system

Abstract

We provide the system. [Solution] A means of providing an interface for users to enter inquiries, Means for receiving the user's inquiry, A means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, A means of returning the searched answer to the user, A system that includes this.

Description

Technical Field

[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

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern business environment, responding to inquiries from agents and customers is a significant burden on corporate operations. In particular, for frequently received routine inquiries, it may consume the resources of the person in charge and impede other important tasks. Furthermore, there is also a risk of variation in the quality and consistency of answers when the person in charge answers manually. To solve such problems and improve business efficiency and answer quality, it is necessary to introduce an automatic response system for inquiries.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system that includes means for providing an interface for a user to input an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, and means for returning the retrieved answer to the user. Specifically, the user inputs a question through an interface, and after receiving it, the question is analyzed using natural language processing technology. Based on the analysis results, an appropriate answer is searched from an answer database containing keywords, and that answer is returned to the user. In this way, the automation of inquiry handling can be achieved, ensuring operational efficiency and consistency of answers.

[0006] A "user" refers to an individual or organization that uses the system to make inquiries.

[0007] "Inquiry" refers to the act of a user entering and submitting information or questions they want to know into a system, or the content of such submissions.

[0008] The term "interface" refers to the part of the system that provides the screen and input functions for users to enter and submit inquiries.

[0009] "Means" refers to the methods, schemes, or functional blocks used to implement a specific function within a system.

[0010] "Receiving" refers to the process by which a system receives a query sent by a user.

[0011] "Analysis" refers to the process of understanding an incoming inquiry and deriving an appropriate response based on its content.

[0012] An "answer database" refers to a database that stores a set of pre-prepared questions and their corresponding answers.

[0013] "Searching" refers to the process of finding answers that match or are related to the inquiry in the answer database.

[0014] "Natural language processing" refers to the technology or methods used to enable computers to understand and process human language.

[0015] "Replying" refers to the process by which the system returns the searched answer to the user and displays it. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

[0017] 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.

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

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

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

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The embodiments for carrying out the present invention will now be described. This system automatically provides appropriate answers to user inquiries and functions primarily through cooperation between a server, a terminal, and the user.

[0038] System Configuration

[0039] This system includes the following components:

[0040] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0041] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[0042] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0043] Receiving module: Receives inquiries sent from terminals.

[0044] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0045] Database module: Holds and searches a database of pre-prepared questions and answers.

[0046] Response module: Generates an appropriate response and sends it back to the terminal.

[0047] Program processing

[0048] The following describes the process flow when a program is executed, using natural language.

[0049] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[0050] 2. Terminal: The terminal receives the input query and converts it into JSON format. The terminal then sends the constructed request to the server's API endpoint.

[0051] 3. Server: The server's receiving module receives the request sent from the terminal.

[0052] 4. Server: The server analyzes the requests received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours".

[0053] 5. Server: The server searches the answer database based on the keywords extracted by the database module. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[0054] 6. Server: The response module generates the retrieved response in JSON format and sends it to the terminal.

[0055] 7. Terminal: The terminal receives the JSON-formatted response sent from the server.

[0056] 8. Terminal: Analyzes the received response and displays the answer on the user interface. It will be displayed in the format of "Our business hours are 9:00 to 18:00."

[0057] 9. User: The user reviews the response displayed on their device and obtains appropriate information regarding their inquiry.

[0058] Specific example

[0059] As a concrete example, when making an inquiry about business hours, the user types "What are your business hours?" and sends it. The terminal sends this inquiry to the server, and after the server receives it, the parsing module extracts the keyword "business hours". The database module searches for the appropriate answer based on this keyword, and the response module generates the answer "Our business hours are 9:00 to 18:00" and returns it to the terminal. Finally, the user can confirm this answer on their terminal.

[0060] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[0061] The following describes the processing flow.

[0062] Step 1:

[0063] The user enters their inquiry. They specify the information they want to know and press the submit button. For example, they might enter, "Please tell me your business hours."

[0064] Step 2:

[0065] The terminal receives user input and converts it into JSON format. It then packages the query content as data and prepares to send it to the server.

[0066] Step 3:

[0067] The device sends a POST request to the server's API endpoint. The query data, converted to JSON format, is sent to the server.

[0068] Step 4:

[0069] The server receives a POST request. The server's receiving module receives the sent data and passes it on to the next process.

[0070] Step 5:

[0071] The server's analysis module analyzes the received query. Specifically, it uses natural language processing techniques to extract keywords and context from the query. For example, it might extract the keyword "business hours."

[0072] Step 6:

[0073] The server's database module searches the answer database based on the extracted keywords. It then searches the answer database for entries related to "business hours".

[0074] Step 7:

[0075] The server's database module retrieves the appropriate answer. For example, it retrieves the entry "Our business hours are 9:00 AM to 6:00 PM" from the database.

[0076] Step 8:

[0077] The server's response module generates the retrieved response as JSON-formatted response data. It then converts it into a data format for sending a reply to the user.

[0078] Step 9:

[0079] The server sends the generated response data to the terminal. It returns data in JSON format as a response to the POST request.

[0080] Step 10:

[0081] The terminal receives a response from the server. It parses the response data and prepares it for display on the user interface.

[0082] Step 11:

[0083] The terminal analyzes the response and displays it on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed in a user-friendly format.

[0084] Step 12:

[0085] The user checks the response displayed on their device. They check the displayed response to obtain appropriate information regarding their inquiry.

[0086] As described above, the entire system works together to provide fast and accurate automated responses to user inquiries.

[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] Conventional automated response systems struggled to provide timely and appropriate answers to user inquiries. Furthermore, they lacked the technology to accurately analyze user inquiries and provide precise responses based on those analyses. There was also a need to improve the overall data processing efficiency and usability of the system.

[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 a user to input an inquiry, means for a terminal to convert the user's inquiry into JSON format and send it to the server, means for the server to analyze the received inquiry and extract keywords, means for searching a pre-prepared answer database based on the keywords, means for generating the retrieved answer in JSON format and sending it to the terminal, and means for the terminal to display the received answer to the user. This enables a rapid and accurate automated response to the user's inquiry.

[0092] A "user" is a person or entity that makes inquiries to a system.

[0093] "Means" refers to a method, apparatus, or component within a system used to achieve a particular function or process.

[0094] An "inquiry" is a question that a user enters into the system about a problem they want to solve or information they want to know.

[0095] A "terminal" is a device used by a user to access a system, and includes PCs, smartphones, tablets, and other similar devices.

[0096] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and is a text format for structuring data as key-value pairs.

[0097] A "server" is a computer system that receives requests, processes data, and returns responses.

[0098] "Analysis" is the process of understanding inquiries received from users and extracting important keywords.

[0099] "Keywords" are words or phrases that are considered particularly important from the content of the inquiry.

[0100] A "response database" is a collection of data in which answers to inquiries are recorded in advance.

[0101] "Searching" is the process of finding the appropriate answer from a database of answers based on keywords.

[0102] "Transmission" is the process of moving data or a request from one system to another.

[0103] "Receiving" is the process by which one system takes in data or requests sent from another system.

[0104] "Display" refers to the process of providing information to a user visually on a device.

[0105] This document describes embodiments for carrying out the present invention. This system automatically provides appropriate answers to user inquiries and primarily functions through the cooperation of a server, terminal, and user. Specific hardware and software, as well as methods for data processing and calculation, will also be clearly described.

[0106] System Configuration

[0107] This system includes the following components:

[0108] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0109] 2. Terminal: A terminal has the function of receiving user input, converting it into JSON format, and sending it to the server. This includes PCs, smartphones, and tablets.

[0110] 3. Server: A server is a central device that processes inquiries received from terminals and provides appropriate answers. It has the following functions:

[0111] Receiving module: Receives queries sent from terminals. Apache® HTTP Server or Nginx are typically used.

[0112] Analysis Module: Analyzes received queries and extracts keywords and semantics. This analysis uses natural language processing technologies such as NLTK and spaCy.

[0113] Database Module: Maintains and allows searching a database of pre-defined questions and answers. MySQL® or PostgreSQL are used for the database.

[0114] Response module: Generates the retrieved response in JSON format and sends it back to the terminal. Flask or Django are often used for this.

[0115] Program processing

[0116] The following describes the process flow when a program is executed, using natural language.

[0117] First, the user enters their inquiry. For example, they might enter "Please tell me your business hours." The terminal receives this input, converts it to JSON format, and sends it to the server's API endpoint.

[0118] Next, the server's receiving module receives the request sent from the terminal. The parsing module analyzes the received request and uses natural language processing techniques to extract keywords such as "business hours."

[0119] The database module then searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer, "Our business hours are 9:00 AM to 6:00 PM."

[0120] The response module generates the retrieved response in JSON format and sends it to the terminal. The terminal receives the JSON response sent from the server, parses it, and displays it in the user interface. Finally, the user checks the response displayed on the terminal and obtains the appropriate information regarding the inquiry.

[0121] Specific example

[0122] For example, if a user asks, "What are your business hours?", it would look like this:

[0123] 1. The user types "Please tell me your business hours" and submits it.

[0124] 2. The terminal converts this query into JSON format and sends it to the server.

[0125] 3. The server analyzes the received request and extracts the keyword "business hours".

[0126] 4. The database module searches for answers based on this keyword.

[0127] 5. The response obtained is, "Our business hours are 9:00 AM to 6:00 PM."

[0128] 6. The response module generates this answer in JSON format and sends it to the terminal.

[0129] 7. The device displays the answer in the user interface.

[0130] 8. The user reviews the displayed answer.

[0131] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[0132] Examples of prompts for generative AI models

[0133] The following are examples of prompts for training a generative AI model with new questions:

[0134] Input: "What day of the week are you closed?"

[0135] Output: "Our company is closed every Sunday."

[0136] Input: "Please tell me the address of the branch."

[0137] Output: "Our branch office is located at 1-1-1 Marunouchi, Chiyoda-ku, Tokyo."

[0138] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0139] Step 1:

[0140] The user enters an inquiry.

[0141] The user enters their inquiry using a terminal. This is often done through a web form in a browser or a text field in an application. In this example, the user enters "Please tell me your business hours." The input data is sent to the terminal in text format.

[0142] Step 2:

[0143] The terminal converts the query into JSON format and sends it to the server.

[0144] The terminal receives text entered by the user and converts it into JSON format. Next, it sends the converted JSON data to the server as an HTTP POST request. Specifically, it converts it as a JSON object like {"question": "Please tell me your business hours"}. The converted JSON data is then sent to the server's API endpoint.

[0145] Step 3:

[0146] The server receives the request.

[0147] The server's receiving module receives an HTTP POST request sent from the terminal. The received data is in JSON format, in the form of {"question": "Please tell me your business hours"}. This data is then passed to the next parsing module.

[0148] Step 4:

[0149] The server parses the request.

[0150] The server's parsing module parses the received JSON data. Using natural language processing techniques (for example, libraries like NLTK or spaCy), it extracts the keyword "business hours". Specifically, it parses the text "Please tell me your business hours" and generates a keyword list ["business hours"]. This keyword list is then passed to the next database module.

[0151] Step 5:

[0152] The server searches the database.

[0153] The server's database module searches the database based on the keyword list received from the analysis module. This database stores pre-defined question-and-answer pairs. For example, it searches the database for entries matching the keyword "business hours" and retrieves the answer "Our business hours are 9:00 to 18:00." This answer data is then passed to the response module.

[0154] Step 6:

[0155] The server generates the response in JSON format.

[0156] The server's response module converts the retrieved answer into JSON format. Specifically, it generates a JSON object with the format {"answer": "Our business hours are 9:00 to 18:00"}. This JSON data is then sent to the terminal.

[0157] Step 7:

[0158] The device receives and displays the response.

[0159] The terminal receives data in JSON format sent from the server. Next, it parses this data and extracts the response. The response obtained as a result of the analysis, "Our business hours are 9:00 to 18:00," is displayed on the user interface. Specifically, this text is displayed in a designated area on the browser.

[0160] Step 8:

[0161] The user confirms the answer.

[0162] The user checks the response displayed on their device and obtains appropriate information regarding their inquiry. By reading the displayed response, "Our business hours are 9:00 AM to 6:00 PM," the user can quickly and accurately obtain the necessary information.

[0163] (Application Example 1)

[0164] 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."

[0165] In today's diverse device environment, it is crucial to respond quickly and accurately to user inquiries. However, existing systems are dependent on specific devices and interfaces, limiting their effectiveness in communicating with users via smart devices. Furthermore, the automation of natural language processing for analysis and response generation is insufficient, leading to decreased user satisfaction. A flexible and highly responsive system is needed to address these challenges.

[0166] 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.

[0167] In this invention, the server includes means for providing an interface in which a user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for returning the retrieved answer to the user, and means for displaying it on the user interface via a smart device. This enables quick and accurate responses using smartphones or head-mounted displays.

[0168] A "user inquiry interface" is an input method for users to enter questions or requests for information, and typically includes a text box on the screen or a voice input function.

[0169] A "smart device" refers to a portable electronic device, such as a smartphone or head-mounted display, that can connect to the internet and has advanced processing capabilities.

[0170] A "server" is a central device that receives requests from users, analyzes them, and generates and sends responses; it is a computer system that provides services over a network.

[0171] "Natural language processing" refers to the technology of understanding, analyzing, and generating human language using computers, and is a field of technology applied to text analysis, speech recognition, machine translation, and more.

[0172] A "response database" is a data storage system that organizes and stores pre-prepared answers to inquiries, and its purpose is to quickly provide appropriate answers to user questions.

[0173] A "receiving module" is a part of the software that receives user inquiries, which then reach the server.

[0174] A "parsing module" is a software module that analyzes received inquiries to extract keywords and meanings, and often uses natural language processing.

[0175] A "response module" is a software module that searches a database for the appropriate answer based on the analysis results, generates that answer, and sends it back to the user.

[0176] The present invention will now be described in terms of embodiments. This system provides quick and accurate answers to user inquiries using smart devices, and its main components include a server, a terminal, and a user interface. Furthermore, it utilizes natural language processing and an answer database.

[0177] System components

[0178] 1. User Inquiry Interface: Provide an interface for users to input inquiries using smart devices (e.g., smartphones or head-mounted displays). For example, a user might input "How do I set the night mode on my security camera?" through a smartphone app.

[0179] 2. Terminal: This is a device that receives user inquiries, such as a smartphone or head-mounted display. The terminal has the function of sending the received inquiries to the server in JSON format.

[0180] 3. Server: As a central device, it has the following functions.

[0181] Receiving module: Receives user inquiries from the terminal.

[0182] Analysis module: Analyzes received queries and extracts keywords and meanings using natural language processing.

[0183] Database module: Searches a pre-prepared answer database to retrieve the appropriate answer.

[0184] Response module: Generates the searched answer in JSON format and sends it back to the terminal.

[0185] Processing flow

[0186] User:

[0187] The user opens the app on their smartphone and enters their inquiry. For example, they might type, "How do I set the night mode on my security camera?"

[0188] Terminal:

[0189] The terminal receives the input query, converts it to JSON format, and sends it to the server's API endpoint.

[0190] server:

[0191] The server's receiving module receives requests from the terminal. Next, the analysis module analyzes the received requests and extracts keywords such as "security camera," "night mode," and "settings."

[0192] The database module searches the answer database based on this keyword and retrieves the answer, "To set night mode for your security camera, select Night Vision from the settings menu and turn it on."

[0193] The response module generates the retrieved response in JSON format and sends it to the terminal.

[0194] Terminal:

[0195] The terminal receives a response from the server, parses it, and displays it on the user interface.

[0196] User:

[0197] The user checks the answers displayed on the device and obtains the necessary information.

[0198] Hardware and software to use

[0199] Frontend: React Native (User interface development)

[0200] Backend: Node.js + Express (server processing)

[0201] Natural Language Processing: Natural (Node.js natural language processing library)

[0202] Database: MongoDB or MySQL (database for questions and answers)

[0203] Presentation of specific examples

[0204] As a concrete example of this system, consider a scenario where a user asks, "How do I set the night mode on my security camera?" The user enters this inquiry using their smartphone, and the device sends it to the server. The server uses natural language processing to analyze the inquiry, searches its database for the appropriate answer, and sends it back to the user.

[0205] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0206] Step 1:

[0207] The user enters their inquiry into the smart device's inquiry interface and presses the "Send" button. At this point, the user's input (e.g., "How do I set night mode on my security camera?") is generated as text data.

[0208] Step 2:

[0209] The terminal receives user input and converts this text data into JSON format. This process involves data processing that stores the input text in a key:value format. The generated JSON data is then sent to the server's API endpoint.

[0210] Step 3:

[0211] The server's receiving module receives JSON data sent from the terminal. It parses the received JSON data to extract the original text data.

[0212] Step 4:

[0213] The server's analysis module analyzes the received text data and extracts important keywords using natural language processing techniques (e.g., "security camera," "night mode," "settings"). This process involves tokenization and stemming of the text.

[0214] Step 5:

[0215] The server's database module searches the answer database based on the extracted keywords. It uses a search algorithm to retrieve the answer that best matches the keywords (e.g., "To set night mode for your security camera, select Night Vision from the settings menu and turn it on").

[0216] Step 6:

[0217] The server's response module converts the received response into JSON format and generates reply data for the terminal. Here, new key:value format JSON data is generated.

[0218] Step 7:

[0219] The terminal receives the JSON-formatted response sent from the server, parses it, and extracts the original text-formatted response. This text is then formatted appropriately for display in the user interface.

[0220] Step 8:

[0221] The user checks the response displayed on their device and obtains the necessary information. Specifically, the user sees a response on their smartphone screen that says, "To set the security camera's night mode, select Night Vision from the settings menu and turn it on."

[0222] This allows users to obtain information quickly and accurately, improving their satisfaction with inquiries.

[0223] 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.

[0224] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state. This system primarily functions in cooperation with the server, terminal, and user.

[0225] System Configuration

[0226] This system includes the following components:

[0227] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0228] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[0229] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0230] Receiving module: Receives inquiries sent from terminals.

[0231] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0232] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[0233] Database module: Holds and searches a database of pre-prepared questions and answers.

[0234] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[0235] Program processing

[0236] The following describes the process flow when a program is executed, using natural language.

[0237] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[0238] 2. Terminal: The terminal receives the input query and converts it into JSON format. It packages the query content as data and prepares to send it to the server.

[0239] 3. Terminal: Sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[0240] 4. Server: The server's receiving module receives the POST request. It receives the sent data and passes it on to the next process.

[0241] 5. Server: The server analyzes the queries received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours." Furthermore, the emotion engine recognizes the user's emotional state based on the analysis results. For example, it determines whether the user is angry, happy, or in a neutral emotional state.

[0242] 6. Server: The database module searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[0243] 7. Server: Based on the emotion engine's recognition results, the response module adjusts its response. For example, if the user is angry, it can choose a response that uses more polite and calm language.

[0244] 8. Server: The response module generates the adjusted response as JSON-formatted response data. It then converts it to a data format for sending back to the user.

[0245] 9. Server: Sends the generated response data to the terminal. Returns data in JSON format as a response to the POST request.

[0246] 10. Terminal: Receives the response from the server. Analyzes the received response data and prepares it for display on the user interface.

[0247] 11. Terminal: Display the analyzed responses in the user interface. Specific responses, such as "Our business hours are 9:00 AM to 6:00 PM," should be displayed in a user-friendly format.

[0248] 12. User: Check the answer displayed on the device. Check the displayed answer to obtain appropriate information regarding the inquiry.

[0249] Specific example

[0250] For example, when a user makes an inquiry about business hours, they type "What are your business hours?" and send it. The terminal sends this inquiry to the server, and after the server receives it, the analysis module extracts the keyword "business hours." Furthermore, the sentiment engine recognizes the user's emotional state, and if the user is, for example, favorable, it selects a polite response that reflects that emotion. The database module searches for an appropriate answer based on this keyword and generates a response such as "Our business hours are 9:00 to 18:00" and sends it back to the terminal. Finally, the user can confirm this answer on their terminal.

[0251] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user enters their inquiry. For example, they might type "Please tell me your business hours" and press the submit button.

[0255] Step 2:

[0256] The terminal receives user input and converts it into JSON format. It then packages the inquiry content as data and prepares to send it to the server.

[0257] Step 3:

[0258] The device sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[0259] Step 4:

[0260] The server's receiving module accepts the POST request. It receives the sent data and prepares it for the next processing step.

[0261] Step 5:

[0262] The server's analysis module analyzes the received query. It uses natural language processing techniques to extract keywords such as "business hours."

[0263] Step 6:

[0264] The server's emotion engine recognizes the user's emotional state based on the query content it analyzes. For example, it determines whether the user is angry, happy, or neutral based on their writing style and tone.

[0265] Step 7:

[0266] The server's database module searches the answer database based on the extracted keywords. For example, it searches the database for entries corresponding to "business hours".

[0267] Step 8:

[0268] The server's response module generates an answer based on the search results. During this process, it adjusts the answer to suit the user's emotions based on the emotion engine's recognition results. For example, an angry user will receive a polite and calm response.

[0269] Step 9:

[0270] The server sends the generated response to the terminal as JSON response data. The final response data is then sent back to the terminal.

[0271] Step 10:

[0272] The terminal receives a response from the server. It analyzes the received response data and prepares it to be displayed on the user interface.

[0273] Step 11:

[0274] The terminal analyzes the data and displays the results on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed to the user.

[0275] Step 12:

[0276] The user checks the answer displayed on their device. They can then obtain an appropriate answer to their inquiry.

[0277] Thus, the system of the present invention not only provides a rapid and accurate automated response to user inquiries, but also achieves higher user satisfaction by providing an optimal answer that takes into account the user's emotional state.

[0278] (Example 2)

[0279] 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".

[0280] Traditional inquiry systems have a problem of lowering user satisfaction by providing uniform, standardized answers without considering the user's emotional state. Furthermore, if users make inquiries while feeling dissatisfied or anxious, inappropriate responses can lead to further dissatisfaction. Therefore, there is a need for a system that takes the user's emotional state into consideration and provides more personalized responses.

[0281] 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.

[0282] In this invention, the server includes means for receiving a user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for recognizing the user's emotional state based on the analyzed inquiry content, and means for adjusting the answer retrieved according to the recognized user's emotional state. Thereby, it becomes possible to provide an appropriate answer according to the user's emotional state.

[0283] The means for receiving a user's inquiry refers to the function of receiving the inquiry input by the user through the terminal and sending it to the server.

[0284] The means for analyzing the received inquiry refers to the technology for understanding the content of the transmitted inquiry and extracting keywords and intentions.

[0285] The means for searching for a matching answer from the answer database refers to the function of searching the pre-prepared answer database based on the analyzed keywords and intentions and finding the optimal answer.

[0286] The means for recognizing the user's emotional state refers to the technology for judging the user's emotion based on the analyzed inquiry content and identifying states such as joy, anger, sorrow, and happiness.

[0287] The means for adjusting the answer retrieved according to the recognized user's emotional state refers to the function of modifying the tone and content of the answer according to the user's emotion and providing it to the user in a more appropriate form.

[0288] This invention is a system that automatically and appropriately responds to a user's inquiry. This system aims to improve the user's satisfaction by recognizing the user's emotional state. This system mainly functions in cooperation among the server, the terminal, and the user.

[0289] System Configuration

[0290] This system includes the following components:

[0291] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0292] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, tablets, etc.

[0293] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0294] Receiving module: Receives inquiries sent from terminals.

[0295] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0296] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[0297] Database module: Holds and searches a database of pre-prepared questions and answers.

[0298] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[0299] Program processing

[0300] Program Overview

[0301] The program of this invention integrates natural language processing (NLP) technology and sentiment analysis technology. This allows it to analyze user inquiries and generate appropriate responses. The software used includes natural language processing libraries (e.g., NLTK, Spacy) and sentiment analysis engines (e.g., GCP's Natural Language API).

[0302] Hardware to be used

[0303] Server: Cloud services (e.g., AWS (registered trademark), GCP, Azure (registered trademark)) that execute programs and store data are used.

[0304] Terminal: A terminal for users to make inquiries. PCs, smartphones, tablets, etc. are applicable.

[0305] Explanation of specific operations

[0306] The specific operations of the system from receiving an inquiry from a user to analysis, answer generation, and answer provision are as follows.

[0307] 1. User: The user enters "Please tell me the business hours" and sends it.

[0308] 2. Terminal: The terminal prepares to send this inquiry to the server. It converts the input inquiry into JSON format and sends it as a POST request to the server's API endpoint.

[0309] Server: The server's reception module receives the POST request and passes the transmitted data to the next stage of processing. The analysis module analyzes the received data and extracts the keyword "business hours".

[0310] Server: Based on this analysis result, the emotion engine recognizes the user's emotion. For example, it determines that the user is in a neutral state.

[0311] Server: The database module searches for an answer based on the corresponding keyword "business hours" and obtains "Our business hours are from 9:00 to 18:00".

[0312] 6. Server: The response module takes the results of the sentiment engine into account and generates an appropriate response. A standard response is set for neutral sentiment states.

[0313] 7. Server: Sends the generated response to the terminal as JSON response data.

[0314] 8. Terminal: The terminal receives the response from the server, parses it, and displays it on the user interface. Specifically, it displays "Our business hours are 9:00 to 18:00."

[0315] Specific example operations

[0316] For example, if a user asks, "What are your business hours?", the following would occur:

[0317] 1. User: The user types "Please tell me your business hours" on their device and taps the send button.

[0318] 2. Terminal: The terminal receives this query, converts it to JSON format, and sends it to the server.

[0319] 3. Server: The server receives the data and performs the following steps: analysis, sentiment recognition, database search, and response generation.

[0320] 4. Terminal: Receives a response from the server and displays "Our business hours are 9:00 AM to 6:00 PM" on the user interface.

[0321] 5. User: The user reviews the displayed answer.

[0322] Example of a prompt

[0323] Here are some examples of prompts for a generative AI model:

[0324] "For the user's inquiry, 'What are your business hours?', search the answer database for the relevant response and generate an appropriate answer assuming the user's sentiment is neutral."

[0325] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] User: The user enters their inquiry.

[0329] Specific action: The user opens the application on their smartphone or PC and enters "Please tell me your business hours" into the text input field on the screen.

[0330] Input: User's text input.

[0331] Output: User inquiry ("What are your business hours?").

[0332] Step 2:

[0333] Terminal: The terminal converts the query into JSON format.

[0334] Specific operation: The application on the device captures the entered text and converts it into JSON data like this:

[0335] json

[0336] {

[0337] "query": "Please tell me your business hours."

[0338] }

[0339] Input: User's inquiry.

[0340] Output: Query data in JSON format.

[0341] Step 3:

[0342] Terminal: The terminal sends a POST request to the server.

[0343] Specific operation: The device sends JSON data as an HTTP POST request to the server's API endpoint. The endpoint URL is https: / / api.example.com / query as an example.

[0344] Input: Query data in JSON format.

[0345] Output: POST request sent to the server.

[0346] Step 4:

[0347] Server: The server receives the POST request.

[0348] Specific operation: The server's receiving module receives a POST request and retrieves JSON data from the HTTP request body. It also verifies that the request is in the correct format.

[0349] Input: HTTP POST request.

[0350] Output: Query data in JSON format.

[0351] Step 5:

[0352] Server: The server parses the query.

[0353] Specific operation: The server's parsing module parses the JSON data and extracts the text "Please tell me your business hours" from the "query" field. Furthermore, it uses natural language processing techniques to extract the keyword "business hours". The techniques used include NLTK and Spacy.

[0354] Input: Query data in JSON format.

[0355] Output: Extracted keyword "business hours".

[0356] Step 6:

[0357] Server: The server recognizes the user's emotional state.

[0358] Specific operation: The server's sentiment engine determines the user's emotional state from the analyzed text. For example, it uses GCP's Natural Language API to perform sentiment analysis on the text and determine that the user's emotion is neutral.

[0359] Input: Analyzed text data.

[0360] Output: User's emotional state (neutral).

[0361] Step 7:

[0362] Server: The server searches the answer database.

[0363] Specific operation: The database module searches a pre-prepared answer database based on the keyword "business hours". For example, if an entry for "business hours" exists in the database, it will find the corresponding answer "Our business hours are 9:00 to 18:00".

[0364] Input: Extracted keyword "Business Hours".

[0365] Output: Search results ("Our business hours are 9:00 AM to 6:00 PM").

[0366] Step 8:

[0367] Server: The server generates the answer.

[0368] Specific operation: The response module generates an appropriate response considering the search results and the user's sentiment state. For a neutral sentiment, it selects the standard response "Our business hours are 9:00 AM to 6:00 PM."

[0369] Input: Search results, user's emotional state.

[0370] Output: Generated response ("Our business hours are 9:00 AM to 6:00 PM").

[0371] Step 9:

[0372] Server: The server sends the response to the terminal.

[0373] Specific operation: The server sends the generated response to the terminal as JSON-formatted response data. It returns the POST request response as an HTTP response.

[0374] Input: Generated response.

[0375] Output: Response data in JSON format.

[0376] Step 10:

[0377] Terminal: The terminal receives a response from the server.

[0378] Specific operation: The terminal receives a JSON response from the server and parses the data. Specifically, it extracts "Our business hours are 9:00 to 18:00" from the "response" field.

[0379] Input: Response data in JSON format.

[0380] Output: Analyzed response text.

[0381] Step 11:

[0382] Terminal: The terminal displays the answer in the user interface.

[0383] Specific operation: Execute code to display the analyzed response text on the user interface, showing "Our business hours are 9:00 AM to 6:00 PM" on the screen.

[0384] Input: Analyzed response text.

[0385] Output: The answer displayed to the user.

[0386] Step 12:

[0387] User: The user checks the displayed answer.

[0388] Specific action: The user confirms the response displayed on their device screen, "Our business hours are 9:00 AM to 6:00 PM," and obtains an answer to their inquiry.

[0389] Input: The answer displayed on the device.

[0390] Output: Information obtained by the user.

[0391] (Application Example 2)

[0392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0393] Conventional automated response systems simply provide standardized answers based on keywords to user inquiries, failing to consider the user's emotional state. This resulted in insufficient improvement in user satisfaction, and particularly in handling emotionally charged inquiries. Furthermore, there is a need to efficiently provide interfaces compatible with various devices such as smartphones. Solving these problems is the challenge.

[0394] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface in which the user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for recognizing the content of the received inquiry and the user's emotional state, means for generating an optimal answer based on the emotional state, and means for returning the retrieved answer to the user. This makes it possible to provide an optimized answer that takes the user's emotional state into consideration. Furthermore, it can be used as an application installed on smartphones, smart glasses, head-mounted displays, or robots, enabling support for a variety of devices.

[0395] "Means of providing an interface for users to input inquiries" refers to functions that provide input methods such as GUIs (Graphical User Interfaces), forms, and chatbots for users to enter information.

[0396] "Means for receiving user inquiries" refers to a function that sends user-entered inquiries to a server or cloud service and then receives them.

[0397] "A means of analyzing received inquiries and searching for matching answers from a pre-prepared answer database" refers to a function that uses natural language processing technology to analyze the content of user inquiries, extracts relevant keywords and phrases, and searches for appropriate answers from the database.

[0398] "Means for recognizing the content of received inquiries and the user's emotional state" refers to a function that identifies the user's emotional state (anger, joy, sadness, etc.) using an emotion analysis engine, along with the analyzed inquiry.

[0399] "Means for generating optimal responses based on emotional state" refers to a function that adjusts responses to provide users with appropriate tone and wording based on their recognized emotional state.

[0400] "Means of sending search results back to the user" refers to a function that sends the adjusted answers back to the user's device and displays them in the user interface.

[0401] "Sentiment analysis" is a technology used to identify a user's emotions from the content of an inquiry received.

[0402] "Natural language processing" is a technology that enables computers to understand, analyze, and generate appropriate responses to human language.

[0403] Modes for carrying out the invention

[0404] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state.

[0405] System Configuration

[0406] This system includes the following components.

[0407] 1. User Inquiry Interface:

[0408] This is an interface used by users to enter and submit inquiries. It is typically provided as a web form, a smartphone app, or an input field for smart glasses, head-mounted displays, or robots.

[0409] 2. Terminal:

[0410] This refers to a device that allows users to input inquiries and send them to a server. Examples include personal computers, smartphones, and tablets.

[0411] 3. Server:

[0412] It is a central device for processing inquiries received from terminals and providing appropriate answers. The server includes the following functions:

[0413] Receiving module:

[0414] Receive inquiries sent from the device.

[0415] Analysis module:

[0416] The system analyzes received inquiries and extracts keywords and semantics. Natural language processing techniques are used.

[0417] Emotion analysis engine:

[0418] Based on the analyzed inquiry content, the system recognizes the user's emotional state. For example, this can be done using the Amazon Comprehend API.

[0419] Database module:

[0420] It maintains a database of pre-prepared questions and answers, which can be searched.

[0421] Response module:

[0422] It searches for and generates a refined response, and sends it back to the terminal.

[0423] Program Processing Description

[0424] 1. The server provides a means of providing a user interface, which is an interface for users to input inquiries. For example, this is the chatbot screen of a smartphone application.

[0425] 2. The server's receiving module receives the query entered by the user. At this time, the query content is sent to the server in JSON format.

[0426] 3. The analysis module uses natural language processing technology (e.g., Google® Cloud Natural Language API) to analyze incoming inquiries and extract keywords. For example, it extracts the keyword "product features" from the inquiry "Please tell me about the features of the product."

[0427] 4. The emotion analysis engine recognizes the user's emotional state. For example, it uses Amazon Comprehend to recognize emotions such as "interested."

[0428] 5. The database module searches the answer database based on the extracted keywords and retrieves relevant information. For example, it searches for product feature information related to "product features".

[0429] 6. The response module generates the most appropriate response based on the user's emotional state. For example, if the user is feeling positive emotions, it will generate a response such as, "This product is made from high-quality materials and is highly recommended."

[0430] 7. The server sends the generated response back to the terminal and displays it in the user interface.

[0431] Specific example

[0432] Specific examples of how the system works

[0433] As a concrete example, a user enters "Please tell me the features of this product" into a smartphone application and sends it. The server receives the message, the analysis module performs natural language processing, and the sentiment analysis engine recognizes the user's sentiment as "very interested." Subsequently, the database module searches for the features of related products, and the response module generates and sends back a response that matches the sentiment, such as "This product is made from high-quality materials and is highly recommended." The user can then view this response on their smartphone.

[0434] Example of a prompt

[0435] User prompt: "Please tell me about the features of this product."

[0436] AI Model Prompt: Uses a product search API to search for "features" and an emotion engine API to recognize the user's emotional state. If the user is friendly, it generates a response such as, "This product is made from high-quality materials and is highly recommended."

[0437] In this way, the system of the present invention can provide a rapid and accurate automated response to user inquiries, and further enhance customer satisfaction by providing the optimal answer while taking into account the user's emotional state.

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The user enters their inquiry. The user types "Please tell me the features of this product" on the chatbot screen of the smartphone app. The entered data is converted to JSON format depending on the device.

[0441] Step 2:

[0442] The device sends the received query to the server. Specifically, the query data, converted to JSON format, is sent as a POST request to the server's API endpoint.

[0443] Step 3:

[0444] The server's receiving module receives a POST request. The received data is passed to the parsing module. The input is query data in JSON format, and the output is data ready for parsing.

[0445] Step 4:

[0446] The analysis module analyzes the received inquiry. It uses natural language processing technologies such as the Google Cloud Natural Language API to extract keywords such as "product features." The input is the received inquiry data, and the output is the extracted keywords.

[0447] Step 5:

[0448] The results of the analysis module are passed to the sentiment analysis engine to recognize the user's emotional state. For example, the Amazon Comprehend API is used to analyze emotions such as "interested." The input is the analyzed query data, and the output is the user's emotional state.

[0449] Step 6:

[0450] The database module searches the answer database based on the extracted keywords. For example, it uses MySQL or MongoDB to retrieve information related to "product features." The input is the extracted keywords, and the output is data about the searched product features.

[0451] Step 7:

[0452] The response module generates the most appropriate response based on the user's sentiment and search results. For example, if the user is favorable using the Google Cloud Natural Language API, it might generate a response such as, "This product is made with high-quality materials and is highly recommended." The input is the user's sentiment and search results data, and the output is the optimized response.

[0453] Step 8:

[0454] The server sends the generated response back to the terminal in JSON format. The terminal parses the received response data and prepares it for display in the user interface. The input is the generated response data, and the output is the data to be displayed in the user interface.

[0455] Step 9:

[0456] The device receives a response from the server and displays it in the user interface. Specifically, a response such as "This product is made from high-quality materials and is highly recommended" is displayed on the chatbot screen of the smartphone app. Finally, the user can confirm the displayed response.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] [Second Embodiment]

[0461] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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".

[0473] The embodiments for carrying out the present invention will now be described. This system automatically provides appropriate answers to user inquiries and functions primarily through cooperation between a server, a terminal, and the user.

[0474] System Configuration

[0475] This system includes the following components:

[0476] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0477] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[0478] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0479] Receiving module: Receives inquiries sent from terminals.

[0480] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0481] Database module: Holds and searches a database of pre-prepared questions and answers.

[0482] Response module: Generates an appropriate response and sends it back to the terminal.

[0483] Program processing

[0484] The following describes the process flow when a program is executed, using natural language.

[0485] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[0486] 2. Terminal: The terminal receives the input query and converts it into JSON format. The terminal then sends the constructed request to the server's API endpoint.

[0487] 3. Server: The server's receiving module receives the request sent from the terminal.

[0488] 4. Server: The server analyzes the requests received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours".

[0489] 5. Server: The server searches the answer database based on the keywords extracted by the database module. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[0490] 6. Server: The response module generates the retrieved response in JSON format and sends it to the terminal.

[0491] 7. Terminal: The terminal receives the JSON-formatted response sent from the server.

[0492] 8. Terminal: Analyzes the received response and displays the answer on the user interface. It will be displayed in the format of "Our business hours are 9:00 to 18:00."

[0493] 9. User: The user reviews the response displayed on their device and obtains appropriate information regarding their inquiry.

[0494] Specific example

[0495] As a concrete example, when making an inquiry about business hours, the user types "What are your business hours?" and sends it. The terminal sends this inquiry to the server, and after the server receives it, the parsing module extracts the keyword "business hours". The database module searches for the appropriate answer based on this keyword, and the response module generates the answer "Our business hours are 9:00 to 18:00" and returns it to the terminal. Finally, the user can confirm this answer on their terminal.

[0496] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The user enters their inquiry. They specify the information they want to know and press the submit button. For example, they might enter, "Please tell me your business hours."

[0500] Step 2:

[0501] The terminal receives user input and converts it into JSON format. It then packages the query content as data and prepares to send it to the server.

[0502] Step 3:

[0503] The device sends a POST request to the server's API endpoint. The query data, converted to JSON format, is sent to the server.

[0504] Step 4:

[0505] The server receives a POST request. The server's receiving module receives the sent data and passes it on to the next process.

[0506] Step 5:

[0507] The server's analysis module analyzes the received query. Specifically, it uses natural language processing techniques to extract keywords and context from the query. For example, it might extract the keyword "business hours."

[0508] Step 6:

[0509] The server's database module searches the answer database based on the extracted keywords. It then searches the answer database for entries related to "business hours".

[0510] Step 7:

[0511] The server's database module retrieves the appropriate answer. For example, it retrieves the entry "Our business hours are 9:00 AM to 6:00 PM" from the database.

[0512] Step 8:

[0513] The server's response module generates the retrieved response as JSON-formatted response data. It then converts it into a data format for sending a reply to the user.

[0514] Step 9:

[0515] The server sends the generated response data to the terminal. It returns data in JSON format as a response to the POST request.

[0516] Step 10:

[0517] The terminal receives a response from the server. It parses the response data and prepares it for display on the user interface.

[0518] Step 11:

[0519] The terminal analyzes the response and displays it on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed in a user-friendly format.

[0520] Step 12:

[0521] The user checks the response displayed on their device. They check the displayed response to obtain appropriate information regarding their inquiry.

[0522] As described above, the entire system works together to provide fast and accurate automated responses to user inquiries.

[0523] (Example 1)

[0524] 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."

[0525] Conventional automated response systems struggled to provide timely and appropriate answers to user inquiries. Furthermore, they lacked the technology to accurately analyze user inquiries and provide precise responses based on those analyses. There was also a need to improve the overall data processing efficiency and usability of the system.

[0526] 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.

[0527] In this invention, the server includes means for a user to input an inquiry, means for a terminal to convert the user's inquiry into JSON format and send it to the server, means for the server to analyze the received inquiry and extract keywords, means for searching a pre-prepared answer database based on the keywords, means for generating the retrieved answer in JSON format and sending it to the terminal, and means for the terminal to display the received answer to the user. This enables a rapid and accurate automated response to the user's inquiry.

[0528] A "user" is a person or entity that makes inquiries to a system.

[0529] "Means" refers to a method, apparatus, or component within a system used to achieve a particular function or process.

[0530] An "inquiry" is a question that a user enters into the system about a problem they want to solve or information they want to know.

[0531] A "terminal" is a device used by a user to access a system, and includes PCs, smartphones, tablets, and other similar devices.

[0532] "JSON format" is an abbreviation for JavaScript Object Notation, and it is a text format for structuring data as key-value pairs.

[0533] A "server" is a computer system that receives requests, processes data, and returns responses.

[0534] "Analysis" is the process of understanding inquiries received from users and extracting important keywords.

[0535] "Keywords" are words or phrases that are considered particularly important from the content of the inquiry.

[0536] A "response database" is a collection of data in which answers to inquiries are recorded in advance.

[0537] "Searching" is the process of finding the appropriate answer from a database of answers based on keywords.

[0538] "Transmission" is the process of moving data or a request from one system to another.

[0539] "Receiving" is the process by which one system takes in data or requests sent from another system.

[0540] "Display" refers to the process of providing information to a user visually on a device.

[0541] This document describes embodiments for carrying out the present invention. This system automatically provides appropriate answers to user inquiries and primarily functions through the cooperation of a server, terminal, and user. Specific hardware and software, as well as methods for data processing and calculation, will also be clearly described.

[0542] System Configuration

[0543] This system includes the following components:

[0544] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0545] 2. Terminal: A terminal has the function of receiving user input, converting it into JSON format, and sending it to the server. This includes PCs, smartphones, and tablets.

[0546] 3. Server: A server is a central device that processes inquiries received from terminals and provides appropriate answers. It has the following functions:

[0547] Receiving module: Receives queries sent from terminals. Apache HTTP Server or Nginx are typically used.

[0548] Analysis Module: Analyzes received queries and extracts keywords and semantics. This analysis uses natural language processing technologies such as NLTK and spaCy.

[0549] Database Module: Maintains and allows searching a database of pre-defined questions and answers. MySQL or PostgreSQL are used for the database.

[0550] Response module: Generates the retrieved response in JSON format and sends it back to the terminal. Flask or Django are often used for this.

[0551] Program processing

[0552] The following describes the process flow when a program is executed, using natural language.

[0553] First, the user enters their inquiry. For example, they might enter "Please tell me your business hours." The terminal receives this input, converts it to JSON format, and sends it to the server's API endpoint.

[0554] Next, the server's receiving module receives the request sent from the terminal. The parsing module analyzes the received request and uses natural language processing techniques to extract keywords such as "business hours."

[0555] The database module then searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer, "Our business hours are 9:00 AM to 6:00 PM."

[0556] The response module generates the retrieved response in JSON format and sends it to the terminal. The terminal receives the JSON response sent from the server, parses it, and displays it in the user interface. Finally, the user checks the response displayed on the terminal and obtains the appropriate information regarding the inquiry.

[0557] Specific example

[0558] For example, if a user asks, "What are your business hours?", it would look like this:

[0559] 1. The user types "Please tell me your business hours" and submits it.

[0560] 2. The terminal converts this query into JSON format and sends it to the server.

[0561] 3. The server analyzes the received request and extracts the keyword "business hours".

[0562] 4. The database module searches for answers based on this keyword.

[0563] 5. The response obtained is, "Our business hours are 9:00 AM to 6:00 PM."

[0564] 6. The response module generates this answer in JSON format and sends it to the terminal.

[0565] 7. The device displays the answer in the user interface.

[0566] 8. The user reviews the displayed answer.

[0567] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[0568] Examples of prompts for generative AI models

[0569] The following are examples of prompts for training a generative AI model with new questions:

[0570] Input: "What day of the week are you closed?"

[0571] Output: "Our company is closed every Sunday."

[0572] Input: "Please tell me the address of the branch."

[0573] Output: "Our branch office is located at 1-1-1 Marunouchi, Chiyoda-ku, Tokyo."

[0574] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0575] Step 1:

[0576] The user enters an inquiry.

[0577] The user enters their inquiry using a terminal. This is often done through a web form in a browser or a text field in an application. In this example, the user enters "Please tell me your business hours." The input data is sent to the terminal in text format.

[0578] Step 2:

[0579] The terminal converts the query into JSON format and sends it to the server.

[0580] The terminal receives text entered by the user and converts it into JSON format. Next, it sends the converted JSON data to the server as an HTTP POST request. Specifically, it converts it as a JSON object like {"question": "Please tell me your business hours"}. The converted JSON data is then sent to the server's API endpoint.

[0581] Step 3:

[0582] The server receives the request.

[0583] The server's receiving module receives an HTTP POST request sent from the terminal. The received data is in JSON format, in the form of {"question": "Please tell me your business hours"}. This data is then passed to the next parsing module.

[0584] Step 4:

[0585] The server parses the request.

[0586] The server's parsing module parses the received JSON data. Using natural language processing techniques (for example, libraries like NLTK or spaCy), it extracts the keyword "business hours". Specifically, it parses the text "Please tell me your business hours" and generates a keyword list ["business hours"]. This keyword list is then passed to the next database module.

[0587] Step 5:

[0588] The server searches the database.

[0589] The server's database module searches the database based on the keyword list received from the analysis module. This database stores pre-defined question-and-answer pairs. For example, it searches the database for entries matching the keyword "business hours" and retrieves the answer "Our business hours are 9:00 to 18:00." This answer data is then passed to the response module.

[0590] Step 6:

[0591] The server generates the response in JSON format.

[0592] The server's response module converts the retrieved answer into JSON format. Specifically, it generates a JSON object with the format {"answer": "Our business hours are 9:00 to 18:00"}. This JSON data is then sent to the terminal.

[0593] Step 7:

[0594] The device receives and displays the response.

[0595] The terminal receives data in JSON format sent from the server. Next, it parses this data and extracts the response. The response obtained as a result of the analysis, "Our business hours are 9:00 to 18:00," is displayed on the user interface. Specifically, this text is displayed in a designated area on the browser.

[0596] Step 8:

[0597] The user confirms the answer.

[0598] The user checks the response displayed on their device and obtains appropriate information regarding their inquiry. By reading the displayed response, "Our business hours are 9:00 AM to 6:00 PM," the user can quickly and accurately obtain the necessary information.

[0599] (Application Example 1)

[0600] 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."

[0601] In today's diverse device environment, it is crucial to respond quickly and accurately to user inquiries. However, existing systems are dependent on specific devices and interfaces, limiting their effectiveness in communicating with users via smart devices. Furthermore, the automation of natural language processing for analysis and response generation is insufficient, leading to decreased user satisfaction. A flexible and highly responsive system is needed to address these challenges.

[0602] 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.

[0603] In this invention, the server includes means for providing an interface in which a user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for returning the retrieved answer to the user, and means for displaying it on the user interface via a smart device. This enables quick and accurate responses using smartphones or head-mounted displays.

[0604] A "user inquiry interface" is an input method for users to enter questions or requests for information, and typically includes a text box on the screen or a voice input function.

[0605] A "smart device" refers to a portable electronic device, such as a smartphone or head-mounted display, that can connect to the internet and has advanced processing capabilities.

[0606] A "server" is a central device that receives requests from users, analyzes them, and generates and sends responses; it is a computer system that provides services over a network.

[0607] "Natural language processing" refers to the technology of understanding, analyzing, and generating human language using computers, and is a field of technology applied to text analysis, speech recognition, machine translation, and more.

[0608] A "response database" is a data storage system that organizes and stores pre-prepared answers to inquiries, and its purpose is to quickly provide appropriate answers to user questions.

[0609] A "receiving module" is a part of the software that receives user inquiries, which then reach the server.

[0610] A "parsing module" is a software module that analyzes received inquiries to extract keywords and meanings, and often uses natural language processing.

[0611] A "response module" is a software module that searches a database for the appropriate answer based on the analysis results, generates that answer, and sends it back to the user.

[0612] The present invention will now be described in terms of embodiments. This system provides quick and accurate answers to user inquiries using smart devices, and its main components include a server, a terminal, and a user interface. Furthermore, it utilizes natural language processing and an answer database.

[0613] System components

[0614] 1. User Inquiry Interface: Provide an interface for users to input inquiries using smart devices (e.g., smartphones or head-mounted displays). For example, a user might input "How do I set the night mode on my security camera?" through a smartphone app.

[0615] 2. Terminal: This is a device that receives user inquiries, such as a smartphone or head-mounted display. The terminal has the function of sending the received inquiries to the server in JSON format.

[0616] 3. Server: As a central device, it has the following functions.

[0617] Receiving module: Receives user inquiries from the terminal.

[0618] Analysis module: Analyzes received queries and extracts keywords and meanings using natural language processing.

[0619] Database module: Searches a pre-prepared answer database to retrieve the appropriate answer.

[0620] Response module: Generates the searched answer in JSON format and sends it back to the terminal.

[0621] Processing flow

[0622] User:

[0623] The user opens the app on their smartphone and enters their inquiry. For example, they might type, "How do I set the night mode on my security camera?"

[0624] Terminal:

[0625] The terminal receives the input query, converts it to JSON format, and sends it to the server's API endpoint.

[0626] server:

[0627] The server's receiving module receives requests from the terminal. Next, the analysis module analyzes the received requests and extracts keywords such as "security camera," "night mode," and "settings."

[0628] The database module searches the answer database based on this keyword and retrieves the answer, "To set night mode for your security camera, select Night Vision from the settings menu and turn it on."

[0629] The response module generates the retrieved response in JSON format and sends it to the terminal.

[0630] Terminal:

[0631] The terminal receives a response from the server, parses it, and displays it on the user interface.

[0632] User:

[0633] The user checks the answers displayed on the device and obtains the necessary information.

[0634] Hardware and software to use

[0635] Frontend: React Native (User interface development)

[0636] Backend: Node.js + Express (server processing)

[0637] Natural Language Processing: Natural (Node.js natural language processing library)

[0638] Database: MongoDB or MySQL (database for questions and answers)

[0639] Presentation of specific examples

[0640] As a concrete example of this system, consider a scenario where a user asks, "How do I set the night mode on my security camera?" The user enters this inquiry using their smartphone, and the device sends it to the server. The server uses natural language processing to analyze the inquiry, searches its database for the appropriate answer, and sends it back to the user.

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The user enters their inquiry into the smart device's inquiry interface and presses the "Send" button. At this point, the user's input (e.g., "How do I set night mode on my security camera?") is generated as text data.

[0644] Step 2:

[0645] The terminal receives user input and converts this text data into JSON format. This process involves data processing that stores the input text in a key:value format. The generated JSON data is then sent to the server's API endpoint.

[0646] Step 3:

[0647] The server's receiving module receives JSON data sent from the terminal. It parses the received JSON data to extract the original text data.

[0648] Step 4:

[0649] The server's analysis module analyzes the received text data and extracts important keywords using natural language processing techniques (e.g., "security camera," "night mode," "settings"). This process involves tokenization and stemming of the text.

[0650] Step 5:

[0651] The server's database module searches the answer database based on the extracted keywords. It uses a search algorithm to retrieve the answer that best matches the keywords (e.g., "To set night mode for your security camera, select Night Vision from the settings menu and turn it on").

[0652] Step 6:

[0653] The server's response module converts the received response into JSON format and generates reply data for the terminal. Here, new key:value format JSON data is generated.

[0654] Step 7:

[0655] The terminal receives the JSON-formatted response sent from the server, parses it, and extracts the original text-formatted response. This text is then formatted appropriately for display in the user interface.

[0656] Step 8:

[0657] The user checks the response displayed on their device and obtains the necessary information. Specifically, the user sees a response on their smartphone screen that says, "To set the security camera's night mode, select Night Vision from the settings menu and turn it on."

[0658] This allows users to obtain information quickly and accurately, improving their satisfaction with inquiries.

[0659] 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.

[0660] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state. This system primarily functions in cooperation with the server, terminal, and user.

[0661] System Configuration

[0662] This system includes the following components:

[0663] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0664] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[0665] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0666] Receiving module: Receives inquiries sent from terminals.

[0667] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0668] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[0669] Database module: Holds and searches a database of pre-prepared questions and answers.

[0670] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[0671] Program processing

[0672] The following describes the process flow when a program is executed, using natural language.

[0673] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[0674] 2. Terminal: The terminal receives the input query and converts it into JSON format. It packages the query content as data and prepares to send it to the server.

[0675] 3. Terminal: Sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[0676] 4. Server: The server's receiving module receives the POST request. It receives the sent data and passes it on to the next process.

[0677] 5. Server: The server analyzes the queries received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours." Furthermore, the emotion engine recognizes the user's emotional state based on the analysis results. For example, it determines whether the user is angry, happy, or in a neutral emotional state.

[0678] 6. Server: The database module searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[0679] 7. Server: Based on the emotion engine's recognition results, the response module adjusts its response. For example, if the user is angry, it can choose a response that uses more polite and calm language.

[0680] 8. Server: The response module generates the adjusted response as JSON-formatted response data. It then converts it to a data format for sending back to the user.

[0681] 9. Server: Sends the generated response data to the terminal. Returns data in JSON format as a response to the POST request.

[0682] 10. Terminal: Receives the response from the server. Analyzes the received response data and prepares it for display on the user interface.

[0683] 11. Terminal: Display the analyzed responses in the user interface. Specific responses, such as "Our business hours are 9:00 AM to 6:00 PM," should be displayed in a user-friendly format.

[0684] 12. User: Check the answer displayed on the device. Check the displayed answer to obtain appropriate information regarding the inquiry.

[0685] Specific example

[0686] For example, when a user makes an inquiry about business hours, they type "What are your business hours?" and send it. The terminal sends this inquiry to the server, and after the server receives it, the analysis module extracts the keyword "business hours." Furthermore, the sentiment engine recognizes the user's emotional state, and if the user is, for example, favorable, it selects a polite response that reflects that emotion. The database module searches for an appropriate answer based on this keyword and generates a response such as "Our business hours are 9:00 to 18:00" and sends it back to the terminal. Finally, the user can confirm this answer on their terminal.

[0687] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The user enters their inquiry. For example, they might type "Please tell me your business hours" and press the submit button.

[0691] Step 2:

[0692] The terminal receives user input and converts it into JSON format. It then packages the inquiry content as data and prepares to send it to the server.

[0693] Step 3:

[0694] The device sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[0695] Step 4:

[0696] The server's receiving module accepts the POST request. It receives the sent data and prepares it for the next processing step.

[0697] Step 5:

[0698] The server's analysis module analyzes the received query. It uses natural language processing techniques to extract keywords such as "business hours."

[0699] Step 6:

[0700] The server's emotion engine recognizes the user's emotional state based on the query content it analyzes. For example, it determines whether the user is angry, happy, or neutral based on their writing style and tone.

[0701] Step 7:

[0702] The server's database module searches the answer database based on the extracted keywords. For example, it searches the database for entries corresponding to "business hours".

[0703] Step 8:

[0704] The server's response module generates an answer based on the search results. During this process, it adjusts the answer to suit the user's emotions based on the emotion engine's recognition results. For example, an angry user will receive a polite and calm response.

[0705] Step 9:

[0706] The server sends the generated response to the terminal as JSON response data. The final response data is then sent back to the terminal.

[0707] Step 10:

[0708] The terminal receives a response from the server. It analyzes the received response data and prepares it to be displayed on the user interface.

[0709] Step 11:

[0710] The terminal analyzes the data and displays the results on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed to the user.

[0711] Step 12:

[0712] The user checks the answer displayed on their device. They can then obtain an appropriate answer to their inquiry.

[0713] Thus, the system of the present invention not only provides a rapid and accurate automated response to user inquiries, but also achieves higher user satisfaction by providing an optimal answer that takes into account the user's emotional state.

[0714] (Example 2)

[0715] 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".

[0716] Traditional inquiry systems have a problem of lowering user satisfaction by providing uniform, standardized answers without considering the user's emotional state. Furthermore, if users make inquiries while feeling dissatisfied or anxious, inappropriate responses can lead to further dissatisfaction. Therefore, there is a need for a system that takes the user's emotional state into consideration and provides more personalized responses.

[0717] 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.

[0718] In this invention, the server includes means for receiving user inquiries, means for analyzing the received inquiries and searching for matching answers from a pre-prepared answer database, means for recognizing the user's emotional state based on the analyzed inquiry content, and means for adjusting the retrieved answers according to the recognized emotional state of the user. This makes it possible to provide appropriate answers according to the user's emotional state.

[0719] "Means of receiving user inquiries" refers to the function of sending inquiries entered by users through their terminals to a server and receiving them.

[0720] "Means for analyzing received inquiries" refers to technologies for understanding the content of submitted inquiries and extracting keywords and intents.

[0721] "Means of searching for matching answers from the answer database" refers to a function that searches a pre-prepared answer database based on analyzed keywords and intent to find the most suitable answer.

[0722] "Means of recognizing a user's emotional state" refers to technology that determines a user's emotions based on the analyzed content of their inquiry and identifies states such as joy, anger, sadness, and happiness.

[0723] "Means of adjusting searched answers according to the recognized emotional state of the user" refers to a function that modifies the tone and content of answers according to the user's emotions and provides them to the user in a more appropriate form.

[0724] This invention is a system that automatically and appropriately responds to user inquiries. This system aims to increase user satisfaction by recognizing the user's emotional state. This system primarily functions through collaboration between the server, terminal, and user.

[0725] System Configuration

[0726] This system includes the following components:

[0727] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0728] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, tablets, etc.

[0729] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0730] Receiving module: Receives inquiries sent from terminals.

[0731] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0732] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[0733] Database module: Holds and searches a database of pre-prepared questions and answers.

[0734] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[0735] Program processing

[0736] Program Overview

[0737] The program of this invention integrates natural language processing (NLP) technology and sentiment analysis technology. This allows it to analyze user inquiries and generate appropriate responses. The software used includes natural language processing libraries (e.g., NLTK, Spacy) and sentiment analysis engines (e.g., GCP's Natural Language API).

[0738] Hardware to use

[0739] Server: Cloud services (e.g., AWS, GCP, Azure) are used for program execution and data storage.

[0740] Terminal: A device used by the user to submit inquiries. This includes PCs, smartphones, and tablets.

[0741] Detailed explanation of operation

[0742] The specific actions the system takes from receiving a user inquiry to analyzing it, generating an answer, and providing the answer are as follows:

[0743] 1. User: The user types "Please tell me your business hours" and submits it.

[0744] 2. Terminal: The terminal prepares to send this query to the server. It converts the entered query into JSON format and sends it as a POST request to the server's API endpoint.

[0745] 3. Server: The server's receiving module receives the POST request and passes the sent data to the next stage of processing. The parsing module parses the received data and extracts the keyword "business hours".

[0746] 4. Server: The emotion engine uses this analysis result to recognize the user's emotions. For example, it might determine that the user is in a neutral state.

[0747] 5. Server: The database module searches for the answer based on the keyword "business hours" and retrieves "Our business hours are 9:00 to 18:00".

[0748] 6. Server: The response module takes the results of the sentiment engine into account and generates an appropriate response. A standard response is set for neutral sentiment states.

[0749] 7. Server: Sends the generated response to the terminal as JSON response data.

[0750] 8. Terminal: The terminal receives the response from the server, parses it, and displays it on the user interface. Specifically, it displays "Our business hours are 9:00 to 18:00."

[0751] Specific example operations

[0752] For example, if a user asks, "What are your business hours?", the following would occur:

[0753] 1. User: The user types "Please tell me your business hours" on their device and taps the send button.

[0754] 2. Terminal: The terminal receives this query, converts it to JSON format, and sends it to the server.

[0755] 3. Server: The server receives the data and performs the following steps: analysis, sentiment recognition, database search, and response generation.

[0756] 4. Terminal: Receives a response from the server and displays "Our business hours are 9:00 AM to 6:00 PM" on the user interface.

[0757] 5. User: The user reviews the displayed answer.

[0758] Example of a prompt

[0759] Here are some examples of prompts for a generative AI model:

[0760] "For the user's inquiry, 'What are your business hours?', search the answer database for the relevant response and generate an appropriate answer assuming the user's sentiment is neutral."

[0761] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] User: The user enters their inquiry.

[0765] Specific action: The user opens the application on their smartphone or PC and enters "Please tell me your business hours" into the text input field on the screen.

[0766] Input: User's text input.

[0767] Output: User inquiry ("What are your business hours?").

[0768] Step 2:

[0769] Terminal: The terminal converts the query into JSON format.

[0770] Specific operation: The application on the device captures the entered text and converts it into JSON data like this:

[0771] json

[0772] {

[0773] "query": "Please tell me your business hours."

[0774] }

[0775] Input: User's inquiry.

[0776] Output: Query data in JSON format.

[0777] Step 3:

[0778] Terminal: The terminal sends a POST request to the server.

[0779] Specific operation: The device sends JSON data as an HTTP POST request to the server's API endpoint. The endpoint URL is https: / / api.example.com / query as an example.

[0780] Input: Query data in JSON format.

[0781] Output: POST request sent to the server.

[0782] Step 4:

[0783] Server: The server receives the POST request.

[0784] Specific operation: The server's receiving module receives a POST request and retrieves JSON data from the HTTP request body. It also verifies that the request is in the correct format.

[0785] Input: HTTP POST request.

[0786] Output: Query data in JSON format.

[0787] Step 5:

[0788] Server: The server parses the query.

[0789] Specific operation: The server's parsing module parses the JSON data and extracts the text "Please tell me your business hours" from the "query" field. Furthermore, it uses natural language processing techniques to extract the keyword "business hours". The techniques used include NLTK and Spacy.

[0790] Input: Query data in JSON format.

[0791] Output: Extracted keyword "business hours".

[0792] Step 6:

[0793] Server: The server recognizes the user's emotional state.

[0794] Specific operation: The server's sentiment engine determines the user's emotional state from the analyzed text. For example, it uses GCP's Natural Language API to perform sentiment analysis on the text and determine that the user's emotion is neutral.

[0795] Input: Analyzed text data.

[0796] Output: User's emotional state (neutral).

[0797] Step 7:

[0798] Server: The server searches the answer database.

[0799] Specific operation: The database module searches a pre-prepared answer database based on the keyword "business hours". For example, if an entry for "business hours" exists in the database, it will find the corresponding answer "Our business hours are 9:00 to 18:00".

[0800] Input: Extracted keyword "Business Hours".

[0801] Output: Search results ("Our business hours are 9:00 AM to 6:00 PM").

[0802] Step 8:

[0803] Server: The server generates the answer.

[0804] Specific operation: The response module generates an appropriate response considering the search results and the user's sentiment state. For a neutral sentiment, it selects the standard response "Our business hours are 9:00 AM to 6:00 PM."

[0805] Input: Search results, user's emotional state.

[0806] Output: Generated response ("Our business hours are 9:00 AM to 6:00 PM").

[0807] Step 9:

[0808] Server: The server sends the response to the terminal.

[0809] Specific operation: The server sends the generated response to the terminal as JSON-formatted response data. It returns the POST request response as an HTTP response.

[0810] Input: Generated response.

[0811] Output: Response data in JSON format.

[0812] Step 10:

[0813] Terminal: The terminal receives a response from the server.

[0814] Specific operation: The terminal receives a JSON response from the server and parses the data. Specifically, it extracts "Our business hours are 9:00 to 18:00" from the "response" field.

[0815] Input: Response data in JSON format.

[0816] Output: Analyzed response text.

[0817] Step 11:

[0818] Terminal: The terminal displays the answer in the user interface.

[0819] Specific operation: Execute code to display the analyzed response text on the user interface, showing "Our business hours are 9:00 AM to 6:00 PM" on the screen.

[0820] Input: Analyzed response text.

[0821] Output: The answer displayed to the user.

[0822] Step 12:

[0823] User: The user checks the displayed answer.

[0824] Specific action: The user confirms the response displayed on their device screen, "Our business hours are 9:00 AM to 6:00 PM," and obtains an answer to their inquiry.

[0825] Input: The answer displayed on the device.

[0826] Output: Information obtained by the user.

[0827] (Application Example 2)

[0828] 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."

[0829] Conventional automated response systems simply provide standardized answers based on keywords to user inquiries, failing to consider the user's emotional state. This resulted in insufficient improvement in user satisfaction, and particularly in handling emotionally charged inquiries. Furthermore, there is a need to efficiently provide interfaces compatible with various devices such as smartphones. Solving these problems is the challenge.

[0830] 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 providing an interface in which the user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for recognizing the content of the received inquiry and the user's emotional state, means for generating an optimal answer based on the emotional state, and means for returning the retrieved answer to the user. This makes it possible to provide an optimized answer that takes the user's emotional state into consideration. Furthermore, it can be used as an application installed on smartphones, smart glasses, head-mounted displays, or robots, enabling support for a variety of devices.

[0831] "Means of providing an interface for users to input inquiries" refers to functions that provide input methods such as GUIs (Graphical User Interfaces), forms, and chatbots for users to enter information.

[0832] "Means for receiving user inquiries" refers to a function that sends user-entered inquiries to a server or cloud service and then receives them.

[0833] "A means of analyzing received inquiries and searching for matching answers from a pre-prepared answer database" refers to a function that uses natural language processing technology to analyze the content of user inquiries, extracts relevant keywords and phrases, and searches for appropriate answers from the database.

[0834] "Means for recognizing the content of received inquiries and the user's emotional state" refers to a function that identifies the user's emotional state (anger, joy, sadness, etc.) using an emotion analysis engine, along with the analyzed inquiry.

[0835] "Means for generating optimal responses based on emotional state" refers to a function that adjusts responses to provide users with appropriate tone and wording based on their recognized emotional state.

[0836] "Means of sending search results back to the user" refers to a function that sends the adjusted answers back to the user's device and displays them in the user interface.

[0837] "Sentiment analysis" is a technology used to identify a user's emotions from the content of an inquiry received.

[0838] "Natural language processing" is a technology that enables computers to understand, analyze, and generate appropriate responses to human language.

[0839] Modes for carrying out the invention

[0840] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state.

[0841] System Configuration

[0842] This system includes the following components.

[0843] 1. User Inquiry Interface:

[0844] This is an interface used by users to enter and submit inquiries. It is typically provided as a web form, a smartphone app, or an input field for smart glasses, head-mounted displays, or robots.

[0845] 2. Terminal:

[0846] This refers to a device that allows users to input inquiries and send them to a server. Examples include personal computers, smartphones, and tablets.

[0847] 3. Server:

[0848] It is a central device for processing inquiries received from terminals and providing appropriate answers. The server includes the following functions:

[0849] Receiving module:

[0850] Receive inquiries sent from the device.

[0851] Analysis module:

[0852] The system analyzes received inquiries and extracts keywords and semantics. Natural language processing techniques are used.

[0853] Emotion analysis engine:

[0854] Based on the analyzed inquiry content, the system recognizes the user's emotional state. For example, this can be done using the Amazon Comprehend API.

[0855] Database module:

[0856] It maintains a database of pre-prepared questions and answers, which can be searched.

[0857] Response module:

[0858] It searches for and generates a refined response, and sends it back to the terminal.

[0859] Program Processing Description

[0860] 1. The server provides a means of providing a user interface, which is an interface for users to input inquiries. For example, this is the chatbot screen of a smartphone application.

[0861] 2. The server's receiving module receives the query entered by the user. At this time, the query content is sent to the server in JSON format.

[0862] 3. The analysis module uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze incoming inquiries and extract keywords. For example, it extracts the keyword "product features" from the inquiry "Please tell me about the features of the product."

[0863] 4. The emotion analysis engine recognizes the user's emotional state. For example, it uses Amazon Comprehend to recognize emotions such as "interested."

[0864] 5. The database module searches the answer database based on the extracted keywords and retrieves relevant information. For example, it searches for product feature information related to "product features".

[0865] 6. The response module generates the most appropriate response based on the user's emotional state. For example, if the user is feeling positive emotions, it will generate a response such as, "This product is made from high-quality materials and is highly recommended."

[0866] 7. The server sends the generated response back to the terminal and displays it in the user interface.

[0867] Specific example

[0868] Specific examples of how the system works

[0869] As a concrete example, a user enters "Please tell me the features of this product" into a smartphone application and sends it. The server receives the message, the analysis module performs natural language processing, and the sentiment analysis engine recognizes the user's sentiment as "very interested." Subsequently, the database module searches for the features of related products, and the response module generates and sends back a response that matches the sentiment, such as "This product is made from high-quality materials and is highly recommended." The user can then view this response on their smartphone.

[0870] Example of a prompt

[0871] User prompt: "Please tell me about the features of this product."

[0872] AI Model Prompt: Uses a product search API to search for "features" and an emotion engine API to recognize the user's emotional state. If the user is friendly, it generates a response such as, "This product is made from high-quality materials and is highly recommended."

[0873] In this way, the system of the present invention can provide a rapid and accurate automated response to user inquiries, and further enhance customer satisfaction by providing the optimal answer while taking into account the user's emotional state.

[0874] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0875] Step 1:

[0876] The user enters their inquiry. The user types "Please tell me the features of this product" on the chatbot screen of the smartphone app. The entered data is converted to JSON format depending on the device.

[0877] Step 2:

[0878] The device sends the received query to the server. Specifically, the query data, converted to JSON format, is sent as a POST request to the server's API endpoint.

[0879] Step 3:

[0880] The server's receiving module receives a POST request. The received data is passed to the parsing module. The input is query data in JSON format, and the output is data ready for parsing.

[0881] Step 4:

[0882] The analysis module analyzes the received inquiry. It uses natural language processing technologies such as the Google Cloud Natural Language API to extract keywords such as "product features." The input is the received inquiry data, and the output is the extracted keywords.

[0883] Step 5:

[0884] The results of the analysis module are passed to the sentiment analysis engine to recognize the user's emotional state. For example, the Amazon Comprehend API is used to analyze emotions such as "interested." The input is the analyzed query data, and the output is the user's emotional state.

[0885] Step 6:

[0886] The database module searches the answer database based on the extracted keywords. For example, it uses MySQL or MongoDB to retrieve information related to "product features." The input is the extracted keywords, and the output is data about the searched product features.

[0887] Step 7:

[0888] The response module generates the most appropriate response based on the user's sentiment and search results. For example, if the user is favorable using the Google Cloud Natural Language API, it might generate a response such as, "This product is made with high-quality materials and is highly recommended." The input is the user's sentiment and search results data, and the output is the optimized response.

[0889] Step 8:

[0890] The server sends the generated response back to the terminal in JSON format. The terminal parses the received response data and prepares it for display in the user interface. The input is the generated response data, and the output is the data to be displayed in the user interface.

[0891] Step 9:

[0892] The device receives a response from the server and displays it in the user interface. Specifically, a response such as "This product is made from high-quality materials and is highly recommended" is displayed on the chatbot screen of the smartphone app. Finally, the user can confirm the displayed response.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] [Third Embodiment]

[0897] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0898] 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.

[0899] 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).

[0900] 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.

[0901] 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.

[0902] 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).

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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".

[0909] The embodiments for carrying out the present invention will now be described. This system automatically provides appropriate answers to user inquiries and functions primarily through cooperation between a server, a terminal, and the user.

[0910] System Configuration

[0911] This system includes the following components:

[0912] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0913] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[0914] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[0915] Receiving module: Receives inquiries sent from terminals.

[0916] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[0917] Database module: Holds and searches a database of pre-prepared questions and answers.

[0918] Response module: Generates an appropriate response and sends it back to the terminal.

[0919] Program processing

[0920] The following describes the process flow when a program is executed, using natural language.

[0921] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[0922] 2. Terminal: The terminal receives the input query and converts it into JSON format. The terminal then sends the constructed request to the server's API endpoint.

[0923] 3. Server: The server's receiving module receives the request sent from the terminal.

[0924] 4. Server: The server analyzes the requests received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours".

[0925] 5. Server: The server searches the answer database based on the keywords extracted by the database module. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[0926] 6. Server: The response module generates the retrieved response in JSON format and sends it to the terminal.

[0927] 7. Terminal: The terminal receives the JSON-formatted response sent from the server.

[0928] 8. Terminal: Analyzes the received response and displays the answer on the user interface. It will be displayed in the format of "Our business hours are 9:00 to 18:00."

[0929] 9. User: The user reviews the response displayed on their device and obtains appropriate information regarding their inquiry.

[0930] Specific example

[0931] As a concrete example, when making an inquiry about business hours, the user types "What are your business hours?" and sends it. The terminal sends this inquiry to the server, and after the server receives it, the parsing module extracts the keyword "business hours". The database module searches for the appropriate answer based on this keyword, and the response module generates the answer "Our business hours are 9:00 to 18:00" and returns it to the terminal. Finally, the user can confirm this answer on their terminal.

[0932] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[0933] The following describes the processing flow.

[0934] Step 1:

[0935] The user enters their inquiry. They specify the information they want to know and press the submit button. For example, they might enter, "Please tell me your business hours."

[0936] Step 2:

[0937] The terminal receives user input and converts it into JSON format. It then packages the query content as data and prepares to send it to the server.

[0938] Step 3:

[0939] The device sends a POST request to the server's API endpoint. The query data, converted to JSON format, is sent to the server.

[0940] Step 4:

[0941] The server receives a POST request. The server's receiving module receives the sent data and passes it on to the next process.

[0942] Step 5:

[0943] The server's analysis module analyzes the received query. Specifically, it uses natural language processing techniques to extract keywords and context from the query. For example, it might extract the keyword "business hours."

[0944] Step 6:

[0945] The server's database module searches the answer database based on the extracted keywords. It then searches the answer database for entries related to "business hours".

[0946] Step 7:

[0947] The server's database module retrieves the appropriate answer. For example, it retrieves the entry "Our business hours are 9:00 AM to 6:00 PM" from the database.

[0948] Step 8:

[0949] The server's response module generates the retrieved response as JSON-formatted response data. It then converts it into a data format for sending a reply to the user.

[0950] Step 9:

[0951] The server sends the generated response data to the terminal. It returns data in JSON format as a response to the POST request.

[0952] Step 10:

[0953] The terminal receives a response from the server. It parses the response data and prepares it for display on the user interface.

[0954] Step 11:

[0955] The terminal analyzes the response and displays it on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed in a user-friendly format.

[0956] Step 12:

[0957] The user checks the response displayed on their device. They check the displayed response to obtain appropriate information regarding their inquiry.

[0958] As described above, the entire system works together to provide fast and accurate automated responses to user inquiries.

[0959] (Example 1)

[0960] 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."

[0961] Conventional automated response systems struggled to provide timely and appropriate answers to user inquiries. Furthermore, they lacked the technology to accurately analyze user inquiries and provide precise responses based on those analyses. There was also a need to improve the overall data processing efficiency and usability of the system.

[0962] 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.

[0963] In this invention, the server includes means for a user to input an inquiry, means for a terminal to convert the user's inquiry into JSON format and send it to the server, means for the server to analyze the received inquiry and extract keywords, means for searching a pre-prepared answer database based on the keywords, means for generating the retrieved answer in JSON format and sending it to the terminal, and means for the terminal to display the received answer to the user. This enables a rapid and accurate automated response to the user's inquiry.

[0964] A "user" is a person or entity that makes inquiries to a system.

[0965] "Means" refers to a method, apparatus, or component within a system used to achieve a particular function or process.

[0966] An "inquiry" is a question that a user enters into the system about a problem they want to solve or information they want to know.

[0967] A "terminal" is a device used by a user to access a system, and includes PCs, smartphones, tablets, and other similar devices.

[0968] "JSON format" is an abbreviation for JavaScript Object Notation, and it is a text format for structuring data as key-value pairs.

[0969] A "server" is a computer system that receives requests, processes data, and returns responses.

[0970] "Analysis" is the process of understanding inquiries received from users and extracting important keywords.

[0971] "Keywords" are words or phrases that are considered particularly important from the content of the inquiry.

[0972] A "response database" is a collection of data in which answers to inquiries are recorded in advance.

[0973] "Searching" is the process of finding the appropriate answer from a database of answers based on keywords.

[0974] "Transmission" is the process of moving data or a request from one system to another.

[0975] "Receiving" is the process by which one system takes in data or requests sent from another system.

[0976] "Display" refers to the process of providing information to a user visually on a device.

[0977] This document describes embodiments for carrying out the present invention. This system automatically provides appropriate answers to user inquiries and primarily functions through the cooperation of a server, terminal, and user. Specific hardware and software, as well as methods for data processing and calculation, will also be clearly described.

[0978] System Configuration

[0979] This system includes the following components:

[0980] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[0981] 2. Terminal: A terminal has the function of receiving user input, converting it into JSON format, and sending it to the server. This includes PCs, smartphones, and tablets.

[0982] 3. Server: A server is a central device that processes inquiries received from terminals and provides appropriate answers. It has the following functions:

[0983] Receiving module: Receives queries sent from terminals. Apache HTTP Server or Nginx are typically used.

[0984] Analysis Module: Analyzes received queries and extracts keywords and semantics. This analysis uses natural language processing technologies such as NLTK and spaCy.

[0985] Database Module: Maintains and allows searching a database of pre-defined questions and answers. MySQL or PostgreSQL are used for the database.

[0986] Response module: Generates the retrieved response in JSON format and sends it back to the terminal. Flask or Django are often used for this.

[0987] Program processing

[0988] The following describes the process flow when a program is executed, using natural language.

[0989] First, the user enters their inquiry. For example, they might enter "Please tell me your business hours." The terminal receives this input, converts it to JSON format, and sends it to the server's API endpoint.

[0990] Next, the server's receiving module receives the request sent from the terminal. The parsing module analyzes the received request and uses natural language processing techniques to extract keywords such as "business hours."

[0991] The database module then searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer, "Our business hours are 9:00 AM to 6:00 PM."

[0992] The response module generates the retrieved response in JSON format and sends it to the terminal. The terminal receives the JSON response sent from the server, parses it, and displays it in the user interface. Finally, the user checks the response displayed on the terminal and obtains the appropriate information regarding the inquiry.

[0993] Specific example

[0994] For example, if a user asks, "What are your business hours?", it would look like this:

[0995] 1. The user types "Please tell me your business hours" and submits it.

[0996] 2. The terminal converts this query into JSON format and sends it to the server.

[0997] 3. The server analyzes the received request and extracts the keyword "business hours".

[0998] 4. The database module searches for answers based on this keyword.

[0999] 5. The response obtained is, "Our business hours are 9:00 AM to 6:00 PM."

[1000] 6. The response module generates this answer in JSON format and sends it to the terminal.

[1001] 7. The device displays the answer in the user interface.

[1002] 8. The user reviews the displayed answer.

[1003] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[1004] Examples of prompts for generative AI models

[1005] The following are examples of prompts for training a generative AI model with new questions:

[1006] Input: "What day of the week are you closed?"

[1007] Output: "Our company is closed every Sunday."

[1008] Input: "Please tell me the address of the branch."

[1009] Output: "Our branch office is located at 1-1-1 Marunouchi, Chiyoda-ku, Tokyo."

[1010] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1011] Step 1:

[1012] The user enters an inquiry.

[1013] The user enters their inquiry using a terminal. This is often done through a web form in a browser or a text field in an application. In this example, the user enters "Please tell me your business hours." The input data is sent to the terminal in text format.

[1014] Step 2:

[1015] The terminal converts the query into JSON format and sends it to the server.

[1016] The terminal receives text entered by the user and converts it into JSON format. Next, it sends the converted JSON data to the server as an HTTP POST request. Specifically, it converts it as a JSON object like {"question": "Please tell me your business hours"}. The converted JSON data is then sent to the server's API endpoint.

[1017] Step 3:

[1018] The server receives the request.

[1019] The server's receiving module receives an HTTP POST request sent from the terminal. The received data is in JSON format, in the form of {"question": "Please tell me your business hours"}. This data is then passed to the next parsing module.

[1020] Step 4:

[1021] The server parses the request.

[1022] The server's parsing module parses the received JSON data. Using natural language processing techniques (for example, libraries like NLTK or spaCy), it extracts the keyword "business hours". Specifically, it parses the text "Please tell me your business hours" and generates a keyword list ["business hours"]. This keyword list is then passed to the next database module.

[1023] Step 5:

[1024] The server searches the database.

[1025] The server's database module searches the database based on the keyword list received from the analysis module. This database stores pre-defined question-and-answer pairs. For example, it searches the database for entries matching the keyword "business hours" and retrieves the answer "Our business hours are 9:00 to 18:00." This answer data is then passed to the response module.

[1026] Step 6:

[1027] The server generates the response in JSON format.

[1028] The server's response module converts the retrieved answer into JSON format. Specifically, it generates a JSON object with the format {"answer": "Our business hours are 9:00 to 18:00"}. This JSON data is then sent to the terminal.

[1029] Step 7:

[1030] The device receives and displays the response.

[1031] The terminal receives data in JSON format sent from the server. Next, it parses this data and extracts the response. The response obtained as a result of the analysis, "Our business hours are 9:00 to 18:00," is displayed on the user interface. Specifically, this text is displayed in a designated area on the browser.

[1032] Step 8:

[1033] The user confirms the answer.

[1034] The user checks the response displayed on their device and obtains appropriate information regarding their inquiry. By reading the displayed response, "Our business hours are 9:00 AM to 6:00 PM," the user can quickly and accurately obtain the necessary information.

[1035] (Application Example 1)

[1036] 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."

[1037] In today's diverse device environment, it is crucial to respond quickly and accurately to user inquiries. However, existing systems are dependent on specific devices and interfaces, limiting their effectiveness in communicating with users via smart devices. Furthermore, the automation of natural language processing for analysis and response generation is insufficient, leading to decreased user satisfaction. A flexible and highly responsive system is needed to address these challenges.

[1038] 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.

[1039] In this invention, the server includes means for providing an interface in which a user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for returning the retrieved answer to the user, and means for displaying it on the user interface via a smart device. This enables quick and accurate responses using smartphones or head-mounted displays.

[1040] A "user inquiry interface" is an input method for users to enter questions or requests for information, and typically includes a text box on the screen or a voice input function.

[1041] A "smart device" refers to a portable electronic device, such as a smartphone or head-mounted display, that can connect to the internet and has advanced processing capabilities.

[1042] A "server" is a central device that receives requests from users, analyzes them, and generates and sends responses; it is a computer system that provides services over a network.

[1043] "Natural language processing" refers to the technology of understanding, analyzing, and generating human language using computers, and is a field of technology applied to text analysis, speech recognition, machine translation, and more.

[1044] A "response database" is a data storage system that organizes and stores pre-prepared answers to inquiries, and its purpose is to quickly provide appropriate answers to user questions.

[1045] A "receiving module" is a part of the software that receives user inquiries, which then reach the server.

[1046] A "parsing module" is a software module that analyzes received inquiries to extract keywords and meanings, and often uses natural language processing.

[1047] A "response module" is a software module that searches a database for the appropriate answer based on the analysis results, generates that answer, and sends it back to the user.

[1048] The present invention will now be described in terms of embodiments. This system provides quick and accurate answers to user inquiries using smart devices, and its main components include a server, a terminal, and a user interface. Furthermore, it utilizes natural language processing and an answer database.

[1049] System components

[1050] 1. User Inquiry Interface: Provide an interface for users to input inquiries using smart devices (e.g., smartphones or head-mounted displays). For example, a user might input "How do I set the night mode on my security camera?" through a smartphone app.

[1051] 2. Terminal: This is a device that receives user inquiries, such as a smartphone or head-mounted display. The terminal has the function of sending the received inquiries to the server in JSON format.

[1052] 3. Server: As a central device, it has the following functions.

[1053] Receiving module: Receives user inquiries from the terminal.

[1054] Analysis module: Analyzes received queries and extracts keywords and meanings using natural language processing.

[1055] Database module: Searches a pre-prepared answer database to retrieve the appropriate answer.

[1056] Response module: Generates the searched answer in JSON format and sends it back to the terminal.

[1057] Processing flow

[1058] User:

[1059] The user opens the app on their smartphone and enters their inquiry. For example, they might type, "How do I set the night mode on my security camera?"

[1060] Terminal:

[1061] The terminal receives the input query, converts it to JSON format, and sends it to the server's API endpoint.

[1062] server:

[1063] The server's receiving module receives requests from the terminal. Next, the analysis module analyzes the received requests and extracts keywords such as "security camera," "night mode," and "settings."

[1064] The database module searches the answer database based on this keyword and retrieves the answer, "To set night mode for your security camera, select Night Vision from the settings menu and turn it on."

[1065] The response module generates the retrieved response in JSON format and sends it to the terminal.

[1066] Terminal:

[1067] The terminal receives a response from the server, parses it, and displays it on the user interface.

[1068] User:

[1069] The user checks the answers displayed on the device and obtains the necessary information.

[1070] Hardware and software to use

[1071] Frontend: React Native (User interface development)

[1072] Backend: Node.js + Express (server processing)

[1073] Natural Language Processing: Natural (Node.js natural language processing library)

[1074] Database: MongoDB or MySQL (database for questions and answers)

[1075] Presentation of specific examples

[1076] As a concrete example of this system, consider a scenario where a user asks, "How do I set the night mode on my security camera?" The user enters this inquiry using their smartphone, and the device sends it to the server. The server uses natural language processing to analyze the inquiry, searches its database for the appropriate answer, and sends it back to the user.

[1077] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1078] Step 1:

[1079] The user enters their inquiry into the smart device's inquiry interface and presses the "Send" button. At this point, the user's input (e.g., "How do I set night mode on my security camera?") is generated as text data.

[1080] Step 2:

[1081] The terminal receives user input and converts this text data into JSON format. This process involves data processing that stores the input text in a key:value format. The generated JSON data is then sent to the server's API endpoint.

[1082] Step 3:

[1083] The server's receiving module receives JSON data sent from the terminal. It parses the received JSON data to extract the original text data.

[1084] Step 4:

[1085] The server's analysis module analyzes the received text data and extracts important keywords using natural language processing techniques (e.g., "security camera," "night mode," "settings"). This process involves tokenization and stemming of the text.

[1086] Step 5:

[1087] The server's database module searches the answer database based on the extracted keywords. It uses a search algorithm to retrieve the answer that best matches the keywords (e.g., "To set night mode for your security camera, select Night Vision from the settings menu and turn it on").

[1088] Step 6:

[1089] The server's response module converts the received response into JSON format and generates reply data for the terminal. Here, new key:value format JSON data is generated.

[1090] Step 7:

[1091] The terminal receives the JSON-formatted response sent from the server, parses it, and extracts the original text-formatted response. This text is then formatted appropriately for display in the user interface.

[1092] Step 8:

[1093] The user checks the response displayed on their device and obtains the necessary information. Specifically, the user sees a response on their smartphone screen that says, "To set the security camera's night mode, select Night Vision from the settings menu and turn it on."

[1094] This allows users to obtain information quickly and accurately, improving their satisfaction with inquiries.

[1095] 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.

[1096] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state. This system primarily functions in cooperation with the server, terminal, and user.

[1097] System Configuration

[1098] This system includes the following components:

[1099] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[1100] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[1101] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[1102] Receiving module: Receives inquiries sent from terminals.

[1103] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[1104] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[1105] Database module: Holds and searches a database of pre-prepared questions and answers.

[1106] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[1107] Program processing

[1108] The following describes the process flow when a program is executed, using natural language.

[1109] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[1110] 2. Terminal: The terminal receives the input query and converts it into JSON format. It packages the query content as data and prepares to send it to the server.

[1111] 3. Terminal: Sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[1112] 4. Server: The server's receiving module receives the POST request. It receives the sent data and passes it on to the next process.

[1113] 5. Server: The server analyzes the queries received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours." Furthermore, the emotion engine recognizes the user's emotional state based on the analysis results. For example, it determines whether the user is angry, happy, or in a neutral emotional state.

[1114] 6. Server: The database module searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[1115] 7. Server: Based on the emotion engine's recognition results, the response module adjusts its response. For example, if the user is angry, it can choose a response that uses more polite and calm language.

[1116] 8. Server: The response module generates the adjusted response as JSON-formatted response data. It then converts it to a data format for sending back to the user.

[1117] 9. Server: Sends the generated response data to the terminal. Returns data in JSON format as a response to the POST request.

[1118] 10. Terminal: Receives the response from the server. Analyzes the received response data and prepares it for display on the user interface.

[1119] 11. Terminal: Display the analyzed responses in the user interface. Specific responses, such as "Our business hours are 9:00 AM to 6:00 PM," should be displayed in a user-friendly format.

[1120] 12. User: Check the answer displayed on the device. Check the displayed answer to obtain appropriate information regarding the inquiry.

[1121] Specific example

[1122] For example, when a user makes an inquiry about business hours, they type "What are your business hours?" and send it. The terminal sends this inquiry to the server, and after the server receives it, the analysis module extracts the keyword "business hours." Furthermore, the sentiment engine recognizes the user's emotional state, and if the user is, for example, favorable, it selects a polite response that reflects that emotion. The database module searches for an appropriate answer based on this keyword and generates a response such as "Our business hours are 9:00 to 18:00" and sends it back to the terminal. Finally, the user can confirm this answer on their terminal.

[1123] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[1124] The following describes the processing flow.

[1125] Step 1:

[1126] The user enters their inquiry. For example, they might type "Please tell me your business hours" and press the submit button.

[1127] Step 2:

[1128] The terminal receives user input and converts it into JSON format. It then packages the inquiry content as data and prepares to send it to the server.

[1129] Step 3:

[1130] The device sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[1131] Step 4:

[1132] The server's receiving module accepts the POST request. It receives the sent data and prepares it for the next processing step.

[1133] Step 5:

[1134] The server's analysis module analyzes the received query. It uses natural language processing techniques to extract keywords such as "business hours."

[1135] Step 6:

[1136] The server's emotion engine recognizes the user's emotional state based on the query content it analyzes. For example, it determines whether the user is angry, happy, or neutral based on their writing style and tone.

[1137] Step 7:

[1138] The server's database module searches the answer database based on the extracted keywords. For example, it searches the database for entries corresponding to "business hours".

[1139] Step 8:

[1140] The server's response module generates an answer based on the search results. During this process, it adjusts the answer to suit the user's emotions based on the emotion engine's recognition results. For example, an angry user will receive a polite and calm response.

[1141] Step 9:

[1142] The server sends the generated response to the terminal as JSON response data. The final response data is then sent back to the terminal.

[1143] Step 10:

[1144] The terminal receives a response from the server. It analyzes the received response data and prepares it to be displayed on the user interface.

[1145] Step 11:

[1146] The terminal analyzes the data and displays the results on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed to the user.

[1147] Step 12:

[1148] The user checks the answer displayed on their device. They can then obtain an appropriate answer to their inquiry.

[1149] Thus, the system of the present invention not only provides a rapid and accurate automated response to user inquiries, but also achieves higher user satisfaction by providing an optimal answer that takes into account the user's emotional state.

[1150] (Example 2)

[1151] 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."

[1152] Traditional inquiry systems have a problem of lowering user satisfaction by providing uniform, standardized answers without considering the user's emotional state. Furthermore, if users make inquiries while feeling dissatisfied or anxious, inappropriate responses can lead to further dissatisfaction. Therefore, there is a need for a system that takes the user's emotional state into consideration and provides more personalized responses.

[1153] 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.

[1154] In this invention, the server includes means for receiving user inquiries, means for analyzing the received inquiries and searching for matching answers from a pre-prepared answer database, means for recognizing the user's emotional state based on the analyzed inquiry content, and means for adjusting the retrieved answers according to the recognized emotional state of the user. This makes it possible to provide appropriate answers according to the user's emotional state.

[1155] "Means of receiving user inquiries" refers to the function of sending inquiries entered by users through their terminals to a server and receiving them.

[1156] "Means for analyzing received inquiries" refers to technologies for understanding the content of submitted inquiries and extracting keywords and intents.

[1157] "Means of searching for matching answers from the answer database" refers to a function that searches a pre-prepared answer database based on analyzed keywords and intent to find the most suitable answer.

[1158] "Means of recognizing a user's emotional state" refers to technology that determines a user's emotions based on the analyzed content of their inquiry and identifies states such as joy, anger, sadness, and happiness.

[1159] "Means of adjusting searched answers according to the recognized emotional state of the user" refers to a function that modifies the tone and content of answers according to the user's emotions and provides them to the user in a more appropriate form.

[1160] This invention is a system that automatically and appropriately responds to user inquiries. This system aims to increase user satisfaction by recognizing the user's emotional state. This system primarily functions through collaboration between the server, terminal, and user.

[1161] System Configuration

[1162] This system includes the following components:

[1163] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[1164] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, tablets, etc.

[1165] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[1166] Receiving module: Receives inquiries sent from terminals.

[1167] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[1168] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[1169] Database module: Holds and searches a database of pre-prepared questions and answers.

[1170] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[1171] Program processing

[1172] Program Overview

[1173] The program of this invention integrates natural language processing (NLP) technology and sentiment analysis technology. This allows it to analyze user inquiries and generate appropriate responses. The software used includes natural language processing libraries (e.g., NLTK, Spacy) and sentiment analysis engines (e.g., GCP's Natural Language API).

[1174] Hardware to use

[1175] Server: Cloud services (e.g., AWS, GCP, Azure) are used for program execution and data storage.

[1176] Terminal: A device used by the user to submit inquiries. This includes PCs, smartphones, and tablets.

[1177] Detailed explanation of operation

[1178] The specific actions the system takes from receiving a user inquiry to analyzing it, generating an answer, and providing the answer are as follows:

[1179] 1. User: The user types "Please tell me your business hours" and submits it.

[1180] 2. Terminal: The terminal prepares to send this query to the server. It converts the entered query into JSON format and sends it as a POST request to the server's API endpoint.

[1181] 3. Server: The server's receiving module receives the POST request and passes the sent data to the next stage of processing. The parsing module parses the received data and extracts the keyword "business hours".

[1182] 4. Server: The emotion engine uses this analysis result to recognize the user's emotions. For example, it might determine that the user is in a neutral state.

[1183] 5. Server: The database module searches for the answer based on the keyword "business hours" and retrieves "Our business hours are 9:00 to 18:00".

[1184] 6. Server: The response module takes the results of the sentiment engine into account and generates an appropriate response. A standard response is set for neutral sentiment states.

[1185] 7. Server: Sends the generated response to the terminal as JSON response data.

[1186] 8. Terminal: The terminal receives the response from the server, parses it, and displays it on the user interface. Specifically, it displays "Our business hours are 9:00 to 18:00."

[1187] Specific example operations

[1188] For example, if a user asks, "What are your business hours?", the following would occur:

[1189] 1. User: The user types "Please tell me your business hours" on their device and taps the send button.

[1190] 2. Terminal: The terminal receives this query, converts it to JSON format, and sends it to the server.

[1191] 3. Server: The server receives the data and performs the following steps: analysis, sentiment recognition, database search, and response generation.

[1192] 4. Terminal: Receives a response from the server and displays "Our business hours are 9:00 AM to 6:00 PM" on the user interface.

[1193] 5. User: The user reviews the displayed answer.

[1194] Example of a prompt

[1195] Here are some examples of prompts for a generative AI model:

[1196] "For the user's inquiry, 'What are your business hours?', search the answer database for the relevant response and generate an appropriate answer assuming the user's sentiment is neutral."

[1197] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[1198] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1199] Step 1:

[1200] User: The user enters their inquiry.

[1201] Specific action: The user opens the application on their smartphone or PC and enters "Please tell me your business hours" into the text input field on the screen.

[1202] Input: User's text input.

[1203] Output: User inquiry ("What are your business hours?").

[1204] Step 2:

[1205] Terminal: The terminal converts the query into JSON format.

[1206] Specific operation: The application on the device captures the entered text and converts it into JSON data like this:

[1207] json

[1208] {

[1209] "query": "Please tell me your business hours."

[1210] }

[1211] Input: User's inquiry.

[1212] Output: Query data in JSON format.

[1213] Step 3:

[1214] Terminal: The terminal sends a POST request to the server.

[1215] Specific operation: The device sends JSON data as an HTTP POST request to the server's API endpoint. The endpoint URL is https: / / api.example.com / query as an example.

[1216] Input: Query data in JSON format.

[1217] Output: POST request sent to the server.

[1218] Step 4:

[1219] Server: The server receives the POST request.

[1220] Specific operation: The server's receiving module receives a POST request and retrieves JSON data from the HTTP request body. It also verifies that the request is in the correct format.

[1221] Input: HTTP POST request.

[1222] Output: Query data in JSON format.

[1223] Step 5:

[1224] Server: The server parses the query.

[1225] Specific operation: The server's parsing module parses the JSON data and extracts the text "Please tell me your business hours" from the "query" field. Furthermore, it uses natural language processing techniques to extract the keyword "business hours". The techniques used include NLTK and Spacy.

[1226] Input: Query data in JSON format.

[1227] Output: Extracted keyword "business hours".

[1228] Step 6:

[1229] Server: The server recognizes the user's emotional state.

[1230] Specific operation: The server's sentiment engine determines the user's emotional state from the analyzed text. For example, it uses GCP's Natural Language API to perform sentiment analysis on the text and determine that the user's emotion is neutral.

[1231] Input: Analyzed text data.

[1232] Output: User's emotional state (neutral).

[1233] Step 7:

[1234] Server: The server searches the answer database.

[1235] Specific operation: The database module searches a pre-prepared answer database based on the keyword "business hours". For example, if an entry for "business hours" exists in the database, it will find the corresponding answer "Our business hours are 9:00 to 18:00".

[1236] Input: Extracted keyword "Business Hours".

[1237] Output: Search results ("Our business hours are 9:00 AM to 6:00 PM").

[1238] Step 8:

[1239] Server: The server generates the answer.

[1240] Specific operation: The response module generates an appropriate response considering the search results and the user's sentiment state. For a neutral sentiment, it selects the standard response "Our business hours are 9:00 AM to 6:00 PM."

[1241] Input: Search results, user's emotional state.

[1242] Output: Generated response ("Our business hours are 9:00 AM to 6:00 PM").

[1243] Step 9:

[1244] Server: The server sends the response to the terminal.

[1245] Specific operation: The server sends the generated response to the terminal as JSON-formatted response data. It returns the POST request response as an HTTP response.

[1246] Input: Generated response.

[1247] Output: Response data in JSON format.

[1248] Step 10:

[1249] Terminal: The terminal receives a response from the server.

[1250] Specific operation: The terminal receives a JSON response from the server and parses the data. Specifically, it extracts "Our business hours are 9:00 to 18:00" from the "response" field.

[1251] Input: Response data in JSON format.

[1252] Output: Analyzed response text.

[1253] Step 11:

[1254] Terminal: The terminal displays the answer in the user interface.

[1255] Specific operation: Execute code to display the analyzed response text on the user interface, showing "Our business hours are 9:00 AM to 6:00 PM" on the screen.

[1256] Input: Analyzed response text.

[1257] Output: The answer displayed to the user.

[1258] Step 12:

[1259] User: The user checks the displayed answer.

[1260] Specific action: The user confirms the response displayed on their device screen, "Our business hours are 9:00 AM to 6:00 PM," and obtains an answer to their inquiry.

[1261] Input: The answer displayed on the device.

[1262] Output: Information obtained by the user.

[1263] (Application Example 2)

[1264] 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."

[1265] Conventional automated response systems simply provide standardized answers based on keywords to user inquiries, failing to consider the user's emotional state. This resulted in insufficient improvement in user satisfaction, and particularly in handling emotionally charged inquiries. Furthermore, there is a need to efficiently provide interfaces compatible with various devices such as smartphones. Solving these problems is the challenge.

[1266] 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 providing an interface in which the user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for recognizing the content of the received inquiry and the user's emotional state, means for generating an optimal answer based on the emotional state, and means for returning the retrieved answer to the user. This makes it possible to provide an optimized answer that takes the user's emotional state into consideration. Furthermore, it can be used as an application installed on smartphones, smart glasses, head-mounted displays, or robots, enabling support for a variety of devices.

[1267] "Means of providing an interface for users to input inquiries" refers to functions that provide input methods such as GUIs (Graphical User Interfaces), forms, and chatbots for users to enter information.

[1268] "Means for receiving user inquiries" refers to a function that sends user-entered inquiries to a server or cloud service and then receives them.

[1269] "A means of analyzing received inquiries and searching for matching answers from a pre-prepared answer database" refers to a function that uses natural language processing technology to analyze the content of user inquiries, extracts relevant keywords and phrases, and searches for appropriate answers from the database.

[1270] "Means for recognizing the content of received inquiries and the user's emotional state" refers to a function that identifies the user's emotional state (anger, joy, sadness, etc.) using an emotion analysis engine, along with the analyzed inquiry.

[1271] "Means for generating optimal responses based on emotional state" refers to a function that adjusts responses to provide users with appropriate tone and wording based on their recognized emotional state.

[1272] "Means of sending search results back to the user" refers to a function that sends the adjusted answers back to the user's device and displays them in the user interface.

[1273] "Sentiment analysis" is a technology used to identify a user's emotions from the content of an inquiry received.

[1274] "Natural language processing" is a technology that enables computers to understand, analyze, and generate appropriate responses to human language.

[1275] Modes for carrying out the invention

[1276] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state.

[1277] System Configuration

[1278] This system includes the following components.

[1279] 1. User Inquiry Interface:

[1280] This is an interface used by users to enter and submit inquiries. It is typically provided as a web form, a smartphone app, or an input field for smart glasses, head-mounted displays, or robots.

[1281] 2. Terminal:

[1282] This refers to a device that allows users to input inquiries and send them to a server. Examples include personal computers, smartphones, and tablets.

[1283] 3. Server:

[1284] It is a central device for processing inquiries received from terminals and providing appropriate answers. The server includes the following functions:

[1285] Receiving module:

[1286] Receive inquiries sent from the device.

[1287] Analysis module:

[1288] The system analyzes received inquiries and extracts keywords and semantics. Natural language processing techniques are used.

[1289] Emotion analysis engine:

[1290] Based on the analyzed inquiry content, the system recognizes the user's emotional state. For example, this can be done using the Amazon Comprehend API.

[1291] Database module:

[1292] It maintains a database of pre-prepared questions and answers, which can be searched.

[1293] Response module:

[1294] It searches for and generates a refined response, and sends it back to the terminal.

[1295] Program Processing Description

[1296] 1. The server provides a means of providing a user interface, which is an interface for users to input inquiries. For example, this is the chatbot screen of a smartphone application.

[1297] 2. The server's receiving module receives the query entered by the user. At this time, the query content is sent to the server in JSON format.

[1298] 3. The analysis module uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze incoming inquiries and extract keywords. For example, it extracts the keyword "product features" from the inquiry "Please tell me about the features of the product."

[1299] 4. The emotion analysis engine recognizes the user's emotional state. For example, it uses Amazon Comprehend to recognize emotions such as "interested."

[1300] 5. The database module searches the answer database based on the extracted keywords and retrieves relevant information. For example, it searches for product feature information related to "product features".

[1301] 6. The response module generates the most appropriate response based on the user's emotional state. For example, if the user is feeling positive emotions, it will generate a response such as, "This product is made from high-quality materials and is highly recommended."

[1302] 7. The server sends the generated response back to the terminal and displays it in the user interface.

[1303] Specific example

[1304] Specific examples of how the system works

[1305] As a concrete example, a user enters "Please tell me the features of this product" into a smartphone application and sends it. The server receives the message, the analysis module performs natural language processing, and the sentiment analysis engine recognizes the user's sentiment as "very interested." Subsequently, the database module searches for the features of related products, and the response module generates and sends back a response that matches the sentiment, such as "This product is made from high-quality materials and is highly recommended." The user can then view this response on their smartphone.

[1306] Example of a prompt

[1307] User prompt: "Please tell me about the features of this product."

[1308] AI Model Prompt: Uses a product search API to search for "features" and an emotion engine API to recognize the user's emotional state. If the user is friendly, it generates a response such as, "This product is made from high-quality materials and is highly recommended."

[1309] In this way, the system of the present invention can provide a rapid and accurate automated response to user inquiries, and further enhance customer satisfaction by providing the optimal answer while taking into account the user's emotional state.

[1310] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1311] Step 1:

[1312] The user enters their inquiry. The user types "Please tell me the features of this product" on the chatbot screen of the smartphone app. The entered data is converted to JSON format depending on the device.

[1313] Step 2:

[1314] The device sends the received query to the server. Specifically, the query data, converted to JSON format, is sent as a POST request to the server's API endpoint.

[1315] Step 3:

[1316] The server's receiving module receives a POST request. The received data is passed to the parsing module. The input is query data in JSON format, and the output is data ready for parsing.

[1317] Step 4:

[1318] The analysis module analyzes the received inquiry. It uses natural language processing technologies such as the Google Cloud Natural Language API to extract keywords such as "product features." The input is the received inquiry data, and the output is the extracted keywords.

[1319] Step 5:

[1320] The results of the analysis module are passed to the sentiment analysis engine to recognize the user's emotional state. For example, the Amazon Comprehend API is used to analyze emotions such as "interested." The input is the analyzed query data, and the output is the user's emotional state.

[1321] Step 6:

[1322] The database module searches the answer database based on the extracted keywords. For example, it uses MySQL or MongoDB to retrieve information related to "product features." The input is the extracted keywords, and the output is data about the searched product features.

[1323] Step 7:

[1324] The response module generates the most appropriate response based on the user's sentiment and search results. For example, if the user is favorable using the Google Cloud Natural Language API, it might generate a response such as, "This product is made with high-quality materials and is highly recommended." The input is the user's sentiment and search results data, and the output is the optimized response.

[1325] Step 8:

[1326] The server sends the generated response back to the terminal in JSON format. The terminal parses the received response data and prepares it for display in the user interface. The input is the generated response data, and the output is the data to be displayed in the user interface.

[1327] Step 9:

[1328] The device receives a response from the server and displays it in the user interface. Specifically, a response such as "This product is made from high-quality materials and is highly recommended" is displayed on the chatbot screen of the smartphone app. Finally, the user can confirm the displayed response.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] [Fourth Embodiment]

[1333] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1334] 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.

[1335] 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).

[1336] 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.

[1337] 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.

[1338] 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).

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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".

[1346] The embodiments for carrying out the present invention will now be described. This system automatically provides appropriate answers to user inquiries and functions primarily through cooperation between a server, a terminal, and the user.

[1347] System Configuration

[1348] This system includes the following components:

[1349] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[1350] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[1351] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[1352] Receiving module: Receives inquiries sent from terminals.

[1353] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[1354] Database module: Holds and searches a database of pre-prepared questions and answers.

[1355] Response module: Generates an appropriate response and sends it back to the terminal.

[1356] Program processing

[1357] The following describes the process flow when a program is executed, using natural language.

[1358] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[1359] 2. Terminal: The terminal receives the input query and converts it into JSON format. The terminal then sends the constructed request to the server's API endpoint.

[1360] 3. Server: The server's receiving module receives the request sent from the terminal.

[1361] 4. Server: The server analyzes the requests received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours".

[1362] 5. Server: The server searches the answer database based on the keywords extracted by the database module. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[1363] 6. Server: The response module generates the retrieved response in JSON format and sends it to the terminal.

[1364] 7. Terminal: The terminal receives the JSON-formatted response sent from the server.

[1365] 8. Terminal: Analyzes the received response and displays the answer on the user interface. It will be displayed in the format of "Our business hours are 9:00 to 18:00."

[1366] 9. User: The user reviews the response displayed on their device and obtains appropriate information regarding their inquiry.

[1367] Specific example

[1368] As a concrete example, when making an inquiry about business hours, the user types "What are your business hours?" and sends it. The terminal sends this inquiry to the server, and after the server receives it, the parsing module extracts the keyword "business hours". The database module searches for the appropriate answer based on this keyword, and the response module generates the answer "Our business hours are 9:00 to 18:00" and returns it to the terminal. Finally, the user can confirm this answer on their terminal.

[1369] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[1370] The following describes the processing flow.

[1371] Step 1:

[1372] The user enters their inquiry. They specify the information they want to know and press the submit button. For example, they might enter, "Please tell me your business hours."

[1373] Step 2:

[1374] The terminal receives user input and converts it into JSON format. It then packages the query content as data and prepares to send it to the server.

[1375] Step 3:

[1376] The device sends a POST request to the server's API endpoint. The query data, converted to JSON format, is sent to the server.

[1377] Step 4:

[1378] The server receives a POST request. The server's receiving module receives the sent data and passes it on to the next process.

[1379] Step 5:

[1380] The server's analysis module analyzes the received query. Specifically, it uses natural language processing techniques to extract keywords and context from the query. For example, it might extract the keyword "business hours."

[1381] Step 6:

[1382] The server's database module searches the answer database based on the extracted keywords. It then searches the answer database for entries related to "business hours".

[1383] Step 7:

[1384] The server's database module retrieves the appropriate answer. For example, it retrieves the entry "Our business hours are 9:00 AM to 6:00 PM" from the database.

[1385] Step 8:

[1386] The server's response module generates the retrieved response as JSON-formatted response data. It then converts it into a data format for sending a reply to the user.

[1387] Step 9:

[1388] The server sends the generated response data to the terminal. It returns data in JSON format as a response to the POST request.

[1389] Step 10:

[1390] The terminal receives a response from the server. It parses the response data and prepares it for display on the user interface.

[1391] Step 11:

[1392] The terminal analyzes the response and displays it on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed in a user-friendly format.

[1393] Step 12:

[1394] The user checks the response displayed on their device. They check the displayed response to obtain appropriate information regarding their inquiry.

[1395] As described above, the entire system works together to provide fast and accurate automated responses to user inquiries.

[1396] (Example 1)

[1397] 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".

[1398] Conventional automated response systems struggled to provide timely and appropriate answers to user inquiries. Furthermore, they lacked the technology to accurately analyze user inquiries and provide precise responses based on those analyses. There was also a need to improve the overall data processing efficiency and usability of the system.

[1399] 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.

[1400] In this invention, the server includes means for a user to input an inquiry, means for a terminal to convert the user's inquiry into JSON format and send it to the server, means for the server to analyze the received inquiry and extract keywords, means for searching a pre-prepared answer database based on the keywords, means for generating the retrieved answer in JSON format and sending it to the terminal, and means for the terminal to display the received answer to the user. This enables a rapid and accurate automated response to the user's inquiry.

[1401] A "user" is a person or entity that makes inquiries to a system.

[1402] "Means" refers to a method, apparatus, or component within a system used to achieve a particular function or process.

[1403] An "inquiry" is a question that a user enters into the system about a problem they want to solve or information they want to know.

[1404] A "terminal" is a device used by a user to access a system, and includes PCs, smartphones, tablets, and other similar devices.

[1405] "JSON format" is an abbreviation for JavaScript Object Notation, and it is a text format for structuring data as key-value pairs.

[1406] A "server" is a computer system that receives requests, processes data, and returns responses.

[1407] "Analysis" is the process of understanding inquiries received from users and extracting important keywords.

[1408] "Keywords" are words or phrases that are considered particularly important from the content of the inquiry.

[1409] A "response database" is a collection of data in which answers to inquiries are recorded in advance.

[1410] "Searching" is the process of finding the appropriate answer from a database of answers based on keywords.

[1411] "Transmission" is the process of moving data or a request from one system to another.

[1412] "Receiving" is the process by which one system takes in data or requests sent from another system.

[1413] "Display" refers to the process of providing information to a user visually on a device.

[1414] This document describes embodiments for carrying out the present invention. This system automatically provides appropriate answers to user inquiries and primarily functions through the cooperation of a server, terminal, and user. Specific hardware and software, as well as methods for data processing and calculation, will also be clearly described.

[1415] System Configuration

[1416] This system includes the following components:

[1417] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[1418] 2. Terminal: A terminal has the function of receiving user input, converting it into JSON format, and sending it to the server. This includes PCs, smartphones, and tablets.

[1419] 3. Server: A server is a central device that processes inquiries received from terminals and provides appropriate answers. It has the following functions:

[1420] Receiving module: Receives queries sent from terminals. Apache HTTP Server or Nginx are typically used.

[1421] Analysis Module: Analyzes received queries and extracts keywords and semantics. This analysis uses natural language processing technologies such as NLTK and spaCy.

[1422] Database Module: Maintains and allows searching a database of pre-defined questions and answers. MySQL or PostgreSQL are used for the database.

[1423] Response module: Generates the retrieved response in JSON format and sends it back to the terminal. Flask or Django are often used for this.

[1424] Program processing

[1425] The following describes the process flow when a program is executed, using natural language.

[1426] First, the user enters their inquiry. For example, they might enter "Please tell me your business hours." The terminal receives this input, converts it to JSON format, and sends it to the server's API endpoint.

[1427] Next, the server's receiving module receives the request sent from the terminal. The parsing module analyzes the received request and uses natural language processing techniques to extract keywords such as "business hours."

[1428] The database module then searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer, "Our business hours are 9:00 AM to 6:00 PM."

[1429] The response module generates the retrieved response in JSON format and sends it to the terminal. The terminal receives the JSON response sent from the server, parses it, and displays it in the user interface. Finally, the user checks the response displayed on the terminal and obtains the appropriate information regarding the inquiry.

[1430] Specific example

[1431] For example, if a user asks, "What are your business hours?", it would look like this:

[1432] 1. The user types "Please tell me your business hours" and submits it.

[1433] 2. The terminal converts this query into JSON format and sends it to the server.

[1434] 3. The server analyzes the received request and extracts the keyword "business hours".

[1435] 4. The database module searches for answers based on this keyword.

[1436] 5. The response obtained is, "Our business hours are 9:00 AM to 6:00 PM."

[1437] 6. The response module generates this answer in JSON format and sends it to the terminal.

[1438] 7. The device displays the answer in the user interface.

[1439] 8. The user reviews the displayed answer.

[1440] Thus, the system of the present invention can provide a rapid and accurate automated response to user inquiries, ensuring operational efficiency and consistency in responses.

[1441] Examples of prompts for generative AI models

[1442] The following are examples of prompts for training a generative AI model with new questions:

[1443] Input: "What day of the week are you closed?"

[1444] Output: "Our company is closed every Sunday."

[1445] Input: "Please tell me the address of the branch."

[1446] Output: "Our branch office is located at 1-1-1 Marunouchi, Chiyoda-ku, Tokyo."

[1447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1448] Step 1:

[1449] The user enters an inquiry.

[1450] The user enters their inquiry using a terminal. This is often done through a web form in a browser or a text field in an application. In this example, the user enters "Please tell me your business hours." The input data is sent to the terminal in text format.

[1451] Step 2:

[1452] The terminal converts the query into JSON format and sends it to the server.

[1453] The terminal receives text entered by the user and converts it into JSON format. Next, it sends the converted JSON data to the server as an HTTP POST request. Specifically, it converts it as a JSON object like {"question": "Please tell me your business hours"}. The converted JSON data is then sent to the server's API endpoint.

[1454] Step 3:

[1455] The server receives the request.

[1456] The server's receiving module receives an HTTP POST request sent from the terminal. The received data is in JSON format, in the form of {"question": "Please tell me your business hours"}. This data is then passed to the next parsing module.

[1457] Step 4:

[1458] The server parses the request.

[1459] The server's parsing module parses the received JSON data. Using natural language processing techniques (for example, libraries like NLTK or spaCy), it extracts the keyword "business hours". Specifically, it parses the text "Please tell me your business hours" and generates a keyword list ["business hours"]. This keyword list is then passed to the next database module.

[1460] Step 5:

[1461] The server searches the database.

[1462] The server's database module searches the database based on the keyword list received from the analysis module. This database stores pre-defined question-and-answer pairs. For example, it searches the database for entries matching the keyword "business hours" and retrieves the answer "Our business hours are 9:00 to 18:00." This answer data is then passed to the response module.

[1463] Step 6:

[1464] The server generates the response in JSON format.

[1465] The server's response module converts the retrieved answer into JSON format. Specifically, it generates a JSON object with the format {"answer": "Our business hours are 9:00 to 18:00"}. This JSON data is then sent to the terminal.

[1466] Step 7:

[1467] The device receives and displays the response.

[1468] The terminal receives data in JSON format sent from the server. Next, it parses this data and extracts the response. The response obtained as a result of the analysis, "Our business hours are 9:00 to 18:00," is displayed on the user interface. Specifically, this text is displayed in a designated area on the browser.

[1469] Step 8:

[1470] The user confirms the answer.

[1471] The user checks the response displayed on their device and obtains appropriate information regarding their inquiry. By reading the displayed response, "Our business hours are 9:00 AM to 6:00 PM," the user can quickly and accurately obtain the necessary information.

[1472] (Application Example 1)

[1473] 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".

[1474] In today's diverse device environment, it is crucial to respond quickly and accurately to user inquiries. However, existing systems are dependent on specific devices and interfaces, limiting their effectiveness in communicating with users via smart devices. Furthermore, the automation of natural language processing for analysis and response generation is insufficient, leading to decreased user satisfaction. A flexible and highly responsive system is needed to address these challenges.

[1475] 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.

[1476] In this invention, the server includes means for providing an interface in which a user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for returning the retrieved answer to the user, and means for displaying it on the user interface via a smart device. This enables quick and accurate responses using smartphones or head-mounted displays.

[1477] A "user inquiry interface" is an input method for users to enter questions or requests for information, and typically includes a text box on the screen or a voice input function.

[1478] A "smart device" refers to a portable electronic device, such as a smartphone or head-mounted display, that can connect to the internet and has advanced processing capabilities.

[1479] A "server" is a central device that receives requests from users, analyzes them, and generates and sends responses; it is a computer system that provides services over a network.

[1480] "Natural language processing" refers to the technology of understanding, analyzing, and generating human language using computers, and is a field of technology applied to text analysis, speech recognition, machine translation, and more.

[1481] A "response database" is a data storage system that organizes and stores pre-prepared answers to inquiries, and its purpose is to quickly provide appropriate answers to user questions.

[1482] A "receiving module" is a part of the software that receives user inquiries, which then reach the server.

[1483] A "parsing module" is a software module that analyzes received inquiries to extract keywords and meanings, and often uses natural language processing.

[1484] A "response module" is a software module that searches a database for the appropriate answer based on the analysis results, generates that answer, and sends it back to the user.

[1485] The present invention will now be described in terms of embodiments. This system provides quick and accurate answers to user inquiries using smart devices, and its main components include a server, a terminal, and a user interface. Furthermore, it utilizes natural language processing and an answer database.

[1486] System components

[1487] 1. User Inquiry Interface: Provide an interface for users to input inquiries using smart devices (e.g., smartphones or head-mounted displays). For example, a user might input "How do I set the night mode on my security camera?" through a smartphone app.

[1488] 2. Terminal: This is a device that receives user inquiries, such as a smartphone or head-mounted display. The terminal has the function of sending the received inquiries to the server in JSON format.

[1489] 3. Server: As a central device, it has the following functions.

[1490] Receiving module: Receives user inquiries from the terminal.

[1491] Analysis module: Analyzes received queries and extracts keywords and meanings using natural language processing.

[1492] Database module: Searches a pre-prepared answer database to retrieve the appropriate answer.

[1493] Response module: Generates the searched answer in JSON format and sends it back to the terminal.

[1494] Processing flow

[1495] User:

[1496] The user opens the app on their smartphone and enters their inquiry. For example, they might type, "How do I set the night mode on my security camera?"

[1497] Terminal:

[1498] The terminal receives the input query, converts it to JSON format, and sends it to the server's API endpoint.

[1499] server:

[1500] The server's receiving module receives requests from the terminal. Next, the analysis module analyzes the received requests and extracts keywords such as "security camera," "night mode," and "settings."

[1501] The database module searches the answer database based on this keyword and retrieves the answer, "To set night mode for your security camera, select Night Vision from the settings menu and turn it on."

[1502] The response module generates the retrieved response in JSON format and sends it to the terminal.

[1503] Terminal:

[1504] The terminal receives a response from the server, parses it, and displays it on the user interface.

[1505] User:

[1506] The user checks the answers displayed on the device and obtains the necessary information.

[1507] Hardware and software to use

[1508] Frontend: React Native (User interface development)

[1509] Backend: Node.js + Express (server processing)

[1510] Natural Language Processing: Natural (Node.js natural language processing library)

[1511] Database: MongoDB or MySQL (database for questions and answers)

[1512] Presentation of specific examples

[1513] As a concrete example of this system, consider a scenario where a user asks, "How do I set the night mode on my security camera?" The user enters this inquiry using their smartphone, and the device sends it to the server. The server uses natural language processing to analyze the inquiry, searches its database for the appropriate answer, and sends it back to the user.

[1514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1515] Step 1:

[1516] The user enters their inquiry into the smart device's inquiry interface and presses the "Send" button. At this point, the user's input (e.g., "How do I set night mode on my security camera?") is generated as text data.

[1517] Step 2:

[1518] The terminal receives user input and converts this text data into JSON format. This process involves data processing that stores the input text in a key:value format. The generated JSON data is then sent to the server's API endpoint.

[1519] Step 3:

[1520] The server's receiving module receives JSON data sent from the terminal. It parses the received JSON data to extract the original text data.

[1521] Step 4:

[1522] The server's analysis module analyzes the received text data and extracts important keywords using natural language processing techniques (e.g., "security camera," "night mode," "settings"). This process involves tokenization and stemming of the text.

[1523] Step 5:

[1524] The server's database module searches the answer database based on the extracted keywords. It uses a search algorithm to retrieve the answer that best matches the keywords (e.g., "To set night mode for your security camera, select Night Vision from the settings menu and turn it on").

[1525] Step 6:

[1526] The server's response module converts the received response into JSON format and generates reply data for the terminal. Here, new key:value format JSON data is generated.

[1527] Step 7:

[1528] The terminal receives the JSON-formatted response sent from the server, parses it, and extracts the original text-formatted response. This text is then formatted appropriately for display in the user interface.

[1529] Step 8:

[1530] The user checks the response displayed on their device and obtains the necessary information. Specifically, the user sees a response on their smartphone screen that says, "To set the security camera's night mode, select Night Vision from the settings menu and turn it on."

[1531] This allows users to obtain information quickly and accurately, improving their satisfaction with inquiries.

[1532] 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.

[1533] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state. This system primarily functions in cooperation with the server, terminal, and user.

[1534] System Configuration

[1535] This system includes the following components:

[1536] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[1537] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, and tablets.

[1538] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[1539] Receiving module: Receives inquiries sent from terminals.

[1540] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[1541] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[1542] Database module: Holds and searches a database of pre-prepared questions and answers.

[1543] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[1544] Program processing

[1545] The following describes the process flow when a program is executed, using natural language.

[1546] 1. User: The user enters their inquiry. For example, they might enter "What are your business hours?".

[1547] 2. Terminal: The terminal receives the input query and converts it into JSON format. It packages the query content as data and prepares to send it to the server.

[1548] 3. Terminal: Sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[1549] 4. Server: The server's receiving module receives the POST request. It receives the sent data and passes it on to the next process.

[1550] 5. Server: The server analyzes the queries received by the analysis module. Specifically, it uses natural language processing techniques to extract keywords such as "business hours." Furthermore, the emotion engine recognizes the user's emotional state based on the analysis results. For example, it determines whether the user is angry, happy, or in a neutral emotional state.

[1551] 6. Server: The database module searches the answer database based on the extracted keywords. For example, if an entry for "business hours" exists in the database, it retrieves the corresponding answer "Our business hours are 9:00 to 18:00".

[1552] 7. Server: Based on the emotion engine's recognition results, the response module adjusts its response. For example, if the user is angry, it can choose a response that uses more polite and calm language.

[1553] 8. Server: The response module generates the adjusted response as JSON-formatted response data. It then converts it to a data format for sending back to the user.

[1554] 9. Server: Sends the generated response data to the terminal. Returns data in JSON format as a response to the POST request.

[1555] 10. Terminal: Receives the response from the server. Analyzes the received response data and prepares it for display on the user interface.

[1556] 11. Terminal: Display the analyzed responses in the user interface. Specific responses, such as "Our business hours are 9:00 AM to 6:00 PM," should be displayed in a user-friendly format.

[1557] 12. User: Check the answer displayed on the device. Check the displayed answer to obtain appropriate information regarding the inquiry.

[1558] Specific example

[1559] For example, when a user makes an inquiry about business hours, they type "What are your business hours?" and send it. The terminal sends this inquiry to the server, and after the server receives it, the analysis module extracts the keyword "business hours." Furthermore, the sentiment engine recognizes the user's emotional state, and if the user is, for example, favorable, it selects a polite response that reflects that emotion. The database module searches for an appropriate answer based on this keyword and generates a response such as "Our business hours are 9:00 to 18:00" and sends it back to the terminal. Finally, the user can confirm this answer on their terminal.

[1560] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[1561] The following describes the processing flow.

[1562] Step 1:

[1563] The user enters their inquiry. For example, they might type "Please tell me your business hours" and press the submit button.

[1564] Step 2:

[1565] The terminal receives user input and converts it into JSON format. It then packages the inquiry content as data and prepares to send it to the server.

[1566] Step 3:

[1567] The device sends the query data, converted to JSON format, as a POST request to the server's API endpoint.

[1568] Step 4:

[1569] The server's receiving module accepts the POST request. It receives the sent data and prepares it for the next processing step.

[1570] Step 5:

[1571] The server's analysis module analyzes the received query. It uses natural language processing techniques to extract keywords such as "business hours."

[1572] Step 6:

[1573] The server's emotion engine recognizes the user's emotional state based on the query content it analyzes. For example, it determines whether the user is angry, happy, or neutral based on their writing style and tone.

[1574] Step 7:

[1575] The server's database module searches the answer database based on the extracted keywords. For example, it searches the database for entries corresponding to "business hours".

[1576] Step 8:

[1577] The server's response module generates an answer based on the search results. During this process, it adjusts the answer to suit the user's emotions based on the emotion engine's recognition results. For example, an angry user will receive a polite and calm response.

[1578] Step 9:

[1579] The server sends the generated response to the terminal as JSON response data. The final response data is then sent back to the terminal.

[1580] Step 10:

[1581] The terminal receives a response from the server. It analyzes the received response data and prepares it to be displayed on the user interface.

[1582] Step 11:

[1583] The terminal analyzes the data and displays the results on the user interface. Specific answers, such as "Our business hours are 9:00 AM to 6:00 PM," are displayed to the user.

[1584] Step 12:

[1585] The user checks the answer displayed on their device. They can then obtain an appropriate answer to their inquiry.

[1586] Thus, the system of the present invention not only provides a rapid and accurate automated response to user inquiries, but also achieves higher user satisfaction by providing an optimal answer that takes into account the user's emotional state.

[1587] (Example 2)

[1588] 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".

[1589] Traditional inquiry systems have a problem of lowering user satisfaction by providing uniform, standardized answers without considering the user's emotional state. Furthermore, if users make inquiries while feeling dissatisfied or anxious, inappropriate responses can lead to further dissatisfaction. Therefore, there is a need for a system that takes the user's emotional state into consideration and provides more personalized responses.

[1590] 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.

[1591] In this invention, the server includes means for receiving user inquiries, means for analyzing the received inquiries and searching for matching answers from a pre-prepared answer database, means for recognizing the user's emotional state based on the analyzed inquiry content, and means for adjusting the retrieved answers according to the recognized emotional state of the user. This makes it possible to provide appropriate answers according to the user's emotional state.

[1592] "Means of receiving user inquiries" refers to the function of sending inquiries entered by users through their terminals to a server and receiving them.

[1593] "Means for analyzing received inquiries" refers to technologies for understanding the content of submitted inquiries and extracting keywords and intents.

[1594] "Means of searching for matching answers from the answer database" refers to a function that searches a pre-prepared answer database based on analyzed keywords and intent to find the most suitable answer.

[1595] "Means of recognizing a user's emotional state" refers to technology that determines a user's emotions based on the analyzed content of their inquiry and identifies states such as joy, anger, sadness, and happiness.

[1596] "Means of adjusting searched answers according to the recognized emotional state of the user" refers to a function that modifies the tone and content of answers according to the user's emotions and provides them to the user in a more appropriate form.

[1597] This invention is a system that automatically and appropriately responds to user inquiries. This system aims to increase user satisfaction by recognizing the user's emotional state. This system primarily functions through collaboration between the server, terminal, and user.

[1598] System Configuration

[1599] This system includes the following components:

[1600] 1. User Inquiry Interface: An interface used by users to enter and submit inquiries. Typically provided as a web form or an input field in an application.

[1601] 2. Terminal: A device that has the function of allowing the user to input inquiries and send them to the server. This includes PCs, smartphones, tablets, etc.

[1602] 3. Server: A central device for processing inquiries received from terminals and providing appropriate answers. It has the following functions:

[1603] Receiving module: Receives inquiries sent from terminals.

[1604] Analysis module: Analyzes received inquiries and extracts keywords and semantic information.

[1605] Emotion Engine: Recognizes the user's emotional state based on the analyzed inquiry content.

[1606] Database module: Holds and searches a database of pre-prepared questions and answers.

[1607] Response module: Generates a searched and adjusted response and sends it back to the terminal.

[1608] Program processing

[1609] Program Overview

[1610] The program of this invention integrates natural language processing (NLP) technology and sentiment analysis technology. This allows it to analyze user inquiries and generate appropriate responses. The software used includes natural language processing libraries (e.g., NLTK, Spacy) and sentiment analysis engines (e.g., GCP's Natural Language API).

[1611] Hardware to use

[1612] Server: Cloud services (e.g., AWS, GCP, Azure) are used for program execution and data storage.

[1613] Terminal: A device used by the user to submit inquiries. This includes PCs, smartphones, and tablets.

[1614] Detailed explanation of operation

[1615] The specific actions the system takes from receiving a user inquiry to analyzing it, generating an answer, and providing the answer are as follows:

[1616] 1. User: The user types "Please tell me your business hours" and submits it.

[1617] 2. Terminal: The terminal prepares to send this query to the server. It converts the entered query into JSON format and sends it as a POST request to the server's API endpoint.

[1618] 3. Server: The server's receiving module receives the POST request and passes the sent data to the next stage of processing. The parsing module parses the received data and extracts the keyword "business hours".

[1619] 4. Server: The emotion engine uses this analysis result to recognize the user's emotions. For example, it might determine that the user is in a neutral state.

[1620] 5. Server: The database module searches for the answer based on the keyword "business hours" and retrieves "Our business hours are 9:00 to 18:00".

[1621] 6. Server: The response module takes the results of the sentiment engine into account and generates an appropriate response. A standard response is set for neutral sentiment states.

[1622] 7. Server: Sends the generated response to the terminal as JSON response data.

[1623] 8. Terminal: The terminal receives the response from the server, parses it, and displays it on the user interface. Specifically, it displays "Our business hours are 9:00 to 18:00."

[1624] Specific example operations

[1625] For example, if a user asks, "What are your business hours?", the following would occur:

[1626] 1. User: The user types "Please tell me your business hours" on their device and taps the send button.

[1627] 2. Terminal: The terminal receives this query, converts it to JSON format, and sends it to the server.

[1628] 3. Server: The server receives the data and performs the following steps: analysis, sentiment recognition, database search, and response generation.

[1629] 4. Terminal: Receives a response from the server and displays "Our business hours are 9:00 AM to 6:00 PM" on the user interface.

[1630] 5. User: The user reviews the displayed answer.

[1631] Example of a prompt

[1632] Here are some examples of prompts for a generative AI model:

[1633] "For the user's inquiry, 'What are your business hours?', search the answer database for the relevant response and generate an appropriate answer assuming the user's sentiment is neutral."

[1634] Thus, the system of the present invention can not only provide a fast and accurate automated response to user inquiries, but also achieve a higher level of satisfaction by providing an optimal answer that takes into account the user's emotional state.

[1635] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1636] Step 1:

[1637] User: The user enters their inquiry.

[1638] Specific action: The user opens the application on their smartphone or PC and enters "Please tell me your business hours" into the text input field on the screen.

[1639] Input: User's text input.

[1640] Output: User inquiry ("What are your business hours?").

[1641] Step 2:

[1642] Terminal: The terminal converts the query into JSON format.

[1643] Specific operation: The application on the device captures the entered text and converts it into JSON data like this:

[1644] json

[1645] {

[1646] "query": "Please tell me your business hours."

[1647] }

[1648] Input: User's inquiry.

[1649] Output: Query data in JSON format.

[1650] Step 3:

[1651] Terminal: The terminal sends a POST request to the server.

[1652] Specific operation: The device sends JSON data as an HTTP POST request to the server's API endpoint. The endpoint URL is https: / / api.example.com / query as an example.

[1653] Input: Query data in JSON format.

[1654] Output: POST request sent to the server.

[1655] Step 4:

[1656] Server: The server receives the POST request.

[1657] Specific operation: The server's receiving module receives a POST request and retrieves JSON data from the HTTP request body. It also verifies that the request is in the correct format.

[1658] Input: HTTP POST request.

[1659] Output: Query data in JSON format.

[1660] Step 5:

[1661] Server: The server parses the query.

[1662] Specific operation: The server's parsing module parses the JSON data and extracts the text "Please tell me your business hours" from the "query" field. Furthermore, it uses natural language processing techniques to extract the keyword "business hours". The techniques used include NLTK and Spacy.

[1663] Input: Query data in JSON format.

[1664] Output: Extracted keyword "business hours".

[1665] Step 6:

[1666] Server: The server recognizes the user's emotional state.

[1667] Specific operation: The server's sentiment engine determines the user's emotional state from the analyzed text. For example, it uses GCP's Natural Language API to perform sentiment analysis on the text and determine that the user's emotion is neutral.

[1668] Input: Analyzed text data.

[1669] Output: User's emotional state (neutral).

[1670] Step 7:

[1671] Server: The server searches the answer database.

[1672] Specific operation: The database module searches a pre-prepared answer database based on the keyword "business hours". For example, if an entry for "business hours" exists in the database, it will find the corresponding answer "Our business hours are 9:00 to 18:00".

[1673] Input: Extracted keyword "Business Hours".

[1674] Output: Search results ("Our business hours are 9:00 AM to 6:00 PM").

[1675] Step 8:

[1676] Server: The server generates the answer.

[1677] Specific operation: The response module generates an appropriate response considering the search results and the user's sentiment state. For a neutral sentiment, it selects the standard response "Our business hours are 9:00 AM to 6:00 PM."

[1678] Input: Search results, user's emotional state.

[1679] Output: Generated response ("Our business hours are 9:00 AM to 6:00 PM").

[1680] Step 9:

[1681] Server: The server sends the response to the terminal.

[1682] Specific operation: The server sends the generated response to the terminal as JSON-formatted response data. It returns the POST request response as an HTTP response.

[1683] Input: Generated response.

[1684] Output: Response data in JSON format.

[1685] Step 10:

[1686] Terminal: The terminal receives a response from the server.

[1687] Specific operation: The terminal receives a JSON response from the server and parses the data. Specifically, it extracts "Our business hours are 9:00 to 18:00" from the "response" field.

[1688] Input: Response data in JSON format.

[1689] Output: Analyzed response text.

[1690] Step 11:

[1691] Terminal: The terminal displays the answer in the user interface.

[1692] Specific operation: Execute code to display the analyzed response text on the user interface, showing "Our business hours are 9:00 AM to 6:00 PM" on the screen.

[1693] Input: Analyzed response text.

[1694] Output: The answer displayed to the user.

[1695] Step 12:

[1696] User: The user checks the displayed answer.

[1697] Specific action: The user confirms the response displayed on their device screen, "Our business hours are 9:00 AM to 6:00 PM," and obtains an answer to their inquiry.

[1698] Input: The answer displayed on the device.

[1699] Output: Information obtained by the user.

[1700] (Application Example 2)

[1701] 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".

[1702] Conventional automated response systems simply provide standardized answers based on keywords to user inquiries, failing to consider the user's emotional state. This resulted in insufficient improvement in user satisfaction, and particularly in handling emotionally charged inquiries. Furthermore, there is a need to efficiently provide interfaces compatible with various devices such as smartphones. Solving these problems is the challenge.

[1703] 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 providing an interface in which the user inputs an inquiry, means for receiving the user's inquiry, means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, means for recognizing the content of the received inquiry and the user's emotional state, means for generating an optimal answer based on the emotional state, and means for returning the retrieved answer to the user. This makes it possible to provide an optimized answer that takes the user's emotional state into consideration. Furthermore, it can be used as an application installed on smartphones, smart glasses, head-mounted displays, or robots, enabling support for a variety of devices.

[1704] "Means of providing an interface for users to input inquiries" refers to functions that provide input methods such as GUIs (Graphical User Interfaces), forms, and chatbots for users to enter information.

[1705] "Means for receiving user inquiries" refers to a function that sends user-entered inquiries to a server or cloud service and then receives them.

[1706] "A means of analyzing received inquiries and searching for matching answers from a pre-prepared answer database" refers to a function that uses natural language processing technology to analyze the content of user inquiries, extracts relevant keywords and phrases, and searches for appropriate answers from the database.

[1707] "Means for recognizing the content of received inquiries and the user's emotional state" refers to a function that identifies the user's emotional state (anger, joy, sadness, etc.) using an emotion analysis engine, along with the analyzed inquiry.

[1708] "Means for generating optimal responses based on emotional state" refers to a function that adjusts responses to provide users with appropriate tone and wording based on their recognized emotional state.

[1709] "Means of sending search results back to the user" refers to a function that sends the adjusted answers back to the user's device and displays them in the user interface.

[1710] "Sentiment analysis" is a technology used to identify a user's emotions from the content of an inquiry received.

[1711] "Natural language processing" is a technology that enables computers to understand, analyze, and generate appropriate responses to human language.

[1712] Modes for carrying out the invention

[1713] This invention is a system that automatically provides appropriate answers to user inquiries. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide the optimal answer according to the content of the inquiry and the user's emotional state.

[1714] System Configuration

[1715] This system includes the following components.

[1716] 1. User Inquiry Interface:

[1717] This is an interface used by users to enter and submit inquiries. It is typically provided as a web form, a smartphone app, or an input field for smart glasses, head-mounted displays, or robots.

[1718] 2. Terminal:

[1719] This refers to a device that allows users to input inquiries and send them to a server. Examples include personal computers, smartphones, and tablets.

[1720] 3. Server:

[1721] It is a central device for processing inquiries received from terminals and providing appropriate answers. The server includes the following functions:

[1722] Receiving module:

[1723] Receive inquiries sent from the device.

[1724] Analysis module:

[1725] The system analyzes received inquiries and extracts keywords and semantics. Natural language processing techniques are used.

[1726] Emotion analysis engine:

[1727] Based on the analyzed inquiry content, the system recognizes the user's emotional state. For example, this can be done using the Amazon Comprehend API.

[1728] Database module:

[1729] It maintains a database of pre-prepared questions and answers, which can be searched.

[1730] Response module:

[1731] It searches for and generates a refined response, and sends it back to the terminal.

[1732] Program Processing Description

[1733] 1. The server provides a means of providing a user interface, which is an interface for users to input inquiries. For example, this is the chatbot screen of a smartphone application.

[1734] 2. The server's receiving module receives the query entered by the user. At this time, the query content is sent to the server in JSON format.

[1735] 3. The analysis module uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze incoming inquiries and extract keywords. For example, it extracts the keyword "product features" from the inquiry "Please tell me about the features of the product."

[1736] 4. The emotion analysis engine recognizes the user's emotional state. For example, it uses Amazon Comprehend to recognize emotions such as "interested."

[1737] 5. The database module searches the answer database based on the extracted keywords and retrieves relevant information. For example, it searches for product feature information related to "product features".

[1738] 6. The response module generates the most appropriate response based on the user's emotional state. For example, if the user is feeling positive emotions, it will generate a response such as, "This product is made from high-quality materials and is highly recommended."

[1739] 7. The server sends the generated response back to the terminal and displays it in the user interface.

[1740] Specific example

[1741] Specific examples of how the system works

[1742] As a concrete example, a user enters "Please tell me the features of this product" into a smartphone application and sends it. The server receives the message, the analysis module performs natural language processing, and the sentiment analysis engine recognizes the user's sentiment as "very interested." Subsequently, the database module searches for the features of related products, and the response module generates and sends back a response that matches the sentiment, such as "This product is made from high-quality materials and is highly recommended." The user can then view this response on their smartphone.

[1743] Example of a prompt

[1744] User prompt: "Please tell me about the features of this product."

[1745] AI Model Prompt: Uses a product search API to search for "features" and an emotion engine API to recognize the user's emotional state. If the user is friendly, it generates a response such as, "This product is made from high-quality materials and is highly recommended."

[1746] In this way, the system of the present invention can provide a rapid and accurate automated response to user inquiries, and further enhance customer satisfaction by providing the optimal answer while taking into account the user's emotional state.

[1747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1748] Step 1:

[1749] The user enters their inquiry. The user types "Please tell me the features of this product" on the chatbot screen of the smartphone app. The entered data is converted to JSON format depending on the device.

[1750] Step 2:

[1751] The device sends the received query to the server. Specifically, the query data, converted to JSON format, is sent as a POST request to the server's API endpoint.

[1752] Step 3:

[1753] The server's receiving module receives a POST request. The received data is passed to the parsing module. The input is query data in JSON format, and the output is data ready for parsing.

[1754] Step 4:

[1755] The analysis module analyzes the received inquiry. It uses natural language processing technologies such as the Google Cloud Natural Language API to extract keywords such as "product features." The input is the received inquiry data, and the output is the extracted keywords.

[1756] Step 5:

[1757] The results of the analysis module are passed to the sentiment analysis engine to recognize the user's emotional state. For example, the Amazon Comprehend API is used to analyze emotions such as "interested." The input is the analyzed query data, and the output is the user's emotional state.

[1758] Step 6:

[1759] The database module searches the answer database based on the extracted keywords. For example, it uses MySQL or MongoDB to retrieve information related to "product features." The input is the extracted keywords, and the output is data about the searched product features.

[1760] Step 7:

[1761] The response module generates the most appropriate response based on the user's sentiment and search results. For example, if the user is favorable using the Google Cloud Natural Language API, it might generate a response such as, "This product is made with high-quality materials and is highly recommended." The input is the user's sentiment and search results data, and the output is the optimized response.

[1762] Step 8:

[1763] The server sends the generated response back to the terminal in JSON format. The terminal parses the received response data and prepares it for display in the user interface. The input is the generated response data, and the output is the data to be displayed in the user interface.

[1764] Step 9:

[1765] The device receives a response from the server and displays it in the user interface. Specifically, a response such as "This product is made from high-quality materials and is highly recommended" is displayed on the chatbot screen of the smartphone app. Finally, the user can confirm the displayed response.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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."

[1775] 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.

[1776] 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.

[1777] 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.

[1778] 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.

[1779] 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.

[1780] 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.

[1781] 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.

[1782] 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.

[1783] 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.

[1784] 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.

[1785] 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.

[1786] 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.

[1787] The following is further disclosed regarding the embodiments described above.

[1788] (Claim 1)

[1789] A means of providing an interface for users to enter inquiries,

[1790] Means for receiving the user's inquiry,

[1791] A means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database,

[1792] A means of returning the searched answer to the user,

[1793] A system that includes this.

[1794] (Claim 2)

[1795] The system according to claim 1, which searches the aforementioned answer database based on keywords.

[1796] (Claim 3)

[1797] The system according to claim 1, wherein natural language processing is used when analyzing the user's inquiry.

[1798] "Example 1"

[1799] (Claim 1)

[1800] The means by which users enter inquiries,

[1801] A means for converting the user's inquiry into JSON format using a terminal and sending it to the server,

[1802] The server analyzes the received query and extracts keywords,

[1803] A means for searching a pre-prepared answer database based on the aforementioned keywords,

[1804] A means of generating the searched answers in JSON format and sending them to the terminal,

[1805] The means by which the terminal displays the response received to the user,

[1806] A system that includes this.

[1807] (Claim 2)

[1808] The system according to claim 1, which searches the aforementioned answer database based on keywords.

[1809] (Claim 3)

[1810] The system according to claim 1, wherein natural language processing is used when analyzing the user's inquiry.

[1811] "Application Example 1"

[1812] (Claim 1)

[1813] A means of providing an interface for users to enter inquiries,

[1814] Means for receiving the user's inquiry,

[1815] A means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database,

[1816] A means of returning the searched answer to the user,

[1817] Means for displaying on a user interface via a smart device,

[1818] A system that includes this.

[1819] (Claim 2)

[1820] The system according to claim 1, which searches the aforementioned answer database based on keywords.

[1821] (Claim 3)

[1822] The system according to claim 1, wherein natural language processing is used when analyzing the user's inquiry.

[1823] (Claim 4)

[1824] The system according to claim 1, which uses a smartphone or head-mounted display to receive, analyze, and respond to inquiries.

[1825] "Example 2 of combining an emotion engine"

[1826] (Claim 1)

[1827] A means for users to enter inquiries,

[1828] Means for receiving inquiries from the aforementioned user,

[1829] A means for analyzing received inquiries and searching for matching answers from a pre-prepared answer database,

[1830] A means for returning the searched answer to the user,

[1831] A means for recognizing the user's emotional state based on the analyzed inquiry content,

[1832] A means for adjusting the retrieved response according to the recognized emotional state of the user,

[1833] A system that includes this.

[1834] (Claim 2)

[1835] The system according to claim 1, which searches the aforementioned answer database based on keywords.

[1836] (Claim 3)

[1837] The system according to claim 1, wherein natural language processing is used when analyzing the user's inquiry.

[1838] "Application example 2 when combining with an emotional engine"

[1839] (Claim 1)

[1840] A means of providing an interface for users to enter inquiries,

[1841] Means for receiving the user's inquiry,

[1842] A means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database,

[1843] The means for recognizing the received inquiry content and the user's emotional state,

[1844] means for generating an optimal response based on the aforementioned emotional state,

[1845] A means of returning the searched answer to the user,

[1846] A system that includes this.

[1847] (Claim 2)

[1848] The system according to claim 1, which searches the aforementioned answer database based on keywords.

[1849] (Claim 3)

[1850] The system according to claim 1, wherein natural language processing is used when analyzing the user's inquiry.

[1851] (Claim 4)

[1852] The system according to claim 1, which uses sentiment analysis to recognize the emotional state of the user.

[1853] (Claim 5)

[1854] The system according to claim 1, which has means for adjusting the response based on the aforementioned emotional state.

[1855] (Claim 6)

[1856] The system according to claim 1, wherein the interface includes an application installed on a smartphone, smart glasses, head-mounted display, or robot. [Explanation of symbols]

[1857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing an interface for users to enter inquiries, Means for receiving the user's inquiry, A means for analyzing the received inquiry and searching for a matching answer from a pre-prepared answer database, A means of returning the searched answer to the user, A system that includes this.

2. The system according to claim 1, which searches the aforementioned answer database based on keywords.

3. The system according to claim 1, wherein natural language processing is used when analyzing the user's inquiry.

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