system

Abstract

We provide the system. [Solution] A means for analyzing query information received from an information processing device and identifying conditions related to the query information, Based on the above conditions, means for collecting relevant information from external sources and internal databases, A means for constructing a virtual environment and performing operational checks related to the aforementioned conditions, A means for generating proposed answers based on collected information and the results of operational checks, A means for presenting the aforementioned proposed answer via an information processing device, 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a conventional inquiry response system, it is difficult to respond quickly and accurately to complex and highly specialized inquiries, and there is a problem of increasing the burden on the person in charge. Also, when it is necessary to confirm the operation of external information sources or devices, manual information collection and verification are required, which consumes a lot of time and labor, and thus there is a problem that efficient support cannot be provided. 【Means for Solving the Problems】 【0005】 This invention provides a means for analyzing inquiry content using an AI agent, identifying relevant conditions, and automatically collecting relevant information from external information sources and internal databases. It also includes a means for rapidly generating proposed answers to inquiries by performing operational verification in a virtual environment and, if necessary, confirming specifications with external organizations. This reduces the burden on personnel handling complex inquiries and enables efficient responses. 【0006】 An "information processing device" is a system or device capable of processing, transmitting, and receiving data. 【0007】 "Inquiry information" refers to technical or product-related questions or requests submitted by users via information processing equipment. 【0008】 "Analysis" is the process of breaking down specific information or data to understand its content and meaning. 【0009】 A "condition" is a specific request or item identified based on the inquiry information. 【0010】 An "external information source" is an information source that exists outside of the information processing device, such as an online information base or web page. 【0011】 An "internal database" is a data storage system installed within a company or organization that can be accessed by information processing devices. 【0012】 A "virtual environment" is a computer-generated environment built on software to simulate a physical system. 【0013】 "Operational verification" is the process of verifying whether a system or device functions as intended. 【0014】 A "draft response" is a proposed or draft answer generated based on the information provided in the inquiry. 【0015】 "Presentation via an information processing device" refers to the act of displaying or transmitting the generated answer to the user through digital means. 【Brief Description of the Drawings】 【0016】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine. 【Embodiments 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 numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0020】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0021】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【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】 This invention is an inquiry response system utilizing an AI agent, which includes inquiry analysis by an information processing device, automatic collection of relevant information, operational verification in a virtual environment, automatic inquiries to vendors, and generation and presentation of proposed answers. The system begins with an analysis of the inquiry content and performs a series of automated processes to provide prompt and accurate support. 【0038】 First, the server receives inquiry information from the user. This information includes technical questions and requests regarding a specific product or service. The server analyzes the inquiry information and uses natural language processing to extract keywords and intent. Through this analysis, the server identifies the specific conditions that need to be processed. 【0039】 Next, the server automatically collects relevant information from external sources and internal databases based on specified conditions. External sources include online knowledge bases and forums, while internal databases store past inquiry history and FAQs. The server combines this information to gather background information for the inquiry. 【0040】 The server then builds a virtual environment and performs operational checks related to the inquiry. For example, if a user reports a connection problem with a device, the server simulates that device, reproduces the problem, and searches for a solution. 【0041】 Furthermore, the server automatically makes inquiries to external organizations to verify specifications as needed. This allows for confirmation of any missing information or new specification changes. 【0042】 Ultimately, the server generates a proposed solution based on the collected information and the results of its operational checks. This proposed solution includes the solution itself, steps, and relevant links, and is presented to the user in an appropriate format. This allows the user to quickly obtain the information necessary to resolve the problem. 【0043】 In one embodiment of the present invention, for example, when handling inquiries regarding network configuration problems, the server identifies the cause of the connection error and provides the user with the latest drivers and configuration examples to facilitate a quick resolution. In this way, it is possible to reduce the workload on the person in charge and improve user satisfaction. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 The server receives inquiry information from the user. The user enters questions related to products or services using the system. The server receives this data and begins preparing to process it. 【0047】 Step 2: 【0048】 The server analyzes the query information. Using natural language processing, it analyzes keywords and sentence structure to identify the intent of the query. Based on this analysis, the server determines what processing is required. 【0049】 Step 3: 【0050】 The server collects relevant information based on the analysis results. It searches for necessary information from external knowledge bases and forums, and explores past similar cases and FAQs in its internal database. This provides the basic information needed to respond to inquiries. 【0051】 Step 4: 【0052】 The server builds a virtual environment and performs operational verification. This involves simulating the systems and devices related to the inquiry. By reproducing the problem, a concrete solution can be found. 【0053】 Step 5: 【0054】 The server will contact external organizations for specification inquiries as needed. In particular, if new problems or unknown specification changes are discovered, it will obtain accurate information by checking with vendors and manufacturers. 【0055】 Step 6: 【0056】 The server generates a proposed solution based on the collected information and the results of its operational checks. It organizes the information in the most useful format for the user and proposes a solution. This proposed solution includes steps, links, and additional resources. 【0057】 Step 7: 【0058】 The server presents the user with a proposed answer. Information is provided immediately via email or chat system to assist the user in resolving their problem. The user can then use the received information to resolve their inquiry. 【0059】 (Example 1) 【0060】 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." 【0061】 Conventional information inquiry response systems require considerable time and effort to process from analyzing inquiry content to generating responses, making it difficult to respond quickly and accurately. To solve this problem, a system is needed that enables efficient analysis of inquiry information, rapid collection of related information, and verification of operation in a virtual environment. 【0062】 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. 【0063】 In this invention, the server includes means for analyzing data received from an information processing device and identifying conditions related to the data, means for collecting relevant information from external information sources and internal data areas, and means for constructing a virtual area and verifying its operation. This makes it possible to efficiently perform tasks ranging from analyzing query information to collecting relevant information, and further to reproducing problems and presenting solutions. 【0064】 An "information processing device" is a digital device that analyzes received data and efficiently handles inquiries. 【0065】 "Data" refers to a collection of information, including the content of questions and inquiries received from users. 【0066】 A "condition" is a specific element of a problem or issue that needs to be solved, identified from the analyzed data. 【0067】 "External information sources" refer to external databases, such as online knowledge bases and forums, that are consulted to gather information. 【0068】 "Internal data area" refers to databases maintained within an organization, such as past inquiry history and FAQs. 【0069】 "Relevant information" refers to information collected based on specific criteria that is useful for solving a problem. 【0070】 A "virtual domain" is a virtual space on a computer that is built to simulate a real-world environment. 【0071】 "Operational verification" is the process of reproducing a problem situation based on identified conditions in a virtual environment and verifying the solution. 【0072】 A "response" is advice or a suggestion for resolving a problem for the user, generated based on the collected relevant information and the results of operational checks. 【0073】 This invention is a system that uses an AI agent to respond to inquiries quickly and accurately. The central component of the system is a server, which receives user inquiries and performs efficient information processing. The server uses the following specific software and processes: 【0074】 First, the server analyzes the data received from the user's terminal. This analysis uses a generative AI model and leverages natural language processing techniques to extract relevant conditions from the data. The goal of this step is to identify keywords and intent from the text sent by the user. For example, if a user reports "unstable network connection," the server will extract the keywords "network" and "unstable." 【0075】 Next, the server automatically collects relevant information from external sources and internal data areas. External sources include online knowledge bases and forums, which are accessed by the server through web scraping and API calls. Internal data areas store FAQs and history of past inquiries within the organization, and a database management system (DBMS) supports its operation. 【0076】 Furthermore, the server creates a virtual area and performs simulations based on specified conditions. The virtual environment is created using virtualization technology and has the capability to reproduce reported problems. For example, it can virtually reproduce unstable network behavior and perform tests to identify the cause. 【0077】 Ultimately, the server uses a generated AI model to create and present an answer to the user based on the results of the operational checks and the collected information. The answer includes steps and relevant links to help the user solve their problem. For example, sending a prompt such as "Please show me the steps to improve my network connection" will prompt the AI ​​model to provide appropriate advice. 【0078】 This system allows servers to respond quickly and effectively to user issues, improving the efficiency of inquiry handling and user satisfaction. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The server retrieves inquiry data received from the user's terminal. It receives the user's inquiry text as input and analyzes it using natural language processing technology. Through this analysis, the server extracts keywords and intent from the inquiry content and outputs the conditions necessary to identify the problem. Specifically, it uses a generative AI model to analyze the structure of the text, for example, to clarify information such as "the network connection is interrupted." 【0082】 Step 2: 【0083】 The server collects relevant information from external sources and internal data areas based on the extracted criteria. The inputs used are the keywords and intents identified in step 1. Based on these keywords, the server accesses online knowledge bases and forums, and searches its internal FAQ database. This process generates and executes database queries, outputting the resulting information. For example, it includes the specific action of collecting information on "common solutions to network connectivity problems." 【0084】 Step 3: 【0085】 The server constructs a virtual area and performs operational checks related to the query. The input includes information collected in step 2 and data necessary for the simulation. The server constructs a virtual environment, specifically setting up a virtual network, and simulates the problem. This process reproduces the problem and generates output results to identify its cause. For example, it reproduces network delays or connection failures and analyzes their causes. 【0086】 Step 4: 【0087】 The server generates a response based on the results of the operational checks and the collected information. The input used is the results obtained in step 3 and related information. Utilizing a generative AI model, the server creates a response that includes suggestions and solutions for the user. Specifically, it proposes improvement measures based on the identified cause of the problem, generating output that details, for example, "how to update the network driver" or "procedures for changing settings." 【0088】 Step 5: 【0089】 The server presents the generated response to the user. The input includes the response generated in step 4. The server sends this to the user's terminal in the appropriate format. The output is presented to the user as a notification or email. Specific actions include displaying text containing the suggested steps on the user's terminal screen. 【0090】 (Application Example 1) 【0091】 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." 【0092】 Modern electronic payment services require quick and accurate resolution of customer inquiries and transaction-related issues. However, current systems often take a long time to identify and resolve problems, potentially leading to decreased customer satisfaction. This issue is particularly serious for critical inquiries such as payment errors and double billing. 【0093】 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. 【0094】 In this invention, the server includes means for analyzing query information received from an information processing device and identifying conditions related to the query information, means for collecting relevant information from external information sources and internal databases, and means for constructing a virtual environment for verifying the operation of transactions in an electronic payment service. This makes it possible to quickly and accurately identify transaction-related problems and provide appropriate solutions. 【0095】 An "information processing device" is an electronic device used to receive, analyze, and process data, and includes servers and computers. 【0096】 "Inquiry information" refers to data provided by users to resolve specific problems or questions, and specifically pertains to products or services. 【0097】 A "condition" is an element that indicates a specific criterion or state extracted from the inquiry information. 【0098】 "External information sources" refer to external data providers such as online knowledge bases and forums on the internet. 【0099】 An "internal database" is an information repository that stores past inquiry history and FAQs maintained by a company or organization. 【0100】 A "virtual environment" is a simulation space constructed using software rather than physical means, and is used to verify operation under specific conditions. 【0101】 "Electronic payment services" refer to services that facilitate the transfer and transaction of funds using digital means. 【0102】 A "transaction" refers to a monetary transaction or settlement process conducted through an electronic payment service. 【0103】 "Operational verification" is the process of verifying whether a system or process functions correctly under specific conditions. 【0104】 A "proposed solution" refers to a solution or proposal created based on information obtained through analysis and operational verification. 【0105】 The system for realizing this invention is configured with an information processing device at its core. The system receives inquiry information sent from the user's terminal via a server and analyzes it using natural language processing. Based on the analysis results, the server extracts specific conditions and, based on these, collects relevant information from external information sources (such as online knowledge bases and forums) and internal databases (past inquiry history and FAQ databases). 【0106】 Next, the server builds a virtual environment to verify the operation of transactions in the electronic payment service. In this virtual environment, it reproduces transaction-related problems and explores ways to identify and resolve issues such as payment errors and double billing. 【0107】 Ultimately, the server automatically generates appropriate solutions based on the collected information and the results of testing in the virtual environment. These solutions are then presented to the user's terminal in the appropriate format via an information processing device, enabling rapid resolution. 【0108】 This system utilizes servers running on cloud services such as AWS®, and leverages libraries like spaCy and NLTK for natural language processing. The system's stability and processing power are guaranteed by using common database management systems such as MySQL®. For example, if a user reports a payment error using a smartphone app, the system will quickly identify the cause of the error and provide appropriate steps to resolve it. 【0109】 An example of a prompt for a generative AI model (e.g., OpenAI® GPT) is as follows: "To identify the cause of the payment error reported by the user, collect relevant information and present an appropriate solution based on the simulation results." 【0110】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0111】 Step 1: 【0112】 The server receives query information from the user's terminal. The input is text data provided by the user, and the output is plain text to be parsed. This text data is stored in a database and used for subsequent parsing processes. 【0113】 Step 2: 【0114】 The server analyzes received query information using natural language processing. The input is query information in plain text format, and the output is the intent of the query and related keywords. Specifically, it uses libraries such as spaCy and NLTK to extract keywords and intent from the text. 【0115】 Step 3: 【0116】 The server collects relevant information from external sources and internal databases based on the conditions extracted in step 2. The input is the extracted keywords or conditions, and the output is a list of relevant information. Online knowledge bases are used as external sources, and past query history is used as an internal source. Specifically, the server executes API queries and retrieves the corresponding data entries. 【0117】 Step 4: 【0118】 The server will build a virtual environment for verifying the operation of the electronic payment service. The inputs will be collected information and transaction data, and the output will be the simulation results. In this step, virtual user operations will be performed to confirm that the transaction is functioning correctly. 【0119】 Step 5: 【0120】 The server generates proposed answers based on the collected information and the results of testing in the virtual environment. The input is a list of simulation results and related information, and the output is a proposed answer as a solution. A generative AI model is used to construct the proposed answers using prompt statements. Specifically, the generative model is given a prompt such as, "To identify the cause of the payment error reported by the user, please collect related information and provide an appropriate solution based on the simulation results." 【0121】 Step 6: 【0122】 The terminal presents the user with suggested answers sent from the server. The input is the suggested answer generated by the server, and the output is the display of the solution for the user to review. Specifically, the terminal renders the suggested answer on the user interface, providing it as a guideline for the next action the user should take. 【0123】 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. 【0124】 This invention relates to an inquiry response system that effectively processes user inquiry information and generates responses while taking into account the user's emotions. This system operates with a server at its core that receives user inquiries and analyzes their content using natural language processing. Furthermore, the server utilizes an emotion engine to recognize the user's emotional state and reflects this in the analysis results. 【0125】 Specifically, the server analyzes the inquiry information received from the user, and the emotion engine detects the sentiment and tone contained within it. For example, if the user uses words that express dissatisfaction, the emotion engine accurately recognizes this dissatisfaction and records that state. This sentiment analysis helps determine whether the user's inquiry is a simple request for information or a case requiring urgent support. 【0126】 Next, the server searches for necessary information from external knowledge bases and internal databases based on the given conditions. Using the results of the sentiment engine, the server performs an optimized search and adjusts suggested answers according to the user's sentiment. During this process, operational verification in a virtual environment is also performed in parallel, and specifications are confirmed with external organizations if necessary. 【0127】 Based on the collected information and the results of operational checks, the server generates response suggestions that take emotions into account. For example, if a user is dissatisfied with an inquiry, the server will generate a response with a more friendly and encouraging tone. By presenting these suggested responses to the user quickly, the server supports a smoother problem-solving process. 【0128】 Not only does the system enable users to resolve problems through the answers they receive, but it also learns users' emotional patterns over time, providing even more improved and customized support. This reduces the workload on staff and significantly improves user satisfaction. 【0129】 The following describes the processing flow. 【0130】 Step 1: 【0131】 Users submit inquiry information via an information processing device. This inquiry information includes the question itself and related background information. The user's input method is typically text-based. 【0132】 Step 2: 【0133】 The server receives the query information from the user as text data and prepares it for analysis. In this analysis, the server identifies the query category and requirements in order to process the information accurately. 【0134】 Step 3: 【0135】 The server uses an emotion engine to analyze the user's emotions contained in the inquiry information. Here, emotional states such as anxiety, dissatisfaction, and doubt are identified, and the results are used for subsequent processing. This analysis forms the basis for understanding the user's emotions. 【0136】 Step 4: 【0137】 Based on the analysis results, the server collects relevant information to derive an appropriate response. It searches past solutions in its internal database and also explores external knowledge bases and forums. It adjusts the focus and priority of information gathering according to the user's sentiment. 【0138】 Step 5: 【0139】 The server creates a virtual environment and performs operational checks related to the query. For example, when attempting to reproduce a problem related to a specific software version, the results of the execution in the virtual environment are recorded. 【0140】 Step 6: 【0141】 The server will automatically make inquiries to external organizations as needed. In particular, if specific specifications or new problems are anticipated, it will check specifications with vendors and manufacturers to obtain the latest information. This step improves the accuracy and reliability of the responses. 【0142】 Step 7: 【0143】 Based on the collected information and the results of operational checks, the server generates emotionally sensitive response suggestions. It adjusts the tone of the response according to the user's emotional state, incorporating expressions that convey a sense of reassurance and trust. 【0144】 Step 8: 【0145】 The server presents the generated suggested answers to the user. By receiving these answers, the user can learn about specific solutions and the next steps. Immediate feedback can be received via the device, and additional support is also available. 【0146】 (Example 2) 【0147】 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 will be referred to as the "terminal." 【0148】 Traditional customer service systems often provided uniform answers without adequately considering user feelings, frequently leading to user dissatisfaction. Furthermore, this resulted in a heavy workload for staff, making efficient responses difficult. Therefore, improving user satisfaction and reducing the burden on staff became key challenges. 【0149】 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. 【0150】 In this invention, the server includes means for analyzing query information and identifying conditions and emotional states related to the query information; means for collecting relevant information from external information sources and internal databases based on the conditions and emotional states and adjusting the priority of information according to the user's emotions; and means for generating emotionally conscious response proposals based on the collected information, operational verification results, and emotion analysis results. This enables a quick and appropriate response that takes the user's emotions into consideration. 【0151】 "Inquiry information" refers to data related to questions and requests provided by users through information processing devices. 【0152】 "Conditions" refer to elements of circumstances or content identified in relation to the inquiry information, and serve as the basis for collecting relevant information and generating responses. 【0153】 "Emotional state" refers to the psychological state or emotional expression that a user displays when making an inquiry. 【0154】 "External information sources" refer to databases, online resources, and other sources of information located outside the company. 【0155】 An "internal database" is a data storage or information resource maintained within an organization or company. 【0156】 "Emotional analysis" refers to the process of detecting and evaluating the emotions and psychological state of a user based on the inquiry information they provide. 【0157】 A "draft response" is the content of a response generated to provide appropriate and useful information in response to a user's inquiry. 【0158】 "Customized support" refers to individualized services or support activities tailored to each user's specific needs and feelings. 【0159】 This inquiry handling system aims to receive user inquiry information and generate responses that take emotions into consideration. At the core of the system is a server, which is responsible for processing user inquiries. 【0160】 The server first receives inquiry information from the user. This information is then analyzed using natural language processing technology. Specifically, generative AI models such as Google's BERT and OpenAI's GPT series are used to extract the intent and sentiment of the inquiry. This allows the server to accurately understand the type of information and support the user is seeking. 【0161】 Next, the server performs sentiment analysis to identify the emotional state behind the query. Sentiment analysis uses an emotion engine to analyze the user's expressions and tone. For example, if the user is expressing dissatisfaction, the server accurately detects the emotion of "dissatisfaction" and uses that information to process the query. 【0162】 Subsequently, the server collects relevant information from external sources and internal databases based on the analysis results. This process allows for highly customized information retrieval, taking user emotions into consideration and adjusting information priorities accordingly. 【0163】 Ultimately, the server combines the collected information with the results of sentiment analysis to generate suggested answers that correspond to the user's emotions. These suggested answers are then quickly presented to the user to support problem-solving. 【0164】 (Specific example) 【0165】 User: "My ordered item hasn't arrived yet, and I'm very worried. What's going on?" 【0166】 An example of a prompt statement is as follows: 【0167】 "The user is dissatisfied because their product hasn't arrived. Please explain the reason for the delay and the shipping status in a friendly and encouraging tone, and provide additional information and support." 【0168】 Based on this prompt, the generative AI model creates answers that alleviate user anxiety, thereby increasing user satisfaction. 【0169】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0170】 Step 1: 【0171】 The user enters the inquiry information using a terminal and presses the send button. The input is a text-based inquiry message. This inquiry information is sent to the server via a data communication protocol. The server receives this data and passes it on to the next parsing step. 【0172】 Step 2: 【0173】 The server analyzes the received query information using natural language processing (NLP) tools. The input for this step is the user's text message. The server uses a generative AI model (e.g., BERT, GPT series) to extract the intent and keywords of the query. The output of this analysis is structured data as the analysis result. 【0174】 Step 3: 【0175】 The server performs sentiment analysis based on structured data. The input here is the structured data obtained in the previous step. The sentiment engine evaluates the emotional state and identifies emotions such as "dissatisfaction" and "joy." The output of this step is a dataset containing the emotional states. 【0176】 Step 4: 【0177】 The server searches for relevant information from external sources and internal databases based on the results of sentiment analysis and structured data. The input consists of emotional states and structured data. In retrieving relevant information, the identified emotions influence the priority of the information. The output is a list of information useful to the user. 【0178】 Step 5: 【0179】 The server combines relevant information and sentiment analysis results to generate suggested answers. The inputs include emotional state, structured data, and relevant information. In this step, a generative AI model is used to create an appropriate tone of response that matches the user's emotions. The output of this process is the final suggested answer. 【0180】 Step 6: 【0181】 The server sends the generated answer to the terminal and presents it to the user. The input is the completed answer, and the output is the answer message displayed on the user's terminal. The terminal displays the received answer on its screen and provides the user with information to solve the problem. 【0182】 (Application Example 2) 【0183】 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". 【0184】 There is a need to provide prompt and emotionally sensitive responses to the problems customers face with electronic payment services. However, conventional automated response systems often fail to adequately consider user emotions, resulting in insufficient support. Furthermore, in the case of urgent inquiries or complaints, appropriate responses may be delayed, leading to decreased user satisfaction. Technologies are needed to solve these problems. 【0185】 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. 【0186】 In this invention, the server includes means for analyzing query information and identifying conditions, means for collecting relevant information from external sources and an internal knowledge base based on the conditions and analyzed sentiment, and means for constructing a virtual work environment and performing operational checks related to the conditions and sentiment. This makes it possible to generate and present a quick and optimal response while taking the user's sentiment into consideration. 【0187】 An "information processing unit" is a device that receives data from an external source and processes that data based on specific conditions. 【0188】 "Inquiry information" refers to data related to specific requests or questions provided by users. 【0189】 "External information sources" refer to external information repositories and information supply services other than internal databases. 【0190】 An "internal knowledge base" is a database managed within an organization, where past information and knowledge are accumulated. 【0191】 A "virtual work environment" is a virtual space simulated on a computer, an environment in which it is possible to perform operational verification based on a specific scenario. 【0192】 "Sentiment analysis" is the process of extracting emotional nuances from user inquiries and recognizing their emotional state. 【0193】 A "suggested response" is proposed data generated based on certain information, serving as a response to the user. 【0194】 The server first receives user inquiry information through an information processing unit. This information is analyzed using natural language processing technology to identify the content of the inquiry and its associated sentiment. Dedicated sentiment engine software is used for this sentiment analysis. 【0195】 When a user submits an inquiry expressing dissatisfaction, such as "I was charged twice," the sentiment engine detects the complaint. Based on the conditions and sentiment, the server searches external sources and its internal knowledge base to gather the information needed for a resolution. 【0196】 The virtual work environment performs simulations and verifies its operation. During this process, a generative AI model is used to create proposed answers to be provided to the user. For example, this might include explaining the progress of a rapid refund process to the user. 【0197】 The generated response is presented via the information processing unit, with its tone adjusted according to the user's emotional state. For example, to alleviate dissatisfaction, a friendly tone such as "We apologize for the inconvenience. We will address this promptly" might be used. 【0198】 The AI ​​model is instructed to generate an answer using a prompt such as, "The user has made an angry inquiry. Please provide an appropriate response to soothe their emotions." 【0199】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0200】 Step 1: 【0201】 The server uses an information processing unit to receive inquiry information from users. It receives the user's inquiry message as input and sends it to the analysis process in its original form. 【0202】 Step 2: 【0203】 The server uses a natural language processing engine to analyze the received query information. The analysis breaks down the message content and extracts sentiment and conditions from the text data. This process outputs the sentiment state and the query topic. 【0204】 Step 3: 【0205】 The server uses an emotion engine to further analyze the extracted emotions. It receives the emotion text to be analyzed as input and identifies the type of emotion (e.g., anger, joy, sadness). The output is the identified emotion type and its associated intensity. 【0206】 Step 4: 【0207】 Based on the analysis of conditions and sentiments, the server searches external information sources and internal knowledge bases. This search process generates search queries to collect relevant information, resulting in the output of a list of the required information. 【0208】 Step 5: 【0209】 The server builds a virtual work environment and performs operational verification using the information obtained through the search. It takes relevant information as input and verifies its accuracy through simulation. The output is an evaluation of whether the information is valid as a result of the operational verification. 【0210】 Step 6: 【0211】 The server utilizes a generative AI model to generate suggested responses to the user based on the results of operational verification and sentiment analysis. It receives evaluated information as input and outputs suggested responses with adjusted tone and content. 【0212】 Step 7: 【0213】 The server presents suggested answers to the user via an information processing unit. By using the generated suggested answers as input and displaying feedback on the user's screen based on that input, rapid support is completed. 【0214】 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. 【0215】 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. 【0216】 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. 【0217】 [Second Embodiment] 【0218】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0219】 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. 【0220】 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). 【0221】 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. 【0222】 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. 【0223】 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). 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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. 【0229】 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". 【0230】 This invention is an inquiry response system utilizing an AI agent, which includes inquiry analysis by an information processing device, automatic collection of relevant information, operational verification in a virtual environment, automatic inquiries to vendors, and generation and presentation of proposed answers. The system begins with an analysis of the inquiry content and performs a series of automated processes to provide prompt and accurate support. 【0231】 First, the server receives inquiry information from the user. This information includes technical questions and requests regarding a specific product or service. The server analyzes the inquiry information and uses natural language processing to extract keywords and intent. Through this analysis, the server identifies the specific conditions that need to be processed. 【0232】 Next, the server automatically collects relevant information from external sources and internal databases based on specified conditions. External sources include online knowledge bases and forums, while internal databases store past inquiry history and FAQs. The server combines this information to gather background information for the inquiry. 【0233】 The server then builds a virtual environment and performs operational checks related to the inquiry. For example, if a user reports a connection problem with a device, the server simulates that device, reproduces the problem, and searches for a solution. 【0234】 Furthermore, the server automatically makes inquiries to external organizations to verify specifications as needed. This allows for confirmation of any missing information or new specification changes. 【0235】 Ultimately, the server generates a proposed solution based on the collected information and the results of its operational checks. This proposed solution includes the solution itself, steps, and relevant links, and is presented to the user in an appropriate format. This allows the user to quickly obtain the information necessary to resolve the problem. 【0236】 In one embodiment of the present invention, for example, when handling inquiries regarding network configuration problems, the server identifies the cause of the connection error and provides the user with the latest drivers and configuration examples to facilitate a quick resolution. In this way, it is possible to reduce the workload on the person in charge and improve user satisfaction. 【0237】 The following describes the processing flow. 【0238】 Step 1: 【0239】 The server receives inquiry information from the user. The user enters questions related to products or services using the system. The server receives this data and begins preparing to process it. 【0240】 Step 2: 【0241】 The server analyzes the query information. Using natural language processing, it analyzes keywords and sentence structure to identify the intent of the query. Based on this analysis, the server determines what processing is required. 【0242】 Step 3: 【0243】 The server collects relevant information based on the analysis results. It searches for necessary information from external knowledge bases and forums, and explores past similar cases and FAQs in its internal database. This provides the basic information needed to respond to inquiries. 【0244】 Step 4: 【0245】 The server builds a virtual environment and performs operational verification. This involves simulating the systems and devices related to the inquiry. By reproducing the problem, a concrete solution can be found. 【0246】 Step 5: 【0247】 The server will contact external organizations for specification inquiries as needed. In particular, if new problems or unknown specification changes are discovered, it will obtain accurate information by checking with vendors and manufacturers. 【0248】 Step 6: 【0249】 The server generates a proposed solution based on the collected information and the results of its operational checks. It organizes the information in the most useful format for the user and proposes a solution. This proposed solution includes steps, links, and additional resources. 【0250】 Step 7: 【0251】 The server presents the user with a proposed answer. Information is provided immediately via email or chat system to assist the user in resolving their problem. The user can then use the received information to resolve their inquiry. 【0252】 (Example 1) 【0253】 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". 【0254】 Conventional information inquiry response systems require considerable time and effort to process from analyzing inquiry content to generating responses, making it difficult to respond quickly and accurately. To solve this problem, a system is needed that enables efficient analysis of inquiry information, rapid collection of related information, and verification of operation in a virtual environment. 【0255】 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. 【0256】 In this invention, the server includes means for analyzing data received from an information processing device and identifying conditions related to the data, means for collecting relevant information from external information sources and internal data areas, and means for constructing a virtual area and verifying its operation. This makes it possible to efficiently perform tasks ranging from analyzing query information to collecting relevant information, and further to reproducing problems and presenting solutions. 【0257】 An "information processing device" is a digital device that analyzes received data and efficiently handles inquiries. 【0258】 "Data" refers to a collection of information, including the content of questions and inquiries received from users. 【0259】 A "condition" is a specific element of a problem or issue that needs to be solved, identified from the analyzed data. 【0260】 "External information sources" refer to external databases, such as online knowledge bases and forums, that are consulted to gather information. 【0261】 "Internal data area" refers to databases maintained within an organization, such as past inquiry history and FAQs. 【0262】 "Relevant information" refers to information collected based on specific criteria that is useful for solving a problem. 【0263】 A "virtual domain" is a virtual space on a computer that is built to simulate a real-world environment. 【0264】 "Operational verification" is the process of reproducing a problem situation based on identified conditions in a virtual environment and verifying the solution. 【0265】 A "response" is advice or a suggestion for resolving a problem for the user, generated based on the collected relevant information and the results of operational checks. 【0266】 This invention is a system that uses an AI agent to respond to inquiries quickly and accurately. The central component of the system is a server, which receives user inquiries and performs efficient information processing. The server uses the following specific software and processes: 【0267】 First, the server analyzes the data received from the user's terminal. This analysis uses a generative AI model and leverages natural language processing techniques to extract relevant conditions from the data. The goal of this step is to identify keywords and intent from the text sent by the user. For example, if a user reports "unstable network connection," the server will extract the keywords "network" and "unstable." 【0268】 Next, the server automatically collects relevant information from external sources and internal data areas. External sources include online knowledge bases and forums, which are accessed by the server through web scraping and API calls. Internal data areas store FAQs and history of past inquiries within the organization, and a database management system (DBMS) supports its operation. 【0269】 Furthermore, the server creates a virtual area and performs simulations based on specified conditions. The virtual environment is created using virtualization technology and has the capability to reproduce reported problems. For example, it can virtually reproduce unstable network behavior and perform tests to identify the cause. 【0270】 Ultimately, the server uses a generated AI model to create and present an answer to the user based on the results of the operational checks and the collected information. The answer includes steps and relevant links to help the user solve their problem. For example, sending a prompt such as "Please show me the steps to improve my network connection" will prompt the AI ​​model to provide appropriate advice. 【0271】 This system allows servers to respond quickly and effectively to user issues, improving the efficiency of inquiry handling and user satisfaction. 【0272】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0273】 Step 1: 【0274】 The server retrieves inquiry data received from the user's terminal. It receives the user's inquiry text as input and analyzes it using natural language processing technology. Through this analysis, the server extracts keywords and intent from the inquiry content and outputs the conditions necessary to identify the problem. Specifically, it uses a generative AI model to analyze the structure of the text, for example, to clarify information such as "the network connection is interrupted." 【0275】 Step 2: 【0276】 The server collects relevant information from external sources and internal data areas based on the extracted criteria. The inputs used are the keywords and intents identified in step 1. Based on these keywords, the server accesses online knowledge bases and forums, and searches its internal FAQ database. This process generates and executes database queries, outputting the resulting information. For example, it includes the specific action of collecting information on "common solutions to network connectivity problems." 【0277】 Step 3: 【0278】 The server constructs a virtual area and performs operational checks related to the query. The input includes information collected in step 2 and data necessary for the simulation. The server constructs a virtual environment, specifically setting up a virtual network, and simulates the problem. This process reproduces the problem and generates output results to identify its cause. For example, it reproduces network delays or connection failures and analyzes their causes. 【0279】 Step 4: 【0280】 The server generates an answer based on the results of the operation check and the collected information. As input, the results and related information obtained in Step 3 are used. Utilizing the generation AI model, the server creates an answer that includes proposals and solutions for the user. In specific operations, improvement measures are proposed based on the identified causes of the problem, and outputs that detail, for example, "how to update the network driver" or "procedure for changing settings" are generated. 【0281】 Step 5: 【0282】 The server presents the generated answer to the user. As input, the answer generated in Step 4 is included. The server sends this to the user terminal in an appropriate format. As output, information is presented to the user as a notification or email. Specific operations include the process of displaying the text containing the proposed procedure on the user's terminal screen. 【0283】 (Application Example 1) 【0284】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0285】 In modern electronic payment services, it is required to quickly and accurately solve problems related to customer inquiries and transactions. However, in the current system, it often takes a long time to identify the cause of the problem and solve it, which may lead to a decrease in customer satisfaction. This issue is particularly serious in important inquiries such as payment errors and double billing. 【0286】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0287】 In this invention, the server includes means for analyzing query information received from an information processing device and identifying conditions related to the query information, means for collecting relevant information from external information sources and internal databases, and means for constructing a virtual environment for verifying the operation of transactions in an electronic payment service. This makes it possible to quickly and accurately identify transaction-related problems and provide appropriate solutions. 【0288】 An "information processing device" is an electronic device used to receive, analyze, and process data, and includes servers and computers. 【0289】 "Inquiry information" refers to data provided by users to resolve specific problems or questions, and specifically pertains to products or services. 【0290】 A "condition" is an element that indicates a specific criterion or state extracted from the inquiry information. 【0291】 "External information sources" refer to external data providers such as online knowledge bases and forums on the internet. 【0292】 An "internal database" is an information repository that stores past inquiry history and FAQs maintained by a company or organization. 【0293】 A "virtual environment" is a simulation space constructed using software rather than physical means, and is used to verify operation under specific conditions. 【0294】 "Electronic payment services" refer to services that facilitate the transfer and transaction of funds using digital means. 【0295】 A "transaction" refers to a monetary transaction or settlement process conducted through an electronic payment service. 【0296】 "Operational verification" is the process of verifying whether a system or process functions correctly under specific conditions. 【0297】 A "proposed solution" refers to a solution or proposal created based on information obtained through analysis and operational verification. 【0298】 The system for realizing this invention is configured with an information processing device at its core. The system receives inquiry information sent from the user's terminal via a server and analyzes it using natural language processing. Based on the analysis results, the server extracts specific conditions and, based on these, collects relevant information from external information sources (such as online knowledge bases and forums) and internal databases (past inquiry history and FAQ databases). 【0299】 Next, the server builds a virtual environment to verify the operation of transactions in the electronic payment service. In this virtual environment, it reproduces transaction-related problems and explores ways to identify and resolve issues such as payment errors and double billing. 【0300】 Ultimately, the server automatically generates appropriate solutions based on the collected information and the results of testing in the virtual environment. These solutions are then presented to the user's terminal in the appropriate format via an information processing device, enabling rapid resolution. 【0301】 This system utilizes servers running on cloud services such as AWS, and leverages libraries like spaCy and NLTK for natural language processing. The use of common database management systems such as MySQL ensures system stability and processing power. For example, if a user reports a payment error using a smartphone app, the system will quickly identify the cause of the error and provide appropriate steps to resolve it. 【0302】 Examples of prompt texts for generating AI models (e.g., OpenAI GPT) are as follows. "Please collect relevant information and present appropriate solutions based on the simulation results to identify the causes of payment errors reported by users." 【0303】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0304】 Step 1: 【0305】 The server receives inquiry information from the user's terminal. The input is the text data provided by the user, and the output is the plain text to be analyzed. This text data is stored in a database and used for the next analysis process. 【0306】 Step 2: 【0307】 The server analyzes the received inquiry information using natural language processing. The input is the inquiry information in plain text format, and the output is the intention of the inquiry and related keywords. As a specific operation, libraries such as spaCy and NLTK are used to extract keywords and intentions from the text. 【0308】 Step 3: 【0309】 The server collects relevant information from external information sources and internal databases based on the conditions extracted in Step 2. The input is the extracted keywords and conditions, and the output is a list of related information. An online knowledge base is used as an external information source, and the past inquiry history is used as an internal information source. As a specific operation, API queries are executed to obtain corresponding data entries. 【0310】 Step 4: 【0311】 The server will build a virtual environment for verifying the operation of the electronic payment service. The inputs will be collected information and transaction data, and the output will be the simulation results. In this step, virtual user operations will be performed to confirm that the transaction is functioning correctly. 【0312】 Step 5: 【0313】 The server generates proposed answers based on the collected information and the results of testing in the virtual environment. The input is a list of simulation results and related information, and the output is a proposed answer as a solution. A generative AI model is used to construct the proposed answers using prompt statements. Specifically, the generative model is given a prompt such as, "To identify the cause of the payment error reported by the user, please collect related information and provide an appropriate solution based on the simulation results." 【0314】 Step 6: 【0315】 The terminal presents the user with suggested answers sent from the server. The input is the suggested answer generated by the server, and the output is the display of the solution for the user to review. Specifically, the terminal renders the suggested answer on the user interface, providing it as a guideline for the next action the user should take. 【0316】 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. 【0317】 This invention relates to an inquiry response system that effectively processes user inquiry information and generates responses while taking into account the user's emotions. This system operates with a server at its core that receives user inquiries and analyzes their content using natural language processing. Furthermore, the server utilizes an emotion engine to recognize the user's emotional state and reflects this in the analysis results. 【0318】 Specifically, the server analyzes the inquiry information received from the user, and the emotion engine detects the sentiment and tone contained within it. For example, if the user uses words that express dissatisfaction, the emotion engine accurately recognizes this dissatisfaction and records that state. This sentiment analysis helps determine whether the user's inquiry is a simple request for information or a case requiring urgent support. 【0319】 Next, the server searches for necessary information from external knowledge bases and internal databases based on the given conditions. Using the results of the sentiment engine, the server performs an optimized search and adjusts suggested answers according to the user's sentiment. During this process, operational verification in a virtual environment is also performed in parallel, and specifications are confirmed with external organizations if necessary. 【0320】 Based on the collected information and the results of operational checks, the server generates response suggestions that take emotions into account. For example, if a user is dissatisfied with an inquiry, the server will generate a response with a more friendly and encouraging tone. By presenting these suggested responses to the user quickly, the server supports a smoother problem-solving process. 【0321】 Not only does the system enable users to resolve problems through the answers they receive, but it also learns users' emotional patterns over time, providing even more improved and customized support. This reduces the workload on staff and significantly improves user satisfaction. 【0322】 The following describes the processing flow. 【0323】 Step 1: 【0324】 Users submit inquiry information via an information processing device. This inquiry information includes the question itself and related background information. The user's input method is typically text-based. 【0325】 Step 2: 【0326】 The server receives the query information from the user as text data and prepares it for analysis. In this analysis, the server identifies the query category and requirements in order to process the information accurately. 【0327】 Step 3: 【0328】 The server uses an emotion engine to analyze the user's emotions contained in the inquiry information. Here, emotional states such as anxiety, dissatisfaction, and doubt are identified, and the results are used for subsequent processing. This analysis forms the basis for understanding the user's emotions. 【0329】 Step 4: 【0330】 Based on the analysis results, the server collects relevant information to derive an appropriate response. It searches past solutions in its internal database and also explores external knowledge bases and forums. It adjusts the focus and priority of information gathering according to the user's sentiment. 【0331】 Step 5: 【0332】 The server creates a virtual environment and performs operational checks related to the query. For example, when attempting to reproduce a problem related to a specific software version, the results of the execution in the virtual environment are recorded. 【0333】 Step 6: 【0334】 The server will automatically make inquiries to external organizations as needed. In particular, if specific specifications or new problems are anticipated, it will check specifications with vendors and manufacturers to obtain the latest information. This step improves the accuracy and reliability of the responses. 【0335】 Step 7: 【0336】 Based on the collected information and the results of operational checks, the server generates emotionally sensitive response suggestions. It adjusts the tone of the response according to the user's emotional state, incorporating expressions that convey a sense of reassurance and trust. 【0337】 Step 8: 【0338】 The server presents the generated suggested answers to the user. By receiving these answers, the user can learn about specific solutions and the next steps. Immediate feedback can be received via the device, and additional support is also available. 【0339】 (Example 2) 【0340】 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". 【0341】 Traditional customer service systems often provided uniform answers without adequately considering user feelings, frequently leading to user dissatisfaction. Furthermore, this resulted in a heavy workload for staff, making efficient responses difficult. Therefore, improving user satisfaction and reducing the burden on staff became key challenges. 【0342】 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. 【0343】 In this invention, the server includes means for analyzing query information and identifying conditions and emotional states related to the query information; means for collecting relevant information from external information sources and internal databases based on the conditions and emotional states and adjusting the priority of information according to the user's emotions; and means for generating emotionally conscious response proposals based on the collected information, operational verification results, and emotion analysis results. This enables a quick and appropriate response that takes the user's emotions into consideration. 【0344】 "Inquiry information" refers to data related to questions and requests provided by users through information processing devices. 【0345】 "Conditions" refer to elements of circumstances or content identified in relation to the inquiry information, and serve as the basis for collecting relevant information and generating responses. 【0346】 "Emotional state" refers to the psychological state or emotional expression that a user displays when making an inquiry. 【0347】 "External information sources" refer to databases, online resources, and other sources of information located outside the company. 【0348】 An "internal database" is a data storage or information resource maintained within an organization or company. 【0349】 "Emotional analysis" refers to the process of detecting and evaluating the emotions and psychological state of a user based on the inquiry information they provide. 【0350】 A "draft response" is the content of a response generated to provide appropriate and useful information in response to a user's inquiry. 【0351】 "Customized support" refers to individualized services or support activities tailored to each user's specific needs and feelings. 【0352】 This inquiry handling system aims to receive user inquiry information and generate responses that take emotions into consideration. At the core of the system is a server, which is responsible for processing user inquiries. 【0353】 The server first receives inquiry information from the user. This information is then analyzed using natural language processing techniques. Specifically, generative AI models such as Google's BERT and OpenAI's GPT series are used to extract the intent and sentiment of the inquiry. This allows the server to accurately understand the type of information and support the user is seeking. 【0354】 Next, the server performs sentiment analysis to identify the emotional state behind the query. Sentiment analysis uses an emotion engine to analyze the user's expressions and tone. For example, if the user is expressing dissatisfaction, the server accurately detects the emotion of "dissatisfaction" and uses that information to process the query. 【0355】 Subsequently, the server collects relevant information from external sources and internal databases based on the analysis results. This process allows for highly customized information retrieval, taking user emotions into consideration and adjusting information priorities accordingly. 【0356】 Ultimately, the server combines the collected information with the results of sentiment analysis to generate suggested answers that correspond to the user's emotions. These suggested answers are then quickly presented to the user to support problem-solving. 【0357】 (Specific example) 【0358】 User: "My ordered item hasn't arrived yet, and I'm very worried. What's going on?" 【0359】 An example of a prompt statement is as follows: 【0360】 "The user is dissatisfied because their product hasn't arrived. Please explain the reason for the delay and the shipping status in a friendly and encouraging tone, and provide additional information and support." 【0361】 Based on this prompt, the generative AI model creates answers that alleviate user anxiety, thereby increasing user satisfaction. 【0362】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0363】 Step 1: 【0364】 The user enters the inquiry information using a terminal and presses the send button. The input is a text-based inquiry message. This inquiry information is sent to the server via a data communication protocol. The server receives this data and passes it on to the next parsing step. 【0365】 Step 2: 【0366】 The server analyzes the received query information using natural language processing (NLP) tools. The input for this step is the user's text message. The server uses a generative AI model (e.g., BERT, GPT series) to extract the intent and keywords of the query. The output of this analysis is structured data as the analysis result. 【0367】 Step 3: 【0368】 The server performs sentiment analysis based on structured data. The input here is the structured data obtained in the previous step. The sentiment engine evaluates the emotional state and identifies emotions such as "dissatisfaction" and "joy." The output of this step is a dataset containing the emotional states. 【0369】 Step 4: 【0370】 The server searches for relevant information from external sources and internal databases based on the results of sentiment analysis and structured data. The input consists of emotional states and structured data. In retrieving relevant information, the identified emotions influence the priority of the information. The output is a list of information useful to the user. 【0371】 Step 5: 【0372】 The server combines relevant information and sentiment analysis results to generate suggested answers. The inputs include emotional state, structured data, and relevant information. In this step, a generative AI model is used to create an appropriate tone of response that matches the user's emotions. The output of this process is the final suggested answer. 【0373】 Step 6: 【0374】 The server sends the generated answer to the terminal and presents it to the user. The input is the completed answer, and the output is the answer message displayed on the user's terminal. The terminal displays the received answer on its screen and provides the user with information to solve the problem. 【0375】 (Application Example 2) 【0376】 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." 【0377】 There is a need to provide prompt and emotionally sensitive responses to the problems customers face with electronic payment services. However, conventional automated response systems often fail to adequately consider user emotions, resulting in insufficient support. Furthermore, in the case of urgent inquiries or complaints, appropriate responses may be delayed, leading to decreased user satisfaction. Technologies are needed to solve these problems. 【0378】 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. 【0379】 In this invention, the server includes means for analyzing query information and identifying conditions, means for collecting relevant information from external sources and an internal knowledge base based on the conditions and analyzed sentiment, and means for constructing a virtual work environment and performing operational checks related to the conditions and sentiment. This makes it possible to generate and present a quick and optimal response while taking the user's sentiment into consideration. 【0380】 An "information processing unit" is a device that receives data from an external source and processes that data based on specific conditions. 【0381】 "Inquiry information" refers to data related to specific requests or questions provided by users. 【0382】 "External information sources" refer to external information repositories and information supply services other than internal databases. 【0383】 An "internal knowledge base" is a database managed within an organization, where past information and knowledge are accumulated. 【0384】 A "virtual work environment" is a virtual space simulated on a computer, an environment in which it is possible to perform operational verification based on a specific scenario. 【0385】 "Sentiment analysis" is the process of extracting emotional nuances from user inquiries and recognizing their emotional state. 【0386】 A "suggested response" is proposed data generated based on certain information, serving as a response to the user. 【0387】 The server first receives user inquiry information through an information processing unit. This information is analyzed using natural language processing technology to identify the content of the inquiry and its associated sentiment. Dedicated sentiment engine software is used for this sentiment analysis. 【0388】 When a user submits an inquiry expressing dissatisfaction, such as "I was charged twice," the sentiment engine detects the complaint. Based on the conditions and sentiment, the server searches external sources and its internal knowledge base to gather the information needed for a resolution. 【0389】 The virtual work environment performs simulations and verifies its operation. During this process, a generative AI model is used to create proposed answers to be provided to the user. For example, this might include explaining the progress of a rapid refund process to the user. 【0390】 The generated response is presented via the information processing unit, with its tone adjusted according to the user's emotional state. For example, to alleviate dissatisfaction, a friendly tone such as "We apologize for the inconvenience. We will address this promptly" might be used. 【0391】 The AI ​​model is instructed to generate an answer using a prompt such as, "The user has made an angry inquiry. Please provide an appropriate response to soothe their emotions." 【0392】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0393】 Step 1: 【0394】 The server uses an information processing unit to receive inquiry information from users. It receives the user's inquiry message as input and sends it to the analysis process in its original form. 【0395】 Step 2: 【0396】 The server uses a natural language processing engine to analyze the received query information. The analysis breaks down the message content and extracts sentiment and conditions from the text data. This process outputs the sentiment state and the query topic. 【0397】 Step 3: 【0398】 The server uses an emotion engine to further analyze the extracted emotions. It receives the emotion text to be analyzed as input and identifies the type of emotion (e.g., anger, joy, sadness). The output is the identified emotion type and its associated intensity. 【0399】 Step 4: 【0400】 Based on the analysis of conditions and sentiments, the server searches external information sources and internal knowledge bases. This search process generates search queries to collect relevant information, resulting in the output of a list of the required information. 【0401】 Step 5: 【0402】 The server builds a virtual work environment and performs operational verification using the information obtained through the search. It takes relevant information as input and verifies its accuracy through simulation. The output is an evaluation of whether the information is valid as a result of the operational verification. 【0403】 Step 6: 【0404】 The server utilizes a generative AI model to generate suggested responses to the user based on the results of operational verification and sentiment analysis. It receives evaluated information as input and outputs suggested responses with adjusted tone and content. 【0405】 Step 7: 【0406】 The server presents suggested answers to the user via an information processing unit. By using the generated suggested answers as input and displaying feedback on the user's screen based on that input, rapid support is completed. 【0407】 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. 【0408】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0409】 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. 【0410】 [Third Embodiment] 【0411】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0412】 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. 【0413】 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). 【0414】 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. 【0415】 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. 【0416】 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). 【0417】 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. 【0418】 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. 【0419】 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. 【0420】 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. 【0421】 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. 【0422】 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". 【0423】 This invention is an inquiry response system utilizing an AI agent, which includes inquiry analysis by an information processing device, automatic collection of relevant information, operational verification in a virtual environment, automatic inquiries to vendors, and generation and presentation of proposed answers. The system begins with an analysis of the inquiry content and performs a series of automated processes to provide prompt and accurate support. 【0424】 First, the server receives inquiry information from the user. This information includes technical questions and requests regarding a specific product or service. The server analyzes the inquiry information and uses natural language processing to extract keywords and intent. Through this analysis, the server identifies the specific conditions that need to be processed. 【0425】 Next, the server automatically collects relevant information from external sources and internal databases based on specified conditions. External sources include online knowledge bases and forums, while internal databases store past inquiry history and FAQs. The server combines this information to gather background information for the inquiry. 【0426】 The server then builds a virtual environment and performs operational checks related to the inquiry. For example, if a user reports a connection problem with a device, the server simulates that device, reproduces the problem, and searches for a solution. 【0427】 Furthermore, the server automatically makes inquiries to external organizations to verify specifications as needed. This allows for confirmation of any missing information or new specification changes. 【0428】 Ultimately, the server generates a proposed solution based on the collected information and the results of its operational checks. This proposed solution includes the solution itself, steps, and relevant links, and is presented to the user in an appropriate format. This allows the user to quickly obtain the information necessary to resolve the problem. 【0429】 In one embodiment of the present invention, for example, when handling inquiries regarding network configuration problems, the server identifies the cause of the connection error and provides the user with the latest drivers and configuration examples to facilitate a quick resolution. In this way, it is possible to reduce the workload on the person in charge and improve user satisfaction. 【0430】 The following describes the processing flow. 【0431】 Step 1: 【0432】 The server receives inquiry information from the user. The user enters questions related to products or services using the system. The server receives this data and begins preparing to process it. 【0433】 Step 2: 【0434】 The server analyzes the query information. Using natural language processing, it analyzes keywords and sentence structure to identify the intent of the query. Based on this analysis, the server determines what processing is required. 【0435】 Step 3: 【0436】 The server collects relevant information based on the analysis results. It searches for necessary information from external knowledge bases and forums, and explores past similar cases and FAQs in its internal database. This provides the basic information needed to respond to inquiries. 【0437】 Step 4: 【0438】 The server builds a virtual environment and performs operational verification. This involves simulating the systems and devices related to the inquiry. By reproducing the problem, a concrete solution can be found. 【0439】 Step 5: 【0440】 The server will contact external organizations for specification inquiries as needed. In particular, if new problems or unknown specification changes are discovered, it will obtain accurate information by checking with vendors and manufacturers. 【0441】 Step 6: 【0442】 The server generates a proposed solution based on the collected information and the results of its operational checks. It organizes the information in the most useful format for the user and proposes a solution. This proposed solution includes steps, links, and additional resources. 【0443】 Step 7: 【0444】 The server presents the user with a proposed answer. Information is provided immediately via email or chat system to assist the user in resolving their problem. The user can then use the received information to resolve their inquiry. 【0445】 (Example 1) 【0446】 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." 【0447】 Conventional information inquiry response systems require considerable time and effort to process from analyzing inquiry content to generating responses, making it difficult to respond quickly and accurately. To solve this problem, a system is needed that enables efficient analysis of inquiry information, rapid collection of related information, and verification of operation in a virtual environment. 【0448】 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. 【0449】 In this invention, the server includes means for analyzing data received from an information processing device and identifying conditions related to the data, means for collecting relevant information from external information sources and internal data areas, and means for constructing a virtual area and verifying its operation. This makes it possible to efficiently perform tasks ranging from analyzing query information to collecting relevant information, and further to reproducing problems and presenting solutions. 【0450】 An "information processing device" is a digital device that analyzes received data and efficiently handles inquiries. 【0451】 "Data" refers to a collection of information, including the content of questions and inquiries received from users. 【0452】 A "condition" is a specific element of a problem or issue that needs to be solved, identified from the analyzed data. 【0453】 "External information sources" refer to external databases, such as online knowledge bases and forums, that are consulted to gather information. 【0454】 "Internal data area" refers to databases maintained within an organization, such as past inquiry history and FAQs. 【0455】 "Relevant information" refers to information collected based on specific criteria that is useful for solving a problem. 【0456】 A "virtual domain" is a virtual space on a computer that is built to simulate a real-world environment. 【0457】 "Operational verification" is the process of reproducing a problem situation based on identified conditions in a virtual environment and verifying the solution. 【0458】 A "response" is advice or a suggestion for resolving a problem for the user, generated based on the collected relevant information and the results of operational checks. 【0459】 This invention is a system that uses an AI agent to respond to inquiries quickly and accurately. The central component of the system is a server, which receives user inquiries and performs efficient information processing. The server uses the following specific software and processes: 【0460】 First, the server analyzes the data received from the user's terminal. This analysis uses a generative AI model and leverages natural language processing techniques to extract relevant conditions from the data. The goal of this step is to identify keywords and intent from the text sent by the user. For example, if a user reports "unstable network connection," the server will extract the keywords "network" and "unstable." 【0461】 Next, the server automatically collects relevant information from external sources and internal data areas. External sources include online knowledge bases and forums, which are accessed by the server through web scraping and API calls. Internal data areas store FAQs and history of past inquiries within the organization, and a database management system (DBMS) supports its operation. 【0462】 Furthermore, the server creates a virtual area and performs simulations based on specified conditions. The virtual environment is created using virtualization technology and has the capability to reproduce reported problems. For example, it can virtually reproduce unstable network behavior and perform tests to identify the cause. 【0463】 Ultimately, the server uses a generated AI model to create and present an answer to the user based on the results of the operational checks and the collected information. The answer includes steps and relevant links to help the user solve their problem. For example, sending a prompt such as "Please show me the steps to improve my network connection" will prompt the AI ​​model to provide appropriate advice. 【0464】 This system allows servers to respond quickly and effectively to user issues, improving the efficiency of inquiry handling and user satisfaction. 【0465】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0466】 Step 1: 【0467】 The server retrieves inquiry data received from the user's terminal. It receives the user's inquiry text as input and analyzes it using natural language processing technology. Through this analysis, the server extracts keywords and intent from the inquiry content and outputs the conditions necessary to identify the problem. Specifically, it uses a generative AI model to analyze the structure of the text, for example, to clarify information such as "the network connection is interrupted." 【0468】 Step 2: 【0469】 The server collects relevant information from external sources and internal data areas based on the extracted criteria. The inputs used are the keywords and intents identified in step 1. Based on these keywords, the server accesses online knowledge bases and forums, and searches its internal FAQ database. This process generates and executes database queries, outputting the resulting information. For example, it includes the specific action of collecting information on "common solutions to network connectivity problems." 【0470】 Step 3: 【0471】 The server constructs a virtual area and performs operational checks related to the query. The input includes information collected in step 2 and data necessary for the simulation. The server constructs a virtual environment, specifically setting up a virtual network, and simulates the problem. This process reproduces the problem and generates output results to identify its cause. For example, it reproduces network delays or connection failures and analyzes their causes. 【0472】 Step 4: 【0473】 The server generates a response based on the results of the operational checks and the collected information. The input used is the results obtained in step 3 and related information. Utilizing a generative AI model, the server creates a response that includes suggestions and solutions for the user. Specifically, it proposes improvement measures based on the identified cause of the problem, generating output that details, for example, "how to update the network driver" or "procedures for changing settings." 【0474】 Step 5: 【0475】 The server presents the generated response to the user. The input includes the response generated in step 4. The server sends this to the user's terminal in the appropriate format. The output is presented to the user as a notification or email. Specific actions include displaying text containing the suggested steps on the user's terminal screen. 【0476】 (Application Example 1) 【0477】 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." 【0478】 Modern electronic payment services require quick and accurate resolution of customer inquiries and transaction-related issues. However, current systems often take a long time to identify and resolve problems, potentially leading to decreased customer satisfaction. This issue is particularly serious for critical inquiries such as payment errors and double billing. 【0479】 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. 【0480】 In this invention, the server includes means for analyzing query information received from an information processing device and identifying conditions related to the query information, means for collecting relevant information from external information sources and internal databases, and means for constructing a virtual environment for verifying the operation of transactions in an electronic payment service. This makes it possible to quickly and accurately identify transaction-related problems and provide appropriate solutions. 【0481】 An "information processing device" is an electronic device used to receive, analyze, and process data, and includes servers and computers. 【0482】 "Inquiry information" refers to data provided by users to resolve specific problems or questions, and specifically pertains to products or services. 【0483】 A "condition" is an element that indicates a specific criterion or state extracted from the inquiry information. 【0484】 "External information sources" refer to external data providers such as online knowledge bases and forums on the internet. 【0485】 An "internal database" is an information repository that stores past inquiry history and FAQs maintained by a company or organization. 【0486】 A "virtual environment" is a simulation space constructed using software rather than physical means, and is used to verify operation under specific conditions. 【0487】 "Electronic payment services" refer to services that facilitate the transfer and transaction of funds using digital means. 【0488】 A "transaction" refers to a monetary transaction or settlement process conducted through an electronic payment service. 【0489】 "Operational verification" is the process of verifying whether a system or process functions correctly under specific conditions. 【0490】 A "proposed solution" refers to a solution or proposal created based on information obtained through analysis and operational verification. 【0491】 The system for realizing this invention is configured with an information processing device at its core. The system receives inquiry information sent from the user's terminal via a server and analyzes it using natural language processing. Based on the analysis results, the server extracts specific conditions and, based on these, collects relevant information from external information sources (such as online knowledge bases and forums) and internal databases (past inquiry history and FAQ databases). 【0492】 Next, the server builds a virtual environment to verify the operation of transactions in the electronic payment service. In this virtual environment, it reproduces transaction-related problems and explores ways to identify and resolve issues such as payment errors and double billing. 【0493】 Ultimately, the server automatically generates appropriate solutions based on the collected information and the results of testing in the virtual environment. These solutions are then presented to the user's terminal in the appropriate format via an information processing device, enabling rapid resolution. 【0494】 This system utilizes servers running on cloud services such as AWS, and leverages libraries like spaCy and NLTK for natural language processing. The use of common database management systems such as MySQL ensures system stability and processing power. For example, if a user reports a payment error using a smartphone app, the system will quickly identify the cause of the error and provide appropriate steps to resolve it. 【0495】 An example of a prompt for a generated AI model (e.g., OpenAI GPT) is as follows: "To identify the cause of the payment error reported by the user, collect relevant information and present an appropriate solution based on the simulation results." 【0496】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0497】 Step 1: 【0498】 The server receives query information from the user's terminal. The input is text data provided by the user, and the output is plain text to be parsed. This text data is stored in a database and used for subsequent parsing processes. 【0499】 Step 2: 【0500】 The server analyzes received query information using natural language processing. The input is query information in plain text format, and the output is the intent of the query and related keywords. Specifically, it uses libraries such as spaCy and NLTK to extract keywords and intent from the text. 【0501】 Step 3: 【0502】 The server collects relevant information from external sources and internal databases based on the conditions extracted in step 2. The input is the extracted keywords or conditions, and the output is a list of relevant information. Online knowledge bases are used as external sources, and past query history is used as an internal source. Specifically, the server executes API queries and retrieves the corresponding data entries. 【0503】 Step 4: 【0504】 The server will build a virtual environment for verifying the operation of the electronic payment service. The inputs will be collected information and transaction data, and the output will be the simulation results. In this step, virtual user operations will be performed to confirm that the transaction is functioning correctly. 【0505】 Step 5: 【0506】 The server generates proposed answers based on the collected information and the results of testing in the virtual environment. The input is a list of simulation results and related information, and the output is a proposed answer as a solution. A generative AI model is used to construct the proposed answers using prompt statements. Specifically, the generative model is given a prompt such as, "To identify the cause of the payment error reported by the user, please collect related information and provide an appropriate solution based on the simulation results." 【0507】 Step 6: 【0508】 The terminal presents the user with suggested answers sent from the server. The input is the suggested answer generated by the server, and the output is the display of the solution for the user to review. Specifically, the terminal renders the suggested answer on the user interface, providing it as a guideline for the next action the user should take. 【0509】 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. 【0510】 This invention relates to an inquiry response system that effectively processes user inquiry information and generates responses while taking into account the user's emotions. This system operates with a server at its core that receives user inquiries and analyzes their content using natural language processing. Furthermore, the server utilizes an emotion engine to recognize the user's emotional state and reflects this in the analysis results. 【0511】 Specifically, the server analyzes the inquiry information received from the user, and the emotion engine detects the sentiment and tone contained within it. For example, if the user uses words that express dissatisfaction, the emotion engine accurately recognizes this dissatisfaction and records that state. This sentiment analysis helps determine whether the user's inquiry is a simple request for information or a case requiring urgent support. 【0512】 Next, the server searches for necessary information from external knowledge bases and internal databases based on the given conditions. Using the results of the sentiment engine, the server performs an optimized search and adjusts suggested answers according to the user's sentiment. During this process, operational verification in a virtual environment is also performed in parallel, and specifications are confirmed with external organizations if necessary. 【0513】 Based on the collected information and the results of operational checks, the server generates response suggestions that take emotions into account. For example, if a user is dissatisfied with an inquiry, the server will generate a response with a more friendly and encouraging tone. By presenting these suggested responses to the user quickly, the server supports a smoother problem-solving process. 【0514】 Not only does the system enable users to resolve problems through the answers they receive, but it also learns users' emotional patterns over time, providing even more improved and customized support. This reduces the workload on staff and significantly improves user satisfaction. 【0515】 The following describes the processing flow. 【0516】 Step 1: 【0517】 Users submit inquiry information via an information processing device. This inquiry information includes the question itself and related background information. The user's input method is typically text-based. 【0518】 Step 2: 【0519】 The server receives the query information from the user as text data and prepares it for analysis. In this analysis, the server identifies the query category and requirements in order to process the information accurately. 【0520】 Step 3: 【0521】 The server uses an emotion engine to analyze the user's emotions contained in the inquiry information. Here, emotional states such as anxiety, dissatisfaction, and doubt are identified, and the results are used for subsequent processing. This analysis forms the basis for understanding the user's emotions. 【0522】 Step 4: 【0523】 Based on the analysis results, the server collects relevant information to derive an appropriate response. It searches past solutions in its internal database and also explores external knowledge bases and forums. It adjusts the focus and priority of information gathering according to the user's sentiment. 【0524】 Step 5: 【0525】 The server creates a virtual environment and performs operational checks related to the query. For example, when attempting to reproduce a problem related to a specific software version, the results of the execution in the virtual environment are recorded. 【0526】 Step 6: 【0527】 The server will automatically make inquiries to external organizations as needed. In particular, if specific specifications or new problems are anticipated, it will check specifications with vendors and manufacturers to obtain the latest information. This step improves the accuracy and reliability of the responses. 【0528】 Step 7: 【0529】 Based on the collected information and the results of operational checks, the server generates emotionally sensitive response suggestions. It adjusts the tone of the response according to the user's emotional state, incorporating expressions that convey a sense of reassurance and trust. 【0530】 Step 8: 【0531】 The server presents the generated suggested answers to the user. By receiving these answers, the user can learn about specific solutions and the next steps. Immediate feedback can be received via the device, and additional support is also available. 【0532】 (Example 2) 【0533】 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." 【0534】 Traditional customer service systems often provided uniform answers without adequately considering user feelings, frequently leading to user dissatisfaction. Furthermore, this resulted in a heavy workload for staff, making efficient responses difficult. Therefore, improving user satisfaction and reducing the burden on staff became key challenges. 【0535】 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. 【0536】 In this invention, the server includes means for analyzing query information and identifying conditions and emotional states related to the query information; means for collecting relevant information from external information sources and internal databases based on the conditions and emotional states and adjusting the priority of information according to the user's emotions; and means for generating emotionally conscious response proposals based on the collected information, operational verification results, and emotion analysis results. This enables a quick and appropriate response that takes the user's emotions into consideration. 【0537】 "Inquiry information" refers to data related to questions and requests provided by users through information processing devices. 【0538】 "Conditions" refer to elements of circumstances or content identified in relation to the inquiry information, and serve as the basis for collecting relevant information and generating responses. 【0539】 "Emotional state" refers to the psychological state or emotional expression that a user displays when making an inquiry. 【0540】 "External information sources" refer to databases, online resources, and other sources of information located outside the company. 【0541】 An "internal database" is a data storage or information resource maintained within an organization or company. 【0542】 "Emotional analysis" refers to the process of detecting and evaluating the emotions and psychological state of a user based on the inquiry information they provide. 【0543】 A "draft response" is the content of a response generated to provide appropriate and useful information in response to a user's inquiry. 【0544】 "Customized support" refers to individualized services or support activities tailored to each user's specific needs and feelings. 【0545】 This inquiry handling system aims to receive user inquiry information and generate responses that take emotions into consideration. At the core of the system is a server, which is responsible for processing user inquiries. 【0546】 The server first receives inquiry information from the user. This information is then analyzed using natural language processing techniques. Specifically, generative AI models such as Google's BERT and OpenAI's GPT series are used to extract the intent and sentiment of the inquiry. This allows the server to accurately understand the type of information and support the user is seeking. 【0547】 Next, the server performs sentiment analysis to identify the emotional state behind the query. Sentiment analysis uses an emotion engine to analyze the user's expressions and tone. For example, if the user is expressing dissatisfaction, the server accurately detects the emotion of "dissatisfaction" and uses that information to process the query. 【0548】 Subsequently, the server collects relevant information from external sources and internal databases based on the analysis results. This process allows for highly customized information retrieval, taking user emotions into consideration and adjusting information priorities accordingly. 【0549】 Ultimately, the server combines the collected information with the results of sentiment analysis to generate suggested answers that correspond to the user's emotions. These suggested answers are then quickly presented to the user to support problem-solving. 【0550】 (Specific example) 【0551】 User: "My ordered item hasn't arrived yet, and I'm very worried. What's going on?" 【0552】 An example of a prompt statement is as follows: 【0553】 "The user is dissatisfied because their product hasn't arrived. Please explain the reason for the delay and the shipping status in a friendly and encouraging tone, and provide additional information and support." 【0554】 Based on this prompt, the generative AI model creates answers that alleviate user anxiety, thereby increasing user satisfaction. 【0555】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0556】 Step 1: 【0557】 The user enters the inquiry information using a terminal and presses the send button. The input is a text-based inquiry message. This inquiry information is sent to the server via a data communication protocol. The server receives this data and passes it on to the next parsing step. 【0558】 Step 2: 【0559】 The server analyzes the received query information using natural language processing (NLP) tools. The input for this step is the user's text message. The server uses a generative AI model (e.g., BERT, GPT series) to extract the intent and keywords of the query. The output of this analysis is structured data as the analysis result. 【0560】 Step 3: 【0561】 The server performs sentiment analysis based on structured data. The input here is the structured data obtained in the previous step. The sentiment engine evaluates the emotional state and identifies emotions such as "dissatisfaction" and "joy." The output of this step is a dataset containing the emotional states. 【0562】 Step 4: 【0563】 The server searches for relevant information from external sources and internal databases based on the results of sentiment analysis and structured data. The input consists of emotional states and structured data. In retrieving relevant information, the identified emotions influence the priority of the information. The output is a list of information useful to the user. 【0564】 Step 5: 【0565】 The server combines relevant information and sentiment analysis results to generate suggested answers. The inputs include emotional state, structured data, and relevant information. In this step, a generative AI model is used to create an appropriate tone of response that matches the user's emotions. The output of this process is the final suggested answer. 【0566】 Step 6: 【0567】 The server sends the generated answer to the terminal and presents it to the user. The input is the completed answer, and the output is the answer message displayed on the user's terminal. The terminal displays the received answer on its screen and provides the user with information to solve the problem. 【0568】 (Application Example 2) 【0569】 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." 【0570】 There is a need to provide prompt and emotionally sensitive responses to the problems customers face with electronic payment services. However, conventional automated response systems often fail to adequately consider user emotions, resulting in insufficient support. Furthermore, in the case of urgent inquiries or complaints, appropriate responses may be delayed, leading to decreased user satisfaction. Technologies are needed to solve these problems. 【0571】 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. 【0572】 In this invention, the server includes means for analyzing query information and identifying conditions, means for collecting relevant information from external sources and an internal knowledge base based on the conditions and analyzed sentiment, and means for constructing a virtual work environment and performing operational checks related to the conditions and sentiment. This makes it possible to generate and present a quick and optimal response while taking the user's sentiment into consideration. 【0573】 An "information processing unit" is a device that receives data from an external source and processes that data based on specific conditions. 【0574】 "Inquiry information" refers to data related to specific requests or questions provided by users. 【0575】 "External information sources" refer to external information repositories and information supply services other than internal databases. 【0576】 An "internal knowledge base" is a database managed within an organization, where past information and knowledge are accumulated. 【0577】 A "virtual work environment" is a virtual space simulated on a computer, an environment in which it is possible to perform operational verification based on a specific scenario. 【0578】 "Sentiment analysis" is the process of extracting emotional nuances from user inquiries and recognizing their emotional state. 【0579】 A "suggested response" is proposed data generated based on certain information, serving as a response to the user. 【0580】 The server first receives user inquiry information through an information processing unit. This information is analyzed using natural language processing technology to identify the content of the inquiry and its associated sentiment. Dedicated sentiment engine software is used for this sentiment analysis. 【0581】 When a user submits an inquiry expressing dissatisfaction, such as "I was charged twice," the sentiment engine detects the complaint. Based on the conditions and sentiment, the server searches external sources and its internal knowledge base to gather the information needed for a resolution. 【0582】 The virtual work environment performs simulations and verifies its operation. During this process, a generative AI model is used to create proposed answers to be provided to the user. For example, this might include explaining the progress of a rapid refund process to the user. 【0583】 The generated response is presented via the information processing unit, with its tone adjusted according to the user's emotional state. For example, to alleviate dissatisfaction, a friendly tone such as "We apologize for the inconvenience. We will address this promptly" might be used. 【0584】 The AI ​​model is instructed to generate an answer using a prompt such as, "The user has made an angry inquiry. Please provide an appropriate response to soothe their emotions." 【0585】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0586】 Step 1: 【0587】 The server uses an information processing unit to receive inquiry information from users. It receives the user's inquiry message as input and sends it to the analysis process in its original form. 【0588】 Step 2: 【0589】 The server uses a natural language processing engine to analyze the received query information. The analysis breaks down the message content and extracts sentiment and conditions from the text data. This process outputs the sentiment state and the query topic. 【0590】 Step 3: 【0591】 The server uses an emotion engine to further analyze the extracted emotions. It receives the emotion text to be analyzed as input and identifies the type of emotion (e.g., anger, joy, sadness). The output is the identified emotion type and its associated intensity. 【0592】 Step 4: 【0593】 Based on the analysis of conditions and sentiments, the server searches external information sources and internal knowledge bases. This search process generates search queries to collect relevant information, resulting in the output of a list of the required information. 【0594】 Step 5: 【0595】 The server builds a virtual work environment and performs operational verification using the information obtained through the search. It takes relevant information as input and verifies its accuracy through simulation. The output is an evaluation of whether the information is valid as a result of the operational verification. 【0596】 Step 6: 【0597】 The server utilizes a generative AI model to generate suggested responses to the user based on the results of operational verification and sentiment analysis. It receives evaluated information as input and outputs suggested responses with adjusted tone and content. 【0598】 Step 7: 【0599】 The server presents suggested answers to the user via an information processing unit. By using the generated suggested answers as input and displaying feedback on the user's screen based on that input, rapid support is completed. 【0600】 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. 【0601】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0602】 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. 【0603】 [Fourth Embodiment] 【0604】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0605】 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. 【0606】 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). 【0607】 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. 【0608】 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. 【0609】 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). 【0610】 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. 【0611】 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. 【0612】 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. 【0613】 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. 【0614】 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. 【0615】 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. 【0616】 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". 【0617】 This invention is an inquiry response system utilizing an AI agent, which includes inquiry analysis by an information processing device, automatic collection of relevant information, operational verification in a virtual environment, automatic inquiries to vendors, and generation and presentation of proposed answers. The system begins with an analysis of the inquiry content and performs a series of automated processes to provide prompt and accurate support. 【0618】 First, the server receives inquiry information from the user. This information includes technical questions and requests regarding a specific product or service. The server analyzes the inquiry information and uses natural language processing to extract keywords and intent. Through this analysis, the server identifies the specific conditions that need to be processed. 【0619】 Next, the server automatically collects relevant information from external sources and internal databases based on specified conditions. External sources include online knowledge bases and forums, while internal databases store past inquiry history and FAQs. The server combines this information to gather background information for the inquiry. 【0620】 The server then builds a virtual environment and performs operational checks related to the inquiry. For example, if a user reports a connection problem with a device, the server simulates that device, reproduces the problem, and searches for a solution. 【0621】 Furthermore, the server automatically makes inquiries to external organizations to verify specifications as needed. This allows for confirmation of any missing information or new specification changes. 【0622】 Ultimately, the server generates a proposed solution based on the collected information and the results of its operational checks. This proposed solution includes the solution itself, steps, and relevant links, and is presented to the user in an appropriate format. This allows the user to quickly obtain the information necessary to resolve the problem. 【0623】 In one embodiment of the present invention, for example, when handling inquiries regarding network configuration problems, the server identifies the cause of the connection error and provides the user with the latest drivers and configuration examples to facilitate a quick resolution. In this way, it is possible to reduce the workload on the person in charge and improve user satisfaction. 【0624】 The following describes the processing flow. 【0625】 Step 1: 【0626】 The server receives inquiry information from the user. The user enters questions related to products or services using the system. The server receives this data and begins preparing to process it. 【0627】 Step 2: 【0628】 The server analyzes the query information. Using natural language processing, it analyzes keywords and sentence structure to identify the intent of the query. Based on this analysis, the server determines what processing is required. 【0629】 Step 3: 【0630】 The server collects relevant information based on the analysis results. It searches for necessary information from external knowledge bases and forums, and explores past similar cases and FAQs in its internal database. This provides the foundational information needed to respond to inquiries. 【0631】 Step 4: 【0632】 The server builds a virtual environment and performs operational verification. This involves simulating the systems and devices related to the inquiry. By reproducing the problem, a concrete solution can be found. 【0633】 Step 5: 【0634】 The server will contact external organizations for specification inquiries as needed. In particular, if new problems or unknown specification changes are discovered, it will obtain accurate information by checking with vendors and manufacturers. 【0635】 Step 6: 【0636】 The server generates a proposed solution based on the collected information and the results of its operational checks. It organizes the information in the most useful format for the user and proposes a solution. This proposed solution includes steps, links, and additional resources. 【0637】 Step 7: 【0638】 The server presents the user with a proposed answer. Information is provided immediately via email or chat system to assist the user in resolving their problem. The user can then use the received information to resolve their inquiry. 【0639】 (Example 1) 【0640】 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". 【0641】 Conventional information inquiry response systems require considerable time and effort to process from analyzing inquiry content to generating responses, making it difficult to respond quickly and accurately. To solve this problem, a system is needed that enables efficient analysis of inquiry information, rapid collection of related information, and verification of operation in a virtual environment. 【0642】 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. 【0643】 In this invention, the server includes means for analyzing data received from an information processing device and identifying conditions related to the data, means for collecting relevant information from external information sources and internal data areas, and means for constructing a virtual area and verifying its operation. This makes it possible to efficiently perform tasks ranging from analyzing query information to collecting relevant information, and further to reproducing problems and presenting solutions. 【0644】 An "information processing device" is a digital device that analyzes received data and efficiently handles inquiries. 【0645】 "Data" refers to a collection of information, including the content of questions and inquiries received from users. 【0646】 A "condition" is a specific element of a problem or issue that needs to be solved, identified from the analyzed data. 【0647】 "External information sources" refer to external databases, such as online knowledge bases and forums, that are consulted to gather information. 【0648】 "Internal data area" refers to databases maintained within an organization, such as past inquiry history and FAQs. 【0649】 "Relevant information" refers to information collected based on specific criteria that is useful for solving a problem. 【0650】 A "virtual domain" is a virtual space on a computer that is built to simulate a real-world environment. 【0651】 "Operational verification" is the process of reproducing a problem situation based on identified conditions in a virtual environment and verifying the solution. 【0652】 A "response" is advice or a suggestion for resolving a problem for the user, generated based on the collected relevant information and the results of operational checks. 【0653】 This invention is a system that uses an AI agent to respond to inquiries quickly and accurately. The central component of the system is a server, which receives user inquiries and performs efficient information processing. The server uses the following specific software and processes: 【0654】 First, the server analyzes the data received from the user's terminal. This analysis uses a generative AI model and leverages natural language processing techniques to extract relevant conditions from the data. The goal of this step is to identify keywords and intent from the text sent by the user. For example, if a user reports "unstable network connection," the server will extract the keywords "network" and "unstable." 【0655】 Next, the server automatically collects relevant information from external sources and internal data areas. External sources include online knowledge bases and forums, which are accessed by the server through web scraping and API calls. Internal data areas store FAQs and history of past inquiries within the organization, and a database management system (DBMS) supports its operation. 【0656】 Furthermore, the server creates a virtual area and performs simulations based on specified conditions. The virtual environment is created using virtualization technology and has the capability to reproduce reported problems. For example, it can virtually reproduce unstable network behavior and perform tests to identify the cause. 【0657】 Ultimately, the server uses a generated AI model to create and present an answer to the user based on the results of the operational checks and the collected information. The answer includes steps and relevant links to help the user solve their problem. For example, sending a prompt such as "Please show me the steps to improve my network connection" will prompt the AI ​​model to provide appropriate advice. 【0658】 This system allows servers to respond quickly and effectively to user issues, improving the efficiency of inquiry handling and user satisfaction. 【0659】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0660】 Step 1: 【0661】 The server retrieves inquiry data received from the user's terminal. It receives the user's inquiry text as input and analyzes it using natural language processing technology. Through this analysis, the server extracts keywords and intent from the inquiry content and outputs the conditions necessary to identify the problem. Specifically, it uses a generative AI model to analyze the structure of the text, for example, to clarify information such as "the network connection is interrupted." 【0662】 Step 2: 【0663】 The server collects relevant information from external sources and internal data areas based on the extracted criteria. The inputs used are the keywords and intents identified in step 1. Based on these keywords, the server accesses online knowledge bases and forums, and searches its internal FAQ database. This process generates and executes database queries, outputting the resulting information. For example, it includes the specific action of collecting information on "common solutions to network connectivity problems." 【0664】 Step 3: 【0665】 The server constructs a virtual area and performs operational checks related to the query. The input includes information collected in step 2 and data necessary for the simulation. The server constructs a virtual environment, specifically setting up a virtual network, and simulates the problem. This process reproduces the problem and generates output results to identify its cause. For example, it reproduces network delays or connection failures and analyzes their causes. 【0666】 Step 4: 【0667】 The server generates a response based on the results of the operational checks and the collected information. The input used is the results obtained in step 3 and related information. Utilizing a generative AI model, the server creates a response that includes suggestions and solutions for the user. Specifically, it proposes improvement measures based on the identified cause of the problem, generating output that details, for example, "how to update the network driver" or "procedures for changing settings." 【0668】 Step 5: 【0669】 The server presents the generated response to the user. The input includes the response generated in step 4. The server sends this to the user's terminal in the appropriate format. The output is presented to the user as a notification or email. Specific actions include displaying text containing the suggested steps on the user's terminal screen. 【0670】 (Application Example 1) 【0671】 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". 【0672】 Modern electronic payment services require quick and accurate resolution of customer inquiries and transaction-related issues. However, current systems often take a long time to identify and resolve problems, potentially leading to decreased customer satisfaction. This issue is particularly serious for critical inquiries such as payment errors and double billing. 【0673】 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. 【0674】 In this invention, the server includes means for analyzing query information received from an information processing device and identifying conditions related to the query information, means for collecting relevant information from external information sources and internal databases, and means for constructing a virtual environment for verifying the operation of transactions in an electronic payment service. This makes it possible to quickly and accurately identify transaction-related problems and provide appropriate solutions. 【0675】 An "information processing device" is an electronic device used to receive, analyze, and process data, and includes servers and computers. 【0676】 "Inquiry information" refers to data provided by users to resolve specific problems or questions, and specifically pertains to products or services. 【0677】 A "condition" is an element that indicates a specific criterion or state extracted from the inquiry information. 【0678】 "External information sources" refer to external data providers such as online knowledge bases and forums on the internet. 【0679】 An "internal database" is an information repository that stores past inquiry history and FAQs maintained by a company or organization. 【0680】 A "virtual environment" is a simulation space constructed using software rather than physical means, and is used to verify operation under specific conditions. 【0681】 "Electronic payment services" refer to services that facilitate the transfer and transaction of funds using digital means. 【0682】 A "transaction" refers to a monetary transaction or settlement process conducted through an electronic payment service. 【0683】 "Operational verification" is the process of verifying whether a system or process functions correctly under specific conditions. 【0684】 A "proposed solution" refers to a solution or proposal created based on information obtained through analysis and operational verification. 【0685】 The system for realizing this invention is configured with an information processing device at its core. The system receives inquiry information sent from the user's terminal via a server and analyzes it using natural language processing. Based on the analysis results, the server extracts specific conditions and, based on these, collects relevant information from external information sources (such as online knowledge bases and forums) and internal databases (past inquiry history and FAQ databases). 【0686】 Next, the server builds a virtual environment to verify the operation of transactions in the electronic payment service. In this virtual environment, it reproduces transaction-related problems and explores ways to identify and resolve issues such as payment errors and double billing. 【0687】 Ultimately, the server automatically generates appropriate solutions based on the collected information and the results of testing in the virtual environment. These solutions are then presented to the user's terminal in the appropriate format via an information processing device, enabling rapid resolution. 【0688】 This system utilizes servers running on cloud services such as AWS, and leverages libraries like spaCy and NLTK for natural language processing. The use of common database management systems such as MySQL ensures system stability and processing power. For example, if a user reports a payment error using a smartphone app, the system will quickly identify the cause of the error and provide appropriate steps to resolve it. 【0689】 An example of a prompt for a generated AI model (e.g., OpenAI GPT) is as follows: "To identify the cause of the payment error reported by the user, collect relevant information and present an appropriate solution based on the simulation results." 【0690】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0691】 Step 1: 【0692】 The server receives query information from the user's terminal. The input is text data provided by the user, and the output is plain text to be parsed. This text data is stored in a database and used for subsequent parsing processes. 【0693】 Step 2: 【0694】 The server analyzes received query information using natural language processing. The input is query information in plain text format, and the output is the intent of the query and related keywords. Specifically, it uses libraries such as spaCy and NLTK to extract keywords and intent from the text. 【0695】 Step 3: 【0696】 The server collects relevant information from external sources and internal databases based on the conditions extracted in step 2. The input is the extracted keywords or conditions, and the output is a list of relevant information. Online knowledge bases are used as external sources, and past query history is used as an internal source. Specifically, the server executes API queries and retrieves the corresponding data entries. 【0697】 Step 4: 【0698】 The server will build a virtual environment for verifying the operation of the electronic payment service. The inputs will be collected information and transaction data, and the output will be the simulation results. In this step, virtual user operations will be performed to confirm that the transaction is functioning correctly. 【0699】 Step 5: 【0700】 The server generates proposed answers based on the collected information and the results of testing in the virtual environment. The input is a list of simulation results and related information, and the output is a proposed answer as a solution. A generative AI model is used to construct the proposed answers using prompt statements. Specifically, the generative model is given a prompt such as, "To identify the cause of the payment error reported by the user, please collect related information and provide an appropriate solution based on the simulation results." 【0701】 Step 6: 【0702】 The terminal presents the user with suggested answers sent from the server. The input is the suggested answer generated by the server, and the output is the display of the solution for the user to review. Specifically, the terminal renders the suggested answer on the user interface, providing it as a guideline for the next action the user should take. 【0703】 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. 【0704】 This invention relates to an inquiry response system that effectively processes user inquiry information and generates responses while taking into account the user's emotions. This system operates with a server at its core that receives user inquiries and analyzes their content using natural language processing. Furthermore, the server utilizes an emotion engine to recognize the user's emotional state and reflects this in the analysis results. 【0705】 Specifically, the server analyzes the inquiry information received from the user, and the emotion engine detects the sentiment and tone contained within it. For example, if the user uses words that express dissatisfaction, the emotion engine accurately recognizes this dissatisfaction and records that state. This sentiment analysis helps determine whether the user's inquiry is a simple request for information or a case requiring urgent support. 【0706】 Next, the server searches for necessary information from external knowledge bases and internal databases based on the given conditions. Using the results of the sentiment engine, the server performs an optimized search and adjusts suggested answers according to the user's sentiment. During this process, operational verification in a virtual environment is also performed in parallel, and specifications are confirmed with external organizations if necessary. 【0707】 Based on the collected information and the results of operational checks, the server generates response suggestions that take emotions into account. For example, if a user is dissatisfied with an inquiry, the server will generate a response with a more friendly and encouraging tone. By presenting these suggested responses to the user quickly, the server supports a smoother problem-solving process. 【0708】 Not only does the system enable users to resolve problems through the answers they receive, but it also learns users' emotional patterns over time, providing even more improved and customized support. This reduces the workload on staff and significantly improves user satisfaction. 【0709】 The following describes the processing flow. 【0710】 Step 1: 【0711】 Users submit inquiry information via an information processing device. This inquiry information includes the question itself and related background information. The user's input method is typically text-based. 【0712】 Step 2: 【0713】 The server receives the query information from the user as text data and prepares it for analysis. In this analysis, the server identifies the query category and requirements in order to process the information accurately. 【0714】 Step 3: 【0715】 The server uses an emotion engine to analyze the user's emotions contained in the inquiry information. Here, emotional states such as anxiety, dissatisfaction, and doubt are identified, and the results are used for subsequent processing. This analysis forms the basis for understanding the user's emotions. 【0716】 Step 4: 【0717】 Based on the analysis results, the server collects relevant information to derive an appropriate response. It searches past solutions in its internal database and also explores external knowledge bases and forums. It adjusts the focus and priority of information gathering according to the user's sentiment. 【0718】 Step 5: 【0719】 The server creates a virtual environment and performs operational checks related to the query. For example, when attempting to reproduce a problem related to a specific software version, the results of the execution in the virtual environment are recorded. 【0720】 Step 6: 【0721】 The server will automatically make inquiries to external organizations as needed. In particular, if specific specifications or new problems are anticipated, it will check specifications with vendors and manufacturers to obtain the latest information. This step improves the accuracy and reliability of the responses. 【0722】 Step 7: 【0723】 Based on the collected information and the results of operational checks, the server generates emotionally sensitive response suggestions. It adjusts the tone of the response according to the user's emotional state, incorporating expressions that convey a sense of reassurance and trust. 【0724】 Step 8: 【0725】 The server presents the generated suggested answers to the user. By receiving these answers, the user can learn about specific solutions and the next steps. Immediate feedback can be received via the device, and additional support is also available. 【0726】 (Example 2) 【0727】 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". 【0728】 Traditional customer service systems often provided uniform answers without adequately considering user feelings, frequently leading to user dissatisfaction. Furthermore, this resulted in a heavy workload for staff, making efficient responses difficult. Therefore, improving user satisfaction and reducing the burden on staff became key challenges. 【0729】 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. 【0730】 In this invention, the server includes means for analyzing query information and identifying conditions and emotional states related to the query information; means for collecting relevant information from external information sources and internal databases based on the conditions and emotional states and adjusting the priority of information according to the user's emotions; and means for generating emotionally conscious response proposals based on the collected information, operational verification results, and emotion analysis results. This enables a quick and appropriate response that takes the user's emotions into consideration. 【0731】 "Inquiry information" refers to data related to questions and requests provided by users through information processing devices. 【0732】 "Conditions" refer to elements of circumstances or content identified in relation to the inquiry information, and serve as the basis for collecting relevant information and generating responses. 【0733】 "Emotional state" refers to the psychological state or emotional expression that a user displays when making an inquiry. 【0734】 "External information sources" refer to databases, online resources, and other sources of information located outside the company. 【0735】 An "internal database" is a data storage or information resource maintained within an organization or company. 【0736】 "Emotional analysis" refers to the process of detecting and evaluating the emotions and psychological state of a user based on the inquiry information they provide. 【0737】 A "draft response" is the content of a response generated to provide appropriate and useful information in response to a user's inquiry. 【0738】 "Customized support" refers to individualized services or support activities tailored to each user's specific needs and feelings. 【0739】 This inquiry handling system aims to receive user inquiry information and generate responses that take emotions into consideration. At the core of the system is a server, which is responsible for processing user inquiries. 【0740】 The server first receives inquiry information from the user. This information is then analyzed using natural language processing techniques. Specifically, generative AI models such as Google's BERT and OpenAI's GPT series are used to extract the intent and sentiment of the inquiry. This allows the server to accurately understand the type of information and support the user is seeking. 【0741】 Next, the server performs sentiment analysis to identify the emotional state behind the query. Sentiment analysis uses an emotion engine to analyze the user's expressions and tone. For example, if the user is expressing dissatisfaction, the server accurately detects the emotion of "dissatisfaction" and uses that information to process the query. 【0742】 Subsequently, the server collects relevant information from external sources and internal databases based on the analysis results. This process allows for highly customized information retrieval, taking user emotions into consideration and adjusting information priorities accordingly. 【0743】 Ultimately, the server combines the collected information with the results of sentiment analysis to generate suggested answers that correspond to the user's emotions. These suggested answers are then quickly presented to the user to support problem-solving. 【0744】 (Specific example) 【0745】 User: "My ordered item hasn't arrived yet, and I'm very worried. What's going on?" 【0746】 An example of a prompt statement is as follows: 【0747】 "The user is dissatisfied because their product hasn't arrived. Please explain the reason for the delay and the shipping status in a friendly and encouraging tone, and provide additional information and support." 【0748】 Based on this prompt, the generative AI model creates answers that alleviate user anxiety, thereby increasing user satisfaction. 【0749】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0750】 Step 1: 【0751】 The user enters the inquiry information using a terminal and presses the send button. The input is a text-based inquiry message. This inquiry information is sent to the server via a data communication protocol. The server receives this data and passes it on to the next parsing step. 【0752】 Step 2: 【0753】 The server analyzes the received query information using natural language processing (NLP) tools. The input for this step is the user's text message. The server uses a generative AI model (e.g., BERT, GPT series) to extract the intent and keywords of the query. The output of this analysis is structured data as the analysis result. 【0754】 Step 3: 【0755】 The server performs sentiment analysis based on structured data. The input here is the structured data obtained in the previous step. The sentiment engine evaluates the emotional state and identifies emotions such as "dissatisfaction" and "joy." The output of this step is a dataset containing the emotional states. 【0756】 Step 4: 【0757】 The server searches for relevant information from external sources and internal databases based on the results of sentiment analysis and structured data. The input consists of emotional states and structured data. In retrieving relevant information, the identified emotions influence the priority of the information. The output is a list of information useful to the user. 【0758】 Step 5: 【0759】 The server combines relevant information and sentiment analysis results to generate suggested answers. The inputs include emotional state, structured data, and relevant information. In this step, a generative AI model is used to create an appropriate tone of response that matches the user's emotions. The output of this process is the final suggested answer. 【0760】 Step 6: 【0761】 The server sends the generated answer to the terminal and presents it to the user. The input is the completed answer, and the output is the answer message displayed on the user's terminal. The terminal displays the received answer on its screen and provides the user with information to solve the problem. 【0762】 (Application Example 2) 【0763】 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". 【0764】 There is a need to provide prompt and emotionally sensitive responses to the problems customers face with electronic payment services. However, conventional automated response systems often fail to adequately consider user emotions, resulting in insufficient support. Furthermore, in the case of urgent inquiries or complaints, appropriate responses may be delayed, leading to decreased user satisfaction. Technologies are needed to solve these problems. 【0765】 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. 【0766】 In this invention, the server includes means for analyzing query information and identifying conditions, means for collecting relevant information from external sources and an internal knowledge base based on the conditions and analyzed sentiment, and means for constructing a virtual work environment and performing operational checks related to the conditions and sentiment. This makes it possible to generate and present a quick and optimal response while taking the user's sentiment into consideration. 【0767】 An "information processing unit" is a device that receives data from an external source and processes that data based on specific conditions. 【0768】 "Inquiry information" refers to data related to specific requests or questions provided by users. 【0769】 "External information sources" refer to external information repositories and information supply services other than internal databases. 【0770】 An "internal knowledge base" is a database managed within an organization, where past information and knowledge are accumulated. 【0771】 A "virtual work environment" is a virtual space simulated on a computer, an environment in which it is possible to perform operational verification based on a specific scenario. 【0772】 "Sentiment analysis" is the process of extracting emotional nuances from user inquiries and recognizing their emotional state. 【0773】 A "suggested response" is proposed data generated based on certain information, serving as a response to the user. 【0774】 The server first receives user inquiry information through an information processing unit. This information is analyzed using natural language processing technology to identify the content of the inquiry and its associated sentiment. Dedicated sentiment engine software is used for this sentiment analysis. 【0775】 When a user submits an inquiry expressing dissatisfaction, such as "I was charged twice," the sentiment engine detects the complaint. Based on the conditions and sentiment, the server searches external sources and its internal knowledge base to gather the information needed for a resolution. 【0776】 The virtual work environment performs simulations and verifies its operation. During this process, a generative AI model is used to create proposed answers to be provided to the user. For example, this might include explaining the progress of a rapid refund process to the user. 【0777】 The generated response is presented via the information processing unit, with its tone adjusted according to the user's emotional state. For example, to alleviate dissatisfaction, a friendly tone such as "We apologize for the inconvenience. We will address this promptly" might be used. 【0778】 The AI ​​model is instructed to generate an answer using a prompt such as, "The user has made an angry inquiry. Please provide an appropriate response to soothe their emotions." 【0779】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0780】 Step 1: 【0781】 The server uses an information processing unit to receive inquiry information from users. It receives the user's inquiry message as input and sends it to the analysis process in its original form. 【0782】 Step 2: 【0783】 The server uses a natural language processing engine to analyze the received query information. The analysis breaks down the message content and extracts sentiment and conditions from the text data. This process outputs the sentiment state and the query topic. 【0784】 Step 3: 【0785】 The server uses an emotion engine to further analyze the extracted emotions. It receives the emotion text to be analyzed as input and identifies the type of emotion (e.g., anger, joy, sadness). The output is the identified emotion type and its associated intensity. 【0786】 Step 4: 【0787】 Based on the analysis of conditions and sentiments, the server searches external information sources and internal knowledge bases. This search process generates search queries to collect relevant information, resulting in the output of a list of the required information. 【0788】 Step 5: 【0789】 The server builds a virtual work environment and performs operational verification using the information obtained through the search. It takes relevant information as input and verifies its accuracy through simulation. The output is an evaluation of whether the information is valid as a result of the operational verification. 【0790】 Step 6: 【0791】 The server utilizes a generative AI model to generate suggested responses to the user based on the results of operational verification and sentiment analysis. It receives evaluated information as input and outputs suggested responses with adjusted tone and content. 【0792】 Step 7: 【0793】 The server presents suggested answers to the user via an information processing unit. By using the generated suggested answers as input and displaying feedback on the user's screen based on that input, rapid support is completed. 【0794】 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. 【0795】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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." 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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 as being incorporated by reference. 【0815】 The following is further disclosed regarding the embodiments described above. 【0816】 (Claim 1) 【0817】 A means for analyzing query information received from an information processing device and identifying conditions related to the query information, 【0818】 Based on the above conditions, means for collecting relevant information from external sources and internal databases, 【0819】 A means for constructing a virtual environment and performing operational checks related to the aforementioned conditions, 【0820】 A means for generating proposed answers based on collected information and the results of operational checks, 【0821】 A means for presenting the aforementioned proposed answer via an information processing device, 【0822】 A system that includes this. 【0823】 (Claim 2) 【0824】 The system according to claim 1, wherein the aforementioned operational verification means includes means for verifying specifications with an external organization. 【0825】 (Claim 3) 【0826】 The system according to claim 1, wherein the means for collecting relevant information based on the aforementioned conditions includes means for searching online knowledge bases and support forums. 【0827】 "Example 1" 【0828】 (Claim 1) 【0829】 A means for analyzing data received from an information processing device and identifying conditions related to the data, 【0830】 Based on the above conditions, means for collecting relevant information from external information sources and internal data areas, 【0831】 A means for constructing a virtual area and verifying the operation related to the aforementioned conditions, 【0832】 A means of generating an answer based on the collected information and the results of operational checks, 【0833】 A means for presenting the aforementioned answer via an information processing device, 【0834】 A system that includes this. 【0835】 (Claim 2) 【0836】 The system according to claim 1, wherein the aforementioned operational verification means includes means for verifying specifications with an external organization. 【0837】 (Claim 3) 【0838】 The system according to claim 1, wherein the means for collecting relevant information based on the aforementioned conditions includes means for searching online knowledge domains and support forums. 【0839】 "Application Example 1" 【0840】 (Claim 1) 【0841】 A means for analyzing query information received from an information processing device and identifying conditions related to the query information, 【0842】 Based on the above conditions, means for collecting relevant information from external sources and internal databases, 【0843】 A means for constructing a virtual environment and performing operational checks related to the aforementioned conditions, 【0844】 A means of constructing a virtual environment for verifying the operation of transactions in electronic payment services, 【0845】 A means for generating proposed answers based on collected information and the results of operational checks, 【0846】 A means for presenting the aforementioned proposed answer via an information processing device, 【0847】 A system that includes this. 【0848】 (Claim 2) 【0849】 The system according to claim 1, wherein the aforementioned operational verification means includes means for verifying specifications with an external organization. 【0850】 (Claim 3) 【0851】 The system according to claim 1, wherein the means for collecting relevant information based on the aforementioned conditions includes means for searching online knowledge bases and support forums. 【0852】 "Example 2 of combining an emotion engine" 【0853】 (Claim 1) 【0854】 A means for analyzing inquiry information and identifying conditions and emotional states related to the inquiry information, 【0855】 A means for collecting relevant information from external sources and internal databases based on the aforementioned conditions and emotional state, and for adjusting the priority of information according to the user's emotions, 【0856】 A means for constructing a virtual environment and performing operational checks related to the aforementioned conditions, 【0857】 A means for generating response suggestions that take emotions into account based on collected information, operational verification results, and sentiment analysis results, 【0858】 A means for quickly presenting the aforementioned proposed answer via an information processing device, 【0859】 A means of learning the user's emotional patterns and providing customized support, 【0860】 A system that includes this. 【0861】 (Claim 2) 【0862】 The system according to claim 1, wherein the operational verification means includes means for verifying specifications with an external organization. 【0863】 (Claim 3) 【0864】 The system according to claim 1, wherein the means for collecting relevant information based on the aforementioned conditions and emotional state includes means for searching online knowledge bases and support forums. 【0865】 "Application example 2 when combining with an emotional engine" 【0866】 (Claim 1) 【0867】 A means for analyzing query information received from an information processing unit and identifying conditions related to the query information, 【0868】 Based on the aforementioned conditions and analyzed emotions, means for collecting relevant information from external sources and internal knowledge bases, 【0869】 A means for constructing a virtual work environment and performing behavioral checks related to the aforementioned conditions and emotions, 【0870】 A means for generating response suggestions that take emotions into account, based on the collected information and the results of operational checks, 【0871】 A means for presenting response options that take the aforementioned emotions into consideration via an information processing unit, 【0872】 A system that includes this. 【0873】 (Claim 2) 【0874】 The system according to claim 1, wherein the aforementioned operational verification means includes means for verifying specifications with an external organization and means for sentiment analysis. 【0875】 (Claim 3) 【0876】 The system according to claim 1, wherein the means for collecting relevant information based on the aforementioned conditions includes means for searching considering online knowledge bases, support forums, and sentiment recognition results. [Explanation of symbols] 【0877】 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

[Claim 1] A means for analyzing query information received from an information processing device and identifying conditions related to the query information, Based on the above conditions, means for collecting relevant information from external sources and internal databases, A means for constructing a virtual environment and performing operational checks related to the aforementioned conditions, A means of generating proposed answers based on collected information and the results of operational checks, A means for presenting the aforementioned proposed answer via an information processing device, A system that includes this. [Claim 2] The system according to claim 1, wherein the aforementioned operational verification means includes means for verifying specifications with an external organization. [Claim 3] The system according to claim 1, wherein the means for collecting relevant information based on the aforementioned conditions includes means for searching online knowledge bases and support forums.

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