system

Abstract

We provide the system. [Solution] A means of registering and updating information on corporate customers, A means of receiving customer inquiries, analyzing them, and generating appropriate responses, A means of permanently managing inquiries and responses as a history, A means for generating an account plan based on user information and historical data, A method for automatically creating handover documents when the person in charge changes, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 services targeted at corporate customers, there is a problem that the quality of customer support becomes unstable due to changes in the person in charge. As a result, problems such as a decrease in customer satisfaction and a loss of business opportunities occur. Furthermore, it requires a great deal of time and effort for the handover of the person in charge, making efficient customer support difficult. 【Means for Solving the Problems】 【0005】 This invention provides means for registering and updating information on corporate customers, receiving inquiries, analyzing them, and generating responses. Furthermore, it ensures the consistency and reliability of information by permanently managing inquiry and response content. By adding means for generating an optimal account plan based on user information and historical data, it enables the provision of services tailored to customer needs. When the person in charge changes, it supports a smooth handover and improves the quality of customer service by automatically creating the necessary handover documents. 【0006】 "Corporate customers" refer to companies or organizations that provide goods or services, and are customers whose purpose is for those companies or organizations to use those goods or services. 【0007】 "Means for registering and updating information" refers to elements that provide functions for inputting a user's basic information and contract details into a database, and for changing or adding that information as needed. 【0008】 "Means for receiving, analyzing, and generating appropriate responses to inquiries" refers to the elements that receive questions from users, interpret their content, and implement the process of providing the user with the most appropriate answer. 【0009】 "Means for permanently managing inquiries and responses as a history" refers to elements that have the functionality to record past inquiries and their responses, and to retain and access them over a long period of time. 【0010】 "Means for generating account plans" refers to elements that have the functionality to create service plans optimized for user needs based on user data and usage patterns. 【0011】 "Methods for automatically creating handover documents" refers to elements that have a process for automatically creating documents that allow the new person in charge to start their duties smoothly by aggregating the necessary information when the person in charge changes. [Brief explanation of the drawing] 【0012】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 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. 【0014】 First, the terms used in the following description will be explained. 【0015】 In the following embodiments, a labeled 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. 【0016】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, a labeled 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. 【0018】 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). 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 This invention provides a system for optimizing customer service for corporate clients. This system utilizes AI technology to handle customer inquiries and aims to minimize the impact of changes in assigned personnel. 【0034】 At the heart of the system is an AI agent running on a server. The AI ​​agent receives inquiries sent by users on the server and analyzes their content using natural language processing technology. Based on the analysis, it refers to past inquiry history and FAQs stored in the database to generate the most appropriate answer. This generated answer is sent to the user's terminal in real time, allowing the user to instantly check the answer on their screen. 【0035】 All inquiries and their responses are stored on the server and permanently managed as a history. This history management ensures that past inquiries and responses are passed on to the relevant staff, maintaining a consistent quality of customer service. The system also has a function to analyze user information and this history data to generate an optimal account plan for each user. This proposed plan is notified to the user's device, and the user can adjust the plan according to their preferences. 【0036】 Furthermore, if the person in charge changes, the server automatically generates the necessary handover documents. These documents include important customer information and past inquiry history, allowing the new person in charge to smoothly take over duties and continue customer service without interruption. 【0037】 For example, if a user inquires about how to use a new service, the AI ​​agent will quickly interpret the inquiry and provide detailed usage instructions based on its past database. The user can then effectively use the service based on this response and can ask additional questions to the server if necessary. 【0038】 Thus, the system of the present invention can improve productivity and reliability in dealing with corporate customers, and enhance customer satisfaction and loyalty for companies. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 Users log in to the corporate portal and enter or update their basic information and contract details. The information entered by the user is sent to the server via their device. 【0042】 Step 2: 【0043】 The server receives the transmitted information and registers or updates it in the database. Here, it checks the data's integrity and performs validation as needed. 【0044】 Step 3: 【0045】 Users can make inquiries to the AI ​​agent on the corporate portal page. For example, they might submit a question such as, "Please tell me what services are available." 【0046】 Step 4: 【0047】 The server analyzes the inquiry from the terminal. Using natural language processing technology, it understands the received inquiry and then forms an appropriate response. 【0048】 Step 5: 【0049】 The server references past inquiry history and FAQ databases to ensure the consistency and accuracy of the generated answers. It aggregates the necessary information to construct a single, definitive answer. 【0050】 Step 6: 【0051】 The server sends the answer to the terminal, and the answer is displayed on the user's terminal. Based on this information, the user can understand the requested details. 【0052】 Step 7: 【0053】 The server permanently records the history of inquiries and their responses in a database. This creates an information infrastructure that can be used for future reference and for handover when personnel changes occur. 【0054】 Step 8: 【0055】 The server suggests an appropriate account plan based on the user's past history and current usage. The generated plan is notified to the user via their device, and the user can review its contents. 【0056】 Step 9: 【0057】 When a person in charge changes, the server automatically collects the necessary handover information for the new person in charge and generates handover documents. These documents are sent to the new person in charge's terminal to support a smooth transition of duties. 【0058】 (Example 1) 【0059】 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." 【0060】 In handling inquiries from corporate clients, it is essential to minimize inconsistencies in responses due to changes in the person in charge and the diversity of inquiry content. Furthermore, providing prompt and consistent answers is necessary to improve customer satisfaction. Additionally, efficiently managing these processes to reduce the workload on employees is a key challenge. 【0061】 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. 【0062】 In this invention, the server includes means for storing and editing information about corporate customers, means for receiving customer inquiries, analyzing them, and generating optimal responses, and means for storing the content of inquiries and responses as data. This ensures consistency in customer service even when there are changes in personnel, and enables the provision of quick and appropriate answers. 【0063】 A "corporate customer" is a customer who has the attributes of a company or organization, and who purchases goods or uses services as a business entity rather than as an individual. 【0064】 "Means of storing and compiling information" refers to methods of collecting necessary customer information using databases or recording media, and storing and updating it for later use. 【0065】 "Means for receiving inquiries, analyzing them, and generating optimal responses" refers to process technologies that first receive questions or requests from users, understand their content, and provide appropriate responses or answers. 【0066】 "Means of saving as data" refers to technologies that record inquiries and the resulting responses in digital format and retain them for later reference. 【0067】 "Methods for designing user plans" refers to methods of building and proposing service plans optimized for each customer based on their history and current information. 【0068】 "Means for automatically generating necessary documents" refers to technologies that automatically create and provide required documents and information when specific conditions are met. 【0069】 "Means of interpreting in natural language" refers to methods for understanding received text in human language so that its meaning and intent can be grasped. 【0070】 "Means of utilizing reference information to provide continuous responses" refers to a process that uses previous data and literature to ensure that a series of responses and answers are consistent. 【0071】 This invention is a system for optimizing customer inquiry handling for corporate clients. The server plays a central role in this system and uses the following components to implement various functions. 【0072】 The server first stores information about corporate customers in a database, and then manages and edits this information using programming languages ​​such as Python and SQL, as well as database management systems. A specific example would be using a PostgreSQL database. 【0073】 When receiving inquiries from customers, the server retrieves inquiry data using the HTTP protocol. The received inquiry content is then parsed using spaCy, a Python natural language processing library, to understand the intent of the inquiry. 【0074】 The server then uses a generative AI model to generate the optimal response. For example, OpenAI's GPT can be used as the generative AI model. This ensures that natural and appropriate responses are provided to user inquiries. 【0075】 Inquiries and their responses are stored in a database for future reference. This allows the server to automatically generate necessary handover documents based on past history when personnel changes occur, supporting a smooth continuation of operations. 【0076】 Users can make inquiries and receive responses from the server via their own devices. The use of the HTTPS protocol ensures the security of user data. 【0077】 For example, if a user asks, "I want to know the warranty period for a new product," the server analyzes this inquiry, generates a response such as, "The warranty period for the new product A is 2 years," and sends it to the user's terminal in real time. The user can then use this response to instantly obtain the information they need. 【0078】 Example prompt: "Please provide details on how to configure the new service." 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The user enters their inquiry using a terminal and sends it to the server. This input includes specific questions and requests. The input data is sent in HTTP request format, and the server receives it. 【0082】 Step 2: 【0083】 The server analyzes received queries using natural language processing techniques. Specifically, it uses a Python natural language processing library to tokenize the query content and tag it with parts of speech. This process allows for data processing that clearly understands the intent of the query. The input is the user's query, and the output is structured data as a result of the analysis. 【0084】 Step 3: 【0085】 The server searches past inquiry history and FAQ databases based on the analysis results. This step uses PostgreSQL to find relevant information. SQL queries are issued to search for history with similar inquiries and related FAQs. The input is the parsed token information, and the output is the relevant information as search results. 【0086】 Step 4: 【0087】 The server generates the optimal response using a generative AI model. Here, OpenAI's generative AI model is used to create natural and contextual responses. The generative AI model takes historical information and analysis results as input and generates a response sentence based on that. The output is the response sentence that the server prepares to return to the user. 【0088】 Step 5: 【0089】 The server sends the generated response to the user's device. The response is sent securely using the HTTPS protocol. The input is the generated response text, and the output is the display data that the user can view on their device. 【0090】 Step 6: 【0091】 The server stores queries and their answers in a database for future reference. The server uses this data to automatically generate the necessary handover documents when personnel changes occur. In this step, the input is a new query-answer pair, and the output is updated historical data. 【0092】 (Application Example 1) 【0093】 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." 【0094】 In serving corporate clients, there is a demand for prompt and appropriate information provision and improved operational efficiency. However, frequent changes in personnel within companies and the increasing complexity of inquiries in logistics operations can lead to delays in responses. Furthermore, the inability to propose appropriate plans based on past inquiry history contributes to decreased customer satisfaction. To address these challenges, there is a need to provide consistent customer support through a system. 【0095】 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. 【0096】 In this invention, the server includes means for registering and updating data related to corporate customers, means for receiving, analyzing, and generating appropriate responses to customer inquiries, and means for permanently managing inquiry and response content as a history. This enables rapid and consistent response to corporate customers. Furthermore, by generating efficiency plans related to logistics management and providing information to customers in real time using smartphones, further operational efficiency and improved customer satisfaction can be achieved. 【0097】 "Corporate clients" refer to companies and organizations, and are organizational business partners rather than individual customers. 【0098】 "Data" refers to a collection of numbers, characters, and symbols that can be registered and updated as information. 【0099】 An "inquiry" refers to a question or request made by a customer seeking information or support. 【0100】 "Analysis" is the act of breaking down acquired information or data in order to understand its content. 【0101】 "Answer" refers to the solution or information provided in response to an inquiry. 【0102】 "History" refers to a record kept in chronological order of past inquiries and responses. 【0103】 A "plan" is a strategy or set of instructions formulated to achieve a specific goal. 【0104】 "Documents" refer to documents and data that compile the information necessary when handing over duties. 【0105】 "Logistics management" refers to the work of planning and managing the efficient distribution of goods and products. 【0106】 A "smartphone" is a mobile phone device that utilizes information and communication technology to provide multiple functions. 【0107】 "Real-time" refers to a process where tasks are processed instantly at the moment they are performed, and the results are provided immediately. 【0108】 This invention aims to realize a system that responds quickly and accurately to inquiries from corporate clients. The server utilizes a database management system (e.g., MySQL® or PostgreSQL) to appropriately register and update data related to corporate clients. It also receives inquiries from clients and analyzes their content using natural language processing technology. This analysis uses Python natural language processing libraries (e.g., NLTK or spaCy). Based on the analysis results, it generates appropriate answers and sends them in real time to user terminals such as smartphones. 【0109】 The user's terminal is equipped with a function that receives responses from the server and displays them immediately on the screen. This allows users to quickly obtain the necessary information. Furthermore, when the person in charge changes, the server automatically generates the necessary handover documents and provides them to the new person's terminal. These documents include past inquiry history and important corporate customer information, enabling the new person in charge to smoothly take over the duties. 【0110】 For example, when a user inquires about inventory status, the server receives and analyzes the inquiry. Based on the analysis results, it accesses the relevant logistics database to generate a response and sends it to the user's terminal in real time. 【0111】 Examples of prompt messages include the following: 【0112】 User inquiry: Please tell me the stock status. 【0113】 1. Analyze the meaning of this inquiry and refer to the logistics information database. 【0114】 2. Consider detailed steps to generate and provide the optimal answer to the user. 【0115】 In this way, users can obtain the information they need in a timely manner. 【0116】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0117】 Step 1: 【0118】 The server receives queries from user terminals. It receives query messages from users in natural language format as input. This message is initially processed and prepared for the next step of analysis. 【0119】 Step 2: 【0120】 The server analyzes received queries using natural language processing (NLP) techniques. Here, a Python NLP library is used to extract intent from the text. The input is the user's query message, and the output is the analyzed semantic information. This analysis clarifies the purpose and content of the query. 【0121】 Step 3: 【0122】 The server generates an appropriate response by referencing relevant historical and FAQ databases based on the analysis results. The input is the analyzed semantic information, and the output is the optimal response. This data referencing ensures consistent responses. 【0123】 Step 4: 【0124】 The generated response is sent from the server to the user's terminal in real time. The terminal receives this response and displays it on the user's screen. The input is the generated response, and the output is text information that the user can verify. This process allows the user to obtain a solution quickly. 【0125】 Step 5: 【0126】 When the person in charge changes, the server automatically generates handover documents based on the latest inquiry history and corporate customer data. The input is past inquiry history data, and the output is a document summarizing the necessary information for the next person in charge. Providing this document to the new person in charge ensures continuity of operations. 【0127】 Step 6: 【0128】 Through their smartphones, users are guided through further efficiency improvement plans for logistics management, and optimal measures are suggested. The input is the current logistics data status, and the output is a proposed efficiency improvement plan. This function allows users to identify areas for improvement in their business processes. 【0129】 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. 【0130】 This invention combines an AI-powered system for optimizing inquiry handling to improve customer satisfaction in corporate customer service with an emotion engine that recognizes user emotions. This system makes it possible to understand the customer's emotional state and provide an optimal response accordingly. 【0131】 The system primarily consists of a server, user terminals, and an emotion engine. Users access the corporate portal and enter inquiries and requests. This input information is sent to the server via the terminal. The server has an integrated emotion engine that analyzes the user's emotions from the received inquiries. This analysis utilizes natural language processing technology to identify emotions from the context and wording of the input text. By recognizing emotions such as happiness, distress, or dissatisfaction, the emotion engine can select an appropriate response. 【0132】 The server considers the user's emotional data along with the inquiry content to generate the optimal response. This response is adjusted according to the emotional data, aiming to match the user's emotional state. For example, if the user is dissatisfied, a more polite and comprehensive response can be generated than usual. The generated response is provided to the user in real time through the terminal. 【0133】 Furthermore, the server records all inquiry history, including emotional data, in a database and uses it as needed to handle future inquiries. This history helps maintain consistency in responses, understand users' emotional tendencies, and contribute to building long-term relationships. 【0134】 For example, when a user inquires about how to use a new service, if the emotion engine detects the user's anxiety, the server will generate a response that includes more detailed instructions and additional support options. In this way, flexible responses tailored to user needs can be achieved, leading to improved customer satisfaction and loyalty. 【0135】 The following describes the processing flow. 【0136】 Step 1: 【0137】 Users log in to the corporate portal and enter inquiries and requests on their devices. The input includes questions and related information. 【0138】 Step 2: 【0139】 The terminal sends the inquiry content entered by the user to the server. The data sent includes user information. 【0140】 Step 3: 【0141】 The server passes the received query to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses natural language processing to analyze the context of the text and identify the emotion. 【0142】 Step 4: 【0143】 The server uses sentiment data obtained from the sentiment engine to generate appropriate responses to inquiries. If it detects that the user is dissatisfied, it considers providing a more polite and explanatory response than usual. 【0144】 Step 5: 【0145】 The server sends the generated response to the user's device. The user can then review the response on their device and submit another inquiry if necessary. 【0146】 Step 6: 【0147】 The server records queries, their responses, and user sentiment data in a database. This allows for the accumulation of historical data, including the progression of emotions over time. 【0148】 Step 7: 【0149】 The server analyzes accumulated historical data to understand trends for each user. This analysis is then used to handle future inquiries and optimize account plans. 【0150】 Step 8: 【0151】 When a user makes another inquiry, the server refers to past history data and implements the most appropriate response based on their past emotional state. This enables the provision of customized services tailored to each individual user. 【0152】 (Example 2) 【0153】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0154】 Traditionally, responses to inquiries from corporate clients have often been handled uniformly without considering individual circumstances or customer emotional states, resulting in a failure to adequately enhance customer satisfaction. In particular, there is a demand for flexible and consistent responses based on customer emotions and past inquiry history, but the means to achieve this are insufficient. 【0155】 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. 【0156】 In this invention, the server includes means for recording and modifying data relating to corporate customers, means for receiving customer inquiries, identifying customer emotions using natural language processing technology, and generating optimal responses, and means for recording inquiry and response content as history, and generating consistent responses by referring to past inquiry history and an information database. This enables flexible responses tailored to individual customer needs and emotional states, thereby improving customer satisfaction. 【0157】 "Corporate clients" refer to clients with legal personality, such as companies or organizations, and are distinguished from individual clients. 【0158】 "Data" is a set of codes and symbols used to represent information, in a format that can be manipulated and processed within a computer system. 【0159】 "Emotions" refer to the psychological states of humans and generally encompass different types such as joy, sadness, anger, and anxiety. 【0160】 "Natural language processing technology" refers to the techniques and methods used to process and understand human language using computers, and is used for text analysis and generation. 【0161】 "History" refers to past records of searches, operations, etc., and in particular, to the history of user actions and inquiries within a system. 【0162】 An "information database" is a system that structurally stores and manages information, and allows for the rapid retrieval of information under specific conditions. 【0163】 "Response" refers to the reply generated in response to an received inquiry, and in particular, to the solutions or information that the system provides to the user. 【0164】 This invention is a system that generates optimal responses to inquiries from corporate clients, taking into account the user's emotions. The system mainly consists of a server, a user terminal, and an emotion engine. 【0165】 Users access the corporate portal and enter inquiries and requests on their terminals. The terminals receive this information, format it, and then send it to the server. The server has an emotion engine built in that analyzes the received inquiries using natural language processing technology to identify the user's emotions. This utilizes open-source natural language processing libraries such as "spaCy" and the "BERT" model. 【0166】 The server uses a generative AI model to generate the optimal response based on the sentiment analysis results. The generated response is sent to the user via the terminal. This response is adjusted to correspond to the user's emotional state. For example, if the user is feeling anxious about how to use a new service, it can provide detailed guidelines such as, "Don't worry, here's how to set up the new service." 【0167】 This system records all inquiry history and associated sentiment data in a database, which will be used for future inquiries. This historical data helps maintain consistency in past responses. 【0168】 A concrete example of a prompt would be, "Identify the feelings corporate customers have about using the new service and generate a response that includes helpful support options." Based on this prompt, the system will provide flexible responses tailored to the customer's feelings, leading to improved customer satisfaction. 【0169】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0170】 Step 1: 【0171】 Users access the corporate portal and enter inquiries or requests. The terminal receives this text input and converts it into well-formed data. The input data is, for example, text entered on a web form, and is formatted as request data to be sent to the server as output. 【0172】 Step 2: 【0173】 The terminal sends well-formed data to the server. The server receives this data and begins analyzing the query. The input data is the user's query text, and the output contains analysis data used to prepare for sentiment analysis. 【0174】 Step 3: 【0175】 The server uses an emotion engine to analyze the query text with natural language processing techniques and identify the user's emotions. It analyzes the context of the input text using models such as "spaCy" and "BERT" and generates emotion labels such as joy and anxiety as output. 【0176】 Step 4: 【0177】 The server utilizes a generative AI model to generate the optimal response based on the results of sentiment analysis. The input consists of sentiment labels and the query content, while the output is the response text provided to the user. This response is tailored to the user's emotions. 【0178】 Step 5: 【0179】 The server sends a response text back to the terminal. The terminal displays this text to the user. This allows the user to receive solutions and support information in real time. The output is the information that is ultimately displayed to the user and is applied to the user interface. 【0180】 Step 6: 【0181】 The server records all query and sentiment data in a database. This historical data is used to improve future query handling. The input is all the data processed, and the output is the information recorded in the historical database. This process helps to improve responses in the future. 【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】 Traditional customer service systems often provide mechanical responses without considering the user's emotional state. This is particularly problematic in electronic payment services, where rapid and appropriate problem resolution is crucial, yet customer satisfaction remains low. While there is a need to respond quickly to user dissatisfaction and anxiety, conventional technologies struggle to address such emotions. 【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 registering and updating information about corporate customers, means for recognizing the user's emotional state and adjusting the response, and means for generating a consistent answer by referring to past inquiry history, emotional data, and an FAQ database. This makes it possible to provide an optimal response tailored to the user's emotions in real time. 【0187】 A "corporate customer" refers to a customer who uses products or services as a company or organization. 【0188】 An "inquiry" is a question or request made by a customer seeking information about a product or service, or to resolve a problem. 【0189】 "Natural language processing technology" is a technology that uses computers to analyze and understand human language. 【0190】 "Emotional state" refers to the psychological state or reaction a user exhibits in a particular situation. 【0191】 An "FAQ database" is a collection of frequently asked questions and their answers, used to support quick problem-solving. 【0192】 "Identifying user emotions" means analyzing the user's emotions from the input information and classifying them into specific emotional categories. 【0193】 A "consistent response" is a response that is generated by referring to past history and data, while maintaining consistency with the surrounding context. 【0194】 The system for realizing this invention consists of a server for registering and updating information on corporate customers, a terminal for users to make inquiries, and a communication network connecting these. 【0195】 The server analyzes user inquiries using natural language processing technology and identifies the user's emotional state from the text and audio data. Google® Cloud Natural Language API and IBM Watson® are used as emotion engines for this emotion identification. Based on the analysis results, the server then references past inquiry history and FAQ databases to generate the most appropriate response for the user's emotional state. During this process, a generative AI model is used to adjust the response to ensure consistency with the user's emotional state. 【0196】 The final response is provided to the user via the terminal, allowing the user to resolve the issue in real time. The server also records all inquiry history and sentiment data in a database, which is used to improve future inquiry handling. 【0197】 For example, if a user asks, "I haven't received notifications about recent transactions; can you check them?", the server will sense the user's concern. In this case, the server will generate a detailed response, including instructions on how to check the transaction history and links to additional support, and provide it to the user through their device. 【0198】 An example of a prompt message would be, "Analyze the user's emotions when they make the inquiry, and suggest a way to select and provide an appropriate response." 【0199】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0200】 Step 1: 【0201】 The user uses a terminal to input their inquiry as text or voice. The entered data is transmitted from the user's terminal to the server via communication. The input in this step is the inquiry message from the user, and the server receives that message as output. 【0202】 Step 2: 【0203】 The server performs text analysis on the received query data using natural language processing techniques. Specifically, it uses Google Cloud Natural Language API and IBM Watson to perform data analysis to understand the context of the query. It receives the query text as input and outputs metadata such as the user's emotional state. 【0204】 Step 3: 【0205】 The server uses an emotion engine to identify the user's emotions from the analyzed query data. It analyzes the user's language and context to determine an emotion category (e.g., reassured, dissatisfied, neutral). The input for this step is the analyzed text data, and the output is the user's emotional state. 【0206】 Step 4: 【0207】 The server takes the user's emotional state into account and generates a consistent and optimal response by referring to past inquiry history and FAQ databases. A generative AI model is used to ensure the response is appropriate to the user's emotions. This process takes past data and analysis results as input and outputs an optimized response. 【0208】 Step 5: 【0209】 The server sends the generated response to the terminal and presents it to the user in real time. The user can then use this response to resolve the problem. The input for this step is the generated response data, and the output is the display of the answer to the user. 【0210】 Step 6: 【0211】 The server records all inquiries, their response history, and sentiment data in a database. This data is used for analysis to improve future inquiries. The input is the user's inquiry history and sentiment data, and the output is the updated history information in the database. 【0212】 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. 【0213】 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. 【0214】 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. 【0215】 [Second Embodiment] 【0216】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0217】 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. 【0218】 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). 【0219】 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. 【0220】 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. 【0221】 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). 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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". 【0228】 This invention provides a system for optimizing customer service for corporate clients. This system utilizes AI technology to handle customer inquiries and aims to minimize the impact of changes in assigned personnel. 【0229】 At the heart of the system is an AI agent running on a server. The AI ​​agent receives inquiries sent by users on the server and analyzes their content using natural language processing technology. Based on the analysis, it refers to past inquiry history and FAQs stored in the database to generate the most appropriate answer. This generated answer is sent to the user's terminal in real time, allowing the user to instantly check the answer on their screen. 【0230】 All inquiries and their responses are stored on the server and permanently managed as a history. This history management ensures that past inquiries and responses are passed on to the relevant staff, maintaining a consistent quality of customer service. The system also has a function to analyze user information and this history data to generate an optimal account plan for each user. This proposed plan is notified to the user's device, and the user can adjust the plan according to their preferences. 【0231】 Furthermore, if the person in charge changes, the server automatically generates the necessary handover documents. These documents include important customer information and past inquiry history, allowing the new person in charge to smoothly take over duties and continue customer service without interruption. 【0232】 For example, if a user inquires about how to use a new service, the AI ​​agent will quickly interpret the inquiry and provide detailed usage instructions based on its past database. The user can then effectively use the service based on this response and can ask additional questions to the server if necessary. 【0233】 Thus, the system of the present invention can improve productivity and reliability in dealing with corporate customers, and enhance customer satisfaction and loyalty for companies. 【0234】 The following describes the processing flow. 【0235】 Step 1: 【0236】 Users log in to the corporate portal and enter or update their basic information and contract details. The information entered by the user is sent to the server via their device. 【0237】 Step 2: 【0238】 The server receives the transmitted information and registers or updates it in the database. Here, it checks the data's integrity and performs validation as needed. 【0239】 Step 3: 【0240】 Users can make inquiries to the AI ​​agent on the corporate portal page. For example, they might submit a question such as, "Please tell me what services are available." 【0241】 Step 4: 【0242】 The server analyzes the inquiry from the terminal. Using natural language processing technology, it understands the received inquiry and then forms an appropriate response. 【0243】 Step 5: 【0244】 The server references past inquiry history and FAQ databases to ensure the consistency and accuracy of the generated answers. It aggregates the necessary information to construct a single, definitive answer. 【0245】 Step 6: 【0246】 The server sends the answer to the terminal, and the answer is displayed on the user's terminal. Based on this information, the user can understand the requested details. 【0247】 Step 7: 【0248】 The server permanently records the history of inquiries and their responses in a database. This creates an information infrastructure that can be used for future reference and for handover when personnel changes occur. 【0249】 Step 8: 【0250】 The server suggests an appropriate account plan based on the user's past history and current usage. The generated plan is notified to the user via their device, and the user can review its contents. 【0251】 Step 9: 【0252】 When a person in charge changes, the server automatically collects the necessary handover information for the new person in charge and generates handover documents. These documents are sent to the new person in charge's terminal to support a smooth transition of duties. 【0253】 (Example 1) 【0254】 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". 【0255】 In handling inquiries from corporate clients, it is essential to minimize inconsistencies in responses due to changes in the person in charge and the diversity of inquiry content. Furthermore, providing prompt and consistent answers is necessary to improve customer satisfaction. Additionally, efficiently managing these processes to reduce the workload on employees is a key challenge. 【0256】 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. 【0257】 In this invention, the server includes means for storing and editing information about corporate customers, means for receiving customer inquiries, analyzing them, and generating optimal responses, and means for storing the content of inquiries and responses as data. This ensures consistency in customer service even when there are changes in personnel, and enables the provision of quick and appropriate answers. 【0258】 A "corporate customer" is a customer who has the attributes of a company or organization, and who purchases goods or uses services as a business entity rather than as an individual. 【0259】 "Means of storing and compiling information" refers to methods of collecting necessary customer information using databases or recording media, and storing and updating it for later use. 【0260】 "Means for receiving inquiries, analyzing them, and generating optimal responses" refers to process technologies that first receive questions or requests from users, understand their content, and provide appropriate responses or answers. 【0261】 "Means of saving as data" refers to technologies that record inquiries and the resulting responses in digital format and retain them for later reference. 【0262】 "Methods for designing user plans" refers to methods of building and proposing service plans optimized for each customer based on their history and current information. 【0263】 "Means for automatically generating necessary documents" refers to technologies that automatically create and provide required documents and information when specific conditions are met. 【0264】 "Means of interpreting in natural language" refers to methods for understanding received text in human language so that its meaning and intent can be grasped. 【0265】 "Means of utilizing reference information to provide continuous responses" refers to a process that uses previous data and literature to ensure that a series of responses and answers are consistent. 【0266】 This invention is a system for optimizing customer inquiry handling for corporate clients. The server plays a central role in this system and uses the following components to implement various functions. 【0267】 The server first stores information about corporate customers in a database, and then manages and edits this information using programming languages ​​such as Python and SQL, as well as database management systems. A specific example would be using a PostgreSQL database. 【0268】 When receiving inquiries from customers, the server retrieves inquiry data using the HTTP protocol. The received inquiry content is then parsed using spaCy, a Python natural language processing library, to understand the intent of the inquiry. 【0269】 The server then uses a generative AI model to generate the optimal response. For example, OpenAI's GPT can be used as the generative AI model. This ensures that natural and appropriate responses are provided to user inquiries. 【0270】 Inquiries and their responses are stored in a database for future reference. This allows the server to automatically generate necessary handover documents based on past history when personnel changes occur, supporting a smooth continuation of operations. 【0271】 Users can make inquiries and receive responses from the server via their own devices. The use of the HTTPS protocol ensures the security of user data. 【0272】 For example, if a user asks, "I want to know the warranty period for a new product," the server analyzes this inquiry, generates a response such as, "The warranty period for the new product A is 2 years," and sends it to the user's terminal in real time. The user can then use this response to instantly obtain the information they need. 【0273】 Example prompt: "Please provide details on how to configure the new service." 【0274】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0275】 Step 1: 【0276】 The user enters their inquiry using a terminal and sends it to the server. This input includes specific questions and requests. The input data is sent in HTTP request format, and the server receives it. 【0277】 Step 2: 【0278】 The server analyzes received queries using natural language processing techniques. Specifically, it uses a Python natural language processing library to tokenize the query content and tag it with parts of speech. This process allows for data processing that clearly understands the intent of the query. The input is the user's query, and the output is structured data as a result of the analysis. 【0279】 Step 3: 【0280】 The server searches past inquiry history and FAQ databases based on the analysis results. This step uses PostgreSQL to find relevant information. SQL queries are issued to search for history with similar inquiries and related FAQs. The input is the parsed token information, and the output is the relevant information as search results. 【0281】 Step 4: 【0282】 The server uses a generative AI model to generate an optimal answer. Here, the generative AI model of OpenAI is used to create a natural and contextually appropriate answer. The generative AI model takes in past information and analysis results as input and generates a response sentence based on them. The output is the response sentence prepared for the server to return to the user. 【0283】 Step 5: 【0284】 The server sends the generated answer to the user's terminal. The answer is securely sent using the HTTPS protocol. The input is the generated response sentence, and the output is the display data that the user can view on the terminal. 【0285】 Step 6: 【0286】 The server saves the inquiry and its answer in the database for future reference. The server uses this data to automatically generate handover materials required when the person in charge changes. In this step, the input is a new pair of inquiry and answer, and the output is the updated history data. 【0287】 (Application Example 1) 【0288】 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". 【0289】 In dealing with corporate customers, it is required to provide prompt and appropriate information and improve business efficiency. However, due to frequent changes in the person in charge within the company or the complexity of inquiries in the logistics business, there may be delays in responses. Also, the inability to propose an appropriate plan using past inquiry history has led to a decrease in customer satisfaction. To address these issues, it is required to provide consistent customer support with the system. 【0290】 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. 【0291】 In this invention, the server includes means for registering and updating data related to corporate customers, means for receiving, analyzing, and generating appropriate responses to customer inquiries, and means for permanently managing inquiry and response content as a history. This enables rapid and consistent response to corporate customers. Furthermore, by generating efficiency plans related to logistics management and providing information to customers in real time using smartphones, further operational efficiency and improved customer satisfaction can be achieved. 【0292】 "Corporate clients" refer to companies and organizations, and are organizational business partners rather than individual customers. 【0293】 "Data" refers to a collection of numbers, characters, and symbols that can be registered and updated as information. 【0294】 An "inquiry" refers to a question or request made by a customer seeking information or support. 【0295】 "Analysis" is the act of breaking down acquired information or data in order to understand its content. 【0296】 "Answer" refers to the solution or information provided in response to an inquiry. 【0297】 "History" refers to a record kept in chronological order of past inquiries and responses. 【0298】 A "plan" is a strategy or set of instructions formulated to achieve a specific goal. 【0299】 "Documents" refer to documents and data that compile the information necessary when handing over duties. 【0300】 "Logistics management" refers to the work of planning and managing the efficient distribution of goods and products. 【0301】 A "smartphone" is a mobile phone device that utilizes information and communication technology to provide multiple functions. 【0302】 "Real-time" refers to a process where tasks are processed instantly at the moment they are performed, and the results are provided immediately. 【0303】 This invention aims to realize a system that responds quickly and accurately to inquiries from corporate clients. The server utilizes a database management system (e.g., MySQL or PostgreSQL) to appropriately register and update data related to corporate clients. It also receives inquiries from clients and analyzes their content using natural language processing technology. This analysis uses Python natural language processing libraries (e.g., NLTK or spaCy). Based on the analysis results, it generates appropriate answers and sends them in real time to user terminals such as smartphones. 【0304】 The user's terminal is equipped with a function that receives responses from the server and displays them immediately on the screen. This allows users to quickly obtain the necessary information. Furthermore, when the person in charge changes, the server automatically generates the necessary handover documents and provides them to the new person's terminal. These documents include past inquiry history and important corporate customer information, enabling the new person in charge to smoothly take over the duties. 【0305】 For example, when a user inquires about inventory status, the server receives and analyzes the inquiry. Based on the analysis results, it accesses the relevant logistics database to generate a response and sends it to the user's terminal in real time. 【0306】 Examples of prompt messages include the following: 【0307】 User Inquiry: Please inform me of the inventory status 【0308】 1. Analyze the meaning of this inquiry and refer to the logistics information database 【0309】 2. Consider the detailed procedures for generating the optimal answer and providing it to the user 【0310】 In this way, the user can obtain the required information in a timely manner. 【0311】 The flow of the specific process in Application Example 1 will be described using Figure 12. 【0312】 Step 1: 【0313】 The server receives an inquiry from the user terminal. As input, it receives a natural language form inquiry message from the user. This message is initially processed and prepared for the analysis in the next step. 【0314】 Step 2: 【0315】 The server analyzes the received inquiry using natural language processing technology. Here, the natural language processing library of Python is used to extract the intention from the text. The input is the user's inquiry message, and the output is the analyzed semantic information. Through this analysis, the purpose and content of the inquiry are clarified. 【0316】 Step 3: 【0317】 The server generates an appropriate answer by referring to the relevant history database and FAQ database based on the analysis result. The input is the analyzed semantic information, and the output is the optimal response content. Through this data reference, a consistent answer can be obtained. 【0318】 Step 4: 【0319】 The generated response is sent from the server to the user's terminal in real time. The terminal receives this response and displays it on the user's screen. The input is the generated response, and the output is text information that the user can verify. This process allows the user to obtain a solution quickly. 【0320】 Step 5: 【0321】 When the person in charge changes, the server automatically generates handover documents based on the latest inquiry history and corporate customer data. The input is past inquiry history data, and the output is a document summarizing the necessary information for the next person in charge. Providing this document to the new person in charge ensures continuity of operations. 【0322】 Step 6: 【0323】 Through their smartphones, users are guided through further efficiency improvement plans for logistics management, and optimal measures are suggested. The input is the current logistics data status, and the output is a proposed efficiency improvement plan. This function allows users to identify areas for improvement in their business processes. 【0324】 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. 【0325】 This invention combines an AI-powered system for optimizing inquiry handling to improve customer satisfaction in corporate customer service with an emotion engine that recognizes user emotions. This system makes it possible to understand the customer's emotional state and provide an optimal response accordingly. 【0326】 The system primarily consists of a server, user terminals, and an emotion engine. Users access the corporate portal and enter inquiries and requests. This input information is sent to the server via the terminal. The server has an integrated emotion engine that analyzes the user's emotions from the received inquiries. This analysis utilizes natural language processing technology to identify emotions from the context and wording of the input text. By recognizing emotions such as happiness, distress, or dissatisfaction, the emotion engine can select an appropriate response. 【0327】 The server considers the user's emotional data along with the inquiry content to generate the optimal response. This response is adjusted according to the emotional data, aiming to match the user's emotional state. For example, if the user is dissatisfied, a more polite and comprehensive response can be generated than usual. The generated response is provided to the user in real time through the terminal. 【0328】 Furthermore, the server records all inquiry history, including emotional data, in a database and uses it as needed to handle future inquiries. This history helps maintain consistency in responses, understand users' emotional tendencies, and contribute to building long-term relationships. 【0329】 For example, when a user inquires about how to use a new service, if the emotion engine detects the user's anxiety, the server will generate a response that includes more detailed instructions and additional support options. In this way, flexible responses tailored to user needs can be achieved, leading to improved customer satisfaction and loyalty. 【0330】 The following describes the processing flow. 【0331】 Step 1: 【0332】 Users log in to the corporate portal and enter inquiries and requests on their devices. The input includes questions and related information. 【0333】 Step 2: 【0334】 The terminal sends the inquiry content entered by the user to the server. The data sent includes user information. 【0335】 Step 3: 【0336】 The server passes the received query to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses natural language processing to analyze the context of the text and identify the emotion. 【0337】 Step 4: 【0338】 The server uses sentiment data obtained from the sentiment engine to generate appropriate responses to inquiries. If it detects that the user is dissatisfied, it considers providing a more polite and explanatory response than usual. 【0339】 Step 5: 【0340】 The server sends the generated response to the user's device. The user can then review the response on their device and submit another inquiry if necessary. 【0341】 Step 6: 【0342】 The server records queries, their responses, and user sentiment data in a database. This allows for the accumulation of historical data, including the progression of emotions over time. 【0343】 Step 7: 【0344】 The server analyzes accumulated historical data to understand trends for each user. This analysis is then used to handle future inquiries and optimize account plans. 【0345】 Step 8: 【0346】 When a user makes another inquiry, the server refers to past history data and implements the most appropriate response based on their past emotional state. This enables the provision of customized services tailored to each individual user. 【0347】 (Example 2) 【0348】 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". 【0349】 Traditionally, responses to inquiries from corporate clients have often been handled uniformly without considering individual circumstances or customer emotional states, resulting in a failure to adequately enhance customer satisfaction. In particular, there is a demand for flexible and consistent responses based on customer emotions and past inquiry history, but the means to achieve this are insufficient. 【0350】 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. 【0351】 In this invention, the server includes means for recording and modifying data relating to corporate customers, means for receiving customer inquiries, identifying customer emotions using natural language processing technology, and generating optimal responses, and means for recording inquiry and response content as history, and generating consistent responses by referring to past inquiry history and an information database. This enables flexible responses tailored to individual customer needs and emotional states, thereby improving customer satisfaction. 【0352】 "Corporate clients" refer to clients with legal personality, such as companies or organizations, and are distinguished from individual clients. 【0353】 "Data" is a set of codes and symbols used to represent information, in a format that can be manipulated and processed within a computer system. 【0354】 "Emotions" refer to the psychological states of humans and generally encompass different types such as joy, sadness, anger, and anxiety. 【0355】 "Natural language processing technology" refers to the techniques and methods used to process and understand human language using computers, and is used for text analysis and generation. 【0356】 "History" refers to past records of searches, operations, etc., and in particular, to the history of user actions and inquiries within a system. 【0357】 An "information database" is a system that structurally stores and manages information, and allows for the rapid retrieval of information under specific conditions. 【0358】 "Response" refers to the reply generated in response to an received inquiry, and in particular, to the solutions or information that the system provides to the user. 【0359】 This invention is a system that generates optimal responses to inquiries from corporate clients, taking into account the user's emotions. The system mainly consists of a server, a user terminal, and an emotion engine. 【0360】 Users access the corporate portal and enter inquiries and requests on their terminals. The terminals receive this information, format it, and then send it to the server. The server has an emotion engine built in that analyzes the received inquiries using natural language processing technology to identify the user's emotions. This utilizes open-source natural language processing libraries such as "spaCy" and the "BERT" model. 【0361】 The server uses a generative AI model to generate the optimal response based on the sentiment analysis results. The generated response is sent to the user via the terminal. This response is adjusted to correspond to the user's emotional state. For example, if the user is feeling anxious about how to use a new service, it can provide detailed guidelines such as, "Don't worry, here's how to set up the new service." 【0362】 This system records all inquiry history and associated sentiment data in a database, which will be used for future inquiries. This historical data helps maintain consistency in past responses. 【0363】 A concrete example of a prompt would be, "Identify the feelings corporate customers have about using the new service and generate a response that includes helpful support options." Based on this prompt, the system will provide flexible responses tailored to the customer's feelings, leading to improved customer satisfaction. 【0364】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0365】 Step 1: 【0366】 Users access the corporate portal and enter inquiries or requests. The terminal receives this text input and converts it into well-formed data. The input data is, for example, text entered on a web form, and is formatted as request data to be sent to the server as output. 【0367】 Step 2: 【0368】 The terminal sends well-formed data to the server. The server receives this data and begins analyzing the query. The input data is the user's query text, and the output contains analysis data used to prepare for sentiment analysis. 【0369】 Step 3: 【0370】 The server uses an emotion engine to analyze the query text with natural language processing techniques and identify the user's emotions. It analyzes the context of the input text using models such as "spaCy" and "BERT" and generates emotion labels such as joy and anxiety as output. 【0371】 Step 4: 【0372】 The server utilizes a generative AI model to generate the optimal response based on the results of sentiment analysis. The input consists of sentiment labels and the query content, while the output is the response text provided to the user. This response is tailored to the user's emotions. 【0373】 Step 5: 【0374】 The server sends a response text back to the terminal. The terminal displays this text to the user. This allows the user to receive solutions and support information in real time. The output is the information that is ultimately displayed to the user and is applied to the user interface. 【0375】 Step 6: 【0376】 The server records all query and sentiment data in a database. This historical data is used to improve future query handling. The input is all the data processed, and the output is the information recorded in the historical database. This process helps to improve responses in the future. 【0377】 (Application Example 2) 【0378】 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 as the "terminal". 【0379】 Traditional customer service systems often provide mechanical responses without considering the user's emotional state. This is particularly problematic in electronic payment services, where rapid and appropriate problem resolution is crucial, yet customer satisfaction remains low. While there is a need to respond quickly to user dissatisfaction and anxiety, conventional technologies struggle to address such emotions. 【0380】 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. 【0381】 In this invention, the server includes means for registering and updating information about corporate customers, means for recognizing the user's emotional state and adjusting the response, and means for generating a consistent answer by referring to past inquiry history, emotional data, and an FAQ database. This makes it possible to provide an optimal response tailored to the user's emotions in real time. 【0382】 A "corporate customer" refers to a customer who uses products or services as a company or organization. 【0383】 An "inquiry" is a question or request made by a customer seeking information about a product or service, or to resolve a problem. 【0384】 "Natural language processing technology" is a technology that uses computers to analyze and understand human language. 【0385】 "Emotional state" refers to the psychological state or reaction a user exhibits in a particular situation. 【0386】 An "FAQ database" is a collection of frequently asked questions and their answers, used to support quick problem-solving. 【0387】 "Identifying user emotions" means analyzing the user's emotions from the input information and classifying them into specific emotional categories. 【0388】 A "consistent response" is a response that is generated by referring to past history and data, while maintaining consistency with the surrounding context. 【0389】 The system for realizing this invention consists of a server for registering and updating information on corporate customers, a terminal for users to make inquiries, and a communication network connecting these. 【0390】 The server analyzes user inquiries using natural language processing technology and identifies the user's emotional state from the text and audio data. Google Cloud Natural Language API and IBM Watson are used as emotion engines for this emotion identification. Based on the analysis results, the server then references past inquiry history and FAQ databases to generate the most appropriate response for the user's emotional state. During this process, a generative AI model is used to adjust the response to ensure consistency with the user's emotional state. 【0391】 The final response is provided to the user via the terminal, allowing the user to resolve the issue in real time. The server also records all inquiry history and sentiment data in a database, which is used to improve future inquiry handling. 【0392】 For example, if a user asks, "I haven't received notifications about recent transactions; can you check them?", the server will sense the user's concern. In this case, the server will generate a detailed response, including instructions on how to check the transaction history and links to additional support, and provide it to the user through their device. 【0393】 An example of a prompt message would be, "Analyze the user's emotions when they make the inquiry, and suggest a way to select and provide an appropriate response." 【0394】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0395】 Step 1: 【0396】 The user uses a terminal to input their inquiry as text or voice. The entered data is transmitted from the user's terminal to the server via communication. The input in this step is the inquiry message from the user, and the server receives that message as output. 【0397】 Step 2: 【0398】 The server performs text analysis on the received query data using natural language processing techniques. Specifically, it uses Google Cloud Natural Language API and IBM Watson to perform data analysis to understand the context of the query. It receives the query text as input and outputs metadata such as the user's emotional state. 【0399】 Step 3: 【0400】 The server uses an emotion engine to identify the user's emotions from the analyzed query data. It analyzes the user's language and context to determine an emotion category (e.g., reassured, dissatisfied, neutral). The input for this step is the analyzed text data, and the output is the user's emotional state. 【0401】 Step 4: 【0402】 The server takes the user's emotional state into account and generates a consistent and optimal response by referring to past inquiry history and FAQ databases. A generative AI model is used to ensure the response is appropriate to the user's emotions. This process takes past data and analysis results as input and outputs an optimized response. 【0403】 Step 5: 【0404】 The server sends the generated response to the terminal and presents it to the user in real time. The user can then use this response to resolve the problem. The input for this step is the generated response data, and the output is the display of the answer to the user. 【0405】 Step 6: 【0406】 The server records all inquiries, their response history, and sentiment data in a database. This data is used for analysis to improve future inquiries. The input is the user's inquiry history and sentiment data, and the output is the updated history information in the database. 【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). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【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 provides a system for optimizing customer service for corporate clients. This system utilizes AI technology to handle customer inquiries and aims to minimize the impact of changes in assigned personnel. 【0424】 At the heart of the system is an AI agent running on a server. The AI ​​agent receives inquiries sent by users on the server and analyzes their content using natural language processing technology. Based on the analysis, it refers to past inquiry history and FAQs stored in the database to generate the most appropriate answer. This generated answer is sent to the user's terminal in real time, allowing the user to instantly check the answer on their screen. 【0425】 All inquiries and their responses are stored on the server and permanently managed as a history. This history management ensures that past inquiries and responses are passed on to the relevant staff, maintaining a consistent quality of customer service. The system also has a function to analyze user information and this history data to generate an optimal account plan for each user. This proposed plan is notified to the user's device, and the user can adjust the plan according to their preferences. 【0426】 Furthermore, if the person in charge changes, the server automatically generates the necessary handover documents. These documents include important customer information and past inquiry history, allowing the new person in charge to smoothly take over duties and continue customer service without interruption. 【0427】 For example, if a user inquires about how to use a new service, the AI ​​agent will quickly interpret the inquiry and provide detailed usage instructions based on its past database. The user can then effectively use the service based on this response and can ask additional questions to the server if necessary. 【0428】 Thus, the system of the present invention can improve productivity and reliability in dealing with corporate customers, and enhance customer satisfaction and loyalty for companies. 【0429】 The following describes the processing flow. 【0430】 Step 1: 【0431】 Users log in to the corporate portal and enter or update their basic information and contract details. The information entered by the user is sent to the server via their device. 【0432】 Step 2: 【0433】 The server receives the transmitted information and registers or updates it in the database. Here, it checks the data's integrity and performs validation as needed. 【0434】 Step 3: 【0435】 Users can make inquiries to the AI ​​agent on the corporate portal page. For example, they might submit a question such as, "Please tell me what services are available." 【0436】 Step 4: 【0437】 The server analyzes the inquiry from the terminal. Using natural language processing technology, it understands the received inquiry and then forms an appropriate response. 【0438】 Step 5: 【0439】 The server references past inquiry history and FAQ databases to ensure the consistency and accuracy of the generated answers. It aggregates the necessary information to construct a single, definitive answer. 【0440】 Step 6: 【0441】 The server sends the answer to the terminal, and the answer is displayed on the user's terminal. Based on this information, the user can understand the requested details. 【0442】 Step 7: 【0443】 The server permanently records the history of inquiries and their responses in a database. This creates an information infrastructure that can be used for future reference and for handover when personnel changes occur. 【0444】 Step 8: 【0445】 The server suggests an appropriate account plan based on the user's past history and current usage. The generated plan is notified to the user via their device, and the user can review its contents. 【0446】 Step 9: 【0447】 When a person in charge changes, the server automatically collects the necessary handover information for the new person in charge and generates handover documents. These documents are sent to the new person in charge's terminal to support a smooth transition of duties. 【0448】 (Example 1) 【0449】 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." 【0450】 In handling inquiries from corporate clients, it is essential to minimize inconsistencies in responses due to changes in the person in charge and the diversity of inquiry content. Furthermore, providing prompt and consistent answers is necessary to improve customer satisfaction. Additionally, efficiently managing these processes to reduce the workload on employees is a key challenge. 【0451】 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. 【0452】 In this invention, the server includes means for storing and editing information about corporate customers, means for receiving customer inquiries, analyzing them, and generating optimal responses, and means for storing the content of inquiries and responses as data. This ensures consistency in customer service even when there are changes in personnel, and enables the provision of quick and appropriate answers. 【0453】 A "corporate customer" is a customer who has the attributes of a company or organization, and who purchases goods or uses services as a business entity rather than as an individual. 【0454】 "Means of storing and compiling information" refers to methods of collecting necessary customer information using databases or recording media, and storing and updating it for later use. 【0455】 "Means for receiving inquiries, analyzing them, and generating optimal responses" refers to process technologies that first receive questions or requests from users, understand their content, and provide appropriate responses or answers. 【0456】 "Means of saving as data" refers to technologies that record inquiries and the resulting responses in digital format and retain them for later reference. 【0457】 "Methods for designing user plans" refers to methods of building and proposing service plans optimized for each customer based on their history and current information. 【0458】 "Means for automatically generating necessary documents" refers to technologies that automatically create and provide required documents and information when specific conditions are met. 【0459】 "Means of interpreting in natural language" refers to methods for understanding received text in human language so that its meaning and intent can be grasped. 【0460】 "Means of utilizing reference information to provide continuous responses" refers to a process that uses previous data and literature to ensure that a series of responses and answers are consistent. 【0461】 This invention is a system for optimizing customer inquiry handling for corporate clients. The server plays a central role in this system and uses the following components to implement various functions. 【0462】 The server first stores information about corporate customers in a database, and then manages and edits this information using programming languages ​​such as Python and SQL, as well as database management systems. A specific example would be using a PostgreSQL database. 【0463】 When receiving inquiries from customers, the server retrieves inquiry data using the HTTP protocol. The received inquiry content is then parsed using spaCy, a Python natural language processing library, to understand the intent of the inquiry. 【0464】 The server then uses a generative AI model to generate the optimal response. For example, OpenAI's GPT can be used as the generative AI model. This ensures that natural and appropriate responses are provided to user inquiries. 【0465】 Inquiries and their responses are stored in a database for future reference. This allows the server to automatically generate necessary handover documents based on past history when personnel changes occur, supporting a smooth continuation of operations. 【0466】 Users can make inquiries and receive responses from the server via their own devices. The use of the HTTPS protocol ensures the security of user data. 【0467】 For example, if a user asks, "I want to know the warranty period for a new product," the server analyzes this inquiry, generates a response such as, "The warranty period for the new product A is 2 years," and sends it to the user's terminal in real time. The user can then use this response to instantly obtain the information they need. 【0468】 Example prompt: "Please provide details on how to configure the new service." 【0469】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0470】 Step 1: 【0471】 The user enters their inquiry using a terminal and sends it to the server. This input includes specific questions and requests. The input data is sent in HTTP request format, and the server receives it. 【0472】 Step 2: 【0473】 The server analyzes received queries using natural language processing techniques. Specifically, it uses a Python natural language processing library to tokenize the query content and tag it with parts of speech. This process allows for data processing that clearly understands the intent of the query. The input is the user's query, and the output is structured data as a result of the analysis. 【0474】 Step 3: 【0475】 The server searches past inquiry history and FAQ databases based on the analysis results. This step uses PostgreSQL to find relevant information. SQL queries are issued to search for history with similar inquiries and related FAQs. The input is the parsed token information, and the output is the relevant information as search results. 【0476】 Step 4: 【0477】 The server generates the optimal response using a generative AI model. Here, OpenAI's generative AI model is used to create natural and contextual responses. The generative AI model takes historical information and analysis results as input and generates a response sentence based on that. The output is the response sentence that the server prepares to return to the user. 【0478】 Step 5: 【0479】 The server sends the generated response to the user's device. The response is sent securely using the HTTPS protocol. The input is the generated response text, and the output is the display data that the user can view on their device. 【0480】 Step 6: 【0481】 The server stores queries and their answers in a database for future reference. The server uses this data to automatically generate the necessary handover documents when personnel changes occur. In this step, the input is a new query-answer pair, and the output is updated historical data. 【0482】 (Application Example 1) 【0483】 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." 【0484】 In serving corporate clients, there is a demand for prompt and appropriate information provision and improved operational efficiency. However, frequent changes in personnel within companies and the increasing complexity of inquiries in logistics operations can lead to delays in responses. Furthermore, the inability to propose appropriate plans based on past inquiry history contributes to decreased customer satisfaction. To address these challenges, there is a need to provide consistent customer support through a system. 【0485】 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. 【0486】 In this invention, the server includes means for registering and updating data related to corporate customers, means for receiving, analyzing, and generating appropriate responses to customer inquiries, and means for permanently managing inquiry and response content as a history. This enables rapid and consistent response to corporate customers. Furthermore, by generating efficiency plans related to logistics management and providing information to customers in real time using smartphones, further operational efficiency and improved customer satisfaction can be achieved. 【0487】 "Corporate clients" refer to companies and organizations, and are organizational business partners rather than individual customers. 【0488】 "Data" refers to a collection of numbers, characters, and symbols that can be registered and updated as information. 【0489】 An "inquiry" refers to a question or request made by a customer seeking information or support. 【0490】 "Analysis" is the act of breaking down acquired information or data in order to understand its content. 【0491】 "Answer" refers to the solution or information provided in response to an inquiry. 【0492】 "History" refers to a record kept in chronological order of past inquiries and responses. 【0493】 A "plan" is a strategy or set of instructions formulated to achieve a specific goal. 【0494】 "Documents" refer to documents and data that compile the information necessary when handing over duties. 【0495】 "Logistics management" refers to the work of planning and managing the efficient distribution of goods and products. 【0496】 A "smartphone" is a mobile phone device that utilizes information and communication technology to provide multiple functions. 【0497】 "Real-time" refers to a process where tasks are processed instantly at the moment they are performed, and the results are provided immediately. 【0498】 This invention aims to realize a system that responds quickly and accurately to inquiries from corporate clients. The server utilizes a database management system (e.g., MySQL or PostgreSQL) to appropriately register and update data related to corporate clients. It also receives inquiries from clients and analyzes their content using natural language processing technology. This analysis uses Python natural language processing libraries (e.g., NLTK or spaCy). Based on the analysis results, it generates appropriate answers and sends them in real time to user terminals such as smartphones. 【0499】 The user's terminal is equipped with a function that receives responses from the server and displays them immediately on the screen. This allows users to quickly obtain the necessary information. Furthermore, when the person in charge changes, the server automatically generates the necessary handover documents and provides them to the new person's terminal. These documents include past inquiry history and important corporate customer information, enabling the new person in charge to smoothly take over the duties. 【0500】 For example, when a user inquires about inventory status, the server receives and analyzes the inquiry. Based on the analysis results, it accesses the relevant logistics database to generate a response and sends it to the user's terminal in real time. 【0501】 Examples of prompt messages include the following: 【0502】 User inquiry: Please tell me the stock status. 【0503】 1. Analyze the meaning of this inquiry and refer to the logistics information database. 【0504】 2. Consider detailed steps to generate and provide the optimal answer to the user. 【0505】 In this way, users can obtain the information they need in a timely manner. 【0506】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0507】 Step 1: 【0508】 The server receives queries from user terminals. It receives query messages from users in natural language format as input. This message is initially processed and prepared for the next step of analysis. 【0509】 Step 2: 【0510】 The server analyzes received queries using natural language processing (NLP) techniques. Here, a Python NLP library is used to extract intent from the text. The input is the user's query message, and the output is the analyzed semantic information. This analysis clarifies the purpose and content of the query. 【0511】 Step 3: 【0512】 The server generates an appropriate response by referencing relevant historical and FAQ databases based on the analysis results. The input is the analyzed semantic information, and the output is the optimal response. This data referencing ensures consistent responses. 【0513】 Step 4: 【0514】 The generated response is sent from the server to the user's terminal in real time. The terminal receives this response and displays it on the user's screen. The input is the generated response, and the output is text information that the user can verify. This process allows the user to obtain a solution quickly. 【0515】 Step 5: 【0516】 When the person in charge changes, the server automatically generates handover documents based on the latest inquiry history and corporate customer data. The input is past inquiry history data, and the output is a document summarizing the necessary information for the next person in charge. Providing this document to the new person in charge ensures continuity of operations. 【0517】 Step 6: 【0518】 Through their smartphones, users are guided through further efficiency improvement plans for logistics management, and optimal measures are suggested. The input is the current logistics data status, and the output is a proposed efficiency improvement plan. This function allows users to identify areas for improvement in their business processes. 【0519】 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. 【0520】 This invention combines an AI-powered system for optimizing inquiry handling to improve customer satisfaction in corporate customer service with an emotion engine that recognizes user emotions. This system makes it possible to understand the customer's emotional state and provide an optimal response accordingly. 【0521】 The system primarily consists of a server, user terminals, and an emotion engine. Users access the corporate portal and enter inquiries and requests. This input information is sent to the server via the terminal. The server has an integrated emotion engine that analyzes the user's emotions from the received inquiries. This analysis utilizes natural language processing technology to identify emotions from the context and wording of the input text. By recognizing emotions such as happiness, distress, or dissatisfaction, the emotion engine can select an appropriate response. 【0522】 The server considers the user's emotional data along with the inquiry content to generate the optimal response. This response is adjusted according to the emotional data, aiming to match the user's emotional state. For example, if the user is dissatisfied, a more polite and comprehensive response can be generated than usual. The generated response is provided to the user in real time through the terminal. 【0523】 Furthermore, the server records all inquiry history, including emotional data, in a database and uses it as needed to handle future inquiries. This history helps maintain consistency in responses, understand users' emotional tendencies, and contribute to building long-term relationships. 【0524】 For example, when a user inquires about how to use a new service, if the emotion engine detects the user's anxiety, the server will generate a response that includes more detailed instructions and additional support options. In this way, flexible responses tailored to user needs can be achieved, leading to improved customer satisfaction and loyalty. 【0525】 The following describes the processing flow. 【0526】 Step 1: 【0527】 Users log in to the corporate portal and enter inquiries and requests on their devices. The input includes questions and related information. 【0528】 Step 2: 【0529】 The terminal sends the inquiry content entered by the user to the server. The data sent includes user information. 【0530】 Step 3: 【0531】 The server passes the received query to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses natural language processing to analyze the context of the text and identify the emotion. 【0532】 Step 4: 【0533】 The server uses sentiment data obtained from the sentiment engine to generate appropriate responses to inquiries. If it detects that the user is dissatisfied, it considers providing a more polite and explanatory response than usual. 【0534】 Step 5: 【0535】 The server sends the generated response to the user's device. The user can then review the response on their device and submit another inquiry if necessary. 【0536】 Step 6: 【0537】 The server records queries, their responses, and user sentiment data in a database. This allows for the accumulation of historical data, including the progression of emotions over time. 【0538】 Step 7: 【0539】 The server analyzes accumulated historical data to understand trends for each user. This analysis is then used to handle future inquiries and optimize account plans. 【0540】 Step 8: 【0541】 When a user makes another inquiry, the server refers to past history data and implements the most appropriate response based on their past emotional state. This enables the provision of customized services tailored to each individual user. 【0542】 (Example 2) 【0543】 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." 【0544】 Traditionally, responses to inquiries from corporate clients have often been handled uniformly without considering individual circumstances or customer emotional states, resulting in a failure to adequately enhance customer satisfaction. In particular, there is a demand for flexible and consistent responses based on customer emotions and past inquiry history, but the means to achieve this are insufficient. 【0545】 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. 【0546】 In this invention, the server includes means for recording and modifying data relating to corporate customers, means for receiving customer inquiries, identifying customer emotions using natural language processing technology, and generating optimal responses, and means for recording inquiry and response content as history, and generating consistent responses by referring to past inquiry history and an information database. This enables flexible responses tailored to individual customer needs and emotional states, thereby improving customer satisfaction. 【0547】 "Corporate clients" refer to clients with legal personality, such as companies or organizations, and are distinguished from individual clients. 【0548】 "Data" is a set of codes and symbols used to represent information, in a format that can be manipulated and processed within a computer system. 【0549】 "Emotions" refer to the psychological states of humans and generally encompass different types such as joy, sadness, anger, and anxiety. 【0550】 "Natural language processing technology" refers to the techniques and methods used to process and understand human language using computers, and is used for text analysis and generation. 【0551】 "History" refers to past records of searches, operations, etc., and in particular, to the history of user actions and inquiries within a system. 【0552】 An "information database" is a system that structurally stores and manages information, and allows for the rapid retrieval of information under specific conditions. 【0553】 "Response" refers to the reply generated in response to an received inquiry, and in particular, to the solutions or information that the system provides to the user. 【0554】 This invention is a system that generates optimal responses to inquiries from corporate clients, taking into account the user's emotions. The system mainly consists of a server, a user terminal, and an emotion engine. 【0555】 Users access the corporate portal and enter inquiries and requests on their terminals. The terminals receive this information, format it, and then send it to the server. The server has an emotion engine built in that analyzes the received inquiries using natural language processing technology to identify the user's emotions. This utilizes open-source natural language processing libraries such as "spaCy" and the "BERT" model. 【0556】 The server uses a generative AI model to generate the optimal response based on the sentiment analysis results. The generated response is sent to the user via the terminal. This response is adjusted to correspond to the user's emotional state. For example, if the user is feeling anxious about how to use a new service, it can provide detailed guidelines such as, "Don't worry, here's how to set up the new service." 【0557】 This system records all inquiry history and associated sentiment data in a database, which will be used for future inquiries. This historical data helps maintain consistency in past responses. 【0558】 A concrete example of a prompt would be, "Identify the feelings corporate customers have about using the new service and generate a response that includes helpful support options." Based on this prompt, the system will provide flexible responses tailored to the customer's feelings, leading to improved customer satisfaction. 【0559】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0560】 Step 1: 【0561】 Users access the corporate portal and enter inquiries or requests. The terminal receives this text input and converts it into well-formed data. The input data is, for example, text entered on a web form, and is formatted as request data to be sent to the server as output. 【0562】 Step 2: 【0563】 The terminal sends well-formed data to the server. The server receives this data and begins analyzing the query. The input data is the user's query text, and the output contains analysis data used to prepare for sentiment analysis. 【0564】 Step 3: 【0565】 The server uses an emotion engine to analyze the query text with natural language processing techniques and identify the user's emotions. It analyzes the context of the input text using models such as "spaCy" and "BERT" and generates emotion labels such as joy and anxiety as output. 【0566】 Step 4: 【0567】 The server utilizes a generative AI model to generate the optimal response based on the results of sentiment analysis. The input consists of sentiment labels and the query content, while the output is the response text provided to the user. This response is tailored to the user's emotions. 【0568】 Step 5: 【0569】 The server sends a response text back to the terminal. The terminal displays this text to the user. This allows the user to receive solutions and support information in real time. The output is the information that is ultimately displayed to the user and is applied to the user interface. 【0570】 Step 6: 【0571】 The server records all query and sentiment data in a database. This historical data is used to improve future query handling. The input is all the data processed, and the output is the information recorded in the historical database. This process helps to improve responses in the future. 【0572】 (Application Example 2) 【0573】 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." 【0574】 Traditional customer service systems often provide mechanical responses without considering the user's emotional state. This is particularly problematic in electronic payment services, where rapid and appropriate problem resolution is crucial, yet customer satisfaction remains low. While there is a need to respond quickly to user dissatisfaction and anxiety, conventional technologies struggle to address such emotions. 【0575】 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. 【0576】 In this invention, the server includes means for registering and updating information about corporate customers, means for recognizing the user's emotional state and adjusting the response, and means for generating a consistent answer by referring to past inquiry history, emotional data, and an FAQ database. This makes it possible to provide an optimal response tailored to the user's emotions in real time. 【0577】 A "corporate customer" refers to a customer who uses products or services as a company or organization. 【0578】 An "inquiry" is a question or request made by a customer seeking information about a product or service, or to resolve a problem. 【0579】 "Natural language processing technology" is a technology that uses computers to analyze and understand human language. 【0580】 "Emotional state" refers to the psychological state or reaction a user exhibits in a particular situation. 【0581】 An "FAQ database" is a collection of frequently asked questions and their answers, used to support quick problem-solving. 【0582】 "Identifying user emotions" means analyzing the user's emotions from the input information and classifying them into specific emotional categories. 【0583】 A "consistent response" is a response that is generated by referring to past history and data, while maintaining consistency with the surrounding context. 【0584】 The system for realizing this invention consists of a server for registering and updating information on corporate customers, a terminal for users to make inquiries, and a communication network connecting these. 【0585】 The server analyzes user inquiries using natural language processing technology and identifies the user's emotional state from the text and audio data. Google Cloud Natural Language API and IBM Watson are used as emotion engines for this emotion identification. Based on the analysis results, the server then references past inquiry history and FAQ databases to generate the most appropriate response for the user's emotional state. During this process, a generative AI model is used to adjust the response to ensure consistency with the user's emotional state. 【0586】 The final response is provided to the user via the terminal, allowing the user to resolve the issue in real time. The server also records all inquiry history and sentiment data in a database, which is used to improve future inquiry handling. 【0587】 For example, if a user asks, "I haven't received notifications about recent transactions; can you check them?", the server will sense the user's concern. In this case, the server will generate a detailed response, including instructions on how to check the transaction history and links to additional support, and provide it to the user through their device. 【0588】 An example of a prompt message would be, "Analyze the user's emotions when they make the inquiry, and suggest a way to select and provide an appropriate response." 【0589】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0590】 Step 1: 【0591】 The user uses a terminal to input their inquiry as text or voice. The entered data is transmitted from the user's terminal to the server via communication. The input in this step is the inquiry message from the user, and the server receives that message as output. 【0592】 Step 2: 【0593】 The server performs text analysis on the received query data using natural language processing techniques. Specifically, it uses Google Cloud Natural Language API and IBM Watson to perform data analysis to understand the context of the query. It receives the query text as input and outputs metadata such as the user's emotional state. 【0594】 Step 3: 【0595】 The server uses an emotion engine to identify the user's emotions from the analyzed query data. It analyzes the user's language and context to determine an emotion category (e.g., reassured, dissatisfied, neutral). The input for this step is the analyzed text data, and the output is the user's emotional state. 【0596】 Step 4: 【0597】 The server takes the user's emotional state into account and generates a consistent and optimal response by referring to past inquiry history and FAQ databases. A generative AI model is used to ensure the response is appropriate to the user's emotions. This process takes past data and analysis results as input and outputs an optimized response. 【0598】 Step 5: 【0599】 The server sends the generated response to the terminal and presents it to the user in real time. The user can then use this response to resolve the problem. The input for this step is the generated response data, and the output is the display of the answer to the user. 【0600】 Step 6: 【0601】 The server records all inquiries, their response history, and sentiment data in a database. This data is used for analysis to improve future inquiries. The input is the user's inquiry history and sentiment data, and the output is the updated history information in the database. 【0602】 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. 【0603】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0604】 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. 【0605】 [Fourth Embodiment] 【0606】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0607】 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. 【0608】 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). 【0609】 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. 【0610】 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. 【0611】 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). 【0612】 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. 【0613】 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. 【0614】 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. 【0615】 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. 【0616】 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. 【0617】 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. 【0618】 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". 【0619】 This invention provides a system for optimizing customer service for corporate clients. This system utilizes AI technology to handle customer inquiries and aims to minimize the impact of changes in assigned personnel. 【0620】 At the heart of the system is an AI agent running on a server. The AI ​​agent receives inquiries sent by users on the server and analyzes their content using natural language processing technology. Based on the analysis, it refers to past inquiry history and FAQs stored in the database to generate the most appropriate answer. This generated answer is sent to the user's terminal in real time, allowing the user to instantly check the answer on their screen. 【0621】 All inquiries and their responses are stored on the server and permanently managed as a history. This history management ensures that past inquiries and responses are passed on to the relevant staff, maintaining a consistent quality of customer service. The system also has a function to analyze user information and this history data to generate an optimal account plan for each user. This proposed plan is notified to the user's device, and the user can adjust the plan according to their preferences. 【0622】 Furthermore, if the person in charge changes, the server automatically generates the necessary handover documents. These documents include important customer information and past inquiry history, allowing the new person in charge to smoothly take over duties and continue customer service without interruption. 【0623】 For example, if a user inquires about how to use a new service, the AI ​​agent will quickly interpret the inquiry and provide detailed usage instructions based on its past database. The user can then effectively use the service based on this response and can ask additional questions to the server if necessary. 【0624】 Thus, the system of the present invention can improve productivity and reliability in dealing with corporate customers, and enhance customer satisfaction and loyalty for companies. 【0625】 The following describes the processing flow. 【0626】 Step 1: 【0627】 Users log in to the corporate portal and enter or update their basic information and contract details. The information entered by the user is sent to the server via their device. 【0628】 Step 2: 【0629】 The server receives the transmitted information and registers or updates it in the database. Here, it checks the data's integrity and performs validation as needed. 【0630】 Step 3: 【0631】 Users can make inquiries to the AI ​​agent on the corporate portal page. For example, they might submit a question such as, "Please tell me what services are available." 【0632】 Step 4: 【0633】 The server analyzes the inquiry from the terminal. Using natural language processing technology, it understands the received inquiry and then forms an appropriate response. 【0634】 Step 5: 【0635】 The server references past inquiry history and FAQ databases to ensure the consistency and accuracy of the generated answers. It aggregates the necessary information to construct a single, definitive answer. 【0636】 Step 6: 【0637】 The server sends the answer to the terminal, and the answer is displayed on the user's terminal. Based on this information, the user can understand the requested details. 【0638】 Step 7: 【0639】 The server permanently records the history of inquiries and their responses in a database. This creates an information infrastructure that can be used for future reference and for handover when personnel changes occur. 【0640】 Step 8: 【0641】 The server suggests an appropriate account plan based on the user's past history and current usage. The generated plan is notified to the user via their device, and the user can review its contents. 【0642】 Step 9: 【0643】 When a person in charge changes, the server automatically collects the necessary handover information for the new person in charge and generates handover documents. These documents are sent to the new person in charge's terminal to support a smooth transition of duties. 【0644】 (Example 1) 【0645】 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". 【0646】 In handling inquiries from corporate clients, it is essential to minimize inconsistencies in responses due to changes in the person in charge and the diversity of inquiry content. Furthermore, providing prompt and consistent answers is necessary to improve customer satisfaction. Additionally, efficiently managing these processes to reduce the workload on employees is a key challenge. 【0647】 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. 【0648】 In this invention, the server includes means for storing and editing information about corporate customers, means for receiving customer inquiries, analyzing them, and generating optimal responses, and means for storing the content of inquiries and responses as data. This ensures consistency in customer service even when there are changes in personnel, and enables the provision of quick and appropriate answers. 【0649】 A "corporate customer" is a customer who has the attributes of a company or organization, and who purchases goods or uses services as a business entity rather than as an individual. 【0650】 "Means of storing and compiling information" refers to methods of collecting necessary customer information using databases or recording media, and storing and updating it for later use. 【0651】 "Means for receiving inquiries, analyzing them, and generating optimal responses" refers to process technologies that first receive questions or requests from users, understand their content, and provide appropriate responses or answers. 【0652】 "Means of saving as data" refers to technologies that record inquiries and the resulting responses in digital format and retain them for later reference. 【0653】 "Methods for designing user plans" refers to methods of building and proposing service plans optimized for each customer based on their history and current information. 【0654】 "Means for automatically generating necessary documents" refers to technologies that automatically create and provide required documents and information when specific conditions are met. 【0655】 "Means of interpreting in natural language" refers to methods for understanding received text in human language so that its meaning and intent can be grasped. 【0656】 "Means of utilizing reference information to provide continuous responses" refers to a process that uses previous data and literature to ensure that a series of responses and answers are consistent. 【0657】 This invention is a system for optimizing customer inquiry handling for corporate clients. The server plays a central role in this system and uses the following components to implement various functions. 【0658】 The server first stores information about corporate customers in a database, and then manages and edits this information using programming languages ​​such as Python and SQL, as well as database management systems. A specific example would be using a PostgreSQL database. 【0659】 When receiving inquiries from customers, the server retrieves inquiry data using the HTTP protocol. The received inquiry content is then parsed using spaCy, a Python natural language processing library, to understand the intent of the inquiry. 【0660】 The server then uses a generative AI model to generate the optimal response. For example, OpenAI's GPT can be used as the generative AI model. This ensures that natural and appropriate responses are provided to user inquiries. 【0661】 Inquiries and their responses are stored in a database for future reference. This allows the server to automatically generate necessary handover documents based on past history when personnel changes occur, supporting a smooth continuation of operations. 【0662】 Users can make inquiries and receive responses from the server via their own devices. The use of the HTTPS protocol ensures the security of user data. 【0663】 For example, if a user asks, "I want to know the warranty period for a new product," the server analyzes this inquiry, generates a response such as, "The warranty period for the new product A is 2 years," and sends it to the user's terminal in real time. The user can then use this response to instantly obtain the information they need. 【0664】 Example prompt: "Please provide details on how to configure the new service." 【0665】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0666】 Step 1: 【0667】 The user enters their inquiry using a terminal and sends it to the server. This input includes specific questions and requests. The input data is sent in HTTP request format, and the server receives it. 【0668】 Step 2: 【0669】 The server analyzes received queries using natural language processing techniques. Specifically, it uses a Python natural language processing library to tokenize the query content and tag it with parts of speech. This process allows for data processing that clearly understands the intent of the query. The input is the user's query, and the output is structured data as a result of the analysis. 【0670】 Step 3: 【0671】 The server searches past inquiry history and FAQ databases based on the analysis results. This step uses PostgreSQL to find relevant information. SQL queries are issued to search for history with similar inquiries and related FAQs. The input is the parsed token information, and the output is the relevant information as search results. 【0672】 Step 4: 【0673】 The server generates the optimal response using a generative AI model. Here, OpenAI's generative AI model is used to create natural and contextual responses. The generative AI model takes historical information and analysis results as input and generates a response sentence based on that. The output is the response sentence that the server prepares to return to the user. 【0674】 Step 5: 【0675】 The server sends the generated response to the user's device. The response is sent securely using the HTTPS protocol. The input is the generated response text, and the output is the display data that the user can view on their device. 【0676】 Step 6: 【0677】 The server stores queries and their answers in a database for future reference. The server uses this data to automatically generate the necessary handover documents when personnel changes occur. In this step, the input is a new query-answer pair, and the output is updated historical data. 【0678】 (Application Example 1) 【0679】 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". 【0680】 In serving corporate clients, there is a demand for prompt and appropriate information provision and improved operational efficiency. However, frequent changes in personnel within companies and the increasing complexity of inquiries in logistics operations can lead to delays in responses. Furthermore, the inability to propose appropriate plans based on past inquiry history contributes to decreased customer satisfaction. To address these challenges, there is a need to provide consistent customer support through a system. 【0681】 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. 【0682】 In this invention, the server includes means for registering and updating data related to corporate customers, means for receiving, analyzing, and generating appropriate responses to customer inquiries, and means for permanently managing inquiry and response content as a history. This enables rapid and consistent response to corporate customers. Furthermore, by generating efficiency plans related to logistics management and providing information to customers in real time using smartphones, further operational efficiency and improved customer satisfaction can be achieved. 【0683】 "Corporate clients" refer to companies and organizations, and are organizational business partners rather than individual customers. 【0684】 "Data" refers to a collection of numbers, characters, and symbols that can be registered and updated as information. 【0685】 An "inquiry" refers to a question or request made by a customer seeking information or support. 【0686】 "Analysis" is the act of breaking down acquired information or data in order to understand its content. 【0687】 "Answer" refers to the solution or information provided in response to an inquiry. 【0688】 "History" refers to a record kept in chronological order of past inquiries and responses. 【0689】 A "plan" is a strategy or set of instructions formulated to achieve a specific goal. 【0690】 "Documents" refer to documents and data that compile the information necessary when handing over duties. 【0691】 "Logistics management" refers to the work of planning and managing the efficient distribution of goods and products. 【0692】 A "smartphone" is a mobile phone device that utilizes information and communication technology to provide multiple functions. 【0693】 "Real-time" refers to a process where tasks are processed instantly at the moment they are performed, and the results are provided immediately. 【0694】 This invention aims to realize a system that responds quickly and accurately to inquiries from corporate clients. The server utilizes a database management system (e.g., MySQL or PostgreSQL) to appropriately register and update data related to corporate clients. It also receives inquiries from clients and analyzes their content using natural language processing technology. This analysis uses Python natural language processing libraries (e.g., NLTK or spaCy). Based on the analysis results, it generates appropriate answers and sends them in real time to user terminals such as smartphones. 【0695】 The user's terminal is equipped with a function that receives responses from the server and displays them immediately on the screen. This allows users to quickly obtain the necessary information. Furthermore, when the person in charge changes, the server automatically generates the necessary handover documents and provides them to the new person's terminal. These documents include past inquiry history and important corporate customer information, enabling the new person in charge to smoothly take over the duties. 【0696】 For example, when a user inquires about inventory status, the server receives and analyzes the inquiry. Based on the analysis results, it accesses the relevant logistics database to generate a response and sends it to the user's terminal in real time. 【0697】 Examples of prompt messages include the following: 【0698】 User inquiry: Please tell me the stock status. 【0699】 1. Analyze the meaning of this inquiry and refer to the logistics information database. 【0700】 2. Consider detailed steps to generate and provide the optimal answer to the user. 【0701】 In this way, users can obtain the information they need in a timely manner. 【0702】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0703】 Step 1: 【0704】 The server receives queries from user terminals. It receives query messages from users in natural language format as input. This message is initially processed and prepared for the next step of analysis. 【0705】 Step 2: 【0706】 The server analyzes received queries using natural language processing (NLP) techniques. Here, a Python NLP library is used to extract intent from the text. The input is the user's query message, and the output is the analyzed semantic information. This analysis clarifies the purpose and content of the query. 【0707】 Step 3: 【0708】 The server generates an appropriate response by referencing relevant historical and FAQ databases based on the analysis results. The input is the analyzed semantic information, and the output is the optimal response. This data referencing ensures consistent responses. 【0709】 Step 4: 【0710】 The generated response is sent from the server to the user's terminal in real time. The terminal receives this response and displays it on the user's screen. The input is the generated response, and the output is text information that the user can verify. This process allows the user to obtain a solution quickly. 【0711】 Step 5: 【0712】 When the person in charge changes, the server automatically generates handover documents based on the latest inquiry history and corporate customer data. The input is past inquiry history data, and the output is a document summarizing the necessary information for the next person in charge. Providing this document to the new person in charge ensures continuity of operations. 【0713】 Step 6: 【0714】 Through their smartphones, users are guided through further efficiency improvement plans for logistics management, and optimal measures are suggested. The input is the current logistics data status, and the output is a proposed efficiency improvement plan. This function allows users to identify areas for improvement in their business processes. 【0715】 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. 【0716】 This invention combines an AI-powered system for optimizing inquiry handling to improve customer satisfaction in corporate customer service with an emotion engine that recognizes user emotions. This system makes it possible to understand the customer's emotional state and provide an optimal response accordingly. 【0717】 The system primarily consists of a server, user terminals, and an emotion engine. Users access the corporate portal and enter inquiries and requests. This input information is sent to the server via the terminal. The server has an integrated emotion engine that analyzes the user's emotions from the received inquiries. This analysis utilizes natural language processing technology to identify emotions from the context and wording of the input text. By recognizing emotions such as happiness, distress, or dissatisfaction, the emotion engine can select an appropriate response. 【0718】 The server considers the user's emotional data along with the inquiry content to generate the optimal response. This response is adjusted according to the emotional data, aiming to match the user's emotional state. For example, if the user is dissatisfied, a more polite and comprehensive response can be generated than usual. The generated response is provided to the user in real time through the terminal. 【0719】 Furthermore, the server records all inquiry history, including emotional data, in a database and uses it as needed to handle future inquiries. This history helps maintain consistency in responses, understand users' emotional tendencies, and contribute to building long-term relationships. 【0720】 For example, when a user inquires about how to use a new service, if the emotion engine detects the user's anxiety, the server will generate a response that includes more detailed instructions and additional support options. In this way, flexible responses tailored to user needs can be achieved, leading to improved customer satisfaction and loyalty. 【0721】 The following describes the processing flow. 【0722】 Step 1: 【0723】 Users log in to the corporate portal and enter inquiries and requests on their devices. The input includes questions and related information. 【0724】 Step 2: 【0725】 The terminal sends the inquiry content entered by the user to the server. The data sent includes user information. 【0726】 Step 3: 【0727】 The server passes the received query to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses natural language processing to analyze the context of the text and identify the emotion. 【0728】 Step 4: 【0729】 The server uses sentiment data obtained from the sentiment engine to generate appropriate responses to inquiries. If it detects that the user is dissatisfied, it considers providing a more polite and explanatory response than usual. 【0730】 Step 5: 【0731】 The server sends the generated response to the user's device. The user can then review the response on their device and submit another inquiry if necessary. 【0732】 Step 6: 【0733】 The server records queries, their responses, and user sentiment data in a database. This allows for the accumulation of historical data, including the progression of emotions over time. 【0734】 Step 7: 【0735】 The server analyzes accumulated historical data to understand trends for each user. This analysis is then used to handle future inquiries and optimize account plans. 【0736】 Step 8: 【0737】 When a user makes another inquiry, the server refers to past history data and implements the most appropriate response based on their past emotional state. This enables the provision of customized services tailored to each individual user. 【0738】 (Example 2) 【0739】 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". 【0740】 Traditionally, responses to inquiries from corporate clients have often been handled uniformly without considering individual circumstances or customer emotional states, resulting in a failure to adequately enhance customer satisfaction. In particular, there is a demand for flexible and consistent responses based on customer emotions and past inquiry history, but the means to achieve this are insufficient. 【0741】 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. 【0742】 In this invention, the server includes means for recording and modifying data relating to corporate customers, means for receiving customer inquiries, identifying customer emotions using natural language processing technology, and generating optimal responses, and means for recording inquiry and response content as history, and generating consistent responses by referring to past inquiry history and an information database. This enables flexible responses tailored to individual customer needs and emotional states, thereby improving customer satisfaction. 【0743】 "Corporate clients" refer to clients with legal personality, such as companies or organizations, and are distinguished from individual clients. 【0744】 "Data" is a set of codes and symbols used to represent information, in a format that can be manipulated and processed within a computer system. 【0745】 "Emotions" refer to the psychological states of humans and generally encompass different types such as joy, sadness, anger, and anxiety. 【0746】 "Natural language processing technology" refers to the techniques and methods used to process and understand human language using computers, and is used for text analysis and generation. 【0747】 "History" refers to past records of searches, operations, etc., and in particular, to the history of user actions and inquiries within a system. 【0748】 An "information database" is a system that structurally stores and manages information, and allows for the rapid retrieval of information under specific conditions. 【0749】 "Response" refers to the reply generated in response to an received inquiry, and in particular, to the solutions or information that the system provides to the user. 【0750】 This invention is a system that generates optimal responses to inquiries from corporate clients, taking into account the user's emotions. The system mainly consists of a server, a user terminal, and an emotion engine. 【0751】 Users access the corporate portal and enter inquiries and requests on their terminals. The terminals receive this information, format it, and then send it to the server. The server has an emotion engine built in that analyzes the received inquiries using natural language processing technology to identify the user's emotions. This utilizes open-source natural language processing libraries such as "spaCy" and the "BERT" model. 【0752】 The server uses a generative AI model to generate the optimal response based on the sentiment analysis results. The generated response is sent to the user via the terminal. This response is adjusted to correspond to the user's emotional state. For example, if the user is feeling anxious about how to use a new service, it can provide detailed guidelines such as, "Don't worry, here's how to set up the new service." 【0753】 This system records all inquiry history and associated sentiment data in a database, which will be used for future inquiries. This historical data helps maintain consistency in past responses. 【0754】 A concrete example of a prompt would be, "Identify the feelings corporate customers have about using the new service and generate a response that includes helpful support options." Based on this prompt, the system will provide flexible responses tailored to the customer's feelings, leading to improved customer satisfaction. 【0755】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0756】 Step 1: 【0757】 Users access the corporate portal and enter inquiries or requests. The terminal receives this text input and converts it into well-formed data. The input data is, for example, text entered on a web form, and is formatted as request data to be sent to the server as output. 【0758】 Step 2: 【0759】 The terminal sends well-formed data to the server. The server receives this data and begins analyzing the query. The input data is the user's query text, and the output contains analysis data used to prepare for sentiment analysis. 【0760】 Step 3: 【0761】 The server uses an emotion engine to analyze the query text with natural language processing techniques and identify the user's emotions. It analyzes the context of the input text using models such as "spaCy" and "BERT" and generates emotion labels such as joy and anxiety as output. 【0762】 Step 4: 【0763】 The server utilizes a generative AI model to generate the optimal response based on the results of sentiment analysis. The input consists of sentiment labels and the query content, while the output is the response text provided to the user. This response is tailored to the user's emotions. 【0764】 Step 5: 【0765】 The server sends a response text back to the terminal. The terminal displays this text to the user. This allows the user to receive solutions and support information in real time. The output is the information that is ultimately displayed to the user and is applied to the user interface. 【0766】 Step 6: 【0767】 The server records all query and sentiment data in a database. This historical data is used to improve future query handling. The input is all the data processed, and the output is the information recorded in the historical database. This process helps to improve responses in the future. 【0768】 (Application Example 2) 【0769】 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". 【0770】 Traditional customer service systems often provide mechanical responses without considering the user's emotional state. This is particularly problematic in electronic payment services, where rapid and appropriate problem resolution is crucial, yet customer satisfaction remains low. While there is a need to respond quickly to user dissatisfaction and anxiety, conventional technologies struggle to address such emotions. 【0771】 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. 【0772】 In this invention, the server includes means for registering and updating information about corporate customers, means for recognizing the user's emotional state and adjusting the response, and means for generating a consistent answer by referring to past inquiry history, emotional data, and an FAQ database. This makes it possible to provide an optimal response tailored to the user's emotions in real time. 【0773】 A "corporate customer" refers to a customer who uses products or services as a company or organization. 【0774】 An "inquiry" is a question or request made by a customer seeking information about a product or service, or to resolve a problem. 【0775】 "Natural language processing technology" is a technology that uses computers to analyze and understand human language. 【0776】 "Emotional state" refers to the psychological state or reaction a user exhibits in a particular situation. 【0777】 An "FAQ database" is a collection of frequently asked questions and their answers, used to support quick problem-solving. 【0778】 "Identifying user emotions" means analyzing the user's emotions from the input information and classifying them into specific emotional categories. 【0779】 A "consistent response" is a response that is generated by referring to past history and data, while maintaining consistency with the surrounding context. 【0780】 The system for realizing this invention consists of a server for registering and updating information on corporate customers, a terminal for users to make inquiries, and a communication network connecting these. 【0781】 The server analyzes user inquiries using natural language processing technology and identifies the user's emotional state from the text and audio data. Google Cloud Natural Language API and IBM Watson are used as emotion engines for this emotion identification. Based on the analysis results, the server then references past inquiry history and FAQ databases to generate the most appropriate response for the user's emotional state. During this process, a generative AI model is used to adjust the response to ensure consistency with the user's emotional state. 【0782】 The final response is provided to the user via the terminal, allowing the user to resolve the issue in real time. The server also records all inquiry history and sentiment data in a database, which is used to improve future inquiry handling. 【0783】 For example, if a user asks, "I haven't received notifications about recent transactions; can you check them?", the server will sense the user's concern. In this case, the server will generate a detailed response, including instructions on how to check the transaction history and links to additional support, and provide it to the user through their device. 【0784】 An example of a prompt message would be, "Analyze the user's emotions when they make the inquiry, and suggest a way to select and provide an appropriate response." 【0785】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0786】 Step 1: 【0787】 The user uses a terminal to input their inquiry as text or voice. The entered data is transmitted from the user's terminal to the server via communication. The input in this step is the inquiry message from the user, and the server receives that message as output. 【0788】 Step 2: 【0789】 The server performs text analysis on the received query data using natural language processing techniques. Specifically, it uses Google Cloud Natural Language API and IBM Watson to perform data analysis to understand the context of the query. It receives the query text as input and outputs metadata such as the user's emotional state. 【0790】 Step 3: 【0791】 The server uses an emotion engine to identify the user's emotions from the analyzed query data. It analyzes the user's language and context to determine an emotion category (e.g., reassured, dissatisfied, neutral). The input for this step is the analyzed text data, and the output is the user's emotional state. 【0792】 Step 4: 【0793】 The server takes the user's emotional state into account and generates a consistent and optimal response by referring to past inquiry history and FAQ databases. A generative AI model is used to ensure the response is appropriate to the user's emotions. This process takes past data and analysis results as input and outputs an optimized response. 【0794】 Step 5: 【0795】 The server sends the generated response to the terminal and presents it to the user in real time. The user can then use this response to resolve the problem. The input for this step is the generated response data, and the output is the display of the answer to the user. 【0796】 Step 6: 【0797】 The server records all inquiries, their response history, and sentiment data in a database. This data is used for analysis to improve future inquiries. The input is the user's inquiry history and sentiment data, and the output is the updated history information in the database. 【0798】 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. 【0799】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0800】 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 robot 414. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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." 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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 this memory. 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0819】 The following is further disclosed regarding the embodiments described above. 【0820】 (Claim 1) 【0821】 A means of registering and updating information on corporate customers, 【0822】 A means of receiving customer inquiries, analyzing them, and generating appropriate responses, 【0823】 A means of permanently managing inquiries and responses as a history, 【0824】 A means for generating an account plan based on user information and historical data, 【0825】 A method for automatically creating handover documents when the person in charge changes, 【0826】 A system that includes this. 【0827】 (Claim 2) 【0828】 The system according to claim 1, which analyzes customer inquiries using natural language processing technology. 【0829】 (Claim 3) 【0830】 The system according to claim 1, which generates consistent answers by referring to past inquiry history and an FAQ database. 【0831】 "Example 1" 【0832】 (Claim 1) 【0833】 A means of accumulating and compiling information on corporate customers, 【0834】 A means for receiving customer inquiries, analyzing them, and generating optimal responses, 【0835】 A means of saving inquiry and response content as data, 【0836】 A means of designing user plans based on customer information and data, 【0837】 A method for automatically generating necessary documents when the person in charge changes, 【0838】 A means of interpreting the content of an inquiry in natural language, 【0839】 A means of providing continuous responses by utilizing inquiry history and reference information, 【0840】 A system that includes this. 【0841】 (Claim 2) 【0842】 The system according to claim 1, which processes the content of an inquiry using natural language processing technology. 【0843】 (Claim 3) 【0844】 The system according to claim 1, which utilizes past inquiry records and reference information to provide a consistent response. 【0845】 "Application Example 1" 【0846】 (Claim 1) 【0847】 A means for registering and updating data related to corporate customers, 【0848】 A means of receiving customer inquiries, analyzing them, and generating appropriate responses, 【0849】 A means of permanently managing inquiries and responses as a history, 【0850】 A means for generating an account plan based on user information and historical data, 【0851】 A method for automatically creating handover documents when the person in charge changes, 【0852】 A means of generating efficiency plans related to logistics management, 【0853】 A means of providing information to customers in real time using smartphones, 【0854】 A system that includes this. 【0855】 (Claim 2) 【0856】 The system according to claim 1, which analyzes customer inquiries using natural language processing technology and generates responses by referring to logistics data. 【0857】 (Claim 3) 【0858】 The system according to claim 1, which generates consistent answers by referring to past inquiry history and an FAQ database, thereby supporting improved logistics efficiency. 【0859】 "Example 2 of combining an emotion engine" 【0860】 (Claim 1) 【0861】 Means for recording and correcting data relating to corporate customers, 【0862】 A means of receiving customer inquiries, analyzing them, and creating the optimal response, 【0863】 A means for analyzing emotions and generating an appropriate response based on the analysis results, 【0864】 A means for recording the content of inquiries and responses as a history, 【0865】 A means of creating an account plan based on customer data and historical data, 【0866】 A method for automatically creating handover documents when the person in charge changes, 【0867】 A system that includes this. 【0868】 (Claim 2) 【0869】 The system according to claim 1, which analyzes customer inquiries using natural language processing technology to identify customer emotions. 【0870】 (Claim 3) 【0871】 The system according to claim 1, which refers to past inquiry history and an information database to generate a consistent response. 【0872】 "Application example 2 when combining with an emotional engine" 【0873】 (Claim 1) 【0874】 A means of registering and updating information on corporate customers, 【0875】 A means of receiving customer inquiries, analyzing them, and generating appropriate responses, 【0876】 A means of permanently managing inquiries and responses as a history, 【0877】 A means for generating an account plan based on user information and historical data, 【0878】 A method for automatically creating handover documents when the person in charge changes, 【0879】 A means of recognizing the user's emotional state and adjusting the response, 【0880】 A system that includes this. 【0881】 (Claim 2) 【0882】 The system according to claim 1, which analyzes customer inquiries using natural language processing technology to identify the user's emotions. 【0883】 (Claim 3) 【0884】 The system according to claim 1, which generates consistent answers by referring to past inquiry history, sentiment data, and an FAQ database. [Explanation of symbols] 【0885】 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 of registering and updating information on corporate customers, A means of receiving customer inquiries, analyzing them, and generating appropriate responses, A means of permanently managing inquiries and responses as a history, A means for generating an account plan based on user information and historical data, A method for automatically creating handover documents when the person in charge changes, A system that includes this. [Claim 2] The system according to claim 1, which analyzes customer inquiries using natural language processing technology. [Claim 3] The system according to claim 1, which generates consistent answers by referring to past inquiry history and an FAQ database.

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