System

Abstract

A system is provided.SOLUTION: A system comprising: customer service robot means loaded with a generative artificial intelligence model; database means for storing customer service skill data; generator means for receiving a user input and generating an appropriate response based on the input; and display means for displaying or communicating the generated response 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] The hospitality industry faces challenges such as staff shortages and inconsistent customer service quality. Traditional customer service methods rely on the skill and knowledge of staff, so they do not necessarily provide consistently high-quality service. In addition, hiring and training staff requires significant costs and time, placing a heavy burden on management. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a customer service robot system equipped with a generative artificial intelligence model. This system includes a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on the input, and a display that displays or communicates the generated response to the user. The customer service robot also has the function of automatically displaying an initial greeting message based on the defined customer service skill data, and upon receiving user input, the generative artificial intelligence model generates a response based on the input. This method makes it possible to provide consistently high-quality customer service and reduce staff costs and time.

[0006] A "generative artificial intelligence model" refers to a machine learning algorithm and its implementation designed to generate appropriate responses to given inputs.

[0007] A "customer service robot" is a humanoid or similar physical device designed to automate customer service tasks, and equipped with artificial intelligence, sensors, and drive mechanisms.

[0008] "Customer service skill data" refers to a set of data stored in digital format that contains the knowledge and skills required for customer service.

[0009] A "database" refers to a digital storage device that systematically stores and manages information required by the system, such as customer service skill data and user information.

[0010] "Generator" refers to a set of computational devices and algorithms for generating an appropriate response based on input data received from a user.

[0011] "Indicator" means a device or function that visually or audibly communicates a generated response or message to a user. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The system of the present invention automates customer service operations and provides high-quality customer service. The system uses a customer service robot equipped with a generative artificial intelligence model and includes a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on the input, and a display that displays or communicates the generated response to the user. The system also automatically displays an initial greeting message based on the defined customer service skill data, and upon receiving input from the user, the generative artificial intelligence model generates a response based on the input.

[0034] Specifically, the system operates in the following manner.

[0035] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings to customers and responses to general inquiries. Then, the terminal displays an initial greeting message to the user from the customer service robot. For example, this greeting message is a phrase such as "Hello, welcome. What can I do for you?"

[0036] Next, the user enters a question or request as text, for example, "Tell me more about this product." The device receives this input and forwards it to the server, which passes the input data to the generator, which uses a generative artificial intelligence model to generate an appropriate response.

[0037] The generated response might be something like, "This product is the latest model and has many features." This response is again passed to the terminal, which displays it to the user. If the user types further questions, responses are generated and displayed in a similar manner. This process continues until the user types "goodbye."

[0038] When the system is finished, the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and the interaction ends. This end-to-end process ensures that users receive consistent, high-quality customer service.

[0039] As a specific scenario, consider the case where a user asks, "Do you have this item in stock?" In this case, the server sends the user's question to the generator, which generates a response such as, "We currently have plenty of this item in stock." This response is then communicated to the user via the terminal. Similarly, by sequentially generating and displaying appropriate responses to multiple questions, highly efficient customer service is achieved.

[0040] The processing flow will be explained below.

[0041] Step 1:

[0042] The server loads the database that stores customer service skill data. This is done by the load_skillset function, and stores basic customer service skills and response messages in memory in dictionary format.

[0043] Step 2:

[0044] The device retrieves the initial greeting message from the loaded skills data and displays it to the user. Specifically, it displays the message defined in skillset['greeting'] on the screen.

[0045] Step 3:

[0046] The user inputs a question or request into the terminal, for example, "Please tell me more about this product."

[0047] Step 4:

[0048] The terminal receives input from the user and sends the data to the server, which sends the input data in the appropriate format to be passed to the generator.

[0049] Step 5:

[0050] The server calls the generator and generates a response message using a generative artificial intelligence model based on the user's input data. This is done in the generate_response function, which calls ChatGPT's API to generate an appropriate response.

[0051] Step 6:

[0052] The server takes the generated response and sends it to the device, extracting and reformatting the response text from the response object from the API.

[0053] Step 7:

[0054] The terminal displays the response sent by the server to the user, specifically by using the print function to display the response on the screen.

[0055] Step 8:

[0056] The user enters additional questions or requests, and this process continues until the user enters "goodbye."

[0057] Step 9:

[0058] The terminal detects that the user has typed "goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day," as defined in skillset['farewell'], and ends the conversation.

[0059] This series of processes enables the system to provide users with consistent, high-quality customer service.

[0060] Example 1

[0061] 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."

[0062] There is a demand for automating customer responses and consistently providing high-quality service. However, conventional systems can only generate standard responses, and have the problem of being unable to provide appropriate responses to the diverse questions and requests of users. Furthermore, the flow of dialogue often becomes intermittent, resulting in a poor customer experience. To solve these issues, a more advanced generative model and a flexible response generation system are required.

[0063] 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.

[0064] In this invention, the server includes an automated dialogue device means equipped with a generative model, an information storage means means for storing dialogue technical data, and a response generation means means for receiving user input and generating an appropriate response based on that input. This makes it possible to smoothly generate and provide appropriate responses to a variety of user inputs.

[0065] An "automated dialogue device equipped with a generative model" is a device that automatically dialogues with a user using a generative artificial intelligence model.

[0066] The "information storage means for storing dialogue technique data" refers to a database or other information storage device for storing information and response patterns required for dialogue with users.

[0067] The "response generation means for receiving a user's input and generating an appropriate response based on the input" is a means for receiving input data from a user and generating an appropriate response based on the input.

[0068] A "display means" is a device for visually or audibly presenting or communicating the generated response to a user.

[0069] The "initial guidance message" is the first guidance message displayed by the automated dialogue device when a user accesses the system for the first time.

[0070] A "generative artificial intelligence model" is an artificial intelligence model for text generation and natural language processing.

[0071] This invention is a system that uses an automatic dialogue device equipped with a generative artificial intelligence model to automate customer service and provide consistently high-quality service. This system operates through the cooperation of a server, terminals, and users.

[0072] First, the server loads a database containing customer service skill data. This database stores dialogue technique data, including initial guidance messages and response patterns for common inquiries. The server manages the data using a database management system such as MySQL or PostgreSQL.

[0073] Next, the terminal starts the customer service robot and displays an initial guidance message to the user. For example, the terminal display might say, "Hello, welcome. How can I help you?" This initial guidance message allows the user to smoothly begin interacting with the system.

[0074] The user inputs text using the device interface, for example, by using a keyboard or touch screen to type, "Please tell me more about this product." This is an example of a specific user input.

[0075] The terminal receives the user's input and transfers it to the server. The server passes the received input data to the response generation means, which generates an appropriate response using a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. The generator analyzes the user's prompt and generates an appropriate response. Specific examples of prompts and their responses are shown below.

[0076] User prompt: "Tell me more about this product."

[0077] Generated response: "This product is the latest model and has many features."

[0078] The generated response is sent from the server to the device, which then displays it to the user. For example, a response such as "This product is the latest model and has many features" is displayed on the device's display. If the user enters additional questions, the same process is repeated.

[0079] If the user types "Goodbye," the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and ends the interaction. This end-to-end process ensures that users receive consistent, high-quality customer service.

[0080] This will concretely show how the system operates, how it automates customer responses, and how it provides efficient, high-quality service. This invention makes it possible to generate and smoothly provide appropriate responses to a variety of user inputs.

[0081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0082] Step 1:

[0083] The server loads a database containing customer service skill data.

[0084] Input: The server establishes a database connection based on the configuration file and database settings.

[0085] Processing: The server uses a database management system such as MySQL or PostgreSQL to load the required skill data, specifically by executing an SQL query such as "SELECT FROM skill_data".

[0086] Output: The customer service skill data is loaded into the server's memory.

[0087] Step 2:

[0088] The terminal will start the customer service robot and display an initial guidance message.

[0089] Input: The device detects that the startup screen has finished initializing.

[0090] Processing: The terminal uses the UI component of the customer service robot to display an initial message: "Hello, welcome. How can we help you?"

[0091] Output: An initial message appears on the screen.

[0092] Step 3:

[0093] The user enters a question or request.

[0094] Input: The user uses a keyboard or touchscreen to enter text, for example, "Tell me more about this product."

[0095] Processing: The entered text data is sent to the terminal.

[0096] Output: The text data is saved to the device.

[0097] Step 4:

[0098] The terminal forwards the user's input to the server.

[0099] Input: Text data containing user input is saved on the device.

[0100] Processing: The terminal sends this text data to the server as an HTTP POST request, which includes the user's input data.

[0101] Output: The server receives the user's input data.

[0102] Step 5:

[0103] The server passes the input data to the response generation means.

[0104] Input: The server has received the user's input data.

[0105] Processing: The server analyzes the input data and passes it to the response generation means (generator) as a prompt sentence.

[0106] Output: The data formatted as a prompt sentence is passed to the response generation means.

[0107] Step 6:

[0108] The generator uses a generative artificial intelligence model to generate a response.

[0109] Input: A formatted prompt is input to the generative AI model. For example, "Please tell me more about this product."

[0110] Processing: A generative AI model (e.g., GPT-3) analyzes the prompt and generates a response. Specifically, the model constructs a response based on the input text, taking into account appropriate context.

[0111] Output: The generated response is output from the generator. For example, "This product is the latest model and has many features."

[0112] Step 7:

[0113] The server passes the generated response to the terminal.

[0114] Input: The state in which the response from the generator is being received.

[0115] Processing: The server sends the received response to the terminal as an HTTP response.

[0116] Output: The response is sent to the terminal.

[0117] Step 8:

[0118] The terminal displays the response to the user.

[0119] Input: The state after receiving the response sent from the server.

[0120] Processing: The terminal displays the received response to the user. For example, it displays "This product is the latest model and has many features."

[0121] Output: The response is displayed on the screen.

[0122] Step 9:

[0123] The user enters a further question or enters "goodbye."

[0124] Input: The user enters an additional question or "goodbye."

[0125] Processing: The user provides input, which is sent to the terminal.

[0126] Output: The new input data is saved to the terminal. If there are any additional questions, restart from step 4. If there is a message that says it's finished, go to the next step.

[0127] Step 10:

[0128] The terminal displays a termination message and ends the conversation.

[0129] Input: The state where the user has received the data "Goodbye".

[0130] Processing: The terminal generates a termination message and displays "Thank you for using the service. Have a nice day" on the screen.

[0131] Output: A termination message is displayed on the screen and the interaction ends.

[0132] (Application example 1)

[0133] 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."

[0134] Conventional customer service systems have difficulty providing consistently high-quality service, and in brick-and-mortar stores in particular, they have relied heavily on the skills of store staff. There is also a risk of customer service quality declining due to staff inexperience or overwork. Furthermore, while quick and accurate responses to customer questions are required, it is also difficult to provide real-time inventory status and product information. A system that can solve these issues and achieve high-quality, efficient customer service is needed.

[0135] 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.

[0136] In this invention, the server includes: [a customer service robot means equipped with a generative artificial intelligence model;] [an information storage area means for storing customer service skill data;] [a response generator means for receiving a user's input and generating an appropriate response based on the input;] [a voice recognition device means for recognizing the user's question and converting it into text; and] [a response display device means displayed on the smart glasses. This makes it possible to quickly and accurately capture a user's question using the smart glasses and provide appropriate responses and inventory information in real time by utilizing the generative AI model.

[0137] A "customer service robot" is a robot equipped with a generative artificial intelligence model that automates customer service tasks and provides high-quality service.

[0138] The "information storage area" refers to a database or storage device for storing customer service skill data.

[0139] A "response generator" is a device or program that generates an appropriate response based on user input data.

[0140] A "display device" is a device or interface for displaying or communicating a generated response to a user.

[0141] A "voice recognition device" is a device that analyzes a user's voice input and converts it into text data.

[0142] "Smart glasses" are wearable devices that have a built-in display and can present information visually.

[0143] The "server" is a system that manages computing resources such as databases and generative artificial intelligence models, and controls the entire system in cooperation with customer service robots.

[0144] A system for carrying out the present invention automates customer service operations and provides high-quality customer service. A specific embodiment of this system will be described below.

[0145] System Configuration

[0146] The system includes the following main measures:

[0147] 1. Customer service robot equipped with a generative artificial intelligence model

[0148] 2. Information storage area for storing customer service skill data

[0149] 3. A response generator that takes user input and generates an appropriate response based on that input.

[0150] 4. A voice recognition device that recognizes the user's voice and converts it into text

[0151] 5. Response display device displayed on smart glasses

[0152] Hardware and software used

[0153] Hardware

[0154] Smart glasses: Examples include Google Glass and Vuzix Blade, which allow store associates to visually view information through the glasses.

[0155] Voice recognition device: A microphone built into smart glasses or an external microphone that captures the user's voice and collects it as voice data.

[0156] Customer service robots: Doll-like and other forms of robots equipped with generative AI models.

[0157] software

[0158] Speech recognition library: Use SpeechRecognition or Google Speech-to-Text API to convert voice data into text data.

[0159] Generative artificial intelligence models, such as OpenAI's GPT-3, generate responses based on text data.

[0160] Database: An information storage area that stores customer information, inventory information, etc. and can be referenced in real time.

[0161] System Operation

[0162] Initial Data Load

[0163] The server loads the necessary data from an information storage area that stores customer service skill data, including initial greeting messages to customers and responses to common inquiries.

[0164] Voice Recognition

[0165] The user (store clerk) uses a voice recognition device to capture the speech of the user (customer) through smart glasses. The speech data is converted into text data using a voice recognition library such as SpeechRecognition.

[0166] Text analysis and response generation

[0167] The server then passes the converted text data to a generative AI model, which uses a response generator to generate an appropriate response. OpenAI's GPT-3 is used as the generative AI model, and a prompt tailored to the user's question is created.

[0168] Response Display

[0169] The generated response is displayed on the smart glasses' display, and the store clerk visually confirms the response and relays it to the customer.

[0170] Specific examples

[0171] For example, if a customer asks, "Do you have this jacket in stock?", the speech recognition device converts this speech into text and passes it to the generative AI model, which then generates a response such as, "We currently have plenty of this jacket in stock." This response is displayed on the smart glasses' display, where a store clerk can confirm it and relay it to the customer.

[0172] Prompt Sentence Examples

[0173] If the user input is "Do you have this jacket in stock?", the prompt sentence might look like this:

[0174] plain

[0175] Customer: Do you have this jacket in stock?

[0176] Customer service robots:

[0177] In this way, the system can quickly and accurately capture user questions via the smart glasses and leverage generative AI models to provide appropriate responses and inventory information in real time.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] The server loads initial greeting messages and response data to inquiries from an information storage area that stores customer service skill data. This data is used in subsequent customer service operations.

[0181] Input: Reference to the information storage area where customer service skill data is stored

[0182] Output: Loaded customer service skills data

[0183] Specific operation: The server connects to the information storage area and retrieves the customer service skill data required for customer service, including initial greeting messages and standard responses to common questions.

[0184] Step 2:

[0185] The user (store clerk) puts on the smart glasses and gets ready. The smart glasses detect when the user (customer) enters the store.

[0186] Input: None (Staff ready)

[0187] Output: Ready state

[0188] Specific operation: The store clerk activates the smart glasses and checks their readiness. The smart glasses' sensors also detect the customer's presence and complete the necessary initial setup.

[0189] Step 3:

[0190] A voice recognition device installed in the smart glasses captures the user's (customer's) voice input and collects it as voice data.

[0191] Input: User's voice

[0192] Output: Audio data

[0193] Specific operation: The microphone in the smart glasses captures the user's voice and records it as audio data.

[0194] Step 4:

[0195] A speech recognizer converts the audio data into text data, which is done by a speech recognition library such as SpeechRecognition or the Google Speech-to-Text API.

[0196] Input: Audio data

[0197] Output: Text data

[0198] Specific operation: The speech recognition device analyzes the speech data in detail and converts the content into corresponding text data. High-precision text conversion is performed using a speech recognition library.

[0199] Step 5:

[0200] The server receives the converted text data and passes it to a generative AI model, which generates a prompt sentence and inputs it to a response generator.

[0201] Input: Text data

[0202] Output: prompt statement

[0203] Specific operation: The server analyzes the text data and generates a prompt suitable for the generative AI model. For example, if the prompt is "Customer: Do you have this jacket in stock?", the server prepares a prompt that includes the part "Customer Robot:."

[0204] Step 6:

[0205] The generative AI model generates an appropriate response based on the prompt, and the generated result is sent to the server as text data.

[0206] Input: prompt statement

[0207] Output: Response data

[0208] What it does: A generative AI model (e.g., GPT-3) receives a prompt and generates a response that matches it, such as, "We currently have plenty of this jacket in stock."

[0209] Step 7:

[0210] The server transmits the generated response data to the smart glasses, which then transfers the data to a display device for visual display to the store clerk.

[0211] Input: Response data

[0212] Output: The response displayed

[0213] Specific operation: The server receives the response data from the generative AI model and sends it to the smart glasses to be displayed on the screen. The store clerk checks the displayed response and communicates it appropriately to the customer.

[0214] 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.

[0215] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion engine that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion engine.

[0216] Specifically, the system operates in the following manner.

[0217] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings and responses to general inquiries required when interacting with users. Next, the terminal uses the loaded skill data to have the customer service robot display an initial greeting message to the user. For example, the phrase might be, "Hello, welcome. What can I do for you?"

[0218] Next, the user inputs a question or request into the device. For example, they might input, "Can you tell me more about this product?" At this time, the emotion engine recognizes emotions from the user's facial expressions, voice tone, or text input. The emotion engine provides this emotion data to the generator, which then generates a response that takes the user's emotions into account. For example, if the user is a little angry, the generator will generate a soft and polite response such as, "I'm sorry. I'll get back to you with more details about the product right away."

[0219] The server takes the generated response and sends it to the terminal. The terminal displays this response to the user and repeats this process until the user is satisfied. If the user types "goodbye", the terminal displays a closing greeting message "Thank you for using our service. Have a nice day" and ends the interaction.

[0220] As a concrete example, consider a case where a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion. In this case, the emotion engine recognizes dissatisfaction from the user's facial expression and tone of voice and provides that information to the generator. The generator then generates a response such as, "We currently have plenty of this item in stock. We apologize for any inconvenience," and the device displays it to the user.

[0221] In this way, the system of the present invention can provide a better user experience by generating appropriate responses while taking into account the user's emotions.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] The server loads the database that stores customer service skill data. It calls the load_skillset function and stores basic customer service messages and response patterns in memory in dictionary format.

[0225] Step 2:

[0226] The terminal extracts the initial greeting message from the loaded skill data and displays it to the user. Specifically, it displays the message "Hello, welcome. How can I help you?" defined in skillset['greeting'] on the screen.

[0227] Step 3:

[0228] The user enters a question or request into the terminal as text, for example, "Please tell me more about this product."

[0229] Step 4:

[0230] The device receives input from the user and sends the data to the emotion engine, which analyzes the user's input text, facial expressions, tone of voice, etc. to recognize the user's emotions.

[0231] Step 5:

[0232] The emotion engine identifies the user's emotion and provides emotion data to the generator. For example, if the user speaks in a frustrated tone, it passes the frustrated emotion data to the generator.

[0233] Step 6:

[0234] The server uses a generator to generate a response message incorporating emotion data. The generate_response function is called, and a generative AI model (e.g., ChatGPT) is used to generate a response such as, "This product is the latest model and has many features. Thank you for your question."

[0235] Step 7:

[0236] The server retrieves the generated response and sends it to the terminal, which formats the generated response message appropriately and provides it to the terminal.

[0237] Step 8:

[0238] The terminal displays the response message sent from the server to the user, for example, "This product is the latest model and has many features. Thank you for your question."

[0239] Step 9:

[0240] The user enters additional questions or requests. This process repeats until the user enters "goodbye." For each entry, steps 4 through 8 above are repeated.

[0241] Step 10:

[0242] The terminal detects that the user has typed "Goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day.", as defined in skillset['farewell'], and ends the conversation.

[0243] Through this series of processes, the system can provide consistent, high-quality customer service that takes user emotions into consideration.

[0244] Example 2

[0245] 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."

[0246] Conventional customer service systems have difficulty providing responses that take the user's emotions into account, and have been unable to sufficiently increase user satisfaction. Furthermore, they have limited ability to generate appropriate responses to initial greetings and general inquiries, making it difficult to provide a consistent level of customer service. Furthermore, they lack a mechanism for smoothly ending a dialogue when the user inputs an end command.

[0247] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0248] In this invention, the server includes customer service robot means equipped with a generative artificial intelligence model, database means for storing customer service skill data, generator means for receiving user input and generating an appropriate response based on the input, display means for displaying or audibly communicating the generated response to the user, emotion recognition device means for recognizing the user's emotions, generator means for generating a response based on emotion data provided by the emotion recognition device, and server means for transmitting response data to the terminal.

[0249] This allows the system to automatically generate and provide appropriate responses while taking the user's feelings into consideration, enabling higher quality customer service. It also makes it possible to smoothly end the dialogue when the user inputs an instruction to end the dialogue.

[0250] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that automatically generates appropriate responses and content based on input data.

[0251] A "customer service robot" refers to a mechanical device equipped with a generative artificial intelligence model that can automatically interact with users.

[0252] "Database" refers to an information system for storing and managing customer service skill data in a structured format.

[0253] "Generator" refers to a device or software that generates an appropriate response based on user input data and emotional data.

[0254] "Indicator" refers to a device that visually or audibly communicates the generated response to the user.

[0255] An "emotion recognition device" refers to a device that analyzes and recognizes emotions from a user's facial expressions and voice.

[0256] "Server" refers to a central computing device that manages data and controls communication between the customer service robot, database, generator, display, and emotion recognition device.

[0257] "User" refers to a person who uses the system to receive customer service.

[0258] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion recognition device that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion recognition device.

[0259] The server first loads a database containing customer service skill data. The database contains initial greetings and responses to common customer inquiries. Specifically, the server establishes a database connection and loads the customer service skill dataset into memory. For example, using a MySQL database, it executes the query SELECT FROM skill_data;

[0260] Next, the terminal displays an initial greeting message to the user based on the loaded skill data. For example, the terminal can display a message such as "Hello, welcome. How can I help you?" The terminal communicates this message visually and audibly through the display and speaker.

[0261] A user uses the terminal to enter a question or request, such as "Can you tell me more about this product?" This input is captured digitally by the terminal software.

[0262] An emotion recognition device recognizes emotions from a user's facial expressions, voice tone, or text input. Specifically, an emotion recognition device uses a camera or microphone to collect the user's facial expression data and voice data, and analyzes them with a machine learning model (e.g., OpenCV) to estimate emotions.

[0263] The generator uses a generative artificial intelligence model (e.g., GPT-4) to generate an appropriate response based on the emotion data provided by the emotion recognizer. For example, if the user is angry, it generates a soft and polite response such as, "I'm sorry. I'll get back to you with the details of the product right away."

[0264] The server receives the generated response and sends it to the terminal. Specifically, it transfers the generated text data to the terminal via an HTTP request or the like.

[0265] The terminal displays this response to the user, for example by displaying a response message on the display or by communicating it audibly through a speaker.

[0266] This process is repeated until the user is satisfied. Finally, if the user types "goodbye," the terminal displays a closing greeting message: "Thank you for using the service. Have a nice day," and ends the interaction.

[0267] As a specific example, if a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion, the emotion recognition device will recognize dissatisfaction from the user's facial expression and tone of voice and provide that information to the generator. The generator will generate a response saying, "We currently have plenty of stock of this item. We apologize for any inconvenience," and the device will display it to the user.

[0268] Example prompt sentence:

[0269] A user asks "Do you have this item in stock?" and the emotion recognizer recognizes a slightly dissatisfied emotion. Generate an appropriate and polite response for the dissatisfied user.

[0270] This allows the system to generate and provide appropriate responses that take the user's emotions into consideration, resulting in higher quality customer service.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1:

[0273] The server loads a database containing customer service skill data.

[0274] Input: Database connection information.

[0275] Specifically, the server establishes a database connection and executes the query SELECT FROM skill_data; to load the customer service skills dataset into memory.

[0276] Output: Customer service skill data loaded.

[0277] Step 2:

[0278] Based on the loaded skill data, the terminal displays an initial greeting message to the user from the customer service robot.

[0279] Input: Customer service skill data.

[0280] Specifically, the terminal visually and audibly communicates the message "Hello, welcome. How can I help you?" to the user through the display and speaker.

[0281] Output: An initial greeting message is displayed or spoken to the user.

[0282] Step 3:

[0283] The user inputs a question or request into the terminal.

[0284] Input: User question or request (e.g., "Tell me more about this product").

[0285] Specifically, a user inputs information using a keyboard or touch screen, and the input is captured as digital data.

[0286] Output: The state in which a user's question or request is digitally recorded on a terminal.

[0287] Step 4:

[0288] An emotion recognizer recognizes emotions from a user's facial expression, voice tone, or text input.

[0289] Input: User's facial expression data, voice data, and text input data.

[0290] Specifically, the emotion recognition device collects facial expressions and voices using a camera and microphone, and analyzes them using a machine learning model to estimate emotions.

[0291] Output: The state in which the user's emotion data is provided to the generator.

[0292] Step 5:

[0293] The generator generates a response that takes into account the user's emotions.

[0294] Input: User sentiment data and question or request (e.g., "Tell me more about this product").

[0295] Specifically, the generator sends the emotion data and input data as a prompt to a generative AI model (e.g., GPT-4), which then generates an appropriate response.

[0296] Output: The generated response data.

[0297] Step 6:

[0298] The server takes the generated response and sends it to the terminal.

[0299] Input: The generated response data.

[0300] Specifically, the server transfers the generated response data to the terminal via an HTTP request or the like.

[0301] Output: The response data sent to the device.

[0302] Step 7:

[0303] The terminal displays this response to the user.

[0304] Input: The response data sent (e.g. "Sorry, we'll get back to you with more details about that product shortly").

[0305] Specifically, the terminal displays a response message on the display or transmits a response audibly through the speaker.

[0306] Output: The response message is displayed or spoken to the user.

[0307] Step 8:

[0308] If the user types "goodbye", the dialogue ends.

[0309] Input: User exit instruction ("Goodbye").

[0310] Specifically, the user inputs "goodbye," and the terminal receives this and displays or speaks a closing greeting message, "Thank you for using the service. Have a nice day."

[0311] Output: The conversation has been successfully terminated.

[0312] (Application example 2)

[0313] 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."

[0314] In traditional customer service work, it has been difficult to accurately grasp a customer's emotions and provide appropriate responses accordingly. In brick-and-mortar stores in particular, staff are required to be skilled enough to quickly read a customer's emotions from their facial expressions and tone of voice and respond accordingly. However, in reality, not all staff have this skill, which leads to inconsistencies in the quality of service.

[0315] The identification processing by the identification 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 database means for storing customer service skill data, customer service robot means equipped with a generative artificial intelligence model, generator means for receiving user input and generating an appropriate response based on the input, emotion recognition device means for recognizing the user's emotion in real time using a wearable display device, emotion analysis unit means for transmitting the user's emotion data to the generator, and visual presentation unit means for displaying an emotion-based response on the wearable display device. This enables the customer service robot and staff to grasp customer emotions in real time and automatically generate and display high-quality responses accordingly.

[0316] A "customer service robot equipped with a generative artificial intelligence model" is an artificial intelligence system that has the function of generating responses based on user input during customer service operations and communicating those responses to the user.

[0317] The "database for storing customer service skill data" is a database system for storing and managing skills and response examples required for customer service.

[0318] A "generator that receives user input and generates an appropriate response based on that input" is a device or software that analyzes information provided by a user and generates an appropriate reply or action.

[0319] A "display device that displays or communicates a generated response to a user" is an interface that provides a response generated by a generator to a user visually or audibly.

[0320] An "emotion recognition device that recognizes a user's emotions in real time using a wearable display device" is a device that analyzes a user's facial expressions and voice through a wearable display device and recognizes their emotional state in real time.

[0321] An "emotion analysis unit that communicates user emotion data to the generator" is a device or software that transmits emotion data obtained from an emotion recognition device to the generator for use as part of the response generation process.

[0322] The "visual presentation unit for displaying emotion-based responses on a wearable display device" is a device or software for visually displaying responses generated based on data from the emotion analysis unit on a wearable display.

[0323] The system of the present invention is designed to automate customer service operations in brick-and-mortar stores and provide high-quality, emotionally sensitive customer service. The system is composed of various hardware and software components.

[0324] The main components include a customer service robot equipped with a generative artificial intelligence model, a database that stores customer service skill data, a generator that receives user input and generates an appropriate response, a display that displays or transmits the generated response, an emotion recognition device that recognizes the user's emotions in real time using a wearable display device, and an emotion analysis unit.

[0325] 1. Program processing and use of hardware and software

[0326] The server first loads a database that stores customer service skill data. This database includes examples of initial greetings and responses to general inquiries that are necessary when dealing with users. The customer service robot uses this data to provide an initial greeting to users who visit the store.

[0327] Specifically, the customer service robot displays the message "Hello, welcome. How can I help you?". The terminal then receives input from the user, and the input information is analyzed along with the user's facial expressions and tone of voice through an emotion recognition device. Emotion engine software (e.g., Microsoft Azure Emotion API) is used for this analysis.

[0328] The sentiment analysis unit transmits the analysis results to the generator, which is equipped with a generative AI model (e.g., GPT-4) that generates an optimal response taking into account the user's emotional data. For example, if the user is slightly angry, the generative AI model will generate a response such as, "Sorry. We'll get back to you with more details about the product shortly."

[0329] The generated response is displayed on a wearable display device (e.g., smart glasses) via a visual presentation unit, allowing staff to instantly obtain information for responding to the customer and take appropriate action.

[0330] 2. Examples of concrete examples and prompts

[0331] As a concrete scenario, consider a situation where a customer comes into a store, picks up a product, and gently asks, "Do you have this product in other colors?" The emotion recognition device analyzes the user's facial expression and voice tone to recognize a calm emotional state.

[0332] An example of a prompt for a generative AI model would be:

[0333] Customer's facial expression: Smiling

[0334] Voice tone: gentle

[0335] User Input: "Does this item come in other colors?"

[0336] Prompt for generative artificial intelligence model:

[0337] "A smiling, calm-toned customer asks if this product is available in other colors. Generate an appropriate response."

[0338] Based on this prompt, the generator generates a response, "Of course! We also have red and blue. Which one would you like to try?", and displays it on the wearable display. Staff can respond to customers based on this information, thereby increasing customer satisfaction.

[0339] As described above, the system of the present invention can significantly improve the efficiency of customer service operations in brick-and-mortar stores and improve service quality by automatically generating and displaying customer service responses that take emotions into account in real time.

[0340] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0341] Step 1:

[0342] The server loads a database that stores customer service skill data. The database contains initial greetings and responses to common inquiries that are necessary when interacting with users. The server retrieves the skill data from this database.

[0343] Input: Database connection information

[0344] Output: Customer service skill data

[0345] Action: Read customer service skill data from the database

[0346] Step 2:

[0347] The user receives initial input from the device (e.g., "Hello, welcome. How can I help you?"). The device displays an initial greeting message based on the acquired skill data.

[0348] Input: Customer service skill data

[0349] Output: Show initial greeting message

[0350] Action: The terminal displays a greeting message to the user.

[0351] Step 3:

[0352] The user inputs a question or request into the terminal (e.g., "Please tell me more about this product"), and the terminal sends the input data to the emotion recognition device.

[0353] Input: User input (question or request)

[0354] Output: Send data to emotion recognition device

[0355] Action: The device collects user input and sends it to the emotion recognition device.

[0356] Step 4:

[0357] The emotion recognition device analyzes the user's facial expressions and voice tone to determine their emotional state, and the emotion data is transmitted to the emotion analysis unit.

[0358] Input: User's facial expression, voice tone

[0359] Output: Emotion data

[0360] Operation: The emotion recognition device performs emotion analysis and transmits the results to the emotion analysis unit.

[0361] Step 5:

[0362] The emotion analysis unit sends emotion data to the generator, which is equipped with a generative artificial intelligence model and receives the emotion data and user input as a prompt sentence.

[0363] Input: Emotion data, user input

[0364] Output: prompt statement

[0365] How it works: The emotion analysis unit sends emotion data and user input to the generator.

[0366] Step 6:

[0367] The generator uses a generative artificial intelligence model to generate the optimal response based on the sentiment data (e.g., "Sorry, we'll get back to you with more details about that product shortly").

[0368] Input: prompt statement

[0369] Output: The generated response

[0370] How it works: A generative AI model generates a response based on a prompt.

[0371] Step 7:

[0372] The generated response is displayed on a wearable display (e.g., smart glasses) through a visual presentation unit. The staff member can then view the response and respond appropriately to the customer.

[0373] Input: Generated response

[0374] Output: Display on the screen

[0375] Action: The visual presentation unit displays the response on the wearable display.

[0376] Step 8:

[0377] Staff will then use the information displayed on the wearable display to actually provide the appropriate response to the customer.

[0378] Input: Response shown on the display

[0379] Output: Respond appropriately to the customer

[0380] Action: Staff responds to customers based on the information displayed

[0381] This is the process flow of this system, which allows staff to accurately grasp the customer's emotions and provide the most appropriate response.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Second embodiment]

[0386] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0397] 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."

[0398] The system of the present invention automates customer service operations and provides high-quality customer service. The system uses a customer service robot equipped with a generative artificial intelligence model and includes a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on the input, and a display that displays or communicates the generated response to the user. The system also automatically displays an initial greeting message based on the defined customer service skill data, and upon receiving input from the user, the generative artificial intelligence model generates a response based on the input.

[0399] Specifically, the system operates in the following manner.

[0400] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings to customers and responses to general inquiries. Then, the terminal displays an initial greeting message to the user from the customer service robot. For example, this greeting message is a phrase such as "Hello, welcome. What can I do for you?"

[0401] Next, the user enters a question or request as text, for example, "Tell me more about this product." The device receives this input and forwards it to the server, which passes the input data to the generator, which uses a generative artificial intelligence model to generate an appropriate response.

[0402] The generated response might be something like, "This product is the latest model and has many features." This response is again passed to the terminal, which displays it to the user. If the user types further questions, responses are generated and displayed in a similar manner. This process continues until the user types "goodbye."

[0403] When the system is finished, the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and the interaction ends. This end-to-end process ensures that users receive consistent, high-quality customer service.

[0404] As a specific scenario, consider the case where a user asks, "Do you have this item in stock?" In this case, the server sends the user's question to the generator, which generates a response such as, "We currently have plenty of this item in stock." This response is then communicated to the user via the terminal. Similarly, by sequentially generating and displaying appropriate responses to multiple questions, highly efficient customer service is achieved.

[0405] The processing flow will be explained below.

[0406] Step 1:

[0407] The server loads the database that stores customer service skill data. This is done by the load_skillset function, and stores basic customer service skills and response messages in memory in dictionary format.

[0408] Step 2:

[0409] The device retrieves the initial greeting message from the loaded skills data and displays it to the user. Specifically, it displays the message defined in skillset['greeting'] on the screen.

[0410] Step 3:

[0411] The user inputs a question or request into the terminal, for example, "Please tell me more about this product."

[0412] Step 4:

[0413] The terminal receives input from the user and sends the data to the server, which sends the input data in the appropriate format to be passed to the generator.

[0414] Step 5:

[0415] The server calls the generator and generates a response message using a generative artificial intelligence model based on the user's input data. This is done in the generate_response function, which calls ChatGPT's API to generate an appropriate response.

[0416] Step 6:

[0417] The server takes the generated response and sends it to the device, extracting and reformatting the response text from the response object from the API.

[0418] Step 7:

[0419] The terminal displays the response sent by the server to the user, specifically by using the print function to display the response on the screen.

[0420] Step 8:

[0421] The user enters additional questions or requests, and this process continues until the user enters "goodbye."

[0422] Step 9:

[0423] The terminal detects that the user has typed "goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day," as defined in skillset['farewell'], and ends the conversation.

[0424] This series of processes enables the system to provide users with consistent, high-quality customer service.

[0425] Example 1

[0426] 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."

[0427] There is a demand for automating customer responses and consistently providing high-quality service. However, conventional systems can only generate standard responses, and have the problem of being unable to provide appropriate responses to the diverse questions and requests of users. Furthermore, the flow of dialogue often becomes intermittent, resulting in a poor customer experience. To solve these issues, a more advanced generative model and a flexible response generation system are required.

[0428] 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.

[0429] In this invention, the server includes an automated dialogue device means equipped with a generative model, an information storage means means for storing dialogue technical data, and a response generation means means for receiving user input and generating an appropriate response based on that input. This makes it possible to smoothly generate and provide appropriate responses to a variety of user inputs.

[0430] An "automated dialogue device equipped with a generative model" is a device that automatically dialogues with a user using a generative artificial intelligence model.

[0431] The "information storage means for storing dialogue technique data" refers to a database or other information storage device for storing information and response patterns required for dialogue with users.

[0432] The "response generation means for receiving a user's input and generating an appropriate response based on the input" is a means for receiving input data from a user and generating an appropriate response based on the input.

[0433] A "display means" is a device for visually or audibly presenting or communicating the generated response to a user.

[0434] The "initial guidance message" is the first guidance message displayed by the automated dialogue device when a user accesses the system for the first time.

[0435] A "generative artificial intelligence model" is an artificial intelligence model for text generation and natural language processing.

[0436] This invention is a system that uses an automatic dialogue device equipped with a generative artificial intelligence model to automate customer service and provide consistently high-quality service. This system operates through the cooperation of a server, terminals, and users.

[0437] First, the server loads a database containing customer service skill data. This database stores dialogue technique data, including initial guidance messages and response patterns for common inquiries. The server manages the data using a database management system such as MySQL or PostgreSQL.

[0438] Next, the terminal starts the customer service robot and displays an initial guidance message to the user. For example, the terminal display might say, "Hello, welcome. How can I help you?" This initial guidance message allows the user to smoothly begin interacting with the system.

[0439] The user inputs text using the device interface, for example, by using a keyboard or touch screen to type, "Please tell me more about this product." This is an example of a specific user input.

[0440] The terminal receives the user's input and transfers it to the server. The server passes the received input data to the response generation means, which generates an appropriate response using a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. The generator analyzes the user's prompt and generates an appropriate response. Specific examples of prompts and their responses are shown below.

[0441] User prompt: "Tell me more about this product."

[0442] Generated response: "This product is the latest model and has many features."

[0443] The generated response is sent from the server to the device, which then displays it to the user. For example, a response such as "This product is the latest model and has many features" is displayed on the device's display. If the user enters additional questions, the same process is repeated.

[0444] If the user types "Goodbye," the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and ends the interaction. This end-to-end process ensures that users receive consistent, high-quality customer service.

[0445] This will concretely show how the system operates, how it automates customer responses, and how it provides efficient, high-quality service. This invention makes it possible to generate and smoothly provide appropriate responses to a variety of user inputs.

[0446] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0447] Step 1:

[0448] The server loads a database containing customer service skill data.

[0449] Input: The server establishes a database connection based on the configuration file and database settings.

[0450] Processing: The server uses a database management system such as MySQL or PostgreSQL to load the required skill data, specifically by executing an SQL query such as "SELECT FROM skill_data".

[0451] Output: The customer service skill data is loaded into the server's memory.

[0452] Step 2:

[0453] The terminal will start the customer service robot and display an initial guidance message.

[0454] Input: The device detects that the startup screen has finished initializing.

[0455] Processing: The terminal uses the UI component of the customer service robot to display an initial message: "Hello, welcome. How can we help you?"

[0456] Output: An initial message appears on the screen.

[0457] Step 3:

[0458] The user enters a question or request.

[0459] Input: The user uses a keyboard or touchscreen to enter text, for example, "Tell me more about this product."

[0460] Processing: The entered text data is sent to the terminal.

[0461] Output: The text data is saved to the device.

[0462] Step 4:

[0463] The terminal forwards the user's input to the server.

[0464] Input: Text data containing user input is saved on the device.

[0465] Processing: The terminal sends this text data to the server as an HTTP POST request, which includes the user's input data.

[0466] Output: The server receives the user's input data.

[0467] Step 5:

[0468] The server passes the input data to the response generation means.

[0469] Input: The server has received the user's input data.

[0470] Processing: The server analyzes the input data and passes it to the response generation means (generator) as a prompt sentence.

[0471] Output: The data formatted as a prompt sentence is passed to the response generation means.

[0472] Step 6:

[0473] The generator uses a generative artificial intelligence model to generate a response.

[0474] Input: A formatted prompt is input to the generative AI model. For example, "Please tell me more about this product."

[0475] Processing: A generative AI model (e.g., GPT-3) analyzes the prompt and generates a response. Specifically, the model constructs a response based on the input text, taking into account appropriate context.

[0476] Output: The generated response is output from the generator. For example, "This product is the latest model and has many features."

[0477] Step 7:

[0478] The server passes the generated response to the terminal.

[0479] Input: The state in which the response from the generator is being received.

[0480] Processing: The server sends the received response to the terminal as an HTTP response.

[0481] Output: The response is sent to the terminal.

[0482] Step 8:

[0483] The terminal displays the response to the user.

[0484] Input: The state after receiving the response sent from the server.

[0485] Processing: The terminal displays the received response to the user. For example, it displays "This product is the latest model and has many features."

[0486] Output: The response is displayed on the screen.

[0487] Step 9:

[0488] The user enters a further question or enters "goodbye."

[0489] Input: The user enters an additional question or "goodbye."

[0490] Processing: The user provides input, which is sent to the terminal.

[0491] Output: The new input data is saved to the terminal. If there are any additional questions, restart from step 4. If there is a message that says it's finished, go to the next step.

[0492] Step 10:

[0493] The terminal displays a termination message and ends the conversation.

[0494] Input: The state where the user has received the data "Goodbye".

[0495] Processing: The terminal generates a termination message and displays "Thank you for using the service. Have a nice day" on the screen.

[0496] Output: A termination message is displayed on the screen and the interaction ends.

[0497] (Application example 1)

[0498] 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."

[0499] Conventional customer service systems have difficulty providing consistently high-quality service, and in brick-and-mortar stores in particular, they have relied heavily on the skills of store staff. There is also a risk of customer service quality declining due to staff inexperience or overwork. Furthermore, while quick and accurate responses to customer questions are required, it is also difficult to provide real-time inventory status and product information. A system that can solve these issues and achieve high-quality, efficient customer service is needed.

[0500] 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.

[0501] In this invention, the server includes: [a customer service robot means equipped with a generative artificial intelligence model;] [an information storage area means for storing customer service skill data;] [a response generator means for receiving a user's input and generating an appropriate response based on the input;] [a voice recognition device means for recognizing the user's question and converting it into text; and] [a response display device means displayed on the smart glasses. This makes it possible to quickly and accurately capture a user's question using the smart glasses and provide appropriate responses and inventory information in real time by utilizing the generative AI model.

[0502] A "customer service robot" is a robot equipped with a generative artificial intelligence model that automates customer service tasks and provides high-quality service.

[0503] The "information storage area" refers to a database or storage device for storing customer service skill data.

[0504] A "response generator" is a device or program that generates an appropriate response based on user input data.

[0505] A "display device" is a device or interface for displaying or communicating a generated response to a user.

[0506] A "voice recognition device" is a device that analyzes a user's voice input and converts it into text data.

[0507] "Smart glasses" are wearable devices that have a built-in display and can present information visually.

[0508] The "server" is a system that manages computing resources such as databases and generative artificial intelligence models, and controls the entire system in cooperation with customer service robots.

[0509] A system for carrying out the present invention automates customer service operations and provides high-quality customer service. A specific embodiment of this system will be described below.

[0510] System Configuration

[0511] The system includes the following main measures:

[0512] 1. Customer service robot equipped with a generative artificial intelligence model

[0513] 2. Information storage area for storing customer service skill data

[0514] 3. A response generator that takes user input and generates an appropriate response based on that input.

[0515] 4. A voice recognition device that recognizes the user's voice and converts it into text

[0516] 5. Response display device displayed on smart glasses

[0517] Hardware and software used

[0518] Hardware

[0519] Smart glasses: Examples include Google Glass and Vuzix Blade, which allow store associates to visually view information through the glasses.

[0520] Voice recognition device: A microphone built into smart glasses or an external microphone that captures the user's voice and collects it as voice data.

[0521] Customer service robots: Doll-like and other forms of robots equipped with generative AI models.

[0522] software

[0523] Speech recognition library: Use SpeechRecognition or Google Speech-to-Text API to convert voice data into text data.

[0524] Generative artificial intelligence models, such as OpenAI's GPT-3, generate responses based on text data.

[0525] Database: An information storage area that stores customer information, inventory information, etc. and can be referenced in real time.

[0526] System Operation

[0527] Initial Data Load

[0528] The server loads the necessary data from an information storage area that stores customer service skill data, including initial greeting messages to customers and responses to common inquiries.

[0529] Voice Recognition

[0530] The user (store clerk) uses a voice recognition device to capture the speech of the user (customer) through smart glasses. The speech data is converted into text data using a voice recognition library such as SpeechRecognition.

[0531] Text analysis and response generation

[0532] The server then passes the converted text data to a generative AI model, which uses a response generator to generate an appropriate response. OpenAI's GPT-3 is used as the generative AI model, and a prompt tailored to the user's question is created.

[0533] Response Display

[0534] The generated response is displayed on the smart glasses' display, and the store clerk visually confirms the response and relays it to the customer.

[0535] Specific examples

[0536] For example, if a customer asks, "Do you have this jacket in stock?", the speech recognition device converts this speech into text and passes it to the generative AI model, which then generates a response such as, "We currently have plenty of this jacket in stock." This response is displayed on the smart glasses' display, where a store clerk can confirm it and relay it to the customer.

[0537] Prompt Sentence Examples

[0538] If the user input is "Do you have this jacket in stock?", the prompt sentence might look like this:

[0539] plain

[0540] Customer: Do you have this jacket in stock?

[0541] Customer service robots:

[0542] In this way, the system can quickly and accurately capture user questions via the smart glasses and leverage generative AI models to provide appropriate responses and inventory information in real time.

[0543] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0544] Step 1:

[0545] The server loads initial greeting messages and response data to inquiries from an information storage area that stores customer service skill data. This data is used in subsequent customer service operations.

[0546] Input: Reference to the information storage area where customer service skill data is stored

[0547] Output: Loaded customer service skills data

[0548] Specific operation: The server connects to the information storage area and retrieves the customer service skill data required for customer service, including initial greeting messages and standard responses to common questions.

[0549] Step 2:

[0550] The user (store clerk) puts on the smart glasses and gets ready. The smart glasses detect when the user (customer) enters the store.

[0551] Input: None (Staff ready)

[0552] Output: Ready state

[0553] Specific operation: The store clerk activates the smart glasses and checks their readiness. The smart glasses' sensors also detect the customer's presence and complete the necessary initial setup.

[0554] Step 3:

[0555] A voice recognition device installed in the smart glasses captures the user's (customer's) voice input and collects it as voice data.

[0556] Input: User's voice

[0557] Output: Audio data

[0558] Specific operation: The microphone in the smart glasses captures the user's voice and records it as audio data.

[0559] Step 4:

[0560] A speech recognizer converts the audio data into text data, which is done by a speech recognition library such as SpeechRecognition or the Google Speech-to-Text API.

[0561] Input: Audio data

[0562] Output: Text data

[0563] Specific operation: The speech recognition device analyzes the speech data in detail and converts the content into corresponding text data. High-precision text conversion is performed using a speech recognition library.

[0564] Step 5:

[0565] The server receives the converted text data and passes it to a generative AI model, which generates a prompt sentence and inputs it to a response generator.

[0566] Input: Text data

[0567] Output: prompt statement

[0568] Specific operation: The server analyzes the text data and generates a prompt suitable for the generative AI model. For example, if the prompt is "Customer: Do you have this jacket in stock?", the server prepares a prompt that includes the part "Customer Robot:."

[0569] Step 6:

[0570] The generative AI model generates an appropriate response based on the prompt, and the generated result is sent to the server as text data.

[0571] Input: prompt statement

[0572] Output: Response data

[0573] What it does: A generative AI model (e.g., GPT-3) receives a prompt and generates a response that matches it, such as, "We currently have plenty of this jacket in stock."

[0574] Step 7:

[0575] The server transmits the generated response data to the smart glasses, which then transfers the data to a display device for visual display to the store clerk.

[0576] Input: Response data

[0577] Output: The response displayed

[0578] Specific operation: The server receives the response data from the generative AI model and sends it to the smart glasses to be displayed on the screen. The store clerk checks the displayed response and communicates it appropriately to the customer.

[0579] 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.

[0580] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion engine that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion engine.

[0581] Specifically, the system operates in the following manner.

[0582] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings and responses to general inquiries required when interacting with users. Next, the terminal uses the loaded skill data to have the customer service robot display an initial greeting message to the user. For example, the phrase might be, "Hello, welcome. What can I do for you?"

[0583] Next, the user inputs a question or request into the device. For example, they might input, "Can you tell me more about this product?" At this time, the emotion engine recognizes emotions from the user's facial expressions, voice tone, or text input. The emotion engine provides this emotion data to the generator, which then generates a response that takes the user's emotions into account. For example, if the user is a little angry, the generator will generate a soft and polite response such as, "I'm sorry. I'll get back to you with more details about the product right away."

[0584] The server takes the generated response and sends it to the terminal. The terminal displays this response to the user and repeats this process until the user is satisfied. If the user types "goodbye", the terminal displays a closing greeting message "Thank you for using our service. Have a nice day" and ends the interaction.

[0585] As a concrete example, consider a case where a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion. In this case, the emotion engine recognizes dissatisfaction from the user's facial expression and tone of voice and provides that information to the generator. The generator then generates a response such as, "We currently have plenty of this item in stock. We apologize for any inconvenience," and the device displays it to the user.

[0586] In this way, the system of the present invention can provide a better user experience by generating appropriate responses while taking into account the user's emotions.

[0587] The processing flow will be explained below.

[0588] Step 1:

[0589] The server loads the database that stores customer service skill data. It calls the load_skillset function and stores basic customer service messages and response patterns in memory in dictionary format.

[0590] Step 2:

[0591] The terminal extracts the initial greeting message from the loaded skill data and displays it to the user. Specifically, it displays the message "Hello, welcome. How can I help you?" defined in skillset['greeting'] on the screen.

[0592] Step 3:

[0593] The user enters a question or request into the terminal as text, for example, "Please tell me more about this product."

[0594] Step 4:

[0595] The device receives input from the user and sends the data to the emotion engine, which analyzes the user's input text, facial expressions, tone of voice, etc. to recognize the user's emotions.

[0596] Step 5:

[0597] The emotion engine identifies the user's emotion and provides emotion data to the generator. For example, if the user speaks in a frustrated tone, it passes the frustrated emotion data to the generator.

[0598] Step 6:

[0599] The server uses a generator to generate a response message incorporating emotion data. The generate_response function is called, and a generative AI model (e.g., ChatGPT) is used to generate a response such as, "This product is the latest model and has many features. Thank you for your question."

[0600] Step 7:

[0601] The server retrieves the generated response and sends it to the terminal, which formats the generated response message appropriately and provides it to the terminal.

[0602] Step 8:

[0603] The terminal displays the response message sent from the server to the user, for example, "This product is the latest model and has many features. Thank you for your question."

[0604] Step 9:

[0605] The user enters additional questions or requests. This process repeats until the user enters "goodbye." For each entry, steps 4 through 8 above are repeated.

[0606] Step 10:

[0607] The terminal detects that the user has typed "Goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day.", as defined in skillset['farewell'], and ends the conversation.

[0608] Through this series of processes, the system can provide consistent, high-quality customer service that takes user emotions into consideration.

[0609] Example 2

[0610] 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."

[0611] Conventional customer service systems have difficulty providing responses that take the user's emotions into account, and have been unable to sufficiently increase user satisfaction. Furthermore, they have limited ability to generate appropriate responses to initial greetings and general inquiries, making it difficult to provide a consistent level of customer service. Furthermore, they lack a mechanism for smoothly ending a dialogue when the user inputs an end command.

[0612] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0613] In this invention, the server includes customer service robot means equipped with a generative artificial intelligence model, database means for storing customer service skill data, generator means for receiving user input and generating an appropriate response based on the input, display means for displaying or audibly communicating the generated response to the user, emotion recognition device means for recognizing the user's emotions, generator means for generating a response based on emotion data provided by the emotion recognition device, and server means for transmitting response data to the terminal.

[0614] This allows the system to automatically generate and provide appropriate responses while taking the user's feelings into consideration, enabling higher quality customer service. It also makes it possible to smoothly end the dialogue when the user inputs an instruction to end the dialogue.

[0615] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that automatically generates appropriate responses and content based on input data.

[0616] A "customer service robot" refers to a mechanical device equipped with a generative artificial intelligence model that can automatically interact with users.

[0617] "Database" refers to an information system for storing and managing customer service skill data in a structured format.

[0618] "Generator" refers to a device or software that generates an appropriate response based on user input data and emotional data.

[0619] "Indicator" refers to a device that visually or audibly communicates the generated response to the user.

[0620] An "emotion recognition device" refers to a device that analyzes and recognizes emotions from a user's facial expressions and voice.

[0621] "Server" refers to a central computing device that manages data and controls communication between the customer service robot, database, generator, display, and emotion recognition device.

[0622] "User" refers to a person who uses the system to receive customer service.

[0623] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion recognition device that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion recognition device.

[0624] The server first loads a database containing customer service skill data. The database contains initial greetings and responses to common customer inquiries. Specifically, the server establishes a database connection and loads the customer service skill dataset into memory. For example, using a MySQL database, it executes the query SELECT FROM skill_data;

[0625] Next, the terminal displays an initial greeting message to the user based on the loaded skill data. For example, the terminal can display a message such as "Hello, welcome. How can I help you?" The terminal communicates this message visually and audibly through the display and speaker.

[0626] A user uses the terminal to enter a question or request, such as "Can you tell me more about this product?" This input is captured digitally by the terminal software.

[0627] An emotion recognition device recognizes emotions from a user's facial expressions, voice tone, or text input. Specifically, an emotion recognition device uses a camera or microphone to collect the user's facial expression data and voice data, and analyzes them with a machine learning model (e.g., OpenCV) to estimate emotions.

[0628] The generator uses a generative artificial intelligence model (e.g., GPT-4) to generate an appropriate response based on the emotion data provided by the emotion recognizer. For example, if the user is angry, it generates a soft and polite response such as, "I'm sorry. I'll get back to you with the details of the product right away."

[0629] The server receives the generated response and sends it to the terminal. Specifically, it transfers the generated text data to the terminal via an HTTP request or the like.

[0630] The terminal displays this response to the user, for example by displaying a response message on the display or by communicating it audibly through a speaker.

[0631] This process is repeated until the user is satisfied. Finally, if the user types "goodbye," the terminal displays a closing greeting message: "Thank you for using the service. Have a nice day," and ends the interaction.

[0632] As a specific example, if a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion, the emotion recognition device will recognize dissatisfaction from the user's facial expression and tone of voice and provide that information to the generator. The generator will generate a response saying, "We currently have plenty of stock of this item. We apologize for any inconvenience," and the device will display it to the user.

[0633] Example prompt sentence:

[0634] A user asks "Do you have this item in stock?" and the emotion recognizer recognizes a slightly dissatisfied emotion. Generate an appropriate and polite response for the dissatisfied user.

[0635] This allows the system to generate and provide appropriate responses that take the user's emotions into consideration, resulting in higher quality customer service.

[0636] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] The server loads a database containing customer service skill data.

[0639] Input: Database connection information.

[0640] Specifically, the server establishes a database connection and executes the query SELECT FROM skill_data; to load the customer service skills dataset into memory.

[0641] Output: Customer service skill data loaded.

[0642] Step 2:

[0643] Based on the loaded skill data, the terminal displays an initial greeting message to the user from the customer service robot.

[0644] Input: Customer service skill data.

[0645] Specifically, the terminal visually and audibly communicates the message "Hello, welcome. How can I help you?" to the user through the display and speaker.

[0646] Output: An initial greeting message is displayed or spoken to the user.

[0647] Step 3:

[0648] The user inputs a question or request into the terminal.

[0649] Input: User question or request (e.g., "Tell me more about this product").

[0650] Specifically, a user inputs information using a keyboard or touch screen, and the input is captured as digital data.

[0651] Output: The state in which a user's question or request is digitally recorded on a terminal.

[0652] Step 4:

[0653] An emotion recognizer recognizes emotions from a user's facial expression, voice tone, or text input.

[0654] Input: User's facial expression data, voice data, and text input data.

[0655] Specifically, the emotion recognition device collects facial expressions and voices using a camera and microphone, and analyzes them using a machine learning model to estimate emotions.

[0656] Output: The state in which the user's emotion data is provided to the generator.

[0657] Step 5:

[0658] The generator generates a response that takes into account the user's emotions.

[0659] Input: User sentiment data and question or request (e.g., "Tell me more about this product").

[0660] Specifically, the generator sends the emotion data and input data as a prompt to a generative AI model (e.g., GPT-4), which then generates an appropriate response.

[0661] Output: The generated response data.

[0662] Step 6:

[0663] The server takes the generated response and sends it to the terminal.

[0664] Input: The generated response data.

[0665] Specifically, the server transfers the generated response data to the terminal via an HTTP request or the like.

[0666] Output: The response data sent to the device.

[0667] Step 7:

[0668] The terminal displays this response to the user.

[0669] Input: The response data sent (e.g. "Sorry, we'll get back to you with more details about that product shortly").

[0670] Specifically, the terminal displays a response message on the display or transmits a response audibly through the speaker.

[0671] Output: The response message is displayed or spoken to the user.

[0672] Step 8:

[0673] If the user types "goodbye", the dialogue ends.

[0674] Input: User exit instruction ("Goodbye").

[0675] Specifically, the user inputs "goodbye," and the terminal receives this and displays or speaks a closing greeting message, "Thank you for using the service. Have a nice day."

[0676] Output: The conversation has been successfully terminated.

[0677] (Application example 2)

[0678] 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."

[0679] In traditional customer service work, it has been difficult to accurately grasp a customer's emotions and provide appropriate responses accordingly. In brick-and-mortar stores in particular, staff are required to be skilled enough to quickly read a customer's emotions from their facial expressions and tone of voice and respond accordingly. However, in reality, not all staff have this skill, which leads to inconsistencies in the quality of service.

[0680] The identification processing by the identification 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 database means for storing customer service skill data, customer service robot means equipped with a generative artificial intelligence model, generator means for receiving user input and generating an appropriate response based on the input, emotion recognition device means for recognizing the user's emotion in real time using a wearable display device, emotion analysis unit means for transmitting the user's emotion data to the generator, and visual presentation unit means for displaying an emotion-based response on the wearable display device. This enables the customer service robot and staff to grasp customer emotions in real time and automatically generate and display high-quality responses accordingly.

[0681] A "customer service robot equipped with a generative artificial intelligence model" is an artificial intelligence system that has the function of generating responses based on user input during customer service operations and communicating those responses to the user.

[0682] The "database for storing customer service skill data" is a database system for storing and managing skills and response examples required for customer service.

[0683] A "generator that receives user input and generates an appropriate response based on that input" is a device or software that analyzes information provided by a user and generates an appropriate reply or action.

[0684] A "display device that displays or communicates a generated response to a user" is an interface that provides a response generated by a generator to a user visually or audibly.

[0685] An "emotion recognition device that recognizes a user's emotions in real time using a wearable display device" is a device that analyzes a user's facial expressions and voice through a wearable display device and recognizes their emotional state in real time.

[0686] An "emotion analysis unit that communicates user emotion data to the generator" is a device or software that transmits emotion data obtained from an emotion recognition device to the generator for use as part of the response generation process.

[0687] The "visual presentation unit for displaying emotion-based responses on a wearable display device" is a device or software for visually displaying responses generated based on data from the emotion analysis unit on a wearable display.

[0688] The system of the present invention is designed to automate customer service operations in brick-and-mortar stores and provide high-quality, emotionally sensitive customer service. The system is composed of various hardware and software components.

[0689] The main components include a customer service robot equipped with a generative artificial intelligence model, a database that stores customer service skill data, a generator that receives user input and generates an appropriate response, a display that displays or transmits the generated response, an emotion recognition device that recognizes the user's emotions in real time using a wearable display device, and an emotion analysis unit.

[0690] 1. Program processing and use of hardware and software

[0691] The server first loads a database that stores customer service skill data. This database includes examples of initial greetings and responses to general inquiries that are necessary when dealing with users. The customer service robot uses this data to provide an initial greeting to users who visit the store.

[0692] Specifically, the customer service robot displays the message "Hello, welcome. How can I help you?". The terminal then receives input from the user, and the input information is analyzed along with the user's facial expressions and tone of voice through an emotion recognition device. Emotion engine software (e.g., Microsoft Azure Emotion API) is used for this analysis.

[0693] The sentiment analysis unit transmits the analysis results to the generator, which is equipped with a generative AI model (e.g., GPT-4) that generates an optimal response taking into account the user's emotional data. For example, if the user is slightly angry, the generative AI model will generate a response such as, "Sorry. We'll get back to you with more details about the product shortly."

[0694] The generated response is displayed on a wearable display device (e.g., smart glasses) via a visual presentation unit, allowing staff to instantly obtain information for responding to the customer and take appropriate action.

[0695] 2. Examples of concrete examples and prompts

[0696] As a concrete scenario, consider a situation where a customer comes into a store, picks up a product, and gently asks, "Do you have this product in other colors?" The emotion recognition device analyzes the user's facial expression and voice tone to recognize a calm emotional state.

[0697] An example of a prompt for a generative AI model would be:

[0698] Customer's facial expression: Smiling

[0699] Voice tone: gentle

[0700] User Input: "Does this item come in other colors?"

[0701] Prompt for generative artificial intelligence model:

[0702] "A smiling, calm-toned customer asks if this product is available in other colors. Generate an appropriate response."

[0703] Based on this prompt, the generator generates a response, "Of course! We also have red and blue. Which one would you like to try?", and displays it on the wearable display. Staff can respond to customers based on this information, thereby increasing customer satisfaction.

[0704] As described above, the system of the present invention can significantly improve the efficiency of customer service operations in brick-and-mortar stores and improve service quality by automatically generating and displaying customer service responses that take emotions into account in real time.

[0705] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0706] Step 1:

[0707] The server loads a database that stores customer service skill data. The database contains initial greetings and responses to common inquiries that are necessary when interacting with users. The server retrieves the skill data from this database.

[0708] Input: Database connection information

[0709] Output: Customer service skill data

[0710] Action: Read customer service skill data from the database

[0711] Step 2:

[0712] The user receives initial input from the device (e.g., "Hello, welcome. How can I help you?"). The device displays an initial greeting message based on the acquired skill data.

[0713] Input: Customer service skill data

[0714] Output: Show initial greeting message

[0715] Action: The terminal displays a greeting message to the user.

[0716] Step 3:

[0717] The user inputs a question or request into the terminal (e.g., "Please tell me more about this product"), and the terminal sends the input data to the emotion recognition device.

[0718] Input: User input (question or request)

[0719] Output: Send data to emotion recognition device

[0720] Action: The device collects user input and sends it to the emotion recognition device.

[0721] Step 4:

[0722] The emotion recognition device analyzes the user's facial expressions and voice tone to determine their emotional state, and the emotion data is transmitted to the emotion analysis unit.

[0723] Input: User's facial expression, voice tone

[0724] Output: Emotion data

[0725] Operation: The emotion recognition device performs emotion analysis and transmits the results to the emotion analysis unit.

[0726] Step 5:

[0727] The emotion analysis unit sends emotion data to the generator, which is equipped with a generative artificial intelligence model and receives the emotion data and user input as a prompt sentence.

[0728] Input: Emotion data, user input

[0729] Output: prompt statement

[0730] How it works: The emotion analysis unit sends emotion data and user input to the generator.

[0731] Step 6:

[0732] The generator uses a generative artificial intelligence model to generate the optimal response based on the sentiment data (e.g., "Sorry, we'll get back to you with more details about that product shortly").

[0733] Input: prompt statement

[0734] Output: The generated response

[0735] How it works: A generative AI model generates a response based on a prompt.

[0736] Step 7:

[0737] The generated response is displayed on a wearable display (e.g., smart glasses) through a visual presentation unit. The staff member can then view the response and respond appropriately to the customer.

[0738] Input: Generated response

[0739] Output: Display on the screen

[0740] Action: The visual presentation unit displays the response on the wearable display.

[0741] Step 8:

[0742] Staff will then use the information displayed on the wearable display to actually provide the appropriate response to the customer.

[0743] Input: Response shown on the display

[0744] Output: Respond appropriately to the customer

[0745] Action: Staff responds to customers based on the information displayed

[0746] This is the process flow of this system, which allows staff to accurately grasp the customer's emotions and provide the most appropriate response.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] [Third embodiment]

[0751] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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).

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] The system of the present invention automates customer service operations and provides high-quality customer service. The system uses a customer service robot equipped with a generative artificial intelligence model and includes a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on the input, and a display that displays or communicates the generated response to the user. The system also automatically displays an initial greeting message based on the defined customer service skill data, and upon receiving input from the user, the generative artificial intelligence model generates a response based on the input.

[0764] Specifically, the system operates in the following manner.

[0765] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings to customers and responses to general inquiries. Then, the terminal displays an initial greeting message to the user from the customer service robot. For example, this greeting message is a phrase such as "Hello, welcome. What can I do for you?"

[0766] Next, the user enters a question or request as text, for example, "Tell me more about this product." The device receives this input and forwards it to the server, which passes the input data to the generator, which uses a generative artificial intelligence model to generate an appropriate response.

[0767] The generated response might be something like, "This product is the latest model and has many features." This response is again passed to the terminal, which displays it to the user. If the user types further questions, responses are generated and displayed in a similar manner. This process continues until the user types "goodbye."

[0768] When the system is finished, the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and the interaction ends. This end-to-end process ensures that users receive consistent, high-quality customer service.

[0769] As a specific scenario, consider the case where a user asks, "Do you have this item in stock?" In this case, the server sends the user's question to the generator, which generates a response such as, "We currently have plenty of this item in stock." This response is then communicated to the user via the terminal. Similarly, by sequentially generating and displaying appropriate responses to multiple questions, highly efficient customer service is achieved.

[0770] The processing flow will be explained below.

[0771] Step 1:

[0772] The server loads the database that stores customer service skill data. This is done by the load_skillset function, and stores basic customer service skills and response messages in memory in dictionary format.

[0773] Step 2:

[0774] The device retrieves the initial greeting message from the loaded skills data and displays it to the user. Specifically, it displays the message defined in skillset['greeting'] on the screen.

[0775] Step 3:

[0776] The user inputs a question or request into the terminal, for example, "Please tell me more about this product."

[0777] Step 4:

[0778] The terminal receives input from the user and sends the data to the server, which sends the input data in the appropriate format to be passed to the generator.

[0779] Step 5:

[0780] The server calls the generator and generates a response message using a generative artificial intelligence model based on the user's input data. This is done in the generate_response function, which calls ChatGPT's API to generate an appropriate response.

[0781] Step 6:

[0782] The server takes the generated response and sends it to the device, extracting and reformatting the response text from the response object from the API.

[0783] Step 7:

[0784] The terminal displays the response sent by the server to the user, specifically by using the print function to display the response on the screen.

[0785] Step 8:

[0786] The user enters additional questions or requests, and this process continues until the user enters "goodbye."

[0787] Step 9:

[0788] The terminal detects that the user has typed "goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day," as defined in skillset['farewell'], and ends the conversation.

[0789] This series of processes enables the system to provide users with consistent, high-quality customer service.

[0790] Example 1

[0791] 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."

[0792] There is a demand for automating customer responses and consistently providing high-quality service. However, conventional systems can only generate standard responses, and have the problem of being unable to provide appropriate responses to the diverse questions and requests of users. Furthermore, the flow of dialogue often becomes intermittent, resulting in a poor customer experience. To solve these issues, a more advanced generative model and a flexible response generation system are required.

[0793] 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.

[0794] In this invention, the server includes an automated dialogue device means equipped with a generative model, an information storage means means for storing dialogue technical data, and a response generation means means for receiving user input and generating an appropriate response based on that input. This makes it possible to smoothly generate and provide appropriate responses to a variety of user inputs.

[0795] An "automated dialogue device equipped with a generative model" is a device that automatically dialogues with a user using a generative artificial intelligence model.

[0796] The "information storage means for storing dialogue technique data" refers to a database or other information storage device for storing information and response patterns required for dialogue with users.

[0797] The "response generation means for receiving a user's input and generating an appropriate response based on the input" is a means for receiving input data from a user and generating an appropriate response based on the input.

[0798] A "display means" is a device for visually or audibly presenting or communicating the generated response to a user.

[0799] The "initial guidance message" is the first guidance message displayed by the automated dialogue device when a user accesses the system for the first time.

[0800] A "generative artificial intelligence model" is an artificial intelligence model for text generation and natural language processing.

[0801] This invention is a system that uses an automatic dialogue device equipped with a generative artificial intelligence model to automate customer service and provide consistently high-quality service. This system operates through the cooperation of a server, terminals, and users.

[0802] First, the server loads a database containing customer service skill data. This database stores dialogue technique data, including initial guidance messages and response patterns for common inquiries. The server manages the data using a database management system such as MySQL or PostgreSQL.

[0803] Next, the terminal starts the customer service robot and displays an initial guidance message to the user. For example, the terminal display might say, "Hello, welcome. How can I help you?" This initial guidance message allows the user to smoothly begin interacting with the system.

[0804] The user inputs text using the device interface, for example, by using a keyboard or touch screen to type, "Please tell me more about this product." This is an example of a specific user input.

[0805] The terminal receives the user's input and transfers it to the server. The server passes the received input data to the response generation means, which generates an appropriate response using a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. The generator analyzes the user's prompt and generates an appropriate response. Specific examples of prompts and their responses are shown below.

[0806] User prompt: "Tell me more about this product."

[0807] Generated response: "This product is the latest model and has many features."

[0808] The generated response is sent from the server to the device, which then displays it to the user. For example, a response such as "This product is the latest model and has many features" is displayed on the device's display. If the user enters additional questions, the same process is repeated.

[0809] If the user types "Goodbye," the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and ends the interaction. This end-to-end process ensures that users receive consistent, high-quality customer service.

[0810] This will concretely show how the system operates, how it automates customer responses, and how it provides efficient, high-quality service. This invention makes it possible to generate and smoothly provide appropriate responses to a variety of user inputs.

[0811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0812] Step 1:

[0813] The server loads a database containing customer service skill data.

[0814] Input: The server establishes a database connection based on the configuration file and database settings.

[0815] Processing: The server uses a database management system such as MySQL or PostgreSQL to load the required skill data, specifically by executing an SQL query such as "SELECT FROM skill_data".

[0816] Output: The customer service skill data is loaded into the server's memory.

[0817] Step 2:

[0818] The terminal will start the customer service robot and display an initial guidance message.

[0819] Input: The device detects that the startup screen has finished initializing.

[0820] Processing: The terminal uses the UI component of the customer service robot to display an initial message: "Hello, welcome. How can we help you?"

[0821] Output: An initial message appears on the screen.

[0822] Step 3:

[0823] The user enters a question or request.

[0824] Input: The user uses a keyboard or touchscreen to enter text, for example, "Tell me more about this product."

[0825] Processing: The entered text data is sent to the terminal.

[0826] Output: The text data is saved to the device.

[0827] Step 4:

[0828] The terminal forwards the user's input to the server.

[0829] Input: Text data containing user input is saved on the device.

[0830] Processing: The terminal sends this text data to the server as an HTTP POST request, which includes the user's input data.

[0831] Output: The server receives the user's input data.

[0832] Step 5:

[0833] The server passes the input data to the response generation means.

[0834] Input: The server has received the user's input data.

[0835] Processing: The server analyzes the input data and passes it to the response generation means (generator) as a prompt sentence.

[0836] Output: The data formatted as a prompt sentence is passed to the response generation means.

[0837] Step 6:

[0838] The generator uses a generative artificial intelligence model to generate a response.

[0839] Input: A formatted prompt is input to the generative AI model. For example, "Please tell me more about this product."

[0840] Processing: A generative AI model (e.g., GPT-3) analyzes the prompt and generates a response. Specifically, the model constructs a response based on the input text, taking into account appropriate context.

[0841] Output: The generated response is output from the generator. For example, "This product is the latest model and has many features."

[0842] Step 7:

[0843] The server passes the generated response to the terminal.

[0844] Input: The state in which the response from the generator is being received.

[0845] Processing: The server sends the received response to the terminal as an HTTP response.

[0846] Output: The response is sent to the terminal.

[0847] Step 8:

[0848] The terminal displays the response to the user.

[0849] Input: The state after receiving the response sent from the server.

[0850] Processing: The terminal displays the received response to the user. For example, it displays "This product is the latest model and has many features."

[0851] Output: The response is displayed on the screen.

[0852] Step 9:

[0853] The user enters a further question or enters "goodbye."

[0854] Input: The user enters an additional question or "goodbye."

[0855] Processing: The user provides input, which is sent to the terminal.

[0856] Output: The new input data is saved to the terminal. If there are any additional questions, restart from step 4. If there is a message that says it's finished, go to the next step.

[0857] Step 10:

[0858] The terminal displays a termination message and ends the conversation.

[0859] Input: The state where the user has received the data "Goodbye".

[0860] Processing: The terminal generates a termination message and displays "Thank you for using the service. Have a nice day" on the screen.

[0861] Output: A termination message is displayed on the screen and the interaction ends.

[0862] (Application example 1)

[0863] 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."

[0864] Conventional customer service systems have difficulty providing consistently high-quality service, and in brick-and-mortar stores in particular, they have relied heavily on the skills of store staff. There is also a risk of customer service quality declining due to staff inexperience or overwork. Furthermore, while quick and accurate responses to customer questions are required, it is also difficult to provide real-time inventory status and product information. A system that can solve these issues and achieve high-quality, efficient customer service is needed.

[0865] 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.

[0866] In this invention, the server includes: [a customer service robot means equipped with a generative artificial intelligence model;] [an information storage area means for storing customer service skill data;] [a response generator means for receiving a user's input and generating an appropriate response based on the input;] [a voice recognition device means for recognizing the user's question and converting it into text; and] [a response display device means displayed on the smart glasses. This makes it possible to quickly and accurately capture a user's question using the smart glasses and provide appropriate responses and inventory information in real time by utilizing the generative AI model.

[0867] A "customer service robot" is a robot equipped with a generative artificial intelligence model that automates customer service tasks and provides high-quality service.

[0868] The "information storage area" refers to a database or storage device for storing customer service skill data.

[0869] A "response generator" is a device or program that generates an appropriate response based on user input data.

[0870] A "display device" is a device or interface for displaying or communicating a generated response to a user.

[0871] A "voice recognition device" is a device that analyzes a user's voice input and converts it into text data.

[0872] "Smart glasses" are wearable devices that have a built-in display and can present information visually.

[0873] The "server" is a system that manages computing resources such as databases and generative artificial intelligence models, and controls the entire system in cooperation with customer service robots.

[0874] A system for carrying out the present invention automates customer service operations and provides high-quality customer service. A specific embodiment of this system will be described below.

[0875] System Configuration

[0876] The system includes the following main measures:

[0877] 1. Customer service robot equipped with a generative artificial intelligence model

[0878] 2. Information storage area for storing customer service skill data

[0879] 3. A response generator that takes user input and generates an appropriate response based on that input.

[0880] 4. A voice recognition device that recognizes the user's voice and converts it into text

[0881] 5. Response display device displayed on smart glasses

[0882] Hardware and software used

[0883] Hardware

[0884] Smart glasses: Examples include Google Glass and Vuzix Blade, which allow store associates to visually view information through the glasses.

[0885] Voice recognition device: A microphone built into smart glasses or an external microphone that captures the user's voice and collects it as voice data.

[0886] Customer service robots: Doll-like and other forms of robots equipped with generative AI models.

[0887] software

[0888] Speech recognition library: Use SpeechRecognition or Google Speech-to-Text API to convert voice data into text data.

[0889] Generative artificial intelligence models, such as OpenAI's GPT-3, generate responses based on text data.

[0890] Database: An information storage area that stores customer information, inventory information, etc. and can be referenced in real time.

[0891] System Operation

[0892] Initial Data Load

[0893] The server loads the necessary data from an information storage area that stores customer service skill data, including initial greeting messages to customers and responses to common inquiries.

[0894] Voice Recognition

[0895] The user (store clerk) uses a voice recognition device to capture the speech of the user (customer) through smart glasses. The speech data is converted into text data using a voice recognition library such as SpeechRecognition.

[0896] Text analysis and response generation

[0897] The server then passes the converted text data to a generative AI model, which uses a response generator to generate an appropriate response. OpenAI's GPT-3 is used as the generative AI model, and a prompt tailored to the user's question is created.

[0898] Response Display

[0899] The generated response is displayed on the smart glasses' display, and the store clerk visually confirms the response and relays it to the customer.

[0900] Specific examples

[0901] For example, if a customer asks, "Do you have this jacket in stock?", the speech recognition device converts this speech into text and passes it to the generative AI model, which then generates a response such as, "We currently have plenty of this jacket in stock." This response is displayed on the smart glasses' display, where a store clerk can confirm it and relay it to the customer.

[0902] Prompt Sentence Examples

[0903] If the user input is "Do you have this jacket in stock?", the prompt sentence might look like this:

[0904] plain

[0905] Customer: Do you have this jacket in stock?

[0906] Customer service robots:

[0907] In this way, the system can quickly and accurately capture user questions via the smart glasses and leverage generative AI models to provide appropriate responses and inventory information in real time.

[0908] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0909] Step 1:

[0910] The server loads initial greeting messages and response data to inquiries from an information storage area that stores customer service skill data. This data is used in subsequent customer service operations.

[0911] Input: Reference to the information storage area where customer service skill data is stored

[0912] Output: Loaded customer service skills data

[0913] Specific operation: The server connects to the information storage area and retrieves the customer service skill data required for customer service, including initial greeting messages and standard responses to common questions.

[0914] Step 2:

[0915] The user (store clerk) puts on the smart glasses and gets ready. The smart glasses detect when the user (customer) enters the store.

[0916] Input: None (Staff ready)

[0917] Output: Ready state

[0918] Specific operation: The store clerk activates the smart glasses and checks their readiness. The smart glasses' sensors also detect the customer's presence and complete the necessary initial setup.

[0919] Step 3:

[0920] A voice recognition device installed in the smart glasses captures the user's (customer's) voice input and collects it as voice data.

[0921] Input: User's voice

[0922] Output: Audio data

[0923] Specific operation: The microphone in the smart glasses captures the user's voice and records it as audio data.

[0924] Step 4:

[0925] A speech recognizer converts the audio data into text data, which is done by a speech recognition library such as SpeechRecognition or the Google Speech-to-Text API.

[0926] Input: Audio data

[0927] Output: Text data

[0928] Specific operation: The speech recognition device analyzes the speech data in detail and converts the content into corresponding text data. High-precision text conversion is performed using a speech recognition library.

[0929] Step 5:

[0930] The server receives the converted text data and passes it to a generative AI model, which generates a prompt sentence and inputs it to a response generator.

[0931] Input: Text data

[0932] Output: prompt statement

[0933] Specific operation: The server analyzes the text data and generates a prompt suitable for the generative AI model. For example, if the prompt is "Customer: Do you have this jacket in stock?", the server prepares a prompt that includes the part "Customer Robot:."

[0934] Step 6:

[0935] The generative AI model generates an appropriate response based on the prompt, and the generated result is sent to the server as text data.

[0936] Input: prompt statement

[0937] Output: Response data

[0938] What it does: A generative AI model (e.g., GPT-3) receives a prompt and generates a response that matches it, such as, "We currently have plenty of this jacket in stock."

[0939] Step 7:

[0940] The server transmits the generated response data to the smart glasses, which then transfers the data to a display device for visual display to the store clerk.

[0941] Input: Response data

[0942] Output: The response displayed

[0943] Specific operation: The server receives the response data from the generative AI model and sends it to the smart glasses to be displayed on the screen. The store clerk checks the displayed response and communicates it appropriately to the customer.

[0944] 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.

[0945] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion engine that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion engine.

[0946] Specifically, the system operates in the following manner.

[0947] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings and responses to general inquiries required when interacting with users. Next, the terminal uses the loaded skill data to have the customer service robot display an initial greeting message to the user. For example, the phrase might be, "Hello, welcome. What can I do for you?"

[0948] Next, the user inputs a question or request into the device. For example, they might input, "Can you tell me more about this product?" At this time, the emotion engine recognizes emotions from the user's facial expressions, voice tone, or text input. The emotion engine provides this emotion data to the generator, which then generates a response that takes the user's emotions into account. For example, if the user is a little angry, the generator will generate a soft and polite response such as, "I'm sorry. I'll get back to you with more details about the product right away."

[0949] The server takes the generated response and sends it to the terminal. The terminal displays this response to the user and repeats this process until the user is satisfied. If the user types "goodbye", the terminal displays a closing greeting message "Thank you for using our service. Have a nice day" and ends the interaction.

[0950] As a concrete example, consider a case where a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion. In this case, the emotion engine recognizes dissatisfaction from the user's facial expression and tone of voice and provides that information to the generator. The generator then generates a response such as, "We currently have plenty of this item in stock. We apologize for any inconvenience," and the device displays it to the user.

[0951] In this way, the system of the present invention can provide a better user experience by generating appropriate responses while taking into account the user's emotions.

[0952] The processing flow will be explained below.

[0953] Step 1:

[0954] The server loads the database that stores customer service skill data. It calls the load_skillset function and stores basic customer service messages and response patterns in memory in dictionary format.

[0955] Step 2:

[0956] The terminal extracts the initial greeting message from the loaded skill data and displays it to the user. Specifically, it displays the message "Hello, welcome. How can I help you?" defined in skillset['greeting'] on the screen.

[0957] Step 3:

[0958] The user enters a question or request into the terminal as text, for example, "Please tell me more about this product."

[0959] Step 4:

[0960] The device receives input from the user and sends the data to the emotion engine, which analyzes the user's input text, facial expressions, tone of voice, etc. to recognize the user's emotions.

[0961] Step 5:

[0962] The emotion engine identifies the user's emotion and provides emotion data to the generator. For example, if the user speaks in a frustrated tone, it passes the frustrated emotion data to the generator.

[0963] Step 6:

[0964] The server uses a generator to generate a response message incorporating emotion data. The generate_response function is called, and a generative AI model (e.g., ChatGPT) is used to generate a response such as, "This product is the latest model and has many features. Thank you for your question."

[0965] Step 7:

[0966] The server retrieves the generated response and sends it to the terminal, which formats the generated response message appropriately and provides it to the terminal.

[0967] Step 8:

[0968] The terminal displays the response message sent from the server to the user, for example, "This product is the latest model and has many features. Thank you for your question."

[0969] Step 9:

[0970] The user enters additional questions or requests. This process repeats until the user enters "goodbye." For each entry, steps 4 through 8 above are repeated.

[0971] Step 10:

[0972] The terminal detects that the user has typed "Goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day.", as defined in skillset['farewell'], and ends the conversation.

[0973] Through this series of processes, the system can provide consistent, high-quality customer service that takes user emotions into consideration.

[0974] Example 2

[0975] 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."

[0976] Conventional customer service systems have difficulty providing responses that take the user's emotions into account, and have been unable to sufficiently increase user satisfaction. Furthermore, they have limited ability to generate appropriate responses to initial greetings and general inquiries, making it difficult to provide a consistent level of customer service. Furthermore, they lack a mechanism for smoothly ending a dialogue when the user inputs an end command.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0978] In this invention, the server includes customer service robot means equipped with a generative artificial intelligence model, database means for storing customer service skill data, generator means for receiving user input and generating an appropriate response based on the input, display means for displaying or audibly communicating the generated response to the user, emotion recognition device means for recognizing the user's emotions, generator means for generating a response based on emotion data provided by the emotion recognition device, and server means for transmitting response data to the terminal.

[0979] This allows the system to automatically generate and provide appropriate responses while taking the user's feelings into consideration, enabling higher quality customer service. It also makes it possible to smoothly end the dialogue when the user inputs an instruction to end the dialogue.

[0980] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that automatically generates appropriate responses and content based on input data.

[0981] A "customer service robot" refers to a mechanical device equipped with a generative artificial intelligence model that can automatically interact with users.

[0982] "Database" refers to an information system for storing and managing customer service skill data in a structured format.

[0983] "Generator" refers to a device or software that generates an appropriate response based on user input data and emotional data.

[0984] "Indicator" refers to a device that visually or audibly communicates the generated response to the user.

[0985] An "emotion recognition device" refers to a device that analyzes and recognizes emotions from a user's facial expressions and voice.

[0986] "Server" refers to a central computing device that manages data and controls communication between the customer service robot, database, generator, display, and emotion recognition device.

[0987] "User" refers to a person who uses the system to receive customer service.

[0988] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion recognition device that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion recognition device.

[0989] The server first loads a database containing customer service skill data. The database contains initial greetings and responses to common customer inquiries. Specifically, the server establishes a database connection and loads the customer service skill dataset into memory. For example, using a MySQL database, it executes the query SELECT FROM skill_data;

[0990] Next, the terminal displays an initial greeting message to the user based on the loaded skill data. For example, the terminal can display a message such as "Hello, welcome. How can I help you?" The terminal communicates this message visually and audibly through the display and speaker.

[0991] A user uses the terminal to enter a question or request, such as "Can you tell me more about this product?" This input is captured digitally by the terminal software.

[0992] An emotion recognition device recognizes emotions from a user's facial expressions, voice tone, or text input. Specifically, an emotion recognition device uses a camera or microphone to collect the user's facial expression data and voice data, and analyzes them with a machine learning model (e.g., OpenCV) to estimate emotions.

[0993] The generator uses a generative artificial intelligence model (e.g., GPT-4) to generate an appropriate response based on the emotion data provided by the emotion recognizer. For example, if the user is angry, it generates a soft and polite response such as, "I'm sorry. I'll get back to you with the details of the product right away."

[0994] The server receives the generated response and sends it to the terminal. Specifically, it transfers the generated text data to the terminal via an HTTP request or the like.

[0995] The terminal displays this response to the user, for example by displaying a response message on the display or by communicating it audibly through a speaker.

[0996] This process is repeated until the user is satisfied. Finally, if the user types "goodbye," the terminal displays a closing greeting message: "Thank you for using the service. Have a nice day," and ends the interaction.

[0997] As a specific example, if a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion, the emotion recognition device will recognize dissatisfaction from the user's facial expression and tone of voice and provide that information to the generator. The generator will generate a response saying, "We currently have plenty of stock of this item. We apologize for any inconvenience," and the device will display it to the user.

[0998] Example prompt sentence:

[0999] A user asks "Do you have this item in stock?" and the emotion recognizer recognizes a slightly dissatisfied emotion. Generate an appropriate and polite response for the dissatisfied user.

[1000] This allows the system to generate and provide appropriate responses that take the user's emotions into consideration, resulting in higher quality customer service.

[1001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1002] Step 1:

[1003] The server loads a database containing customer service skill data.

[1004] Input: Database connection information.

[1005] Specifically, the server establishes a database connection and executes the query SELECT FROM skill_data; to load the customer service skills dataset into memory.

[1006] Output: Customer service skill data loaded.

[1007] Step 2:

[1008] Based on the loaded skill data, the terminal displays an initial greeting message to the user from the customer service robot.

[1009] Input: Customer service skill data.

[1010] Specifically, the terminal visually and audibly communicates the message "Hello, welcome. How can I help you?" to the user through the display and speaker.

[1011] Output: An initial greeting message is displayed or spoken to the user.

[1012] Step 3:

[1013] The user inputs a question or request into the terminal.

[1014] Input: User question or request (e.g., "Tell me more about this product").

[1015] Specifically, a user inputs information using a keyboard or touch screen, and the input is captured as digital data.

[1016] Output: The state in which a user's question or request is digitally recorded on a terminal.

[1017] Step 4:

[1018] An emotion recognizer recognizes emotions from a user's facial expression, voice tone, or text input.

[1019] Input: User's facial expression data, voice data, and text input data.

[1020] Specifically, the emotion recognition device collects facial expressions and voices using a camera and microphone, and analyzes them using a machine learning model to estimate emotions.

[1021] Output: The state in which the user's emotion data is provided to the generator.

[1022] Step 5:

[1023] The generator generates a response that takes into account the user's emotions.

[1024] Input: User sentiment data and question or request (e.g., "Tell me more about this product").

[1025] Specifically, the generator sends the emotion data and input data as a prompt to a generative AI model (e.g., GPT-4), which then generates an appropriate response.

[1026] Output: The generated response data.

[1027] Step 6:

[1028] The server takes the generated response and sends it to the terminal.

[1029] Input: The generated response data.

[1030] Specifically, the server transfers the generated response data to the terminal via an HTTP request or the like.

[1031] Output: The response data sent to the device.

[1032] Step 7:

[1033] The terminal displays this response to the user.

[1034] Input: The response data sent (e.g. "Sorry, we'll get back to you with more details about that product shortly").

[1035] Specifically, the terminal displays a response message on the display or transmits a response audibly through the speaker.

[1036] Output: The response message is displayed or spoken to the user.

[1037] Step 8:

[1038] If the user types "goodbye", the dialogue ends.

[1039] Input: User exit instruction ("Goodbye").

[1040] Specifically, the user inputs "goodbye," and the terminal receives this and displays or speaks a closing greeting message, "Thank you for using the service. Have a nice day."

[1041] Output: The conversation has been successfully terminated.

[1042] (Application example 2)

[1043] 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."

[1044] In traditional customer service work, it has been difficult to accurately grasp a customer's emotions and provide appropriate responses accordingly. In brick-and-mortar stores in particular, staff are required to be skilled enough to quickly read a customer's emotions from their facial expressions and tone of voice and respond accordingly. However, in reality, not all staff have this skill, which leads to inconsistencies in the quality of service.

[1045] The identification processing by the identification 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 database means for storing customer service skill data, customer service robot means equipped with a generative artificial intelligence model, generator means for receiving user input and generating an appropriate response based on the input, emotion recognition device means for recognizing the user's emotion in real time using a wearable display device, emotion analysis unit means for transmitting the user's emotion data to the generator, and visual presentation unit means for displaying an emotion-based response on the wearable display device. This enables the customer service robot and staff to grasp customer emotions in real time and automatically generate and display high-quality responses accordingly.

[1046] A "customer service robot equipped with a generative artificial intelligence model" is an artificial intelligence system that has the function of generating responses based on user input during customer service operations and communicating those responses to the user.

[1047] The "database for storing customer service skill data" is a database system for storing and managing skills and response examples required for customer service.

[1048] A "generator that receives user input and generates an appropriate response based on that input" is a device or software that analyzes information provided by a user and generates an appropriate reply or action.

[1049] A "display device that displays or communicates a generated response to a user" is an interface that provides a response generated by a generator to a user visually or audibly.

[1050] An "emotion recognition device that recognizes a user's emotions in real time using a wearable display device" is a device that analyzes a user's facial expressions and voice through a wearable display device and recognizes their emotional state in real time.

[1051] An "emotion analysis unit that communicates user emotion data to the generator" is a device or software that transmits emotion data obtained from an emotion recognition device to the generator for use as part of the response generation process.

[1052] The "visual presentation unit for displaying emotion-based responses on a wearable display device" is a device or software for visually displaying responses generated based on data from the emotion analysis unit on a wearable display.

[1053] The system of the present invention is designed to automate customer service operations in brick-and-mortar stores and provide high-quality, emotionally sensitive customer service. The system is composed of various hardware and software components.

[1054] The main components include a customer service robot equipped with a generative artificial intelligence model, a database that stores customer service skill data, a generator that receives user input and generates an appropriate response, a display that displays or transmits the generated response, an emotion recognition device that recognizes the user's emotions in real time using a wearable display device, and an emotion analysis unit.

[1055] 1. Program processing and use of hardware and software

[1056] The server first loads a database that stores customer service skill data. This database includes examples of initial greetings and responses to general inquiries that are necessary when dealing with users. The customer service robot uses this data to provide an initial greeting to users who visit the store.

[1057] Specifically, the customer service robot displays the message "Hello, welcome. How can I help you?". The terminal then receives input from the user, and the input information is analyzed along with the user's facial expressions and tone of voice through an emotion recognition device. Emotion engine software (e.g., Microsoft Azure Emotion API) is used for this analysis.

[1058] The sentiment analysis unit transmits the analysis results to the generator, which is equipped with a generative AI model (e.g., GPT-4) that generates an optimal response taking into account the user's emotional data. For example, if the user is slightly angry, the generative AI model will generate a response such as, "Sorry. We'll get back to you with more details about the product shortly."

[1059] The generated response is displayed on a wearable display device (e.g., smart glasses) via a visual presentation unit, allowing staff to instantly obtain information for responding to the customer and take appropriate action.

[1060] 2. Examples of concrete examples and prompts

[1061] As a concrete scenario, consider a situation where a customer comes into a store, picks up a product, and gently asks, "Do you have this product in other colors?" The emotion recognition device analyzes the user's facial expression and voice tone to recognize a calm emotional state.

[1062] An example of a prompt for a generative AI model would be:

[1063] Customer's facial expression: Smiling

[1064] Voice tone: gentle

[1065] User Input: "Does this item come in other colors?"

[1066] Prompt for generative artificial intelligence model:

[1067] "A smiling, calm-toned customer asks if this product is available in other colors. Generate an appropriate response."

[1068] Based on this prompt, the generator generates a response, "Of course! We also have red and blue. Which one would you like to try?", and displays it on the wearable display. Staff can respond to customers based on this information, thereby increasing customer satisfaction.

[1069] As described above, the system of the present invention can significantly improve the efficiency of customer service operations in brick-and-mortar stores and improve service quality by automatically generating and displaying customer service responses that take emotions into account in real time.

[1070] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1071] Step 1:

[1072] The server loads a database that stores customer service skill data. The database contains initial greetings and responses to common inquiries that are necessary when interacting with users. The server retrieves the skill data from this database.

[1073] Input: Database connection information

[1074] Output: Customer service skill data

[1075] Action: Read customer service skill data from the database

[1076] Step 2:

[1077] The user receives initial input from the device (e.g., "Hello, welcome. How can I help you?"). The device displays an initial greeting message based on the acquired skill data.

[1078] Input: Customer service skill data

[1079] Output: Show initial greeting message

[1080] Action: The terminal displays a greeting message to the user.

[1081] Step 3:

[1082] The user inputs a question or request into the terminal (e.g., "Please tell me more about this product"), and the terminal sends the input data to the emotion recognition device.

[1083] Input: User input (question or request)

[1084] Output: Send data to emotion recognition device

[1085] Action: The device collects user input and sends it to the emotion recognition device.

[1086] Step 4:

[1087] The emotion recognition device analyzes the user's facial expressions and voice tone to determine their emotional state, and the emotion data is transmitted to the emotion analysis unit.

[1088] Input: User's facial expression, voice tone

[1089] Output: Emotion data

[1090] Operation: The emotion recognition device performs emotion analysis and transmits the results to the emotion analysis unit.

[1091] Step 5:

[1092] The emotion analysis unit sends emotion data to the generator, which is equipped with a generative artificial intelligence model and receives the emotion data and user input as a prompt sentence.

[1093] Input: Emotion data, user input

[1094] Output: prompt statement

[1095] How it works: The emotion analysis unit sends emotion data and user input to the generator.

[1096] Step 6:

[1097] The generator uses a generative artificial intelligence model to generate the optimal response based on the sentiment data (e.g., "Sorry, we'll get back to you with more details about that product shortly").

[1098] Input: prompt statement

[1099] Output: The generated response

[1100] How it works: A generative AI model generates a response based on a prompt.

[1101] Step 7:

[1102] The generated response is displayed on a wearable display (e.g., smart glasses) through a visual presentation unit. The staff member can then view the response and respond appropriately to the customer.

[1103] Input: Generated response

[1104] Output: Display on the screen

[1105] Action: The visual presentation unit displays the response on the wearable display.

[1106] Step 8:

[1107] Staff will then use the information displayed on the wearable display to actually provide the appropriate response to the customer.

[1108] Input: Response shown on the display

[1109] Output: Respond appropriately to the customer

[1110] Action: Staff responds to customers based on the information displayed

[1111] This is the process flow of this system, which allows staff to accurately grasp the customer's emotions and provide the most appropriate response.

[1112] 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.

[1113] 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.

[1114] 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.

[1115] [Fourth embodiment]

[1116] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1117] 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.

[1118] 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).

[1119] 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.

[1120] 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.

[1121] 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).

[1122] 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.

[1123] 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.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] 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.

[1128] 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."

[1129] The system of the present invention automates customer service operations and provides high-quality customer service. The system uses a customer service robot equipped with a generative artificial intelligence model and includes a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on the input, and a display that displays or communicates the generated response to the user. The system also automatically displays an initial greeting message based on the defined customer service skill data, and upon receiving input from the user, the generative artificial intelligence model generates a response based on the input.

[1130] Specifically, the system operates in the following manner.

[1131] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings to customers and responses to general inquiries. Then, the terminal displays an initial greeting message to the user from the customer service robot. For example, this greeting message is a phrase such as "Hello, welcome. What can I do for you?"

[1132] Next, the user enters a question or request as text, for example, "Tell me more about this product." The device receives this input and forwards it to the server, which passes the input data to the generator, which uses a generative artificial intelligence model to generate an appropriate response.

[1133] The generated response might be something like, "This product is the latest model and has many features." This response is again passed to the terminal, which displays it to the user. If the user types further questions, responses are generated and displayed in a similar manner. This process continues until the user types "goodbye."

[1134] When the system is finished, the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and the interaction ends. This end-to-end process ensures that users receive consistent, high-quality customer service.

[1135] As a specific scenario, consider the case where a user asks, "Do you have this item in stock?" In this case, the server sends the user's question to the generator, which generates a response such as, "We currently have plenty of this item in stock." This response is then communicated to the user via the terminal. Similarly, by sequentially generating and displaying appropriate responses to multiple questions, highly efficient customer service is achieved.

[1136] The processing flow will be explained below.

[1137] Step 1:

[1138] The server loads the database that stores customer service skill data. This is done by the load_skillset function, and stores basic customer service skills and response messages in memory in dictionary format.

[1139] Step 2:

[1140] The device retrieves the initial greeting message from the loaded skills data and displays it to the user. Specifically, it displays the message defined in skillset['greeting'] on the screen.

[1141] Step 3:

[1142] The user inputs a question or request into the terminal, for example, "Please tell me more about this product."

[1143] Step 4:

[1144] The terminal receives input from the user and sends the data to the server, which sends the input data in the appropriate format to be passed to the generator.

[1145] Step 5:

[1146] The server calls the generator and generates a response message using a generative artificial intelligence model based on the user's input data. This is done in the generate_response function, which calls ChatGPT's API to generate an appropriate response.

[1147] Step 6:

[1148] The server takes the generated response and sends it to the device, extracting and reformatting the response text from the response object from the API.

[1149] Step 7:

[1150] The terminal displays the response sent by the server to the user, specifically by using the print function to display the response on the screen.

[1151] Step 8:

[1152] The user enters additional questions or requests, and this process continues until the user enters "goodbye."

[1153] Step 9:

[1154] The terminal detects that the user has typed "goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day," as defined in skillset['farewell'], and ends the conversation.

[1155] This series of processes enables the system to provide users with consistent, high-quality customer service.

[1156] Example 1

[1157] 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."

[1158] There is a demand for automating customer responses and consistently providing high-quality service. However, conventional systems can only generate standard responses, and have the problem of being unable to provide appropriate responses to the diverse questions and requests of users. Furthermore, the flow of dialogue often becomes intermittent, resulting in a poor customer experience. To solve these issues, a more advanced generative model and a flexible response generation system are required.

[1159] 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.

[1160] In this invention, the server includes an automated dialogue device means equipped with a generative model, an information storage means means for storing dialogue technical data, and a response generation means means for receiving user input and generating an appropriate response based on that input. This makes it possible to smoothly generate and provide appropriate responses to a variety of user inputs.

[1161] An "automated dialogue device equipped with a generative model" is a device that automatically dialogues with a user using a generative artificial intelligence model.

[1162] The "information storage means for storing dialogue technique data" refers to a database or other information storage device for storing information and response patterns required for dialogue with users.

[1163] The "response generation means for receiving a user's input and generating an appropriate response based on the input" is a means for receiving input data from a user and generating an appropriate response based on the input.

[1164] A "display means" is a device for visually or audibly presenting or communicating the generated response to a user.

[1165] The "initial guidance message" is the first guidance message displayed by the automated dialogue device when a user accesses the system for the first time.

[1166] A "generative artificial intelligence model" is an artificial intelligence model for text generation and natural language processing.

[1167] This invention is a system that uses an automatic dialogue device equipped with a generative artificial intelligence model to automate customer service and provide consistently high-quality service. This system operates through the cooperation of a server, terminals, and users.

[1168] First, the server loads a database containing customer service skill data. This database stores dialogue technique data, including initial guidance messages and response patterns for common inquiries. The server manages the data using a database management system such as MySQL or PostgreSQL.

[1169] Next, the terminal starts the customer service robot and displays an initial guidance message to the user. For example, the terminal display might say, "Hello, welcome. How can I help you?" This initial guidance message allows the user to smoothly begin interacting with the system.

[1170] The user inputs text using the device interface, for example, by using a keyboard or touch screen to type, "Please tell me more about this product." This is an example of a specific user input.

[1171] The terminal receives the user's input and transfers it to the server. The server passes the received input data to the response generation means, which generates an appropriate response using a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. The generator analyzes the user's prompt and generates an appropriate response. Specific examples of prompts and their responses are shown below.

[1172] User prompt: "Tell me more about this product."

[1173] Generated response: "This product is the latest model and has many features."

[1174] The generated response is sent from the server to the device, which then displays it to the user. For example, a response such as "This product is the latest model and has many features" is displayed on the device's display. If the user enters additional questions, the same process is repeated.

[1175] If the user types "Goodbye," the terminal displays a closing message such as "Thank you for using our service. Have a nice day," and ends the interaction. This end-to-end process ensures that users receive consistent, high-quality customer service.

[1176] This will concretely show how the system operates, how it automates customer responses, and how it provides efficient, high-quality service. This invention makes it possible to generate and smoothly provide appropriate responses to a variety of user inputs.

[1177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1178] Step 1:

[1179] The server loads a database containing customer service skill data.

[1180] Input: The server establishes a database connection based on the configuration file and database settings.

[1181] Processing: The server uses a database management system such as MySQL or PostgreSQL to load the required skill data, specifically by executing an SQL query such as "SELECT FROM skill_data".

[1182] Output: The customer service skill data is loaded into the server's memory.

[1183] Step 2:

[1184] The terminal will start the customer service robot and display an initial guidance message.

[1185] Input: The device detects that the startup screen has finished initializing.

[1186] Processing: The terminal uses the UI component of the customer service robot to display an initial message: "Hello, welcome. How can we help you?"

[1187] Output: An initial message appears on the screen.

[1188] Step 3:

[1189] The user enters a question or request.

[1190] Input: The user uses a keyboard or touchscreen to enter text, for example, "Tell me more about this product."

[1191] Processing: The entered text data is sent to the terminal.

[1192] Output: The text data is saved to the device.

[1193] Step 4:

[1194] The terminal forwards the user's input to the server.

[1195] Input: Text data containing user input is saved on the device.

[1196] Processing: The terminal sends this text data to the server as an HTTP POST request, which includes the user's input data.

[1197] Output: The server receives the user's input data.

[1198] Step 5:

[1199] The server passes the input data to the response generation means.

[1200] Input: The server has received the user's input data.

[1201] Processing: The server analyzes the input data and passes it to the response generation means (generator) as a prompt sentence.

[1202] Output: The data formatted as a prompt sentence is passed to the response generation means.

[1203] Step 6:

[1204] The generator uses a generative artificial intelligence model to generate a response.

[1205] Input: A formatted prompt is input to the generative AI model. For example, "Please tell me more about this product."

[1206] Processing: A generative AI model (e.g., GPT-3) analyzes the prompt and generates a response. Specifically, the model constructs a response based on the input text, taking into account appropriate context.

[1207] Output: The generated response is output from the generator. For example, "This product is the latest model and has many features."

[1208] Step 7:

[1209] The server passes the generated response to the terminal.

[1210] Input: The state in which the response from the generator is being received.

[1211] Processing: The server sends the received response to the terminal as an HTTP response.

[1212] Output: The response is sent to the terminal.

[1213] Step 8:

[1214] The terminal displays the response to the user.

[1215] Input: The state after receiving the response sent from the server.

[1216] Processing: The terminal displays the received response to the user. For example, it displays "This product is the latest model and has many features."

[1217] Output: The response is displayed on the screen.

[1218] Step 9:

[1219] The user enters a further question or enters "goodbye."

[1220] Input: The user enters an additional question or "goodbye."

[1221] Processing: The user provides input, which is sent to the terminal.

[1222] Output: The new input data is saved to the terminal. If there are any additional questions, restart from step 4. If there is a message that says it's finished, go to the next step.

[1223] Step 10:

[1224] The terminal displays a termination message and ends the conversation.

[1225] Input: The state where the user has received the data "Goodbye".

[1226] Processing: The terminal generates a termination message and displays "Thank you for using the service. Have a nice day" on the screen.

[1227] Output: A termination message is displayed on the screen and the interaction ends.

[1228] (Application example 1)

[1229] 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."

[1230] Conventional customer service systems have difficulty providing consistently high-quality service, and in brick-and-mortar stores in particular, they have relied heavily on the skills of store staff. There is also a risk of customer service quality declining due to staff inexperience or overwork. Furthermore, while quick and accurate responses to customer questions are required, it is also difficult to provide real-time inventory status and product information. A system that can solve these issues and achieve high-quality, efficient customer service is needed.

[1231] 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.

[1232] In this invention, the server includes: [a customer service robot means equipped with a generative artificial intelligence model;] [an information storage area means for storing customer service skill data;] [a response generator means for receiving a user's input and generating an appropriate response based on the input;] [a voice recognition device means for recognizing the user's question and converting it into text; and] [a response display device means displayed on the smart glasses. This makes it possible to quickly and accurately capture a user's question using the smart glasses and provide appropriate responses and inventory information in real time by utilizing the generative AI model.

[1233] A "customer service robot" is a robot equipped with a generative artificial intelligence model that automates customer service tasks and provides high-quality service.

[1234] The "information storage area" refers to a database or storage device for storing customer service skill data.

[1235] A "response generator" is a device or program that generates an appropriate response based on user input data.

[1236] A "display device" is a device or interface for displaying or communicating a generated response to a user.

[1237] A "voice recognition device" is a device that analyzes a user's voice input and converts it into text data.

[1238] "Smart glasses" are wearable devices that have a built-in display and can present information visually.

[1239] The "server" is a system that manages computing resources such as databases and generative artificial intelligence models, and controls the entire system in cooperation with customer service robots.

[1240] A system for carrying out the present invention automates customer service operations and provides high-quality customer service. A specific embodiment of this system will be described below.

[1241] System Configuration

[1242] The system includes the following main measures:

[1243] 1. Customer service robot equipped with a generative artificial intelligence model

[1244] 2. Information storage area for storing customer service skill data

[1245] 3. A response generator that takes user input and generates an appropriate response based on that input.

[1246] 4. A voice recognition device that recognizes the user's voice and converts it into text

[1247] 5. Response display device displayed on smart glasses

[1248] Hardware and software used

[1249] Hardware

[1250] Smart glasses: Examples include Google Glass and Vuzix Blade, which allow store associates to visually view information through the glasses.

[1251] Voice recognition device: A microphone built into smart glasses or an external microphone that captures the user's voice and collects it as voice data.

[1252] Customer service robots: Doll-like and other forms of robots equipped with generative AI models.

[1253] software

[1254] Speech recognition library: Use SpeechRecognition or Google Speech-to-Text API to convert voice data into text data.

[1255] Generative artificial intelligence models, such as OpenAI's GPT-3, generate responses based on text data.

[1256] Database: An information storage area that stores customer information, inventory information, etc. and can be referenced in real time.

[1257] System Operation

[1258] Initial Data Load

[1259] The server loads the necessary data from an information storage area that stores customer service skill data, including initial greeting messages to customers and responses to common inquiries.

[1260] Voice Recognition

[1261] The user (store clerk) uses a voice recognition device to capture the speech of the user (customer) through smart glasses. The speech data is converted into text data using a voice recognition library such as SpeechRecognition.

[1262] Text analysis and response generation

[1263] The server then passes the converted text data to a generative AI model, which uses a response generator to generate an appropriate response. OpenAI's GPT-3 is used as the generative AI model, and a prompt tailored to the user's question is created.

[1264] Response Display

[1265] The generated response is displayed on the smart glasses' display, and the store clerk visually confirms the response and relays it to the customer.

[1266] Specific examples

[1267] For example, if a customer asks, "Do you have this jacket in stock?", the speech recognition device converts this speech into text and passes it to the generative AI model, which then generates a response such as, "We currently have plenty of this jacket in stock." This response is displayed on the smart glasses' display, where a store clerk can confirm it and relay it to the customer.

[1268] Prompt Sentence Examples

[1269] If the user input is "Do you have this jacket in stock?", the prompt sentence might look like this:

[1270] plain

[1271] Customer: Do you have this jacket in stock?

[1272] Customer service robots:

[1273] In this way, the system can quickly and accurately capture user questions via the smart glasses and leverage generative AI models to provide appropriate responses and inventory information in real time.

[1274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1275] Step 1:

[1276] The server loads initial greeting messages and response data to inquiries from an information storage area that stores customer service skill data. This data is used in subsequent customer service operations.

[1277] Input: Reference to the information storage area where customer service skill data is stored

[1278] Output: Loaded customer service skills data

[1279] Specific operation: The server connects to the information storage area and retrieves the customer service skill data required for customer service, including initial greeting messages and standard responses to common questions.

[1280] Step 2:

[1281] The user (store clerk) puts on the smart glasses and gets ready. The smart glasses detect when the user (customer) enters the store.

[1282] Input: None (Staff ready)

[1283] Output: Ready state

[1284] Specific operation: The store clerk activates the smart glasses and checks their readiness. The smart glasses' sensors also detect the customer's presence and complete the necessary initial setup.

[1285] Step 3:

[1286] A voice recognition device installed in the smart glasses captures the user's (customer's) voice input and collects it as voice data.

[1287] Input: User's voice

[1288] Output: Audio data

[1289] Specific operation: The microphone in the smart glasses captures the user's voice and records it as audio data.

[1290] Step 4:

[1291] A speech recognizer converts the audio data into text data, which is done by a speech recognition library such as SpeechRecognition or the Google Speech-to-Text API.

[1292] Input: Audio data

[1293] Output: Text data

[1294] Specific operation: The speech recognition device analyzes the speech data in detail and converts the content into corresponding text data. High-precision text conversion is performed using a speech recognition library.

[1295] Step 5:

[1296] The server receives the converted text data and passes it to a generative AI model, which generates a prompt sentence and inputs it to a response generator.

[1297] Input: Text data

[1298] Output: prompt statement

[1299] Specific operation: The server analyzes the text data and generates a prompt suitable for the generative AI model. For example, if the prompt is "Customer: Do you have this jacket in stock?", the server prepares a prompt that includes the part "Customer Robot:."

[1300] Step 6:

[1301] The generative AI model generates an appropriate response based on the prompt, and the generated result is sent to the server as text data.

[1302] Input: prompt statement

[1303] Output: Response data

[1304] What it does: A generative AI model (e.g., GPT-3) receives a prompt and generates a response that matches it, such as, "We currently have plenty of this jacket in stock."

[1305] Step 7:

[1306] The server transmits the generated response data to the smart glasses, which then transfers the data to a display device for visual display to the store clerk.

[1307] Input: Response data

[1308] Output: The response displayed

[1309] Specific operation: The server receives the response data from the generative AI model and sends it to the smart glasses to be displayed on the screen. The store clerk checks the displayed response and communicates it appropriately to the customer.

[1310] 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.

[1311] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion engine that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion engine.

[1312] Specifically, the system operates in the following manner.

[1313] First, the server loads a database containing customer service skill data. The customer service skill data includes initial greetings and responses to general inquiries required when interacting with users. Next, the terminal uses the loaded skill data to have the customer service robot display an initial greeting message to the user. For example, the phrase might be, "Hello, welcome. What can I do for you?"

[1314] Next, the user inputs a question or request into the device. For example, they might input, "Can you tell me more about this product?" At this time, the emotion engine recognizes emotions from the user's facial expressions, voice tone, or text input. The emotion engine provides this emotion data to the generator, which then generates a response that takes the user's emotions into account. For example, if the user is a little angry, the generator will generate a soft and polite response such as, "I'm sorry. I'll get back to you with more details about the product right away."

[1315] The server takes the generated response and sends it to the terminal. The terminal displays this response to the user and repeats this process until the user is satisfied. If the user types "goodbye", the terminal displays a closing greeting message "Thank you for using our service. Have a nice day" and ends the interaction.

[1316] As a concrete example, consider a case where a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion. In this case, the emotion engine recognizes dissatisfaction from the user's facial expression and tone of voice and provides that information to the generator. The generator then generates a response such as, "We currently have plenty of this item in stock. We apologize for any inconvenience," and the device displays it to the user.

[1317] In this way, the system of the present invention can provide a better user experience by generating appropriate responses while taking into account the user's emotions.

[1318] The processing flow will be explained below.

[1319] Step 1:

[1320] The server loads the database that stores customer service skill data. It calls the load_skillset function and stores basic customer service messages and response patterns in memory in dictionary format.

[1321] Step 2:

[1322] The terminal extracts the initial greeting message from the loaded skill data and displays it to the user. Specifically, it displays the message "Hello, welcome. How can I help you?" defined in skillset['greeting'] on the screen.

[1323] Step 3:

[1324] The user enters a question or request into the terminal as text, for example, "Please tell me more about this product."

[1325] Step 4:

[1326] The device receives input from the user and sends the data to the emotion engine, which analyzes the user's input text, facial expressions, tone of voice, etc. to recognize the user's emotions.

[1327] Step 5:

[1328] The emotion engine identifies the user's emotion and provides emotion data to the generator. For example, if the user speaks in a frustrated tone, it passes the frustrated emotion data to the generator.

[1329] Step 6:

[1330] The server uses a generator to generate a response message incorporating emotion data. The generate_response function is called, and a generative AI model (e.g., ChatGPT) is used to generate a response such as, "This product is the latest model and has many features. Thank you for your question."

[1331] Step 7:

[1332] The server retrieves the generated response and sends it to the terminal, which formats the generated response message appropriately and provides it to the terminal.

[1333] Step 8:

[1334] The terminal displays the response message sent from the server to the user, for example, "This product is the latest model and has many features. Thank you for your question."

[1335] Step 9:

[1336] The user enters additional questions or requests. This process repeats until the user enters "goodbye." For each entry, steps 4 through 8 above are repeated.

[1337] Step 10:

[1338] The terminal detects that the user has typed "Goodbye" and displays a closing greeting message: "Thank you for using the service. Have a nice day.", as defined in skillset['farewell'], and ends the conversation.

[1339] Through this series of processes, the system can provide consistent, high-quality customer service that takes user emotions into consideration.

[1340] Example 2

[1341] 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."

[1342] Conventional customer service systems have difficulty providing responses that take the user's emotions into account, and have been unable to sufficiently increase user satisfaction. Furthermore, they have limited ability to generate appropriate responses to initial greetings and general inquiries, making it difficult to provide a consistent level of customer service. Furthermore, they lack a mechanism for smoothly ending a dialogue when the user inputs an end command.

[1343] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1344] In this invention, the server includes customer service robot means equipped with a generative artificial intelligence model, database means for storing customer service skill data, generator means for receiving user input and generating an appropriate response based on the input, display means for displaying or audibly communicating the generated response to the user, emotion recognition device means for recognizing the user's emotions, generator means for generating a response based on emotion data provided by the emotion recognition device, and server means for transmitting response data to the terminal.

[1345] This allows the system to automatically generate and provide appropriate responses while taking the user's feelings into consideration, enabling higher quality customer service. It also makes it possible to smoothly end the dialogue when the user inputs an instruction to end the dialogue.

[1346] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that automatically generates appropriate responses and content based on input data.

[1347] A "customer service robot" refers to a mechanical device equipped with a generative artificial intelligence model that can automatically interact with users.

[1348] "Database" refers to an information system for storing and managing customer service skill data in a structured format.

[1349] "Generator" refers to a device or software that generates an appropriate response based on user input data and emotional data.

[1350] "Indicator" refers to a device that visually or audibly communicates the generated response to the user.

[1351] An "emotion recognition device" refers to a device that analyzes and recognizes emotions from a user's facial expressions and voice.

[1352] "Server" refers to a central computing device that manages data and controls communication between the customer service robot, database, generator, display, and emotion recognition device.

[1353] "User" refers to a person who uses the system to receive customer service.

[1354] The system of the present invention automates customer service operations and provides high-quality customer service that takes user emotions into consideration. This system uses a customer service robot equipped with a generative artificial intelligence model and an emotion recognition device that recognizes user emotions. The main components of the system are a database that stores customer service skill data, a generator that receives user input and generates an appropriate response based on that input, a display that displays or communicates the generated response to the user, and the emotion recognition device.

[1355] The server first loads a database containing customer service skill data. The database contains initial greetings and responses to common customer inquiries. Specifically, the server establishes a database connection and loads the customer service skill dataset into memory. For example, using a MySQL database, it executes the query SELECT FROM skill_data;

[1356] Next, the terminal displays an initial greeting message to the user based on the loaded skill data. For example, the terminal can display a message such as "Hello, welcome. How can I help you?" The terminal communicates this message visually and audibly through the display and speaker.

[1357] A user uses the terminal to enter a question or request, such as "Can you tell me more about this product?" This input is captured digitally by the terminal software.

[1358] An emotion recognition device recognizes emotions from a user's facial expressions, voice tone, or text input. Specifically, an emotion recognition device uses a camera or microphone to collect the user's facial expression data and voice data, and analyzes them with a machine learning model (e.g., OpenCV) to estimate emotions.

[1359] The generator uses a generative artificial intelligence model (e.g., GPT-4) to generate an appropriate response based on the emotion data provided by the emotion recognizer. For example, if the user is angry, it generates a soft and polite response such as, "I'm sorry. I'll get back to you with the details of the product right away."

[1360] The server receives the generated response and sends it to the terminal. Specifically, it transfers the generated text data to the terminal via an HTTP request or the like.

[1361] The terminal displays this response to the user, for example by displaying a response message on the display or by communicating it audibly through a speaker.

[1362] This process is repeated until the user is satisfied. Finally, if the user types "goodbye," the terminal displays a closing greeting message: "Thank you for using the service. Have a nice day," and ends the interaction.

[1363] As a specific example, if a user asks, "Do you have this item in stock?" and expresses a slightly dissatisfied emotion, the emotion recognition device will recognize dissatisfaction from the user's facial expression and tone of voice and provide that information to the generator. The generator will generate a response saying, "We currently have plenty of stock of this item. We apologize for any inconvenience," and the device will display it to the user.

[1364] Example prompt sentence:

[1365] A user asks "Do you have this item in stock?" and the emotion recognizer recognizes a slightly dissatisfied emotion. Generate an appropriate and polite response for the dissatisfied user.

[1366] This allows the system to generate and provide appropriate responses that take the user's emotions into consideration, resulting in higher quality customer service.

[1367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1368] Step 1:

[1369] The server loads a database containing customer service skill data.

[1370] Input: Database connection information.

[1371] Specifically, the server establishes a database connection and executes the query SELECT FROM skill_data; to load the customer service skills dataset into memory.

[1372] Output: Customer service skill data loaded.

[1373] Step 2:

[1374] Based on the loaded skill data, the terminal displays an initial greeting message to the user from the customer service robot.

[1375] Input: Customer service skill data.

[1376] Specifically, the terminal visually and audibly communicates the message "Hello, welcome. How can I help you?" to the user through the display and speaker.

[1377] Output: An initial greeting message is displayed or spoken to the user.

[1378] Step 3:

[1379] The user inputs a question or request into the terminal.

[1380] Input: User question or request (e.g., "Tell me more about this product").

[1381] Specifically, a user inputs information using a keyboard or touch screen, and the input is captured as digital data.

[1382] Output: The state in which a user's question or request is digitally recorded on a terminal.

[1383] Step 4:

[1384] An emotion recognizer recognizes emotions from a user's facial expression, voice tone, or text input.

[1385] Input: User's facial expression data, voice data, and text input data.

[1386] Specifically, the emotion recognition device collects facial expressions and voices using a camera and microphone, and analyzes them using a machine learning model to estimate emotions.

[1387] Output: The state in which the user's emotion data is provided to the generator.

[1388] Step 5:

[1389] The generator generates a response that takes into account the user's emotions.

[1390] Input: User sentiment data and question or request (e.g., "Tell me more about this product").

[1391] Specifically, the generator sends the emotion data and input data as a prompt to a generative AI model (e.g., GPT-4), which then generates an appropriate response.

[1392] Output: The generated response data.

[1393] Step 6:

[1394] The server takes the generated response and sends it to the terminal.

[1395] Input: The generated response data.

[1396] Specifically, the server transfers the generated response data to the terminal via an HTTP request or the like.

[1397] Output: The response data sent to the device.

[1398] Step 7:

[1399] The terminal displays this response to the user.

[1400] Input: The response data sent (e.g. "Sorry, we'll get back to you with more details about that product shortly").

[1401] Specifically, the terminal displays a response message on the display or transmits a response audibly through the speaker.

[1402] Output: The response message is displayed or spoken to the user.

[1403] Step 8:

[1404] If the user types "goodbye", the dialogue ends.

[1405] Input: User exit instruction ("Goodbye").

[1406] Specifically, the user inputs "goodbye," and the terminal receives this and displays or speaks a closing greeting message, "Thank you for using the service. Have a nice day."

[1407] Output: The conversation has been successfully terminated.

[1408] (Application example 2)

[1409] 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."

[1410] In traditional customer service work, it has been difficult to accurately grasp a customer's emotions and provide appropriate responses accordingly. In brick-and-mortar stores in particular, staff are required to be skilled enough to quickly read a customer's emotions from their facial expressions and tone of voice and respond accordingly. However, in reality, not all staff have this skill, which leads to inconsistencies in the quality of service.

[1411] The identification processing by the identification 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 database means for storing customer service skill data, customer service robot means equipped with a generative artificial intelligence model, generator means for receiving user input and generating an appropriate response based on the input, emotion recognition device means for recognizing the user's emotion in real time using a wearable display device, emotion analysis unit means for transmitting the user's emotion data to the generator, and visual presentation unit means for displaying an emotion-based response on the wearable display device. This enables the customer service robot and staff to grasp customer emotions in real time and automatically generate and display high-quality responses accordingly.

[1412] A "customer service robot equipped with a generative artificial intelligence model" is an artificial intelligence system that has the function of generating responses based on user input during customer service operations and communicating those responses to the user.

[1413] The "database for storing customer service skill data" is a database system for storing and managing skills and response examples required for customer service.

[1414] A "generator that receives user input and generates an appropriate response based on that input" is a device or software that analyzes information provided by a user and generates an appropriate reply or action.

[1415] A "display device that displays or communicates a generated response to a user" is an interface that provides a response generated by a generator to a user visually or audibly.

[1416] An "emotion recognition device that recognizes a user's emotions in real time using a wearable display device" is a device that analyzes a user's facial expressions and voice through a wearable display device and recognizes their emotional state in real time.

[1417] An "emotion analysis unit that communicates user emotion data to the generator" is a device or software that transmits emotion data obtained from an emotion recognition device to the generator for use as part of the response generation process.

[1418] The "visual presentation unit for displaying emotion-based responses on a wearable display device" is a device or software for visually displaying responses generated based on data from the emotion analysis unit on a wearable display.

[1419] The system of the present invention is designed to automate customer service operations in brick-and-mortar stores and provide high-quality, emotionally sensitive customer service. The system is composed of various hardware and software components.

[1420] The main components include a customer service robot equipped with a generative artificial intelligence model, a database that stores customer service skill data, a generator that receives user input and generates an appropriate response, a display that displays or transmits the generated response, an emotion recognition device that recognizes the user's emotions in real time using a wearable display device, and an emotion analysis unit.

[1421] 1. Program processing and use of hardware and software

[1422] The server first loads a database that stores customer service skill data. This database includes examples of initial greetings and responses to general inquiries that are necessary when dealing with users. The customer service robot uses this data to provide an initial greeting to users who visit the store.

[1423] Specifically, the customer service robot displays the message "Hello, welcome. How can I help you?". The terminal then receives input from the user, and the input information is analyzed along with the user's facial expressions and tone of voice through an emotion recognition device. Emotion engine software (e.g., Microsoft Azure Emotion API) is used for this analysis.

[1424] The sentiment analysis unit transmits the analysis results to the generator, which is equipped with a generative AI model (e.g., GPT-4) that generates an optimal response taking into account the user's emotional data. For example, if the user is slightly angry, the generative AI model will generate a response such as, "Sorry. We'll get back to you with more details about the product shortly."

[1425] The generated response is displayed on a wearable display device (e.g., smart glasses) via a visual presentation unit, allowing staff to instantly obtain information for responding to the customer and take appropriate action.

[1426] 2. Examples of concrete examples and prompts

[1427] As a concrete scenario, consider a situation where a customer comes into a store, picks up a product, and gently asks, "Do you have this product in other colors?" The emotion recognition device analyzes the user's facial expression and voice tone to recognize a calm emotional state.

[1428] An example of a prompt for a generative AI model would be:

[1429] Customer's facial expression: Smiling

[1430] Voice tone: gentle

[1431] User Input: "Does this item come in other colors?"

[1432] Prompt for generative artificial intelligence model:

[1433] "A smiling, calm-toned customer asks if this product is available in other colors. Generate an appropriate response."

[1434] Based on this prompt, the generator generates a response, "Of course! We also have red and blue. Which one would you like to try?", and displays it on the wearable display. Staff can respond to customers based on this information, thereby increasing customer satisfaction.

[1435] As described above, the system of the present invention can significantly improve the efficiency of customer service operations in brick-and-mortar stores and improve service quality by automatically generating and displaying customer service responses that take emotions into account in real time.

[1436] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1437] Step 1:

[1438] The server loads a database that stores customer service skill data. The database contains initial greetings and responses to common inquiries that are necessary when interacting with users. The server retrieves the skill data from this database.

[1439] Input: Database connection information

[1440] Output: Customer service skill data

[1441] Action: Read customer service skill data from the database

[1442] Step 2:

[1443] The user receives initial input from the device (e.g., "Hello, welcome. How can I help you?"). The device displays an initial greeting message based on the acquired skill data.

[1444] Input: Customer service skill data

[1445] Output: Show initial greeting message

[1446] Action: The terminal displays a greeting message to the user.

[1447] Step 3:

[1448] The user inputs a question or request into the terminal (e.g., "Please tell me more about this product"), and the terminal sends the input data to the emotion recognition device.

[1449] Input: User input (question or request)

[1450] Output: Send data to emotion recognition device

[1451] Action: The device collects user input and sends it to the emotion recognition device.

[1452] Step 4:

[1453] The emotion recognition device analyzes the user's facial expressions and voice tone to determine their emotional state, and the emotion data is transmitted to the emotion analysis unit.

[1454] Input: User's facial expression, voice tone

[1455] Output: Emotion data

[1456] Operation: The emotion recognition device performs emotion analysis and transmits the results to the emotion analysis unit.

[1457] Step 5:

[1458] The emotion analysis unit sends emotion data to the generator, which is equipped with a generative artificial intelligence model and receives the emotion data and user input as a prompt sentence.

[1459] Input: Emotion data, user input

[1460] Output: prompt statement

[1461] How it works: The emotion analysis unit sends emotion data and user input to the generator.

[1462] Step 6:

[1463] The generator uses a generative artificial intelligence model to generate the optimal response based on the sentiment data (e.g., "Sorry, we'll get back to you with more details about that product shortly").

[1464] Input: prompt statement

[1465] Output: The generated response

[1466] How it works: A generative AI model generates a response based on a prompt.

[1467] Step 7:

[1468] The generated response is displayed on a wearable display (e.g., smart glasses) through a visual presentation unit. The staff member can then view the response and respond appropriately to the customer.

[1469] Input: Generated response

[1470] Output: Display on the screen

[1471] Action: The visual presentation unit displays the response on the wearable display.

[1472] Step 8:

[1473] Staff will then use the information displayed on the wearable display to actually provide the appropriate response to the customer.

[1474] Input: Response shown on the display

[1475] Output: Respond appropriately to the customer

[1476] Action: Staff responds to customers based on the information displayed

[1477] This is the process flow of this system, which allows staff to accurately grasp the customer's emotions and provide the most appropriate response.

[1478] 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.

[1479] 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.

[1480] 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 robot 414.

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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).

[1485] 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.

[1486] 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."

[1487] 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.

[1488] 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).

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] The following is further disclosed regarding the above embodiment.

[1500] (Claim 1)

[1501] [A customer service robot equipped with a generative artificial intelligence model,

[1502] [a database means for storing customer service skill data;

[1503] [a generator means for receiving user input and generating an appropriate response based on the input;

[1504] [indicator means for displaying or communicating the generated response to a user;

[1505] A system including:

[1506] (Claim 2)

[1507] [The system according to claim 1, wherein the customer service robot automatically displays an initial greeting message based on the defined customer service skill data.

[1508] (Claim 3)

[1509] [The system of claim 1, wherein upon receiving input from a user, the generative artificial intelligence model generates a response based on the input.

[1510] "Example 1"

[1511] (Claim 1)

[1512] [An automatic dialogue device means equipped with a generative model;

[1513] [information storage means for storing dialogue technology data;

[1514] [a response generation means for receiving user input and generating an appropriate response based on the input;

[1515] [a display means for presenting or communicating the generated response to a user; and

[1516] A system including:

[1517] (Claim 2)

[1518] The system of claim 1, wherein the automatic dialogue device automatically displays an initial guidance message based on the defined dialogue technique data.

[1519] (Claim 3)

[1520] [The system of claim 1, wherein, upon receiving input from a user, the generative model generates a response based on the input.

[1521] "Application Example 1"

[1522] (Claim 1)

[1523] [A customer service robot equipped with a generative artificial intelligence model,

[1524] [information storage area means for storing customer service skill data;

[1525] [a response generator means for receiving user input and generating an appropriate response based on the input;

[1526] [a display means for displaying or communicating the generated response to a user;

[1527] [a voice recognition device means for recognizing a user's voice and converting it into text;

[1528] [a responsive display means displayed on the smart glasses;

[1529] A system including:

[1530] (Claim 2)

[1531] [The system according to claim 1, wherein the customer service robot automatically displays an initial greeting message based on the defined customer service skill data.

[1532] (Claim 3)

[1533] [The system of claim 1, wherein, upon receiving input from a user, the generative artificial intelligence model generates a response based on the input.

[1534] (Claim 4)

[1535] [The system of claim 1, wherein the smart glasses capture a user's question through a voice recognition device and then convert the voice data into text data.

[1536] (Claim 5)

[1537] [The system of claim 1, wherein the generated response is displayed on a display of the smart glasses, allowing the store clerk to visually confirm it.

[1538] "Example 2: Combining Emotion Engines"

[1539] (Claim 1)

[1540] [A customer service robot equipped with a generative artificial intelligence model,

[1541] [a database means for storing customer service skill data;

[1542] [a generator means for receiving user input and generating an appropriate response based on the input;

[1543] [indicator means for displaying or audibly communicating the generated response to a user;

[1544] [an emotion recognition device means for recognizing an emotion of a user;

[1545] [a generator means for generating a response based on emotion data provided by the emotion recognition device;

[1546] [a server means for transmitting response data to a terminal;

[1547] A system including:

[1548] (Claim 2)

[1549] [The system according to claim 1, wherein the customer service robot automatically displays an initial greeting message based on the defined customer service skill data.

[1550] (Claim 3)

[1551] [The system of claim 1, wherein upon receiving input from a user, the generative artificial intelligence model generates a response based on the input.

[1552] (Claim 4)

[1553] [The system of claim 1 provides responses generated based on the user's emotional data.

[1554] (Claim 5)

[1555] [The system according to claim 1, wherein when the user inputs an instruction to terminate, a greeting message is displayed to terminate the dialogue.

[1556] "Application example 2 when combining emotion engines"

[1557] (Claim 1)

[1558] [A customer service robot equipped with a generative artificial intelligence model,

[1559] [a database means for storing customer service skill data;

[1560] [a generator means for receiving user input and generating an appropriate response based on the input;

[1561] [indicator means for displaying or communicating the generated response to a user;

[1562] [an emotion recognition device means for recognizing the emotion of a user in real time by a wearable display device;

[1563] [an emotion analysis unit means for transmitting user emotion data to the generator;

[1564] a visual presentation unit means for displaying an emotion-based response on a wearable display device;

[1565] A system including:

[1566] (Claim 2)

[1567] [The system according to claim 1, wherein the customer service robot automatically displays an initial greeting message based on the defined customer service skill data.

[1568] (Claim 3)

[1569] [The system of claim 1, wherein upon receiving input from a user, the generative artificial intelligence model generates a response based on the input. [Explanation of symbols]

[1570] 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. A customer service robot equipped with a generative artificial intelligence model, a database means for storing customer service skill data; generator means for receiving user input and generating an appropriate response based on the input; display means for displaying or communicating the generated response to a user; A system including:

2. The system according to claim 1, wherein the customer service robot automatically displays an initial greeting message based on the defined customer service skill data.

3. 10. The system of claim 1, wherein upon receiving an input from a user, the generative artificial intelligence model generates a response based on the input.

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