidance" refers to educational or corrective advice or directional information provided to users, and is a type of feedback generated based on dialogue and emotions.

[0785] This invention is a system that enables users to interact with virtual characters using a communication device, providing more natural and adaptive responses. The communication device used is a common mobile communication device such as a smartphone or tablet. This device incorporates software for implementing a generative AI model and hardware for emotion recognition.

[0786] The communication device's functionality is activated when the user logs in through the application and initiates a conversation. A generative AI model analyzes text or voice input from the user and generates a corresponding response from a virtual character. Natural language processing technology is used as the generative AI model for response generation, and it incorporates a deep learning algorithm.

[0787] Furthermore, the communication device incorporates an emotion engine that analyzes the user's voice tone and facial expression data in real time and records emotional data. This emotional data is reflected in the virtual character's responses to enhance the naturalness of the responses. For example, if the user shows anxiety, the virtual character will respond calmly and adjust to provide a sense of reassurance.

[0788] After the interaction, the communication device sends the recorded interaction data and emotional data to the server. The server analyzes this data and generates feedback for each user. This feedback includes specific advice and suggestions for improvement to help the user improve their interaction skills. The generated feedback is returned to the communication device and displayed to the user.

[0789] As a concrete example, in a "customer complaint handling" scenario, a virtual character expresses dissatisfaction, and the user apologizes. If the user's voice indicates anxiety, the emotion engine recognizes this and instructs the virtual character to respond calmly.

[0790] An example of a prompt might be, "I want to practice how to respond to customer complaints." Based on this prompt, the communication device selects the most appropriate dialogue scenario and provides a flow of dialogue using a virtual character.

[0791] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0792] Step 1:

[0793] The user launches the application using a communication device and enters login information. This login information is used for authentication by the communication device, allowing the user to access the application's main screen. Specific actions include filling out the login form and clicking the submit button.

[0794] Step 2:

[0795] The user selects a dialogue scenario. Based on this input, the communication device collects the data necessary to generate a virtual character that matches the selected scenario and provides it to the generation AI model. The generation AI model analyzes this data and generates the initial dialogue content of the virtual character in accordance with the selected scenario.

[0796] Step 3:

[0797] The terminal displays the generated virtual character and begins interacting with the user. When the user provides input (text or voice), the terminal captures it and passes it to the generating AI model. The generating AI model analyzes this input and generates the optimal response. Specific actions include processing voice input through the user's microphone and sending text chat messages.

[0798] Step 4:

[0799] The device incorporates an emotion engine that acquires emotional data by analyzing the user's voice tone or facial expressions. The user's voice tone and facial expressions are used as input, and the emotion engine performs analysis based on this. The results of this analysis are reflected in the virtual character's response, and the device adjusts the response before presenting it to the user.

[0800] Step 5:

[0801] After the conversation ends or after a certain period of time has elapsed, the terminal sends the recorded conversation data and emotional data to the server. The server receives the data and analyzes its quality and content. This analysis includes tracking emotional changes and evaluating the overall conversation using data processing algorithms.

[0802] Step 6:

[0803] The server generates feedback for the user based on the analysis results. This feedback includes specific advice to help improve the user's conversational skills. The generated feedback is sent from the server to the terminal and displayed to the user. Importantly, detailed feedback is provided based on the previously analyzed sentiment data.

[0804] (Application Example 2)

[0805] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0806] In many brick-and-mortar stores, improving the customer service skills of staff is crucial, but traditional training methods have limitations in providing training that includes realistic situational responses and emotional recognition. In particular, there is a lack of effective training methods to cultivate the ability to appropriately recognize emotions and adjust responses during conversations, thus creating a need for more practical methods to improve customer service skills that are tailored to individual circumstances.

[0807] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0808] In this invention, the server includes means for displaying a virtual person using a generative model that interacts with a user via a communication device, means for causing the virtual person to recognize the user's emotions and adjust its response, and means for recording the emotion recognition and response. This makes it possible for store employees to effectively improve their customer service skills in realistic situations.

[0809] A "communication device" is a device used to send and receive information via digital signals.

[0810] A "user" is an individual or group that uses the system to interact with a virtual character.

[0811] A "generative model" is an algorithm that uses machine learning techniques to automatically generate natural-sounding responses.

[0812] A "virtual character" is a digital character created using a generative model to interact with users.

[0813] "Emotion recognition" is the process of analyzing a user's emotional state from voice, facial expressions, and text data.

[0814] "Adjusting responses" means dynamically changing the content and tone of a virtual character's responses based on recognized emotional data.

[0815] "Recording" refers to the act of saving the content of a conversation and data on the user's emotions.

[0816] "Feedback" refers to rating information generated to return the results of the dialogue and areas for improvement to the user.

[0817] "Analyzing" is a method of extracting meaning and trends using collected data.

[0818] "Practice in a physical store" refers to training activities in which store employees simulate an actual sales environment to hone their customer service skills.

[0819] This invention can be specifically implemented as a training system to improve the customer service skills of store employees. The server executes a generative model via a communication device and displays a virtual character. The virtual character interacts with the user and dynamically adjusts its response based on emotional input from the user (voice, facial expressions, text). Software technology that analyzes voice and facial expressions is used for emotion recognition.

[0820] The server also records the content and emotional data of this interaction. This includes the ability to save the user's voice tone and facial expression changes as data. The recorded data is then used to generate feedback for the user. This feedback includes an evaluation of the quality of the interaction and areas for improvement.

[0821] The terminal can be implemented as part of this system using smart glasses or head-mounted displays such as Microsoft HoloLens. This allows users to simulate a real store environment.

[0822] As a concrete example, a flower shop employee can hone their skills in handling customer complaints through dialogue with virtual customers. In this process, the system analyzes the user's emotions, such as anxiety and confusion, and provides appropriate feedback. For example, if the user shows anxiety, the system will provide feedback emphasizing the importance of responding calmly.

[0823] An example of a prompt to the generating AI model would be: "The current situation is a severe customer complaint. Please tell us what the appropriate response would be if the user is showing signs of anxiety." In this way, the system helps improve customer service skills to be more practical and personalized through emotion recognition and appropriate responses.

[0824] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0825] Step 1:

[0826] The user activates the device and logs into the customer service training application. The information entered here is the user's login information, which the device sends to the authentication server for authentication. Upon successful authentication, the user gains access to the main menu.

[0827] Step 2:

[0828] The user selects the scenario they want to train. The input here is an instruction for scenario selection, and the device sends this selection information to the server. The server uses a generative AI model to generate a virtual character based on the selected scenario and sends it to the device.

[0829] Step 3:

[0830] A virtual character is generated and displayed on the device. The user begins interacting with the virtual character, providing input via voice and text. The input data also includes the user's facial expressions. The device sends this data to an emotion recognition engine.

[0831] Step 4:

[0832] The server uses an emotion recognition engine to analyze the user's voice and facial expression data to identify their emotional state. The input is the user's voice and facial expression data, and the output is the recognized emotion information.

[0833] Step 5:

[0834] The server uses a generated AI model to adjust the virtual character's response based on the recognized emotion. The input here is emotion information, and the output is the adjusted response. The server returns this response to the terminal.

[0835] Step 6:

[0836] The device displays a pre-arranged response from a virtual character to the user. The user can continue the conversation and, if necessary, select other scenarios.

[0837] Step 7:

[0838] After all conversations have ended, the terminal records the conversation content and emotional data and sends it to the server.

[0839] Step 8:

[0840] The server analyzes the received data and generates feedback. Input data includes dialogue content and sentiment data, while output is specific feedback. This feedback includes the user's strengths and areas for improvement.

[0841] Step 9:

[0842] The server sends feedback to the terminal, which then displays it to the user. The user can then review the feedback and use it to improve future training sessions.

[0843] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0844] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0846] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0847] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0848] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0849] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0850] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0851] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0852] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0853] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0854] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0855] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0856] 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.

[0857] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0858] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0859] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0860] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0861] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0862] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0863] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A means of displaying a virtual character using a generative model that interacts with the user via a communication terminal,

[0867] Means for recording the content of the aforementioned dialogue,

[0868] A means for analyzing the aforementioned recorded content and generating feedback for the user,

[0869] means for displaying the aforementioned feedback on the communication terminal,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the content of the virtual character's dialogue is adjusted based on a scenario selected by the user.

[0873] (Claim 3)

[0874] The system according to claim 1, which evaluates the feedback generation by comparing it with the dialogue content of other users.

[0875] "Example 1"

[0876] (Claim 1)

[0877] A means for displaying a virtual object using a generated data model that interacts with the user via a communication device,

[0878] means for recording the information of the aforementioned dialogue,

[0879] A means for analyzing the aforementioned recorded information and generating evaluation information for the user,

[0880] Means for displaying the evaluation information on the communication device,

[0881] A means of using prompts to quantitatively analyze user responses,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, which adjusts the interaction information of the virtual object based on the situation selected by the user.

[0885] (Claim 3)

[0886] The system according to claim 1, which performs an evaluation by comparing it with the dialogue information of other users when generating the evaluation information.

[0887] "Application Example 1"

[0888] (Claim 1)

[0889] A means of displaying a virtual person using a generative model that interacts with the user via a communication device,

[0890] means for recording the information of the aforementioned dialogue,

[0891] A means for analyzing the aforementioned recorded information and generating an evaluation for the user,

[0892] Means for displaying the evaluation on the communication device,

[0893] A means of displaying a virtual person using a visual device and simulating the user's actions in the real world,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which adjusts the content of the virtual character's dialogue based on a scene selected by the user.

[0897] (Claim 3)

[0898] The system according to claim 1, which performs the evaluation by comparing it with the dialogue information of other users when generating the evaluation.

[0899] "Example 2 of combining an emotion engine"

[0900] (Claim 1)

[0901] A means for displaying a virtual character using a generative model that exchanges information with the user via a communication device,

[0902] A means for detecting the user's emotions when performing the aforementioned information exchange in real time,

[0903] Means for recording the content of the information exchange and the detected emotion data,

[0904] A means for analyzing the aforementioned record contents and generating educational guidelines for users,

[0905] Means for presenting the aforementioned guidelines to the communication device,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, which adapts the information exchange content of the virtual characters based on a situation selected by the user.

[0909] (Claim 3)

[0910] The system according to claim 1, which performs relative evaluation by comparing it with the information exchanged by other users when generating the aforementioned educational guidelines.

[0911] "Application example 2 when combining with an emotional engine"

[0912] (Claim 1)

[0913] A means of displaying a virtual person using a generative model that interacts with the user via a communication device,

[0914] A means for making the aforementioned virtual character recognize the user's emotions and adjust its response,

[0915] The means for recording the aforementioned emotion recognition and response,

[0916] A means for analyzing the recorded emotional information and generating comprehensive feedback for the user,

[0917] Means for displaying the aforementioned feedback on the communication device,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, which adjusts the content of the virtual character's dialogue based on a situation selected by the user, thereby supporting practice in a physical store.

[0921] (Claim 3)

[0922] The system according to claim 1, which evaluates the feedback generation process by comparing it with the emotional changes of other users. [Explanation of Symbols]

[0923] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of displaying a virtual character using a generative model that interacts with the user via a communication terminal, Means for recording the content of the aforementioned dialogue, A means for analyzing the aforementioned recorded content and generating feedback for the user, means for displaying the aforementioned feedback on the communication terminal, A system that includes this.

2. The system according to claim 1, wherein the content of the virtual character's dialogue is adjusted based on a scenario selected by the user.

3. The system according to claim 1, which evaluates the feedback generation by comparing it with the content of conversations with other users.

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