system

Abstract

We provide the system. [Solution] A means to automatically analyze information received from the client and generate a summary, A means for identifying ambiguities and proposing corrective measures based on the analyzed information, Means for searching for and providing reference information from relevant laws, guidelines, and regulations, A means for classifying the aforementioned consultation content based on categories and automatically assigning a consultant to each consultation, A means of monitoring the progress of consultations and notifying staff and clients of updates, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Consulting services related to information security impose a great burden on both the consultants and the window staff due to their high specialization. Therefore, it is an issue that the efficiency of consultation response cannot be improved and there are variations in consultation quality. Furthermore, in order to provide a prompt and appropriate response, highly reliable information provision is essential, but it is difficult to provide sufficient response with the current method. 【Means for Solving the Problems】 【0005】 To address this challenge, the present invention provides a means for automatically analyzing information from clients and generating a summary. Furthermore, it employs a means for identifying ambiguities based on the analyzed information and proposing corrective measures. The effectiveness of consultations is enhanced by searching for and providing reference information from relevant laws, guidelines, and regulations. The invention also includes a means for classifying consultation content based on categories and automatically assigning appropriate consultants, enabling efficient consultation handling. In addition, it has a function to monitor the progress of consultation handling and notify consultants and clients of updates as needed, thereby stabilizing consultation quality and reducing the burden on those handling the consultations. 【0006】 A "consultant" is an individual or organization that uses the system and seeks questions or advice regarding information security. 【0007】 A "receptionist" is an individual or group responsible for responding to inquiries from clients and providing appropriate support. 【0008】 "Automated analysis" refers to the process of using artificial intelligence technology to mechanically analyze input information through a program, summarizing and classifying it in a format that is easy for humans to understand. 【0009】 A "summary" is a shortened version of text that extracts the key points from detailed information and presents them in a concise and easy-to-understand format. 【0010】 "Ambiguous points" are areas where information or context is unclear and require additional explanation or clarification. 【0011】 A "corrective plan" is a specific proposal or method to resolve identified ambiguities. 【0012】 "Laws and regulations" refer to a general term for rules established by the government or authoritative bodies that must be complied with. 【0013】 "Guidelines" are a series of recommendations that provide standards and guidelines for taking appropriate action in specific situations or fields. 【0014】 "Regulations" are documents outlining the rules and policies agreed upon within an organization, setting out guidelines that stakeholders should follow. 【0015】 A "category" is a unit of classification that groups together information with similar characteristics or features, and serves as a factor in organization and retrieval. 【0016】 "Progress status" is an indicator that shows what stage a particular task or process is at and how much progress has been made. [Brief explanation of the drawing] 【0017】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0019】 First, the terms used in the following description will be explained. 【0020】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of 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. 【0021】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0022】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0024】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0028】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0029】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0030】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0031】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0032】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0035】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0036】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0037】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0038】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting information security-related consultation details into a terminal. 【0039】 The user enters their specific consultation details as text through the terminal's interface. The entered information is immediately sent from the terminal to the server. On the server, the entered text data is automatically analyzed using natural language processing (NLP) technology. The server generates a text summary of the consultation and also searches a database for similar past cases. 【0040】 Furthermore, the server analyzes any ambiguities in the consultation and generates corrective measures. Ambiguities include, for example, unclear technical terms or areas where the content lacks sufficient confirmation. This process references relevant laws, guidelines, and internal regulations. Based on this reference information, it provides specific information such as the next steps the user should take and drafts of necessary application forms. 【0041】 The server then categorizes the inquiry into the appropriate category, searches the database for the most suitable contact person based on the category, and automatically assigns them. Each contact person is notified of the detailed information of the inquiry, along with any risk points they should be aware of. 【0042】 During the operational phase, the server continuously monitors the progress of the consultation and notifies the staff member and user of the progress each time there is an update. This allows users to always know what stage their consultation is at, and enables the staff member to efficiently prepare for the next steps. 【0043】 For example, if a user requests information about the risks associated with the application of a new security protocol, the server summarizes the request and provides a compilation of reference information, including legal risks and technical concerns related to the protocol. Simultaneously, it automatically distributes this request to the department responsible for formulating security policies and assists in generating a report analyzing the relevant risks. This enables efficient and effective information security consultation for both the user and the support staff. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 The user enters specific details of their information security inquiry through the terminal's interface. The entered information is then sent from the terminal to the server by pressing the send button. 【0047】 Step 2: 【0048】 The server receives the consultation content sent from the terminal and analyzes it using natural language processing (NLP) technology. This analysis extracts a summary of the consultation content and key keywords. 【0049】 Step 3: 【0050】 The server uses the extracted keywords to search its database for similar past consultation cases. These search results are saved as examples that the consultant can refer to. 【0051】 Step 4: 【0052】 The server identifies ambiguities in the consultation based on the analysis results. Ambiguous sections include contextual inconsistencies and unclear technical terms. The server then generates corrective suggestions. 【0053】 Step 5: 【0054】 The server searches for relevant laws, guidelines, and internal regulations for reference information and provides it to the user, along with information on legal aspects and procedures as needed. 【0055】 Step 6: 【0056】 The server categorizes inquiries and selects the most suitable contact person from the database. The inquiry is then automatically assigned to this contact person. 【0057】 Step 7: 【0058】 The server notifies the person in charge of potential risk points and detailed information about the consultation. This information is displayed on the person in charge's dashboard screen. 【0059】 Step 8: 【0060】 The server monitors the progress of consultations in real time and notifies the staff and users of updates whenever there is progress. This ensures that all parties involved are always aware of the latest status. 【0061】 (Example 1) 【0062】 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." 【0063】 In information security consulting services, a significant challenge arises from the time and effort required to interpret the consultation content, gather relevant information, and assign it to the appropriate person. This makes it difficult to respond quickly to those seeking advice, leading to a decrease in the overall efficiency of the consulting service. 【0064】 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. 【0065】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for identifying ambiguities and suggesting corrective measures, and means for searching for and providing reference information from relevant laws, guidelines, and regulations. This enables a rapid understanding and response to the content of the consultation. 【0066】 A "consultant" is someone who seeks information or advice. 【0067】 "Automatically analyzing information" means analyzing received data using a program without requiring human intervention. 【0068】 "Generating a summary" means extracting the main points by condensing detailed information into a concise form. 【0069】 "Identifying ambiguities" means finding unclear or incomplete parts. 【0070】 "To propose corrective measures" means to suggest appropriate countermeasures or amendments to identified ambiguities or problems. 【0071】 "Searching for relevant laws, guidelines, and regulations" means finding information about specific standards or systems in databases or documents. 【0072】 "Classifying consultation content based on categories" means classifying and organizing the information received based on its nature and characteristics. 【0073】 "Automatically assigning a consultant" means that the system selects the most suitable person and assigns them to handle the task based on that selection. 【0074】 "Monitoring progress" means continuously observing how far a task or project is progressing. 【0075】 "Notifying about updates" means informing relevant parties about new data or changes. 【0076】 A "generative model" is an artificial intelligence technique trained to perform specific tasks based on large amounts of data. 【0077】 This system is designed to handle information security inquiries in an automated manner. Users input their information security questions and concerns through a terminal interface. The terminal immediately transmits this input information to the server. 【0078】 The server is implemented in programming languages ​​such as Python and Java®, and utilizes natural language processing libraries such as TENSORFLOW® and PyTorch to analyze the received text data. This analysis uses generative AI models (e.g., GPT-3® and BERT), enabling semantic understanding and summarization. 【0079】 Specifically, the server analyzes the consultation content, identifying ambiguous or unclear points. It then consults databases of relevant laws, guidelines, and regulations to generate corrective solutions for these issues. The system then categorizes the consultation content and automatically assigns the most suitable person to handle it. Furthermore, it regularly monitors the progress of the consultation and notifies the person in charge and the person making the inquiry of any updates. 【0080】 As a concrete example, consider a case where a user submits a request for a security assessment of a new cloud service. The server analyzes this information, summarizes the relevant security standards, and collects information on necessary legal matters and technical evaluation points. Based on this information, it notifies the cloud security officer, who can then proceed with the assessment based on the specified risk factors. 【0081】 Examples of prompt statements to input into the generative AI model are as follows: 【0082】 "A user has requested to know about the risks associated with the implementation of a new security protocol. Please summarize this request and generate information that includes the legal risks and technical concerns related to the protocol. Also, please prepare to distribute this information to the relevant departments." 【0083】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0084】 Step 1: 【0085】 The user enters their inquiry into the terminal interface. This input data includes specific questions and requests regarding information security. This text input serves as the foundational data for subsequent data processing. 【0086】 Step 2: 【0087】 The terminal securely transmits user input data to the server. The input here is text data of the user's inquiry, and the output is the completion of the data transfer to the server. The HTTPS protocol is used for transmission to maintain data security. 【0088】 Step 3: 【0089】 The server automatically analyzes the received text data. The input data consists of the user's consultation content, which is analyzed using Natural Language Processing (NLP) technology. Generative AI models (such as GPT-3 and BERT) are utilized to extract key points from the input content and generate a summary output. 【0090】 Step 4: 【0091】 The server identifies ambiguities from the analyzed data and generates corrective action plans. The input here is the summarized text, which is the output of the previous analysis step. By accessing legal and guideline databases and incorporating necessary supplementary information, the server obtains output that generates corrective action plans. 【0092】 Step 5: 【0093】 The server categorizes consultations based on their content and then searches the database for and assigns the appropriate consultant based on that information. The input is the consultation data after analysis is complete, and the output is the notification to the consultant and information that the assignment is complete. 【0094】 Step 6: 【0095】 The server constantly monitors the progress of consultations and notifies the person in charge and the user when there are any developments or updates. Input is progress log information, and output is the completion of the delivery of progress report notifications. An automated notification system is used to ensure that stakeholders are aware of the situation in real time. 【0096】 (Application Example 1) 【0097】 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." 【0098】 In information security consulting services, it is crucial to provide an environment where users can receive prompt and accurate support. However, current systems often require manual information analysis and progress management, which is time-consuming and labor-intensive, thus necessitating increased efficiency. Furthermore, the lack of an interface that allows users to consult anytime, anywhere is a significant challenge. 【0099】 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. 【0100】 In this invention, the server includes a processing device that automatically analyzes information received from a client and generates a summary, a processing device that identifies ambiguities and proposes corrective actions, and a processing device that searches for and provides reference information from relevant norms, guidelines, and regulations. This enables users to receive appropriate support in real time via their mobile devices. 【0101】 A "consultant" is an individual or organization that uses the system to resolve questions or problems related to information security. 【0102】 "Information" refers to the data that will be analyzed based on the text data and content provided by the person seeking advice. 【0103】 "Analysis" is the process of analyzing received information to summarize it and identify ambiguities. 【0104】 A "summary" is a concise text that compiles the analyzed information and is provided to the client or the person in charge. 【0105】 "Ambiguous points" refer to unclear technical terms or missing information contained within the data. 【0106】 A "corrective proposal" is a suggestion that outlines improvements or solutions to ambiguous points. 【0107】 A "norm" is a legally and industryally accepted standard or criterion. 【0108】 A "guideline" is a set of guidelines that outlines policies and directions regarding information security. 【0109】 "Regulations" refer to the rules and procedures established within an organization. 【0110】 "Reference information" refers to supplementary information based on laws and guidelines, which serves as a guide for the next course of action the person seeking advice should take. 【0111】 A "processing unit" is a combination of hardware and software designed to perform a specific task. 【0112】 A "personal digital assistant" (PDA) is a portable electronic device such as a smartphone or tablet. 【0113】 "Real-time" refers to processing that takes place immediately and information being provided instantaneously. 【0114】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting their information security consultation into a mobile device. 【0115】 Users first input specific information security-related inquiries as text using a mobile device such as a smartphone or tablet. This entered text data is then sent by the device to a cloud-based server. The server uses natural language processing (NLP) technology to analyze the input data and generate a summary. Natural language processing libraries such as Google® Cloud Natural Language API and SpaCy are used in this process. 【0116】 Next, the server identifies ambiguities based on the analysis results and creates necessary corrective actions. These corrective action suggestions include a function to retrieve and provide reference information from relevant norms, guidelines, and regulations. This allows users to clearly understand what actions they should take next. 【0117】 Furthermore, the server classifies inquiries based on their categories and automatically assigns the most suitable consultant to each inquiry. The consultant is notified of the detailed information of the inquiry, along with any potential risk factors to be aware of. This function allows consultants to prepare to perform their duties efficiently. 【0118】 Furthermore, the server has a system in place to monitor the progress of consultations in real time and notify staff and clients of updates. This allows clients to always be aware of the status of their consultation and proceed to the next step with confidence. 【0119】 For example, if a company's IT manager has questions about the security risks associated with the introduction of a new system, they can input "What are the information leakage risks when introducing a new system?" into their terminal. The server will then quickly analyze the situation, generate specific suggestions including past similar cases and risk mitigation measures, and provide them to the user. An example of a prompt in this case would be, "Please generate advice based on past cases regarding the risks to be aware of when introducing a new system in terms of information security." 【0120】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0121】 Step 1: 【0122】 Users input specific information security-related inquiries as text using their mobile devices. The entered text data is then sent from the device to a cloud-based server. This registers the user's inquiry in the system. 【0123】 Step 2: 【0124】 The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to perform grammatical analysis and key phrase extraction, and then summarizes the input content. This process extracts the main points of the text data and generates a summarized version. 【0125】 Step 3: 【0126】 The server identifies ambiguities based on the analysis results. For the identified ambiguities, it refers to relevant norms, guidelines, and regulations to generate appropriate corrective actions. This provides concrete solutions to the problems the user is facing. 【0127】 Step 4: 【0128】 The server classifies inquiries based on categories and automatically assigns a suitable person to each category. Specifically, it compares the inquiry content with existing inquiry categories and searches the database for the most suitable person to select. The selected person receives detailed information about the inquiry, along with any potential risk factors to be aware of. 【0129】 Step 5: 【0130】 The server monitors the progress of consultations in real time and sends notifications to the staff member and the user whenever there is progress. The progress status is regularly updated in the management system on the server, and this information is automatically notified. This allows users to always know the current status of the consultation, and staff members to efficiently prepare for the next steps. 【0131】 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. 【0132】 To implement this invention, an emotion engine is integrated into an AI agent system that supports information security consultations. The system begins with the user inputting their information security consultation into a terminal. 【0133】 Users input detailed information about their consultation via their device, and the naturally expressed text and voice data are used for emotion recognition. The information entered by the user is immediately sent to the server by the device. 【0134】 The server first analyzes the received consultation content using natural language processing (NLP) techniques and generates a summary. Simultaneously, the server uses an emotion engine to detect emotions from the user's text and voice. Specifically, it determines emotions from the user's word choice, tone of speech, and intonation. 【0135】 The detected emotional information is added to the analysis results of the consultation. If the user is experiencing particularly strong doubts or anxieties, the server creates a summary that highlights those points and notifies the contact person, including the emotional information. 【0136】 Furthermore, this emotional information can be used to change the guidelines for how customer service representatives should respond. For example, if a user indicates anxiety, the system can be configured to recommend a more courteous and reassuring approach. 【0137】 Furthermore, the server can provide users with appropriate feedback and emotional care information based on the emotion recognition results. For example, if a user is experiencing high levels of stress, it can automatically suggest stress management advice and resources. 【0138】 For example, if a user texts, "I'm worried about how secure the new security protocol is," the server summarizes it, and the emotion engine detects the emotion of "anxiety." In this case, the server provides relevant laws and guidelines, instructs the support staff to "prioritize providing information to alleviate anxiety," and provides the user with "reference materials to reassure them." In this way, incorporating an emotion engine improves the quality of care for individual clients and enables more personalized support. 【0139】 The following describes the processing flow. 【0140】 Step 1: 【0141】 The user enters specific details of their information security concerns via a terminal. The entered information includes text and audio data designed to infer the user's emotions. 【0142】 Step 2: 【0143】 The terminal transmits the user's input, along with audio data, to the server in real time. 【0144】 Step 3: 【0145】 The server analyzes the received data using natural language processing (NLP) techniques. It generates a summary from the text data and extracts important keywords. 【0146】 Step 4: 【0147】 The server simultaneously uses an emotion engine to recognize the user's emotional state. Specifically, it analyzes characteristics such as word choice, tone of voice, speed, and intonation to classify the user's emotions into categories such as "anxiety," "relief," and "excitement." 【0148】 Step 5: 【0149】 The server integrates the generated summary and sentiment information and compiles it into an analysis result. This information helps clarify the content of the consultation and is used for subsequent processing. 【0150】 Step 6: 【0151】 The server classifies the consultation content into the appropriate category based on the analysis results and automatically assigns the most suitable contact person from the database. During this process, it also considers emotional information and instructs the contact person on the appropriate course of action. 【0152】 Step 7: 【0153】 The server notifies the person in charge of the details of the consultation and emotional information, along with any risk points to be aware of and other relevant information. This notification allows the person in charge to choose the appropriate course of action. 【0154】 Step 8: 【0155】 The server monitors the progress of ongoing consultations and notifies the staff member and user of any updates. It also improves the user experience by providing feedback and emotional care information that takes the user's emotional state into consideration. 【0156】 (Example 2) 【0157】 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". 【0158】 In today's information society, the information security issues faced by those seeking advice are becoming increasingly complex and diverse. In this context, it is essential to accurately understand not only the specific content of the consultation but also the psychological state of the person seeking advice, and to provide support optimized for each individual. However, conventional systems are insufficient in recognizing and responding to emotions, often resulting in a uniform quality of support for all clients. Therefore, the challenge lies in achieving flexible and accurate support that takes into account the emotions of the person seeking advice. 【0159】 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. 【0160】 In this invention, the server includes means for automatically analyzing and summarizing information received from the client, means for recognizing emotions from the received information and integrating the corresponding emotional information into the analysis results, and means for providing appropriate response guidelines to the person in charge based on the recognized emotional information. This makes it possible to provide personalized support that is tailored to the client's emotions. 【0161】 "Means for automatically analyzing and summarizing received information" refers to a function in which a processing device automatically processes information provided by a client using a program and generates data that summarizes its contents. 【0162】 "Means for identifying ambiguities and proposing corrective measures" refers to a function that identifies unclear or uncertain parts within the analyzed information and proposes concrete plans for improvement or resolution. 【0163】 "Means for searching and providing reference information from relevant rules, guidelines, and policies" refers to a function that investigates laws, standards, and guidelines related to the consultation content from information resources and provides them to the user as useful information. 【0164】 "A means of classifying based on categories and automatically assigning a consultant" refers to a function that classifies consultation content according to pre-set categories and allows the system to automatically select the most suitable consultant to handle the situation. 【0165】 "A means of monitoring the progress of consultations and notifying updates" refers to a function that constantly monitors the progress of processing consultations and communicates the latest information to the consultant and the person seeking consultation. 【0166】 "Means for recognizing emotions and integrating corresponding emotional information into the analysis results" refers to a function that automatically identifies emotions from the information provided by the client and includes that emotional information in the analysis results of the consultation. 【0167】 "Means of providing appropriate response guidelines to those in charge" refers to a function that presents guidelines and recommended actions on how the person in charge should respond, based on the recognized emotional information and the content of the consultation. 【0168】 "A means of generating and providing feedback tailored to the client's psychological state" refers to a function that automatically creates and provides feedback and information optimized for the client's psychological state, based on recognized emotional information. 【0169】 A description of the embodiment for carrying out the invention will be given. 【0170】 This invention is a system that provides more accurate support to users when they seek advice regarding information security by analyzing the content of their consultations and recognizing their emotions. Users input their consultations via text or voice through a terminal. The terminal is responsible for transmitting the input data to the server. 【0171】 The server uses natural language processing techniques to analyze the received data. Specifically, it summarizes the data using systems such as the generative AI model OpenAI® and natural language processing libraries such as NLTK and spaCy. At the same time, it utilizes a TensorFlow-based emotion engine to recognize emotions from the user's input data. This recognized emotion information is then integrated into the analysis results. 【0172】 Based on the analysis results and recognized emotional information, the server searches for rules and guidelines related to the consultation content and provides the necessary information to the person in charge. Through this process, the person in charge of consultation can obtain appropriate guidance for responding to the user. In addition, by generating feedback that is empathetic to the feelings of the person in charge, personalized information can be provided to each individual person in charge. 【0173】 For example, if a user enters "I'm worried about how secure the new security protocol is," the server analyzes this information and summarizes it using a generative AI model. Additionally, an emotion engine detects the emotion of "anxiety" and integrates it into the analysis results. As a result, the server directs relevant information to the appropriate personnel and provides the user with reference materials to alleviate their anxiety. 【0174】 An example of a prompt is, "Please instruct us on what information to provide if the user expresses concern." Using this prompt makes it possible to provide more appropriate and personalized support. 【0175】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0176】 Step 1: 【0177】 The user enters their information security inquiry into the terminal. The user can type text using the keyboard or use voice input. This input data directly triggers the next process. The output of the input is temporarily stored on the terminal as text or audio data. 【0178】 Step 2: 【0179】 The terminal sends data entered by the user to the server. The data is delivered to the server in a digital format via network communication. The input is the data on the terminal, and the output is the data transferred to the server. This process prepares the data for the server to begin processing. 【0180】 Step 3: 【0181】 The server analyzes the received data using natural language processing techniques. It uses a generative AI model to summarize the input text and audio data and organize the information. Specifically, it analyzes language structure using Python libraries such as NLTK and spaCy. The input is the data transferred to the server, and the output is the analyzed summary result. 【0182】 Step 4: 【0183】 The server uses an emotion engine to recognize emotions from user input data. It employs a TensorFlow-based deep learning model to analyze the tone and intonation of speech and text. The input is the analyzed data, and the output is the recognized emotion information. Emotion identification occurs during this process. 【0184】 Step 5: 【0185】 The server integrates the analysis results and recognized emotional information to create a detailed summary of the consultation. In particular, if emotions such as "anxiety" or "doubt" are detected, the server generates a summary that emphasizes this information. The input is the analyzed summary and emotional information, and the output is the integrated result. This result becomes the information that will be used further. 【0186】 Step 6: 【0187】 The server searches a database of relevant rules and guidelines based on the integrated results and provides the consultant with the most relevant information. This involves using a search algorithm to extract highly relevant information. The input is integrated data, and the output is instructional information for the consultant. 【0188】 Step 7: 【0189】 The server generates and provides personalized feedback based on the client's emotional state. This process uses prompts to automatically generate the feedback content. For example, a prompt such as "Please specify what information to provide if the user expresses anxiety" might be used. The input is a request for emotionally-based feedback generation, and the output is the generated feedback. 【0190】 (Application Example 2) 【0191】 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". 【0192】 In information security consultations, it is challenging to appropriately understand the emotions of the person seeking advice and to provide responses tailored to their individual needs. Current systems have difficulty detecting emotions from text and audio, making it difficult to enhance the person's sense of security or provide appropriate feedback. Furthermore, the lack of emotion-based prioritization and guidelines for countermeasures hinders the ability to respond quickly and accurately to the person's anxieties and doubts. 【0193】 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. 【0194】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for detecting and highlighting emotions based on the analyzed information, and means for evaluating the priority of the consultation and setting response guidelines based on the emotional information. This makes it possible to quickly grasp the client's emotions and provide appropriate feedback and emotional care information. 【0195】 A "consultant" is an individual or organization that has questions or concerns regarding information security and seeks advice through the system. 【0196】 "Automatic analysis" means processing user-inputted information using artificial intelligence technology without requiring human intervention. 【0197】 "Means for generating summaries" refers to a function that performs the process of compressing information received from the client into a concise and easy-to-understand format. 【0198】 "Means for detecting and highlighting emotions" refers to a function that determines emotions from the user's text and voice data and reports them in a way that makes them particularly noticeable. 【0199】 "Means of prioritizing and setting response guidelines" refers to the process of determining the importance of a consultation based on the type and intensity of emotions involved, and then deciding on an appropriate response strategy. 【0200】 "Means of providing feedback and emotional care information" refers to a function that implements procedures to provide reassurance and useful information to the client in response to detected emotions. 【0201】 The system for implementing this invention begins with a user entering information security-related inquiries via a terminal such as a smartphone or computer. The text or voice input from the user to the terminal is immediately transmitted to the server. 【0202】 The server is equipped with natural language processing (NLP) technology and an emotion recognition engine, utilizing emotion analysis models from TensorFlow and OpenAI. First, the server analyzes the received consultation content using NLP technology and generates a summary. Next, the emotion recognition engine analyzes the wording, tone, and voice of the input data to detect the emotions of the person seeking advice. 【0203】 The detected emotional information is added to the summary, the priority of the consultation is evaluated, and a response guideline is set. If the emotion is strong, the content is highlighted and the consultant is notified immediately. The consultant uses the emotional information to provide the best possible response to the user. 【0204】 Furthermore, the server provides feedback and emotional care information to the client based on the emotion recognition results. This feedback is generated using a generative AI model and, for example, presents resources to provide reassurance if the user is feeling anxious. 【0205】 For example, if a user enters "I'm worried about whether the new security measures are effective," the server summarizes this and detects the emotion of "anxiety." Based on this result, the server instructs service personnel to "prioritize providing information to alleviate anxiety" and automatically provides the user with "reference materials to reassure them." 【0206】 An example of a prompt message would be: "If the user has recently expressed increased security concerns, what specific information or actions can you provide?" 【0207】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0208】 Step 1: 【0209】 Users input their information security concerns via text or voice through their smartphones or computers. The input data is immediately transmitted to the server by the device. The input consists of the user's questions or concerns, and the output indicates that the data has been successfully transferred to the server. 【0210】 Step 2: 【0211】 The server inputs received text or audio data into a natural language processing (NLP) model. Text data is converted directly, while audio data is converted to text using speech recognition technology. The server then analyzes the meaning of this data and generates a summary. The input is the user's raw text or audio data, and the output is summarized text data. 【0212】 Step 3: 【0213】 The server processes the summarized text through an emotion recognition engine to analyze the user's emotions. The emotion analysis model used is OpenAI's emotion analysis tool. The detected emotion information is determined from the tone and terminology of the text. The input is summarized text, and the output is an emotion label (e.g., "anxious" or "relieved"). 【0214】 Step 4: 【0215】 The server adds emotional information to the summary and uses an algorithm to evaluate priority and determine the priority of the consultation content. This allows for the creation of a response plan based on the intensity and urgency of the emotions. The input is a summary text with emotional labels attached, and the output is the consultation content with priority set. 【0216】 Step 5: 【0217】 The server notifies the assigned counselor of prioritized information. A response guideline is sent to the counselor via the notification system. This guideline includes the user's emotional state, which the counselor uses to select the appropriate response. The input is the prioritized consultation content, and the output is a notification to the counselor. 【0218】 Step 6: 【0219】 The server uses a generative AI model to automatically create and provide feedback and emotional support information to the client. This feedback is personalized based on emotions and includes content designed to alleviate the user's anxiety. Specific prompt sentences are input to the model, which generates appropriate feedback. The input consists of emotional information and prompt sentences, while the output is personalized feedback. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 [Second Embodiment] 【0224】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0225】 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. 【0226】 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). 【0227】 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. 【0228】 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. 【0229】 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). 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 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". 【0236】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting information security-related consultation details into a terminal. 【0237】 The user enters their specific consultation details as text through the terminal's interface. The entered information is immediately sent from the terminal to the server. On the server, the entered text data is automatically analyzed using natural language processing (NLP) technology. The server generates a text summary of the consultation and also searches a database for similar past cases. 【0238】 Furthermore, the server analyzes any ambiguities in the consultation and generates corrective measures. Ambiguities include, for example, unclear technical terms or areas where the content lacks sufficient confirmation. This process references relevant laws, guidelines, and internal regulations. Based on this reference information, it provides specific information such as the next steps the user should take and drafts of necessary application forms. 【0239】 The server then categorizes the inquiry into the appropriate category, searches the database for the most suitable contact person based on the category, and automatically assigns them. Each contact person is notified of the detailed information of the inquiry, along with any risk points they should be aware of. 【0240】 During the operational phase, the server continuously monitors the progress of the consultation and notifies the staff member and user of the progress each time there is an update. This allows users to always know what stage their consultation is at, and enables the staff member to efficiently prepare for the next steps. 【0241】 For example, if a user requests information about the risks associated with the application of a new security protocol, the server summarizes the request and provides a compilation of reference information, including legal risks and technical concerns related to the protocol. Simultaneously, it automatically distributes this request to the department responsible for formulating security policies and assists in generating a report analyzing the relevant risks. This enables efficient and effective information security consultation for both the user and the support staff. 【0242】 The following describes the processing flow. 【0243】 Step 1: 【0244】 The user enters specific details of their information security inquiry through the terminal's interface. The entered information is then sent from the terminal to the server by pressing the send button. 【0245】 Step 2: 【0246】 The server receives the consultation content sent from the terminal and analyzes it using natural language processing (NLP) technology. This analysis extracts a summary of the consultation content and key keywords. 【0247】 Step 3: 【0248】 The server uses the extracted keywords to search its database for similar past consultation cases. These search results are saved as examples that the consultant can refer to. 【0249】 Step 4: 【0250】 The server identifies ambiguities in the consultation based on the analysis results. Ambiguous sections include contextual inconsistencies and unclear technical terms. The server then generates corrective suggestions. 【0251】 Step 5: 【0252】 The server searches for relevant laws, guidelines, and internal regulations for reference information and provides it to the user, along with information on legal aspects and procedures as needed. 【0253】 Step 6: 【0254】 The server categorizes inquiries and selects the most suitable contact person from the database. The inquiry is then automatically assigned to this contact person. 【0255】 Step 7: 【0256】 The server notifies the person in charge of potential risk points and detailed information about the consultation. This information is displayed on the person in charge's dashboard screen. 【0257】 Step 8: 【0258】 The server monitors the progress of consultations in real time and notifies the staff and users of updates whenever there is progress. This ensures that all parties involved are always aware of the latest status. 【0259】 (Example 1) 【0260】 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." 【0261】 In information security consulting services, a significant challenge arises from the time and effort required to interpret the consultation content, gather relevant information, and assign it to the appropriate person. This makes it difficult to respond quickly to those seeking advice, leading to a decrease in the overall efficiency of the consulting service. 【0262】 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. 【0263】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for identifying ambiguities and suggesting corrective measures, and means for searching for and providing reference information from relevant laws, guidelines, and regulations. This enables a rapid understanding and response to the content of the consultation. 【0264】 A "consultant" is someone who seeks information or advice. 【0265】 "Automatically analyzing information" means analyzing received data using a program without requiring human intervention. 【0266】 "Generating a summary" means extracting the main points by condensing detailed information into a concise form. 【0267】 "Identifying ambiguities" means finding unclear or incomplete parts. 【0268】 "To propose corrective measures" means to suggest appropriate countermeasures or amendments to identified ambiguities or problems. 【0269】 "Searching for relevant laws, guidelines, and regulations" means finding information about specific standards or systems in databases or documents. 【0270】 "Classifying consultation content based on categories" means classifying and organizing the information received based on its nature and characteristics. 【0271】 "Automatically assigning a consultant" means that the system selects the most suitable person and assigns them to handle the task based on that selection. 【0272】 "Monitoring progress" means continuously observing how far a task or project is progressing. 【0273】 "Notifying about updates" means informing relevant parties about new data or changes. 【0274】 A "generative model" is an artificial intelligence technique trained to perform specific tasks based on large amounts of data. 【0275】 This system is designed to handle information security inquiries in an automated manner. Users input their information security questions and concerns through a terminal interface. The terminal immediately transmits this input information to the server. 【0276】 The server is implemented in programming languages ​​such as Python or Java, and utilizes natural language processing libraries such as TensorFlow and PyTorch to analyze the received text data. This analysis uses generative AI models (e.g., GPT-3 and BERT), enabling semantic understanding and summarization. 【0277】 Specifically, the server analyzes the consultation content and identifies ambiguous parts and unclear points. Then, to generate solutions for these problems, it refers to databases of relevant laws, guidelines, regulations, etc. After that, the system classifies the consultation content based on categories and automatically assigns the most suitable person in charge. Furthermore, it regularly monitors the progress of the consultation response and notifies the person in charge and the consultant of the updated information. 【0278】 As a specific example, consider the case where a user inputs a consultation saying "I want to conduct a security assessment of a new cloud service". The server analyzes this information, summarizes the relevant security criteria, and collects information on necessary legal matters and technical evaluation points. Then, based on that information, it notifies the person in charge of cloud security, and the person in charge can proceed with the evaluation based on the specified risk factors. 【0279】 Examples of prompt texts to be input into the generation AI model are as follows. 【0280】 "The user has consulted about 'wanting to know the risks associated with the application of a new security protocol'. Please summarize this content and generate information including legal risks and technical concerns related to the protocol. Also, prepare to distribute this information to the relevant departments." 【0281】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0282】 Step 1: 【0283】 The user inputs the consultation content into the interface of the terminal. The input data includes specific questions and requests regarding information security. This text input becomes the basic data for subsequent data processing. 【0284】 Step 2: 【0285】 The terminal securely sends the input data from the user to the server. Here, the input is the text data of the user's consultation content, and the output is the completion of data transfer to the server. The HTTPS protocol is used for transmission to maintain data security. 【0286】 Step 3: 【0287】 The server automatically analyzes the received text data. The input data is the user's consultation content, and this is analyzed using Natural Language Processing (NLP) technology. By leveraging generative AI models (such as GPT-3 and BERT), an output is obtained that extracts key points from the input content and generates a summary. 【0288】 Step 4: 【0289】 The server identifies ambiguous points from the analyzed data and generates a correction plan. Here, the input is the summary text, which is the output of the previous analysis step. By accessing legal and guideline databases and incorporating the necessary supplementary information, an output is obtained that generates a correction plan. 【0290】 Step 5: 【0291】 The server classifies the consultation content based on categories, and based on that information, searches for and assigns an appropriate consultant from the database. The input is the consultation data after analysis completion, and the output is the information of notification to the consultant and completion of assignment. 【0292】 Step 6: 【0293】 The server constantly monitors the progress of consultation handling, and when there are progress or updates, notifies the person in charge and the user. The input is the log information of the progress status, and the output is the completion of distribution of the progress report notification. An automatic notification system is used to enable relevant parties to grasp the situation in real time. 【0294】 (Application Example 1) 【0295】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0296】 In information security consulting services, it is crucial to provide an environment where users can receive prompt and accurate support. However, current systems often require manual information analysis and progress management, which is time-consuming and labor-intensive, thus necessitating increased efficiency. Furthermore, the lack of an interface that allows users to consult anytime, anywhere is a significant challenge. 【0297】 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. 【0298】 In this invention, the server includes a processing device that automatically analyzes information received from a client and generates a summary, a processing device that identifies ambiguities and proposes corrective actions, and a processing device that searches for and provides reference information from relevant norms, guidelines, and regulations. This enables users to receive appropriate support in real time via their mobile devices. 【0299】 A "consultant" is an individual or organization that uses the system to resolve questions or problems related to information security. 【0300】 "Information" refers to the data that will be analyzed based on the text data and content provided by the person seeking advice. 【0301】 "Analysis" is the process of analyzing received information to summarize it and identify ambiguities. 【0302】 A "summary" is a concise text that compiles the analyzed information and is provided to the client or the person in charge. 【0303】 "Ambiguous points" refer to unclear technical terms or missing information contained within the data. 【0304】 The "rectification plan" is a proposal that shows improvement measures and solutions for ambiguous points. 【0305】 The "specification" is a legally and industrially acceptable standard or criterion. 【0306】 The "guideline" is a guideline that shows policies and directions regarding information security. 【0307】 The "regulation" refers to the rules and procedures defined within an organization. 【0308】 The "reference information" is auxiliary information based on laws and guidelines, which serves as a reference for the actions to be taken by the consultee next. 【0309】 The "processing device" is a combination of hardware and software for executing a specific task. 【0310】 The "portable information terminal" is a portable electronic device such as a smartphone or a tablet. 【0311】 "Real time" means that processing is performed immediately and information is provided instantaneously. 【0312】 To implement this invention, an AI agent system that efficiently supports information security consultations is constructed. The system starts when a consultee inputs an information security consultation into a portable information terminal. 【0313】 Users first input specific information security-related inquiries as text using a mobile device such as a smartphone or tablet. This entered text data is then sent by the device to a cloud-based server. The server uses natural language processing (NLP) technology to analyze the input data and generate a summary. Natural language processing libraries such as Google Cloud Natural Language API and SpaCy are used in this process. 【0314】 Next, the server identifies ambiguities based on the analysis results and creates necessary corrective actions. These corrective action suggestions include a function to retrieve and provide reference information from relevant norms, guidelines, and regulations. This allows users to clearly understand what actions they should take next. 【0315】 Furthermore, the server classifies inquiries based on their categories and automatically assigns the most suitable consultant to each inquiry. The consultant is notified of the detailed information of the inquiry, along with any potential risk factors to be aware of. This function allows consultants to prepare to perform their duties efficiently. 【0316】 Furthermore, the server has a system in place to monitor the progress of consultations in real time and notify staff and clients of updates. This allows clients to always be aware of the status of their consultation and proceed to the next step with confidence. 【0317】 For example, if a company's IT manager has questions about the security risks associated with the introduction of a new system, they can input "What are the information leakage risks when introducing a new system?" into their terminal. The server will then quickly analyze the situation, generate specific suggestions including past similar cases and risk mitigation measures, and provide them to the user. An example of a prompt in this case would be, "Please generate advice based on past cases regarding the risks to be aware of when introducing a new system in terms of information security." 【0318】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0319】 Step 1: 【0320】 Users input specific information security-related inquiries as text using their mobile devices. The entered text data is then sent from the device to a cloud-based server. This registers the user's inquiry in the system. 【0321】 Step 2: 【0322】 The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to perform grammatical analysis and key phrase extraction, and then summarizes the input content. This process extracts the main points of the text data and generates a summarized version. 【0323】 Step 3: 【0324】 The server identifies ambiguities based on the analysis results. For the identified ambiguities, it refers to relevant norms, guidelines, and regulations to generate appropriate corrective actions. This provides concrete solutions to the problems the user is facing. 【0325】 Step 4: 【0326】 The server classifies inquiries based on categories and automatically assigns a suitable person to each category. Specifically, it compares the inquiry content with existing inquiry categories and searches the database for the most suitable person to select. The selected person receives detailed information about the inquiry, along with any potential risk factors to be aware of. 【0327】 Step 5: 【0328】 The server monitors the progress of consultations in real time and sends notifications to the staff member and the user whenever there is progress. The progress status is regularly updated in the management system on the server, and this information is automatically notified. This allows users to always know the current status of the consultation, and staff members to efficiently prepare for the next steps. 【0329】 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. 【0330】 To implement this invention, an emotion engine is integrated into an AI agent system that supports information security consultations. The system begins with the user inputting their information security consultation into a terminal. 【0331】 Users input detailed information about their consultation via their device, and the naturally expressed text and voice data are used for emotion recognition. The information entered by the user is immediately sent to the server by the device. 【0332】 The server first analyzes the received consultation content using natural language processing (NLP) techniques and generates a summary. Simultaneously, the server uses an emotion engine to detect emotions from the user's text and voice. Specifically, it determines emotions from the user's word choice, tone of speech, and intonation. 【0333】 The detected emotional information is added to the analysis results of the consultation. If the user is experiencing particularly strong doubts or anxieties, the server creates a summary that highlights those points and notifies the contact person, including the emotional information. 【0334】 Furthermore, this emotional information can be used to change the guidelines for how customer service representatives should respond. For example, if a user indicates anxiety, the system can be configured to recommend a more courteous and reassuring approach. 【0335】 Furthermore, the server can provide users with appropriate feedback and emotional care information based on the emotion recognition results. For example, if a user is experiencing high levels of stress, it can automatically suggest stress management advice and resources. 【0336】 For example, if a user texts, "I'm worried about how secure the new security protocol is," the server summarizes it, and the emotion engine detects the emotion of "anxiety." In this case, the server provides relevant laws and guidelines, instructs the support staff to "prioritize providing information to alleviate anxiety," and provides the user with "reference materials to reassure them." In this way, incorporating an emotion engine improves the quality of care for individual clients and enables more personalized support. 【0337】 The following describes the processing flow. 【0338】 Step 1: 【0339】 The user enters specific details of their information security concerns via a terminal. The entered information includes text and audio data designed to infer the user's emotions. 【0340】 Step 2: 【0341】 The terminal transmits the user's input, along with audio data, to the server in real time. 【0342】 Step 3: 【0343】 The server analyzes the received data using natural language processing (NLP) techniques. It generates a summary from the text data and extracts important keywords. 【0344】 Step 4: 【0345】 The server simultaneously uses an emotion engine to recognize the user's emotional state. Specifically, it analyzes characteristics such as word choice, tone of voice, speed, and intonation to classify the user's emotions into categories such as "anxiety," "relief," and "excitement." 【0346】 Step 5: 【0347】 The server integrates the generated summary and sentiment information and compiles it into an analysis result. This information helps clarify the content of the consultation and is used for subsequent processing. 【0348】 Step 6: 【0349】 The server classifies the consultation content into the appropriate category based on the analysis results and automatically assigns the most suitable contact person from the database. During this process, it also considers emotional information and instructs the contact person on the appropriate course of action. 【0350】 Step 7: 【0351】 The server notifies the person in charge of the details of the consultation and emotional information, along with any risk points to be aware of and other relevant information. This notification allows the person in charge to choose the appropriate course of action. 【0352】 Step 8: 【0353】 The server monitors the progress of ongoing consultations and notifies the staff member and user of any updates. It also improves the user experience by providing feedback and emotional care information that takes the user's emotional state into consideration. 【0354】 (Example 2) 【0355】 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". 【0356】 In today's information society, the information security issues faced by those seeking advice are becoming increasingly complex and diverse. In this context, it is essential to accurately understand not only the specific content of the consultation but also the psychological state of the person seeking advice, and to provide support optimized for each individual. However, conventional systems are insufficient in recognizing and responding to emotions, often resulting in a uniform quality of support for all clients. Therefore, the challenge lies in achieving flexible and accurate support that takes into account the emotions of the person seeking advice. 【0357】 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. 【0358】 In this invention, the server includes means for automatically analyzing and summarizing information received from the client, means for recognizing emotions from the received information and integrating the corresponding emotional information into the analysis results, and means for providing appropriate response guidelines to the person in charge based on the recognized emotional information. This makes it possible to provide personalized support that is tailored to the client's emotions. 【0359】 "Means for automatically analyzing and summarizing received information" refers to a function in which a processing device automatically processes information provided by a client using a program and generates data that summarizes its contents. 【0360】 "Means for identifying ambiguities and proposing corrective measures" refers to a function that identifies unclear or uncertain parts within the analyzed information and proposes concrete plans for improvement or resolution. 【0361】 "Means for searching and providing reference information from relevant rules, guidelines, and policies" refers to a function that investigates laws, standards, and guidelines related to the consultation content from information resources and provides them to the user as useful information. 【0362】 "A means of classifying based on categories and automatically assigning a consultant" refers to a function that classifies consultation content according to pre-set categories and allows the system to automatically select the most suitable consultant to handle the situation. 【0363】 "A means of monitoring the progress of consultations and notifying updates" refers to a function that constantly monitors the progress of processing consultations and communicates the latest information to the consultant and the person seeking consultation. 【0364】 "Means for recognizing emotions and integrating corresponding emotional information into the analysis results" refers to a function that automatically identifies emotions from the information provided by the client and includes that emotional information in the analysis results of the consultation. 【0365】 "Means of providing appropriate response guidelines to those in charge" refers to a function that presents guidelines and recommended actions on how the person in charge should respond, based on the recognized emotional information and the content of the consultation. 【0366】 "A means of generating and providing feedback tailored to the client's psychological state" refers to a function that automatically creates and provides feedback and information optimized for the client's psychological state, based on recognized emotional information. 【0367】 A description of the embodiment for carrying out the invention will be given. 【0368】 This invention is a system that provides more accurate support to users when they seek advice regarding information security by analyzing the content of their consultations and recognizing their emotions. Users input their consultations via text or voice through a terminal. The terminal is responsible for transmitting the input data to the server. 【0369】 The server uses natural language processing techniques to analyze the received data. Specifically, it summarizes the data using OpenAI, a generative AI model, and natural language processing libraries such as NLTK and spaCy. At the same time, it utilizes a TensorFlow-based emotion engine to recognize emotions from the user's input data. This recognized emotion information is then integrated into the analysis results. 【0370】 Based on the analysis results and recognized emotional information, the server searches for rules and guidelines related to the consultation content and provides the necessary information to the person in charge. Through this process, the person in charge of consultation can obtain appropriate guidance for responding to the user. In addition, by generating feedback that is empathetic to the feelings of the person in charge, personalized information can be provided to each individual person in charge. 【0371】 For example, if a user enters "I'm worried about how secure the new security protocol is," the server analyzes this information and summarizes it using a generative AI model. Additionally, an emotion engine detects the emotion of "anxiety" and integrates it into the analysis results. As a result, the server directs relevant information to the appropriate personnel and provides the user with reference materials to alleviate their anxiety. 【0372】 An example of a prompt is, "Please instruct us on what information to provide if the user expresses concern." Using this prompt makes it possible to provide more appropriate and personalized support. 【0373】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0374】 Step 1: 【0375】 The user enters their information security inquiry into the terminal. The user can type text using the keyboard or use voice input. This input data directly triggers the next process. The output of the input is temporarily stored on the terminal as text or audio data. 【0376】 Step 2: 【0377】 The terminal sends data entered by the user to the server. The data is delivered to the server in a digital format via network communication. The input is the data on the terminal, and the output is the data transferred to the server. This process prepares the data for the server to begin processing. 【0378】 Step 3: 【0379】 The server analyzes the received data using natural language processing techniques. It uses a generative AI model to summarize the input text and audio data and organize the information. Specifically, it analyzes language structure using Python libraries such as NLTK and spaCy. The input is the data transferred to the server, and the output is the analyzed summary result. 【0380】 Step 4: 【0381】 The server uses an emotion engine to recognize emotions from user input data. It employs a TensorFlow-based deep learning model to analyze the tone and intonation of speech and text. The input is the analyzed data, and the output is the recognized emotion information. Emotion identification occurs during this process. 【0382】 Step 5: 【0383】 The server integrates the analysis results and recognized emotional information to create a detailed summary of the consultation. In particular, if emotions such as "anxiety" or "doubt" are detected, the server generates a summary that emphasizes this information. The input is the analyzed summary and emotional information, and the output is the integrated result. This result becomes the information that will be used further. 【0384】 Step 6: 【0385】 The server searches a database of relevant rules and guidelines based on the integrated results and provides the consultant with the most relevant information. This involves using a search algorithm to extract highly relevant information. The input is integrated data, and the output is instructional information for the consultant. 【0386】 Step 7: 【0387】 The server generates and provides personalized feedback based on the client's emotional state. This process uses prompts to automatically generate the feedback content. For example, a prompt such as "Please specify what information to provide if the user expresses anxiety" might be used. The input is a request for emotionally-based feedback generation, and the output is the generated feedback. 【0388】 (Application Example 2) 【0389】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0390】 In information security consultations, it is challenging to appropriately understand the emotions of the person seeking advice and to provide responses tailored to their individual needs. Current systems have difficulty detecting emotions from text and audio, making it difficult to enhance the person's sense of security or provide appropriate feedback. Furthermore, the lack of emotion-based prioritization and guidelines for countermeasures hinders the ability to respond quickly and accurately to the person's anxieties and doubts. 【0391】 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. 【0392】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for detecting and highlighting emotions based on the analyzed information, and means for evaluating the priority of the consultation and setting response guidelines based on the emotional information. This makes it possible to quickly grasp the client's emotions and provide appropriate feedback and emotional care information. 【0393】 A "consultant" is an individual or organization that has questions or concerns regarding information security and seeks advice through the system. 【0394】 "Automatic analysis" means processing user-inputted information using artificial intelligence technology without requiring human intervention. 【0395】 "Means for generating summaries" refers to a function that performs the process of compressing information received from the client into a concise and easy-to-understand format. 【0396】 "Means for detecting and highlighting emotions" refers to a function that determines emotions from the user's text and voice data and reports them in a way that makes them particularly noticeable. 【0397】 "Means of prioritizing and setting response guidelines" refers to the process of determining the importance of a consultation based on the type and intensity of emotions involved, and then deciding on an appropriate response strategy. 【0398】 "Means of providing feedback and emotional care information" refers to a function that implements procedures to provide reassurance and useful information to the client in response to detected emotions. 【0399】 The system for implementing this invention begins with a user entering information security-related inquiries via a terminal such as a smartphone or computer. The text or voice input from the user to the terminal is immediately transmitted to the server. 【0400】 The server is equipped with natural language processing (NLP) technology and an emotion recognition engine, utilizing emotion analysis models from TensorFlow and OpenAI. First, the server analyzes the received consultation content using NLP technology and generates a summary. Next, the emotion recognition engine analyzes the wording, tone, and voice of the input data to detect the emotions of the person seeking advice. 【0401】 The detected emotional information is added to the summary, the priority of the consultation is evaluated, and a response guideline is set. If the emotion is strong, the content is highlighted and the consultant is notified immediately. The consultant uses the emotional information to provide the best possible response to the user. 【0402】 Furthermore, the server provides feedback and emotional care information to the client based on the emotion recognition results. This feedback is generated using a generative AI model and, for example, presents resources to provide reassurance if the user is feeling anxious. 【0403】 For example, if a user enters "I'm worried about whether the new security measures are effective," the server summarizes this and detects the emotion of "anxiety." Based on this result, the server instructs service personnel to "prioritize providing information to alleviate anxiety" and automatically provides the user with "reference materials to reassure them." 【0404】 An example of a prompt message would be: "If the user has recently expressed increased security concerns, what specific information or actions can you provide?" 【0405】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0406】 Step 1: 【0407】 Users input their information security concerns via text or voice through their smartphones or computers. The input data is immediately transmitted to the server by the device. The input consists of the user's questions or concerns, and the output indicates that the data has been successfully transferred to the server. 【0408】 Step 2: 【0409】 The server inputs received text or audio data into a natural language processing (NLP) model. Text data is converted directly, while audio data is converted to text using speech recognition technology. The server then analyzes the meaning of this data and generates a summary. The input is the user's raw text or audio data, and the output is summarized text data. 【0410】 Step 3: 【0411】 The server processes the summarized text through an emotion recognition engine to analyze the user's emotions. The emotion analysis model used is OpenAI's emotion analysis tool. The detected emotion information is determined from the tone and terminology of the text. The input is summarized text, and the output is an emotion label (e.g., "anxious" or "relieved"). 【0412】 Step 4: 【0413】 The server adds emotional information to the summary and uses an algorithm to evaluate priority and determine the priority of the consultation content. This allows for the creation of a response plan based on the intensity and urgency of the emotions. The input is a summary text with emotional labels attached, and the output is the consultation content with priority set. 【0414】 Step 5: 【0415】 The server notifies the assigned counselor of prioritized information. A response guideline is sent to the counselor via the notification system. This guideline includes the user's emotional state, which the counselor uses to select the appropriate response. The input is the prioritized consultation content, and the output is a notification to the counselor. 【0416】 Step 6: 【0417】 The server uses a generative AI model to automatically create and provide feedback and emotional support information to the client. This feedback is personalized based on emotions and includes content designed to alleviate the user's anxiety. Specific prompt sentences are input to the model, which generates appropriate feedback. The input consists of emotional information and prompt sentences, while the output is personalized feedback. 【0418】 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. 【0419】 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. 【0420】 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. 【0421】 [Third Embodiment] 【0422】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0423】 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. 【0424】 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). 【0425】 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. 【0426】 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. 【0427】 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). 【0428】 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. 【0429】 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. 【0430】 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. 【0431】 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. 【0432】 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. 【0433】 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". 【0434】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting information security-related consultation details into a terminal. 【0435】 The user enters their specific consultation details as text through the terminal's interface. The entered information is immediately sent from the terminal to the server. On the server, the entered text data is automatically analyzed using natural language processing (NLP) technology. The server generates a text summary of the consultation and also searches a database for similar past cases. 【0436】 Furthermore, the server analyzes any ambiguities in the consultation and generates corrective measures. Ambiguities include, for example, unclear technical terms or areas where the content lacks sufficient confirmation. This process references relevant laws, guidelines, and internal regulations. Based on this reference information, it provides specific information such as the next steps the user should take and drafts of necessary application forms. 【0437】 The server then categorizes the inquiry into the appropriate category, searches the database for the most suitable contact person based on the category, and automatically assigns them. Each contact person is notified of the detailed information of the inquiry, along with any risk points they should be aware of. 【0438】 During the operational phase, the server continuously monitors the progress of the consultation and notifies the staff member and user of the progress each time there is an update. This allows users to always know what stage their consultation is at, and enables the staff member to efficiently prepare for the next steps. 【0439】 For example, if a user requests information about the risks associated with the application of a new security protocol, the server summarizes the request and provides a compilation of reference information, including legal risks and technical concerns related to the protocol. Simultaneously, it automatically distributes this request to the department responsible for formulating security policies and assists in generating a report analyzing the relevant risks. This enables efficient and effective information security consultation for both the user and the support staff. 【0440】 The following describes the processing flow. 【0441】 Step 1: 【0442】 The user enters specific details of their information security inquiry through the terminal's interface. The entered information is then sent from the terminal to the server by pressing the send button. 【0443】 Step 2: 【0444】 The server receives the consultation content sent from the terminal and analyzes it using natural language processing (NLP) technology. This analysis extracts a summary of the consultation content and key keywords. 【0445】 Step 3: 【0446】 The server uses the extracted keywords to search its database for similar past consultation cases. These search results are saved as examples that the consultant can refer to. 【0447】 Step 4: 【0448】 The server identifies ambiguities in the consultation based on the analysis results. Ambiguous sections include contextual inconsistencies and unclear technical terms. The server then generates corrective suggestions. 【0449】 Step 5: 【0450】 The server searches for relevant laws, guidelines, and internal regulations for reference information and provides it to the user, along with information on legal aspects and procedures as needed. 【0451】 Step 6: 【0452】 The server categorizes inquiries and selects the most suitable contact person from the database. The inquiry is then automatically assigned to this contact person. 【0453】 Step 7: 【0454】 The server notifies the person in charge of potential risk points and detailed information about the consultation. This information is displayed on the person in charge's dashboard screen. 【0455】 Step 8: 【0456】 The server monitors the progress of consultations in real time and notifies the staff and users of updates whenever there is progress. This ensures that all parties involved are always aware of the latest status. 【0457】 (Example 1) 【0458】 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." 【0459】 In information security consulting services, a significant challenge arises from the time and effort required to interpret the consultation content, gather relevant information, and assign it to the appropriate person. This makes it difficult to respond quickly to those seeking advice, leading to a decrease in the overall efficiency of the consulting service. 【0460】 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. 【0461】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for identifying ambiguities and suggesting corrective measures, and means for searching for and providing reference information from relevant laws, guidelines, and regulations. This enables a rapid understanding and response to the content of the consultation. 【0462】 A "consultant" is someone who seeks information or advice. 【0463】 "Automatically analyzing information" means analyzing received data using a program without requiring human intervention. 【0464】 "Generating a summary" means extracting the main points by condensing detailed information into a concise form. 【0465】 "Identifying ambiguities" means finding unclear or incomplete parts. 【0466】 "To propose corrective measures" means to suggest appropriate countermeasures or amendments to identified ambiguities or problems. 【0467】 "Searching for relevant laws, guidelines, and regulations" means finding information about specific standards or systems in databases or documents. 【0468】 "Classifying consultation content based on categories" means classifying and organizing the information received based on its nature and characteristics. 【0469】 "Automatically assigning a consultant" means that the system selects the most suitable person and assigns them to handle the task based on that selection. 【0470】 "Monitoring progress" means continuously observing how far a task or project is progressing. 【0471】 "Notifying about updates" means informing relevant parties about new data or changes. 【0472】 A "generative model" is an artificial intelligence technique trained to perform specific tasks based on large amounts of data. 【0473】 This system is designed to handle information security inquiries in an automated manner. Users input their information security questions and concerns through a terminal interface. The terminal immediately transmits this input information to the server. 【0474】 The server is implemented in programming languages ​​such as Python or Java, and utilizes natural language processing libraries such as TensorFlow and PyTorch to analyze the received text data. This analysis uses generative AI models (e.g., GPT-3 and BERT), enabling semantic understanding and summarization. 【0475】 Specifically, the server analyzes the consultation content, identifying ambiguous or unclear points. It then consults databases of relevant laws, guidelines, and regulations to generate corrective solutions for these issues. The system then categorizes the consultation content and automatically assigns the most suitable person to handle it. Furthermore, it regularly monitors the progress of the consultation and notifies the person in charge and the person making the inquiry of any updates. 【0476】 As a concrete example, consider a case where a user submits a request for a security assessment of a new cloud service. The server analyzes this information, summarizes the relevant security standards, and collects information on necessary legal matters and technical evaluation points. Based on this information, it notifies the cloud security officer, who can then proceed with the assessment based on the specified risk factors. 【0477】 Examples of prompt statements to input into the generative AI model are as follows: 【0478】 "A user has requested to know about the risks associated with the implementation of a new security protocol. Please summarize this request and generate information that includes the legal risks and technical concerns related to the protocol. Also, please prepare to distribute this information to the relevant departments." 【0479】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0480】 Step 1: 【0481】 The user enters their inquiry into the terminal interface. This input data includes specific questions and requests regarding information security. This text input serves as the foundational data for subsequent data processing. 【0482】 Step 2: 【0483】 The terminal securely transmits user input data to the server. The input here is text data of the user's inquiry, and the output is the completion of the data transfer to the server. The HTTPS protocol is used for transmission to maintain data security. 【0484】 Step 3: 【0485】 The server automatically analyzes the received text data. The input data consists of the user's consultation content, which is analyzed using Natural Language Processing (NLP) technology. Generative AI models (such as GPT-3 and BERT) are utilized to extract key points from the input content and generate a summary output. 【0486】 Step 4: 【0487】 The server identifies ambiguities from the analyzed data and generates corrective action plans. The input here is the summarized text, which is the output of the previous analysis step. By accessing legal and guideline databases and incorporating necessary supplementary information, the server obtains output that generates corrective action plans. 【0488】 Step 5: 【0489】 The server categorizes consultations based on their content and then searches the database for and assigns the appropriate consultant based on that information. The input is the consultation data after analysis is complete, and the output is the notification to the consultant and information that the assignment is complete. 【0490】 Step 6: 【0491】 The server constantly monitors the progress of consultations and notifies the person in charge and the user when there are any developments or updates. Input is progress log information, and output is the completion of the delivery of progress report notifications. An automated notification system is used to ensure that stakeholders are aware of the situation in real time. 【0492】 (Application Example 1) 【0493】 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." 【0494】 In information security consulting services, it is crucial to provide an environment where users can receive prompt and accurate support. However, current systems often require manual information analysis and progress management, which is time-consuming and labor-intensive, thus necessitating increased efficiency. Furthermore, the lack of an interface that allows users to consult anytime, anywhere is a significant challenge. 【0495】 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. 【0496】 In this invention, the server includes a processing device that automatically analyzes information received from a client and generates a summary, a processing device that identifies ambiguities and proposes corrective actions, and a processing device that searches for and provides reference information from relevant norms, guidelines, and regulations. This enables users to receive appropriate support in real time via their mobile devices. 【0497】 A "consultant" is an individual or organization that uses the system to resolve questions or problems related to information security. 【0498】 "Information" refers to the data that will be analyzed based on the text data and content provided by the person seeking advice. 【0499】 "Analysis" is the process of analyzing received information to summarize it and identify ambiguities. 【0500】 A "summary" is a concise text that compiles the analyzed information and is provided to the client or the person in charge. 【0501】 "Ambiguous points" refer to unclear technical terms or missing information contained within the data. 【0502】 A "corrective proposal" is a suggestion that outlines improvements or solutions to ambiguous points. 【0503】 A "norm" is a legally and industryally accepted standard or criterion. 【0504】 A "guideline" is a set of guidelines that outlines policies and directions regarding information security. 【0505】 "Regulations" refer to the rules and procedures established within an organization. 【0506】 "Reference information" refers to supplementary information based on laws and guidelines, which serves as a guide for the next course of action the person seeking advice should take. 【0507】 A "processing unit" is a combination of hardware and software designed to perform a specific task. 【0508】 A "personal digital assistant" (PDA) is a portable electronic device such as a smartphone or tablet. 【0509】 "Real-time" refers to processing that takes place immediately and information being provided instantaneously. 【0510】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting their information security consultation into a mobile device. 【0511】 Users first input specific information security-related inquiries as text using a mobile device such as a smartphone or tablet. This entered text data is then sent by the device to a cloud-based server. The server uses natural language processing (NLP) technology to analyze the input data and generate a summary. Natural language processing libraries such as Google Cloud Natural Language API and SpaCy are used in this process. 【0512】 Next, the server identifies ambiguities based on the analysis results and creates necessary corrective actions. These corrective action suggestions include a function to retrieve and provide reference information from relevant norms, guidelines, and regulations. This allows users to clearly understand what actions they should take next. 【0513】 Furthermore, the server classifies inquiries based on their categories and automatically assigns the most suitable consultant to each inquiry. The consultant is notified of the detailed information of the inquiry, along with any potential risk factors to be aware of. This function allows consultants to prepare to perform their duties efficiently. 【0514】 Furthermore, the server has a system in place to monitor the progress of consultations in real time and notify staff and clients of updates. This allows clients to always be aware of the status of their consultation and proceed to the next step with confidence. 【0515】 For example, if a company's IT manager has questions about the security risks associated with the introduction of a new system, they can input "What are the information leakage risks when introducing a new system?" into their terminal. The server will then quickly analyze the situation, generate specific suggestions including past similar cases and risk mitigation measures, and provide them to the user. An example of a prompt in this case would be, "Please generate advice based on past cases regarding the risks to be aware of when introducing a new system in terms of information security." 【0516】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0517】 Step 1: 【0518】 Users input specific information security-related inquiries as text using their mobile devices. The entered text data is then sent from the device to a cloud-based server. This registers the user's inquiry in the system. 【0519】 Step 2: 【0520】 The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to perform grammatical analysis and key phrase extraction, and then summarizes the input content. This process extracts the main points of the text data and generates a summarized version. 【0521】 Step 3: 【0522】 The server identifies ambiguities based on the analysis results. For the identified ambiguities, it refers to relevant norms, guidelines, and regulations to generate appropriate corrective actions. This provides concrete solutions to the problems the user is facing. 【0523】 Step 4: 【0524】 The server classifies inquiries based on categories and automatically assigns a suitable person to each category. Specifically, it compares the inquiry content with existing inquiry categories and searches the database for the most suitable person to select. The selected person receives detailed information about the inquiry, along with any potential risk factors to be aware of. 【0525】 Step 5: 【0526】 The server monitors the progress of consultations in real time and sends notifications to the staff member and the user whenever there is progress. The progress status is regularly updated in the management system on the server, and this information is automatically notified. This allows users to always know the current status of the consultation, and staff members to efficiently prepare for the next steps. 【0527】 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. 【0528】 To implement this invention, an emotion engine is integrated into an AI agent system that supports information security consultations. The system begins with the user inputting their information security consultation into a terminal. 【0529】 Users input detailed information about their consultation via their device, and the naturally expressed text and voice data are used for emotion recognition. The information entered by the user is immediately sent to the server by the device. 【0530】 The server first analyzes the received consultation content using natural language processing (NLP) techniques and generates a summary. Simultaneously, the server uses an emotion engine to detect emotions from the user's text and voice. Specifically, it determines emotions from the user's word choice, tone of speech, and intonation. 【0531】 The detected emotional information is added to the analysis results of the consultation. If the user is experiencing particularly strong doubts or anxieties, the server creates a summary that highlights those points and notifies the contact person, including the emotional information. 【0532】 Furthermore, this emotional information can be used to change the guidelines for how customer service representatives should respond. For example, if a user indicates anxiety, the system can be configured to recommend a more courteous and reassuring approach. 【0533】 Furthermore, the server can provide users with appropriate feedback and emotional care information based on the emotion recognition results. For example, if a user is experiencing high levels of stress, it can automatically suggest stress management advice and resources. 【0534】 For example, if a user texts, "I'm worried about how secure the new security protocol is," the server summarizes it, and the emotion engine detects the emotion of "anxiety." In this case, the server provides relevant laws and guidelines, instructs the support staff to "prioritize providing information to alleviate anxiety," and provides the user with "reference materials to reassure them." In this way, incorporating an emotion engine improves the quality of care for individual clients and enables more personalized support. 【0535】 The following describes the processing flow. 【0536】 Step 1: 【0537】 The user enters specific details of their information security concerns via a terminal. The entered information includes text and audio data designed to infer the user's emotions. 【0538】 Step 2: 【0539】 The terminal transmits the user's input, along with audio data, to the server in real time. 【0540】 Step 3: 【0541】 The server analyzes the received data using natural language processing (NLP) techniques. It generates a summary from the text data and extracts important keywords. 【0542】 Step 4: 【0543】 The server simultaneously uses an emotion engine to recognize the user's emotional state. Specifically, it analyzes characteristics such as word choice, tone of voice, speed, and intonation to classify the user's emotions into categories such as "anxiety," "relief," and "excitement." 【0544】 Step 5: 【0545】 The server integrates the generated summary and sentiment information and compiles it into an analysis result. This information helps clarify the content of the consultation and is used for subsequent processing. 【0546】 Step 6: 【0547】 The server classifies the consultation content into the appropriate category based on the analysis results and automatically assigns the most suitable contact person from the database. During this process, it also considers emotional information and instructs the contact person on the appropriate course of action. 【0548】 Step 7: 【0549】 The server notifies the person in charge of the details of the consultation and emotional information, along with any risk points to be aware of and other relevant information. This notification allows the person in charge to choose the appropriate course of action. 【0550】 Step 8: 【0551】 The server monitors the progress of ongoing consultations and notifies the staff member and user of any updates. It also improves the user experience by providing feedback and emotional care information that takes the user's emotional state into consideration. 【0552】 (Example 2) 【0553】 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." 【0554】 In today's information society, the information security issues faced by those seeking advice are becoming increasingly complex and diverse. In this context, it is essential to accurately understand not only the specific content of the consultation but also the psychological state of the person seeking advice, and to provide support optimized for each individual. However, conventional systems are insufficient in recognizing and responding to emotions, often resulting in a uniform quality of support for all clients. Therefore, the challenge lies in achieving flexible and accurate support that takes into account the emotions of the person seeking advice. 【0555】 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. 【0556】 In this invention, the server includes means for automatically analyzing and summarizing information received from the client, means for recognizing emotions from the received information and integrating the corresponding emotional information into the analysis results, and means for providing appropriate response guidelines to the person in charge based on the recognized emotional information. This makes it possible to provide personalized support that is tailored to the client's emotions. 【0557】 "Means for automatically analyzing and summarizing received information" refers to a function in which a processing device automatically processes information provided by a client using a program and generates data that summarizes its contents. 【0558】 "Means for identifying ambiguities and proposing corrective measures" refers to a function that identifies unclear or uncertain parts within the analyzed information and proposes concrete plans for improvement or resolution. 【0559】 "Means for searching and providing reference information from relevant rules, guidelines, and policies" refers to a function that investigates laws, standards, and guidelines related to the consultation content from information resources and provides them to the user as useful information. 【0560】 "A means of classifying based on categories and automatically assigning a consultant" refers to a function that classifies consultation content according to pre-set categories and allows the system to automatically select the most suitable consultant to handle the situation. 【0561】 "A means of monitoring the progress of consultations and notifying updates" refers to a function that constantly monitors the progress of processing consultations and communicates the latest information to the consultant and the person seeking consultation. 【0562】 "Means for recognizing emotions and integrating corresponding emotional information into the analysis results" refers to a function that automatically identifies emotions from the information provided by the client and includes that emotional information in the analysis results of the consultation. 【0563】 "Means of providing appropriate response guidelines to those in charge" refers to a function that presents guidelines and recommended actions on how the person in charge should respond, based on the recognized emotional information and the content of the consultation. 【0564】 "A means of generating and providing feedback tailored to the client's psychological state" refers to a function that automatically creates and provides feedback and information optimized for the client's psychological state, based on recognized emotional information. 【0565】 A description of the embodiment for carrying out the invention will be given. 【0566】 This invention is a system that provides more accurate support to users when they seek advice regarding information security by analyzing the content of their consultations and recognizing their emotions. Users input their consultations via text or voice through a terminal. The terminal is responsible for transmitting the input data to the server. 【0567】 The server uses natural language processing techniques to analyze the received data. Specifically, it summarizes the data using OpenAI, a generative AI model, and natural language processing libraries such as NLTK and spaCy. At the same time, it utilizes a TensorFlow-based emotion engine to recognize emotions from the user's input data. This recognized emotion information is then integrated into the analysis results. 【0568】 Based on the analysis results and recognized emotional information, the server searches for rules and guidelines related to the consultation content and provides the necessary information to the person in charge. Through this process, the person in charge of consultation can obtain appropriate guidance for responding to the user. In addition, by generating feedback that is empathetic to the feelings of the person in charge, personalized information can be provided to each individual person in charge. 【0569】 For example, if a user enters "I'm worried about how secure the new security protocol is," the server analyzes this information and summarizes it using a generative AI model. Additionally, an emotion engine detects the emotion of "anxiety" and integrates it into the analysis results. As a result, the server directs relevant information to the appropriate personnel and provides the user with reference materials to alleviate their anxiety. 【0570】 An example of a prompt is, "Please instruct us on what information to provide if the user expresses concern." Using this prompt makes it possible to provide more appropriate and personalized support. 【0571】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0572】 Step 1: 【0573】 The user enters their information security inquiry into the terminal. The user can type text using the keyboard or use voice input. This input data directly triggers the next process. The output of the input is temporarily stored on the terminal as text or audio data. 【0574】 Step 2: 【0575】 The terminal sends data entered by the user to the server. The data is delivered to the server in a digital format via network communication. The input is the data on the terminal, and the output is the data transferred to the server. This process prepares the data for the server to begin processing. 【0576】 Step 3: 【0577】 The server analyzes the received data using natural language processing techniques. It uses a generative AI model to summarize the input text and audio data and organize the information. Specifically, it analyzes language structure using Python libraries such as NLTK and spaCy. The input is the data transferred to the server, and the output is the analyzed summary result. 【0578】 Step 4: 【0579】 The server uses an emotion engine to recognize emotions from user input data. It employs a TensorFlow-based deep learning model to analyze the tone and intonation of speech and text. The input is the analyzed data, and the output is the recognized emotion information. Emotion identification occurs during this process. 【0580】 Step 5: 【0581】 The server integrates the analysis results and recognized emotional information to create a detailed summary of the consultation. In particular, if emotions such as "anxiety" or "doubt" are detected, the server generates a summary that emphasizes this information. The input is the analyzed summary and emotional information, and the output is the integrated result. This result becomes the information that will be used further. 【0582】 Step 6: 【0583】 The server searches a database of relevant rules and guidelines based on the integrated results and provides the consultant with the most relevant information. This involves using a search algorithm to extract highly relevant information. The input is integrated data, and the output is instructional information for the consultant. 【0584】 Step 7: 【0585】 The server generates and provides personalized feedback based on the client's emotional state. This process uses prompts to automatically generate the feedback content. For example, a prompt such as "Please specify what information to provide if the user expresses anxiety" might be used. The input is a request for emotionally-based feedback generation, and the output is the generated feedback. 【0586】 (Application Example 2) 【0587】 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." 【0588】 In information security consultations, it is challenging to appropriately understand the emotions of the person seeking advice and to provide responses tailored to their individual needs. Current systems have difficulty detecting emotions from text and audio, making it difficult to enhance the person's sense of security or provide appropriate feedback. Furthermore, the lack of emotion-based prioritization and guidelines for countermeasures hinders the ability to respond quickly and accurately to the person's anxieties and doubts. 【0589】 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. 【0590】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for detecting and highlighting emotions based on the analyzed information, and means for evaluating the priority of the consultation and setting response guidelines based on the emotional information. This makes it possible to quickly grasp the client's emotions and provide appropriate feedback and emotional care information. 【0591】 A "consultant" is an individual or organization that has questions or concerns regarding information security and seeks advice through the system. 【0592】 "Automatic analysis" means processing user-inputted information using artificial intelligence technology without requiring human intervention. 【0593】 "Means for generating summaries" refers to a function that performs the process of compressing information received from the client into a concise and easy-to-understand format. 【0594】 "Means for detecting and highlighting emotions" refers to a function that determines emotions from the user's text and voice data and reports them in a way that makes them particularly noticeable. 【0595】 "Means of prioritizing and setting response guidelines" refers to the process of determining the importance of a consultation based on the type and intensity of emotions involved, and then deciding on an appropriate response strategy. 【0596】 "Means of providing feedback and emotional care information" refers to a function that implements procedures to provide reassurance and useful information to the client in response to detected emotions. 【0597】 The system for implementing this invention begins with a user entering information security-related inquiries via a terminal such as a smartphone or computer. The text or voice input from the user to the terminal is immediately transmitted to the server. 【0598】 The server is equipped with natural language processing (NLP) technology and an emotion recognition engine, utilizing emotion analysis models from TensorFlow and OpenAI. First, the server analyzes the received consultation content using NLP technology and generates a summary. Next, the emotion recognition engine analyzes the wording, tone, and voice of the input data to detect the emotions of the person seeking advice. 【0599】 The detected emotional information is added to the summary, the priority of the consultation is evaluated, and a response guideline is set. If the emotion is strong, the content is highlighted and the consultant is notified immediately. The consultant uses the emotional information to provide the best possible response to the user. 【0600】 Furthermore, the server provides feedback and emotional care information to the client based on the emotion recognition results. This feedback is generated using a generative AI model and, for example, presents resources to provide reassurance if the user is feeling anxious. 【0601】 For example, if a user enters "I'm worried about whether the new security measures are effective," the server summarizes this and detects the emotion of "anxiety." Based on this result, the server instructs service personnel to "prioritize providing information to alleviate anxiety" and automatically provides the user with "reference materials to reassure them." 【0602】 An example of a prompt message would be: "If the user has recently expressed increased security concerns, what specific information or actions can you provide?" 【0603】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0604】 Step 1: 【0605】 Users input their information security concerns via text or voice through their smartphones or computers. The input data is immediately transmitted to the server by the device. The input consists of the user's questions or concerns, and the output indicates that the data has been successfully transferred to the server. 【0606】 Step 2: 【0607】 The server inputs received text or audio data into a natural language processing (NLP) model. Text data is converted directly, while audio data is converted to text using speech recognition technology. The server then analyzes the meaning of this data and generates a summary. The input is the user's raw text or audio data, and the output is summarized text data. 【0608】 Step 3: 【0609】 The server processes the summarized text through an emotion recognition engine to analyze the user's emotions. The emotion analysis model used is OpenAI's emotion analysis tool. The detected emotion information is determined from the tone and terminology of the text. The input is summarized text, and the output is an emotion label (e.g., "anxious" or "relieved"). 【0610】 Step 4: 【0611】 The server adds emotional information to the summary and uses an algorithm to evaluate priority and determine the priority of the consultation content. This allows for the creation of a response plan based on the intensity and urgency of the emotions. The input is a summary text with emotional labels attached, and the output is the consultation content with priority set. 【0612】 Step 5: 【0613】 The server notifies the assigned counselor of prioritized information. A response guideline is sent to the counselor via the notification system. This guideline includes the user's emotional state, which the counselor uses to select the appropriate response. The input is the prioritized consultation content, and the output is a notification to the counselor. 【0614】 Step 6: 【0615】 The server uses a generative AI model to automatically create and provide feedback and emotional support information to the client. This feedback is personalized based on emotions and includes content designed to alleviate the user's anxiety. Specific prompt sentences are input to the model, which generates appropriate feedback. The input consists of emotional information and prompt sentences, while the output is personalized feedback. 【0616】 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. 【0617】 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. 【0618】 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. 【0619】 [Fourth Embodiment] 【0620】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0621】 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. 【0622】 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). 【0623】 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. 【0624】 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. 【0625】 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). 【0626】 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. 【0627】 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. 【0628】 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. 【0629】 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. 【0630】 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. 【0631】 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. 【0632】 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". 【0633】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting information security-related consultation details into a terminal. 【0634】 The user enters their specific consultation details as text through the terminal's interface. The entered information is immediately sent from the terminal to the server. On the server, the entered text data is automatically analyzed using natural language processing (NLP) technology. The server generates a text summary of the consultation and also searches a database for similar past cases. 【0635】 Furthermore, the server analyzes any ambiguities in the consultation and generates corrective measures. Ambiguities include, for example, unclear technical terms or areas where the content lacks sufficient confirmation. This process references relevant laws, guidelines, and internal regulations. Based on this reference information, it provides specific information such as the next steps the user should take and drafts of necessary application forms. 【0636】 The server then categorizes the inquiry into the appropriate category, searches the database for the most suitable contact person based on the category, and automatically assigns them. Each contact person is notified of the detailed information of the inquiry, along with any risk points they should be aware of. 【0637】 During the operational phase, the server continuously monitors the progress of the consultation and notifies the staff member and user of the progress each time there is an update. This allows users to always know what stage their consultation is at, and enables the staff member to efficiently prepare for the next steps. 【0638】 For example, if a user requests information about the risks associated with the application of a new security protocol, the server summarizes the request and provides a compilation of reference information, including legal risks and technical concerns related to the protocol. Simultaneously, it automatically distributes this request to the department responsible for formulating security policies and assists in generating a report analyzing the relevant risks. This enables efficient and effective information security consultation for both the user and the support staff. 【0639】 The following describes the processing flow. 【0640】 Step 1: 【0641】 The user enters specific details of their information security inquiry through the terminal's interface. The entered information is then sent from the terminal to the server by pressing the send button. 【0642】 Step 2: 【0643】 The server receives the consultation content sent from the terminal and analyzes it using natural language processing (NLP) technology. This analysis extracts a summary of the consultation content and key keywords. 【0644】 Step 3: 【0645】 The server uses the extracted keywords to search its database for similar past consultation cases. These search results are saved as examples that the consultant can refer to. 【0646】 Step 4: 【0647】 The server identifies ambiguities in the consultation based on the analysis results. Ambiguous sections include contextual inconsistencies and unclear technical terms. The server then generates corrective suggestions. 【0648】 Step 5: 【0649】 The server searches for relevant laws, guidelines, and internal regulations for reference information and provides it to the user, along with information on legal aspects and procedures as needed. 【0650】 Step 6: 【0651】 The server categorizes inquiries and selects the most suitable contact person from the database. The inquiry is then automatically assigned to this contact person. 【0652】 Step 7: 【0653】 The server notifies the person in charge of potential risk points and detailed information about the consultation. This information is displayed on the person in charge's dashboard screen. 【0654】 Step 8: 【0655】 The server monitors the progress of consultations in real time and notifies the staff and users of updates whenever there is progress. This ensures that all parties involved are always aware of the latest status. 【0656】 (Example 1) 【0657】 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". 【0658】 In information security consulting services, a significant challenge arises from the time and effort required to interpret the consultation content, gather relevant information, and assign it to the appropriate person. This makes it difficult to respond quickly to those seeking advice, leading to a decrease in the overall efficiency of the consulting service. 【0659】 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. 【0660】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for identifying ambiguities and suggesting corrective measures, and means for searching for and providing reference information from relevant laws, guidelines, and regulations. This enables a rapid understanding and response to the content of the consultation. 【0661】 A "consultant" is someone who seeks information or advice. 【0662】 "Automatically analyzing information" means analyzing received data using a program without requiring human intervention. 【0663】 "Generating a summary" means extracting the main points by condensing detailed information into a concise form. 【0664】 "Identifying ambiguities" means finding unclear or incomplete parts. 【0665】 "To propose corrective measures" means to suggest appropriate countermeasures or amendments to identified ambiguities or problems. 【0666】 "Searching for relevant laws, guidelines, and regulations" means finding information about specific standards or systems in databases or documents. 【0667】 "Classifying consultation content based on categories" means classifying and organizing the information received based on its nature and characteristics. 【0668】 "Automatically assigning a consultant" means that the system selects the most suitable person and assigns them to handle the task based on that selection. 【0669】 "Monitoring progress" means continuously observing how far a task or project is progressing. 【0670】 "Notifying about updates" means informing relevant parties about new data or changes. 【0671】 A "generative model" is an artificial intelligence technique trained to perform specific tasks based on large amounts of data. 【0672】 This system is designed to handle information security inquiries in an automated manner. Users input their information security questions and concerns through a terminal interface. The terminal immediately transmits this input information to the server. 【0673】 The server is implemented in programming languages ​​such as Python or Java, and utilizes natural language processing libraries such as TensorFlow and PyTorch to analyze the received text data. This analysis uses generative AI models (e.g., GPT-3 and BERT), enabling semantic understanding and summarization. 【0674】 Specifically, the server analyzes the consultation content, identifying ambiguous or unclear points. It then consults databases of relevant laws, guidelines, and regulations to generate corrective solutions for these issues. The system then categorizes the consultation content and automatically assigns the most suitable person to handle it. Furthermore, it regularly monitors the progress of the consultation and notifies the person in charge and the person making the inquiry of any updates. 【0675】 As a concrete example, consider a case where a user submits a request for a security assessment of a new cloud service. The server analyzes this information, summarizes the relevant security standards, and collects information on necessary legal matters and technical evaluation points. Based on this information, it notifies the cloud security officer, who can then proceed with the assessment based on the specified risk factors. 【0676】 Examples of prompt statements to input into the generative AI model are as follows: 【0677】 "A user has requested to know about the risks associated with the implementation of a new security protocol. Please summarize this request and generate information that includes the legal risks and technical concerns related to the protocol. Also, please prepare to distribute this information to the relevant departments." 【0678】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0679】 Step 1: 【0680】 The user enters their inquiry into the terminal interface. This input data includes specific questions and requests regarding information security. This text input serves as the foundational data for subsequent data processing. 【0681】 Step 2: 【0682】 The terminal securely transmits user input data to the server. The input here is text data of the user's inquiry, and the output is the completion of the data transfer to the server. The HTTPS protocol is used for transmission to maintain data security. 【0683】 Step 3: 【0684】 The server automatically analyzes the received text data. The input data consists of the user's consultation content, which is analyzed using Natural Language Processing (NLP) technology. Generative AI models (such as GPT-3 and BERT) are utilized to extract key points from the input content and generate a summary output. 【0685】 Step 4: 【0686】 The server identifies ambiguities from the analyzed data and generates corrective action plans. The input here is the summarized text, which is the output of the previous analysis step. By accessing legal and guideline databases and incorporating necessary supplementary information, the server obtains output that generates corrective action plans. 【0687】 Step 5: 【0688】 The server categorizes consultations based on their content and then searches the database for and assigns the appropriate consultant based on that information. The input is the consultation data after analysis is complete, and the output is the notification to the consultant and information that the assignment is complete. 【0689】 Step 6: 【0690】 The server constantly monitors the progress of consultations and notifies the person in charge and the user when there are any developments or updates. Input is progress log information, and output is the completion of the delivery of progress report notifications. An automated notification system is used to ensure that stakeholders are aware of the situation in real time. 【0691】 (Application Example 1) 【0692】 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". 【0693】 In information security consulting services, it is crucial to provide an environment where users can receive prompt and accurate support. However, current systems often require manual information analysis and progress management, which is time-consuming and labor-intensive, thus necessitating increased efficiency. Furthermore, the lack of an interface that allows users to consult anytime, anywhere is a significant challenge. 【0694】 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. 【0695】 In this invention, the server includes a processing device that automatically analyzes information received from a client and generates a summary, a processing device that identifies ambiguities and proposes corrective actions, and a processing device that searches for and provides reference information from relevant norms, guidelines, and regulations. This enables users to receive appropriate support in real time via their mobile devices. 【0696】 A "consultant" is an individual or organization that uses the system to resolve questions or problems related to information security. 【0697】 "Information" refers to the data that will be analyzed based on the text data and content provided by the person seeking advice. 【0698】 "Analysis" is the process of analyzing received information to summarize it and identify ambiguities. 【0699】 A "summary" is a concise text that compiles the analyzed information and is provided to the client or the person in charge. 【0700】 "Ambiguous points" refer to unclear technical terms or missing information contained within the data. 【0701】 A "corrective proposal" is a suggestion that outlines improvements or solutions to ambiguous points. 【0702】 A "norm" is a legally and industryally accepted standard or criterion. 【0703】 A "guideline" is a set of guidelines that outlines policies and directions regarding information security. 【0704】 "Regulations" refer to the rules and procedures established within an organization. 【0705】 "Reference information" refers to supplementary information based on laws and guidelines, which serves as a guide for the next course of action the person seeking advice should take. 【0706】 A "processing unit" is a combination of hardware and software designed to perform a specific task. 【0707】 A "personal digital assistant" (PDA) is a portable electronic device such as a smartphone or tablet. 【0708】 "Real-time" refers to processing that takes place immediately and information being provided instantaneously. 【0709】 To implement this invention, an AI agent system will be constructed to efficiently support information security consultations. The system begins with the user inputting their information security consultation into a mobile device. 【0710】 Users first input specific information security-related inquiries as text using a mobile device such as a smartphone or tablet. This entered text data is then sent by the device to a cloud-based server. The server uses natural language processing (NLP) technology to analyze the input data and generate a summary. Natural language processing libraries such as Google Cloud Natural Language API and SpaCy are used in this process. 【0711】 Next, the server identifies ambiguities based on the analysis results and creates necessary corrective actions. These corrective action suggestions include a function to retrieve and provide reference information from relevant norms, guidelines, and regulations. This allows users to clearly understand what actions they should take next. 【0712】 Furthermore, the server classifies inquiries based on their categories and automatically assigns the most suitable consultant to each inquiry. The consultant is notified of the detailed information of the inquiry, along with any potential risk factors to be aware of. This function allows consultants to prepare to perform their duties efficiently. 【0713】 Furthermore, the server has a system in place to monitor the progress of consultations in real time and notify staff and clients of updates. This allows clients to always be aware of the status of their consultation and proceed to the next step with confidence. 【0714】 For example, if a company's IT manager has questions about the security risks associated with the introduction of a new system, they can input "What are the information leakage risks when introducing a new system?" into their terminal. The server will then quickly analyze the situation, generate specific suggestions including past similar cases and risk mitigation measures, and provide them to the user. An example of a prompt in this case would be, "Please generate advice based on past cases regarding the risks to be aware of when introducing a new system in terms of information security." 【0715】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0716】 Step 1: 【0717】 Users input specific information security-related inquiries as text using their mobile devices. The entered text data is then sent from the device to a cloud-based server. This registers the user's inquiry in the system. 【0718】 Step 2: 【0719】 The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to perform grammatical analysis and key phrase extraction, and then summarizes the input content. This process extracts the main points of the text data and generates a summarized version. 【0720】 Step 3: 【0721】 The server identifies ambiguities based on the analysis results. For the identified ambiguities, it refers to relevant norms, guidelines, and regulations to generate appropriate corrective actions. This provides concrete solutions to the problems the user is facing. 【0722】 Step 4: 【0723】 The server classifies inquiries based on categories and automatically assigns a suitable person to each category. Specifically, it compares the inquiry content with existing inquiry categories and searches the database for the most suitable person to select. The selected person receives detailed information about the inquiry, along with any potential risk factors to be aware of. 【0724】 Step 5: 【0725】 The server monitors the progress of consultations in real time and sends notifications to the staff member and the user whenever there is progress. The progress status is regularly updated in the management system on the server, and this information is automatically notified. This allows users to always know the current status of the consultation, and staff members to efficiently prepare for the next steps. 【0726】 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. 【0727】 To implement this invention, an emotion engine is integrated into an AI agent system that supports information security consultations. The system begins with the user inputting their information security consultation into a terminal. 【0728】 Users input detailed information about their consultation via their device, and the naturally expressed text and voice data are used for emotion recognition. The information entered by the user is immediately sent to the server by the device. 【0729】 The server first analyzes the received consultation content using natural language processing (NLP) techniques and generates a summary. Simultaneously, the server uses an emotion engine to detect emotions from the user's text and voice. Specifically, it determines emotions from the user's word choice, tone of speech, and intonation. 【0730】 The detected emotional information is added to the analysis results of the consultation. If the user is experiencing particularly strong doubts or anxieties, the server creates a summary that highlights those points and notifies the contact person, including the emotional information. 【0731】 Furthermore, this emotional information can be used to change the guidelines for how customer service representatives should respond. For example, if a user indicates anxiety, the system can be configured to recommend a more courteous and reassuring approach. 【0732】 Furthermore, the server can provide users with appropriate feedback and emotional care information based on the emotion recognition results. For example, if a user is experiencing high levels of stress, it can automatically suggest stress management advice and resources. 【0733】 For example, if a user texts, "I'm worried about how secure the new security protocol is," the server summarizes it, and the emotion engine detects the emotion of "anxiety." In this case, the server provides relevant laws and guidelines, instructs the support staff to "prioritize providing information to alleviate anxiety," and provides the user with "reference materials to reassure them." In this way, incorporating an emotion engine improves the quality of care for individual clients and enables more personalized support. 【0734】 The following describes the processing flow. 【0735】 Step 1: 【0736】 The user enters specific details of their information security concerns via a terminal. The entered information includes text and audio data designed to infer the user's emotions. 【0737】 Step 2: 【0738】 The terminal transmits the user's input, along with audio data, to the server in real time. 【0739】 Step 3: 【0740】 The server analyzes the received data using natural language processing (NLP) techniques. It generates a summary from the text data and extracts important keywords. 【0741】 Step 4: 【0742】 The server simultaneously uses an emotion engine to recognize the user's emotional state. Specifically, it analyzes characteristics such as word choice, tone of voice, speed, and intonation to classify the user's emotions into categories such as "anxiety," "relief," and "excitement." 【0743】 Step 5: 【0744】 The server integrates the generated summary and sentiment information and compiles it into an analysis result. This information helps clarify the content of the consultation and is used for subsequent processing. 【0745】 Step 6: 【0746】 The server classifies the consultation content into the appropriate category based on the analysis results and automatically assigns the most suitable contact person from the database. During this process, it also considers emotional information and instructs the contact person on the appropriate course of action. 【0747】 Step 7: 【0748】 The server notifies the person in charge of the details of the consultation and emotional information, along with any risk points to be aware of and other relevant information. This notification allows the person in charge to choose the appropriate course of action. 【0749】 Step 8: 【0750】 The server monitors the progress of ongoing consultations and notifies the staff member and user of any updates. It also improves the user experience by providing feedback and emotional care information that takes the user's emotional state into consideration. 【0751】 (Example 2) 【0752】 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". 【0753】 In today's information society, the information security issues faced by those seeking advice are becoming increasingly complex and diverse. In this context, it is essential to accurately understand not only the specific content of the consultation but also the psychological state of the person seeking advice, and to provide support optimized for each individual. However, conventional systems are insufficient in recognizing and responding to emotions, often resulting in a uniform quality of support for all clients. Therefore, the challenge lies in achieving flexible and accurate support that takes into account the emotions of the person seeking advice. 【0754】 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. 【0755】 In this invention, the server includes means for automatically analyzing and summarizing information received from the client, means for recognizing emotions from the received information and integrating the corresponding emotional information into the analysis results, and means for providing appropriate response guidelines to the person in charge based on the recognized emotional information. This makes it possible to provide personalized support that is tailored to the client's emotions. 【0756】 "Means for automatically analyzing and summarizing received information" refers to a function in which a processing device automatically processes information provided by a client using a program and generates data that summarizes its contents. 【0757】 "Means for identifying ambiguities and proposing corrective measures" refers to a function that identifies unclear or uncertain parts within the analyzed information and proposes concrete plans for improvement or resolution. 【0758】 "Means for searching and providing reference information from relevant rules, guidelines, and policies" refers to a function that investigates laws, standards, and guidelines related to the consultation content from information resources and provides them to the user as useful information. 【0759】 "A means of classifying based on categories and automatically assigning a consultant" refers to a function that classifies consultation content according to pre-set categories and allows the system to automatically select the most suitable consultant to handle the situation. 【0760】 "A means of monitoring the progress of consultations and notifying updates" refers to a function that constantly monitors the progress of processing consultations and communicates the latest information to the consultant and the person seeking consultation. 【0761】 "Means for recognizing emotions and integrating corresponding emotional information into the analysis results" refers to a function that automatically identifies emotions from the information provided by the client and includes that emotional information in the analysis results of the consultation. 【0762】 "Means of providing appropriate response guidelines to those in charge" refers to a function that presents guidelines and recommended actions on how the person in charge should respond, based on the recognized emotional information and the content of the consultation. 【0763】 "A means of generating and providing feedback tailored to the client's psychological state" refers to a function that automatically creates and provides feedback and information optimized for the client's psychological state, based on recognized emotional information. 【0764】 A description of the embodiment for carrying out the invention will be given. 【0765】 This invention is a system that provides more accurate support to users when they seek advice regarding information security by analyzing the content of their consultations and recognizing their emotions. Users input their consultations via text or voice through a terminal. The terminal is responsible for transmitting the input data to the server. 【0766】 The server uses natural language processing techniques to analyze the received data. Specifically, it summarizes the data using OpenAI, a generative AI model, and natural language processing libraries such as NLTK and spaCy. At the same time, it utilizes a TensorFlow-based emotion engine to recognize emotions from the user's input data. This recognized emotion information is then integrated into the analysis results. 【0767】 Based on the analysis results and recognized emotional information, the server searches for rules and guidelines related to the consultation content and provides the necessary information to the person in charge. Through this process, the person in charge of consultation can obtain appropriate guidance for responding to the user. In addition, by generating feedback that is empathetic to the feelings of the person in charge, personalized information can be provided to each individual person in charge. 【0768】 For example, if a user enters "I'm worried about how secure the new security protocol is," the server analyzes this information and summarizes it using a generative AI model. Additionally, an emotion engine detects the emotion of "anxiety" and integrates it into the analysis results. As a result, the server directs relevant information to the appropriate personnel and provides the user with reference materials to alleviate their anxiety. 【0769】 An example of a prompt is, "Please instruct us on what information to provide if the user expresses concern." Using this prompt makes it possible to provide more appropriate and personalized support. 【0770】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0771】 Step 1: 【0772】 The user enters their information security inquiry into the terminal. The user can type text using the keyboard or use voice input. This input data directly triggers the next process. The output of the input is temporarily stored on the terminal as text or audio data. 【0773】 Step 2: 【0774】 The terminal sends data entered by the user to the server. The data is delivered to the server in a digital format via network communication. The input is the data on the terminal, and the output is the data transferred to the server. This process prepares the data for the server to begin processing. 【0775】 Step 3: 【0776】 The server analyzes the received data using natural language processing techniques. It uses a generative AI model to summarize the input text and audio data and organize the information. Specifically, it analyzes language structure using Python libraries such as NLTK and spaCy. The input is the data transferred to the server, and the output is the analyzed summary result. 【0777】 Step 4: 【0778】 The server uses an emotion engine to recognize emotions from user input data. It employs a TensorFlow-based deep learning model to analyze the tone and intonation of speech and text. The input is the analyzed data, and the output is the recognized emotion information. Emotion identification occurs during this process. 【0779】 Step 5: 【0780】 The server integrates the analysis results and recognized emotional information to create a detailed summary of the consultation. In particular, if emotions such as "anxiety" or "doubt" are detected, the server generates a summary that emphasizes this information. The input is the analyzed summary and emotional information, and the output is the integrated result. This result becomes the information that will be used further. 【0781】 Step 6: 【0782】 The server searches a database of relevant rules and guidelines based on the integrated results and provides the consultant with the most relevant information. This involves using a search algorithm to extract highly relevant information. The input is integrated data, and the output is instructional information for the consultant. 【0783】 Step 7: 【0784】 The server generates and provides personalized feedback based on the client's emotional state. This process uses prompts to automatically generate the feedback content. For example, a prompt such as "Please specify what information to provide if the user expresses anxiety" might be used. The input is a request for emotionally-based feedback generation, and the output is the generated feedback. 【0785】 (Application Example 2) 【0786】 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". 【0787】 In information security consultations, it is challenging to appropriately understand the emotions of the person seeking advice and to provide responses tailored to their individual needs. Current systems have difficulty detecting emotions from text and audio, making it difficult to enhance the person's sense of security or provide appropriate feedback. Furthermore, the lack of emotion-based prioritization and guidelines for countermeasures hinders the ability to respond quickly and accurately to the person's anxieties and doubts. 【0788】 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. 【0789】 In this invention, the server includes means for automatically analyzing information received from the client and generating a summary, means for detecting and highlighting emotions based on the analyzed information, and means for evaluating the priority of the consultation and setting response guidelines based on the emotional information. This makes it possible to quickly grasp the client's emotions and provide appropriate feedback and emotional care information. 【0790】 A "consultant" is an individual or organization that has questions or concerns regarding information security and seeks advice through the system. 【0791】 "Automatic analysis" means processing user-inputted information using artificial intelligence technology without requiring human intervention. 【0792】 "Means for generating summaries" refers to a function that performs the process of compressing information received from the client into a concise and easy-to-understand format. 【0793】 "Means for detecting and highlighting emotions" refers to a function that determines emotions from the user's text and voice data and reports them in a way that makes them particularly noticeable. 【0794】 "Means of prioritizing and setting response guidelines" refers to the process of determining the importance of a consultation based on the type and intensity of emotions involved, and then deciding on an appropriate response strategy. 【0795】 "Means of providing feedback and emotional care information" refers to a function that implements procedures to provide reassurance and useful information to the client in response to detected emotions. 【0796】 The system for implementing this invention begins with a user entering information security-related inquiries via a terminal such as a smartphone or computer. The text or voice input from the user to the terminal is immediately transmitted to the server. 【0797】 The server is equipped with natural language processing (NLP) technology and an emotion recognition engine, utilizing emotion analysis models from TensorFlow and OpenAI. First, the server analyzes the received consultation content using NLP technology and generates a summary. Next, the emotion recognition engine analyzes the wording, tone, and voice of the input data to detect the emotions of the person seeking advice. 【0798】 The detected emotional information is added to the summary, the priority of the consultation is evaluated, and a response guideline is set. If the emotion is strong, the content is highlighted and the consultant is notified immediately. The consultant uses the emotional information to provide the best possible response to the user. 【0799】 Furthermore, the server provides feedback and emotional care information to the client based on the emotion recognition results. This feedback is generated using a generative AI model and, for example, presents resources to provide reassurance if the user is feeling anxious. 【0800】 For example, if a user enters "I'm worried about whether the new security measures are effective," the server summarizes this and detects the emotion of "anxiety." Based on this result, the server instructs service personnel to "prioritize providing information to alleviate anxiety" and automatically provides the user with "reference materials to reassure them." 【0801】 An example of a prompt message would be: "If the user has recently expressed increased security concerns, what specific information or actions can you provide?" 【0802】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0803】 Step 1: 【0804】 Users input their information security concerns via text or voice through their smartphones or computers. The input data is immediately transmitted to the server by the device. The input consists of the user's questions or concerns, and the output indicates that the data has been successfully transferred to the server. 【0805】 Step 2: 【0806】 The server inputs received text or audio data into a natural language processing (NLP) model. Text data is converted directly, while audio data is converted to text using speech recognition technology. The server then analyzes the meaning of this data and generates a summary. The input is the user's raw text or audio data, and the output is summarized text data. 【0807】 Step 3: 【0808】 The server processes the summarized text through an emotion recognition engine to analyze the user's emotions. The emotion analysis model used is OpenAI's emotion analysis tool. The detected emotion information is determined from the tone and terminology of the text. The input is summarized text, and the output is an emotion label (e.g., "anxious" or "relieved"). 【0809】 Step 4: 【0810】 The server adds emotional information to the summary and uses an algorithm to evaluate priority and determine the priority of the consultation content. This allows for the creation of a response plan based on the intensity and urgency of the emotions. The input is a summary text with emotional labels attached, and the output is the consultation content with priority set. 【0811】 Step 5: 【0812】 The server notifies the assigned counselor of prioritized information. A response guideline is sent to the counselor via the notification system. This guideline includes the user's emotional state, which the counselor uses to select the appropriate response. The input is the prioritized consultation content, and the output is a notification to the counselor. 【0813】 Step 6: 【0814】 The server uses a generative AI model to automatically create and provide feedback and emotional support information to the client. This feedback is personalized based on emotions and includes content designed to alleviate the user's anxiety. Specific prompt sentences are input to the model, which generates appropriate feedback. The input consists of emotional information and prompt sentences, while the output is personalized feedback. 【0815】 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. 【0816】 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. 【0817】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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." 【0824】 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. 【0825】 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. 【0826】 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. 【0827】 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. 【0828】 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. 【0829】 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. 【0830】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0831】 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. 【0832】 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. 【0833】 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. 【0834】 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. 【0835】 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. 【0836】 The following is further disclosed regarding the embodiments described above. 【0837】 (Claim 1) 【0838】 A means to automatically analyze information received from the client and generate a summary, 【0839】 A means for identifying ambiguities and proposing corrective measures based on the analyzed information, 【0840】 Means for searching for and providing reference information from relevant laws, guidelines, and regulations, 【0841】 A means for classifying the aforementioned consultation content based on categories and automatically assigning a consultant to each consultation, 【0842】 A means of monitoring the progress of consultations and notifying staff and clients of updates, 【0843】 A system that includes this. 【0844】 (Claim 2) 【0845】 The system according to claim 1, further comprising means for extracting similar cases from a database based on the analyzed information. 【0846】 (Claim 3) 【0847】 The system according to claim 1, further comprising means for extracting risk points that require attention based on the aforementioned consultation category and notifying them along with reference information. 【0848】 "Example 1" 【0849】 (Claim 1) 【0850】 A means to automatically analyze information received from the client and generate a summary, 【0851】 A means for identifying ambiguities and proposing corrective measures based on the analyzed information, 【0852】 Means for searching for and providing reference information from relevant laws, guidelines, and regulations, 【0853】 A means for classifying the aforementioned consultation content based on categories and automatically assigning a consultant to each consultation, 【0854】 A means of monitoring the progress of consultations and notifying staff and clients of updates, 【0855】 In the aforementioned analysis and classification process, a means for efficiently processing text data using a generative model, 【0856】 To optimize the categorization of consultation content, a method is used to apply a learning model based on case data, 【0857】 A system that includes this. 【0858】 (Claim 2) 【0859】 The system according to claim 1, further comprising means for extracting similar cases from a database based on the analyzed information. 【0860】 (Claim 3) 【0861】 The system according to claim 1, further comprising means for extracting risk points that require attention based on the aforementioned consultation category and notifying them along with reference information. 【0862】 "Application Example 1" 【0863】 (Claim 1) 【0864】 A processing device that automatically analyzes information received from a client and generates a summary, 【0865】 A processing device that identifies ambiguities and proposes corrective measures based on the analyzed information, 【0866】 A processing device that retrieves and provides reference information from relevant norms, guidelines, and regulations, 【0867】 A processing device that classifies the aforementioned consultation content based on categories and automatically assigns a consultation officer, 【0868】 A processing unit that monitors the progress of consultations and notifies the person in charge and the person seeking consultation of updates, 【0869】 A processing device that is compatible with mobile devices and can receive information in real time, 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, further comprising a processing device for extracting similar cases from a knowledge base based on the analyzed information. 【0873】 (Claim 3) 【0874】 The system according to claim 1, further comprising a processing device that extracts risk factors requiring attention based on the classification of the consultation and notifies the user of these factors along with reference information. 【0875】 "Example 2 of combining an emotion engine" 【0876】 (Claim 1) 【0877】 A means to automatically analyze information received from the client and generate a summary, 【0878】 A means for identifying ambiguities and proposing corrective measures based on the analyzed information, 【0879】 Means for searching for and providing reference information from relevant rules, guidelines, and policies, 【0880】 A means for classifying the aforementioned consultation content based on categories and automatically assigning a consultant to each consultation, 【0881】 A means of monitoring the progress of consultations and notifying staff and clients of updates, 【0882】 A means of recognizing emotions from received information and integrating the corresponding emotional information into the analysis results, 【0883】 A means of providing appropriate response guidelines to the person in charge based on recognized emotional information, 【0884】 A means of generating and providing feedback tailored to the psychological state of the person seeking advice, 【0885】 A system that includes this. 【0886】 (Claim 2) 【0887】 The system according to claim 1, further comprising means for extracting similar cases from information resources based on the analyzed information. 【0888】 (Claim 3) 【0889】 The system according to claim 1, further comprising means for extracting risk factors that require attention based on the category of the consultation and notifying them along with reference information. 【0890】 "Application example 2 of combining emotional engines" 【0891】 (Claim 1) 【0892】 A means to automatically analyze information received from the client and generate a summary, 【0893】 Based on the analyzed information, means for detecting and emphasizing emotions, 【0894】 A means of evaluating the priority of consultations based on emotional information and setting response guidelines, 【0895】 A means of providing a counselor with a notification containing the aforementioned emotional information, 【0896】 A means of providing feedback and emotional care information to clients using a generative AI model, 【0897】 A system that includes this. 【0898】 (Claim 2) 【0899】 The system according to claim 1, further comprising means for extracting similar cases from a database based on the analyzed information and emotional information, and for presenting emotional response measures. 【0900】 (Claim 3) 【0901】 The system according to claim 1, further comprising means for extracting risk points that require attention based on the aforementioned consultation category and emotional data, and notifying them along with emotional care. [Explanation of Symbols] 【0902】 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 to automatically analyze information received from the client and generate a summary, A means for identifying ambiguities and proposing corrective measures based on the analyzed information, Means for searching for and providing reference information from relevant laws, guidelines, and regulations, A means for classifying the aforementioned consultation content based on categories and automatically assigning a consultant to each consultation, A means of monitoring the progress of consultations and notifying staff and clients of updates, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for extracting similar cases from a database based on the analyzed information. [Claim 3] The system according to claim 1, further comprising means for extracting risk points that require attention based on the category of the consultation and notifying them along with reference information.

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