system

Abstract

We provide the system. [Solution] An interface means for users to enter inquiries, A means of sending the entered query to the server, A means by which the server analyzes the query content and generates an appropriate response, A means for displaying the generated response to the user, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

[0006] A "user" refers to an individual or legal entity that makes an inquiry using the system.

[0007] "Interface means" refers to means such as UI elements and input devices that users use to input inquiries.

[0008] "Transmission means" refers to network communication means used to send user-entered inquiries to the server.

[0009] A "server" refers to a computer system that receives inquiries from users, analyzes them, and generates responses.

[0010] "Analysis means" refers to software or hardware that uses natural language processing techniques to analyze received inquiries and understand their intent.

[0011] "Answer generation means" refers to software or hardware that constructs an appropriate answer based on the analyzed query content.

[0012] "Display means" refers to the means of presenting the generated response to the user using the user's chat screen or other display methods.

[0013] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[0014] A "natural language understanding engine" refers to a software module used as part of natural language processing to analyze the meaning and intent of input text.

[0015] A "database" refers to a system that stores information related to a query and provides the necessary information based on the analysis results.

[0016] "Reliability evaluation means" refers to software or hardware used to evaluate the accuracy and reliability of acquired information. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[0039] First, let's explain the basic configuration of this system. This system includes a terminal that receives user inquiries, a server that analyzes and responds to the received inquiries, and an interface for providing responses based on the analysis results. The terminal provides an interface for users to input inquiries and has the function of sending the input inquiries to the server. The server analyzes the received inquiries, retrieves the necessary information from the database, and generates an appropriate response. The generated response is then displayed to the user again via the terminal.

[0040] Program Processing Overview

[0041] The user enters an inquiry.

[0042] The user enters their inquiry about a specific question or problem into the chat interface. For example, the user might type, "I want to check the status of my order."

[0043] The device sends the query to the server.

[0044] The terminal receives user input and sends this input to the server using an HTTP POST request. Here, the terminal is responsible for sending the query content to the server using the communication protocol.

[0045] The server analyzes the query content.

[0046] The server extracts the query content from the HTTP request it receives and passes it to a natural language processing (NLP) engine. The NLP engine analyzes the text and interprets its intent. For example, the server analyzes the text "I want to check the status of my order" and recognizes the intent as "checking the order status."

[0047] The server generates the appropriate answer.

[0048] Based on the analysis results, the server sends a query to the database. Here, it retrieves the necessary data using information such as the user's order ID. After retrieving the order status from the database, the server generates a response such as "Order ID: 123456 has been shipped."

[0049] The device displays the answer.

[0050] The device receives the generated response as an HTTP response and displays it in the user's chat interface. The user can see the response "Order ID: 123456 has been shipped" on the chat screen.

[0051] Specific example

[0052] Example 1: Checking the order status of your product

[0053] The user types "I want to know the status of my order" into the chat.

[0054] The device sends this query to the server.

[0055] The server analyzes the query and retrieves the order status of the product from the database.

[0056] The server generates the response, "The status of order ID: 654321 is preparing for shipment."

[0057] The device displays the answer to the user.

[0058] Example 2: Updating user account information

[0059] The user types "I want to update my account information" in the chat.

[0060] The device sends this query to the server.

[0061] The server analyzes the user's intent and retrieves information that the user can update.

[0062] The server generates a response such as, "You can update your account information using the following link."

[0063] The device displays the answer with a link to the user.

[0064] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[0065] The following describes the processing flow.

[0066] Step 1:

[0067] The user types "I want to check the status of my order" into the chat interface.

[0068] Step 2:

[0069] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[0070] Step 3:

[0071] The server receives an HTTP request and parses the JSON data in the request body to extract the query details.

[0072] Step 4:

[0073] The server passes the extracted text to a natural language processing (NLP) engine for text analysis. For example, the text "I want to check the status of my order" is analyzed as having the intent "Check order status".

[0074] Step 5:

[0075] The server generates queries to retrieve the necessary information from the database based on the analysis results. For example, it might retrieve the status of "Order ID: 123456" based on the user's order ID.

[0076] Step 6:

[0077] The server executes an SQL query against the database to retrieve status information corresponding to the order ID.

[0078] Step 7:

[0079] Based on the information obtained by the server, a response is generated for the user. For example, a response such as "Order ID: 123456 has been shipped" is constructed.

[0080] Step 8:

[0081] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response.

[0082] Step 9:

[0083] The terminal receives an HTTP response from the server and parses the JSON data within the response body.

[0084] Step 10:

[0085] The device displays the analyzed response in the chat interface, and the user confirms the response. The user can see information such as "Order ID: 123456 has been shipped."

[0086] The above outlines the specific processing steps of this system. By executing each step in the correct order, users can obtain quick and accurate answers.

[0087] (Example 1)

[0088] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Traditional customer support systems suffer from inefficiency, as it takes a long time for users to receive appropriate answers after submitting an inquiry. Furthermore, they often fail to accurately analyze user inquiries or quickly retrieve necessary information, resulting in inaccurate and slow responses.

[0090] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0091] In this invention, the server includes means for passing the received query content to a natural language processing engine for analysis, means for sending a query to a database based on the analysis results to obtain the necessary data, and means for generating an appropriate answer based on the obtained data. This makes it possible to provide a quick and accurate answer to the user's inquiry.

[0092] "Interface means" refers to the means by which users enter inquiries, and includes chat interfaces and text input boxes.

[0093] A "communication protocol" is a technology that defines the rules and procedures for sending and receiving data, and includes HTTP and HTTPS.

[0094] A "natural language processing engine" is a system that uses technology to analyze the text of a user's inquiry and understand its intent.

[0095] "Analysis means" refers to a method for extracting query content from received data and passing it to a natural language processing engine for analysis.

[0096] A "database" is a system that systematically organizes and stores information and provides data in response to specified queries.

[0097] "Means of sending queries" refers to the means by which a server sends queries (such as SQL statements) to a database in order to retrieve necessary information.

[0098] "Means for generating responses" refers to the means of creating appropriate responses to provide to users based on acquired data.

[0099] An "HTTP response" is a response message sent by a server to a terminal, and it contains the results of a request made by the user.

[0100] A "terminal" is a device such as a computer or smartphone that a user uses to input an inquiry and display a response from a server.

[0101] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[0102] This system consists of three main elements: firstly, a terminal that receives user inquiries; secondly, a server that analyzes the received inquiries and generates answers; and thirdly, a display means for showing the generated answers to the user.

[0103] terminal

[0104] The terminal provides an interface for users to enter their inquiries. For example, a chat interface or a text input box may be used. The inquiry is initiated when the user types "I would like to check the status of my order" into these interfaces and clicks the submit button.

[0105] server

[0106] The server receives the inquiry sent from the terminal and parses it as an HTTP POST request. Next, the server passes the inquiry content to a natural language processing (NLP) engine (e.g., spaCy, Google® Cloud NLP API) to analyze the text and interpret its intent. For example, if the input is the text "I want to check the status of my order," the NLP engine recognizes the intent as "check order status."

[0107] The server then sends a query to the database based on the analysis results. For example, it might send an SQL query like "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL®, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"), and the server uses this result to generate text such as "The status of order ID: 123456 is Shipped."

[0108] Display the answer

[0109] The generated response is sent from the server to the terminal as an HTTP response. The terminal receives this response and displays it in the user's chat interface. The user can then see on the chat screen that "The status of order ID: 123456 is shipped."

[0110] Specific examples are shown below.

[0111] Example 1: Checking the order status of a product

[0112] 1. The user types "I want to know the status of my order" in the chat.

[0113] 2. The device sends this query to the server.

[0114] 3. The server analyzes the query and retrieves the product order status from the database.

[0115] 4. The server generates a response stating, "The status of order ID: 654321 is preparing for shipment."

[0116] 5. The device displays the answer to the user.

[0117] Example 2: Updating account information

[0118] 1. The user types "I want to update my account information" in the chat.

[0119] 2. The device sends this query to the server.

[0120] 3. The server analyzes the user's intent and retrieves information that the user can update.

[0121] 4. The server generates a response such as, "You can update your account information via the following link."

[0122] 5. The device displays the answer with a link to the user.

[0123] Examples of prompt statements

[0124] Please type "I would like to check the status of my order" in the chat.

[0125] Please type "How do I update my account information?".

[0126] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[0127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0128] Step 1:

[0129] The user enters an inquiry.

[0130] The user enters their questions or concerns into the chat interface. Specifically, they type "I want to check the status of my order" into the text box in the chat interface and click the send button. This input data is captured by the device.

[0131] Step 2:

[0132] The device sends the query to the server.

[0133] The terminal receives input from the user and sends this input to the server as an HTTP POST request. Specifically, the terminal converts the user's chat message into the body of an HTTP request. This request is sent to an endpoint (e.g., https: / / api.example.com / query). The input is the user's inquiry, and the output is the HTTP request.

[0134] Step 3:

[0135] The server analyzes the query content.

[0136] The server extracts the query content from the received HTTP request and passes it to the natural language processing (NLP) engine. Specifically, the server parses the query content from the request body and sends the text "I want to check the status of my order" to the NLP engine (e.g., spaCy, Google Cloud NLP API). The NLP engine parses the text and interprets its intent. The input for this step is the HTTP request body, and the output is the intent of the query.

[0137] Step 4:

[0138] The server generates the appropriate answer.

[0139] The server sends a query to the database based on the analysis results of the NLP engine. Specifically, the server generates an SQL query and sends a command such as "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"). Based on this result, the server generates the text "The status of order ID: 123456 is Shipped." In this step, the input is the intent of the query, and the output is the generated response.

[0140] Step 5:

[0141] The device displays the answer.

[0142] The terminal receives the response sent from the server as an HTTP response and displays it in the user's chat interface. Specifically, the terminal parses the response from the server (in JSON format, etc.) and displays the answer in the chat window. The user can then see on the chat screen that "The status of order ID: 123456 is shipped." The input for this step is the HTTP response from the server, and the output is the displayed answer.

[0143] (Application Example 1)

[0144] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0145] Traditional customer support systems often make it difficult for users to quickly resolve payment-related questions and problems. In particular, complex inquiries such as checking payment history, resolving payment errors, and verifying points require accurate and prompt responses. To address these challenges, an efficient and reliable support system is necessary.

[0146] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0147] In this invention, the server includes means for quickly responding to payment-related inquiries submitted by users, means for retrieving payment-related information from a database, and means for processing queries related to payment transactions. This enables users to efficiently and accurately resolve payment-related questions and problems.

[0148] "Inquiry interface means" refers to a device or software that provides a user interface for a user to input an inquiry.

[0149] "Transmission means" refers to a device or software that includes a communication protocol and means for sending user-entered queries to a server.

[0150] "Analysis means" refers to a device or software that analyzes the content of a query received by a server using natural language processing technology and interprets its intent.

[0151] "Answer generation means" refers to a device or software that generates an appropriate answer based on the analysis results and provides it to the user.

[0152] "Display means" refers to a device or software for displaying the generated response to the user.

[0153] "Payment inquiry response means" refers to a device or software for quickly responding to payment-related inquiries submitted by users.

[0154] "Information acquisition means" refers to a device or software used to acquire necessary information from a database based on the analysis results.

[0155] "Settlement transaction processing means" refers to a device or software for processing queries related to settlement transactions.

[0156] A "natural language understanding engine" is a device or software equipped with natural language processing technology that a server uses to analyze the intent of a query.

[0157] "Reliability evaluation means" refers to a device or software for evaluating the reliability of information acquired by a server.

[0158] "Answer accuracy improvement means" refers to a device or software for improving the accuracy of answers provided to the user based on evaluated information.

[0159] A "natural language processing command" is a command that uses natural language processing technology specifically designed for analyzing payment status and error messages.

[0160] This invention is a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. The system mainly includes a terminal for users to input inquiries, a server for analyzing received inquiries, and an interface means for providing answers based on the analysis results.

[0161] Basic System Configuration

[0162] 1. Terminal:

[0163] It provides a user interface and allows the user to input inquiries.

[0164] The entered query is sent to the server using an HTTP POST request.

[0165] 2. Server:

[0166] The received inquiry is analyzed using natural language processing techniques.

[0167] Based on the analysis results, retrieve the necessary information from the database.

[0168] Generate the appropriate answer and display it to the user again via the device.

[0169] This includes methods for processing queries related to settlement transactions, as well as natural language processing commands.

[0170] Hardware and software to be used

[0171] Server machine: Linux (registered trademark) server or cloud service such as AWS (registered trademark)

[0172] Programming language: Python

[0173] Web framework: Flask

[0174] Natural language processing library: spaCy

[0175] Processing Overview

[0176] 1. The user enters the inquiry:

[0177] The user enters their inquiry about a specific question or problem into the chat interface. For example, they might type, "I want to check the status of my order."

[0178] 2. The device sends the query to the server:

[0179] The terminal receives user input and sends this input to the server.

[0180] 3. The server analyzes the query:

[0181] The server extracts the query content from the received HTTP request and parses the text using spaCy.

[0182] Interpret the intent of the inquiry and recognize, for example, the intent to "check order status."

[0183] 4. The server generates the appropriate answer:

[0184] Based on the analysis results, a query is sent to the database to retrieve the necessary data.

[0185] After retrieving the order status from the database, the system generates a response such as, "Order ID: 123456 has been shipped."

[0186] 5. The device displays the answer:

[0187] The device receives the generated response and displays it in the user's chat interface.

[0188] Specific example

[0189] Example 1:

[0190] The user types "What is the status of order number 123456?" into the chat.

[0191] The device sends this query to the server.

[0192] The server analyzes the query, generates an appropriate response, and provides it to the user.

[0193] Example 2:

[0194] The user types "How do I update my account information?" into the chat.

[0195] The device sends this query to the server.

[0196] The server analyzes the data, generates a link to update the account information, and provides it to the user.

[0197] Example of a prompt

[0198] "A user typed 'What is the status of order number 123456?' into the chat. Please tell me the expected response in JSON format that should be returned as a result of processing the following Python script."

[0199] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0200] Step 1:

[0201] The user enters their inquiry into the chat interface.

[0202] Input: Inquiry text such as "What is the status of order number 123456?"

[0203] Output: The inquiry text is displayed in the input field and saved to the device.

[0204] Step 2:

[0205] The terminal sends the query to the server.

[0206] Input: Inquiry text

[0207] Data processing / data calculation: Convert the query text to JSON format and send it to the server as an HTTP POST request.

[0208] Output: The query text is sent to the server.

[0209] Step 3:

[0210] The server analyzes the received queries using natural language processing techniques.

[0211] Input: Query text included in the HTTP POST request

[0212] Data processing / data calculation: Use the spaCy library to parse query text and interpret its intent (e.g., "Order status confirmation").

[0213] Output: Analysis results are obtained (results of intent recognition).

[0214] Step 4:

[0215] The server retrieves the necessary information from the database based on the analysis results.

[0216] Input: Analysis results (intention recognition results), database query (e.g., "Order ID: 123456")

[0217] Data processing / data calculation: Execute database queries to retrieve necessary information from the database (e.g., "Order ID: 123456, status: Shipped").

[0218] Output: Information retrieved from the database

[0219] Step 5:

[0220] The server generates an appropriate response based on the information it has obtained.

[0221] Input: Information retrieved from the database (e.g., "Shipped")

[0222] Data Processing / Data Calculation: Use the acquired information to generate responses to provide to the user (e.g., "Order ID: 123456 has been shipped").

[0223] Output: Generated answer text

[0224] Step 6:

[0225] The server sends the generated response to the terminal.

[0226] Input: Generated response text

[0227] Data processing / data calculation: Convert the response text to JSON format and send it to the terminal as an HTTP response.

[0228] Output: The answer text is sent to the terminal.

[0229] Step 7:

[0230] The device displays the answer in the user's chat interface.

[0231] Input: Response text sent from the server

[0232] Data Processing / Data Calculation: Analyze response text and format it for display in the user's chat interface.

[0233] Output: A formatted response will be displayed in the chat interface (e.g., "Order ID: 123456 has been shipped").

[0234] This allows users to obtain payment information quickly and accurately.

[0235] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0236] This invention relates to a customer support system that recognizes a user's emotions and provides an appropriate response based on those emotions. The system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[0237] Basic System Configuration

[0238] This system is built around a server that incorporates an emotion engine to recognize user emotions. The main components are as follows:

[0239] 1. User interface means:

[0240] An interface for users to enter inquiries (e.g., a chat window).

[0241] 2. Transmission method:

[0242] A network communication method for sending input queries to a server.

[0243] 3. Server:

[0244] A high-performance server that analyzes inquiries, recognizes user emotions, and generates appropriate responses. Includes an emotion engine and a natural language processing engine.

[0245] 4. Database:

[0246] A database that stores necessary information and allows the server to retrieve that information by executing queries.

[0247] 5. Display means:

[0248] An interface for displaying responses received from the server to the user.

[0249] Program processing

[0250] The user enters an inquiry.

[0251] A user types "I want to check the status of my order" into the chat interface. This input contains both the user's intent and emotion.

[0252] The device sends the query to the server.

[0253] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[0254] The server analyzes the content and sentiment of the inquiry.

[0255] The server receives an HTTP request, parses the JSON data in the request body to extract the inquiry content, and uses an emotion engine to analyze the user's emotions. For example, the text "I want to check the status of my order" is analyzed along with the emotion "anxious".

[0256] The server generates the appropriate answer.

[0257] The server sends a query to the database based on the analysis results. Here, it retrieves the necessary data based on the user's order ID and other information. Based on the retrieved information and the user's sentiment, the server generates a response in an appropriate tone. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured."

[0258] The device displays the answer.

[0259] The server stores the generated response in JSON format and sends it to the terminal as an HTTP response. The terminal receives the HTTP response from the server and parses the JSON data in the response body. The parsed response is displayed in the chat interface. The user can see the response "Order ID: 123456 has been shipped. Please rest assured." on the chat screen.

[0260] Specific example

[0261] Example 1: Checking the order status of your product

[0262] The user anxiously types in the chat, "I want to know the status of my order."

[0263] The device sends this query to the server.

[0264] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[0265] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[0266] The device displays the answer to the user.

[0267] Example 2: Updating user account information

[0268] The user calmly types "I want to update my account information" into the chat.

[0269] The device sends this query to the server.

[0270] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[0271] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[0272] The device displays the answer with a link to the user.

[0273] This system allows users to quickly receive appropriate responses that take their emotions into consideration. Furthermore, by combining an emotion engine with natural language processing technology, the server can significantly improve user satisfaction.

[0274] The following describes the processing flow.

[0275] Step 1:

[0276] The user enters "want to check the order status" into the chat interface. The input content is sent to the terminal in text format.

[0277] Step 2:

[0278] The terminal receives the entered text and converts the query data into JSON format. This data includes the user's text and other relevant metadata.

[0279] Step 3:

[0280] The terminal sends the JSON-formatted query data to the server as an HTTP POST request. The terminal accurately transfers the data via the network.

[0281] Step 4:

[0282] The server receives the HTTP request and extracts the JSON data from the request body. Initial processing is performed to analyze the query content and metadata.

[0283] Step 5:

[0284] The server passes the query content to a natural language processing (NLP) engine to analyze the intent of the query. For example, the text "want to check the order status" is determined to have the intent of "order status check".

[0285] Step 6:

[0286] The server simultaneously passes the query text to an emotion engine to analyze the user's emotion. For example, the emotion of "anxious" is detected from the text.

[0287] Step 7:

[0288] The server generates appropriate queries for the database based on the analyzed intent and emotions. The server searches for the order status based on the user's order ID.

[0289] Step 8:

[0290] The server sends an SQL query to the database to retrieve the necessary order information. The information retrieved from the database includes the order ID and its current status.

[0291] Step 9:

[0292] The server generates a response based on the order information it retrieves and the results of sentiment analysis. For example, a response such as "Order ID: 123456 has been shipped. Please rest assured." might be generated.

[0293] Step 10:

[0294] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response. Here, the response data includes a message corresponding to the emotion.

[0295] Step 11:

[0296] The terminal receives an HTTP response from the server and extracts JSON data from the response body. The extracted data is then parsed for display to the user.

[0297] Step 12:

[0298] The terminal displays the analysis results in the chat interface. The user can see the response, "Order ID: 123456 has been shipped. Please rest assured."

[0299] The above outlines the specific processing steps in this system. Each step works in conjunction to quickly provide appropriate responses that take the user's emotions into consideration.

[0300] (Example 2)

[0301] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0302] Traditional customer support systems could automatically process user inquiries, but they struggled to provide appropriate answers that took user emotions into account. As a result, users often received uniform responses regardless of their emotional state, leading to decreased satisfaction. In particular, users experiencing anxiety or impatience were not provided with answers that considered their emotions, resulting in insufficient effectiveness of customer support.

[0303] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0304] In this invention, the server includes means for analyzing user inquiries using natural language processing technology, means for recognizing the user's emotions from the inquiries using an emotion analysis engine, means for obtaining necessary information from a database based on the analysis results, and means for generating a response based on the obtained information and the recognized emotions. This makes it possible to provide an appropriate response that takes the user's emotions into consideration.

[0305] A "user" is someone who submits an inquiry and receives services or support.

[0306] An "interface means" is an input / output device used by a user to input inquiries and receive responses.

[0307] The "transmission means" is a communication means for transmitting the input inquiry data to the server.

[0308] The "server" is a computer system for analyzing the inquiry content, recognizing the user's emotion, and generating an appropriate answer.

[0309] The "analysis means" is a function of software or hardware used by the server to analyze the received inquiry.

[0310] The "emotion recognition means" is a technical means for the server to recognize emotion from the user's inquiry.

[0311] The "emotion analysis engine" is software for extracting and identifying emotion from the user's text data.

[0312] The "natural language processing technology" is a technology for a computer to understand and process human language.

[0313] The "database" is an information management system for the server to store and search for necessary information.

[0314] The "information acquisition means" is a technical means for the server to execute a query on the database and acquire necessary information.

[0315] The "answer generation means" is a technical means for the server to generate an answer to be provided to the user based on the analyzed content and the acquired information.

[0316] The "display means" is a device or software for visually providing the generated answer to the user.

[0317] This invention relates to a customer support system that recognizes user emotions and provides appropriate responses. Specific embodiments of the system are described below. This system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[0318] Basic System Configuration

[0319] This system is built around a server that incorporates an emotion analysis engine to recognize user emotions. The main components are as follows:

[0320] 1. User interface means:

[0321] This is an interface for users to enter inquiries, such as a chat window on a web browser. It is built using HTML and JavaScript (registered trademark).

[0322] 2. Transmission method:

[0323] This is a means of sending the input query to the server, such as a network communication method using a RESTful API. JSON data is sent via an HTTP POST request.

[0324] 3. Server:

[0325] This is a high-performance server that analyzes user inquiries, recognizes user sentiment, and generates appropriate responses. The Django framework, written in Python, is commonly used. It includes sentiment engines, natural language processing engines, and database access capabilities.

[0326] 4. Natural Language Processing Engine:

[0327] The engine used to analyze the content of inquiries utilizes tools such as the Google Cloud Natural Language API.

[0328] 5. Emotion Analysis Engine:

[0329] The engine used to recognize user emotions includes IBM Watson® Tone Analyzer, among others.

[0330] 6. Database:

[0331] PostgreSQL is a commonly used database for storing necessary information and allowing servers to retrieve it by executing queries.

[0332] 7. Display means:

[0333] This is an interface for displaying responses received from a server to the user; a chat window is a concrete example.

[0334] Specific example

[0335] Example 1: Checking the order status of your product

[0336] The user anxiously types in the chat, "I want to know the status of my order."

[0337] The device sends this query to the server.

[0338] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[0339] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[0340] The device displays the answer to the user.

[0341] Example 2: Updating user account information

[0342] The user calmly types "I want to update my account information" into the chat.

[0343] The device sends this query to the server.

[0344] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[0345] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[0346] The device displays the answer with a link to the user.

[0347] Through these specific examples, the system can quickly provide appropriate responses that take user emotions into consideration. It is expected that the embodiments of the invention will significantly improve user satisfaction.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The user enters an inquiry.

[0351] The user opens a chat window in their web browser and enters their inquiry. The input is accepted in text format, and specific inquiries such as "I would like to check the status of my order" are entered. The entered text is then sent to the terminal.

[0352] Step 2:

[0353] The device sends the query to the server.

[0354] The terminal receives input from the user and converts it into JSON format. The JSON data includes, for example, the following information:

[0355] json

[0356] {

[0357] "message": "I want to check the status of my order",

[0358] "user_id": "123456"

[0359] }

[0360] The terminal sends this JSON data to the server as an HTTP POST request. The terminal's output is a request in JSON format, which the server receives.

[0361] Step 3:

[0362] The server analyzes the query content.

[0363] The server receives an HTTP POST request and parses the JSON data in the request body. It uses the Python json library to read the data and extract the message and user_id fields. Then, it uses a natural language processing engine (e.g., Google Cloud Natural Language API) to parse the message field. The server's input is JSON data, and its output is the parsing result (e.g., the intent of the information).

[0364] Step 4:

[0365] The server recognizes the user's emotions.

[0366] The server passes the parsed message field to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer). The sentiment analysis engine recognizes the user's emotions and returns sentiment information such as "anxious." The server's input is the parsed text, and its output is the result of the sentiment analysis.

[0367] Step 5:

[0368] The server retrieves the necessary information from the database.

[0369] The server executes an SQL query against the database based on relevant information such as the order ID. This retrieves necessary information, such as the order status. An example query is "SELECT status FROM orders WHERE order_id = '123456';". The server's input is the query condition, and its output is the order status information.

[0370] Step 6:

[0371] The server generates the appropriate answer.

[0372] The server incorporates the acquired information into the response in an appropriate tone, based on the analysis results and sentiment analysis results. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured." The server's input is order information and sentiment information, and its output is the generated response.

[0373] Step 7:

[0374] The server sends the generated response to the terminal.

[0375] The server converts the generated response into a JSON format response and sends it to the terminal as an HTTP response. The JSON data in the response includes the following information:

[0376] json

[0377] {

[0378] "response_message": "Order ID: 123456 has been shipped. Please rest assured."

[0379] }

[0380] The server input is the generated response, and the output is a response in JSON format.

[0381] Step 8:

[0382] The device analyzes and displays the answer.

[0383] The terminal receives an HTTP response from the server and parses the JSON data in the response body using JavaScript. The parsed response message is then displayed in the chat interface. The user can see the message "Order ID: 123456 has been shipped. Please rest assured." in the chat window. The terminal's input is the response from the server, and its output is the text message displayed to the user.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0386] Traditional customer support systems provided uniform answers to inquiries without considering user emotions. This resulted in a failure to adequately address situations where users felt anxious or dissatisfied, leading to decreased satisfaction. A system is needed to solve this problem and provide more personalized support to users.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the content of the inquiry and recognizing the user's emotions, means for generating an appropriate response based on the emotions, and means for displaying the generated response to the user. This makes it possible to quickly provide an appropriate response that corresponds to the user's emotions and improve user satisfaction.

[0388] A "user" is an individual or legal entity that uses the system to make an inquiry.

[0389] "Interface means" refers to a device or software used by a user to input inquiries.

[0390] "Transmission means" refers to network communication means used to send user-entered inquiries to the server.

[0391] A "server" refers to a processing unit that analyzes the content of an inquiry, recognizes the user's emotions, and generates an appropriate response.

[0392] "Means of recognizing emotions" refers to an analysis engine that identifies emotions from the content of user inquiries.

[0393] "Natural language processing technology" refers to the technology used to analyze and understand human language.

[0394] "Means for generating appropriate answers" refers to algorithms and software that generate answers based on analyzed emotions and inquiry content.

[0395] A "database" refers to data storage that stores information related to queries, allowing servers to execute queries and retrieve that information.

[0396] "Means for displaying generated answers" refers to display devices or screen display software that visually show the answers created by the server to the user.

[0397] A "natural language understanding engine" refers to a processing unit equipped with artificial intelligence for analyzing the intent behind user inquiries.

[0398] "Means for evaluating reliability" refers to algorithms used to determine the accuracy and reliability of information acquired by a server.

[0399] "Means of improving the accuracy of responses" refers to algorithms and software that enhance the accuracy and validity of responses provided to users based on evaluated information.

[0400] This invention relates to a system that recognizes a user's emotions and provides an appropriate response based on those emotions. The main components of the system include an interface means for the user to input an inquiry, a transmission means for sending the input inquiry to a server, a means for the server to analyze the inquiry content and recognize the user's emotions, a means for generating an appropriate response based on the emotions, and a means for displaying the generated response to the user.

[0401] Hardware and software to use

[0402] 1. Hardware: Standard servers, smartphones

[0403] 2. Software: Python 3, Flask (web framework), transformers (natural language processing library)

[0404] System processing

[0405] User inquiry input and submission

[0406] Users enter their inquiries through a chat interface on their smartphones. This interface is designed to allow users to easily input text. The entered inquiries are sent to the server as JSON data via an HTTP POST request.

[0407] Server-based analysis and emotion recognition

[0408] The server receives an HTTP request and uses the transformers library to analyze the query content using natural language processing techniques to recognize the user's emotions. For example, the emotion analysis pipeline assigns labels such as "NEGATIVE" and "POSITIVE."

[0409] Answer generation

[0410] The server generates appropriate responses based on the perceived emotions. For example, if the user is feeling anxious, a response such as "Don't worry, we'll check on that right away" will be generated. Conversely, if the user is expressing joy, a response such as "Thank you! How can we help you?" will be generated.

[0411] Display the answer

[0412] The generated response is sent to the smartphone as an HTTP response in JSON format and displayed to the user through the chat interface.

[0413] Specific example

[0414] Example 1: Checking the order status

[0415] User: "I'd like to check the status of my order."

[0416] The server analyzes the emotion as "anxiety" and responds, "Please rest assured. We will check on this immediately."

[0417] Specific example 2: Reporting a product defect

[0418] User: "The item I received was broken."

[0419] The server interprets the emotion as "negative" and responds, "We are very sorry. We will resend the same product."

[0420] Example of a prompt

[0421] User: I'd like to check the status of my order.

[0422] AI: Don't worry. I'll check it right away.

[0423] Data processing and calculations performed by the server

[0424] Data processing: Parse the received inquiry into JSON format.

[0425] Data processing: This involves using natural language processing techniques and sentiment analysis pipelines to identify user emotions and generate responses based on those emotions.

[0426] This enables appropriate responses based on user emotions, leading to improved user satisfaction.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The user enters their inquiry.

[0430] The user enters their question into the smartphone's chat interface. The entered text becomes the input data for processing.

[0431] Step 2:

[0432] The terminal sends the query to the server.

[0433] The input text is converted into JSON data, stored in an HTTP POST request, and sent to the server. This request contains the user's inquiry.

[0434] Step 3:

[0435] The server analyzes the queries it receives.

[0436] The server receives the HTTP request and parses the JSON data in the request body to extract the query content. Specifically, the text data of the query content becomes the input data.

[0437] Step 4:

[0438] The server recognizes emotions.

[0439] The server uses the transformers library to pass the extracted text data to the sentiment analysis pipeline. The sentiment analysis pipeline identifies the user's emotions from the text and outputs sentiment labels such as "POSITIVE" and "NEGATIVE".

[0440] Step 5:

[0441] The server generates the appropriate answer.

[0442] Based on the sentiment label and the content of the inquiry, the server generates an appropriate response. For example, if the sentiment is "NEGATIVE," it will generate a response such as "Please rest assured, we will check on this immediately." The output data is the generated response text.

[0443] Step 6:

[0444] The server stores the answer in a JSON-formatted response.

[0445] The generated response text is converted into JSON data and sent to the terminal as an HTTP response. The response contains the generated response.

[0446] Step 7:

[0447] The device receives and displays the response.

[0448] The terminal receives the HTTP response, parses the JSON data in the response body, and extracts the response text. The extracted response text is then displayed in the chat interface and presented to the user.

[0449] The above steps result in a system that provides an appropriate response instantly, tailored to the user's emotions.

[0450] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0451] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0452] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0453] [Second Embodiment]

[0454] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0455] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0456] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0457] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0458] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0459] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0460] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0461] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0462] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0463] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0464] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0465] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0466] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[0467] First, let's explain the basic configuration of this system. This system includes a terminal that receives user inquiries, a server that analyzes and responds to the received inquiries, and an interface for providing responses based on the analysis results. The terminal provides an interface for users to input inquiries and has the function of sending the input inquiries to the server. The server analyzes the received inquiries, retrieves the necessary information from the database, and generates an appropriate response. The generated response is then displayed to the user again via the terminal.

[0468] Program Processing Overview

[0469] The user enters an inquiry.

[0470] The user enters their inquiry about a specific question or problem into the chat interface. For example, the user might type, "I want to check the status of my order."

[0471] The device sends the query to the server.

[0472] The terminal receives user input and sends this input to the server using an HTTP POST request. Here, the terminal is responsible for sending the query content to the server using the communication protocol.

[0473] The server analyzes the query content.

[0474] The server extracts the query content from the HTTP request it receives and passes it to a natural language processing (NLP) engine. The NLP engine analyzes the text and interprets its intent. For example, the server analyzes the text "I want to check the status of my order" and recognizes the intent as "checking the order status."

[0475] The server generates the appropriate answer.

[0476] Based on the analysis results, the server sends a query to the database. Here, it retrieves the necessary data using information such as the user's order ID. After retrieving the order status from the database, the server generates a response such as "Order ID: 123456 has been shipped."

[0477] The device displays the answer.

[0478] The device receives the generated response as an HTTP response and displays it in the user's chat interface. The user can see the response "Order ID: 123456 has been shipped" on the chat screen.

[0479] Specific example

[0480] Example 1: Checking the order status of your product

[0481] The user types "I want to know the status of my order" into the chat.

[0482] The device sends this query to the server.

[0483] The server analyzes the query and retrieves the order status of the product from the database.

[0484] The server generates the response, "The status of order ID: 654321 is preparing for shipment."

[0485] The device displays the answer to the user.

[0486] Example 2: Updating user account information

[0487] The user types "I want to update my account information" in the chat.

[0488] The device sends this query to the server.

[0489] The server analyzes the user's intent and retrieves information that the user can update.

[0490] The server generates a response such as, "You can update your account information using the following link."

[0491] The device displays the answer with a link to the user.

[0492] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The user types "I want to check the status of my order" into the chat interface.

[0496] Step 2:

[0497] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[0498] Step 3:

[0499] The server receives an HTTP request and parses the JSON data in the request body to extract the query details.

[0500] Step 4:

[0501] The server passes the extracted text to a natural language processing (NLP) engine for text analysis. For example, the text "I want to check the status of my order" is analyzed as having the intent "Check order status".

[0502] Step 5:

[0503] The server generates queries to retrieve the necessary information from the database based on the analysis results. For example, it might retrieve the status of "Order ID: 123456" based on the user's order ID.

[0504] Step 6:

[0505] The server executes an SQL query against the database to retrieve status information corresponding to the order ID.

[0506] Step 7:

[0507] Based on the information obtained by the server, a response is generated for the user. For example, a response such as "Order ID: 123456 has been shipped" is constructed.

[0508] Step 8:

[0509] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response.

[0510] Step 9:

[0511] The terminal receives an HTTP response from the server and parses the JSON data within the response body.

[0512] Step 10:

[0513] The device displays the analyzed response in the chat interface, and the user confirms the response. The user can see information such as "Order ID: 123456 has been shipped."

[0514] The above outlines the specific processing steps of this system. By executing each step in the correct order, users can obtain quick and accurate answers.

[0515] (Example 1)

[0516] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0517] Traditional customer support systems suffer from inefficiency, as it takes a long time for users to receive appropriate answers after submitting an inquiry. Furthermore, they often fail to accurately analyze user inquiries or quickly retrieve necessary information, resulting in inaccurate and slow responses.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0519] In this invention, the server includes means for passing the received query content to a natural language processing engine for analysis, means for sending a query to a database based on the analysis results to obtain the necessary data, and means for generating an appropriate answer based on the obtained data. This makes it possible to provide a quick and accurate answer to the user's inquiry.

[0520] "Interface means" refers to the means by which users enter inquiries, and includes chat interfaces and text input boxes.

[0521] A "communication protocol" is a technology that defines the rules and procedures for sending and receiving data, and includes HTTP and HTTPS.

[0522] A "natural language processing engine" is a system that uses technology to analyze the text of a user's inquiry and understand its intent.

[0523] "Analysis means" refers to a method for extracting query content from received data and passing it to a natural language processing engine for analysis.

[0524] A "database" is a system that systematically organizes and stores information and provides data in response to specified queries.

[0525] "Means of sending queries" refers to the means by which a server sends queries (such as SQL statements) to a database in order to retrieve necessary information.

[0526] "Means for generating responses" refers to the means of creating appropriate responses to provide to users based on acquired data.

[0527] An "HTTP response" is a response message sent by a server to a terminal, and it contains the results of a request made by the user.

[0528] A "terminal" is a device such as a computer or smartphone that a user uses to input an inquiry and display a response from a server.

[0529] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[0530] This system consists of three main elements: firstly, a terminal that receives user inquiries; secondly, a server that analyzes the received inquiries and generates answers; and thirdly, a display means for showing the generated answers to the user.

[0531] terminal

[0532] The terminal provides an interface for users to enter their inquiries. For example, a chat interface or a text input box may be used. The inquiry is initiated when the user types "I would like to check the status of my order" into these interfaces and clicks the submit button.

[0533] server

[0534] The server receives the inquiry sent from the terminal and parses it as an HTTP POST request. Next, the server passes the inquiry content to a natural language processing (NLP) engine (e.g., spaCy, Google Cloud NLP API) to analyze the text and interpret its intent. For example, if the input is the text "I want to check the status of my order," the NLP engine recognizes the intent as "check order status."

[0535] The server then sends a query to the database based on the analysis results. For example, it might send an SQL query like "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"), and the server uses this result to generate text such as "The status of order ID: 123456 is Shipped."

[0536] Display the answer

[0537] The generated response is sent from the server to the terminal as an HTTP response. The terminal receives this response and displays it in the user's chat interface. The user can then see on the chat screen that "The status of order ID: 123456 is shipped."

[0538] Specific examples are shown below.

[0539] Example 1: Checking the order status of a product

[0540] 1. The user types "I want to know the status of my order" in the chat.

[0541] 2. The device sends this query to the server.

[0542] 3. The server analyzes the query and retrieves the product order status from the database.

[0543] 4. The server generates a response stating, "The status of order ID: 654321 is preparing for shipment."

[0544] 5. The device displays the answer to the user.

[0545] Example 2: Updating account information

[0546] 1. The user types "I want to update my account information" in the chat.

[0547] 2. The device sends this query to the server.

[0548] 3. The server analyzes the user's intent and retrieves information that the user can update.

[0549] 4. The server generates a response such as, "You can update your account information via the following link."

[0550] 5. The device displays the answer with a link to the user.

[0551] Examples of prompt statements

[0552] Please type "I would like to check the status of my order" in the chat.

[0553] Please type "How do I update my account information?".

[0554] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[0555] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0556] Step 1:

[0557] The user enters an inquiry.

[0558] The user enters their questions or concerns into the chat interface. Specifically, they type "I want to check the status of my order" into the text box in the chat interface and click the send button. This input data is captured by the device.

[0559] Step 2:

[0560] The device sends the query to the server.

[0561] The terminal receives input from the user and sends this input to the server as an HTTP POST request. Specifically, the terminal converts the user's chat message into the body of an HTTP request. This request is sent to an endpoint (e.g., https: / / api.example.com / query). The input is the user's inquiry, and the output is the HTTP request.

[0562] Step 3:

[0563] The server analyzes the query content.

[0564] The server extracts the query content from the received HTTP request and passes it to the natural language processing (NLP) engine. Specifically, the server parses the query content from the request body and sends the text "I want to check the status of my order" to the NLP engine (e.g., spaCy, Google Cloud NLP API). The NLP engine parses the text and interprets its intent. The input for this step is the HTTP request body, and the output is the intent of the query.

[0565] Step 4:

[0566] The server generates the appropriate answer.

[0567] The server sends a query to the database based on the analysis results of the NLP engine. Specifically, the server generates an SQL query and sends a command such as "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"). Based on this result, the server generates the text "The status of order ID: 123456 is Shipped." In this step, the input is the intent of the query, and the output is the generated response.

[0568] Step 5:

[0569] The device displays the answer.

[0570] The terminal receives the response sent from the server as an HTTP response and displays it in the user's chat interface. Specifically, the terminal parses the response from the server (in JSON format, etc.) and displays the answer in the chat window. The user can then see on the chat screen that "The status of order ID: 123456 is shipped." The input for this step is the HTTP response from the server, and the output is the displayed answer.

[0571] (Application Example 1)

[0572] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0573] Traditional customer support systems often make it difficult for users to quickly resolve payment-related questions and problems. In particular, complex inquiries such as checking payment history, resolving payment errors, and verifying points require accurate and prompt responses. To address these challenges, an efficient and reliable support system is necessary.

[0574] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0575] In this invention, the server includes means for quickly responding to payment-related inquiries submitted by users, means for retrieving payment-related information from a database, and means for processing queries related to payment transactions. This enables users to efficiently and accurately resolve payment-related questions and problems.

[0576] "Inquiry interface means" refers to a device or software that provides a user interface for a user to input an inquiry.

[0577] "Transmission means" refers to a device or software that includes a communication protocol and means for sending user-entered queries to a server.

[0578] "Analysis means" refers to a device or software that analyzes the content of a query received by a server using natural language processing technology and interprets its intent.

[0579] "Answer generation means" refers to a device or software that generates an appropriate answer based on the analysis results and provides it to the user.

[0580] "Display means" refers to a device or software for displaying the generated response to the user.

[0581] "Payment inquiry response means" refers to a device or software for quickly responding to payment-related inquiries submitted by users.

[0582] "Information acquisition means" refers to a device or software used to acquire necessary information from a database based on the analysis results.

[0583] "Settlement transaction processing means" refers to a device or software for processing queries related to settlement transactions.

[0584] A "natural language understanding engine" is a device or software equipped with natural language processing technology that a server uses to analyze the intent of a query.

[0585] "Reliability evaluation means" refers to a device or software for evaluating the reliability of information acquired by a server.

[0586] "Answer accuracy improvement means" refers to a device or software for improving the accuracy of answers provided to the user based on evaluated information.

[0587] A "natural language processing command" is a command that uses natural language processing technology specifically designed for analyzing payment status and error messages.

[0588] This invention is a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. The system mainly includes a terminal for users to input inquiries, a server for analyzing received inquiries, and an interface means for providing answers based on the analysis results.

[0589] Basic System Configuration

[0590] 1. Terminal:

[0591] It provides a user interface and allows the user to input inquiries.

[0592] The entered query is sent to the server using an HTTP POST request.

[0593] 2. Server:

[0594] The received inquiry is analyzed using natural language processing techniques.

[0595] Based on the analysis results, retrieve the necessary information from the database.

[0596] Generate the appropriate answer and display it to the user again via the device.

[0597] This includes methods for processing queries related to settlement transactions, as well as natural language processing commands.

[0598] Hardware and software to be used

[0599] Server machine: Linux server or cloud service such as AWS

[0600] Programming language: Python

[0601] Web framework: Flask

[0602] Natural language processing library: spaCy

[0603] Processing Overview

[0604] 1. The user enters the inquiry:

[0605] The user enters their inquiry about a specific question or problem into the chat interface. For example, they might type, "I want to check the status of my order."

[0606] 2. The device sends the query to the server:

[0607] The terminal receives user input and sends this input to the server.

[0608] 3. The server analyzes the query:

[0609] The server extracts the query content from the received HTTP request and parses the text using spaCy.

[0610] Interpret the intent of the inquiry and recognize, for example, the intent to "check order status."

[0611] 4. The server generates the appropriate answer:

[0612] Based on the analysis results, a query is sent to the database to retrieve the necessary data.

[0613] After retrieving the order status from the database, the system generates a response such as, "Order ID: 123456 has been shipped."

[0614] 5. The device displays the answer:

[0615] The device receives the generated response and displays it in the user's chat interface.

[0616] Specific example

[0617] Example 1:

[0618] The user types "What is the status of order number 123456?" into the chat.

[0619] The device sends this query to the server.

[0620] The server analyzes the query, generates an appropriate response, and provides it to the user.

[0621] Example 2:

[0622] The user types "How do I update my account information?" into the chat.

[0623] The device sends this query to the server.

[0624] The server analyzes the data, generates a link to update the account information, and provides it to the user.

[0625] Example of a prompt

[0626] "A user typed 'What is the status of order number 123456?' into the chat. Please tell me the expected response in JSON format that should be returned as a result of processing the following Python script."

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The user enters their inquiry into the chat interface.

[0630] Input: Inquiry text such as "What is the status of order number 123456?"

[0631] Output: The inquiry text is displayed in the input field and saved to the device.

[0632] Step 2:

[0633] The terminal sends the query to the server.

[0634] Input: Inquiry text

[0635] Data processing / data calculation: Convert the query text to JSON format and send it to the server as an HTTP POST request.

[0636] Output: The query text is sent to the server.

[0637] Step 3:

[0638] The server analyzes the received queries using natural language processing techniques.

[0639] Input: Query text included in the HTTP POST request

[0640] Data processing / data calculation: Use the spaCy library to parse query text and interpret its intent (e.g., "Order status confirmation").

[0641] Output: Analysis results are obtained (results of intent recognition).

[0642] Step 4:

[0643] The server retrieves the necessary information from the database based on the analysis results.

[0644] Input: Analysis results (intention recognition results), database query (e.g., "Order ID: 123456")

[0645] Data processing / data calculation: Execute database queries to retrieve necessary information from the database (e.g., "Order ID: 123456, status: Shipped").

[0646] Output: Information retrieved from the database

[0647] Step 5:

[0648] The server generates an appropriate response based on the information it has obtained.

[0649] Input: Information retrieved from the database (e.g., "Shipped")

[0650] Data Processing / Data Calculation: Use the acquired information to generate responses to provide to the user (e.g., "Order ID: 123456 has been shipped").

[0651] Output: Generated answer text

[0652] Step 6:

[0653] The server sends the generated response to the terminal.

[0654] Input: Generated response text

[0655] Data processing / data calculation: Convert the response text to JSON format and send it to the terminal as an HTTP response.

[0656] Output: The answer text is sent to the terminal.

[0657] Step 7:

[0658] The device displays the answer in the user's chat interface.

[0659] Input: Response text sent from the server

[0660] Data Processing / Data Calculation: Analyze response text and format it for display in the user's chat interface.

[0661] Output: A formatted response will be displayed in the chat interface (e.g., "Order ID: 123456 has been shipped").

[0662] This allows users to obtain payment information quickly and accurately.

[0663] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0664] This invention relates to a customer support system that recognizes a user's emotions and provides an appropriate response based on those emotions. The system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[0665] Basic System Configuration

[0666] This system is built around a server that incorporates an emotion engine to recognize user emotions. The main components are as follows:

[0667] 1. User interface means:

[0668] An interface for users to enter inquiries (e.g., a chat window).

[0669] 2. Transmission method:

[0670] A network communication method for sending input queries to a server.

[0671] 3. Server:

[0672] A high-performance server that analyzes inquiries, recognizes user emotions, and generates appropriate responses. Includes an emotion engine and a natural language processing engine.

[0673] 4. Database:

[0674] A database that stores necessary information and allows the server to retrieve that information by executing queries.

[0675] 5. Display means:

[0676] An interface for displaying responses received from the server to the user.

[0677] Program processing

[0678] The user enters an inquiry.

[0679] A user types "I want to check the status of my order" into the chat interface. This input contains both the user's intent and emotion.

[0680] The device sends the query to the server.

[0681] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[0682] The server analyzes the content and sentiment of the inquiry.

[0683] The server receives an HTTP request, parses the JSON data in the request body to extract the inquiry content, and uses an emotion engine to analyze the user's emotions. For example, the text "I want to check the status of my order" is analyzed along with the emotion "anxious".

[0684] The server generates the appropriate answer.

[0685] The server sends a query to the database based on the analysis results. Here, it retrieves the necessary data based on the user's order ID and other information. Based on the retrieved information and the user's sentiment, the server generates a response in an appropriate tone. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured."

[0686] The device displays the answer.

[0687] The server stores the generated response in JSON format and sends it to the terminal as an HTTP response. The terminal receives the HTTP response from the server and parses the JSON data in the response body. The parsed response is displayed in the chat interface. The user can see the response "Order ID: 123456 has been shipped. Please rest assured." on the chat screen.

[0688] Specific example

[0689] Example 1: Checking the order status of your product

[0690] The user anxiously types in the chat, "I want to know the status of my order."

[0691] The device sends this query to the server.

[0692] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[0693] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[0694] The device displays the answer to the user.

[0695] Example 2: Updating user account information

[0696] The user calmly types "I want to update my account information" into the chat.

[0697] The device sends this query to the server.

[0698] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[0699] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[0700] The device displays the answer with a link to the user.

[0701] This system allows users to quickly receive appropriate responses that take their emotions into consideration. Furthermore, by combining an emotion engine with natural language processing technology, the server can significantly improve user satisfaction.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The user types "I want to check the status of my order" into the chat interface. The input is sent to the device in text format.

[0705] Step 2:

[0706] The terminal receives the entered text and converts the query data into JSON format. This data includes the user's text and other relevant metadata.

[0707] Step 3:

[0708] The device sends query data in JSON format to the server as an HTTP POST request. The device accurately transfers the data over the network.

[0709] Step 4:

[0710] The server receives an HTTP request and extracts JSON data from the request body. Initial processing is performed to parse the query content and metadata.

[0711] Step 5:

[0712] The server passes the query content to a natural language processing (NLP) engine, which analyzes the intent of the query. For example, the text "I want to check the status of my order" is determined to have the intent "check the order status."

[0713] Step 6:

[0714] The server simultaneously passes the query text to the emotion engine, which analyzes the user's emotions. For example, the emotion "anxious" might be detected from the text.

[0715] Step 7:

[0716] The server generates appropriate queries for the database based on the analyzed intent and emotions. The server searches for the order status based on the user's order ID.

[0717] Step 8:

[0718] The server sends an SQL query to the database to retrieve the necessary order information. The information retrieved from the database includes the order ID and its current status.

[0719] Step 9:

[0720] The server generates a response based on the order information it retrieves and the results of sentiment analysis. For example, a response such as "Order ID: 123456 has been shipped. Please rest assured." might be generated.

[0721] Step 10:

[0722] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response. Here, the response data includes a message corresponding to the emotion.

[0723] Step 11:

[0724] The terminal receives an HTTP response from the server and extracts JSON data from the response body. The extracted data is then parsed for display to the user.

[0725] Step 12:

[0726] The terminal displays the analysis results in the chat interface. The user can see the response, "Order ID: 123456 has been shipped. Please rest assured."

[0727] The above outlines the specific processing steps in this system. Each step works in conjunction to quickly provide appropriate responses that take the user's emotions into consideration.

[0728] (Example 2)

[0729] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0730] Traditional customer support systems could automatically process user inquiries, but they struggled to provide appropriate answers that took user emotions into account. As a result, users often received uniform responses regardless of their emotional state, leading to decreased satisfaction. In particular, users experiencing anxiety or impatience were not provided with answers that considered their emotions, resulting in insufficient effectiveness of customer support.

[0731] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0732] In this invention, the server includes means for analyzing user inquiries using natural language processing technology, means for recognizing the user's emotions from the inquiries using an emotion analysis engine, means for obtaining necessary information from a database based on the analysis results, and means for generating a response based on the obtained information and the recognized emotions. This makes it possible to provide an appropriate response that takes the user's emotions into consideration.

[0733] A "user" is someone who submits an inquiry and receives services or support.

[0734] An "interface means" is an input / output device used by a user to input inquiries and receive responses.

[0735] "Transmission means" refers to the means of communication used to send the entered query data to the server.

[0736] A "server" is a computer system that analyzes inquiries, recognizes user sentiment, and generates appropriate responses.

[0737] "Analysis means" refers to the software or hardware functions used by a server to analyze queries it receives.

[0738] "Emotion recognition means" refers to the technical means by which a server recognizes emotions from a user's inquiry.

[0739] An "emotion analysis engine" is software that extracts and identifies emotions from a user's text data.

[0740] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0741] A "database" is an information management system that allows a server to store and retrieve necessary information.

[0742] "Information acquisition means" refers to the technical means by which a server executes queries against a database to obtain the necessary information.

[0743] "Answer generation means" refers to a technical means by which the server generates an answer to be provided to the user based on the analyzed content and acquired information.

[0744] "Display means" refers to devices or software that visually present the generated response to the user.

[0745] This invention relates to a customer support system that recognizes user emotions and provides appropriate responses. Specific embodiments of the system are described below. This system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[0746] Basic System Configuration

[0747] This system is built around a server that incorporates an emotion analysis engine to recognize user emotions. The main components are as follows:

[0748] 1. User interface means:

[0749] This is an interface for users to enter inquiries, such as a chat window in a web browser. It is built using HTML and JavaScript.

[0750] 2. Transmission method:

[0751] This is a means of sending the input query to the server, such as a network communication method using a RESTful API. JSON data is sent via an HTTP POST request.

[0752] 3. Server:

[0753] This is a high-performance server that analyzes user inquiries, recognizes user sentiment, and generates appropriate responses. The Django framework, written in Python, is commonly used. It includes sentiment engines, natural language processing engines, and database access capabilities.

[0754] 4. Natural Language Processing Engine:

[0755] The engine used to analyze the content of inquiries utilizes tools such as the Google Cloud Natural Language API.

[0756] 5. Emotion Analysis Engine:

[0757] An engine for recognizing user emotions, such as IBM Watson Tone Analyzer, is used.

[0758] 6. Database:

[0759] PostgreSQL is a commonly used database for storing necessary information and allowing servers to retrieve it by executing queries.

[0760] 7. Display means:

[0761] This is an interface for displaying responses received from a server to the user; a chat window is a concrete example.

[0762] Specific example

[0763] Example 1: Checking the order status of your product

[0764] The user anxiously types in the chat, "I want to know the status of my order."

[0765] The device sends this query to the server.

[0766] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[0767] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[0768] The device displays the answer to the user.

[0769] Example 2: Updating user account information

[0770] The user calmly types "I want to update my account information" into the chat.

[0771] The device sends this query to the server.

[0772] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[0773] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[0774] The device displays the answer with a link to the user.

[0775] Through these specific examples, the system can quickly provide appropriate responses that take user emotions into consideration. It is expected that the embodiments of the invention will significantly improve user satisfaction.

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The user enters an inquiry.

[0779] The user opens a chat window in their web browser and enters their inquiry. The input is accepted in text format, and specific inquiries such as "I would like to check the status of my order" are entered. The entered text is then sent to the terminal.

[0780] Step 2:

[0781] The device sends the query to the server.

[0782] The terminal receives input from the user and converts it into JSON format. The JSON data includes, for example, the following information:

[0783] json

[0784] {

[0785] "message": "I want to check the status of my order",

[0786] "user_id": "123456"

[0787] }

[0788] The terminal sends this JSON data to the server as an HTTP POST request. The terminal's output is a request in JSON format, which the server receives.

[0789] Step 3:

[0790] The server analyzes the query content.

[0791] The server receives an HTTP POST request and parses the JSON data in the request body. It uses the Python json library to read the data and extract the message and user_id fields. Then, it uses a natural language processing engine (e.g., Google Cloud Natural Language API) to parse the message field. The server's input is JSON data, and its output is the parsing result (e.g., the intent of the information).

[0792] Step 4:

[0793] The server recognizes the user's emotions.

[0794] The server passes the parsed message field to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer). The sentiment analysis engine recognizes the user's emotions and returns sentiment information such as "anxious." The server's input is the parsed text, and its output is the result of the sentiment analysis.

[0795] Step 5:

[0796] The server retrieves the necessary information from the database.

[0797] The server executes an SQL query against the database based on relevant information such as the order ID. This retrieves necessary information, such as the order status. An example query is "SELECT status FROM orders WHERE order_id = '123456';". The server's input is the query condition, and its output is the order status information.

[0798] Step 6:

[0799] The server generates the appropriate answer.

[0800] The server incorporates the acquired information into the response in an appropriate tone, based on the analysis results and sentiment analysis results. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured." The server's input is order information and sentiment information, and its output is the generated response.

[0801] Step 7:

[0802] The server sends the generated response to the terminal.

[0803] The server converts the generated response into a JSON format response and sends it to the terminal as an HTTP response. The JSON data in the response includes the following information:

[0804] json

[0805] {

[0806] "response_message": "Order ID: 123456 has been shipped. Please rest assured."

[0807] }

[0808] The server input is the generated response, and the output is a response in JSON format.

[0809] Step 8:

[0810] The device analyzes and displays the answer.

[0811] The terminal receives an HTTP response from the server and parses the JSON data in the response body using JavaScript. The parsed response message is then displayed in the chat interface. The user can see the message "Order ID: 123456 has been shipped. Please rest assured." in the chat window. The terminal's input is the response from the server, and its output is the text message displayed to the user.

[0812] (Application Example 2)

[0813] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0814] Traditional customer support systems provided uniform answers to inquiries without considering user emotions. This resulted in a failure to adequately address situations where users felt anxious or dissatisfied, leading to decreased satisfaction. A system is needed to solve this problem and provide more personalized support to users.

[0815] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the content of the inquiry and recognizing the user's emotions, means for generating an appropriate response based on the emotions, and means for displaying the generated response to the user. This makes it possible to quickly provide an appropriate response that corresponds to the user's emotions and improve user satisfaction.

[0816] A "user" is an individual or legal entity that uses the system to make an inquiry.

[0817] "Interface means" refers to a device or software used by a user to input inquiries.

[0818] "Transmission means" refers to network communication means used to send user-entered inquiries to the server.

[0819] A "server" refers to a processing unit that analyzes the content of an inquiry, recognizes the user's emotions, and generates an appropriate response.

[0820] "Means of recognizing emotions" refers to an analysis engine that identifies emotions from the content of user inquiries.

[0821] "Natural language processing technology" refers to the technology used to analyze and understand human language.

[0822] "Means for generating appropriate answers" refers to algorithms and software that generate answers based on analyzed emotions and inquiry content.

[0823] A "database" refers to data storage that stores information related to queries, allowing servers to execute queries and retrieve that information.

[0824] "Means for displaying generated answers" refers to display devices or screen display software that visually show the answers created by the server to the user.

[0825] A "natural language understanding engine" refers to a processing unit equipped with artificial intelligence for analyzing the intent behind user inquiries.

[0826] "Means for evaluating reliability" refers to algorithms used to determine the accuracy and reliability of information acquired by a server.

[0827] "Means of improving the accuracy of responses" refers to algorithms and software that enhance the accuracy and validity of responses provided to users based on evaluated information.

[0828] This invention relates to a system that recognizes a user's emotions and provides an appropriate response based on those emotions. The main components of the system include an interface means for the user to input an inquiry, a transmission means for sending the input inquiry to a server, a means for the server to analyze the inquiry content and recognize the user's emotions, a means for generating an appropriate response based on the emotions, and a means for displaying the generated response to the user.

[0829] Hardware and software to use

[0830] 1. Hardware: Standard servers, smartphones

[0831] 2. Software: Python 3, Flask (web framework), transformers (natural language processing library)

[0832] System processing

[0833] User inquiry input and submission

[0834] Users enter their inquiries through a chat interface on their smartphones. This interface is designed to allow users to easily input text. The entered inquiries are sent to the server as JSON data via an HTTP POST request.

[0835] Server-based analysis and emotion recognition

[0836] The server receives an HTTP request and uses the transformers library to analyze the query content using natural language processing techniques to recognize the user's emotions. For example, the emotion analysis pipeline assigns labels such as "NEGATIVE" and "POSITIVE."

[0837] Answer generation

[0838] The server generates appropriate responses based on the perceived emotions. For example, if the user is feeling anxious, a response such as "Don't worry, we'll check on that right away" will be generated. Conversely, if the user is expressing joy, a response such as "Thank you! How can we help you?" will be generated.

[0839] Display the answer

[0840] The generated response is sent to the smartphone as an HTTP response in JSON format and displayed to the user through the chat interface.

[0841] Specific example

[0842] Example 1: Checking the order status

[0843] User: "I'd like to check the status of my order."

[0844] The server analyzes the emotion as "anxiety" and responds, "Please rest assured. We will check on this immediately."

[0845] Specific example 2: Reporting a product defect

[0846] User: "The item I received was broken."

[0847] The server interprets the emotion as "negative" and responds, "We are very sorry. We will resend the same product."

[0848] Example of a prompt

[0849] User: I'd like to check the status of my order.

[0850] AI: Don't worry. I'll check it right away.

[0851] Data processing and calculations performed by the server

[0852] Data processing: Parse the received inquiry into JSON format.

[0853] Data processing: This involves using natural language processing techniques and sentiment analysis pipelines to identify user emotions and generate responses based on those emotions.

[0854] This enables appropriate responses based on user emotions, leading to improved user satisfaction.

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The user enters their inquiry.

[0858] The user enters their question into the smartphone's chat interface. The entered text becomes the input data for processing.

[0859] Step 2:

[0860] The terminal sends the query to the server.

[0861] The input text is converted into JSON data, stored in an HTTP POST request, and sent to the server. This request contains the user's inquiry.

[0862] Step 3:

[0863] The server analyzes the queries it receives.

[0864] The server receives the HTTP request and parses the JSON data in the request body to extract the query content. Specifically, the text data of the query content becomes the input data.

[0865] Step 4:

[0866] The server recognizes emotions.

[0867] The server uses the transformers library to pass the extracted text data to the sentiment analysis pipeline. The sentiment analysis pipeline identifies the user's emotions from the text and outputs sentiment labels such as "POSITIVE" and "NEGATIVE".

[0868] Step 5:

[0869] The server generates the appropriate answer.

[0870] Based on the sentiment label and the content of the inquiry, the server generates an appropriate response. For example, if the sentiment is "NEGATIVE," it will generate a response such as "Please rest assured, we will check on this immediately." The output data is the generated response text.

[0871] Step 6:

[0872] The server stores the answer in a JSON-formatted response.

[0873] The generated response text is converted into JSON data and sent to the terminal as an HTTP response. The response contains the generated response.

[0874] Step 7:

[0875] The device receives and displays the response.

[0876] The terminal receives the HTTP response, parses the JSON data in the response body, and extracts the response text. The extracted response text is then displayed in the chat interface and presented to the user.

[0877] The above steps result in a system that provides an appropriate response instantly, tailored to the user's emotions.

[0878] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0879] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0880] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0881] [Third Embodiment]

[0882] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0883] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0884] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0885] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0886] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0887] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0888] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0889] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0890] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0891] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0892] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0893] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0894] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[0895] First, let's explain the basic configuration of this system. This system includes a terminal that receives user inquiries, a server that analyzes and responds to the received inquiries, and an interface for providing responses based on the analysis results. The terminal provides an interface for users to input inquiries and has the function of sending the input inquiries to the server. The server analyzes the received inquiries, retrieves the necessary information from the database, and generates an appropriate response. The generated response is then displayed to the user again via the terminal.

[0896] Program Processing Overview

[0897] The user enters an inquiry.

[0898] The user enters their inquiry about a specific question or problem into the chat interface. For example, the user might type, "I want to check the status of my order."

[0899] The device sends the query to the server.

[0900] The terminal receives user input and sends this input to the server using an HTTP POST request. Here, the terminal is responsible for sending the query content to the server using the communication protocol.

[0901] The server analyzes the query content.

[0902] The server extracts the query content from the HTTP request it receives and passes it to a natural language processing (NLP) engine. The NLP engine analyzes the text and interprets its intent. For example, the server analyzes the text "I want to check the status of my order" and recognizes the intent as "checking the order status."

[0903] The server generates the appropriate answer.

[0904] Based on the analysis results, the server sends a query to the database. Here, it retrieves the necessary data using information such as the user's order ID. After retrieving the order status from the database, the server generates a response such as "Order ID: 123456 has been shipped."

[0905] The device displays the answer.

[0906] The device receives the generated response as an HTTP response and displays it in the user's chat interface. The user can see the response "Order ID: 123456 has been shipped" on the chat screen.

[0907] Specific example

[0908] Example 1: Checking the order status of your product

[0909] The user types "I want to know the status of my order" into the chat.

[0910] The device sends this query to the server.

[0911] The server analyzes the query and retrieves the order status of the product from the database.

[0912] The server generates the response, "The status of order ID: 654321 is preparing for shipment."

[0913] The device displays the answer to the user.

[0914] Example 2: Updating user account information

[0915] The user types "I want to update my account information" in the chat.

[0916] The device sends this query to the server.

[0917] The server analyzes the user's intent and retrieves information that the user can update.

[0918] The server generates a response such as, "You can update your account information using the following link."

[0919] The device displays the answer with a link to the user.

[0920] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[0921] The following describes the processing flow.

[0922] Step 1:

[0923] The user types "I want to check the status of my order" into the chat interface.

[0924] Step 2:

[0925] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[0926] Step 3:

[0927] The server receives an HTTP request and parses the JSON data in the request body to extract the query details.

[0928] Step 4:

[0929] The server passes the extracted text to a natural language processing (NLP) engine for text analysis. For example, the text "I want to check the status of my order" is analyzed as having the intent "Check order status".

[0930] Step 5:

[0931] The server generates queries to retrieve the necessary information from the database based on the analysis results. For example, it might retrieve the status of "Order ID: 123456" based on the user's order ID.

[0932] Step 6:

[0933] The server executes an SQL query against the database to retrieve status information corresponding to the order ID.

[0934] Step 7:

[0935] Based on the information obtained by the server, a response is generated for the user. For example, a response such as "Order ID: 123456 has been shipped" is constructed.

[0936] Step 8:

[0937] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response.

[0938] Step 9:

[0939] The terminal receives an HTTP response from the server and parses the JSON data within the response body.

[0940] Step 10:

[0941] The device displays the analyzed response in the chat interface, and the user confirms the response. The user can see information such as "Order ID: 123456 has been shipped."

[0942] The above outlines the specific processing steps of this system. By executing each step in the correct order, users can obtain quick and accurate answers.

[0943] (Example 1)

[0944] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0945] Traditional customer support systems suffer from inefficiency, as it takes a long time for users to receive appropriate answers after submitting an inquiry. Furthermore, they often fail to accurately analyze user inquiries or quickly retrieve necessary information, resulting in inaccurate and slow responses.

[0946] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0947] In this invention, the server includes means for passing the received query content to a natural language processing engine for analysis, means for sending a query to a database based on the analysis results to obtain the necessary data, and means for generating an appropriate answer based on the obtained data. This makes it possible to provide a quick and accurate answer to the user's inquiry.

[0948] "Interface means" refers to the means by which users enter inquiries, and includes chat interfaces and text input boxes.

[0949] A "communication protocol" is a technology that defines the rules and procedures for sending and receiving data, and includes HTTP and HTTPS.

[0950] A "natural language processing engine" is a system that uses technology to analyze the text of a user's inquiry and understand its intent.

[0951] "Analysis means" refers to a method for extracting query content from received data and passing it to a natural language processing engine for analysis.

[0952] A "database" is a system that systematically organizes and stores information and provides data in response to specified queries.

[0953] "Means of sending queries" refers to the means by which a server sends queries (such as SQL statements) to a database in order to retrieve necessary information.

[0954] "Means for generating responses" refers to the means of creating appropriate responses to provide to users based on acquired data.

[0955] An "HTTP response" is a response message sent by a server to a terminal, and it contains the results of a request made by the user.

[0956] A "terminal" is a device such as a computer or smartphone that a user uses to input an inquiry and display a response from a server.

[0957] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[0958] This system consists of three main elements: firstly, a terminal that receives user inquiries; secondly, a server that analyzes the received inquiries and generates answers; and thirdly, a display means for showing the generated answers to the user.

[0959] terminal

[0960] The terminal provides an interface for users to enter their inquiries. For example, a chat interface or a text input box may be used. The inquiry is initiated when the user types "I would like to check the status of my order" into these interfaces and clicks the submit button.

[0961] server

[0962] The server receives the inquiry sent from the terminal and parses it as an HTTP POST request. Next, the server passes the inquiry content to a natural language processing (NLP) engine (e.g., spaCy, Google Cloud NLP API) to analyze the text and interpret its intent. For example, if the input is the text "I want to check the status of my order," the NLP engine recognizes the intent as "check order status."

[0963] The server then sends a query to the database based on the analysis results. For example, it might send an SQL query like "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"), and the server uses this result to generate text such as "The status of order ID: 123456 is Shipped."

[0964] Display the answer

[0965] The generated response is sent from the server to the terminal as an HTTP response. The terminal receives this response and displays it in the user's chat interface. The user can then see on the chat screen that "The status of order ID: 123456 is shipped."

[0966] Specific examples are shown below.

[0967] Example 1: Checking the order status of a product

[0968] 1. The user types "I want to know the status of my order" in the chat.

[0969] 2. The device sends this query to the server.

[0970] 3. The server analyzes the query and retrieves the product order status from the database.

[0971] 4. The server generates a response stating, "The status of order ID: 654321 is preparing for shipment."

[0972] 5. The device displays the answer to the user.

[0973] Example 2: Updating account information

[0974] 1. The user types "I want to update my account information" in the chat.

[0975] 2. The device sends this query to the server.

[0976] 3. The server analyzes the user's intent and retrieves information that the user can update.

[0977] 4. The server generates a response such as, "You can update your account information via the following link."

[0978] 5. The device displays the answer with a link to the user.

[0979] Examples of prompt statements

[0980] Please type "I would like to check the status of my order" in the chat.

[0981] Please type "How do I update my account information?".

[0982] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[0983] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0984] Step 1:

[0985] The user enters an inquiry.

[0986] The user enters their questions or concerns into the chat interface. Specifically, they type "I want to check the status of my order" into the text box in the chat interface and click the send button. This input data is captured by the device.

[0987] Step 2:

[0988] The device sends the query to the server.

[0989] The terminal receives input from the user and sends this input to the server as an HTTP POST request. Specifically, the terminal converts the user's chat message into the body of an HTTP request. This request is sent to an endpoint (e.g., https: / / api.example.com / query). The input is the user's inquiry, and the output is the HTTP request.

[0990] Step 3:

[0991] The server analyzes the query content.

[0992] The server extracts the query content from the received HTTP request and passes it to the natural language processing (NLP) engine. Specifically, the server parses the query content from the request body and sends the text "I want to check the status of my order" to the NLP engine (e.g., spaCy, Google Cloud NLP API). The NLP engine parses the text and interprets its intent. The input for this step is the HTTP request body, and the output is the intent of the query.

[0993] Step 4:

[0994] The server generates the appropriate answer.

[0995] The server sends a query to the database based on the analysis results of the NLP engine. Specifically, the server generates an SQL query and sends a command such as "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"). Based on this result, the server generates the text "The status of order ID: 123456 is Shipped." In this step, the input is the intent of the query, and the output is the generated response.

[0996] Step 5:

[0997] The device displays the answer.

[0998] The terminal receives the response sent from the server as an HTTP response and displays it in the user's chat interface. Specifically, the terminal parses the response from the server (in JSON format, etc.) and displays the answer in the chat window. The user can then see on the chat screen that "The status of order ID: 123456 is shipped." The input for this step is the HTTP response from the server, and the output is the displayed answer.

[0999] (Application Example 1)

[1000] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1001] Traditional customer support systems often make it difficult for users to quickly resolve payment-related questions and problems. In particular, complex inquiries such as checking payment history, resolving payment errors, and verifying points require accurate and prompt responses. To address these challenges, an efficient and reliable support system is necessary.

[1002] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1003] In this invention, the server includes means for quickly responding to payment-related inquiries submitted by users, means for retrieving payment-related information from a database, and means for processing queries related to payment transactions. This enables users to efficiently and accurately resolve payment-related questions and problems.

[1004] "Inquiry interface means" refers to a device or software that provides a user interface for a user to input an inquiry.

[1005] "Transmission means" refers to a device or software that includes a communication protocol and means for sending user-entered queries to a server.

[1006] "Analysis means" refers to a device or software that analyzes the content of a query received by a server using natural language processing technology and interprets its intent.

[1007] "Answer generation means" refers to a device or software that generates an appropriate answer based on the analysis results and provides it to the user.

[1008] "Display means" refers to a device or software for displaying the generated response to the user.

[1009] "Payment inquiry response means" refers to a device or software for quickly responding to payment-related inquiries submitted by users.

[1010] "Information acquisition means" refers to a device or software used to acquire necessary information from a database based on the analysis results.

[1011] "Settlement transaction processing means" refers to a device or software for processing queries related to settlement transactions.

[1012] A "natural language understanding engine" is a device or software equipped with natural language processing technology that a server uses to analyze the intent of a query.

[1013] "Reliability evaluation means" refers to a device or software for evaluating the reliability of information acquired by a server.

[1014] "Answer accuracy improvement means" refers to a device or software for improving the accuracy of answers provided to the user based on evaluated information.

[1015] A "natural language processing command" is a command that uses natural language processing technology specifically designed for analyzing payment status and error messages.

[1016] This invention is a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. The system mainly includes a terminal for users to input inquiries, a server for analyzing received inquiries, and an interface means for providing answers based on the analysis results.

[1017] Basic System Configuration

[1018] 1. Terminal:

[1019] It provides a user interface and allows the user to input inquiries.

[1020] The entered query is sent to the server using an HTTP POST request.

[1021] 2. Server:

[1022] The received inquiry is analyzed using natural language processing techniques.

[1023] Based on the analysis results, retrieve the necessary information from the database.

[1024] Generate the appropriate answer and display it to the user again via the device.

[1025] This includes methods for processing queries related to settlement transactions, as well as natural language processing commands.

[1026] Hardware and software to be used

[1027] Server machine: Linux server or cloud service such as AWS

[1028] Programming language: Python

[1029] Web framework: Flask

[1030] Natural language processing library: spaCy

[1031] Processing Overview

[1032] 1. The user enters the inquiry:

[1033] The user enters their inquiry about a specific question or problem into the chat interface. For example, they might type, "I want to check the status of my order."

[1034] 2. The device sends the query to the server:

[1035] The terminal receives user input and sends this input to the server.

[1036] 3. The server analyzes the query:

[1037] The server extracts the query content from the received HTTP request and parses the text using spaCy.

[1038] Interpret the intent of the inquiry and recognize, for example, the intent to "check order status."

[1039] 4. The server generates the appropriate answer:

[1040] Based on the analysis results, a query is sent to the database to retrieve the necessary data.

[1041] After retrieving the order status from the database, the system generates a response such as, "Order ID: 123456 has been shipped."

[1042] 5. The device displays the answer:

[1043] The device receives the generated response and displays it in the user's chat interface.

[1044] Specific example

[1045] Example 1:

[1046] The user types "What is the status of order number 123456?" into the chat.

[1047] The device sends this query to the server.

[1048] The server analyzes the query, generates an appropriate response, and provides it to the user.

[1049] Example 2:

[1050] The user types "How do I update my account information?" into the chat.

[1051] The device sends this query to the server.

[1052] The server analyzes the data, generates a link to update the account information, and provides it to the user.

[1053] Example of a prompt

[1054] "A user typed 'What is the status of order number 123456?' into the chat. Please tell me the expected response in JSON format that should be returned as a result of processing the following Python script."

[1055] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1056] Step 1:

[1057] The user enters their inquiry into the chat interface.

[1058] Input: Inquiry text such as "What is the status of order number 123456?"

[1059] Output: The inquiry text is displayed in the input field and saved to the device.

[1060] Step 2:

[1061] The terminal sends the query to the server.

[1062] Input: Inquiry text

[1063] Data processing / data calculation: Convert the query text to JSON format and send it to the server as an HTTP POST request.

[1064] Output: The query text is sent to the server.

[1065] Step 3:

[1066] The server analyzes the received queries using natural language processing techniques.

[1067] Input: Query text included in the HTTP POST request

[1068] Data processing / data calculation: Use the spaCy library to parse query text and interpret its intent (e.g., "Order status confirmation").

[1069] Output: Analysis results are obtained (results of intent recognition).

[1070] Step 4:

[1071] The server retrieves the necessary information from the database based on the analysis results.

[1072] Input: Analysis results (intention recognition results), database query (e.g., "Order ID: 123456")

[1073] Data processing / data calculation: Execute database queries to retrieve necessary information from the database (e.g., "Order ID: 123456, status: Shipped").

[1074] Output: Information retrieved from the database

[1075] Step 5:

[1076] The server generates an appropriate response based on the information it has obtained.

[1077] Input: Information retrieved from the database (e.g., "Shipped")

[1078] Data Processing / Data Calculation: Use the acquired information to generate responses to provide to the user (e.g., "Order ID: 123456 has been shipped").

[1079] Output: Generated answer text

[1080] Step 6:

[1081] The server sends the generated response to the terminal.

[1082] Input: Generated response text

[1083] Data processing / data calculation: Convert the response text to JSON format and send it to the terminal as an HTTP response.

[1084] Output: The answer text is sent to the terminal.

[1085] Step 7:

[1086] The device displays the answer in the user's chat interface.

[1087] Input: Response text sent from the server

[1088] Data Processing / Data Calculation: Analyze response text and format it for display in the user's chat interface.

[1089] Output: A formatted response will be displayed in the chat interface (e.g., "Order ID: 123456 has been shipped").

[1090] This allows users to obtain payment information quickly and accurately.

[1091] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1092] This invention relates to a customer support system that recognizes a user's emotions and provides an appropriate response based on those emotions. The system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[1093] Basic System Configuration

[1094] This system is built around a server that incorporates an emotion engine to recognize user emotions. The main components are as follows:

[1095] 1. User interface means:

[1096] An interface for users to enter inquiries (e.g., a chat window).

[1097] 2. Transmission method:

[1098] A network communication method for sending input queries to a server.

[1099] 3. Server:

[1100] A high-performance server that analyzes inquiries, recognizes user emotions, and generates appropriate responses. Includes an emotion engine and a natural language processing engine.

[1101] 4. Database:

[1102] A database that stores necessary information and allows the server to retrieve that information by executing queries.

[1103] 5. Display means:

[1104] An interface for displaying responses received from the server to the user.

[1105] Program processing

[1106] The user enters an inquiry.

[1107] A user types "I want to check the status of my order" into the chat interface. This input contains both the user's intent and emotion.

[1108] The device sends the query to the server.

[1109] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[1110] The server analyzes the content and sentiment of the inquiry.

[1111] The server receives an HTTP request, parses the JSON data in the request body to extract the inquiry content, and uses an emotion engine to analyze the user's emotions. For example, the text "I want to check the status of my order" is analyzed along with the emotion "anxious".

[1112] The server generates the appropriate answer.

[1113] The server sends a query to the database based on the analysis results. Here, it retrieves the necessary data based on the user's order ID and other information. Based on the retrieved information and the user's sentiment, the server generates a response in an appropriate tone. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured."

[1114] The device displays the answer.

[1115] The server stores the generated response in JSON format and sends it to the terminal as an HTTP response. The terminal receives the HTTP response from the server and parses the JSON data in the response body. The parsed response is displayed in the chat interface. The user can see the response "Order ID: 123456 has been shipped. Please rest assured." on the chat screen.

[1116] Specific example

[1117] Example 1: Checking the order status of your product

[1118] The user anxiously types in the chat, "I want to know the status of my order."

[1119] The device sends this query to the server.

[1120] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[1121] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[1122] The device displays the answer to the user.

[1123] Example 2: Updating user account information

[1124] The user calmly types "I want to update my account information" into the chat.

[1125] The device sends this query to the server.

[1126] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[1127] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[1128] The device displays the answer with a link to the user.

[1129] This system allows users to quickly receive appropriate responses that take their emotions into consideration. Furthermore, by combining an emotion engine with natural language processing technology, the server can significantly improve user satisfaction.

[1130] The following describes the processing flow.

[1131] Step 1:

[1132] The user types "I want to check the status of my order" into the chat interface. The input is sent to the device in text format.

[1133] Step 2:

[1134] The terminal receives the entered text and converts the query data into JSON format. This data includes the user's text and other relevant metadata.

[1135] Step 3:

[1136] The device sends query data in JSON format to the server as an HTTP POST request. The device accurately transfers the data over the network.

[1137] Step 4:

[1138] The server receives an HTTP request and extracts JSON data from the request body. Initial processing is performed to parse the query content and metadata.

[1139] Step 5:

[1140] The server passes the query content to a natural language processing (NLP) engine, which analyzes the intent of the query. For example, the text "I want to check the status of my order" is determined to have the intent "check the order status."

[1141] Step 6:

[1142] The server simultaneously passes the query text to the emotion engine, which analyzes the user's emotions. For example, the emotion "anxious" might be detected from the text.

[1143] Step 7:

[1144] The server generates appropriate queries for the database based on the analyzed intent and emotions. The server searches for the order status based on the user's order ID.

[1145] Step 8:

[1146] The server sends an SQL query to the database to retrieve the necessary order information. The information retrieved from the database includes the order ID and its current status.

[1147] Step 9:

[1148] The server generates a response based on the order information it retrieves and the results of sentiment analysis. For example, a response such as "Order ID: 123456 has been shipped. Please rest assured." might be generated.

[1149] Step 10:

[1150] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response. Here, the response data includes a message corresponding to the emotion.

[1151] Step 11:

[1152] The terminal receives an HTTP response from the server and extracts JSON data from the response body. The extracted data is then parsed for display to the user.

[1153] Step 12:

[1154] The terminal displays the analysis results in the chat interface. The user can see the response, "Order ID: 123456 has been shipped. Please rest assured."

[1155] The above outlines the specific processing steps in this system. Each step works in conjunction to quickly provide appropriate responses that take the user's emotions into consideration.

[1156] (Example 2)

[1157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1158] Traditional customer support systems could automatically process user inquiries, but they struggled to provide appropriate answers that took user emotions into account. As a result, users often received uniform responses regardless of their emotional state, leading to decreased satisfaction. In particular, users experiencing anxiety or impatience were not provided with answers that considered their emotions, resulting in insufficient effectiveness of customer support.

[1159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1160] In this invention, the server includes means for analyzing user inquiries using natural language processing technology, means for recognizing the user's emotions from the inquiries using an emotion analysis engine, means for obtaining necessary information from a database based on the analysis results, and means for generating a response based on the obtained information and the recognized emotions. This makes it possible to provide an appropriate response that takes the user's emotions into consideration.

[1161] A "user" is someone who submits an inquiry and receives services or support.

[1162] An "interface means" is an input / output device used by a user to input inquiries and receive responses.

[1163] "Transmission means" refers to the means of communication used to send the entered query data to the server.

[1164] A "server" is a computer system that analyzes inquiries, recognizes user sentiment, and generates appropriate responses.

[1165] "Analysis means" refers to the software or hardware functions used by a server to analyze queries it receives.

[1166] "Emotion recognition means" refers to the technical means by which a server recognizes emotions from a user's inquiry.

[1167] An "emotion analysis engine" is software that extracts and identifies emotions from a user's text data.

[1168] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1169] A "database" is an information management system that allows a server to store and retrieve necessary information.

[1170] "Information acquisition means" refers to the technical means by which a server executes queries against a database to obtain the necessary information.

[1171] "Answer generation means" refers to a technical means by which the server generates an answer to be provided to the user based on the analyzed content and acquired information.

[1172] "Display means" refers to devices or software that visually present the generated response to the user.

[1173] This invention relates to a customer support system that recognizes user emotions and provides appropriate responses. Specific embodiments of the system are described below. This system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[1174] Basic System Configuration

[1175] This system is built around a server that incorporates an emotion analysis engine to recognize user emotions. The main components are as follows:

[1176] 1. User interface means:

[1177] This is an interface for users to enter inquiries, such as a chat window in a web browser. It is built using HTML and JavaScript.

[1178] 2. Transmission method:

[1179] This is a means of sending the input query to the server, such as a network communication method using a RESTful API. JSON data is sent via an HTTP POST request.

[1180] 3. Server:

[1181] This is a high-performance server that analyzes user inquiries, recognizes user sentiment, and generates appropriate responses. The Django framework, written in Python, is commonly used. It includes sentiment engines, natural language processing engines, and database access capabilities.

[1182] 4. Natural Language Processing Engine:

[1183] The engine used to analyze the content of inquiries utilizes tools such as the Google Cloud Natural Language API.

[1184] 5. Emotion Analysis Engine:

[1185] An engine for recognizing user emotions, such as IBM Watson Tone Analyzer, is used.

[1186] 6. Database:

[1187] PostgreSQL is a commonly used database for storing necessary information and allowing servers to retrieve it by executing queries.

[1188] 7. Display means:

[1189] This is an interface for displaying responses received from a server to the user; a chat window is a concrete example.

[1190] Specific example

[1191] Example 1: Checking the order status of your product

[1192] The user anxiously types in the chat, "I want to know the status of my order."

[1193] The device sends this query to the server.

[1194] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[1195] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[1196] The device displays the answer to the user.

[1197] Example 2: Updating user account information

[1198] The user calmly types "I want to update my account information" into the chat.

[1199] The device sends this query to the server.

[1200] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[1201] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[1202] The device displays the answer with a link to the user.

[1203] Through these specific examples, the system can quickly provide appropriate responses that take user emotions into consideration. It is expected that the embodiments of the invention will significantly improve user satisfaction.

[1204] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1205] Step 1:

[1206] The user enters an inquiry.

[1207] The user opens a chat window in their web browser and enters their inquiry. The input is accepted in text format, and specific inquiries such as "I would like to check the status of my order" are entered. The entered text is then sent to the terminal.

[1208] Step 2:

[1209] The device sends the query to the server.

[1210] The terminal receives input from the user and converts it into JSON format. The JSON data includes, for example, the following information:

[1211] json

[1212] {

[1213] "message": "I want to check the status of my order",

[1214] "user_id": "123456"

[1215] }

[1216] The terminal sends this JSON data to the server as an HTTP POST request. The terminal's output is a request in JSON format, which the server receives.

[1217] Step 3:

[1218] The server analyzes the query content.

[1219] The server receives an HTTP POST request and parses the JSON data in the request body. It uses the Python json library to read the data and extract the message and user_id fields. Then, it uses a natural language processing engine (e.g., Google Cloud Natural Language API) to parse the message field. The server's input is JSON data, and its output is the parsing result (e.g., the intent of the information).

[1220] Step 4:

[1221] The server recognizes the user's emotions.

[1222] The server passes the parsed message field to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer). The sentiment analysis engine recognizes the user's emotions and returns sentiment information such as "anxious." The server's input is the parsed text, and its output is the result of the sentiment analysis.

[1223] Step 5:

[1224] The server retrieves the necessary information from the database.

[1225] The server executes an SQL query against the database based on relevant information such as the order ID. This retrieves necessary information, such as the order status. An example query is "SELECT status FROM orders WHERE order_id = '123456';". The server's input is the query condition, and its output is the order status information.

[1226] Step 6:

[1227] The server generates the appropriate answer.

[1228] The server incorporates the acquired information into the response in an appropriate tone, based on the analysis results and sentiment analysis results. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured." The server's input is order information and sentiment information, and its output is the generated response.

[1229] Step 7:

[1230] The server sends the generated response to the terminal.

[1231] The server converts the generated response into a JSON format response and sends it to the terminal as an HTTP response. The JSON data in the response includes the following information:

[1232] json

[1233] {

[1234] "response_message": "Order ID: 123456 has been shipped. Please rest assured."

[1235] }

[1236] The server input is the generated response, and the output is a response in JSON format.

[1237] Step 8:

[1238] The device analyzes and displays the answer.

[1239] The terminal receives an HTTP response from the server and parses the JSON data in the response body using JavaScript. The parsed response message is then displayed in the chat interface. The user can see the message "Order ID: 123456 has been shipped. Please rest assured." in the chat window. The terminal's input is the response from the server, and its output is the text message displayed to the user.

[1240] (Application Example 2)

[1241] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1242] Traditional customer support systems provided uniform answers to inquiries without considering user emotions. This resulted in a failure to adequately address situations where users felt anxious or dissatisfied, leading to decreased satisfaction. A system is needed to solve this problem and provide more personalized support to users.

[1243] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the content of the inquiry and recognizing the user's emotions, means for generating an appropriate response based on the emotions, and means for displaying the generated response to the user. This makes it possible to quickly provide an appropriate response that corresponds to the user's emotions and improve user satisfaction.

[1244] A "user" is an individual or legal entity that uses the system to make an inquiry.

[1245] "Interface means" refers to a device or software used by a user to input inquiries.

[1246] "Transmission means" refers to network communication means used to send user-entered inquiries to the server.

[1247] A "server" refers to a processing unit that analyzes the content of an inquiry, recognizes the user's emotions, and generates an appropriate response.

[1248] "Means of recognizing emotions" refers to an analysis engine that identifies emotions from the content of user inquiries.

[1249] "Natural language processing technology" refers to the technology used to analyze and understand human language.

[1250] "Means for generating appropriate answers" refers to algorithms and software that generate answers based on analyzed emotions and inquiry content.

[1251] A "database" refers to data storage that stores information related to queries, allowing servers to execute queries and retrieve that information.

[1252] "Means for displaying generated answers" refers to display devices or screen display software that visually show the answers created by the server to the user.

[1253] A "natural language understanding engine" refers to a processing unit equipped with artificial intelligence for analyzing the intent behind user inquiries.

[1254] "Means for evaluating reliability" refers to algorithms used to determine the accuracy and reliability of information acquired by a server.

[1255] "Means of improving the accuracy of responses" refers to algorithms and software that enhance the accuracy and validity of responses provided to users based on evaluated information.

[1256] This invention relates to a system that recognizes a user's emotions and provides an appropriate response based on those emotions. The main components of the system include an interface means for the user to input an inquiry, a transmission means for sending the input inquiry to a server, a means for the server to analyze the inquiry content and recognize the user's emotions, a means for generating an appropriate response based on the emotions, and a means for displaying the generated response to the user.

[1257] Hardware and software to use

[1258] 1. Hardware: Standard servers, smartphones

[1259] 2. Software: Python 3, Flask (web framework), transformers (natural language processing library)

[1260] System processing

[1261] User inquiry input and submission

[1262] Users enter their inquiries through a chat interface on their smartphones. This interface is designed to allow users to easily input text. The entered inquiries are sent to the server as JSON data via an HTTP POST request.

[1263] Server-based analysis and emotion recognition

[1264] The server receives an HTTP request and uses the transformers library to analyze the query content using natural language processing techniques to recognize the user's emotions. For example, the emotion analysis pipeline assigns labels such as "NEGATIVE" and "POSITIVE."

[1265] Answer generation

[1266] The server generates appropriate responses based on the perceived emotions. For example, if the user is feeling anxious, a response such as "Don't worry, we'll check on that right away" will be generated. Conversely, if the user is expressing joy, a response such as "Thank you! How can we help you?" will be generated.

[1267] Display the answer

[1268] The generated response is sent to the smartphone as an HTTP response in JSON format and displayed to the user through the chat interface.

[1269] Specific example

[1270] Example 1: Checking the order status

[1271] User: "I'd like to check the status of my order."

[1272] The server analyzes the emotion as "anxiety" and responds, "Please rest assured. We will check on this immediately."

[1273] Specific example 2: Reporting a product defect

[1274] User: "The item I received was broken."

[1275] The server interprets the emotion as "negative" and responds, "We are very sorry. We will resend the same product."

[1276] Example of a prompt

[1277] User: I'd like to check the status of my order.

[1278] AI: Don't worry. I'll check it right away.

[1279] Data processing and calculations performed by the server

[1280] Data processing: Parse the received inquiry into JSON format.

[1281] Data processing: This involves using natural language processing techniques and sentiment analysis pipelines to identify user emotions and generate responses based on those emotions.

[1282] This enables appropriate responses based on user emotions, leading to improved user satisfaction.

[1283] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1284] Step 1:

[1285] The user enters their inquiry.

[1286] The user enters their question into the smartphone's chat interface. The entered text becomes the input data for processing.

[1287] Step 2:

[1288] The terminal sends the query to the server.

[1289] The input text is converted into JSON data, stored in an HTTP POST request, and sent to the server. This request contains the user's inquiry.

[1290] Step 3:

[1291] The server analyzes the queries it receives.

[1292] The server receives the HTTP request and parses the JSON data in the request body to extract the query content. Specifically, the text data of the query content becomes the input data.

[1293] Step 4:

[1294] The server recognizes emotions.

[1295] The server uses the transformers library to pass the extracted text data to the sentiment analysis pipeline. The sentiment analysis pipeline identifies the user's emotions from the text and outputs sentiment labels such as "POSITIVE" and "NEGATIVE".

[1296] Step 5:

[1297] The server generates the appropriate answer.

[1298] Based on the sentiment label and the content of the inquiry, the server generates an appropriate response. For example, if the sentiment is "NEGATIVE," it will generate a response such as "Please rest assured, we will check on this immediately." The output data is the generated response text.

[1299] Step 6:

[1300] The server stores the answer in a JSON-formatted response.

[1301] The generated response text is converted into JSON data and sent to the terminal as an HTTP response. The response contains the generated response.

[1302] Step 7:

[1303] The device receives and displays the response.

[1304] The terminal receives the HTTP response, parses the JSON data in the response body, and extracts the response text. The extracted response text is then displayed in the chat interface and presented to the user.

[1305] The above steps result in a system that provides an appropriate response instantly, tailored to the user's emotions.

[1306] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1307] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1308] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1309] [Fourth Embodiment]

[1310] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1311] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1312] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1313] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1314] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1315] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1316] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1317] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1318] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1319] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1320] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1321] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1322] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1323] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[1324] First, let's explain the basic configuration of this system. This system includes a terminal that receives user inquiries, a server that analyzes and responds to the received inquiries, and an interface for providing responses based on the analysis results. The terminal provides an interface for users to input inquiries and has the function of sending the input inquiries to the server. The server analyzes the received inquiries, retrieves the necessary information from the database, and generates an appropriate response. The generated response is then displayed to the user again via the terminal.

[1325] Program Processing Overview

[1326] The user enters an inquiry.

[1327] The user enters their inquiry about a specific question or problem into the chat interface. For example, the user might type, "I want to check the status of my order."

[1328] The device sends the query to the server.

[1329] The terminal receives user input and sends this input to the server using an HTTP POST request. Here, the terminal is responsible for sending the query content to the server using the communication protocol.

[1330] The server analyzes the query content.

[1331] The server extracts the query content from the HTTP request it receives and passes it to a natural language processing (NLP) engine. The NLP engine analyzes the text and interprets its intent. For example, the server analyzes the text "I want to check the status of my order" and recognizes the intent as "checking the order status."

[1332] The server generates the appropriate answer.

[1333] Based on the analysis results, the server sends a query to the database. Here, it retrieves the necessary data using information such as the user's order ID. After retrieving the order status from the database, the server generates a response such as "Order ID: 123456 has been shipped."

[1334] The device displays the answer.

[1335] The device receives the generated response as an HTTP response and displays it in the user's chat interface. The user can see the response "Order ID: 123456 has been shipped" on the chat screen.

[1336] Specific example

[1337] Example 1: Checking the order status of your product

[1338] The user types "I want to know the status of my order" into the chat.

[1339] The device sends this query to the server.

[1340] The server analyzes the query and retrieves the order status of the product from the database.

[1341] The server generates the response, "The status of order ID: 654321 is preparing for shipment."

[1342] The device displays the answer to the user.

[1343] Example 2: Updating user account information

[1344] The user types "I want to update my account information" in the chat.

[1345] The device sends this query to the server.

[1346] The server analyzes the user's intent and retrieves information that the user can update.

[1347] The server generates a response such as, "You can update your account information using the following link."

[1348] The device displays the answer with a link to the user.

[1349] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[1350] The following describes the processing flow.

[1351] Step 1:

[1352] The user types "I want to check the status of my order" into the chat interface.

[1353] Step 2:

[1354] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[1355] Step 3:

[1356] The server receives an HTTP request and parses the JSON data in the request body to extract the query details.

[1357] Step 4:

[1358] The server passes the extracted text to a natural language processing (NLP) engine for text analysis. For example, the text "I want to check the status of my order" is analyzed as having the intent "Check order status".

[1359] Step 5:

[1360] The server generates queries to retrieve the necessary information from the database based on the analysis results. For example, it might retrieve the status of "Order ID: 123456" based on the user's order ID.

[1361] Step 6:

[1362] The server executes an SQL query against the database to retrieve status information corresponding to the order ID.

[1363] Step 7:

[1364] Based on the information obtained by the server, a response is generated for the user. For example, a response such as "Order ID: 123456 has been shipped" is constructed.

[1365] Step 8:

[1366] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response.

[1367] Step 9:

[1368] The terminal receives an HTTP response from the server and parses the JSON data within the response body.

[1369] Step 10:

[1370] The device displays the analyzed response in the chat interface, and the user confirms the response. The user can see information such as "Order ID: 123456 has been shipped."

[1371] The above outlines the specific processing steps of this system. By executing each step in the correct order, users can obtain quick and accurate answers.

[1372] (Example 1)

[1373] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1374] Traditional customer support systems suffer from inefficiency, as it takes a long time for users to receive appropriate answers after submitting an inquiry. Furthermore, they often fail to accurately analyze user inquiries or quickly retrieve necessary information, resulting in inaccurate and slow responses.

[1375] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1376] In this invention, the server includes means for passing the received query content to a natural language processing engine for analysis, means for sending a query to a database based on the analysis results to obtain the necessary data, and means for generating an appropriate answer based on the obtained data. This makes it possible to provide a quick and accurate answer to the user's inquiry.

[1377] "Interface means" refers to the means by which users enter inquiries, and includes chat interfaces and text input boxes.

[1378] A "communication protocol" is a technology that defines the rules and procedures for sending and receiving data, and includes HTTP and HTTPS.

[1379] A "natural language processing engine" is a system that uses technology to analyze the text of a user's inquiry and understand its intent.

[1380] "Analysis means" refers to a method for extracting query content from received data and passing it to a natural language processing engine for analysis.

[1381] A "database" is a system that systematically organizes and stores information and provides data in response to specified queries.

[1382] "Means of sending queries" refers to the means by which a server sends queries (such as SQL statements) to a database in order to retrieve necessary information.

[1383] "Means for generating responses" refers to the means of creating appropriate responses to provide to users based on acquired data.

[1384] An "HTTP response" is a response message sent by a server to a terminal, and it contains the results of a request made by the user.

[1385] A "terminal" is a device such as a computer or smartphone that a user uses to input an inquiry and display a response from a server.

[1386] This invention relates to a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. Specific embodiments for implementing this invention are described below.

[1387] This system consists of three main elements: firstly, a terminal that receives user inquiries; secondly, a server that analyzes the received inquiries and generates answers; and thirdly, a display means for showing the generated answers to the user.

[1388] terminal

[1389] The terminal provides an interface for users to enter their inquiries. For example, a chat interface or a text input box may be used. The inquiry is initiated when the user types "I would like to check the status of my order" into these interfaces and clicks the submit button.

[1390] server

[1391] The server receives the inquiry sent from the terminal and parses it as an HTTP POST request. Next, the server passes the inquiry content to a natural language processing (NLP) engine (e.g., spaCy, Google Cloud NLP API) to analyze the text and interpret its intent. For example, if the input is the text "I want to check the status of my order," the NLP engine recognizes the intent as "check order status."

[1392] The server then sends a query to the database based on the analysis results. For example, it might send an SQL query like "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"), and the server uses this result to generate text such as "The status of order ID: 123456 is Shipped."

[1393] Display the answer

[1394] The generated response is sent from the server to the terminal as an HTTP response. The terminal receives this response and displays it in the user's chat interface. The user can then see on the chat screen that "The status of order ID: 123456 is shipped."

[1395] Specific examples are shown below.

[1396] Example 1: Checking the order status of a product

[1397] 1. The user types "I want to know the status of my order" in the chat.

[1398] 2. The device sends this query to the server.

[1399] 3. The server analyzes the query and retrieves the product order status from the database.

[1400] 4. The server generates a response stating, "The status of order ID: 654321 is preparing for shipment."

[1401] 5. The device displays the answer to the user.

[1402] Example 2: Updating account information

[1403] 1. The user types "I want to update my account information" in the chat.

[1404] 2. The device sends this query to the server.

[1405] 3. The server analyzes the user's intent and retrieves information that the user can update.

[1406] 4. The server generates a response such as, "You can update your account information via the following link."

[1407] 5. The device displays the answer with a link to the user.

[1408] Examples of prompt statements

[1409] Please type "I would like to check the status of my order" in the chat.

[1410] Please type "How do I update my account information?".

[1411] Through the above configuration, the present invention realizes efficient and reliable customer support. Users can obtain accurate information quickly, and the burden on the server side is reduced. By implementing this system, it is possible to solve the problems of conventional customer support.

[1412] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1413] Step 1:

[1414] The user enters an inquiry.

[1415] The user enters their questions or concerns into the chat interface. Specifically, they type "I want to check the status of my order" into the text box in the chat interface and click the send button. This input data is captured by the device.

[1416] Step 2:

[1417] The device sends the query to the server.

[1418] The terminal receives input from the user and sends this input to the server as an HTTP POST request. Specifically, the terminal converts the user's chat message into the body of an HTTP request. This request is sent to an endpoint (e.g., https: / / api.example.com / query). The input is the user's inquiry, and the output is the HTTP request.

[1419] Step 3:

[1420] The server analyzes the query content.

[1421] The server extracts the query content from the received HTTP request and passes it to the natural language processing (NLP) engine. Specifically, the server parses the query content from the request body and sends the text "I want to check the status of my order" to the NLP engine (e.g., spaCy, Google Cloud NLP API). The NLP engine parses the text and interprets its intent. The input for this step is the HTTP request body, and the output is the intent of the query.

[1422] Step 4:

[1423] The server generates the appropriate answer.

[1424] The server sends a query to the database based on the analysis results of the NLP engine. Specifically, the server generates an SQL query and sends a command such as "SELECT status FROM orders WHERE order_id = '123456'" to the database (e.g., MySQL, PostgreSQL). The database returns the corresponding order status (e.g., "Shipped"). Based on this result, the server generates the text "The status of order ID: 123456 is Shipped." In this step, the input is the intent of the query, and the output is the generated response.

[1425] Step 5:

[1426] The device displays the answer.

[1427] The terminal receives the response sent from the server as an HTTP response and displays it in the user's chat interface. Specifically, the terminal parses the response from the server (in JSON format, etc.) and displays the answer in the chat window. The user can then see on the chat screen that "The status of order ID: 123456 is shipped." The input for this step is the HTTP response from the server, and the output is the displayed answer.

[1428] (Application Example 1)

[1429] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1430] Traditional customer support systems often make it difficult for users to quickly resolve payment-related questions and problems. In particular, complex inquiries such as checking payment history, resolving payment errors, and verifying points require accurate and prompt responses. To address these challenges, an efficient and reliable support system is necessary.

[1431] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1432] In this invention, the server includes means for quickly responding to payment-related inquiries submitted by users, means for retrieving payment-related information from a database, and means for processing queries related to payment transactions. This enables users to efficiently and accurately resolve payment-related questions and problems.

[1433] "Inquiry interface means" refers to a device or software that provides a user interface for a user to input an inquiry.

[1434] "Transmission means" refers to a device or software that includes a communication protocol and means for sending user-entered queries to a server.

[1435] "Analysis means" refers to a device or software that analyzes the content of a query received by a server using natural language processing technology and interprets its intent.

[1436] "Answer generation means" refers to a device or software that generates an appropriate answer based on the analysis results and provides it to the user.

[1437] "Display means" refers to a device or software for displaying the generated response to the user.

[1438] "Payment inquiry response means" refers to a device or software for quickly responding to payment-related inquiries submitted by users.

[1439] "Information acquisition means" refers to a device or software used to acquire necessary information from a database based on the analysis results.

[1440] "Settlement transaction processing means" refers to a device or software for processing queries related to settlement transactions.

[1441] A "natural language understanding engine" is a device or software equipped with natural language processing technology that a server uses to analyze the intent of a query.

[1442] "Reliability evaluation means" refers to a device or software for evaluating the reliability of information acquired by a server.

[1443] "Answer accuracy improvement means" refers to a device or software for improving the accuracy of answers provided to the user based on evaluated information.

[1444] A "natural language processing command" is a command that uses natural language processing technology specifically designed for analyzing payment status and error messages.

[1445] This invention is a customer support system that allows users to efficiently make inquiries via chat and obtain appropriate answers through a server. The system mainly includes a terminal for users to input inquiries, a server for analyzing received inquiries, and an interface means for providing answers based on the analysis results.

[1446] Basic System Configuration

[1447] 1. Terminal:

[1448] It provides a user interface and allows the user to input inquiries.

[1449] The entered query is sent to the server using an HTTP POST request.

[1450] 2. Server:

[1451] The received inquiry is analyzed using natural language processing techniques.

[1452] Based on the analysis results, retrieve the necessary information from the database.

[1453] Generate the appropriate answer and display it to the user again via the device.

[1454] This includes methods for processing queries related to settlement transactions, as well as natural language processing commands.

[1455] Hardware and software to be used

[1456] Server machine: Linux server or cloud service such as AWS

[1457] Programming language: Python

[1458] Web framework: Flask

[1459] Natural language processing library: spaCy

[1460] Processing Overview

[1461] 1. The user enters the inquiry:

[1462] The user enters their inquiry about a specific question or problem into the chat interface. For example, they might type, "I want to check the status of my order."

[1463] 2. The device sends the query to the server:

[1464] The terminal receives user input and sends this input to the server.

[1465] 3. The server analyzes the query:

[1466] The server extracts the query content from the received HTTP request and parses the text using spaCy.

[1467] Interpret the intent of the inquiry and recognize, for example, the intent to "check order status."

[1468] 4. The server generates the appropriate answer:

[1469] Based on the analysis results, a query is sent to the database to retrieve the necessary data.

[1470] After retrieving the order status from the database, the system generates a response such as, "Order ID: 123456 has been shipped."

[1471] 5. The device displays the answer:

[1472] The device receives the generated response and displays it in the user's chat interface.

[1473] Specific example

[1474] Example 1:

[1475] The user types "What is the status of order number 123456?" into the chat.

[1476] The device sends this query to the server.

[1477] The server analyzes the query, generates an appropriate response, and provides it to the user.

[1478] Example 2:

[1479] The user types "How do I update my account information?" into the chat.

[1480] The device sends this query to the server.

[1481] The server analyzes the data, generates a link to update the account information, and provides it to the user.

[1482] Example of a prompt

[1483] "A user typed 'What is the status of order number 123456?' into the chat. Please tell me the expected response in JSON format that should be returned as a result of processing the following Python script."

[1484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1485] Step 1:

[1486] The user enters their inquiry into the chat interface.

[1487] Input: Inquiry text such as "What is the status of order number 123456?"

[1488] Output: The inquiry text is displayed in the input field and saved to the device.

[1489] Step 2:

[1490] The terminal sends the query to the server.

[1491] Input: Inquiry text

[1492] Data processing / data calculation: Convert the query text to JSON format and send it to the server as an HTTP POST request.

[1493] Output: The query text is sent to the server.

[1494] Step 3:

[1495] The server analyzes the received queries using natural language processing techniques.

[1496] Input: Query text included in the HTTP POST request

[1497] Data processing / data calculation: Use the spaCy library to parse query text and interpret its intent (e.g., "Order status confirmation").

[1498] Output: Analysis results are obtained (results of intent recognition).

[1499] Step 4:

[1500] The server retrieves the necessary information from the database based on the analysis results.

[1501] Input: Analysis results (intention recognition results), database query (e.g., "Order ID: 123456")

[1502] Data processing / data calculation: Execute database queries to retrieve necessary information from the database (e.g., "Order ID: 123456, status: Shipped").

[1503] Output: Information retrieved from the database

[1504] Step 5:

[1505] The server generates an appropriate response based on the information it has obtained.

[1506] Input: Information retrieved from the database (e.g., "Shipped")

[1507] Data Processing / Data Calculation: Use the acquired information to generate responses to provide to the user (e.g., "Order ID: 123456 has been shipped").

[1508] Output: Generated answer text

[1509] Step 6:

[1510] The server sends the generated response to the terminal.

[1511] Input: Generated response text

[1512] Data processing / data calculation: Convert the response text to JSON format and send it to the terminal as an HTTP response.

[1513] Output: The answer text is sent to the terminal.

[1514] Step 7:

[1515] The device displays the answer in the user's chat interface.

[1516] Input: Response text sent from the server

[1517] Data Processing / Data Calculation: Analyze response text and format it for display in the user's chat interface.

[1518] Output: A formatted response will be displayed in the chat interface (e.g., "Order ID: 123456 has been shipped").

[1519] This allows users to obtain payment information quickly and accurately.

[1520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1521] This invention relates to a customer support system that recognizes a user's emotions and provides an appropriate response based on those emotions. The system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[1522] Basic System Configuration

[1523] This system is built around a server that incorporates an emotion engine to recognize user emotions. The main components are as follows:

[1524] 1. User interface means:

[1525] An interface for users to enter inquiries (e.g., a chat window).

[1526] 2. Transmission method:

[1527] A network communication method for sending input queries to a server.

[1528] 3. Server:

[1529] A high-performance server that analyzes inquiries, recognizes user emotions, and generates appropriate responses. Includes an emotion engine and a natural language processing engine.

[1530] 4. Database:

[1531] A database that stores necessary information and allows the server to retrieve that information by executing queries.

[1532] 5. Display means:

[1533] An interface for displaying responses received from the server to the user.

[1534] Program processing

[1535] The user enters an inquiry.

[1536] A user types "I want to check the status of my order" into the chat interface. This input contains both the user's intent and emotion.

[1537] The device sends the query to the server.

[1538] The terminal receives user input, stores this query as JSON data in an HTTP POST request, and sends it to the server.

[1539] The server analyzes the content and sentiment of the inquiry.

[1540] The server receives an HTTP request, parses the JSON data in the request body to extract the inquiry content, and uses an emotion engine to analyze the user's emotions. For example, the text "I want to check the status of my order" is analyzed along with the emotion "anxious".

[1541] The server generates the appropriate answer.

[1542] The server sends a query to the database based on the analysis results. Here, it retrieves the necessary data based on the user's order ID and other information. Based on the retrieved information and the user's sentiment, the server generates a response in an appropriate tone. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured."

[1543] The device displays the answer.

[1544] The server stores the generated response in JSON format and sends it to the terminal as an HTTP response. The terminal receives the HTTP response from the server and parses the JSON data in the response body. The parsed response is displayed in the chat interface. The user can see the response "Order ID: 123456 has been shipped. Please rest assured." on the chat screen.

[1545] Specific example

[1546] Example 1: Checking the order status of your product

[1547] The user anxiously types in the chat, "I want to know the status of my order."

[1548] The device sends this query to the server.

[1549] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[1550] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[1551] The device displays the answer to the user.

[1552] Example 2: Updating user account information

[1553] The user calmly types "I want to update my account information" into the chat.

[1554] The device sends this query to the server.

[1555] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[1556] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[1557] The device displays the answer with a link to the user.

[1558] This system allows users to quickly receive appropriate responses that take their emotions into consideration. Furthermore, by combining an emotion engine with natural language processing technology, the server can significantly improve user satisfaction.

[1559] The following describes the processing flow.

[1560] Step 1:

[1561] The user types "I want to check the status of my order" into the chat interface. The input is sent to the device in text format.

[1562] Step 2:

[1563] The terminal receives the entered text and converts the query data into JSON format. This data includes the user's text and other relevant metadata.

[1564] Step 3:

[1565] The device sends query data in JSON format to the server as an HTTP POST request. The device accurately transfers the data over the network.

[1566] Step 4:

[1567] The server receives an HTTP request and extracts JSON data from the request body. Initial processing is performed to parse the query content and metadata.

[1568] Step 5:

[1569] The server passes the query content to a natural language processing (NLP) engine, which analyzes the intent of the query. For example, the text "I want to check the status of my order" is determined to have the intent "check the order status."

[1570] Step 6:

[1571] The server simultaneously passes the query text to the emotion engine, which analyzes the user's emotions. For example, the emotion "anxious" might be detected from the text.

[1572] Step 7:

[1573] The server generates appropriate queries for the database based on the analyzed intent and emotions. The server searches for the order status based on the user's order ID.

[1574] Step 8:

[1575] The server sends an SQL query to the database to retrieve the necessary order information. The information retrieved from the database includes the order ID and its current status.

[1576] Step 9:

[1577] The server generates a response based on the order information it retrieves and the results of sentiment analysis. For example, a response such as "Order ID: 123456 has been shipped. Please rest assured." might be generated.

[1578] Step 10:

[1579] The server stores the generated response in a JSON format and sends it to the terminal as an HTTP response. Here, the response data includes a message corresponding to the emotion.

[1580] Step 11:

[1581] The terminal receives an HTTP response from the server and extracts JSON data from the response body. The extracted data is then parsed for display to the user.

[1582] Step 12:

[1583] The terminal displays the analysis results in the chat interface. The user can see the response, "Order ID: 123456 has been shipped. Please rest assured."

[1584] The above outlines the specific processing steps in this system. Each step works in conjunction to quickly provide appropriate responses that take the user's emotions into consideration.

[1585] (Example 2)

[1586] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1587] Traditional customer support systems could automatically process user inquiries, but they struggled to provide appropriate answers that took user emotions into account. As a result, users often received uniform responses regardless of their emotional state, leading to decreased satisfaction. In particular, users experiencing anxiety or impatience were not provided with answers that considered their emotions, resulting in insufficient effectiveness of customer support.

[1588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1589] In this invention, the server includes means for analyzing user inquiries using natural language processing technology, means for recognizing the user's emotions from the inquiries using an emotion analysis engine, means for obtaining necessary information from a database based on the analysis results, and means for generating a response based on the obtained information and the recognized emotions. This makes it possible to provide an appropriate response that takes the user's emotions into consideration.

[1590] A "user" is someone who submits an inquiry and receives services or support.

[1591] An "interface means" is an input / output device used by a user to input inquiries and receive responses.

[1592] "Transmission means" refers to the means of communication used to send the entered query data to the server.

[1593] A "server" is a computer system that analyzes inquiries, recognizes user sentiment, and generates appropriate responses.

[1594] "Analysis means" refers to the software or hardware functions used by a server to analyze queries it receives.

[1595] "Emotion recognition means" refers to the technical means by which a server recognizes emotions from a user's inquiry.

[1596] An "emotion analysis engine" is software that extracts and identifies emotions from a user's text data.

[1597] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1598] A "database" is an information management system that allows a server to store and retrieve necessary information.

[1599] "Information acquisition means" refers to the technical means by which a server executes queries against a database to obtain the necessary information.

[1600] "Answer generation means" refers to a technical means by which the server generates an answer to be provided to the user based on the analyzed content and acquired information.

[1601] "Display means" refers to devices or software that visually present the generated response to the user.

[1602] This invention relates to a customer support system that recognizes user emotions and provides appropriate responses. Specific embodiments of the system are described below. This system includes a terminal for receiving user inquiries, a server that analyzes the content of the inquiry and the user's emotions and generates a response, and means for displaying the generated response to the user.

[1603] Basic System Configuration

[1604] This system is built around a server that incorporates an emotion analysis engine to recognize user emotions. The main components are as follows:

[1605] 1. User interface means:

[1606] This is an interface for users to enter inquiries, such as a chat window in a web browser. It is built using HTML and JavaScript.

[1607] 2. Transmission method:

[1608] This is a means of sending the input query to the server, such as a network communication method using a RESTful API. JSON data is sent via an HTTP POST request.

[1609] 3. Server:

[1610] This is a high-performance server that analyzes user inquiries, recognizes user sentiment, and generates appropriate responses. The Django framework, written in Python, is commonly used. It includes sentiment engines, natural language processing engines, and database access capabilities.

[1611] 4. Natural Language Processing Engine:

[1612] The engine used to analyze the content of inquiries utilizes tools such as the Google Cloud Natural Language API.

[1613] 5. Emotion Analysis Engine:

[1614] An engine for recognizing user emotions, such as IBM Watson Tone Analyzer, is used.

[1615] 6. Database:

[1616] PostgreSQL is a commonly used database for storing necessary information and allowing servers to retrieve it by executing queries.

[1617] 7. Display means:

[1618] This is an interface for displaying responses received from a server to the user; a chat window is a concrete example.

[1619] Specific example

[1620] Example 1: Checking the order status of your product

[1621] The user anxiously types in the chat, "I want to know the status of my order."

[1622] The device sends this query to the server.

[1623] The server analyzes the query, and the emotion engine recognizes feelings of anxiety.

[1624] The server retrieves the order status from the database and generates a response saying, "Please rest assured. The status of order ID: 654321 is preparing for shipment."

[1625] The device displays the answer to the user.

[1626] Example 2: Updating user account information

[1627] The user calmly types "I want to update my account information" into the chat.

[1628] The device sends this query to the server.

[1629] The server analyzes the intent, and the emotion engine recognizes mild emotions.

[1630] The server generates a message with a link to indicate that the update process is simple, and then generates a response saying, "The process is easy. You can update your account information using the following link."

[1631] The device displays the answer with a link to the user.

[1632] Through these specific examples, the system can quickly provide appropriate responses that take user emotions into consideration. It is expected that the embodiments of the invention will significantly improve user satisfaction.

[1633] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1634] Step 1:

[1635] The user enters an inquiry.

[1636] The user opens a chat window in their web browser and enters their inquiry. The input is accepted in text format, and specific inquiries such as "I would like to check the status of my order" are entered. The entered text is then sent to the terminal.

[1637] Step 2:

[1638] The device sends the query to the server.

[1639] The terminal receives input from the user and converts it into JSON format. The JSON data includes, for example, the following information:

[1640] json

[1641] {

[1642] "message": "I want to check the status of my order",

[1643] "user_id": "123456"

[1644] }

[1645] The terminal sends this JSON data to the server as an HTTP POST request. The terminal's output is a request in JSON format, which the server receives.

[1646] Step 3:

[1647] The server analyzes the query content.

[1648] The server receives an HTTP POST request and parses the JSON data in the request body. It uses the Python json library to read the data and extract the message and user_id fields. Then, it uses a natural language processing engine (e.g., Google Cloud Natural Language API) to parse the message field. The server's input is JSON data, and its output is the parsing result (e.g., the intent of the information).

[1649] Step 4:

[1650] The server recognizes the user's emotions.

[1651] The server passes the parsed message field to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer). The sentiment analysis engine recognizes the user's emotions and returns sentiment information such as "anxious." The server's input is the parsed text, and its output is the result of the sentiment analysis.

[1652] Step 5:

[1653] The server retrieves the necessary information from the database.

[1654] The server executes an SQL query against the database based on relevant information such as the order ID. This retrieves necessary information, such as the order status. An example query is "SELECT status FROM orders WHERE order_id = '123456';". The server's input is the query condition, and its output is the order status information.

[1655] Step 6:

[1656] The server generates the appropriate answer.

[1657] The server incorporates the acquired information into the response in an appropriate tone, based on the analysis results and sentiment analysis results. For example, it might generate a response such as, "Order ID: 123456 has been shipped. Please rest assured." The server's input is order information and sentiment information, and its output is the generated response.

[1658] Step 7:

[1659] The server sends the generated response to the terminal.

[1660] The server converts the generated response into a JSON format response and sends it to the terminal as an HTTP response. The JSON data in the response includes the following information:

[1661] json

[1662] {

[1663] "response_message": "Order ID: 123456 has been shipped. Please rest assured."

[1664] }

[1665] The server input is the generated response, and the output is a response in JSON format.

[1666] Step 8:

[1667] The device analyzes and displays the answer.

[1668] The terminal receives an HTTP response from the server and parses the JSON data in the response body using JavaScript. The parsed response message is then displayed in the chat interface. The user can see the message "Order ID: 123456 has been shipped. Please rest assured." in the chat window. The terminal's input is the response from the server, and its output is the text message displayed to the user.

[1669] (Application Example 2)

[1670] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1671] Traditional customer support systems provided uniform answers to inquiries without considering user emotions. This resulted in a failure to adequately address situations where users felt anxious or dissatisfied, leading to decreased satisfaction. A system is needed to solve this problem and provide more personalized support to users.

[1672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the content of the inquiry and recognizing the user's emotions, means for generating an appropriate response based on the emotions, and means for displaying the generated response to the user. This makes it possible to quickly provide an appropriate response that corresponds to the user's emotions and improve user satisfaction.

[1673] A "user" is an individual or legal entity that uses the system to make an inquiry.

[1674] "Interface means" refers to a device or software used by a user to input inquiries.

[1675] "Transmission means" refers to network communication means used to send user-entered inquiries to the server.

[1676] A "server" refers to a processing unit that analyzes the content of an inquiry, recognizes the user's emotions, and generates an appropriate response.

[1677] "Means of recognizing emotions" refers to an analysis engine that identifies emotions from the content of user inquiries.

[1678] "Natural language processing technology" refers to the technology used to analyze and understand human language.

[1679] "Means for generating appropriate answers" refers to algorithms and software that generate answers based on analyzed emotions and inquiry content.

[1680] A "database" refers to data storage that stores information related to queries, allowing servers to execute queries and retrieve that information.

[1681] "Means for displaying generated answers" refers to display devices or screen display software that visually show the answers created by the server to the user.

[1682] A "natural language understanding engine" refers to a processing unit equipped with artificial intelligence for analyzing the intent behind user inquiries.

[1683] "Means for evaluating reliability" refers to algorithms used to determine the accuracy and reliability of information acquired by a server.

[1684] "Means of improving the accuracy of responses" refers to algorithms and software that enhance the accuracy and validity of responses provided to users based on evaluated information.

[1685] This invention relates to a system that recognizes a user's emotions and provides an appropriate response based on those emotions. The main components of the system include an interface means for the user to input an inquiry, a transmission means for sending the input inquiry to a server, a means for the server to analyze the inquiry content and recognize the user's emotions, a means for generating an appropriate response based on the emotions, and a means for displaying the generated response to the user.

[1686] Hardware and software to use

[1687] 1. Hardware: Standard servers, smartphones

[1688] 2. Software: Python 3, Flask (web framework), transformers (natural language processing library)

[1689] System processing

[1690] User inquiry input and submission

[1691] Users enter their inquiries through a chat interface on their smartphones. This interface is designed to allow users to easily input text. The entered inquiries are sent to the server as JSON data via an HTTP POST request.

[1692] Server-based analysis and emotion recognition

[1693] The server receives an HTTP request and uses the transformers library to analyze the query content using natural language processing techniques to recognize the user's emotions. For example, the emotion analysis pipeline assigns labels such as "NEGATIVE" and "POSITIVE."

[1694] Answer generation

[1695] The server generates appropriate responses based on the perceived emotions. For example, if the user is feeling anxious, a response such as "Don't worry, we'll check on that right away" will be generated. Conversely, if the user is expressing joy, a response such as "Thank you! How can we help you?" will be generated.

[1696] Display the answer

[1697] The generated response is sent to the smartphone as an HTTP response in JSON format and displayed to the user through the chat interface.

[1698] Specific example

[1699] Example 1: Checking the order status

[1700] User: "I'd like to check the status of my order."

[1701] The server analyzes the emotion as "anxiety" and responds, "Please rest assured. We will check on this immediately."

[1702] Specific example 2: Reporting a product defect

[1703] User: "The item I received was broken."

[1704] The server interprets the emotion as "negative" and responds, "We are very sorry. We will resend the same product."

[1705] Example of a prompt

[1706] User: I'd like to check the status of my order.

[1707] AI: Don't worry. I'll check it right away.

[1708] Data processing and calculations performed by the server

[1709] Data processing: Parse the received inquiry into JSON format.

[1710] Data processing: This involves using natural language processing techniques and sentiment analysis pipelines to identify user emotions and generate responses based on those emotions.

[1711] This enables appropriate responses based on user emotions, leading to improved user satisfaction.

[1712] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1713] Step 1:

[1714] The user enters their inquiry.

[1715] The user enters their question into the smartphone's chat interface. The entered text becomes the input data for processing.

[1716] Step 2:

[1717] The terminal sends the query to the server.

[1718] The input text is converted into JSON data, stored in an HTTP POST request, and sent to the server. This request contains the user's inquiry.

[1719] Step 3:

[1720] The server analyzes the queries it receives.

[1721] The server receives the HTTP request and parses the JSON data in the request body to extract the query content. Specifically, the text data of the query content becomes the input data.

[1722] Step 4:

[1723] The server recognizes emotions.

[1724] The server uses the transformers library to pass the extracted text data to the sentiment analysis pipeline. The sentiment analysis pipeline identifies the user's emotions from the text and outputs sentiment labels such as "POSITIVE" and "NEGATIVE".

[1725] Step 5:

[1726] The server generates the appropriate answer.

[1727] Based on the sentiment label and the content of the inquiry, the server generates an appropriate response. For example, if the sentiment is "NEGATIVE," it will generate a response such as "Please rest assured, we will check on this immediately." The output data is the generated response text.

[1728] Step 6:

[1729] The server stores the answer in a JSON-formatted response.

[1730] The generated response text is converted into JSON data and sent to the terminal as an HTTP response. The response contains the generated response.

[1731] Step 7:

[1732] The device receives and displays the response.

[1733] The terminal receives the HTTP response, parses the JSON data in the response body, and extracts the response text. The extracted response text is then displayed in the chat interface and presented to the user.

[1734] The above steps result in a system that provides an appropriate response instantly, tailored to the user's emotions.

[1735] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1736] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1737] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1738] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1739] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1740] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1741] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1742] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1743] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1744] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1745] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1746] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1747] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1748] 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.

[1749] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1750] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1751] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1752] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1753] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1754] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1755] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1756] The following is further disclosed regarding the embodiments described above.

[1757] (Claim 1)

[1758] An interface means for users to enter inquiries,

[1759] A means of sending the entered query to the server,

[1760] A means by which the server analyzes the query content and generates an appropriate response,

[1761] A means for displaying the generated response to the user,

[1762] A system that includes this.

[1763] (Claim 2)

[1764] A means for analyzing queries received by a server using natural language processing technology,

[1765] A means of obtaining necessary information from the database based on the analysis results,

[1766] A means of generating an answer based on the acquired information,

[1767] The system according to claim 1, further comprising:

[1768] (Claim 3)

[1769] A means including a natural language understanding engine that analyzes the intent of the query content,

[1770] A means for evaluating the reliability of information acquired by the server,

[1771] A means to improve the accuracy of the answers provided to users based on the evaluated information,

[1772] The system according to claim 1, further comprising:

[1773] "Example 1"

[1774] (Claim 1)

[1775] An interface means for users to enter inquiries,

[1776] A means of sending the input query to the server using a communication protocol,

[1777] A means of passing the query content received by the server to a natural language processing engine for analysis,

[1778] A means of sending queries to a database based on the analysis results and obtaining the necessary data,

[1779] A means of generating appropriate answers based on acquired data,

[1780] A means of sending the generated response as an HTTP response to the terminal and displaying it to the user,

[1781] A system that includes this.

[1782] (Claim 2)

[1783] A means for analyzing queries received by a server using natural language processing technology,

[1784] A means of obtaining necessary information from the database based on the analysis results,

[1785] A means of generating an answer based on the acquired information,

[1786] The system according to claim 1, further comprising:

[1787] (Claim 3)

[1788] A means including a natural language understanding engine that analyzes the intent of the query content,

[1789] A means for evaluating the reliability of information acquired by the server,

[1790] A means to improve the accuracy of the answers provided to users based on the evaluated information,

[1791] The system according to claim 1, further comprising:

[1792] "Application Example 1"

[1793] (Claim 1)

[1794] An interface means for users to enter inquiries,

[1795] A means of sending the entered query to the server,

[1796] A means by which the server analyzes the query content and generates an appropriate response,

[1797] A means for displaying the generated response to the user,

[1798] A means to quickly respond to payment-related inquiries submitted by users,

[1799] A means of obtaining payment-related information from a database,

[1800] A system that includes this.

[1801] (Claim 2)

[1802] A means for analyzing queries received by a server using natural language processing technology,

[1803] A means of obtaining necessary information from the database based on the analysis results,

[1804] A means of generating an answer based on the acquired information,

[1805] A means for processing queries related to settlement transactions,

[1806] The system according to claim 1, further comprising:

[1807] (Claim 3)

[1808] A means including a natural language understanding engine that analyzes the intent of the query content,

[1809] A means for evaluating the reliability of information acquired by the server,

[1810] A means to improve the accuracy of the answers provided to users based on the evaluated information,

[1811] A method using natural language processing commands specialized in analyzing payment status and error messages,

[1812] The system according to claim 1, further comprising:

[1813] "Example 2 of combining an emotion engine"

[1814] (Claim 1)

[1815] An interface means for users to enter inquiries,

[1816] A means of sending the entered query to the server,

[1817] The server analyzes the content of the inquiry and has a means to recognize the user's emotions.

[1818] A means of generating appropriate answers while taking emotions into consideration,

[1819] A means of displaying the generated response to the user,

[1820] A system that includes this.

[1821] (Claim 2)

[1822] A means for analyzing queries received by a server using natural language processing technology,

[1823] A means by which the server uses an emotion analysis engine to recognize the user's emotions from the query,

[1824] A means of obtaining necessary information from the database based on the analysis results,

[1825] A means for generating a response based on acquired information and recognized emotions,

[1826] The system according to claim 1, further comprising:

[1827] (Claim 3)

[1828] A means including a natural language understanding engine that analyzes the intent of the query content,

[1829] A means for evaluating the reliability of information acquired by the server,

[1830] Means for improving the accuracy of responses provided to users based on evaluated information and user sentiment,

[1831] The system according to claim 1, further comprising:

[1832] "Application example 2 of combining emotional engines"

[1833] (Claim 1)

[1834] An interface means for users to enter inquiries,

[1835] A means of sending the entered query to the server,

[1836] The server analyzes the content of the inquiry and has a means to recognize the user's emotions.

[1837] A means of generating appropriate answers based on emotions,

[1838] A means for displaying the generated response to the user,

[1839] A system that includes this.

[1840] (Claim 2)

[1841] A means for analyzing queries received by a server using natural language processing technology,

[1842] A means of obtaining necessary information from the database based on the analysis results,

[1843] A means of generating an answer based on the acquired information,

[1844] The system according to claim 1, further comprising:

[1845] (Claim 3)

[1846] A means including a natural language understanding engine that analyzes the intent of the query content,

[1847] A means for evaluating the reliability of information acquired by the server,

[1848] A means to improve the accuracy of the answers provided to users based on the evaluated information,

[1849] The system according to claim 1, further comprising: [Explanation of symbols]

[1850] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An interface means for users to enter inquiries, A means of sending the entered query to the server, A means by which the server analyzes the query content and generates an appropriate response, A means for displaying the generated response to the user, A system that includes this.

2. A means for analyzing queries received by a server using natural language processing technology, A means of obtaining necessary information from the database based on the analysis results, A means of generating an answer based on the acquired information, The system according to claim 1, further comprising:

3. A means including a natural language understanding engine that analyzes the intent of the query content, A means for evaluating the reliability of information acquired by the server, A means to improve the accuracy of the answers provided to users based on the evaluated information, The system according to claim 1, further comprising:

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