System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a request for a conversation simulation from a user; means for causing a generative AI model to generate a conversation scenario based on the request; means for receiving responses and feedback generated by the generative AI model; and means for returning the responses and feedback to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] People often feel anxious when communicating with others. In particular, in situations such as conversations with people for the first time, sales, and interviews, the pressure is great and it is often difficult to speak smoothly. In addition, people who live alone or who are not good at communication can feel lonely, which can affect their mental health. In such situations, people need a way to practice conversations with confidence before actually speaking with others. [Means for solving the problem]

[0005] This invention provides a system that receives a conversation simulation request from a user and generates a conversation scenario from a generative AI model based on the request. Specifically, the system is configured to allow users to simulate conversations in various situations, such as introducing themselves to someone they meet for the first time, making a sales pitch, or having a conversation with a romantic partner. The generative AI model generates responses and feedback based on the user's input and returns them to the user, allowing the user to effectively practice conversation. The system also includes a means for recording the generated responses and feedback and using them in the next conversation simulation. For users living alone, the system also provides a means for the generative AI model to talk to the user as a conversation partner to reduce feelings of loneliness. This allows users to speak with confidence in real situations, improving communication in everyday life and business situations and reducing feelings of loneliness.

[0006] "User" refers to an end user who uses this system to conduct conversation simulations.

[0007] A "terminal" is a device used by a user that sends requests and receives responses.

[0008] A "server" is a device at the core of a system that receives user requests, sends those requests to a generative AI model, and generates a response.

[0009] A "generative AI model" is an artificial intelligence model that generates appropriate conversational responses and feedback based on input from the user.

[0010] A "request" is information that is sent from a terminal used by a user to a server, requesting a specific conversation scenario.

[0011] A "conversation scenario" refers to a topic or theme of a conversation in a specific situation, such as a self-introduction or a sales pitch.

[0012] A "response" is a reply that a generative AI model generates based on a scenario and user input.

[0013] "Feedback" refers to suggestions for improvement or advice that a generative AI model includes when responding to user input.

[0014] "Recording" refers to the process of saving the responses and feedback generated and using them in the next conversation simulation.

[0015] "Conversation Partner" is a feature in which a generative AI model talks to users, with the aim of reducing feelings of loneliness for users living alone. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

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

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0037] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[0038] Overall system configuration

[0039] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0040] Embodiment

[0041] 1. User Request Submission

[0042] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the device. The device then sends this information as a request to the server.

[0043] 2. Request acceptance by the server

[0044] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[0045] 3. Invoking generative AI models

[0046] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[0047] 4. Response Generation Using Generative AI Models

[0048] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0049] 5. Providing feedback from the server to the user

[0050] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0051] 6. Record and use next time

[0052] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[0053] 7. Function as a conversation partner

[0054] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[0055] Specific examples

[0056] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback such as the tone of the conversation and additional advice (e.g., "It would be good to talk more specifically about your hobbies when introducing yourself.").

[0057] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[0058] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[0059] The above is a specific embodiment for carrying out the present invention. By using this system, users can develop the ability to respond quickly and effectively to various conversation situations.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user uses the terminal to select a conversation simulation scenario, such as "self-introduction" or "sales talk."

[0063] Step 2:

[0064] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0065] Step 3:

[0066] The terminal sends the input information to the server as a request, which includes the scenario type and the user input.

[0067] Step 4:

[0068] The server receives the request, parses it, and converts it into the appropriate format.

[0069] Step 5:

[0070] The server sends the parsed request to the generative AI model, requesting a response and feedback for the conversation simulation.

[0071] Step 6:

[0072] The generative AI model generates appropriate conversational responses and feedback based on the request, specifically crafting relevant questions and advice based on user input.

[0073] Step 7:

[0074] The generative AI model sends the generated response and feedback back to the server, such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0075] Step 8:

[0076] The server sends the generated responses and feedback back to the user's device, including suggestions and suggestions for improving the conversation.

[0077] Step 9:

[0078] The terminal receives the response and feedback from the server and displays it to the user, who can review it to improve their conversation skills.

[0079] Step 10:

[0080] The responses and feedback generated are recorded on the server and used in the next conversation simulation.

[0081] Step 11:

[0082] For users who live alone, the server provides a function where the generative AI model periodically speaks to them, helping to reduce the user's sense of loneliness.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] In today's busy society, improving interpersonal communication skills is important for one's career and relationships. However, opportunities to practice in simulated conversation situations are limited, and reducing feelings of loneliness is also a challenge, especially for people living alone. Furthermore, there is a lack of systems that allow people to receive feedback and continuously improve their skills.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving responses and feedback generated by the generative AI model, means for returning the responses and feedback to the user, means for recording the generated responses and feedback and using them in the next conversation simulation, and means for the generative AI model to talk to the user as a conversation partner to reduce feelings of loneliness for users living alone. This allows users to receive feedback while simulating various conversation situations and improve their communication skills. Furthermore, users living alone are provided with a conversation partner to reduce feelings of loneliness.

[0088] "User" refers to a person who uses the system to conduct a conversation simulation.

[0089] "Terminal" refers to a device used by a user, including, for example, a smartphone or a computer.

[0090] "Server" refers to a computer system that performs processes such as receiving requests, analyzing them, invoking generative AI models, and returning responses.

[0091] A "request" refers to a request for a conversational simulation that a user sends to the system using a terminal.

[0092] A "generative AI model" refers to an artificial intelligence model that generates appropriate conversational responses or feedback based on given input.

[0093] A "conversation scenario" refers to a setting or scenario used to simulate a particular conversation situation.

[0094] "Response" refers to the response that a generative AI model generates in response to user input.

[0095] "Feedback" refers to specific improvements and advice regarding the content of a user's conversation.

[0096] "Recording" refers to saving the responses and feedback generated.

[0097] "Conversation Partner" refers to a generative AI model that regularly speaks to users, especially those living alone, to help reduce feelings of loneliness.

[0098] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[0099] Overall system configuration

[0100] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0101] Embodiment

[0102] User request submission

[0103] The user selects a conversation simulation situation from the terminal. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[0104] Request acceptance on the server

[0105] The server receives the request sent from the terminal and analyzes the request content, which includes the conversation simulation scenario and specific inputs from the user.

[0106] Invoking a generative AI model

[0107] Based on the received request, the server invokes a generative AI model (e.g., OpenAI's GPT-4), provides the selected scenario and user input to the generative AI model, and has it generate responses and feedback accordingly.

[0108] Response generation using generative AI models

[0109] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0110] Providing feedback from the server to the user

[0111] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0112] Record and use next time

[0113] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[0114] Functions as a conversation partner

[0115] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[0116] Specific examples

[0117] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[0118] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[0119] Prompt Sentence Examples

[0120] "I'd like to simulate a self-introduction. Please generate appropriate responses and feedback for the following inputs."

[0121] "I'd like to simulate a sales pitch. Please generate appropriate responses and feedback for the following inputs."

[0122] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Program processing flow

[0125] Step 1: User selects and inputs conversation situation

[0126] The user uses the terminal to select a conversation situation from a specified interface and inputs the appropriate conversation content.

[0127] Input: The scenario selection and specific conversation content that the user inputs into the terminal (e.g., "self-introduction," "sales talk," etc.).

[0128] Data processing: The terminal converts the input content into request data.

[0129] Output: Request data (scenario and conversation content).

[0130] Action: A user opens an application on their device, selects "Introduce myself" from the menu, and enters "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0131] Step 2: Sending a request from the device to the server

[0132] The terminal transmits the request data input by the user to the server.

[0133] Input: Terminal-generated request data.

[0134] Data processing: The terminal converts the request data into a data packet.

[0135] Output: Data packets sent to the server.

[0136] How it works: A device sends a data packet to a server over the Internet.

[0137] Step 3: Receiving and parsing the request on the server

[0138] The server accepts the data packets received from the terminal and analyzes the contents.

[0139] Input: Data packets received from the terminal.

[0140] Data processing: The server analyzes the data packets and extracts the scenario and conversation content.

[0141] Output: Analyzed scenario and conversation content.

[0142] What it does: The server parses the received data and extracts "Scenario: Self-introduction" and "User input: Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[0143] Step 4: Invoke the generative AI model

[0144] The server calls a generative AI model based on the analyzed scenario and conversation content.

[0145] Input: Parsed scenario and conversation content.

[0146] Data processing: The server generates API requests for the generative AI model.

[0147] Output: API request to the generative AI model.

[0148] How it works: The server generates an API request and sends it to the generative AI model's endpoint. This request includes the scenario and user input.

[0149] Step 5: Generate AI model responses

[0150] Generative AI models generate responses and feedback based on the scenario and inputs provided.

[0151] Input: An API request to a generative AI model.

[0152] Data processing: Generative AI models use internal algorithms to generate responses and feedback.

[0153] Output: Response and feedback data.

[0154] How it works: The generative AI model responds with, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" along with feedback such as, "You might want to be more specific about your hobbies when introducing yourself."

[0155] Step 6: Providing feedback from the server to the user

[0156] The server sends the responses and feedback received from the generative AI model back to the user.

[0157] Input: The response and feedback data received from the generative AI model.

[0158] Data processing: The server converts the response and feedback data into data packets.

[0159] Output: Data packets sent to the user's device.

[0160] Operation: The server creates a data packet containing the generated response and feedback and sends it to the terminal.

[0161] Step 7: Record your feedback information

[0162] The server records the generated response and feedback for future reference.

[0163] Input: Generated response and feedback data.

[0164] Data processing: The server stores the responses and feedback in a database.

[0165] Output: The saved feedback information.

[0166] How it works: The server stores the response and feedback in a database.

[0167] Step 8: Implementing the conversation partner function

[0168] For users living alone, the server periodically initiates conversations using a generative AI model to reduce feelings of loneliness.

[0169] Input: The time set as the scheduled task.

[0170] Data processing: The server generates periodic requests and sends them to the generative AI model.

[0171] Output: Periodically generated conversation responses.

[0172] How it works: The server calls the generative AI model at a specified time and begins a conversation such as, "Hello, what are your plans for today?"

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] The lack of effective communication between robots and workers in factories is a problem. With conventional technology, workers lack the training to properly interact with robots, which leads to reduced work efficiency and increased errors. It also creates a sense of loneliness for workers who live alone.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes a means for receiving a conversation simulation request from a user, a means for causing a generative AI model to generate a conversation scenario and feedback based on the request, a means for receiving a response and feedback generated by the generative AI model, a means for returning the response and feedback to the user, and a means for simulating communication between a worker and a robot in a factory and providing feedback. This allows workers to learn effective communication methods, improving work efficiency and reducing errors. Furthermore, the generative AI model can function as a conversation partner for workers living alone, helping to reduce feelings of loneliness.

[0178] The "means for receiving a conversation simulation request from a user" is an interface for accepting an input from a user requesting the execution of a conversation simulation based on a specific situation, and for processing the request.

[0179] "Means for causing a generative AI model to generate a conversation scenario and feedback" refers to a process and system for using a generative AI model to generate an appropriate conversation scenario and feedback for user input based on a received request.

[0180] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that aggregates the conversational responses and feedback generated by a generative AI model and receives them on a server or system for use in subsequent steps.

[0181] "Means for sending responses and feedback back to the user" refers to the communications means and user interface for delivering the conversational responses and feedback generated by the generative AI model to the user.

[0182] The "means for simulating communication between workers and robots in a factory and providing feedback" is a process for simulating conversations and communications between workers and robots in a factory, generating feedback based on the results, and using the simulation results to improve work efficiency and communication capabilities.

[0183] The present invention provides a system for simulating communication between a user and a factory robot. This system utilizes a generative AI model to provide conversation simulation and feedback, and an embodiment of the system is described below.

[0184] Overall system configuration

[0185] The system consists of a terminal operated by the user, a server, and a generative AI model. The user uses the terminal to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0186] Embodiment

[0187] 1. User Request Submission

[0188] The user selects the situation in which the conversation simulation will be used from the terminal. For example, there are "self-introduction," "confirmation of work procedures," "reporting a problem," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[0189] 2. Request acceptance by the server

[0190] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[0191] 3. Invoking generative AI models

[0192] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[0193] 4. Response Generation Using Generative AI Models

[0194] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0195] 5. Providing feedback from the server to the user

[0196] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0197] Specific examples

[0198] For example, if a user wants to simulate a self-introduction, they would enter "Nice to meet you, my name is Tanaka. My hobbies are fishing and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Tanaka. Fishing and reading are great hobbies. Where have you been fishing recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[0199] Hardware and software used

[0200] Hardware: Computers controlling factory robots

[0201] Software: OpenAI GPT-3 API, Python

[0202] Prompt Sentence Examples

[0203] User input prompt:

[0204] "Please provide an example prompt for a simulation of introducing yourself to a factory robot."

[0205] Robot response:

[0206] "Hello, this is our new product line. How can I help you today?"

[0207] In this way, by using this system, users can simulate effective communication with factory robots, which can contribute to improving performance in actual workplaces.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] A user uses a terminal to request a conversation simulation. This request includes the selection of a situation, such as "self-introduction," "confirmation of work procedures," or "reporting a problem," and the input of specific conversation content. The input data is sent from the terminal to the server. The input data includes the selected situation and the user's specific conversation content.

[0211] Step 2:

[0212] The server receives the request sent from the terminal, analyzes the received data, and checks the request content. This analysis identifies the situation and user input, and checks the request content.

[0213] Step 3:

[0214] The server calls the generative AI model based on the received request. At the time of the call, the server provides the user's selected situation and specific conversation content as a prompt to the generative AI model. Based on this prompt, the generative AI model generates appropriate conversational responses and feedback.

[0215] Step 4:

[0216] The generative AI model generates conversational responses and feedback based on the provided prompts. Specifically, it generates optimal responses to the user's input, as well as suggestions for improving the conversation and specific feedback. The generated results are sent to the server.

[0217] Step 5:

[0218] The server receives the responses and feedback received from the generative AI model. This received data is compiled into information to be sent back to the user. The compiled information is converted into a transmission format for sending back to the user.

[0219] Step 6:

[0220] The server returns the compiled responses and feedback to the user's terminal, allowing the user to review the responses and feedback on their own terminal, and use this information to improve their conversation skills.

[0221] Step 7:

[0222] The server records the responses and feedback generated, allowing the user to refer to the previous feedback the next time they engage in a conversational simulation, thereby encouraging continuous improvement.

[0223] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0224] This invention provides a system that allows users to simulate various conversation situations, and utilizes a generative AI model and an emotion engine to provide conversation practice and feedback. Specific embodiments of this system are described below.

[0225] Overall system configuration

[0226] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user uses their device to send a conversation simulation request, which the server accepts and passes to the generative AI model, generating appropriate responses and feedback. The emotion engine then recognizes the user's emotional state and adjusts the requests and responses to the generative AI model accordingly.

[0227] Embodiment

[0228] 1. User Request Submission

[0229] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are options such as "self-introduction," "sales talk," and "conversation with a lover," and they input information appropriate to each situation into the device. The device then sends this information as a request to the server.

[0230] 2. Emotional state recognition using the emotion engine

[0231] The emotion engine recognizes the user's emotional state in real time as the user types or during conversation simulations, and generates emotion data using voice analysis, sensor data, and other methods.

[0232] 3. Request acceptance and processing on the server

[0233] The server receives the request and emotion data sent from the device, analyzes the received request and emotion data, and converts them into an appropriate format.

[0234] 4. Invoking generative AI models

[0235] The server invokes the generative AI model based on the analyzed request and emotion data, providing the selected scenario, user input, and corresponding emotional state to the generative AI model, which then generates a response and feedback accordingly.

[0236] 5. Response Generation Using Generative AI Models

[0237] Based on the provided scenario, user input, and emotional state, the generative AI model generates appropriate conversational responses, as well as specific feedback and suggestions for improvement based on the user's input.

[0238] 6. Providing feedback from the server to the user

[0239] The server sends the responses and feedback received from the generative AI model back to the user, who can then use the feedback to improve their conversation skills.

[0240] 7. Record and use next time

[0241] The generated responses and feedback are recorded on the server and used in the next conversation simulation. Emotional data is also recorded to track the user's emotional fluctuations and provide more effective feedback.

[0242] 8. Function as a conversation partner

[0243] For users living alone, the emotion engine monitors the user's emotional state, and the generative AI model acts as a conversation partner, engaging in conversations with appropriate topics based on the user's emotional state to reduce loneliness.

[0244] Specific examples

[0245] For example, if a user wants to simulate a "self-introduction," the user inputs "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into the device. The emotion engine analyzes the user's voice and facial expressions and detects that the user is relaxed. The device sends this information as a request to the server. The server receives this and makes a request to the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, based on the emotion engine data, additional advice is provided to a relaxed user to continue the conversation.

[0246] Similarly, when a user simulates a sales pitch, they type "Hello, I'd like to introduce you to our new product." The emotion engine detects the user's nervous state and sends it to the server. The generative AI model then generates a response such as "Hello, I'm excited to tell you about our new product. First, can you tell me about its features?" and provides feedback to help the user relax. Specific advice to ease the tension is also provided.

[0247] This invention not only helps users speak confidently in real-life situations, but also serves as a conversation partner for users living alone, reducing feelings of loneliness. By utilizing an emotion engine, appropriate responses and feedback are provided according to the user's emotional state, enabling effective conversation practice.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] The user uses the terminal to select a conversation simulation situation, such as "self-introduction" or "sales talk."

[0251] Step 2:

[0252] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0253] Step 3:

[0254] The emotion engine analyzes the user's voice and facial expressions in real time as they type, recognizing their emotional state. For example, it can determine whether the user is relaxed or tense based on their tone of voice and facial expression.

[0255] Step 4:

[0256] The terminal sends the user's input data and emotional data to the server as a request, which includes the selected situation, the user's input, and the emotional state.

[0257] Step 5:

[0258] The server receives the request, analyzes it, and converts the results into an appropriate format for passing to the generative AI model.

[0259] Step 6:

[0260] The server sends the analyzed request and emotional data to the generative AI model, requesting a response and feedback for the conversation simulation.

[0261] Step 7:

[0262] The generative AI model generates appropriate conversational responses and feedback based on the request. For example, if a user inputs a self-introduction, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0263] Step 8:

[0264] The generative AI model sends responses and feedback back to the server, including suggestions for improvement and advice based on the user's emotional state.

[0265] Step 9:

[0266] The server sends the generated response and feedback back to the user's terminal, which receives it and displays it to the user.

[0267] Step 10:

[0268] The user can check the responses and feedback displayed on the device and use this information in the next conversation simulation. The user can also understand their own emotional state based on the data from the emotion engine, improving the quality of their practice.

[0269] Step 11:

[0270] The generated responses and feedback are recorded on the server, along with emotional data, which will be used in the next conversation simulation.

[0271] Step 12:

[0272] For users who live alone, the emotion engine monitors the user's emotional state. If the user feels lonely, the generative AI model generates appropriate conversational content and speaks to the user through the device. For example, it provides conversations tailored to the user's situation, such as "How was your day?" or "What's your favorite movie recently?"

[0273] In this way, the user inputs information via the device, the emotion engine analyzes their emotional state, and the server sends a request to the generative AI model to generate appropriate conversation responses and feedback. This allows users to practice real conversation situations while receiving feedback appropriate to their emotional state. It also functions as a conversation partner to reduce feelings of loneliness.

[0274] Example 2

[0275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0276] Conventional conversation simulation systems generate responses and feedback uniformly without considering the user's emotional state, making it impossible to provide effective feedback optimized for the user's situation and emotions. Furthermore, they lacked functionality to provide emotional care and reduce loneliness for users living alone.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for formatting the request and sending it to the server, means for an emotion engine to recognize the user's emotional state in real time, means for a generative AI model to generate an appropriate response and return it to the server, and means for recording the generated response and feedback and using it for the next conversation simulation. This provides appropriate feedback based on the user's emotional state, enabling users living alone to reduce their sense of loneliness and practice conversation effectively.

[0278] "User" refers to an individual who uses the system to engage in conversational simulations.

[0279] "Terminal" refers to an electronic device through which a user accesses the system.

[0280] A "request" refers to a request for a conversation simulation that a user sends to the system through a terminal.

[0281] "Server" refers to a central computer system that receives and processes requests.

[0282] An "emotion engine" refers to an analysis device that analyzes a user's voice and facial expressions to recognize their emotional state.

[0283] A "generative AI model" refers to an artificial intelligence model that generates conversation scenarios and provides feedback in response to user requests.

[0284] "Response" refers to the conversational content that a generative AI model generates based on a request.

[0285] "Feedback" refers to the improvements and advice that the generative AI model provides to the user regarding their conversation.

[0286] "Recording" refers to the act of the server storing request, response, and feedback data.

[0287] "Utilizing in the next simulation" refers to the process of referencing the recorded data in the next or subsequent simulation to provide more personalized feedback.

[0288] A "conversation partner" is a generative AI model that acts as a conversation partner for the user, reducing feelings of loneliness.

[0289] "Emotional Data" refers to information about a user's emotional state that is analyzed and generated by the Emotion Engine.

[0290] This invention provides a system that allows users to experience and practice various conversation situations. This system uses a generative AI model and an emotion engine based on user input to simulate real conversations. The following describes how this system is specifically implemented.

[0291] Overall system configuration

[0292] The system consists of a terminal used by the user, a central processing server, a generative AI model that generates responses, and an emotion engine that recognizes emotional states.

[0293] 1. User Input

[0294] The user uses the terminal to select a specific conversation situation and input the content of the conversation. For example, options such as "self-introduction," "sales talk," and "conversation with a lover" are provided. An example of specific content that the user can input is "Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[0295] 2. Submitting a Request

[0296] The terminal formats the user's input and sends it to the server as request data, which includes the selected scenario and the user's input.

[0297] 3. Recognizing emotional states

[0298] Upon receiving the request, the server requests data from the emotion engine to collect emotion data. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state (e.g., relaxed, nervous) in real time.

[0299] 4. Integrating Request and Emotion Data

[0300] The server receives the emotion data sent from the emotion engine and integrates it with the request data. This integrated data serves as input for the generative AI model to generate conversational responses.

[0301] 5. Response Generation Using Generative AI Models

[0302] The server passes the integrated data to a generative AI model and asks it to generate the optimal response and feedback. The generative AI model generates appropriate conversational responses and feedback based on this data. An example of a generated response is, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0303] 6. Responding and Providing Feedback

[0304] The server sends the generated responses and feedback back to the device, where the user can view the information on the device screen and use it to improve their conversation skills.

[0305] 7. Record the data and use it again

[0306] The server records requests, responses, feedback, and emotional data, which are then used as a basis for providing individually optimized feedback during subsequent simulations.

[0307] 8. Function as a conversation partner

[0308] For users living alone, a generative AI model can act as a conversation partner. Based on data from the emotion engine, it provides appropriate conversation based on the user's emotions to reduce feelings of loneliness. This function allows users to have a conversation partner without feeling lonely on a daily basis.

[0309] Specific examples

[0310] For example, if a user wants to simulate a "self-introduction," the specific operations are as follows:

[0311] 1. The user types into the terminal: "Hello, my name is Taro. My hobbies are traveling and reading."

[0312] 2. Analysis by emotion engine: Recognizes a relaxed state from voice and facial expression data.

[0313] 3. Response generation using a generative AI model: "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0314] 4. Feedback: Advice is given to help you relax and continue speaking.

[0315] In this way, conversation simulation is carried out, and the user can improve his / her skills in actual conversation situations.

[0316] Prompt Sentence Examples

[0317] User: "Hi, I'd like to tell you about our new product."

[0318] Generative AI model: "Hello, I'm excited to talk to you about your new product. First, can you tell me about its features?"

[0319] Feedback: "You seem nervous. It would be a good idea to write down what you're going to say beforehand. I also recommend taking some deep breaths and relaxing."

[0320] The system provides training to help users become confident and effective speakers in real-life conversation situations.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] The flow of this system's program processing

[0323] Step 1:

[0324] The user sends a request for a conversation simulation from the terminal.

[0325] Specific operation: The user selects a situation and inputs the conversation content in text by operating the device screen. For example, the user might input, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0326] Input: User text input (simulation situation and conversation content)

[0327] Output: Formatted request data

[0328] Step 2:

[0329] The device sends a request to the server.

[0330] Specific operation: The terminal converts the user's input data into packets and sends an HTTP request to the specified API endpoint on the server.

[0331] Input: Formatted request data

[0332] Output: HTTP request data received by the server

[0333] Step 3:

[0334] The server accepts the request and requests the emotion engine for the user's emotion data.

[0335] Specific operation: The server checks the format of the received request data, and if it determines that the data is valid, it sends a request to the emotion engine to analyze the user's emotional state.

[0336] Input: HTTP request data

[0337] Output: Analysis request to the emotion engine

[0338] Step 4:

[0339] The emotion engine recognizes the user's emotional state in real time.

[0340] Specific operation: The emotion engine uses voice and facial expression analysis systems to recognize the user's emotional state from their voice and video data. For example, it detects whether they are relaxed or tense.

[0341] Input: User's audio and video data

[0342] Output: Emotional state data (e.g., relaxed, tense)

[0343] Step 5:

[0344] The server receives and processes the emotion data sent from the emotion engine.

[0345] Specific operation: The server receives the emotion data sent from the emotion engine and integrates it into the request data. It formats the data and prepares it for passing to the generative AI model.

[0346] Input: Emotional state data

[0347] Output: Consolidated request data

[0348] Step 6:

[0349] The server then forwards the processed request and emotion data to the generative AI model.

[0350] What happens: The server sends a POST request to the appropriate API endpoint to transfer the aggregated data to the generative AI model.

[0351] Input: Consolidated request data

[0352] Output: HTTP request to the generative AI model

[0353] Step 7:

[0354] The generative AI model generates an appropriate response and returns it to the server.

[0355] Specific operation: The generative AI model uses natural language processing to generate appropriate responses and feedback based on the input situation and emotional state. For example, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0356] Input: Consolidated request data

[0357] Output: Generated responses and feedback

[0358] Step 8:

[0359] The server sends a response and feedback back to the user's terminal.

[0360] Specific operation: The server formats the response and feedback received from the generative AI model and sends it back to the user's device. The data is sent using an HTTP response.

[0361] Input: Generated responses and feedback

[0362] Output: Response data sent to the user's device

[0363] Step 9:

[0364] The user checks the feedback on the device.

[0365] Specific operation: The user checks the responses and feedback displayed on the device screen and uses it as a reference for improving their conversation skills.

[0366] Input: Response data sent back to the terminal

[0367] Output: Verified feedback

[0368] Step 10:

[0369] The server records the requests and responses and uses them for the next simulation.

[0370] Specific operation: The server stores the request content, response, feedback, and emotion data in a database, allowing past data to be reflected in the responses and feedback in the next simulation.

[0371] Input: Request content, response, feedback, emotion data

[0372] Output: Data recorded in the database

[0373] (Application example 2)

[0374] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0375] Conventional conversation simulation systems have the problem that they do not provide feedback that takes into account the user's emotional state, which means that they are unable to sufficiently improve their ability to adapt to real-world situations. Furthermore, particularly in customer service, there is a lack of real-time training for staff to respond to various situations, making it difficult to improve service quality.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving a response and feedback generated by the generative AI model, means for returning the response and feedback to the user, means for analyzing the user's emotional state using an emotion recognition engine, and means for adjusting requests and responses to the generative AI model based on the emotional state. This allows for the provision of conversational responses and feedback according to the user's emotional state, enabling improvement of customer service skills in physical stores.

[0377] The "means for receiving a conversation simulation request from a user" refers to a device or software that receives a request on the system when a user inputs a request for a conversation simulation through a terminal.

[0378] "Means for generating a conversation scenario for a generative AI model" refers to a device or software that creates an appropriate conversation scenario based on a user request and provides that scenario to a generative AI model.

[0379] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that receives conversational responses and feedback created by a generative AI model on a server or terminal.

[0380] "Means for returning responses and feedback to the user" refers to a device or software that returns and displays the conversational responses and feedback received from the generative AI model to the user's device.

[0381] "Means for analyzing a user's emotional state using an emotion recognition engine" refers to a device or software that analyzes a user's voice, facial expressions, etc., and determines their emotional state in real time.

[0382] "Means for adjusting requests and responses to a generative AI model based on emotional state" refers to a device or software that appropriately modifies requests and responses provided to a generative AI model based on the user's emotional state obtained by an emotion recognition engine.

[0383] MODE FOR CARRYING OUT THE INVENTION

[0384] This invention is a system that allows users to improve their customer service skills and communication abilities through various conversation simulations. This system is composed of a user terminal, a server, a generative AI model, and an emotion recognition engine.

[0385] Overall system configuration

[0386] 1. User Device

[0387] The user terminal uses smart glasses or a head-mounted display (HMD), which allows the user to view conversation scenarios and receive feedback in real time. When the user inputs a conversation simulation request through the terminal, the information is sent to the server.

[0388] 2. Server

[0389] The server analyzes the requests received from the user and generates a conversation scenario from the generative AI model, which uses the OpenAI API to generate appropriate responses and feedback based on the user's input and emotional data.

[0390] 3. Emotion Recognition Engine

[0391] The emotion recognition engine analyzes the user's emotional state in real time. It uses the camera installed in the smart glasses or HMD to collect the user's facial expressions and voice data to recognize emotions. It uses the EmotionRecognition library to analyze emotions and sends the information to the server.

[0392] 4. Generative AI Models

[0393] The generative AI model generates appropriate conversational responses and feedback based on emotional data obtained from the emotion recognition engine and user input. OpenAI's generative AI model is used, and responses are generated by entering prompt sentences via the API.

[0394] Specific program processing

[0395] 1. Recognizing emotional states

[0396] The camera installed in the smart glasses or HMD captures the user's face and uses the EmotionRecognition library to analyze their emotions, for example, determining in real time whether the user is relaxed or nervous.

[0397] 2. Invoking a generative AI model

[0398] The prompt, including the user's input and emotional state, is passed to the OpenAI API to generate an appropriate response and feedback, which is then sent back to the user's device.

[0399] Specific examples

[0400] The user types "Welcome to our store," and a state of nervousness is detected.

[0401] The server uses this information to send the following prompt to the generative AI model:

[0402] User Input: Welcome to our store.

[0403] Emotional state: Tension

[0404] Appropriate response:

[0405] The generative AI model responds, "Thank you. Is there anything in particular you're looking for?" and this is sent back to the user.

[0406] This system not only allows users to improve their customer service skills in physical stores, but also allows them to receive real-time feedback based on their emotional state.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] The user device receives a request for a conversation simulation. Using smart glasses or a head-mounted display (HMD), the user selects the situation they want to simulate and inputs it using text or voice. This input data is sent to the server. For example, an input such as "Welcome to our store" is sent as a request.

[0410] Step 2:

[0411] The device's camera captures the user's face in real time, and the emotion recognition engine analyzes it. Using the EmotionRecognition library, the system determines the user's emotional state (tense, relaxed, etc.) based on their facial expressions and voice data. The analysis results indicate that the user is tense, and this data is sent to the server. The input is the camera image and voice data, and the output is emotional state data.

[0412] Step 3:

[0413] The server receives the user's input data and emotional state data and creates a prompt for the generative AI model. For example, the prompt might look like this:

[0414] User Input: Welcome to our store.

[0415] Emotional state: Tension

[0416] Appropriate response:

[0417] This prompt is sent to the OpenAI API, which receives the user's input data and emotional state data as input, and the generative AI model's response as output.

[0418] Step 4:

[0419] The generative AI model generates an appropriate conversational response based on the prompt. For example, it generates a response such as, "Thank you. Is there anything in particular you're looking for?" The generated response is sent back to the server. The input is the prompt, and the output is the conversational response.

[0420] Step 5:

[0421] The server sends the generated conversational responses back to the user's device. The device displays the responses to the user and reads them aloud, allowing the user to experience a real customer service simulation. The input is the response data of the generative AI model, and the output is feedback to the user's device.

[0422] Step 6:

[0423] The server records the entire conversation simulation and saves it for future training. For example, the user's input, emotional state, generated responses, and their feedback are saved in a database. The input is the entire simulation data, and the output is the saved training data.

[0424] These steps allow users to effectively improve their customer service skills while receiving real-time feedback based on their emotional state.

[0425] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0431] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0432] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0433] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0435] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0436] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0437] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0438] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0439] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0440] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0441] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[0442] Overall system configuration

[0443] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0444] Embodiment

[0445] 1. User Request Submission

[0446] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the device. The device then sends this information as a request to the server.

[0447] 2. Request acceptance by the server

[0448] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[0449] 3. Invoking generative AI models

[0450] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[0451] 4. Response Generation Using Generative AI Models

[0452] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0453] 5. Providing feedback from the server to the user

[0454] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0455] 6. Record and use next time

[0456] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[0457] 7. Function as a conversation partner

[0458] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[0459] Specific examples

[0460] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback such as the tone of the conversation and additional advice (e.g., "It would be good to talk more specifically about your hobbies when introducing yourself.").

[0461] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[0462] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[0463] The above is a specific embodiment for carrying out the present invention. By using this system, users can develop the ability to respond quickly and effectively to various conversation situations.

[0464] The processing flow will be explained below.

[0465] Step 1:

[0466] The user uses the terminal to select a conversation simulation scenario, such as "self-introduction" or "sales talk."

[0467] Step 2:

[0468] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0469] Step 3:

[0470] The terminal sends the input information to the server as a request, which includes the scenario type and the user input.

[0471] Step 4:

[0472] The server receives the request, parses it, and converts it into the appropriate format.

[0473] Step 5:

[0474] The server sends the parsed request to the generative AI model, requesting a response and feedback for the conversation simulation.

[0475] Step 6:

[0476] The generative AI model generates appropriate conversational responses and feedback based on the request, specifically crafting relevant questions and advice based on user input.

[0477] Step 7:

[0478] The generative AI model sends the generated response and feedback back to the server, such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0479] Step 8:

[0480] The server sends the generated responses and feedback back to the user's device, including suggestions and suggestions for improving the conversation.

[0481] Step 9:

[0482] The terminal receives the response and feedback from the server and displays it to the user, who can review it to improve their conversation skills.

[0483] Step 10:

[0484] The responses and feedback generated are recorded on the server and used in the next conversation simulation.

[0485] Step 11:

[0486] For users who live alone, the server provides a function where the generative AI model periodically speaks to them, helping to reduce the user's sense of loneliness.

[0487] Example 1

[0488] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0489] In today's busy society, improving interpersonal communication skills is important for one's career and relationships. However, opportunities to practice in simulated conversation situations are limited, and reducing feelings of loneliness is also a challenge, especially for people living alone. Furthermore, there is a lack of systems that allow people to receive feedback and continuously improve their skills.

[0490] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0491] In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving responses and feedback generated by the generative AI model, means for returning the responses and feedback to the user, means for recording the generated responses and feedback and using them in the next conversation simulation, and means for the generative AI model to talk to the user as a conversation partner to reduce feelings of loneliness for users living alone. This allows users to receive feedback while simulating various conversation situations and improve their communication skills. Furthermore, users living alone are provided with a conversation partner to reduce feelings of loneliness.

[0492] "User" refers to a person who uses the system to conduct a conversation simulation.

[0493] "Terminal" refers to a device used by a user, including, for example, a smartphone or a computer.

[0494] "Server" refers to a computer system that performs processes such as receiving requests, analyzing them, invoking generative AI models, and returning responses.

[0495] A "request" refers to a request for a conversational simulation that a user sends to the system using a terminal.

[0496] A "generative AI model" refers to an artificial intelligence model that generates appropriate conversational responses or feedback based on given input.

[0497] A "conversation scenario" refers to a setting or scenario used to simulate a particular conversation situation.

[0498] "Response" refers to the response that a generative AI model generates in response to user input.

[0499] "Feedback" refers to specific improvements and advice regarding the content of a user's conversation.

[0500] "Recording" refers to saving the responses and feedback generated.

[0501] "Conversation Partner" refers to a generative AI model that regularly speaks to users, especially those living alone, to help reduce feelings of loneliness.

[0502] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[0503] Overall system configuration

[0504] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0505] Embodiment

[0506] User request submission

[0507] The user selects a conversation simulation situation from the terminal. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[0508] Request acceptance on the server

[0509] The server receives the request sent from the terminal and analyzes the request content, which includes the conversation simulation scenario and specific inputs from the user.

[0510] Invoking a generative AI model

[0511] Based on the received request, the server invokes a generative AI model (e.g., OpenAI's GPT-4), provides the selected scenario and user input to the generative AI model, and has it generate responses and feedback accordingly.

[0512] Response generation using generative AI models

[0513] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0514] Providing feedback from the server to the user

[0515] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0516] Record and use next time

[0517] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[0518] Functions as a conversation partner

[0519] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[0520] Specific examples

[0521] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[0522] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[0523] Prompt Sentence Examples

[0524] "I'd like to simulate a self-introduction. Please generate appropriate responses and feedback for the following inputs."

[0525] "I'd like to simulate a sales pitch. Please generate appropriate responses and feedback for the following inputs."

[0526] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[0527] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0528] Program processing flow

[0529] Step 1: User selects and inputs conversation situation

[0530] The user uses the terminal to select a conversation situation from a specified interface and inputs the appropriate conversation content.

[0531] Input: The scenario selection and specific conversation content that the user inputs into the terminal (e.g., "self-introduction," "sales talk," etc.).

[0532] Data processing: The terminal converts the input content into request data.

[0533] Output: Request data (scenario and conversation content).

[0534] Action: A user opens an application on their device, selects "Introduce myself" from the menu, and enters "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0535] Step 2: Sending a request from the device to the server

[0536] The terminal transmits the request data input by the user to the server.

[0537] Input: Terminal-generated request data.

[0538] Data processing: The terminal converts the request data into a data packet.

[0539] Output: Data packets sent to the server.

[0540] How it works: A device sends a data packet to a server over the Internet.

[0541] Step 3: Receiving and parsing the request on the server

[0542] The server accepts the data packets received from the terminal and analyzes the contents.

[0543] Input: Data packets received from the terminal.

[0544] Data processing: The server analyzes the data packets and extracts the scenario and conversation content.

[0545] Output: Analyzed scenario and conversation content.

[0546] What it does: The server parses the received data and extracts "Scenario: Self-introduction" and "User input: Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[0547] Step 4: Invoke the generative AI model

[0548] The server calls a generative AI model based on the analyzed scenario and conversation content.

[0549] Input: Parsed scenario and conversation content.

[0550] Data processing: The server generates API requests for the generative AI model.

[0551] Output: API request to the generative AI model.

[0552] How it works: The server generates an API request and sends it to the generative AI model's endpoint. This request includes the scenario and user input.

[0553] Step 5: Generate AI model responses

[0554] Generative AI models generate responses and feedback based on the scenario and inputs provided.

[0555] Input: An API request to a generative AI model.

[0556] Data processing: Generative AI models use internal algorithms to generate responses and feedback.

[0557] Output: Response and feedback data.

[0558] How it works: The generative AI model responds with, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" along with feedback such as, "You might want to be more specific about your hobbies when introducing yourself."

[0559] Step 6: Providing feedback from the server to the user

[0560] The server sends the responses and feedback received from the generative AI model back to the user.

[0561] Input: The response and feedback data received from the generative AI model.

[0562] Data processing: The server converts the response and feedback data into data packets.

[0563] Output: Data packets sent to the user's device.

[0564] Operation: The server creates a data packet containing the generated response and feedback and sends it to the terminal.

[0565] Step 7: Record your feedback information

[0566] The server records the generated response and feedback for future reference.

[0567] Input: Generated response and feedback data.

[0568] Data processing: The server stores the responses and feedback in a database.

[0569] Output: The saved feedback information.

[0570] How it works: The server stores the response and feedback in a database.

[0571] Step 8: Implementing the conversation partner function

[0572] For users living alone, the server periodically initiates conversations using a generative AI model to reduce feelings of loneliness.

[0573] Input: The time set as the scheduled task.

[0574] Data processing: The server generates periodic requests and sends them to the generative AI model.

[0575] Output: Periodically generated conversation responses.

[0576] How it works: The server calls the generative AI model at a specified time and begins a conversation such as, "Hello, what are your plans for today?"

[0577] (Application example 1)

[0578] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] The lack of effective communication between robots and workers in factories is a problem. With conventional technology, workers lack the training to properly interact with robots, which leads to reduced work efficiency and increased errors. It also creates a sense of loneliness for workers who live alone.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0581] In this invention, the server includes a means for receiving a conversation simulation request from a user, a means for causing a generative AI model to generate a conversation scenario and feedback based on the request, a means for receiving a response and feedback generated by the generative AI model, a means for returning the response and feedback to the user, and a means for simulating communication between a worker and a robot in a factory and providing feedback. This allows workers to learn effective communication methods, improving work efficiency and reducing errors. Furthermore, the generative AI model can function as a conversation partner for workers living alone, helping to reduce feelings of loneliness.

[0582] The "means for receiving a conversation simulation request from a user" is an interface for accepting an input from a user requesting the execution of a conversation simulation based on a specific situation, and for processing the request.

[0583] "Means for causing a generative AI model to generate a conversation scenario and feedback" refers to a process and system for using a generative AI model to generate an appropriate conversation scenario and feedback for user input based on a received request.

[0584] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that aggregates the conversational responses and feedback generated by a generative AI model and receives them on a server or system for use in subsequent steps.

[0585] "Means for sending responses and feedback back to the user" refers to the communications means and user interface for delivering the conversational responses and feedback generated by the generative AI model to the user.

[0586] The "means for simulating communication between workers and robots in a factory and providing feedback" is a process for simulating conversations and communications between workers and robots in a factory, generating feedback based on the results, and using the simulation results to improve work efficiency and communication capabilities.

[0587] The present invention provides a system for simulating communication between a user and a factory robot. This system utilizes a generative AI model to provide conversation simulation and feedback, and an embodiment of the system is described below.

[0588] Overall system configuration

[0589] The system consists of a terminal operated by the user, a server, and a generative AI model. The user uses the terminal to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0590] Embodiment

[0591] 1. User Request Submission

[0592] The user selects the situation in which the conversation simulation will be used from the terminal. For example, there are "self-introduction," "confirmation of work procedures," "reporting a problem," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[0593] 2. Request acceptance by the server

[0594] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[0595] 3. Invoking generative AI models

[0596] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[0597] 4. Response Generation Using Generative AI Models

[0598] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0599] 5. Providing feedback from the server to the user

[0600] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0601] Specific examples

[0602] For example, if a user wants to simulate a self-introduction, they would enter "Nice to meet you, my name is Tanaka. My hobbies are fishing and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Tanaka. Fishing and reading are great hobbies. Where have you been fishing recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[0603] Hardware and software used

[0604] Hardware: Computers controlling factory robots

[0605] Software: OpenAI GPT-3 API, Python

[0606] Prompt Sentence Examples

[0607] User input prompt:

[0608] "Please provide an example prompt for a simulation of introducing yourself to a factory robot."

[0609] Robot response:

[0610] "Hello, this is our new product line. How can I help you today?"

[0611] In this way, by using this system, users can simulate effective communication with factory robots, which can contribute to improving performance in actual workplaces.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1:

[0614] A user uses a terminal to request a conversation simulation. This request includes the selection of a situation, such as "self-introduction," "confirmation of work procedures," or "reporting a problem," and the input of specific conversation content. The input data is sent from the terminal to the server. The input data includes the selected situation and the user's specific conversation content.

[0615] Step 2:

[0616] The server receives the request sent from the terminal, analyzes the received data, and checks the request content. This analysis identifies the situation and user input, and checks the request content.

[0617] Step 3:

[0618] The server calls the generative AI model based on the received request. At the time of the call, the server provides the user's selected situation and specific conversation content as a prompt to the generative AI model. Based on this prompt, the generative AI model generates appropriate conversational responses and feedback.

[0619] Step 4:

[0620] The generative AI model generates conversational responses and feedback based on the provided prompts. Specifically, it generates optimal responses to the user's input, as well as suggestions for improving the conversation and specific feedback. The generated results are sent to the server.

[0621] Step 5:

[0622] The server receives the responses and feedback received from the generative AI model. This received data is compiled into information to be sent back to the user. The compiled information is converted into a transmission format for sending back to the user.

[0623] Step 6:

[0624] The server returns the compiled responses and feedback to the user's terminal, allowing the user to review the responses and feedback on their own terminal, and use this information to improve their conversation skills.

[0625] Step 7:

[0626] The server records the responses and feedback generated, allowing the user to refer to the previous feedback the next time they engage in a conversational simulation, thereby encouraging continuous improvement.

[0627] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0628] This invention provides a system that allows users to simulate various conversation situations, and utilizes a generative AI model and an emotion engine to provide conversation practice and feedback. Specific embodiments of this system are described below.

[0629] Overall system configuration

[0630] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user uses their device to send a conversation simulation request, which the server accepts and passes to the generative AI model, generating appropriate responses and feedback. The emotion engine then recognizes the user's emotional state and adjusts the requests and responses to the generative AI model accordingly.

[0631] Embodiment

[0632] 1. User Request Submission

[0633] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are options such as "self-introduction," "sales talk," and "conversation with a lover," and they input information appropriate to each situation into the device. The device then sends this information as a request to the server.

[0634] 2. Emotional state recognition using the emotion engine

[0635] The emotion engine recognizes the user's emotional state in real time as the user types or during conversation simulations, and generates emotion data using voice analysis, sensor data, and other methods.

[0636] 3. Request acceptance and processing on the server

[0637] The server receives the request and emotion data sent from the device, analyzes the received request and emotion data, and converts them into an appropriate format.

[0638] 4. Invoking generative AI models

[0639] The server invokes the generative AI model based on the analyzed request and emotion data, providing the selected scenario, user input, and corresponding emotional state to the generative AI model, which then generates a response and feedback accordingly.

[0640] 5. Response Generation Using Generative AI Models

[0641] Based on the provided scenario, user input, and emotional state, the generative AI model generates appropriate conversational responses, as well as specific feedback and suggestions for improvement based on the user's input.

[0642] 6. Providing feedback from the server to the user

[0643] The server sends the responses and feedback received from the generative AI model back to the user, who can then use the feedback to improve their conversation skills.

[0644] 7. Record and use next time

[0645] The generated responses and feedback are recorded on the server and used in the next conversation simulation. Emotional data is also recorded to track the user's emotional fluctuations and provide more effective feedback.

[0646] 8. Function as a conversation partner

[0647] For users living alone, the emotion engine monitors the user's emotional state, and the generative AI model acts as a conversation partner, engaging in conversations with appropriate topics based on the user's emotional state to reduce loneliness.

[0648] Specific examples

[0649] For example, if a user wants to simulate a "self-introduction," the user inputs "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into the device. The emotion engine analyzes the user's voice and facial expressions and detects that the user is relaxed. The device sends this information as a request to the server. The server receives this and makes a request to the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, based on the emotion engine data, additional advice is provided to a relaxed user to continue the conversation.

[0650] Similarly, when a user simulates a sales pitch, they type "Hello, I'd like to introduce you to our new product." The emotion engine detects the user's nervous state and sends it to the server. The generative AI model then generates a response such as "Hello, I'm excited to tell you about our new product. First, can you tell me about its features?" and provides feedback to help the user relax. Specific advice to ease the tension is also provided.

[0651] This invention not only helps users speak confidently in real-life situations, but also serves as a conversation partner for users living alone, reducing feelings of loneliness. By utilizing an emotion engine, appropriate responses and feedback are provided according to the user's emotional state, enabling effective conversation practice.

[0652] The processing flow will be explained below.

[0653] Step 1:

[0654] The user uses the terminal to select a conversation simulation situation, such as "self-introduction" or "sales talk."

[0655] Step 2:

[0656] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0657] Step 3:

[0658] The emotion engine analyzes the user's voice and facial expressions in real time as they type, recognizing their emotional state. For example, it can determine whether the user is relaxed or tense based on their tone of voice and facial expression.

[0659] Step 4:

[0660] The terminal sends the user's input data and emotional data to the server as a request, which includes the selected situation, the user's input, and the emotional state.

[0661] Step 5:

[0662] The server receives the request, analyzes it, and converts the results into an appropriate format for passing to the generative AI model.

[0663] Step 6:

[0664] The server sends the analyzed request and emotional data to the generative AI model, requesting a response and feedback for the conversation simulation.

[0665] Step 7:

[0666] The generative AI model generates appropriate conversational responses and feedback based on the request. For example, if a user inputs a self-introduction, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0667] Step 8:

[0668] The generative AI model sends responses and feedback back to the server, including suggestions for improvement and advice based on the user's emotional state.

[0669] Step 9:

[0670] The server sends the generated response and feedback back to the user's terminal, which receives it and displays it to the user.

[0671] Step 10:

[0672] The user can check the responses and feedback displayed on the device and use this information in the next conversation simulation. The user can also understand their own emotional state based on the data from the emotion engine, improving the quality of their practice.

[0673] Step 11:

[0674] The generated responses and feedback are recorded on the server, along with emotional data, which will be used in the next conversation simulation.

[0675] Step 12:

[0676] For users who live alone, the emotion engine monitors the user's emotional state. If the user feels lonely, the generative AI model generates appropriate conversational content and speaks to the user through the device. For example, it provides conversations tailored to the user's situation, such as "How was your day?" or "What's your favorite movie recently?"

[0677] In this way, the user inputs information via the device, the emotion engine analyzes their emotional state, and the server sends a request to the generative AI model to generate appropriate conversation responses and feedback. This allows users to practice real conversation situations while receiving feedback appropriate to their emotional state. It also functions as a conversation partner to reduce feelings of loneliness.

[0678] Example 2

[0679] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0680] Conventional conversation simulation systems generate responses and feedback uniformly without considering the user's emotional state, making it impossible to provide effective feedback optimized for the user's situation and emotions. Furthermore, they lacked functionality to provide emotional care and reduce loneliness for users living alone.

[0681] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for formatting the request and sending it to the server, means for an emotion engine to recognize the user's emotional state in real time, means for a generative AI model to generate an appropriate response and return it to the server, and means for recording the generated response and feedback and using it for the next conversation simulation. This provides appropriate feedback based on the user's emotional state, enabling users living alone to reduce their sense of loneliness and practice conversation effectively.

[0682] "User" refers to an individual who uses the system to engage in conversational simulations.

[0683] "Terminal" refers to an electronic device through which a user accesses the system.

[0684] A "request" refers to a request for a conversation simulation that a user sends to the system through a terminal.

[0685] "Server" refers to a central computer system that receives and processes requests.

[0686] An "emotion engine" refers to an analysis device that analyzes a user's voice and facial expressions to recognize their emotional state.

[0687] A "generative AI model" refers to an artificial intelligence model that generates conversation scenarios and provides feedback in response to user requests.

[0688] "Response" refers to the conversational content that a generative AI model generates based on a request.

[0689] "Feedback" refers to the improvements and advice that the generative AI model provides to the user regarding their conversation.

[0690] "Recording" refers to the act of the server storing request, response, and feedback data.

[0691] "Utilizing in the next simulation" refers to the process of referencing the recorded data in the next or subsequent simulation to provide more personalized feedback.

[0692] A "conversation partner" is a generative AI model that acts as a conversation partner for the user, reducing feelings of loneliness.

[0693] "Emotional Data" refers to information about a user's emotional state that is analyzed and generated by the Emotion Engine.

[0694] This invention provides a system that allows users to experience and practice various conversation situations. This system uses a generative AI model and an emotion engine based on user input to simulate real conversations. The following describes how this system is specifically implemented.

[0695] Overall system configuration

[0696] The system consists of a terminal used by the user, a central processing server, a generative AI model that generates responses, and an emotion engine that recognizes emotional states.

[0697] 1. User Input

[0698] The user uses the terminal to select a specific conversation situation and input the content of the conversation. For example, options such as "self-introduction," "sales talk," and "conversation with a lover" are provided. An example of specific content that the user can input is "Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[0699] 2. Submitting a Request

[0700] The terminal formats the user's input and sends it to the server as request data, which includes the selected scenario and the user's input.

[0701] 3. Recognizing emotional states

[0702] Upon receiving the request, the server requests data from the emotion engine to collect emotion data. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state (e.g., relaxed, nervous) in real time.

[0703] 4. Integrating Request and Emotion Data

[0704] The server receives the emotion data sent from the emotion engine and integrates it with the request data. This integrated data serves as input for the generative AI model to generate conversational responses.

[0705] 5. Response Generation Using Generative AI Models

[0706] The server passes the integrated data to a generative AI model and asks it to generate the optimal response and feedback. The generative AI model generates appropriate conversational responses and feedback based on this data. An example of a generated response is, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0707] 6. Responding and Providing Feedback

[0708] The server sends the generated responses and feedback back to the device, where the user can view the information on the device screen and use it to improve their conversation skills.

[0709] 7. Record the data and use it again

[0710] The server records requests, responses, feedback, and emotional data, which are then used as a basis for providing individually optimized feedback during subsequent simulations.

[0711] 8. Function as a conversation partner

[0712] For users living alone, a generative AI model can act as a conversation partner. Based on data from the emotion engine, it provides appropriate conversation based on the user's emotions to reduce feelings of loneliness. This function allows users to have a conversation partner without feeling lonely on a daily basis.

[0713] Specific examples

[0714] For example, if a user wants to simulate a "self-introduction," the specific operations are as follows:

[0715] 1. The user types into the terminal: "Hello, my name is Taro. My hobbies are traveling and reading."

[0716] 2. Analysis by emotion engine: Recognizes a relaxed state from voice and facial expression data.

[0717] 3. Response generation using a generative AI model: "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0718] 4. Feedback: Advice is given to help you relax and continue speaking.

[0719] In this way, conversation simulation is carried out, and the user can improve his / her skills in actual conversation situations.

[0720] Prompt Sentence Examples

[0721] User: "Hi, I'd like to tell you about our new product."

[0722] Generative AI model: "Hello, I'm excited to talk to you about your new product. First, can you tell me about its features?"

[0723] Feedback: "You seem nervous. It would be a good idea to write down what you're going to say beforehand. I also recommend taking some deep breaths and relaxing."

[0724] The system provides training to help users become confident and effective speakers in real-life conversation situations.

[0725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0726] The flow of this system's program processing

[0727] Step 1:

[0728] The user sends a request for a conversation simulation from the terminal.

[0729] Specific operation: The user selects a situation and inputs the conversation content in text by operating the device screen. For example, the user might input, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0730] Input: User text input (simulation situation and conversation content)

[0731] Output: Formatted request data

[0732] Step 2:

[0733] The device sends a request to the server.

[0734] Specific operation: The terminal converts the user's input data into packets and sends an HTTP request to the specified API endpoint on the server.

[0735] Input: Formatted request data

[0736] Output: HTTP request data received by the server

[0737] Step 3:

[0738] The server accepts the request and requests the emotion engine for the user's emotion data.

[0739] Specific operation: The server checks the format of the received request data, and if it determines that the data is valid, it sends a request to the emotion engine to analyze the user's emotional state.

[0740] Input: HTTP request data

[0741] Output: Analysis request to the emotion engine

[0742] Step 4:

[0743] The emotion engine recognizes the user's emotional state in real time.

[0744] Specific operation: The emotion engine uses voice and facial expression analysis systems to recognize the user's emotional state from their voice and video data. For example, it detects whether they are relaxed or tense.

[0745] Input: User's audio and video data

[0746] Output: Emotional state data (e.g., relaxed, tense)

[0747] Step 5:

[0748] The server receives and processes the emotion data sent from the emotion engine.

[0749] Specific operation: The server receives the emotion data sent from the emotion engine and integrates it into the request data. It formats the data and prepares it for passing to the generative AI model.

[0750] Input: Emotional state data

[0751] Output: Consolidated request data

[0752] Step 6:

[0753] The server then forwards the processed request and emotion data to the generative AI model.

[0754] What happens: The server sends a POST request to the appropriate API endpoint to transfer the aggregated data to the generative AI model.

[0755] Input: Consolidated request data

[0756] Output: HTTP request to the generative AI model

[0757] Step 7:

[0758] The generative AI model generates an appropriate response and returns it to the server.

[0759] Specific operation: The generative AI model uses natural language processing to generate appropriate responses and feedback based on the input situation and emotional state. For example, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0760] Input: Consolidated request data

[0761] Output: Generated responses and feedback

[0762] Step 8:

[0763] The server sends a response and feedback back to the user's terminal.

[0764] Specific operation: The server formats the response and feedback received from the generative AI model and sends it back to the user's device. The data is sent using an HTTP response.

[0765] Input: Generated responses and feedback

[0766] Output: Response data sent to the user's device

[0767] Step 9:

[0768] The user checks the feedback on the device.

[0769] Specific operation: The user checks the responses and feedback displayed on the device screen and uses it as a reference for improving their conversation skills.

[0770] Input: Response data sent back to the terminal

[0771] Output: Verified feedback

[0772] Step 10:

[0773] The server records the requests and responses and uses them for the next simulation.

[0774] Specific operation: The server stores the request content, response, feedback, and emotion data in a database, allowing past data to be reflected in the responses and feedback in the next simulation.

[0775] Input: Request content, response, feedback, emotion data

[0776] Output: Data recorded in the database

[0777] (Application example 2)

[0778] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0779] Conventional conversation simulation systems have the problem that they do not provide feedback that takes into account the user's emotional state, which means that they are unable to sufficiently improve their ability to adapt to real-world situations. Furthermore, particularly in customer service, there is a lack of real-time training for staff to respond to various situations, making it difficult to improve service quality.

[0780] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving a response and feedback generated by the generative AI model, means for returning the response and feedback to the user, means for analyzing the user's emotional state using an emotion recognition engine, and means for adjusting requests and responses to the generative AI model based on the emotional state. This allows for the provision of conversational responses and feedback according to the user's emotional state, enabling improvement of customer service skills in physical stores.

[0781] The "means for receiving a conversation simulation request from a user" refers to a device or software that receives a request on the system when a user inputs a request for a conversation simulation through a terminal.

[0782] "Means for generating a conversation scenario for a generative AI model" refers to a device or software that creates an appropriate conversation scenario based on a user request and provides that scenario to a generative AI model.

[0783] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that receives conversational responses and feedback created by a generative AI model on a server or terminal.

[0784] "Means for returning responses and feedback to the user" refers to a device or software that returns and displays the conversational responses and feedback received from the generative AI model to the user's device.

[0785] "Means for analyzing a user's emotional state using an emotion recognition engine" refers to a device or software that analyzes a user's voice, facial expressions, etc., and determines their emotional state in real time.

[0786] "Means for adjusting requests and responses to a generative AI model based on emotional state" refers to a device or software that appropriately modifies requests and responses provided to a generative AI model based on the user's emotional state obtained by an emotion recognition engine.

[0787] MODE FOR CARRYING OUT THE INVENTION

[0788] This invention is a system that allows users to improve their customer service skills and communication abilities through various conversation simulations. This system is composed of a user terminal, a server, a generative AI model, and an emotion recognition engine.

[0789] Overall system configuration

[0790] 1. User Device

[0791] The user terminal uses smart glasses or a head-mounted display (HMD), which allows the user to view conversation scenarios and receive feedback in real time. When the user inputs a conversation simulation request through the terminal, the information is sent to the server.

[0792] 2. Server

[0793] The server analyzes the requests received from the user and generates a conversation scenario from the generative AI model, which uses the OpenAI API to generate appropriate responses and feedback based on the user's input and emotional data.

[0794] 3. Emotion Recognition Engine

[0795] The emotion recognition engine analyzes the user's emotional state in real time. It uses the camera installed in the smart glasses or HMD to collect the user's facial expressions and voice data to recognize emotions. It uses the EmotionRecognition library to analyze emotions and sends the information to the server.

[0796] 4. Generative AI Models

[0797] The generative AI model generates appropriate conversational responses and feedback based on emotional data obtained from the emotion recognition engine and user input. OpenAI's generative AI model is used, and responses are generated by entering prompt sentences via the API.

[0798] Specific program processing

[0799] 1. Recognizing emotional states

[0800] The camera installed in the smart glasses or HMD captures the user's face and uses the EmotionRecognition library to analyze their emotions, for example, determining in real time whether the user is relaxed or nervous.

[0801] 2. Invoking a generative AI model

[0802] The prompt, including the user's input and emotional state, is passed to the OpenAI API to generate an appropriate response and feedback, which is then sent back to the user's device.

[0803] Specific examples

[0804] The user types "Welcome to our store," and a state of nervousness is detected.

[0805] The server uses this information to send the following prompt to the generative AI model:

[0806] User Input: Welcome to our store.

[0807] Emotional state: Tension

[0808] Appropriate response:

[0809] The generative AI model responds, "Thank you. Is there anything in particular you're looking for?" and this is sent back to the user.

[0810] This system not only allows users to improve their customer service skills in physical stores, but also allows them to receive real-time feedback based on their emotional state.

[0811] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0812] Step 1:

[0813] The user device receives a request for a conversation simulation. Using smart glasses or a head-mounted display (HMD), the user selects the situation they want to simulate and inputs it using text or voice. This input data is sent to the server. For example, an input such as "Welcome to our store" is sent as a request.

[0814] Step 2:

[0815] The device's camera captures the user's face in real time, and the emotion recognition engine analyzes it. Using the EmotionRecognition library, the system determines the user's emotional state (tense, relaxed, etc.) based on their facial expressions and voice data. The analysis results indicate that the user is tense, and this data is sent to the server. The input is the camera image and voice data, and the output is emotional state data.

[0816] Step 3:

[0817] The server receives the user's input data and emotional state data and creates a prompt for the generative AI model. For example, the prompt might look like this:

[0818] User Input: Welcome to our store.

[0819] Emotional state: Tension

[0820] Appropriate response:

[0821] This prompt is sent to the OpenAI API, which receives the user's input data and emotional state data as input, and the generative AI model's response as output.

[0822] Step 4:

[0823] The generative AI model generates an appropriate conversational response based on the prompt. For example, it generates a response such as, "Thank you. Is there anything in particular you're looking for?" The generated response is sent back to the server. The input is the prompt, and the output is the conversational response.

[0824] Step 5:

[0825] The server sends the generated conversational responses back to the user's device. The device displays the responses to the user and reads them aloud, allowing the user to experience a real customer service simulation. The input is the response data of the generative AI model, and the output is feedback to the user's device.

[0826] Step 6:

[0827] The server records the entire conversation simulation and saves it for future training. For example, the user's input, emotional state, generated responses, and their feedback are saved in a database. The input is the entire simulation data, and the output is the saved training data.

[0828] These steps allow users to effectively improve their customer service skills while receiving real-time feedback based on their emotional state.

[0829] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0830] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0832] [Third embodiment]

[0833] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0834] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0835] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0836] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0837] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0838] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0839] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0840] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0841] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0842] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0843] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0844] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0845] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[0846] Overall system configuration

[0847] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0848] Embodiment

[0849] 1. User Request Submission

[0850] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the device. The device then sends this information as a request to the server.

[0851] 2. Request acceptance by the server

[0852] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[0853] 3. Invoking generative AI models

[0854] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[0855] 4. Response Generation Using Generative AI Models

[0856] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0857] 5. Providing feedback from the server to the user

[0858] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0859] 6. Record and use next time

[0860] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[0861] 7. Function as a conversation partner

[0862] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[0863] Specific examples

[0864] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback such as the tone of the conversation and additional advice (e.g., "It would be good to talk more specifically about your hobbies when introducing yourself.").

[0865] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[0866] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[0867] The above is a specific embodiment for carrying out the present invention. By using this system, users can develop the ability to respond quickly and effectively to various conversation situations.

[0868] The processing flow will be explained below.

[0869] Step 1:

[0870] The user uses the terminal to select a conversation simulation scenario, such as "self-introduction" or "sales talk."

[0871] Step 2:

[0872] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0873] Step 3:

[0874] The terminal sends the input information to the server as a request, which includes the scenario type and the user input.

[0875] Step 4:

[0876] The server receives the request, parses it, and converts it into the appropriate format.

[0877] Step 5:

[0878] The server sends the parsed request to the generative AI model, requesting a response and feedback for the conversation simulation.

[0879] Step 6:

[0880] The generative AI model generates appropriate conversational responses and feedback based on the request, specifically crafting relevant questions and advice based on user input.

[0881] Step 7:

[0882] The generative AI model sends the generated response and feedback back to the server, such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[0883] Step 8:

[0884] The server sends the generated responses and feedback back to the user's device, including suggestions and suggestions for improving the conversation.

[0885] Step 9:

[0886] The terminal receives the response and feedback from the server and displays it to the user, who can review it to improve their conversation skills.

[0887] Step 10:

[0888] The responses and feedback generated are recorded on the server and used in the next conversation simulation.

[0889] Step 11:

[0890] For users who live alone, the server provides a function where the generative AI model periodically speaks to them, helping to reduce the user's sense of loneliness.

[0891] Example 1

[0892] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0893] In today's busy society, improving interpersonal communication skills is important for one's career and relationships. However, opportunities to practice in simulated conversation situations are limited, and reducing feelings of loneliness is also a challenge, especially for people living alone. Furthermore, there is a lack of systems that allow people to receive feedback and continuously improve their skills.

[0894] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0895] In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving responses and feedback generated by the generative AI model, means for returning the responses and feedback to the user, means for recording the generated responses and feedback and using them in the next conversation simulation, and means for the generative AI model to talk to the user as a conversation partner to reduce feelings of loneliness for users living alone. This allows users to receive feedback while simulating various conversation situations and improve their communication skills. Furthermore, users living alone are provided with a conversation partner to reduce feelings of loneliness.

[0896] "User" refers to a person who uses the system to conduct a conversation simulation.

[0897] "Terminal" refers to a device used by a user, including, for example, a smartphone or a computer.

[0898] "Server" refers to a computer system that performs processes such as receiving requests, analyzing them, invoking generative AI models, and returning responses.

[0899] A "request" refers to a request for a conversational simulation that a user sends to the system using a terminal.

[0900] A "generative AI model" refers to an artificial intelligence model that generates appropriate conversational responses or feedback based on given input.

[0901] A "conversation scenario" refers to a setting or scenario used to simulate a particular conversation situation.

[0902] "Response" refers to the response that a generative AI model generates in response to user input.

[0903] "Feedback" refers to specific improvements and advice regarding the content of a user's conversation.

[0904] "Recording" refers to saving the responses and feedback generated.

[0905] "Conversation Partner" refers to a generative AI model that regularly speaks to users, especially those living alone, to help reduce feelings of loneliness.

[0906] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[0907] Overall system configuration

[0908] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0909] Embodiment

[0910] User request submission

[0911] The user selects a conversation simulation situation from the terminal. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[0912] Request acceptance on the server

[0913] The server receives the request sent from the terminal and analyzes the request content, which includes the conversation simulation scenario and specific inputs from the user.

[0914] Invoking a generative AI model

[0915] Based on the received request, the server invokes a generative AI model (e.g., OpenAI's GPT-4), provides the selected scenario and user input to the generative AI model, and has it generate responses and feedback accordingly.

[0916] Response generation using generative AI models

[0917] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[0918] Providing feedback from the server to the user

[0919] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[0920] Record and use next time

[0921] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[0922] Functions as a conversation partner

[0923] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[0924] Specific examples

[0925] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[0926] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[0927] Prompt Sentence Examples

[0928] "I'd like to simulate a self-introduction. Please generate appropriate responses and feedback for the following inputs."

[0929] "I'd like to simulate a sales pitch. Please generate appropriate responses and feedback for the following inputs."

[0930] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[0931] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0932] Program processing flow

[0933] Step 1: User selects and inputs conversation situation

[0934] The user uses the terminal to select a conversation situation from a specified interface and inputs the appropriate conversation content.

[0935] Input: The scenario selection and specific conversation content that the user inputs into the terminal (e.g., "self-introduction," "sales talk," etc.).

[0936] Data processing: The terminal converts the input content into request data.

[0937] Output: Request data (scenario and conversation content).

[0938] Action: A user opens an application on their device, selects "Introduce myself" from the menu, and enters "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[0939] Step 2: Sending a request from the device to the server

[0940] The terminal transmits the request data input by the user to the server.

[0941] Input: Terminal-generated request data.

[0942] Data processing: The terminal converts the request data into a data packet.

[0943] Output: Data packets sent to the server.

[0944] How it works: A device sends a data packet to a server over the Internet.

[0945] Step 3: Receiving and parsing the request on the server

[0946] The server accepts the data packets received from the terminal and analyzes the contents.

[0947] Input: Data packets received from the terminal.

[0948] Data processing: The server analyzes the data packets and extracts the scenario and conversation content.

[0949] Output: Analyzed scenario and conversation content.

[0950] What it does: The server parses the received data and extracts "Scenario: Self-introduction" and "User input: Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[0951] Step 4: Invoke the generative AI model

[0952] The server calls a generative AI model based on the analyzed scenario and conversation content.

[0953] Input: Parsed scenario and conversation content.

[0954] Data processing: The server generates API requests for the generative AI model.

[0955] Output: API request to the generative AI model.

[0956] How it works: The server generates an API request and sends it to the generative AI model's endpoint. This request includes the scenario and user input.

[0957] Step 5: Generate AI model responses

[0958] Generative AI models generate responses and feedback based on the scenario and inputs provided.

[0959] Input: An API request to a generative AI model.

[0960] Data processing: Generative AI models use internal algorithms to generate responses and feedback.

[0961] Output: Response and feedback data.

[0962] How it works: The generative AI model responds with, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" along with feedback such as, "You might want to be more specific about your hobbies when introducing yourself."

[0963] Step 6: Providing feedback from the server to the user

[0964] The server sends the responses and feedback received from the generative AI model back to the user.

[0965] Input: The response and feedback data received from the generative AI model.

[0966] Data processing: The server converts the response and feedback data into data packets.

[0967] Output: Data packets sent to the user's device.

[0968] Operation: The server creates a data packet containing the generated response and feedback and sends it to the terminal.

[0969] Step 7: Record your feedback information

[0970] The server records the generated response and feedback for future reference.

[0971] Input: Generated response and feedback data.

[0972] Data processing: The server stores the responses and feedback in a database.

[0973] Output: The saved feedback information.

[0974] How it works: The server stores the response and feedback in a database.

[0975] Step 8: Implementing the conversation partner function

[0976] For users living alone, the server periodically initiates conversations using a generative AI model to reduce feelings of loneliness.

[0977] Input: The time set as the scheduled task.

[0978] Data processing: The server generates periodic requests and sends them to the generative AI model.

[0979] Output: Periodically generated conversation responses.

[0980] How it works: The server calls the generative AI model at a specified time and begins a conversation such as, "Hello, what are your plans for today?"

[0981] (Application example 1)

[0982] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0983] The lack of effective communication between robots and workers in factories is a problem. With conventional technology, workers lack the training to properly interact with robots, which leads to reduced work efficiency and increased errors. It also creates a sense of loneliness for workers who live alone.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0985] In this invention, the server includes a means for receiving a conversation simulation request from a user, a means for causing a generative AI model to generate a conversation scenario and feedback based on the request, a means for receiving a response and feedback generated by the generative AI model, a means for returning the response and feedback to the user, and a means for simulating communication between a worker and a robot in a factory and providing feedback. This allows workers to learn effective communication methods, improving work efficiency and reducing errors. Furthermore, the generative AI model can function as a conversation partner for workers living alone, helping to reduce feelings of loneliness.

[0986] The "means for receiving a conversation simulation request from a user" is an interface for accepting an input from a user requesting the execution of a conversation simulation based on a specific situation, and for processing the request.

[0987] "Means for causing a generative AI model to generate a conversation scenario and feedback" refers to a process and system for using a generative AI model to generate an appropriate conversation scenario and feedback for user input based on a received request.

[0988] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that aggregates the conversational responses and feedback generated by a generative AI model and receives them on a server or system for use in subsequent steps.

[0989] "Means for sending responses and feedback back to the user" refers to the communications means and user interface for delivering the conversational responses and feedback generated by the generative AI model to the user.

[0990] The "means for simulating communication between workers and robots in a factory and providing feedback" is a process for simulating conversations and communications between workers and robots in a factory, generating feedback based on the results, and using the simulation results to improve work efficiency and communication capabilities.

[0991] The present invention provides a system for simulating communication between a user and a factory robot. This system utilizes a generative AI model to provide conversation simulation and feedback, and an embodiment of the system is described below.

[0992] Overall system configuration

[0993] The system consists of a terminal operated by the user, a server, and a generative AI model. The user uses the terminal to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[0994] Embodiment

[0995] 1. User Request Submission

[0996] The user selects the situation in which the conversation simulation will be used from the terminal. For example, there are "self-introduction," "confirmation of work procedures," "reporting a problem," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[0997] 2. Request acceptance by the server

[0998] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[0999] 3. Invoking generative AI models

[1000] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[1001] 4. Response Generation Using Generative AI Models

[1002] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[1003] 5. Providing feedback from the server to the user

[1004] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[1005] Specific examples

[1006] For example, if a user wants to simulate a self-introduction, they would enter "Nice to meet you, my name is Tanaka. My hobbies are fishing and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Tanaka. Fishing and reading are great hobbies. Where have you been fishing recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[1007] Hardware and software used

[1008] Hardware: Computers controlling factory robots

[1009] Software: OpenAI GPT-3 API, Python

[1010] Prompt Sentence Examples

[1011] User input prompt:

[1012] "Please provide an example prompt for a simulation of introducing yourself to a factory robot."

[1013] Robot response:

[1014] "Hello, this is our new product line. How can I help you today?"

[1015] In this way, by using this system, users can simulate effective communication with factory robots, which can contribute to improving performance in actual workplaces.

[1016] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1017] Step 1:

[1018] A user uses a terminal to request a conversation simulation. This request includes the selection of a situation, such as "self-introduction," "confirmation of work procedures," or "reporting a problem," and the input of specific conversation content. The input data is sent from the terminal to the server. The input data includes the selected situation and the user's specific conversation content.

[1019] Step 2:

[1020] The server receives the request sent from the terminal, analyzes the received data, and checks the request content. This analysis identifies the situation and user input, and checks the request content.

[1021] Step 3:

[1022] The server calls the generative AI model based on the received request. At the time of the call, the server provides the user's selected situation and specific conversation content as a prompt to the generative AI model. Based on this prompt, the generative AI model generates appropriate conversational responses and feedback.

[1023] Step 4:

[1024] The generative AI model generates conversational responses and feedback based on the provided prompts. Specifically, it generates optimal responses to the user's input, as well as suggestions for improving the conversation and specific feedback. The generated results are sent to the server.

[1025] Step 5:

[1026] The server receives the responses and feedback received from the generative AI model. This received data is compiled into information to be sent back to the user. The compiled information is converted into a transmission format for sending back to the user.

[1027] Step 6:

[1028] The server returns the compiled responses and feedback to the user's terminal, allowing the user to review the responses and feedback on their own terminal, and use this information to improve their conversation skills.

[1029] Step 7:

[1030] The server records the responses and feedback generated, allowing the user to refer to the previous feedback the next time they engage in a conversational simulation, thereby encouraging continuous improvement.

[1031] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1032] This invention provides a system that allows users to simulate various conversation situations, and utilizes a generative AI model and an emotion engine to provide conversation practice and feedback. Specific embodiments of this system are described below.

[1033] Overall system configuration

[1034] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user uses their device to send a conversation simulation request, which the server accepts and passes to the generative AI model, generating appropriate responses and feedback. The emotion engine then recognizes the user's emotional state and adjusts the requests and responses to the generative AI model accordingly.

[1035] Embodiment

[1036] 1. User Request Submission

[1037] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are options such as "self-introduction," "sales talk," and "conversation with a lover," and they input information appropriate to each situation into the device. The device then sends this information as a request to the server.

[1038] 2. Emotional state recognition using the emotion engine

[1039] The emotion engine recognizes the user's emotional state in real time as the user types or during conversation simulations, and generates emotion data using voice analysis, sensor data, and other methods.

[1040] 3. Request acceptance and processing on the server

[1041] The server receives the request and emotion data sent from the device, analyzes the received request and emotion data, and converts them into an appropriate format.

[1042] 4. Invoking generative AI models

[1043] The server invokes the generative AI model based on the analyzed request and emotion data, providing the selected scenario, user input, and corresponding emotional state to the generative AI model, which then generates a response and feedback accordingly.

[1044] 5. Response Generation Using Generative AI Models

[1045] Based on the provided scenario, user input, and emotional state, the generative AI model generates appropriate conversational responses, as well as specific feedback and suggestions for improvement based on the user's input.

[1046] 6. Providing feedback from the server to the user

[1047] The server sends the responses and feedback received from the generative AI model back to the user, who can then use the feedback to improve their conversation skills.

[1048] 7. Record and use next time

[1049] The generated responses and feedback are recorded on the server and used in the next conversation simulation. Emotional data is also recorded to track the user's emotional fluctuations and provide more effective feedback.

[1050] 8. Function as a conversation partner

[1051] For users living alone, the emotion engine monitors the user's emotional state, and the generative AI model acts as a conversation partner, engaging in conversations with appropriate topics based on the user's emotional state to reduce loneliness.

[1052] Specific examples

[1053] For example, if a user wants to simulate a "self-introduction," the user inputs "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into the device. The emotion engine analyzes the user's voice and facial expressions and detects that the user is relaxed. The device sends this information as a request to the server. The server receives this and makes a request to the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, based on the emotion engine data, additional advice is provided to a relaxed user to continue the conversation.

[1054] Similarly, when a user simulates a sales pitch, they type "Hello, I'd like to introduce you to our new product." The emotion engine detects the user's nervous state and sends it to the server. The generative AI model then generates a response such as "Hello, I'm excited to tell you about our new product. First, can you tell me about its features?" and provides feedback to help the user relax. Specific advice to ease the tension is also provided.

[1055] This invention not only helps users speak confidently in real-life situations, but also serves as a conversation partner for users living alone, reducing feelings of loneliness. By utilizing an emotion engine, appropriate responses and feedback are provided according to the user's emotional state, enabling effective conversation practice.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] The user uses the terminal to select a conversation simulation situation, such as "self-introduction" or "sales talk."

[1059] Step 2:

[1060] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[1061] Step 3:

[1062] The emotion engine analyzes the user's voice and facial expressions in real time as they type, recognizing their emotional state. For example, it can determine whether the user is relaxed or tense based on their tone of voice and facial expression.

[1063] Step 4:

[1064] The terminal sends the user's input data and emotional data to the server as a request, which includes the selected situation, the user's input, and the emotional state.

[1065] Step 5:

[1066] The server receives the request, analyzes it, and converts the results into an appropriate format for passing to the generative AI model.

[1067] Step 6:

[1068] The server sends the analyzed request and emotional data to the generative AI model, requesting a response and feedback for the conversation simulation.

[1069] Step 7:

[1070] The generative AI model generates appropriate conversational responses and feedback based on the request. For example, if a user inputs a self-introduction, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1071] Step 8:

[1072] The generative AI model sends responses and feedback back to the server, including suggestions for improvement and advice based on the user's emotional state.

[1073] Step 9:

[1074] The server sends the generated response and feedback back to the user's terminal, which receives it and displays it to the user.

[1075] Step 10:

[1076] The user can check the responses and feedback displayed on the device and use this information in the next conversation simulation. The user can also understand their own emotional state based on the data from the emotion engine, improving the quality of their practice.

[1077] Step 11:

[1078] The generated responses and feedback are recorded on the server, along with emotional data, which will be used in the next conversation simulation.

[1079] Step 12:

[1080] For users who live alone, the emotion engine monitors the user's emotional state. If the user feels lonely, the generative AI model generates appropriate conversational content and speaks to the user through the device. For example, it provides conversations tailored to the user's situation, such as "How was your day?" or "What's your favorite movie recently?"

[1081] In this way, the user inputs information via the device, the emotion engine analyzes their emotional state, and the server sends a request to the generative AI model to generate appropriate conversation responses and feedback. This allows users to practice real conversation situations while receiving feedback appropriate to their emotional state. It also functions as a conversation partner to reduce feelings of loneliness.

[1082] Example 2

[1083] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1084] Conventional conversation simulation systems generate responses and feedback uniformly without considering the user's emotional state, making it impossible to provide effective feedback optimized for the user's situation and emotions. Furthermore, they lacked functionality to provide emotional care and reduce loneliness for users living alone.

[1085] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for formatting the request and sending it to the server, means for an emotion engine to recognize the user's emotional state in real time, means for a generative AI model to generate an appropriate response and return it to the server, and means for recording the generated response and feedback and using it for the next conversation simulation. This provides appropriate feedback based on the user's emotional state, enabling users living alone to reduce their sense of loneliness and practice conversation effectively.

[1086] "User" refers to an individual who uses the system to engage in conversational simulations.

[1087] "Terminal" refers to an electronic device through which a user accesses the system.

[1088] A "request" refers to a request for a conversation simulation that a user sends to the system through a terminal.

[1089] "Server" refers to a central computer system that receives and processes requests.

[1090] An "emotion engine" refers to an analysis device that analyzes a user's voice and facial expressions to recognize their emotional state.

[1091] A "generative AI model" refers to an artificial intelligence model that generates conversation scenarios and provides feedback in response to user requests.

[1092] "Response" refers to the conversational content that a generative AI model generates based on a request.

[1093] "Feedback" refers to the improvements and advice that the generative AI model provides to the user regarding their conversation.

[1094] "Recording" refers to the act of the server storing request, response, and feedback data.

[1095] "Utilizing in the next simulation" refers to the process of referencing the recorded data in the next or subsequent simulation to provide more personalized feedback.

[1096] A "conversation partner" is a generative AI model that acts as a conversation partner for the user, reducing feelings of loneliness.

[1097] "Emotional Data" refers to information about a user's emotional state that is analyzed and generated by the Emotion Engine.

[1098] This invention provides a system that allows users to experience and practice various conversation situations. This system uses a generative AI model and an emotion engine based on user input to simulate real conversations. The following describes how this system is specifically implemented.

[1099] Overall system configuration

[1100] The system consists of a terminal used by the user, a central processing server, a generative AI model that generates responses, and an emotion engine that recognizes emotional states.

[1101] 1. User Input

[1102] The user uses the terminal to select a specific conversation situation and input the content of the conversation. For example, options such as "self-introduction," "sales talk," and "conversation with a lover" are provided. An example of specific content that the user can input is "Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[1103] 2. Submitting a Request

[1104] The terminal formats the user's input and sends it to the server as request data, which includes the selected scenario and the user's input.

[1105] 3. Recognizing emotional states

[1106] Upon receiving the request, the server requests data from the emotion engine to collect emotion data. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state (e.g., relaxed, nervous) in real time.

[1107] 4. Integrating Request and Emotion Data

[1108] The server receives the emotion data sent from the emotion engine and integrates it with the request data. This integrated data serves as input for the generative AI model to generate conversational responses.

[1109] 5. Response Generation Using Generative AI Models

[1110] The server passes the integrated data to a generative AI model and asks it to generate the optimal response and feedback. The generative AI model generates appropriate conversational responses and feedback based on this data. An example of a generated response is, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1111] 6. Responding and Providing Feedback

[1112] The server sends the generated responses and feedback back to the device, where the user can view the information on the device screen and use it to improve their conversation skills.

[1113] 7. Record the data and use it again

[1114] The server records requests, responses, feedback, and emotional data, which are then used as a basis for providing individually optimized feedback during subsequent simulations.

[1115] 8. Function as a conversation partner

[1116] For users living alone, a generative AI model can act as a conversation partner. Based on data from the emotion engine, it provides appropriate conversation based on the user's emotions to reduce feelings of loneliness. This function allows users to have a conversation partner without feeling lonely on a daily basis.

[1117] Specific examples

[1118] For example, if a user wants to simulate a "self-introduction," the specific operations are as follows:

[1119] 1. The user types into the terminal: "Hello, my name is Taro. My hobbies are traveling and reading."

[1120] 2. Analysis by emotion engine: Recognizes a relaxed state from voice and facial expression data.

[1121] 3. Response generation using a generative AI model: "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1122] 4. Feedback: Advice is given to help you relax and continue speaking.

[1123] In this way, conversation simulation is carried out, and the user can improve his / her skills in actual conversation situations.

[1124] Prompt Sentence Examples

[1125] User: "Hi, I'd like to tell you about our new product."

[1126] Generative AI model: "Hello, I'm excited to talk to you about your new product. First, can you tell me about its features?"

[1127] Feedback: "You seem nervous. It would be a good idea to write down what you're going to say beforehand. I also recommend taking some deep breaths and relaxing."

[1128] The system provides training to help users become confident and effective speakers in real-life conversation situations.

[1129] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1130] The flow of this system's program processing

[1131] Step 1:

[1132] The user sends a request for a conversation simulation from the terminal.

[1133] Specific operation: The user selects a situation and inputs the conversation content in text by operating the device screen. For example, the user might input, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[1134] Input: User text input (simulation situation and conversation content)

[1135] Output: Formatted request data

[1136] Step 2:

[1137] The device sends a request to the server.

[1138] Specific operation: The terminal converts the user's input data into packets and sends an HTTP request to the specified API endpoint on the server.

[1139] Input: Formatted request data

[1140] Output: HTTP request data received by the server

[1141] Step 3:

[1142] The server accepts the request and requests the emotion engine for the user's emotion data.

[1143] Specific operation: The server checks the format of the received request data, and if it determines that the data is valid, it sends a request to the emotion engine to analyze the user's emotional state.

[1144] Input: HTTP request data

[1145] Output: Analysis request to the emotion engine

[1146] Step 4:

[1147] The emotion engine recognizes the user's emotional state in real time.

[1148] Specific operation: The emotion engine uses voice and facial expression analysis systems to recognize the user's emotional state from their voice and video data. For example, it detects whether they are relaxed or tense.

[1149] Input: User's audio and video data

[1150] Output: Emotional state data (e.g., relaxed, tense)

[1151] Step 5:

[1152] The server receives and processes the emotion data sent from the emotion engine.

[1153] Specific operation: The server receives the emotion data sent from the emotion engine and integrates it into the request data. It formats the data and prepares it for passing to the generative AI model.

[1154] Input: Emotional state data

[1155] Output: Consolidated request data

[1156] Step 6:

[1157] The server then forwards the processed request and emotion data to the generative AI model.

[1158] What happens: The server sends a POST request to the appropriate API endpoint to transfer the aggregated data to the generative AI model.

[1159] Input: Consolidated request data

[1160] Output: HTTP request to the generative AI model

[1161] Step 7:

[1162] The generative AI model generates an appropriate response and returns it to the server.

[1163] Specific operation: The generative AI model uses natural language processing to generate appropriate responses and feedback based on the input situation and emotional state. For example, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1164] Input: Consolidated request data

[1165] Output: Generated responses and feedback

[1166] Step 8:

[1167] The server sends a response and feedback back to the user's terminal.

[1168] Specific operation: The server formats the response and feedback received from the generative AI model and sends it back to the user's device. The data is sent using an HTTP response.

[1169] Input: Generated responses and feedback

[1170] Output: Response data sent to the user's device

[1171] Step 9:

[1172] The user checks the feedback on the device.

[1173] Specific operation: The user checks the responses and feedback displayed on the device screen and uses it as a reference for improving their conversation skills.

[1174] Input: Response data sent back to the terminal

[1175] Output: Verified feedback

[1176] Step 10:

[1177] The server records the requests and responses and uses them for the next simulation.

[1178] Specific operation: The server stores the request content, response, feedback, and emotion data in a database, allowing past data to be reflected in the responses and feedback in the next simulation.

[1179] Input: Request content, response, feedback, emotion data

[1180] Output: Data recorded in the database

[1181] (Application example 2)

[1182] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1183] Conventional conversation simulation systems have the problem that they do not provide feedback that takes into account the user's emotional state, which means that they are unable to sufficiently improve their ability to adapt to real-world situations. Furthermore, particularly in customer service, there is a lack of real-time training for staff to respond to various situations, making it difficult to improve service quality.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving a response and feedback generated by the generative AI model, means for returning the response and feedback to the user, means for analyzing the user's emotional state using an emotion recognition engine, and means for adjusting requests and responses to the generative AI model based on the emotional state. This allows for the provision of conversational responses and feedback according to the user's emotional state, enabling improvement of customer service skills in physical stores.

[1185] The "means for receiving a conversation simulation request from a user" refers to a device or software that receives a request on the system when a user inputs a request for a conversation simulation through a terminal.

[1186] "Means for generating a conversation scenario for a generative AI model" refers to a device or software that creates an appropriate conversation scenario based on a user request and provides that scenario to a generative AI model.

[1187] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that receives conversational responses and feedback created by a generative AI model on a server or terminal.

[1188] "Means for returning responses and feedback to the user" refers to a device or software that returns and displays the conversational responses and feedback received from the generative AI model to the user's device.

[1189] "Means for analyzing a user's emotional state using an emotion recognition engine" refers to a device or software that analyzes a user's voice, facial expressions, etc., and determines their emotional state in real time.

[1190] "Means for adjusting requests and responses to a generative AI model based on emotional state" refers to a device or software that appropriately modifies requests and responses provided to a generative AI model based on the user's emotional state obtained by an emotion recognition engine.

[1191] MODE FOR CARRYING OUT THE INVENTION

[1192] This invention is a system that allows users to improve their customer service skills and communication abilities through various conversation simulations. This system is composed of a user terminal, a server, a generative AI model, and an emotion recognition engine.

[1193] Overall system configuration

[1194] 1. User Device

[1195] The user terminal uses smart glasses or a head-mounted display (HMD), which allows the user to view conversation scenarios and receive feedback in real time. When the user inputs a conversation simulation request through the terminal, the information is sent to the server.

[1196] 2. Server

[1197] The server analyzes the requests received from the user and generates a conversation scenario from the generative AI model, which uses the OpenAI API to generate appropriate responses and feedback based on the user's input and emotional data.

[1198] 3. Emotion Recognition Engine

[1199] The emotion recognition engine analyzes the user's emotional state in real time. It uses the camera installed in the smart glasses or HMD to collect the user's facial expressions and voice data to recognize emotions. It uses the EmotionRecognition library to analyze emotions and sends the information to the server.

[1200] 4. Generative AI Models

[1201] The generative AI model generates appropriate conversational responses and feedback based on emotional data obtained from the emotion recognition engine and user input. OpenAI's generative AI model is used, and responses are generated by entering prompt sentences via the API.

[1202] Specific program processing

[1203] 1. Recognizing emotional states

[1204] The camera installed in the smart glasses or HMD captures the user's face and uses the EmotionRecognition library to analyze their emotions, for example, determining in real time whether the user is relaxed or nervous.

[1205] 2. Invoking a generative AI model

[1206] The prompt, including the user's input and emotional state, is passed to the OpenAI API to generate an appropriate response and feedback, which is then sent back to the user's device.

[1207] Specific examples

[1208] The user types "Welcome to our store," and a state of nervousness is detected.

[1209] The server uses this information to send the following prompt to the generative AI model:

[1210] User Input: Welcome to our store.

[1211] Emotional state: Tension

[1212] Appropriate response:

[1213] The generative AI model responds, "Thank you. Is there anything in particular you're looking for?" and this is sent back to the user.

[1214] This system not only allows users to improve their customer service skills in physical stores, but also allows them to receive real-time feedback based on their emotional state.

[1215] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1216] Step 1:

[1217] The user device receives a request for a conversation simulation. Using smart glasses or a head-mounted display (HMD), the user selects the situation they want to simulate and inputs it using text or voice. This input data is sent to the server. For example, an input such as "Welcome to our store" is sent as a request.

[1218] Step 2:

[1219] The device's camera captures the user's face in real time, and the emotion recognition engine analyzes it. Using the EmotionRecognition library, the system determines the user's emotional state (tense, relaxed, etc.) based on their facial expressions and voice data. The analysis results indicate that the user is tense, and this data is sent to the server. The input is the camera image and voice data, and the output is emotional state data.

[1220] Step 3:

[1221] The server receives the user's input data and emotional state data and creates a prompt for the generative AI model. For example, the prompt might look like this:

[1222] User Input: Welcome to our store.

[1223] Emotional state: Tension

[1224] Appropriate response:

[1225] This prompt is sent to the OpenAI API, which receives the user's input data and emotional state data as input, and the generative AI model's response as output.

[1226] Step 4:

[1227] The generative AI model generates an appropriate conversational response based on the prompt. For example, it generates a response such as, "Thank you. Is there anything in particular you're looking for?" The generated response is sent back to the server. The input is the prompt, and the output is the conversational response.

[1228] Step 5:

[1229] The server sends the generated conversational responses back to the user's device. The device displays the responses to the user and reads them aloud, allowing the user to experience a real customer service simulation. The input is the response data of the generative AI model, and the output is feedback to the user's device.

[1230] Step 6:

[1231] The server records the entire conversation simulation and saves it for future training. For example, the user's input, emotional state, generated responses, and their feedback are saved in a database. The input is the entire simulation data, and the output is the saved training data.

[1232] These steps allow users to effectively improve their customer service skills while receiving real-time feedback based on their emotional state.

[1233] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1234] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1235] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1236] [Fourth embodiment]

[1237] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1238] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1239] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1240] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1241] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1243] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1244] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1245] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1246] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1247] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1248] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1249] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1250] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[1251] Overall system configuration

[1252] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[1253] Embodiment

[1254] 1. User Request Submission

[1255] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the device. The device then sends this information as a request to the server.

[1256] 2. Request acceptance by the server

[1257] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[1258] 3. Invoking generative AI models

[1259] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[1260] 4. Response Generation Using Generative AI Models

[1261] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[1262] 5. Providing feedback from the server to the user

[1263] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[1264] 6. Record and use next time

[1265] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[1266] 7. Function as a conversation partner

[1267] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[1268] Specific examples

[1269] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback such as the tone of the conversation and additional advice (e.g., "It would be good to talk more specifically about your hobbies when introducing yourself.").

[1270] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[1271] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[1272] The above is a specific embodiment for carrying out the present invention. By using this system, users can develop the ability to respond quickly and effectively to various conversation situations.

[1273] The processing flow will be explained below.

[1274] Step 1:

[1275] The user uses the terminal to select a conversation simulation scenario, such as "self-introduction" or "sales talk."

[1276] Step 2:

[1277] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[1278] Step 3:

[1279] The terminal sends the input information to the server as a request, which includes the scenario type and the user input.

[1280] Step 4:

[1281] The server receives the request, parses it, and converts it into the appropriate format.

[1282] Step 5:

[1283] The server sends the parsed request to the generative AI model, requesting a response and feedback for the conversation simulation.

[1284] Step 6:

[1285] The generative AI model generates appropriate conversational responses and feedback based on the request, specifically crafting relevant questions and advice based on user input.

[1286] Step 7:

[1287] The generative AI model sends the generated response and feedback back to the server, such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1288] Step 8:

[1289] The server sends the generated responses and feedback back to the user's device, including suggestions and suggestions for improving the conversation.

[1290] Step 9:

[1291] The terminal receives the response and feedback from the server and displays it to the user, who can review it to improve their conversation skills.

[1292] Step 10:

[1293] The responses and feedback generated are recorded on the server and used in the next conversation simulation.

[1294] Step 11:

[1295] For users who live alone, the server provides a function where the generative AI model periodically speaks to them, helping to reduce the user's sense of loneliness.

[1296] Example 1

[1297] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1298] In today's busy society, improving interpersonal communication skills is important for one's career and relationships. However, opportunities to practice in simulated conversation situations are limited, and reducing feelings of loneliness is also a challenge, especially for people living alone. Furthermore, there is a lack of systems that allow people to receive feedback and continuously improve their skills.

[1299] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1300] In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving responses and feedback generated by the generative AI model, means for returning the responses and feedback to the user, means for recording the generated responses and feedback and using them in the next conversation simulation, and means for the generative AI model to talk to the user as a conversation partner to reduce feelings of loneliness for users living alone. This allows users to receive feedback while simulating various conversation situations and improve their communication skills. Furthermore, users living alone are provided with a conversation partner to reduce feelings of loneliness.

[1301] "User" refers to a person who uses the system to conduct a conversation simulation.

[1302] "Terminal" refers to a device used by a user, including, for example, a smartphone or a computer.

[1303] "Server" refers to a computer system that performs processes such as receiving requests, analyzing them, invoking generative AI models, and returning responses.

[1304] A "request" refers to a request for a conversational simulation that a user sends to the system using a terminal.

[1305] A "generative AI model" refers to an artificial intelligence model that generates appropriate conversational responses or feedback based on given input.

[1306] A "conversation scenario" refers to a setting or scenario used to simulate a particular conversation situation.

[1307] "Response" refers to the response that a generative AI model generates in response to user input.

[1308] "Feedback" refers to specific improvements and advice regarding the content of a user's conversation.

[1309] "Recording" refers to saving the responses and feedback generated.

[1310] "Conversation Partner" refers to a generative AI model that regularly speaks to users, especially those living alone, to help reduce feelings of loneliness.

[1311] This invention provides a system that allows users to simulate various conversation situations and provides conversation practice and feedback using a generative AI model. Specific embodiments of this system are described below.

[1312] Overall system configuration

[1313] The system consists of a user's device, a server, and a generative AI model. The user uses the device to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[1314] Embodiment

[1315] User request submission

[1316] The user selects a conversation simulation situation from the terminal. For example, there are "self-introduction," "sales talk," "conversation with a lover," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[1317] Request acceptance on the server

[1318] The server receives the request sent from the terminal and analyzes the request content, which includes the conversation simulation scenario and specific inputs from the user.

[1319] Invoking a generative AI model

[1320] Based on the received request, the server invokes a generative AI model (e.g., OpenAI's GPT-4), provides the selected scenario and user input to the generative AI model, and has it generate responses and feedback accordingly.

[1321] Response generation using generative AI models

[1322] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[1323] Providing feedback from the server to the user

[1324] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[1325] Record and use next time

[1326] The responses and feedback generated are recorded on the server, allowing users to refer to their previous feedback the next time they use the service, enabling continuous improvement.

[1327] Functions as a conversation partner

[1328] For users who live alone, the generative AI model acts as a conversation partner, regularly talking to them to help alleviate feelings of loneliness. This function is also provided by the server, helping users overcome feelings of loneliness.

[1329] Specific examples

[1330] For example, if a user wants to simulate a self-introduction, they enter "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[1331] Similarly, when a user simulates a sales pitch, they input "Hello, I'd like to introduce you to our new product" into their device and send it to the server. The generative AI model then generates a response such as "Hello, what kind of new product is it? Can you tell me about its specific features and benefits?" and sends it back to the user.

[1332] Prompt Sentence Examples

[1333] "I'd like to simulate a self-introduction. Please generate appropriate responses and feedback for the following inputs."

[1334] "I'd like to simulate a sales pitch. Please generate appropriate responses and feedback for the following inputs."

[1335] This allows users to improve their confidence and skills in real-life situations by simulating actual conversation situations.The system not only supports effective conversation practice, but also serves as a conversation partner for users living alone to reduce loneliness.

[1336] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1337] Program processing flow

[1338] Step 1: User selects and inputs conversation situation

[1339] The user uses the terminal to select a conversation situation from a specified interface and inputs the appropriate conversation content.

[1340] Input: The scenario selection and specific conversation content that the user inputs into the terminal (e.g., "self-introduction," "sales talk," etc.).

[1341] Data processing: The terminal converts the input content into request data.

[1342] Output: Request data (scenario and conversation content).

[1343] Action: A user opens an application on their device, selects "Introduce myself" from the menu, and enters "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[1344] Step 2: Sending a request from the device to the server

[1345] The terminal transmits the request data input by the user to the server.

[1346] Input: Terminal-generated request data.

[1347] Data processing: The terminal converts the request data into a data packet.

[1348] Output: Data packets sent to the server.

[1349] How it works: A device sends a data packet to a server over the Internet.

[1350] Step 3: Receiving and parsing the request on the server

[1351] The server accepts the data packets received from the terminal and analyzes the contents.

[1352] Input: Data packets received from the terminal.

[1353] Data processing: The server analyzes the data packets and extracts the scenario and conversation content.

[1354] Output: Analyzed scenario and conversation content.

[1355] What it does: The server parses the received data and extracts "Scenario: Self-introduction" and "User input: Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[1356] Step 4: Invoke the generative AI model

[1357] The server calls a generative AI model based on the analyzed scenario and conversation content.

[1358] Input: Parsed scenario and conversation content.

[1359] Data processing: The server generates API requests for the generative AI model.

[1360] Output: API request to the generative AI model.

[1361] How it works: The server generates an API request and sends it to the generative AI model's endpoint. This request includes the scenario and user input.

[1362] Step 5: Generate AI model responses

[1363] Generative AI models generate responses and feedback based on the scenario and inputs provided.

[1364] Input: An API request to a generative AI model.

[1365] Data processing: Generative AI models use internal algorithms to generate responses and feedback.

[1366] Output: Response and feedback data.

[1367] How it works: The generative AI model responds with, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" along with feedback such as, "You might want to be more specific about your hobbies when introducing yourself."

[1368] Step 6: Providing feedback from the server to the user

[1369] The server sends the responses and feedback received from the generative AI model back to the user.

[1370] Input: The response and feedback data received from the generative AI model.

[1371] Data processing: The server converts the response and feedback data into data packets.

[1372] Output: Data packets sent to the user's device.

[1373] Operation: The server creates a data packet containing the generated response and feedback and sends it to the terminal.

[1374] Step 7: Record your feedback information

[1375] The server records the generated response and feedback for future reference.

[1376] Input: Generated response and feedback data.

[1377] Data processing: The server stores the responses and feedback in a database.

[1378] Output: The saved feedback information.

[1379] How it works: The server stores the response and feedback in a database.

[1380] Step 8: Implementing the conversation partner function

[1381] For users living alone, the server periodically initiates conversations using a generative AI model to reduce feelings of loneliness.

[1382] Input: The time set as the scheduled task.

[1383] Data processing: The server generates periodic requests and sends them to the generative AI model.

[1384] Output: Periodically generated conversation responses.

[1385] How it works: The server calls the generative AI model at a specified time and begins a conversation such as, "Hello, what are your plans for today?"

[1386] (Application example 1)

[1387] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1388] The lack of effective communication between robots and workers in factories is a problem. With conventional technology, workers lack the training to properly interact with robots, which leads to reduced work efficiency and increased errors. It also creates a sense of loneliness for workers who live alone.

[1389] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1390] In this invention, the server includes a means for receiving a conversation simulation request from a user, a means for causing a generative AI model to generate a conversation scenario and feedback based on the request, a means for receiving a response and feedback generated by the generative AI model, a means for returning the response and feedback to the user, and a means for simulating communication between a worker and a robot in a factory and providing feedback. This allows workers to learn effective communication methods, improving work efficiency and reducing errors. Furthermore, the generative AI model can function as a conversation partner for workers living alone, helping to reduce feelings of loneliness.

[1391] The "means for receiving a conversation simulation request from a user" is an interface for accepting an input from a user requesting the execution of a conversation simulation based on a specific situation, and for processing the request.

[1392] "Means for causing a generative AI model to generate a conversation scenario and feedback" refers to a process and system for using a generative AI model to generate an appropriate conversation scenario and feedback for user input based on a received request.

[1393] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that aggregates the conversational responses and feedback generated by a generative AI model and receives them on a server or system for use in subsequent steps.

[1394] "Means for sending responses and feedback back to the user" refers to the communications means and user interface for delivering the conversational responses and feedback generated by the generative AI model to the user.

[1395] The "means for simulating communication between workers and robots in a factory and providing feedback" is a process for simulating conversations and communications between workers and robots in a factory, generating feedback based on the results, and using the simulation results to improve work efficiency and communication capabilities.

[1396] The present invention provides a system for simulating communication between a user and a factory robot. This system utilizes a generative AI model to provide conversation simulation and feedback, and an embodiment of the system is described below.

[1397] Overall system configuration

[1398] The system consists of a terminal operated by the user, a server, and a generative AI model. The user uses the terminal to send a conversation simulation request, which the server accepts and passes to the generative AI model, which generates appropriate responses and feedback.

[1399] Embodiment

[1400] 1. User Request Submission

[1401] The user selects the situation in which the conversation simulation will be used from the terminal. For example, there are "self-introduction," "confirmation of work procedures," "reporting a problem," etc., and inputs information appropriate to each situation into the terminal. The terminal then sends this information as a request to the server.

[1402] 2. Request acceptance by the server

[1403] The server receives requests sent from the terminals, which include a conversation simulation scenario and specific inputs from the users.

[1404] 3. Invoking generative AI models

[1405] Based on the received request, the server invokes the generative AI model, providing the selected scenario and user input to the generative AI model, which then generates a response and feedback accordingly.

[1406] 4. Response Generation Using Generative AI Models

[1407] The generative AI model generates appropriate conversational responses based on the provided scenario and user input, along with specific feedback and suggestions for improvement based on the user's input.

[1408] 5. Providing feedback from the server to the user

[1409] The server sends the responses and feedback received from the generative AI model back to the user, who can then use this feedback information displayed on their device to improve their conversation skills.

[1410] Specific examples

[1411] For example, if a user wants to simulate a self-introduction, they would enter "Nice to meet you, my name is Tanaka. My hobbies are fishing and reading." into their device and send it to the server. The server receives this and requests the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Tanaka. Fishing and reading are great hobbies. Where have you been fishing recently?" Furthermore, the model provides feedback on the tone of the conversation and additional advice (e.g., "You might want to talk more specifically about your hobbies when introducing yourself.").

[1412] Hardware and software used

[1413] Hardware: Computers controlling factory robots

[1414] Software: OpenAI GPT-3 API, Python

[1415] Prompt Sentence Examples

[1416] User input prompt:

[1417] "Please provide an example prompt for a simulation of introducing yourself to a factory robot."

[1418] Robot response:

[1419] "Hello, this is our new product line. How can I help you today?"

[1420] In this way, by using this system, users can simulate effective communication with factory robots, which can contribute to improving performance in actual workplaces.

[1421] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1422] Step 1:

[1423] A user uses a terminal to request a conversation simulation. This request includes the selection of a situation, such as "self-introduction," "confirmation of work procedures," or "reporting a problem," and the input of specific conversation content. The input data is sent from the terminal to the server. The input data includes the selected situation and the user's specific conversation content.

[1424] Step 2:

[1425] The server receives the request sent from the terminal, analyzes the received data, and checks the request content. This analysis identifies the situation and user input, and checks the request content.

[1426] Step 3:

[1427] The server calls the generative AI model based on the received request. At the time of the call, the server provides the user's selected situation and specific conversation content as a prompt to the generative AI model. Based on this prompt, the generative AI model generates appropriate conversational responses and feedback.

[1428] Step 4:

[1429] The generative AI model generates conversational responses and feedback based on the provided prompts. Specifically, it generates optimal responses to the user's input, as well as suggestions for improving the conversation and specific feedback. The generated results are sent to the server.

[1430] Step 5:

[1431] The server receives the responses and feedback received from the generative AI model. This received data is compiled into information to be sent back to the user. The compiled information is converted into a transmission format for sending back to the user.

[1432] Step 6:

[1433] The server returns the compiled responses and feedback to the user's terminal, allowing the user to review the responses and feedback on their own terminal, and use this information to improve their conversation skills.

[1434] Step 7:

[1435] The server records the responses and feedback generated, allowing the user to refer to the previous feedback the next time they engage in a conversational simulation, thereby encouraging continuous improvement.

[1436] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1437] This invention provides a system that allows users to simulate various conversation situations, and utilizes a generative AI model and an emotion engine to provide conversation practice and feedback. Specific embodiments of this system are described below.

[1438] Overall system configuration

[1439] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user uses their device to send a conversation simulation request, which the server accepts and passes to the generative AI model, generating appropriate responses and feedback. The emotion engine then recognizes the user's emotional state and adjusts the requests and responses to the generative AI model accordingly.

[1440] Embodiment

[1441] 1. User Request Submission

[1442] The user selects the situation in which they wish to use the conversation simulation from their device. For example, there are options such as "self-introduction," "sales talk," and "conversation with a lover," and they input information appropriate to each situation into the device. The device then sends this information as a request to the server.

[1443] 2. Emotional state recognition using the emotion engine

[1444] The emotion engine recognizes the user's emotional state in real time as the user types or during conversation simulations, and generates emotion data using voice analysis, sensor data, and other methods.

[1445] 3. Request acceptance and processing on the server

[1446] The server receives the request and emotion data sent from the device, analyzes the received request and emotion data, and converts them into an appropriate format.

[1447] 4. Invoking generative AI models

[1448] The server invokes the generative AI model based on the analyzed request and emotion data, providing the selected scenario, user input, and corresponding emotional state to the generative AI model, which then generates a response and feedback accordingly.

[1449] 5. Response Generation Using Generative AI Models

[1450] Based on the provided scenario, user input, and emotional state, the generative AI model generates appropriate conversational responses, as well as specific feedback and suggestions for improvement based on the user's input.

[1451] 6. Providing feedback from the server to the user

[1452] The server sends the responses and feedback received from the generative AI model back to the user, who can then use the feedback to improve their conversation skills.

[1453] 7. Record and use next time

[1454] The generated responses and feedback are recorded on the server and used in the next conversation simulation. Emotional data is also recorded to track the user's emotional fluctuations and provide more effective feedback.

[1455] 8. Function as a conversation partner

[1456] For users living alone, the emotion engine monitors the user's emotional state, and the generative AI model acts as a conversation partner, engaging in conversations with appropriate topics based on the user's emotional state to reduce loneliness.

[1457] Specific examples

[1458] For example, if a user wants to simulate a "self-introduction," the user inputs "Nice to meet you, my name is Taro. My hobbies are traveling and reading." into the device. The emotion engine analyzes the user's voice and facial expressions and detects that the user is relaxed. The device sends this information as a request to the server. The server receives this and makes a request to the generative AI model. In this case, the generative AI model generates a response such as "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?" Furthermore, based on the emotion engine data, additional advice is provided to a relaxed user to continue the conversation.

[1459] Similarly, when a user simulates a sales pitch, they type "Hello, I'd like to introduce you to our new product." The emotion engine detects the user's nervous state and sends it to the server. The generative AI model then generates a response such as "Hello, I'm excited to tell you about our new product. First, can you tell me about its features?" and provides feedback to help the user relax. Specific advice to ease the tension is also provided.

[1460] This invention not only helps users speak confidently in real-life situations, but also serves as a conversation partner for users living alone, reducing feelings of loneliness. By utilizing an emotion engine, appropriate responses and feedback are provided according to the user's emotional state, enabling effective conversation practice.

[1461] The processing flow will be explained below.

[1462] Step 1:

[1463] The user uses the terminal to select a conversation simulation situation, such as "self-introduction" or "sales talk."

[1464] Step 2:

[1465] The user inputs specific information into the terminal. For example, the user inputs, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[1466] Step 3:

[1467] The emotion engine analyzes the user's voice and facial expressions in real time as they type, recognizing their emotional state. For example, it can determine whether the user is relaxed or tense based on their tone of voice and facial expression.

[1468] Step 4:

[1469] The terminal sends the user's input data and emotional data to the server as a request, which includes the selected situation, the user's input, and the emotional state.

[1470] Step 5:

[1471] The server receives the request, analyzes it, and converts the results into an appropriate format for passing to the generative AI model.

[1472] Step 6:

[1473] The server sends the analyzed request and emotional data to the generative AI model, requesting a response and feedback for the conversation simulation.

[1474] Step 7:

[1475] The generative AI model generates appropriate conversational responses and feedback based on the request. For example, if a user inputs a self-introduction, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1476] Step 8:

[1477] The generative AI model sends responses and feedback back to the server, including suggestions for improvement and advice based on the user's emotional state.

[1478] Step 9:

[1479] The server sends the generated response and feedback back to the user's terminal, which receives it and displays it to the user.

[1480] Step 10:

[1481] The user can check the responses and feedback displayed on the device and use this information in the next conversation simulation. The user can also understand their own emotional state based on the data from the emotion engine, improving the quality of their practice.

[1482] Step 11:

[1483] The generated responses and feedback are recorded on the server, along with emotional data, which will be used in the next conversation simulation.

[1484] Step 12:

[1485] For users who live alone, the emotion engine monitors the user's emotional state. If the user feels lonely, the generative AI model generates appropriate conversational content and speaks to the user through the device. For example, it provides conversations tailored to the user's situation, such as "How was your day?" or "What's your favorite movie recently?"

[1486] In this way, the user inputs information via the device, the emotion engine analyzes their emotional state, and the server sends a request to the generative AI model to generate appropriate conversation responses and feedback. This allows users to practice real conversation situations while receiving feedback appropriate to their emotional state. It also functions as a conversation partner to reduce feelings of loneliness.

[1487] Example 2

[1488] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1489] Conventional conversation simulation systems generate responses and feedback uniformly without considering the user's emotional state, making it impossible to provide effective feedback optimized for the user's situation and emotions. Furthermore, they lacked functionality to provide emotional care and reduce loneliness for users living alone.

[1490] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for formatting the request and sending it to the server, means for an emotion engine to recognize the user's emotional state in real time, means for a generative AI model to generate an appropriate response and return it to the server, and means for recording the generated response and feedback and using it for the next conversation simulation. This provides appropriate feedback based on the user's emotional state, enabling users living alone to reduce their sense of loneliness and practice conversation effectively.

[1491] "User" refers to an individual who uses the system to engage in conversational simulations.

[1492] "Terminal" refers to an electronic device through which a user accesses the system.

[1493] A "request" refers to a request for a conversation simulation that a user sends to the system through a terminal.

[1494] "Server" refers to a central computer system that receives and processes requests.

[1495] An "emotion engine" refers to an analysis device that analyzes a user's voice and facial expressions to recognize their emotional state.

[1496] A "generative AI model" refers to an artificial intelligence model that generates conversation scenarios and provides feedback in response to user requests.

[1497] "Response" refers to the conversational content that a generative AI model generates based on a request.

[1498] "Feedback" refers to the improvements and advice that the generative AI model provides to the user regarding their conversation.

[1499] "Recording" refers to the act of the server storing request, response, and feedback data.

[1500] "Utilizing in the next simulation" refers to the process of referencing the recorded data in the next or subsequent simulation to provide more personalized feedback.

[1501] A "conversation partner" is a generative AI model that acts as a conversation partner for the user, reducing feelings of loneliness.

[1502] "Emotional Data" refers to information about a user's emotional state that is analyzed and generated by the Emotion Engine.

[1503] This invention provides a system that allows users to experience and practice various conversation situations. This system uses a generative AI model and an emotion engine based on user input to simulate real conversations. The following describes how this system is specifically implemented.

[1504] Overall system configuration

[1505] The system consists of a terminal used by the user, a central processing server, a generative AI model that generates responses, and an emotion engine that recognizes emotional states.

[1506] 1. User Input

[1507] The user uses the terminal to select a specific conversation situation and input the content of the conversation. For example, options such as "self-introduction," "sales talk," and "conversation with a lover" are provided. An example of specific content that the user can input is "Nice to meet you, my name is Taro. My hobbies are traveling and reading."

[1508] 2. Submitting a Request

[1509] The terminal formats the user's input and sends it to the server as request data, which includes the selected scenario and the user's input.

[1510] 3. Recognizing emotional states

[1511] Upon receiving the request, the server requests data from the emotion engine to collect emotion data. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state (e.g., relaxed, nervous) in real time.

[1512] 4. Integrating Request and Emotion Data

[1513] The server receives the emotion data sent from the emotion engine and integrates it with the request data. This integrated data serves as input for the generative AI model to generate conversational responses.

[1514] 5. Response Generation Using Generative AI Models

[1515] The server passes the integrated data to a generative AI model and asks it to generate the optimal response and feedback. The generative AI model generates appropriate conversational responses and feedback based on this data. An example of a generated response is, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1516] 6. Responding and Providing Feedback

[1517] The server sends the generated responses and feedback back to the device, where the user can view the information on the device screen and use it to improve their conversation skills.

[1518] 7. Record the data and use it again

[1519] The server records requests, responses, feedback, and emotional data, which are then used as a basis for providing individually optimized feedback during subsequent simulations.

[1520] 8. Function as a conversation partner

[1521] For users living alone, a generative AI model can act as a conversation partner. Based on data from the emotion engine, it provides appropriate conversation based on the user's emotions to reduce feelings of loneliness. This function allows users to have a conversation partner without feeling lonely on a daily basis.

[1522] Specific examples

[1523] For example, if a user wants to simulate a "self-introduction," the specific operations are as follows:

[1524] 1. The user types into the terminal: "Hello, my name is Taro. My hobbies are traveling and reading."

[1525] 2. Analysis by emotion engine: Recognizes a relaxed state from voice and facial expression data.

[1526] 3. Response generation using a generative AI model: "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1527] 4. Feedback: Advice is given to help you relax and continue speaking.

[1528] In this way, conversation simulation is carried out, and the user can improve his / her skills in actual conversation situations.

[1529] Prompt Sentence Examples

[1530] User: "Hi, I'd like to tell you about our new product."

[1531] Generative AI model: "Hello, I'm excited to talk to you about your new product. First, can you tell me about its features?"

[1532] Feedback: "You seem nervous. It would be a good idea to write down what you're going to say beforehand. I also recommend taking some deep breaths and relaxing."

[1533] The system provides training to help users become confident and effective speakers in real-life conversation situations.

[1534] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1535] The flow of this system's program processing

[1536] Step 1:

[1537] The user sends a request for a conversation simulation from the terminal.

[1538] Specific operation: The user selects a situation and inputs the conversation content in text by operating the device screen. For example, the user might input, "Nice to meet you. My name is Taro. My hobbies are traveling and reading."

[1539] Input: User text input (simulation situation and conversation content)

[1540] Output: Formatted request data

[1541] Step 2:

[1542] The device sends a request to the server.

[1543] Specific operation: The terminal converts the user's input data into packets and sends an HTTP request to the specified API endpoint on the server.

[1544] Input: Formatted request data

[1545] Output: HTTP request data received by the server

[1546] Step 3:

[1547] The server accepts the request and requests the emotion engine for the user's emotion data.

[1548] Specific operation: The server checks the format of the received request data, and if it determines that the data is valid, it sends a request to the emotion engine to analyze the user's emotional state.

[1549] Input: HTTP request data

[1550] Output: Analysis request to the emotion engine

[1551] Step 4:

[1552] The emotion engine recognizes the user's emotional state in real time.

[1553] Specific operation: The emotion engine uses voice and facial expression analysis systems to recognize the user's emotional state from their voice and video data. For example, it detects whether they are relaxed or tense.

[1554] Input: User's audio and video data

[1555] Output: Emotional state data (e.g., relaxed, tense)

[1556] Step 5:

[1557] The server receives and processes the emotion data sent from the emotion engine.

[1558] Specific operation: The server receives the emotion data sent from the emotion engine and integrates it into the request data. It formats the data and prepares it for passing to the generative AI model.

[1559] Input: Emotional state data

[1560] Output: Consolidated request data

[1561] Step 6:

[1562] The server then forwards the processed request and emotion data to the generative AI model.

[1563] What happens: The server sends a POST request to the appropriate API endpoint to transfer the aggregated data to the generative AI model.

[1564] Input: Consolidated request data

[1565] Output: HTTP request to the generative AI model

[1566] Step 7:

[1567] The generative AI model generates an appropriate response and returns it to the server.

[1568] Specific operation: The generative AI model uses natural language processing to generate appropriate responses and feedback based on the input situation and emotional state. For example, it generates a response such as, "Nice to meet you, Taro. Traveling and reading are great hobbies. Where have you traveled recently?"

[1569] Input: Consolidated request data

[1570] Output: Generated responses and feedback

[1571] Step 8:

[1572] The server sends a response and feedback back to the user's terminal.

[1573] Specific operation: The server formats the response and feedback received from the generative AI model and sends it back to the user's device. The data is sent using an HTTP response.

[1574] Input: Generated responses and feedback

[1575] Output: Response data sent to the user's device

[1576] Step 9:

[1577] The user checks the feedback on the device.

[1578] Specific operation: The user checks the responses and feedback displayed on the device screen and uses it as a reference for improving their conversation skills.

[1579] Input: Response data sent back to the terminal

[1580] Output: Verified feedback

[1581] Step 10:

[1582] The server records the requests and responses and uses them for the next simulation.

[1583] Specific operation: The server stores the request content, response, feedback, and emotion data in a database, allowing past data to be reflected in the responses and feedback in the next simulation.

[1584] Input: Request content, response, feedback, emotion data

[1585] Output: Data recorded in the database

[1586] (Application example 2)

[1587] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1588] Conventional conversation simulation systems have the problem that they do not provide feedback that takes into account the user's emotional state, which means that they are unable to sufficiently improve their ability to adapt to real-world situations. Furthermore, particularly in customer service, there is a lack of real-time training for staff to respond to various situations, making it difficult to improve service quality.

[1589] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a conversation simulation request from a user, means for causing a generative AI model to generate a conversation scenario based on the request, means for receiving a response and feedback generated by the generative AI model, means for returning the response and feedback to the user, means for analyzing the user's emotional state using an emotion recognition engine, and means for adjusting requests and responses to the generative AI model based on the emotional state. This allows for the provision of conversational responses and feedback according to the user's emotional state, enabling improvement of customer service skills in physical stores.

[1590] The "means for receiving a conversation simulation request from a user" refers to a device or software that receives a request on the system when a user inputs a request for a conversation simulation through a terminal.

[1591] "Means for generating a conversation scenario for a generative AI model" refers to a device or software that creates an appropriate conversation scenario based on a user request and provides that scenario to a generative AI model.

[1592] "Means for receiving responses and feedback generated by a generative AI model" refers to a device or software that receives conversational responses and feedback created by a generative AI model on a server or terminal.

[1593] "Means for returning responses and feedback to the user" refers to a device or software that returns and displays the conversational responses and feedback received from the generative AI model to the user's device.

[1594] "Means for analyzing a user's emotional state using an emotion recognition engine" refers to a device or software that analyzes a user's voice, facial expressions, etc., and determines their emotional state in real time.

[1595] "Means for adjusting requests and responses to a generative AI model based on emotional state" refers to a device or software that appropriately modifies requests and responses provided to a generative AI model based on the user's emotional state obtained by an emotion recognition engine.

[1596] MODE FOR CARRYING OUT THE INVENTION

[1597] This invention is a system that allows users to improve their customer service skills and communication abilities through various conversation simulations. This system is composed of a user terminal, a server, a generative AI model, and an emotion recognition engine.

[1598] Overall system configuration

[1599] 1. User Device

[1600] The user terminal uses smart glasses or a head-mounted display (HMD), which allows the user to view conversation scenarios and receive feedback in real time. When the user inputs a conversation simulation request through the terminal, the information is sent to the server.

[1601] 2. Server

[1602] The server analyzes the requests received from the user and generates a conversation scenario from the generative AI model, which uses the OpenAI API to generate appropriate responses and feedback based on the user's input and emotional data.

[1603] 3. Emotion Recognition Engine

[1604] The emotion recognition engine analyzes the user's emotional state in real time. It uses the camera installed in the smart glasses or HMD to collect the user's facial expressions and voice data to recognize emotions. It uses the EmotionRecognition library to analyze emotions and sends the information to the server.

[1605] 4. Generative AI Models

[1606] The generative AI model generates appropriate conversational responses and feedback based on emotional data obtained from the emotion recognition engine and user input. OpenAI's generative AI model is used, and responses are generated by entering prompt sentences via the API.

[1607] Specific program processing

[1608] 1. Recognizing emotional states

[1609] The camera installed in the smart glasses or HMD captures the user's face and uses the EmotionRecognition library to analyze their emotions, for example, determining in real time whether the user is relaxed or nervous.

[1610] 2. Invoking a generative AI model

[1611] The prompt, including the user's input and emotional state, is passed to the OpenAI API to generate an appropriate response and feedback, which is then sent back to the user's device.

[1612] Specific examples

[1613] The user types "Welcome to our store," and a state of nervousness is detected.

[1614] The server uses this information to send the following prompt to the generative AI model:

[1615] User Input: Welcome to our store.

[1616] Emotional state: Tension

[1617] Appropriate response:

[1618] The generative AI model responds, "Thank you. Is there anything in particular you're looking for?" and this is sent back to the user.

[1619] This system not only allows users to improve their customer service skills in physical stores, but also allows them to receive real-time feedback based on their emotional state.

[1620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1621] Step 1:

[1622] The user device receives a request for a conversation simulation. Using smart glasses or a head-mounted display (HMD), the user selects the situation they want to simulate and inputs it using text or voice. This input data is sent to the server. For example, an input such as "Welcome to our store" is sent as a request.

[1623] Step 2:

[1624] The device's camera captures the user's face in real time, and the emotion recognition engine analyzes it. Using the EmotionRecognition library, the system determines the user's emotional state (tense, relaxed, etc.) based on their facial expressions and voice data. The analysis results indicate that the user is tense, and this data is sent to the server. The input is the camera image and voice data, and the output is emotional state data.

[1625] Step 3:

[1626] The server receives the user's input data and emotional state data and creates a prompt for the generative AI model. For example, the prompt might look like this:

[1627] User Input: Welcome to our store.

[1628] Emotional state: Tension

[1629] Appropriate response:

[1630] This prompt is sent to the OpenAI API, which receives the user's input data and emotional state data as input, and the generative AI model's response as output.

[1631] Step 4:

[1632] The generative AI model generates an appropriate conversational response based on the prompt. For example, it generates a response such as, "Thank you. Is there anything in particular you're looking for?" The generated response is sent back to the server. The input is the prompt, and the output is the conversational response.

[1633] Step 5:

[1634] The server sends the generated conversational responses back to the user's device. The device displays the responses to the user and reads them aloud, allowing the user to experience a real customer service simulation. The input is the response data of the generative AI model, and the output is feedback to the user's device.

[1635] Step 6:

[1636] The server records the entire conversation simulation and saves it for future training. For example, the user's input, emotional state, generated responses, and their feedback are saved in a database. The input is the entire simulation data, and the output is the saved training data.

[1637] These steps allow users to effectively improve their customer service skills while receiving real-time feedback based on their emotional state.

[1638] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1639] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1640] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1641] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1642] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1643] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1644] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1645] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1646] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1647] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1648] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1649] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1650] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1651] 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.

[1652] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1653] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1654] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1655] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1656] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1657] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1658] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1659] The following is further disclosed regarding the above embodiment.

[1660] (Claim 1)

[1661] means for receiving a conversation simulation request from a user;

[1662] means for causing a generative AI model to generate a conversation scenario based on the request;

[1663] means for receiving responses and feedback generated by the generative AI model;

[1664] means for transmitting said response and feedback back to the user;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, further comprising means for recording the generated responses and feedback and utilizing the recorded responses and feedback in a subsequent conversation simulation.

[1668] (Claim 3)

[1669] 10. The system of claim 1, further comprising means for the generative AI model to talk to the user as a conversation partner to reduce loneliness for users living alone.

[1670] "Example 1"

[1671] (Claim 1)

[1672] means for receiving a conversation simulation request from a user;

[1673] means for causing a generative AI model to generate a conversation scenario based on the request;

[1674] means for receiving responses and feedback generated by the generative AI model;

[1675] means for transmitting said response and feedback back to the user;

[1676] means for recording the generated responses and feedback and utilizing them in a next conversation simulation;

[1677] For users living alone, the generative AI model will talk to users as a conversation partner to reduce loneliness,

[1678] A system including:

[1679] (Claim 2)

[1680] The system of claim 1, including a process of sending a request from a user's terminal to a server, and the server calling a generative AI model to generate a response.

[1681] (Claim 3)

[1682] 10. The system of claim 1, further comprising means for improving the user's conversation skills based on feedback provided by the generative AI model.

[1683] "Application Example 1"

[1684] (Claim 1)

[1685] means for receiving a conversation simulation request from a user;

[1686] means for causing a generative AI model to generate a conversation scenario and feedback based on the request;

[1687] means for receiving responses and feedback generated by the generative AI model;

[1688] means for transmitting said response and feedback back to the user;

[1689] means for simulating communication between workers and robots in a factory and providing feedback;

[1690] A system including:

[1691] (Claim 2)

[1692] 10. The system of claim 1, further comprising means for recording the generated responses and feedback and utilizing the recorded responses and feedback in a subsequent conversation simulation.

[1693] (Claim 3)

[1694] 10. The system of claim 1, further comprising means for the generative AI model to talk to the user as a conversation partner to reduce loneliness for users living alone.

[1695] "Example 2: Combining Emotion Engines"

[1696] (Claim 1)

[1697] means for receiving a conversation simulation request from a user;

[1698] means for formatting the request and sending it to a server;

[1699] a means for the server to receive the request and request the emotion engine to provide emotion data of the user;

[1700] a means for the emotion engine to recognize the user's emotional state in real time;

[1701] A means for the server to receive and process the emotion data sent from the emotion engine;

[1702] a means for transmitting the processed request and emotion data to the generative AI model;

[1703] A means for the generative AI model to generate an appropriate response and return it to the server;

[1704] a means for the server to send a response and feedback back to the user's terminal;

[1705] a means for the user to view the feedback on the device;

[1706] A way for the server to record requests and responses and use them for the next simulation.

[1707] A system including:

[1708] (Claim 2)

[1709] 10. The system of claim 1, further comprising means for recording the generated responses and feedback for use in subsequent conversation simulations.

[1710] (Claim 3)

[1711] 10. The system of claim 1, further comprising means for the generative AI model to talk to the user as a conversation partner to reduce loneliness for users living alone.

[1712] "Application example 2 when combining emotion engines"

[1713] (Claim 1)

[1714] means for receiving a conversation simulation request from a user;

[1715] means for causing a generative AI model to generate a conversation scenario based on the request;

[1716] means for receiving responses and feedback generated by the generative AI model;

[1717] means for transmitting said response and feedback back to the user;

[1718] means for analyzing a user's emotional state using an emotion recognition engine;

[1719] means for adjusting requests and responses to a generative AI model based on said emotional state;

[1720] A system including:

[1721] (Claim 2)

[1722] 10. The system of claim 1, further comprising means for recording the generated responses and feedback and utilizing the recorded responses and feedback in a subsequent conversation simulation.

[1723] (Claim 3)

[1724] 10. The system of claim 1, further comprising means for the generative AI model to talk to the user as a conversation partner to reduce loneliness for users living alone. [Explanation of symbols]

[1725] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a conversation simulation request from a user; means for causing a generative AI model to generate a conversation scenario based on the request; means for receiving responses and feedback generated by the generative AI model; means for transmitting said response and feedback back to the user; A system including:

2. The system of claim 1 , further comprising means for recording the generated responses and feedback and utilizing the recorded responses and feedback in subsequent conversation simulations.

3. The system of claim 1, further comprising means for the generative AI model to talk to the user as a conversation partner to reduce loneliness for users living alone.

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