system

Abstract

Provide a system. 【Solution means】 Communication means for receiving information input from a user, Analysis means for analyzing the information received from the user and extracting relevant information, Allocation means for selecting a person in charge who needs to respond based on the extracted relevant information, Visualization means for presenting the risk information identified by the analysis means, Management means for tracking and monitoring the progress of corresponding cases and notifying the progress information to relevant stakeholders, Support means for analyzing the specific consultation content of the user using a generated AI model and providing advice, Display means for visualizing the progress of the consultation content in real time via a terminal, A system including the above.

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In consultation services related to information security, it is required to reduce the burden on responders, improve the efficiency of processing consultation contents, and eliminate variations in response quality. However, in general consultation services, since the analysis of consultation contents, the efficient allocation of responders, and the progress management mechanism are not sufficient, these problems occur. In addition, there are many ambiguous consultation contents, and there is also a problem that methods for accurately and quickly identifying and correcting them are lacking.

Means for Solving the Problems

[0005] This invention provides a system that receives information input from users, automatically analyzes it, and extracts relevant information. Specifically, it acquires information received from users through comprehensive communication means and analyzes it in detail using appropriate analysis means. This analysis process also includes a function to identify ambiguous expressions and propose concrete improvement suggestions. Furthermore, it effectively processes consultations by selecting the most suitable person to handle the matter using an assignment means based on the analysis results and visualizing risk information. In addition, it tracks and monitors cases using a progress management means and notifies stakeholders of progress information in real time, thereby improving the efficiency and consistency of consultation work.

[0006] A "user" refers to an individual or organization that inputs information and seeks advice through the system.

[0007] "Communication means for receiving information input" refers to a means or method for receiving information from users in digital format and transmitting it to a server.

[0008] "Analysis means" refers to algorithms and systems used to process input information and extract and analyze relevant information and risks.

[0009] "Extracting relevant information" refers to information identified through analysis that is closely related to the user's inquiry and necessary for addressing it.

[0010] "Assignment method" refers to a process or system that automatically selects the most suitable person to respond based on the analyzed information.

[0011] "Visualization methods" refer to methods for visually displaying analysis results and risk information, and presenting them in a way that is easy for users and personnel to understand.

[0012] "Management measures" refer to procedures and systems for monitoring progress and managing, updating, and notifying information related to that progress.

[0013] "Vague expressions" refer to descriptions in a consultation that lack specificity or parts whose meaning is difficult to pinpoint.

[0014] An "improvement proposal" is a suggestion to offer clearer and more effective countermeasures for ambiguous expressions or identified risks.

[0015] The "optimal person in charge" refers to an individual or department that possesses the most suitable skills and experience to handle a case, as selected through analytical and assignment methods.

[0016] An "interested party" refers to an individual, group, or organization that has some interest in or influence over the progress or outcome of a consultation case. [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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when 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 described.

[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] This invention is a system for efficiently and effectively providing information security consultation services. The system includes functions to receive user input, analyze it, and automatically perform appropriate processing.

[0039] Specifically, users input their questions or concerns via their device. The device then transmits this input information to a server using a secure communication method. The server analyzes the received information in detail using analytical tools. This analysis process includes tokenizing the information using natural language processing technology, extracting keywords, and referencing past databases related to the consultation content. Ambiguous expressions are automatically extracted, and suggestions for improvement are provided to help users identify their consultation content more specifically.

[0040] Based on these analysis results, the server uses an assignment mechanism to assign the most suitable person to the inquiry. The selected person will use past cases and relevant materials to respond to the inquiry quickly and effectively. The server also visualizes risk information and presents important information to users and assigned personnel in an easily understandable format.

[0041] Furthermore, the server manages the progress of projects and notifies stakeholders of updates in real time. This ensures that users and personnel can always proceed with their work based on the latest information.

[0042] As a concrete example, consider a case where a user consults about "measures to prevent information leakage when introducing a new cloud service." When the user enters this consultation request, the terminal sends it to the server. The server identifies keywords such as "cloud service" and "measures to prevent information leakage," and by referring to relevant past consultation history and laws, provides the user with appropriate risk information and countermeasures. Based on this, the server automatically assigns an information security officer with specialized knowledge, then manages the progress, and sends feedback to the user.

[0043] Thus, the present invention provides a concrete form for achieving increased efficiency and consistent quality improvement in information security consultations.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters their questions and concerns about information security into the terminal. These entries include specific questions and points of confusion.

[0047] Step 2:

[0048] The terminal encrypts the entered consultation content and transmits it to the server via a secure communication channel. This process maintains the integrity and confidentiality of the data.

[0049] Step 3:

[0050] The server processes the received consultation content using analytical tools and tokenizes it using natural language processing. It also extracts important keywords and performs contextual semantic analysis.

[0051] Step 4:

[0052] The server searches its internal database for relevant past consultation cases based on the analyzed information, and uses this to suggest similar consultations and identify risk points.

[0053] Step 5:

[0054] The server automatically generates specific mitigation measures for identified risk points and presents ambiguous statements as suggested improvements. At this point, the user may be asked to provide additional information.

[0055] Step 6:

[0056] Based on the analysis results, the server automatically selects the most suitable consultant using an assignment mechanism. The selection criteria include past response history and expertise.

[0057] Step 7:

[0058] The server notifies assigned personnel of the case information and related documents to be handled, enabling them to begin responding quickly.

[0059] Step 8:

[0060] The server monitors the progress of consultations in real time, and if the monitoring system detects any changes in the progress status, it notifies users and stakeholders as necessary.

[0061] Step 9:

[0062] The user receives final feedback from the server and confirms the problem resolution and future course of action. This completes the resolution of the inquiry.

[0063] (Example 1)

[0064] 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."

[0065] In information security consulting services, the diverse nature of user questions and ambiguous language make it difficult to respond quickly and accurately. Furthermore, effectively utilizing past response records and quickly assigning the most suitable personnel is also a challenge. Additionally, visually presenting risk information and managing case progress in real time are difficult. Therefore, a system is needed to enable appropriate information analysis and efficient operational management.

[0066] 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.

[0067] In this invention, the server includes communication means via a terminal that receives information data, analysis means using a generative AI model that analyzes the information and extracts important terms, and visualization means that visually displays the risks based on the analysis results. This makes it possible to concretize the user's vague questions and quickly present the optimal countermeasures. Furthermore, it is possible to select the most suitable personnel by utilizing past response records and improve the quality and effectiveness of consultations.

[0068] "Communication means" refers to devices and methods for sending and receiving information, and in particular, those that have the function of securely transmitting user input data to a server.

[0069] "Analysis means" refers to systems and methods for analyzing input information and extracting important words and relevant data, and utilizes generative AI models.

[0070] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze text data and extract specific patterns or phrases.

[0071] "Assignment means" refers to a method or system for automatically selecting the most suitable person based on the results of the analysis, and includes the ability to refer to past history.

[0072] "Visualization methods" refer to technologies that visually display analyzed data and present it in a way that is easy for users and personnel to understand.

[0073] "Management means" refers to a method or system that has the function of tracking the progress of a project and notifying relevant parties of that information.

[0074] This invention provides a system for efficiently conducting information security consultations. Users input their questions and concerns regarding information security into a terminal. This terminal transmits the user input information to a server using a secure communication method. The server then analyzes the received information using a generation AI model.

[0075] The server uses natural language processing technology to tokenize user input and extract key terms. This analysis identifies keywords in the user's inquiry and references relevant historical databases. The server identifies ambiguous expressions and, through a generative AI model, presents improvement suggestions to the user. For example, if a user enters a question about "measures to prevent information leakage when introducing a new cloud service," the server extracts keywords such as "cloud service" and "measures to prevent information leakage."

[0076] Based on this, the server automatically selects the most suitable expert to handle the consultation using an assignment mechanism. This selection process utilizes past response history to ensure the assignment of a more appropriate person. The server also visually displays risk information along with the analysis results, providing information to users and staff in a dashboard format.

[0077] Furthermore, the server tracks the progress of the case and notifies stakeholders of updates as they occur. This ensures that users and staff always proceed with their work based on the latest information. As users progress through their consultations, the server updates information in real time, supporting efficient business operations.

[0078] A concrete example of a prompt is, "When introducing a new cloud service, I would like to know the best practices for preventing information leaks. I would also like to know about relevant laws and past cases." Based on this prompt, the system can provide accurate information and countermeasures.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user enters their information security inquiry into the terminal. They type a prompt such as "Measures to prevent information leakage when introducing a new cloud service" into the terminal's input field. Once input is complete, the terminal sends this data to the server via a secure communication method. In this step, the input is the user's inquiry text, and the output is the inquiry data sent to the server.

[0082] Step 2:

[0083] The server analyzes the received consultation data using a generation AI model. Specifically, a natural language processing module within the server tokenizes the text and extracts important keywords. Data processing involves operations such as decomposing strings into tokens and identifying keywords to grasp the core of the consultation content entered by the user. The input for this step is the consultation data sent to the server, and the output is the extracted keywords and related database entries.

[0084] Step 3:

[0085] The server identifies ambiguous expressions identified through analysis and uses a generative AI model to present specific improvement suggestions to the user. This clarifies the user's inquiry and facilitates the next process. The data calculation involves automatic detection of ambiguous expressions and generation of appropriate explanatory texts. The input for this step is extracted keywords, and the output is improvement suggestions for the user.

[0086] Step 4:

[0087] Based on the analysis results, the server automatically selects an appropriate information security expert using an assignment mechanism. This selection takes into account past response history and expertise data. The server refers to the database to determine the most suitable person and configures that information within the system. The input for this step is the analysis results and database information, and the output is the information of the selected person.

[0088] Step 5:

[0089] The server visualizes risk information and provides it to users and personnel. This process generates and displays dashboards and reports based on the analysis results. The server visualizes important information as diagrams and text, displaying it on the system screen in a way that is easy for users and personnel to understand. The input for this step is the analysis results, and the output is visualized risk information.

[0090] Step 6:

[0091] The server continuously monitors project progress and notifies users and relevant organizational members in real time. The server collects progress data and immediately notifies users via email or chat tools when changes occur. This ensures that stakeholders can always act based on the latest information. The input for this step is the progress status, and the output is the notification information for stakeholders.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In information security consulting services, it is essential to quickly and accurately resolve the diverse questions and problems that users have. However, traditional methods often involve manual information analysis and appropriate assignment of personnel, making it difficult to maintain efficiency and consistent quality. Furthermore, progress management and visualization of risk information are prone to information scattering, making it difficult for stakeholders to always keep up-to-date. There is a need to provide new methods to solve these challenges.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes support means for receiving user inquiries, analyzing them in detail using a generated AI model, and providing appropriate advice; display means for visualizing the specific content of the user's inquiry in real time; and assignment means for selecting the most suitable person in charge based on past response history and the generated AI model. This makes it possible to perform information security consultation services efficiently and with high quality.

[0097] "A communication method for receiving information input from users" refers to a method for securely transmitting consultation information from users to a server via a terminal.

[0098] "Analysis methods" refer to techniques for analyzing received information in detail and extracting relevant keywords and problems.

[0099] "Assignment method" refers to a method for automatically selecting the most suitable person based on the results of the analysis.

[0100] "Visualization methods" are techniques for visually representing information and risks and providing them in a format that is easily understandable to users and those in charge.

[0101] "Management measures" refer to methods for tracking the progress of a project and notifying stakeholders of the latest information.

[0102] "A support method that analyzes the specific content of a user's consultation and provides advice using a generative AI model" refers to a method that utilizes AI technology to analyze the content of a user's consultation and present appropriate advice and solutions.

[0103] "A display method that visualizes the progress of consultations in real time via a terminal" refers to a method for displaying the progress of consultations and related information on the user's terminal so that they can instantly check it.

[0104] The system for implementing this invention consists of a server, a terminal, and software including a generative AI model. First, the user inputs information security-related inquiries using a terminal such as a smartphone. This input information is transmitted from the terminal to the server via a secure communication means.

[0105] Upon receiving information, the server uses analysis tools and natural language processing techniques to analyze the input text. Specifically, it uses natural language processing libraries such as spaCy to tokenize the information and extract important keywords and related information. Based on these analysis results, a generative AI model is used to provide the user with appropriate advice. By utilizing AI technology, even ambiguous expressions from the user are transformed into concrete solutions.

[0106] Furthermore, the server references a historical database via an assignment mechanism to select the most suitable person in charge. The case is assigned to the selected person, and its progress is notified to stakeholders in real time through a management mechanism. Risk information and progress status are displayed on the terminal through a visualization mechanism, allowing users to always check the latest information.

[0107] For example, if a user enters "Information leakage prevention measures when introducing a new cloud service" as a consultation request, the server identifies keywords such as "cloud service" and "information leakage prevention measures," and then refers to past cases to suggest appropriate countermeasures. Based on this process, the AI ​​model quickly selects the appropriate information security officer.

[0108] An example of a prompt message to the generating AI model would be, "I would like to consult about the security risks when purchasing new software. What is the best way to implement security measures?" The system will function correctly when a user inputs this kind of message.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses a terminal to input their information security-related inquiries. The entered information is sent to the server via a secure communication method from the terminal. The input here is the user's text message, and the output is the data sent to the server.

[0112] Step 2:

[0113] The server passes the received data to the analysis tool. The analysis tool uses natural language processing techniques to tokenize the received data and extract important keywords and related information. This process uses the spaCy library to parse the text and generate a keyword list. The input here is text data sent by the user, and the output is the analyzed keyword list.

[0114] Step 3:

[0115] Based on the analysis results, the server uses a generative AI model to analyze the user's inquiry in detail and generate specific advice. The input is a keyword list, which is passed to the generative AI model, and the output is suggested advice and solutions. In this process, the AI ​​transforms the user's vague inquiries into specific answers.

[0116] Step 4:

[0117] The server uses an assignment mechanism to reference historical databases and select the most suitable information security officer. This selection includes searches based on past history and current keywords. The input is a keyword list and a database query, and the output is information about the selected officer.

[0118] Step 5:

[0119] The server uses visualization tools to visualize risk information and the progress of consultations, displaying them on the user's terminal. This allows the user to check the latest information. Inputs are analysis results and risk information, and output is visualized information on the user's terminal.

[0120] Step 6:

[0121] The user's terminal receives project progress and update information through a progress management system. This includes a real-time notification system from the server, ensuring the user always receives the latest information. The input is the server's progress information, and the output is the information notified to the user.

[0122] 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.

[0123] This invention is a system for improving the convenience and efficiency of information security consultation services, and in particular aims to further improve the quality of consultation responses by integrating an emotion engine that recognizes the user's emotions. This system receives consultation content from users as input and has means for analyzing that content, as well as an emotion engine that analyzes the user's emotions.

[0124] Specifically, users input their consultation details into the terminal. This input may include not only specific questions and problems, but also words and phrases that suggest the user's emotions. Before sending this input to the server, the terminal performs a preliminary analysis to determine if emotional elements are present.

[0125] The server analyzes the received consultation content in detail. The analysis system has the function of tokenizing the content, extracting keywords, and extracting relevant information. In addition, an emotion engine is incorporated, which makes a judgment of positive or negative emotion based on the user's input. This emotion information is used to adjust the response to the consultation content. For example, if a negative emotion is detected, the system increases the urgency of the response and sends an immediate notification to the person in charge.

[0126] Based on this analysis and emotional information, the server selects the most suitable counselor. The selection process uses an algorithm that considers past interaction history, the counselor's area of ​​expertise, and the user's emotional information. Furthermore, visualization tools present these results and emotional information visually, allowing counselors to easily understand the priority of their involvement and the appropriate course of action.

[0127] For example, if a user expresses concern about the risk of data leakage, the terminal transfers this information to the server, where content analysis and sentiment analysis are performed. The sentiment engine detects expressions indicating the user's anxiety, and the server determines the urgency and sets a top-level response priority. This prepares the support staff to respond quickly and appropriately.

[0128] By utilizing an emotion engine in this way, the present invention can respond more closely to user needs compared to conventional information security consultation systems.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users enter the content of their consultation into the device. They may also enter words or phrases that express their emotions.

[0132] Step 2:

[0133] The device temporarily stores the user's input and filters it to detect emotional elements. This process utilizes an emotion recognition algorithm.

[0134] Step 3:

[0135] The terminal transmits the consultation content, including emotional information, to the server via a secure communication channel. An encryption protocol is used for this transmission.

[0136] Step 4:

[0137] The server tokenizes the received consultation content using an analysis tool, extracts keywords, and identifies relevant information.

[0138] Step 5:

[0139] The server uses an emotion engine to determine the user's emotional state from their input. Based on this, an emotion label (e.g., positive, negative, neutral) is assigned.

[0140] Step 6:

[0141] The server selects the most appropriate counselor using an assignment mechanism based on the analyzed information and emotion labels. Past interaction history and the counselor's expertise are also taken into consideration.

[0142] Step 7:

[0143] The server sends a notification to the selected agent containing the details of the inquiry, relevant information, and an emotion label. This allows the agent to determine the urgency of the response and the resources required.

[0144] Step 8:

[0145] The server monitors the progress of consultations and immediately notifies stakeholders of any significant changes or updates. This ensures that responses are always based on the latest information.

[0146] Step 9:

[0147] The user receives feedback from the server and decides on their course of action based on the proposed solutions and response plans. This marks the successful completion of the consultation process.

[0148] (Example 2)

[0149] 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".

[0150] Conventional information processing systems responded to user inquiries mechanically, making it difficult to provide flexible and appropriate responses that reflected the user's emotions. This resulted in decreased user satisfaction and reduced efficiency in problem-solving. Furthermore, the lack of consideration for user emotions during the selection of personnel sometimes led to inappropriate responses.

[0151] 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.

[0152] In this invention, the server includes a device for receiving information input from a user, including emotions; an analysis means for analyzing the information received from the user and extracting relevant information and emotional information; and an assignment means for selecting a person in charge who needs to respond based on the extracted relevant information and emotional information. This enables a quick and appropriate response that is in line with the user's emotions.

[0153] A "user" refers to someone who inputs information into the system and requests support or consultation.

[0154] "Information input containing emotions" refers to information provided by users through their devices, which includes elements that express emotions in addition to problems and questions.

[0155] "Device" refers to equipment or software configurations used to receive input from users.

[0156] "Analysis means" refers to a system component that has the function of analyzing received information and extracting relevant data and emotional information.

[0157] "Related information" refers to data extracted from user input that is useful for solving problems.

[0158] "Emotional information" refers to data that indicates the emotional state of a user, analyzed from their input.

[0159] An "assignment mechanism" is a system component that has the function of selecting the appropriate person in charge and assigning them the task based on the analysis results.

[0160] "Responsible person" refers to a specialist staff member or support person selected to address users' inquiries and problems.

[0161] "Display means" refers to a device or software function for visually displaying the analyzed results or information.

[0162] "Management tools" are components of a system for tracking the progress of a case and notifying stakeholders of that information.

[0163] An "emotion engine" refers to an algorithm that analyzes the user's emotions from the input information and makes a positive or negative judgment.

[0164] "Urgency" is an indicator that shows whether a problem or consultation requires a prompt response.

[0165] "Past response history" refers to records of responses taken to date, and is data that is referenced when selecting a person in charge.

[0166] This invention is a system aimed at providing prompt and appropriate responses based on the user's emotions in information security consultation services. The system mainly consists of a server, terminals, and users.

[0167] The user first enters their inquiry into the terminal. The terminal receives this information and identifies it as input containing words or phrases that express emotions. Next, the terminal sends the received data to the server. For example, if the user makes an inquiry such as "I'm worried about data breaches," the terminal will send this sentence along with the inquiry to the server.

[0168] The server receives input from the terminal and analyzes emotional information using an emotion engine. This emotion engine has the function of determining whether the emotion is positive or negative. If the user's expression is negative, the server sets the urgency level to high. In addition, the server tokenizes the consultation content using an analysis method and generates relevant information by extracting keywords. The server's analysis information is input into an algorithm that uses past interaction history to select the most suitable person in charge. The selection of a person in charge is based on their area of ​​expertise and emotional information.

[0169] The selected personnel are provided with visualized analysis results and emotional information, enabling them to respond quickly and accurately. The server also includes a progress management function, periodically notifying the personnel and stakeholders of the project's progress and any changes in emotional responses.

[0170] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Given a situation where a user is concerned about a data breach, how can we determine the urgency and optimize the response process?"

[0171] Through the above operations, this invention enables flexible responses that are tailored to the user's emotions, thereby improving the quality of information security consultation services.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user enters their information security concerns into the terminal. This input includes expressions of emotion, such as "I'm worried about data leaks." Text data corresponding to the user's questions and concerns is generated as input data. This text data then enters a processing state within the terminal.

[0175] Step 2:

[0176] The device performs a preliminary analysis of the input data received from the user. Here, it detects emotional keywords within the text data and tags them as metadata. The input is the user's raw text, and the output is text data with emotional tags attached. This process prepares data enhanced with emotional information.

[0177] Step 3:

[0178] The device sends tagged text data to the server. The transmitted data includes text information containing the user's inquiry and sentiment tags, which is then input to the server. Specifically, the server enters a data waiting state due to the packetization and transmission protocol processing of the data.

[0179] Step 4:

[0180] The server analyzes the received data in detail using analytical tools. The input is data transmitted from the terminal, which is tokenized and keywords are extracted. Furthermore, a built-in emotion engine determines the emotional information (positive or negative). This analysis generates a dataset for evaluating the user's situation.

[0181] Step 5:

[0182] The server selects the most suitable person in charge based on the analysis results and sentiment information. The input is the analyzed dataset, and the algorithm is used to search for the most suitable person in charge. The output generates information about the selected person in charge. Specifically, it sets high-priority responses by referring to past response history and the person in charge's area of ​​expertise.

[0183] Step 6:

[0184] The server provides the selection results to the person in charge through a visualization mechanism. The visualized data is provided as input displayed on the person in charge's terminal, and prioritized tasks are output as visual data. This visualization operation allows the person in charge to quickly and accurately formulate countermeasures.

[0185] Step 7:

[0186] The server tracks project progress and periodically notifies project managers and stakeholders of changes in sentiment and progress. This process receives project data to be managed as input, the progress management system processes the data, and status changes are output as notification data. This enables proactive responses tailored to the situation.

[0187] (Application Example 2)

[0188] 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".

[0189] The problem that this invention aims to solve is to provide a more effective information analysis and personnel assignment system in an information security consultation system, in order to accurately analyze the emotions of users and enable a swift and appropriate response based on the results. Furthermore, it aims to realize more user-friendly support for daily life by extending the functions of consumer robots that provide support according to the emotional state of family members and users.

[0190] 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.

[0191] In this invention, the server includes communication means for receiving information input from users, analysis means for analyzing the information and extracting relevant information and emotional information, and assignment means for selecting a responder based on the extracted information. This makes it possible to understand the user's emotional state and provide a quick and appropriate response. Furthermore, it enables life support tailored to the user's emotional information, providing more flexible and appropriate support.

[0192] "Communication means" refers to a function that provides an interface for receiving information input from users and transmitting it to a server.

[0193] "Analysis means" refers to a function that performs a process to analyze the received user information and extract relevant information and emotional information.

[0194] "Emotional information" refers to data about positive or negative emotions extracted from user input.

[0195] The "assignment method" is a function that selects the most suitable person to handle a situation based on the analysis results.

[0196] "Visualization means" refers to a function for visually presenting analyzed emotional information and related information.

[0197] A "response mechanism" is a function that takes action to provide support in accordance with the user's emotional state.

[0198] "Management measures" refer to functions for tracking and monitoring the progress of assigned cases and notifying relevant parties of that information.

[0199] This invention makes it possible to realize a system in which a consumer robot for home use analyzes emotions and provides life support based on that analysis. This system includes support to help users live comfortably in their daily lives in a home environment. Specifically, it consists of a server, a robotic device, and dedicated software.

[0200] The server has a communication method to analyze voice input from users and uses speech recognition software (such as a speech recognition API) to convert the voice data into text. The converted text data is analyzed through an emotion engine (e.g., Microsoft's Text Analytics API) to extract sentiment information.

[0201] The robotic device uses this emotional information to provide situation-appropriate support. For example, if a positive emotion is detected, it displays a congratulatory message; if a negative emotion is detected, it plays relaxing music or offers words of encouragement. These actions are performed by response means assigned based on information provided by the analysis means.

[0202] For example, if the robot detects signs of stress while a family is eating, it can suggest, "Why don't you take it easy for a bit?" By combining the emotion analysis technology and robot motion control technology shown in these application examples, it is possible to create a more harmonious home environment.

[0203] An example of a prompt for a generative AI model is, "Design a function for a home robot that analyzes the emotions of a user's conversation and suggests relaxation techniques based on the results." This is expected to allow the generative AI model to provide information that complements the design of an appropriate function.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] When a user speaks to a home robot, the robot receives the voice. The input is the user's voice data. The robot takes this voice data and prepares to send it to the server.

[0207] Step 2:

[0208] The server converts the received audio data into text data using a speech recognition API. The output of this step is the text converted from the audio data. During the conversion process, a language model analyzes phonemes and identifies corresponding words.

[0209] Step 3:

[0210] The server passes the converted text data to the sentiment analysis engine. The input is the text data obtained in step 2, and the analysis engine extracts sentiment information. In the data calculation, the nuances of the words and phrases in the text are calculated to determine whether the sentiment is positive, negative, or neutral.

[0211] Step 4:

[0212] Upon receiving the emotional information, the server generates instructions for the robot. The input is the emotional information obtained in step 3, and the output is the specific action the robot should perform. The server selects a response appropriate to the situation and sends instructions to the robot, such as a message of joy for positive emotions or guidance to relax for negative emotions.

[0213] Step 5:

[0214] The robot performs actions based on instructions from the server. The input is an action instruction generated in step 4, and the specific actions include responding to the user's emotions, such as playing an audio message, playing music, or displaying a message on the screen.

[0215] 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.

[0216] Data generation model 58 is a type of 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 those described above. 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 shown 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention is a system for efficiently and effectively providing information security consultation services. The system includes functions to receive information input from users, analyze it, and automatically perform appropriate processing.

[0232] Specifically, users input their questions or concerns via their device. The device then transmits this input information to a server using a secure communication method. The server analyzes the received information in detail using analytical tools. This analysis process includes tokenizing the information using natural language processing technology, extracting keywords, and referencing past databases related to the consultation content. Ambiguous expressions are automatically extracted, and suggestions for improvement are provided to help users identify their consultation content more specifically.

[0233] Based on these analysis results, the server uses an assignment mechanism to assign the most suitable person to the inquiry. The selected person will use past cases and relevant materials to respond to the inquiry quickly and effectively. The server also visualizes risk information and presents important information to users and assigned personnel in an easily understandable format.

[0234] Furthermore, the server manages the progress of projects and notifies stakeholders of updates in real time. This ensures that users and personnel can always proceed with their work based on the latest information.

[0235] As a concrete example, consider a case where a user consults about "measures to prevent information leakage when introducing a new cloud service." When the user enters this consultation request, the terminal sends it to the server. The server identifies keywords such as "cloud service" and "measures to prevent information leakage," and by referring to relevant past consultation history and laws, provides the user with appropriate risk information and countermeasures. Based on this, the server automatically assigns an information security officer with specialized knowledge, then manages the progress, and sends feedback to the user.

[0236] Thus, the present invention provides a concrete form for achieving increased efficiency and consistent quality improvement in information security consultations.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user enters their questions and concerns about information security into the terminal. These entries include specific questions and points of confusion.

[0240] Step 2:

[0241] The terminal encrypts the entered consultation content and transmits it to the server via a secure communication channel. This process maintains the integrity and confidentiality of the data.

[0242] Step 3:

[0243] The server processes the received consultation content using analytical tools and tokenizes it using natural language processing. It also extracts important keywords and performs contextual semantic analysis.

[0244] Step 4:

[0245] The server searches its internal database for relevant past consultation cases based on the analyzed information, and uses this to suggest similar consultations and identify risk points.

[0246] Step 5:

[0247] The server automatically generates specific mitigation measures for identified risk points and presents ambiguous statements as suggested improvements. At this point, the user may be asked to provide additional information.

[0248] Step 6:

[0249] Based on the analysis results, the server automatically selects the most suitable consultant using an assignment mechanism. The selection criteria include past response history and expertise.

[0250] Step 7:

[0251] The server notifies assigned personnel of the case information and related documents to be handled, enabling them to begin responding quickly.

[0252] Step 8:

[0253] The server monitors the progress of consultations in real time, and if the monitoring system detects any changes in the progress status, it notifies users and stakeholders as necessary.

[0254] Step 9:

[0255] The user receives final feedback from the server and confirms the problem resolution and future course of action. This completes the resolution of the inquiry.

[0256] (Example 1)

[0257] 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."

[0258] In information security consulting services, the diverse nature of user questions and ambiguous language make it difficult to respond quickly and accurately. Furthermore, effectively utilizing past response records and quickly assigning the most suitable personnel is also a challenge. Additionally, visually presenting risk information and managing case progress in real time are difficult. Therefore, a system is needed to enable appropriate information analysis and efficient operational management.

[0259] 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.

[0260] In this invention, the server includes communication means via a terminal that receives information data, analysis means using a generative AI model that analyzes the information and extracts important terms, and visualization means that visually displays the risks based on the analysis results. This makes it possible to concretize the user's vague questions and quickly present the optimal countermeasures. Furthermore, it is possible to select the most suitable personnel by utilizing past response records and improve the quality and effectiveness of consultations.

[0261] "Communication means" refers to devices and methods for sending and receiving information, and in particular, those that have the function of securely transmitting user input data to a server.

[0262] "Analysis means" refers to systems and methods for analyzing input information and extracting important words and relevant data, and utilizes generative AI models.

[0263] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze text data and extract specific patterns or phrases.

[0264] "Assignment means" refers to a method or system for automatically selecting the most suitable person based on the results of the analysis, and includes the ability to refer to past history.

[0265] "Visualization methods" refer to technologies that visually display analyzed data and present it in a way that is easy for users and personnel to understand.

[0266] "Management means" refers to a method or system that has the function of tracking the progress of a project and notifying relevant parties of that information.

[0267] This invention provides a system for efficiently conducting information security consultations. Users input their questions and concerns regarding information security into a terminal. This terminal transmits the user input information to a server using a secure communication method. The server then analyzes the received information using a generation AI model.

[0268] The server uses natural language processing technology to tokenize user input and extract key terms. This analysis identifies keywords in the user's inquiry and references relevant historical databases. The server identifies ambiguous expressions and, through a generative AI model, presents improvement suggestions to the user. For example, if a user enters a question about "measures to prevent information leakage when introducing a new cloud service," the server extracts keywords such as "cloud service" and "measures to prevent information leakage."

[0269] Based on this, the server automatically selects the most suitable expert to handle the consultation using an assignment mechanism. This selection process utilizes past response history to ensure the assignment of a more appropriate person. The server also visually displays risk information along with the analysis results, providing information to users and staff in a dashboard format.

[0270] Furthermore, the server tracks the progress of the project and notifies stakeholders of updates as they occur. This ensures that users and staff always have the latest information to guide their actions. As users progress through the consultation process, the server updates information in real time, supporting efficient business operations.

[0271] A concrete example of a prompt is, "When introducing a new cloud service, I would like to know the best practices for preventing information leaks. I would also like to know about relevant laws and past cases." Based on this prompt, the system can provide accurate information and countermeasures.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user enters their information security inquiry into the terminal. They type a prompt such as "Measures to prevent information leakage when introducing a new cloud service" into the terminal's input field. Once input is complete, the terminal sends this data to the server via a secure communication method. In this step, the input is the user's inquiry text, and the output is the inquiry data sent to the server.

[0275] Step 2:

[0276] The server analyzes the received consultation data using a generation AI model. Specifically, a natural language processing module within the server tokenizes the text and extracts important keywords. Data processing involves operations such as decomposing strings into tokens and identifying keywords to grasp the core of the consultation content entered by the user. The input for this step is the consultation data sent to the server, and the output is the extracted keywords and related database entries.

[0277] Step 3:

[0278] The server identifies ambiguous expressions identified through analysis and uses a generative AI model to present specific improvement suggestions to the user. This clarifies the user's inquiry and facilitates the next process. The data calculations involve automatic detection of ambiguous expressions and generation of appropriate explanatory texts. The input for this step is extracted keywords, and the output is improvement suggestions for the user.

[0279] Step 4:

[0280] Based on the analysis results, the server automatically selects an appropriate information security expert using an assignment mechanism. This selection takes into account past response history and expertise data. The server refers to the database to determine the most suitable person and configures that information within the system. The input for this step is the analysis results and database information, and the output is the information of the selected person.

[0281] Step 5:

[0282] The server visualizes risk information and provides it to users and responsible persons. In this process, dashboards and reports based on the analysis results are generated and displayed. The server visualizes important information as graphs and texts and displays it on the system screen in a form that is easy for users and responsible persons to understand. The input for this step is the analysis result, and the output is the visualized risk information.

[0283] Step 6:

[0284] The server continuously monitors the progress information of the project and notifies users and relevant organizational members in real time. The server collects progress data and immediately notifies via email or chat tools when there is a change. As a result, relevant parties can always act based on the latest information. The input for this step is the progress status, and the output is the notification information to relevant parties.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In the consulting business regarding information security, it is required to quickly and accurately solve various doubts and problems that users have. However, in the conventional methods, information analysis and appropriate allocation of responsible persons are often performed manually, and there is a problem that it is difficult to maintain efficiency and quality consistency. Also, in progress management and visualization of risk information, information is easily dissipated, and it is difficult for relevant parties to always grasp the latest information. There is a need to provide a new means to solve such problems.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes support means for receiving user inquiries, analyzing them in detail using a generated AI model, and providing appropriate advice; display means for visualizing the specific content of the user's inquiry in real time; and assignment means for selecting the most suitable person in charge based on past response history and the generated AI model. This makes it possible to perform information security consultation services efficiently and with high quality.

[0290] "A communication method for receiving information input from users" refers to a method for securely transmitting consultation information from users to a server via a terminal.

[0291] "Analysis methods" refer to techniques for analyzing received information in detail and extracting relevant keywords and problems.

[0292] "Assignment method" refers to a method for automatically selecting the most suitable person based on the results of the analysis.

[0293] "Visualization methods" are techniques for visually representing information and risks and providing them in a format that is easily understandable to users and those in charge.

[0294] "Management measures" refer to methods for tracking the progress of a project and notifying stakeholders of the latest information.

[0295] "A support method that analyzes the specific content of a user's consultation and provides advice using a generative AI model" refers to a method that utilizes AI technology to analyze the content of a user's consultation and present appropriate advice and solutions.

[0296] "A display method that visualizes the progress of consultations in real time via a terminal" refers to a method for displaying the progress of consultations and related information on the user's terminal so that they can instantly check it.

[0297] The system for implementing this invention consists of a server, a terminal, and software including a generative AI model. First, the user inputs information security-related inquiries using a terminal such as a smartphone. This input information is transmitted from the terminal to the server via a secure communication means.

[0298] Upon receiving information, the server uses analysis tools and natural language processing techniques to analyze the input text. Specifically, it uses natural language processing libraries such as spaCy to tokenize the information and extract important keywords and related information. Based on these analysis results, a generative AI model is used to provide the user with appropriate advice. By utilizing AI technology, even ambiguous expressions from the user are transformed into concrete solutions.

[0299] Furthermore, the server references a historical database via an assignment mechanism to select the most suitable person in charge. The case is assigned to the selected person, and its progress is notified to stakeholders in real time through a management mechanism. Risk information and progress status are displayed on the terminal through a visualization mechanism, allowing users to always check the latest information.

[0300] For example, if a user enters "Information leakage prevention measures when introducing a new cloud service" as a consultation request, the server identifies keywords such as "cloud service" and "information leakage prevention measures," and then refers to past cases to suggest appropriate countermeasures. Based on this process, the AI ​​model quickly selects the appropriate information security officer.

[0301] An example of a prompt message to the generating AI model would be, "I would like to consult about the security risks when purchasing new software. What is the best way to implement security measures?" The system will function correctly when a user inputs this kind of message.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The user uses the terminal to input the consultation content regarding information security. The input information is sent by the terminal to the server using secure communication means. Here, the input is the user's text message, and the output is the data transmitted to the server.

[0305] Step 2:

[0306] The server passes the received data to the analysis means. The analysis means uses natural language processing technology to tokenize the received data and extract important keywords and related information. In this process, the spaCy library is used to parse the text and generate a keyword list. Here, the input is the text data sent by the user, and the output is the analyzed keyword list.

[0307] Step 3:

[0308] Based on the analysis results, the server uses the generative AI model to analyze the user's consultation content in detail and generate specific advice. The input is the keyword list, which is passed to the generative AI model, and the advice or solutions proposed as the output are obtained. In this process, the AI converts the user's ambiguous inquiry into a specific answer.

[0309] Step 4:

[0310] The server refers to the past database through the allocation means and selects the most suitable information security staff. This selection includes a search based on past history and current keywords. The input is the keyword list and the database query, and the output is the information of the selected staff.

[0311] Step 5:

[0312] The server uses visualization tools to visualize risk information and the progress of consultations, displaying them on the user's terminal. This allows the user to check the latest information. Inputs are analysis results and risk information, and output is visualized information on the user's terminal.

[0313] Step 6:

[0314] The user's terminal receives project progress and update information through a progress management system. This includes a real-time notification system from the server, ensuring the user always receives the latest information. The input is the server's progress information, and the output is the information notified to the user.

[0315] 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.

[0316] This invention is a system for improving the convenience and efficiency of information security consultation services, and in particular aims to further improve the quality of consultation responses by integrating an emotion engine that recognizes the user's emotions. This system receives consultation content from users as input and has means for analyzing that content, as well as an emotion engine that analyzes the user's emotions.

[0317] Specifically, users input their consultation details into the terminal. This input may include not only specific questions and problems, but also words and phrases that suggest the user's emotions. Before sending this input to the server, the terminal performs a preliminary analysis to determine if emotional elements are present.

[0318] The server analyzes the received consultation content in detail. The analysis system has the function of tokenizing the content, extracting keywords, and extracting relevant information. In addition, an emotion engine is incorporated, which makes a judgment of positive or negative emotion based on the user's input. This emotion information is used to adjust the response to the consultation content. For example, if a negative emotion is detected, the system increases the urgency of the response and sends an immediate notification to the person in charge.

[0319] Based on this analysis and emotional information, the server selects the most suitable counselor. The selection process uses an algorithm that considers past interaction history, the counselor's area of ​​expertise, and the user's emotional information. Furthermore, visualization tools present these results and emotional information visually, allowing counselors to easily understand the priority of their involvement and the appropriate course of action.

[0320] For example, if a user expresses concern about the risk of data leakage, the terminal transfers this information to the server, where content analysis and sentiment analysis are performed. The sentiment engine detects expressions indicating the user's anxiety, and the server determines the urgency and sets a top-level response priority. This prepares the support staff to respond quickly and appropriately.

[0321] By utilizing an emotion engine in this way, the present invention can respond more closely to user needs compared to conventional information security consultation systems.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] Users enter the content of their consultation into the device. They may also enter words or phrases that express their emotions.

[0325] Step 2:

[0326] The device temporarily stores the user's input and filters it to detect emotional elements. This process utilizes an emotion recognition algorithm.

[0327] Step 3:

[0328] The terminal transmits the consultation content, including emotional information, to the server via a secure communication channel. An encryption protocol is used for this transmission.

[0329] Step 4:

[0330] The server tokenizes the received consultation content using an analysis tool, extracts keywords, and identifies relevant information.

[0331] Step 5:

[0332] The server uses an emotion engine to determine the user's emotional state from their input. Based on this, an emotion label (e.g., positive, negative, neutral) is assigned.

[0333] Step 6:

[0334] The server selects the most appropriate counselor using an assignment mechanism based on the analyzed information and emotion labels. Past interaction history and the counselor's expertise are also taken into consideration.

[0335] Step 7:

[0336] The server sends a notification to the selected agent containing the details of the inquiry, relevant information, and an emotion label. This allows the agent to determine the urgency of the response and the resources required.

[0337] Step 8:

[0338] The server monitors the progress of consultations and immediately notifies stakeholders of any significant changes or updates. This ensures that responses are always based on the latest information.

[0339] Step 9:

[0340] The user receives feedback from the server and decides on their course of action based on the proposed solutions and response plans. This marks the successful completion of the consultation process.

[0341] (Example 2)

[0342] 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".

[0343] Conventional information processing systems responded to user inquiries mechanically, making it difficult to provide flexible and appropriate responses that reflected the user's emotions. This resulted in decreased user satisfaction and reduced efficiency in problem-solving. Furthermore, the lack of consideration for user emotions during the selection of personnel sometimes led to inappropriate responses.

[0344] 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.

[0345] In this invention, the server includes a device for receiving information input from a user, including emotions; an analysis means for analyzing the information received from the user and extracting relevant information and emotional information; and an assignment means for selecting a person in charge who needs to respond based on the extracted relevant information and emotional information. This enables a quick and appropriate response that is in line with the user's emotions.

[0346] A "user" refers to someone who inputs information into the system and requests support or consultation.

[0347] "Information input containing emotions" refers to information provided by users through their devices, which includes elements that express emotions in addition to problems and questions.

[0348] "Device" refers to equipment or software configurations used to receive input from users.

[0349] "Analysis means" refers to a system component that has the function of analyzing received information and extracting relevant data and emotional information.

[0350] "Related information" refers to data extracted from user input that is useful for solving problems.

[0351] "Emotional information" refers to data that indicates the emotional state of a user, analyzed from their input.

[0352] An "assignment mechanism" is a system component that has the function of selecting the appropriate person in charge and assigning them the task based on the analysis results.

[0353] "Responsible person" refers to a specialist staff member or support person selected to address users' inquiries and problems.

[0354] "Display means" refers to a device or software function for visually displaying the analyzed results or information.

[0355] "Management tools" are components of a system for tracking the progress of a case and notifying stakeholders of that information.

[0356] An "emotion engine" refers to an algorithm that analyzes the user's emotions from the input information and makes a positive or negative judgment.

[0357] "Urgency" is an indicator that shows whether a problem or consultation requires a prompt response.

[0358] "Past response history" refers to records of responses taken to date, and is data that is referenced when selecting a person in charge.

[0359] This invention is a system aimed at providing prompt and appropriate responses based on the user's emotions in information security consultation services. The system mainly consists of a server, terminals, and users.

[0360] The user first enters their inquiry into the terminal. The terminal receives this information and identifies it as input containing words or phrases that express emotions. Next, the terminal sends the received data to the server. For example, if the user makes an inquiry such as "I'm worried about data breaches," the terminal will send this sentence along with the inquiry to the server.

[0361] The server receives input from the terminal and analyzes emotional information using an emotion engine. This emotion engine has the function of determining whether the emotion is positive or negative. If the user's expression is negative, the server sets the urgency level to high. In addition, the server tokenizes the consultation content using an analysis method and generates relevant information by extracting keywords. The server's analysis information is input into an algorithm that uses past interaction history to select the most suitable person in charge. The selection of a person in charge is based on their area of ​​expertise and emotional information.

[0362] The selected personnel are provided with visualized analysis results and emotional information, enabling them to respond quickly and accurately. The server also includes a progress management function, periodically notifying the personnel and stakeholders of the project's progress and any changes in emotional responses.

[0363] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Given a situation where a user is concerned about a data breach, how can we determine the urgency and optimize the response process?"

[0364] Through the above operations, this invention enables flexible responses that are tailored to the user's emotions, thereby improving the quality of information security consultation services.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The user enters their information security concerns into the terminal. This input includes expressions of emotion, such as "I'm worried about data leaks." Text data corresponding to the user's questions and concerns is generated as input data. This text data then enters a processing state within the terminal.

[0368] Step 2:

[0369] The device performs a preliminary analysis of the input data received from the user. Here, it detects emotional keywords within the text data and tags them as metadata. The input is the user's raw text, and the output is text data with emotional tags attached. This process prepares data enhanced with emotional information.

[0370] Step 3:

[0371] The device sends tagged text data to the server. The transmitted data includes text information containing the user's inquiry and sentiment tags, which is then input to the server. Specifically, the server enters a data waiting state due to the packetization and transmission protocol processing of the data.

[0372] Step 4:

[0373] The server analyzes the received data in detail using analytical tools. The input is data transmitted from the terminal, which is tokenized and keywords are extracted. Furthermore, a built-in emotion engine determines the emotional information (positive or negative). This analysis generates a dataset for evaluating the user's situation.

[0374] Step 5:

[0375] The server selects the most suitable person in charge based on the analysis results and sentiment information. The input is the analyzed dataset, and the algorithm is used to search for the most suitable person in charge. The output generates information about the selected person in charge. Specifically, it sets high-priority responses by referring to past response history and the person in charge's area of ​​expertise.

[0376] Step 6:

[0377] The server provides the selection results to the person in charge through a visualization mechanism. The visualized data is provided as input displayed on the person in charge's terminal, and prioritized tasks are output as visual data. This visualization operation allows the person in charge to quickly and accurately formulate countermeasures.

[0378] Step 7:

[0379] The server tracks project progress and periodically notifies project managers and stakeholders of changes in sentiment and progress. This process receives project data to be managed as input, the progress management system processes the data, and status changes are output as notification data. This enables proactive responses tailored to the situation.

[0380] (Application Example 2)

[0381] 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."

[0382] The problem that this invention aims to solve is to provide a more effective information analysis and personnel assignment system in an information security consultation system, in order to accurately analyze the emotions of users and enable a swift and appropriate response based on the results. Furthermore, it aims to realize more user-friendly support for daily life by extending the functions of consumer robots that provide support according to the emotional state of family members and users.

[0383] 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.

[0384] In this invention, the server includes communication means for receiving information input from users, analysis means for analyzing the information and extracting relevant information and emotional information, and assignment means for selecting a responder based on the extracted information. This makes it possible to understand the user's emotional state and provide a quick and appropriate response. Furthermore, it enables life support tailored to the user's emotional information, providing more flexible and appropriate support.

[0385] "Communication means" refers to a function that provides an interface for receiving information input from users and transmitting it to a server.

[0386] "Analysis means" refers to a function that performs a process to analyze the received user information and extract relevant information and emotional information.

[0387] "Emotional information" refers to data about positive or negative emotions extracted from user input.

[0388] The "assignment method" is a function that selects the most suitable person to handle a situation based on the analysis results.

[0389] "Visualization means" refers to a function for visually presenting analyzed emotional information and related information.

[0390] A "response mechanism" is a function that takes action to provide support in accordance with the user's emotional state.

[0391] "Management measures" refer to functions for tracking and monitoring the progress of assigned cases and notifying relevant parties of that information.

[0392] This invention makes it possible to realize a system in which a consumer robot for home use analyzes emotions and provides life support based on that analysis. This system includes support to help users live comfortably in their daily lives in a home environment. Specifically, it consists of a server, a robotic device, and dedicated software.

[0393] The server has a communication method to analyze voice input from users and uses speech recognition software (such as a speech recognition API) to convert the voice data into text. The converted text data is then analyzed through an emotion engine (e.g., Microsoft's Text Analytics API) to extract sentiment information.

[0394] The robotic device uses this emotional information to provide situation-appropriate support. For example, if a positive emotion is detected, it displays a congratulatory message; if a negative emotion is detected, it plays relaxing music or offers words of encouragement. These actions are performed by response means assigned based on information provided by the analysis means.

[0395] For example, if the robot detects signs of stress while a family is eating, it can suggest, "Why don't you take it easy for a bit?" By combining the emotion analysis technology and robot motion control technology shown in these application examples, it is possible to create a more harmonious home environment.

[0396] An example of a prompt for a generative AI model is, "Design a function for a home robot that analyzes the emotions of a user's conversation and suggests relaxation techniques based on the results." This is expected to allow the generative AI model to provide information that complements the design of an appropriate function.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] When a user speaks to a home robot, the robot receives the voice. The input is the user's voice data. The robot takes this voice data and prepares to send it to the server.

[0400] Step 2:

[0401] The server converts the received audio data into text data using a speech recognition API. The output of this step is the text converted from the audio data. During the conversion process, a language model analyzes phonemes and identifies corresponding words.

[0402] Step 3:

[0403] The server passes the converted text data to the sentiment analysis engine. The input is the text data obtained in step 2, and the analysis engine extracts sentiment information. In the data calculation, the nuances of the words and phrases in the text are calculated to determine whether the sentiment is positive, negative, or neutral.

[0404] Step 4:

[0405] Upon receiving the emotional information, the server generates instructions for the robot. The input is the emotional information obtained in step 3, and the output is the specific action the robot should perform. The server selects a response appropriate to the situation and sends instructions to the robot, such as a message of joy for positive emotions or guidance to relax for negative emotions.

[0406] Step 5:

[0407] The robot performs actions based on instructions from the server. The input is an action instruction generated in step 4, and the specific actions include responding to the user's emotions, such as playing an audio message, playing music, or displaying a message on the screen.

[0408] 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.

[0409] 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 those described above. 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 shown 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention is a system for efficiently and effectively providing information security consultation services. The system includes functions to receive information input from users, analyze it, and automatically perform appropriate processing.

[0425] Specifically, users input their questions or concerns via their device. The device then transmits this input information to a server using a secure communication method. The server analyzes the received information in detail using analytical tools. This analysis process includes tokenizing the information using natural language processing technology, extracting keywords, and referencing past databases related to the consultation content. Ambiguous expressions are automatically extracted, and suggestions for improvement are provided to help users identify their consultation content more specifically.

[0426] Based on these analysis results, the server uses an assignment mechanism to assign the most suitable person to the inquiry. The selected person will use past cases and relevant materials to respond to the inquiry quickly and effectively. The server also visualizes risk information and presents important information to users and assigned personnel in an easily understandable format.

[0427] Furthermore, the server manages the progress of projects and notifies stakeholders of updates in real time. This ensures that users and personnel can always proceed with their work based on the latest information.

[0428] As a concrete example, consider a case where a user consults about "measures to prevent information leakage when introducing a new cloud service." When the user enters this consultation request, the terminal sends it to the server. The server identifies keywords such as "cloud service" and "measures to prevent information leakage," and by referring to relevant past consultation history and laws, provides the user with appropriate risk information and countermeasures. Based on this, the server automatically assigns an information security officer with specialized knowledge, then manages the progress, and sends feedback to the user.

[0429] Thus, the present invention provides a concrete form for achieving increased efficiency and consistent quality improvement in information security consultations.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The user enters their questions and concerns about information security into the terminal. These entries include specific questions and points of confusion.

[0433] Step 2:

[0434] The terminal encrypts the entered consultation content and transmits it to the server via a secure communication channel. This process maintains the integrity and confidentiality of the data.

[0435] Step 3:

[0436] The server processes the received consultation content using analytical tools and tokenizes it using natural language processing. It also extracts important keywords and performs contextual semantic analysis.

[0437] Step 4:

[0438] The server searches its internal database for relevant past consultation cases based on the analyzed information, and uses this to suggest similar consultations and identify risk points.

[0439] Step 5:

[0440] The server automatically generates specific mitigation measures for identified risk points and presents ambiguous statements as suggested improvements. At this point, the user may be asked to provide additional information.

[0441] Step 6:

[0442] Based on the analysis results, the server automatically selects the most suitable consultant using an assignment mechanism. The selection criteria include past response history and expertise.

[0443] Step 7:

[0444] The server notifies assigned personnel of the case information and related documents to be handled, enabling them to begin responding quickly.

[0445] Step 8:

[0446] The server monitors the progress of consultations in real time, and if the monitoring system detects any changes in the progress status, it notifies users and stakeholders as necessary.

[0447] Step 9:

[0448] The user receives final feedback from the server and confirms the problem resolution and future course of action. This completes the resolution of the inquiry.

[0449] (Example 1)

[0450] 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."

[0451] In information security consulting services, the diverse nature of user questions and ambiguous language make it difficult to respond quickly and accurately. Furthermore, effectively utilizing past response records and quickly assigning the most suitable personnel is also a challenge. Additionally, visually presenting risk information and managing case progress in real time are difficult. Therefore, a system is needed to enable appropriate information analysis and efficient operational management.

[0452] 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.

[0453] In this invention, the server includes communication means via a terminal that receives information data, analysis means using a generative AI model that analyzes the information and extracts important terms, and visualization means that visually displays the risks based on the analysis results. This makes it possible to concretize the user's vague questions and quickly present the optimal countermeasures. Furthermore, it is possible to select the most suitable personnel by utilizing past response records and improve the quality and effectiveness of consultations.

[0454] "Communication means" refers to devices and methods for sending and receiving information, and in particular, those that have the function of securely transmitting user input data to a server.

[0455] "Analysis means" refers to systems and methods for analyzing input information and extracting important words and relevant data, and utilizes generative AI models.

[0456] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze text data and extract specific patterns or phrases.

[0457] "Assignment means" refers to a method or system for automatically selecting the most suitable person based on the results of the analysis, and includes the ability to refer to past history.

[0458] "Visualization methods" refer to technologies that visually display analyzed data and present it in a way that is easy for users and personnel to understand.

[0459] "Management means" refers to a method or system that has the function of tracking the progress of a project and notifying relevant parties of that information.

[0460] This invention provides a system for efficiently conducting information security consultations. Users input their questions and concerns regarding information security into a terminal. This terminal transmits the user input information to a server using a secure communication method. The server then analyzes the received information using a generation AI model.

[0461] The server uses natural language processing technology to tokenize user input and extract key terms. This analysis identifies keywords in the user's inquiry and references relevant historical databases. The server identifies ambiguous expressions and, through a generative AI model, presents improvement suggestions to the user. For example, if a user enters a question about "measures to prevent information leakage when introducing a new cloud service," the server extracts keywords such as "cloud service" and "measures to prevent information leakage."

[0462] Based on this, the server automatically selects the most suitable expert to handle the consultation using an assignment mechanism. This selection process utilizes past response history to ensure the assignment of a more appropriate person. The server also visually displays risk information along with the analysis results, providing information to users and staff in a dashboard format.

[0463] Furthermore, the server tracks the progress of the project and notifies stakeholders of updates as they occur. This ensures that users and staff always have the latest information to guide their actions. As users progress through the consultation process, the server updates information in real time, supporting efficient business operations.

[0464] A concrete example of a prompt is, "When introducing a new cloud service, I would like to know the best practices for preventing information leaks. I would also like to know about relevant laws and past cases." Based on this prompt, the system can provide accurate information and countermeasures.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The user enters their information security inquiry into the terminal. They type a prompt such as "Measures to prevent information leakage when introducing a new cloud service" into the terminal's input field. Once input is complete, the terminal sends this data to the server via a secure communication method. In this step, the input is the user's inquiry text, and the output is the inquiry data sent to the server.

[0468] Step 2:

[0469] The server analyzes the received consultation data using a generation AI model. Specifically, a natural language processing module within the server tokenizes the text and extracts important keywords. Data processing involves operations such as decomposing strings into tokens and identifying keywords to grasp the core of the consultation content entered by the user. The input for this step is the consultation data sent to the server, and the output is the extracted keywords and related database entries.

[0470] Step 3:

[0471] The server identifies ambiguous expressions identified through analysis and uses a generative AI model to present specific improvement suggestions to the user. This clarifies the user's inquiry and facilitates the next process. The data calculations involve automatic detection of ambiguous expressions and generation of appropriate explanatory texts. The input for this step is extracted keywords, and the output is improvement suggestions for the user.

[0472] Step 4:

[0473] Based on the analysis results, the server automatically selects an appropriate information security expert using an assignment mechanism. This selection takes into account past response history and expertise data. The server refers to the database to determine the most suitable person and configures that information within the system. The input for this step is the analysis results and database information, and the output is the information of the selected person.

[0474] Step 5:

[0475] The server visualizes risk information and provides it to users and personnel. This process generates and displays dashboards and reports based on the analysis results. The server visualizes important information as diagrams and text, displaying it on the system screen in a way that is easy for users and personnel to understand. The input for this step is the analysis results, and the output is visualized risk information.

[0476] Step 6:

[0477] The server continuously monitors project progress and notifies users and relevant organizational members in real time. The server collects progress data and immediately notifies users via email or chat tools when changes occur. This ensures that stakeholders can always act based on the latest information. The input for this step is the progress status, and the output is the notification information for stakeholders.

[0478] (Application Example 1)

[0479] 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."

[0480] In information security consulting services, it is essential to quickly and accurately resolve the diverse questions and problems that users have. However, traditional methods often involve manual information analysis and appropriate assignment of personnel, making it difficult to maintain efficiency and consistent quality. Furthermore, progress management and visualization of risk information are prone to information scattering, making it difficult for stakeholders to always keep up-to-date. There is a need to provide new methods to solve these challenges.

[0481] 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.

[0482] In this invention, the server includes support means for receiving user inquiries, analyzing them in detail using a generated AI model, and providing appropriate advice; display means for visualizing the specific content of the user's inquiry in real time; and assignment means for selecting the most suitable person in charge based on past response history and the generated AI model. This makes it possible to perform information security consultation services efficiently and with high quality.

[0483] "A communication method for receiving information input from users" refers to a method for securely transmitting consultation information from users to a server via a terminal.

[0484] "Analysis methods" refer to techniques for analyzing received information in detail and extracting relevant keywords and problems.

[0485] "Assignment method" refers to a method for automatically selecting the most suitable person based on the results of the analysis.

[0486] "Visualization methods" are techniques for visually representing information and risks and providing them in a format that is easily understandable to users and those in charge.

[0487] "Management measures" refer to methods for tracking the progress of a project and notifying stakeholders of the latest information.

[0488] "A support method that analyzes the specific content of a user's consultation and provides advice using a generative AI model" refers to a method that utilizes AI technology to analyze the content of a user's consultation and present appropriate advice and solutions.

[0489] "A display method that visualizes the progress of consultations in real time via a terminal" refers to a method for displaying the progress of consultations and related information on the user's terminal so that they can instantly check it.

[0490] The system for implementing this invention consists of a server, a terminal, and software including a generative AI model. First, the user inputs information security-related inquiries using a terminal such as a smartphone. This input information is transmitted from the terminal to the server via a secure communication means.

[0491] Upon receiving information, the server uses analysis tools and natural language processing techniques to analyze the input text. Specifically, it uses natural language processing libraries such as spaCy to tokenize the information and extract important keywords and related information. Based on these analysis results, a generative AI model is used to provide the user with appropriate advice. By utilizing AI technology, even ambiguous expressions from the user are transformed into concrete solutions.

[0492] Furthermore, the server references a historical database via an assignment mechanism to select the most suitable person in charge. The case is assigned to the selected person, and its progress is notified to stakeholders in real time through a management mechanism. Risk information and progress status are displayed on the terminal through a visualization mechanism, allowing users to always check the latest information.

[0493] For example, if a user enters "Information leakage prevention measures when introducing a new cloud service" as a consultation request, the server identifies keywords such as "cloud service" and "information leakage prevention measures," and then refers to past cases to suggest appropriate countermeasures. Based on this process, the AI ​​model quickly selects the appropriate information security officer.

[0494] An example of a prompt message to the generating AI model would be, "I would like to consult about the security risks when purchasing new software. What is the best way to implement security measures?" The system will function correctly when a user inputs this kind of message.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The user uses a terminal to input their information security-related inquiries. The entered information is sent to the server via a secure communication method from the terminal. The input here is the user's text message, and the output is the data sent to the server.

[0498] Step 2:

[0499] The server passes the received data to the analysis tool. The analysis tool uses natural language processing techniques to tokenize the received data and extract important keywords and related information. This process uses the spaCy library to parse the text and generate a keyword list. The input here is text data sent by the user, and the output is the analyzed keyword list.

[0500] Step 3:

[0501] Based on the analysis results, the server uses a generative AI model to analyze the user's inquiry in detail and generate specific advice. The input is a keyword list, which is passed to the generative AI model, and the output is suggested advice and solutions. In this process, the AI ​​transforms the user's vague inquiries into specific answers.

[0502] Step 4:

[0503] The server uses an assignment mechanism to reference historical databases and select the most suitable information security officer. This selection includes searches based on past history and current keywords. The input is a keyword list and a database query, and the output is information about the selected officer.

[0504] Step 5:

[0505] The server uses visualization tools to visualize risk information and the progress of consultations, displaying them on the user's terminal. This allows the user to check the latest information. Inputs are analysis results and risk information, and output is visualized information on the user's terminal.

[0506] Step 6:

[0507] The user's terminal receives project progress and update information through a progress management system. This includes a real-time notification system from the server, ensuring the user always receives the latest information. The input is the server's progress information, and the output is the information notified to the user.

[0508] 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.

[0509] This invention is a system for improving the convenience and efficiency of information security consultation services, and in particular aims to further improve the quality of consultation responses by integrating an emotion engine that recognizes the user's emotions. This system receives consultation content from users as input and has means for analyzing that content, as well as an emotion engine that analyzes the user's emotions.

[0510] Specifically, users input their consultation details into the terminal. This input may include not only specific questions and problems, but also words and phrases that suggest the user's emotions. Before sending this input to the server, the terminal performs a preliminary analysis to determine if emotional elements are present.

[0511] The server analyzes the received consultation content in detail. The analysis system has the function of tokenizing the content, extracting keywords, and extracting relevant information. In addition, an emotion engine is incorporated, which makes a judgment of positive or negative emotion based on the user's input. This emotion information is used to adjust the response to the consultation content. For example, if a negative emotion is detected, the system increases the urgency of the response and sends an immediate notification to the person in charge.

[0512] Based on this analysis and emotional information, the server selects the most suitable counselor. The selection process uses an algorithm that considers past interaction history, the counselor's area of ​​expertise, and the user's emotional information. Furthermore, visualization tools present these results and emotional information visually, allowing counselors to easily understand the priority of their involvement and the appropriate course of action.

[0513] For example, if a user expresses concern about the risk of data leakage, the terminal transfers this information to the server, where content analysis and sentiment analysis are performed. The sentiment engine detects expressions indicating the user's anxiety, and the server determines the urgency and sets a top-level response priority. This prepares the support staff to respond quickly and appropriately.

[0514] By utilizing an emotion engine in this way, the present invention can respond more closely to user needs compared to conventional information security consultation systems.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] Users enter the content of their consultation into the device. They may also enter words or phrases that express their emotions.

[0518] Step 2:

[0519] The device temporarily stores the user's input and filters it to detect emotional elements. This process utilizes an emotion recognition algorithm.

[0520] Step 3:

[0521] The terminal transmits the consultation content, including emotional information, to the server via a secure communication channel. An encryption protocol is used for this transmission.

[0522] Step 4:

[0523] The server tokenizes the received consultation content using an analysis tool, extracts keywords, and identifies relevant information.

[0524] Step 5:

[0525] The server uses an emotion engine to determine the user's emotional state from their input. Based on this, an emotion label (e.g., positive, negative, neutral) is assigned.

[0526] Step 6:

[0527] The server selects the most appropriate counselor using an assignment mechanism based on the analyzed information and emotion labels. Past interaction history and the counselor's expertise are also taken into consideration.

[0528] Step 7:

[0529] The server sends a notification to the selected agent containing the details of the inquiry, relevant information, and an emotion label. This allows the agent to determine the urgency of the response and the resources required.

[0530] Step 8:

[0531] The server monitors the progress of consultations and immediately notifies stakeholders of any significant changes or updates. This ensures that responses are always based on the latest information.

[0532] Step 9:

[0533] The user receives feedback from the server and decides on their course of action based on the proposed solutions and response plans. This marks the successful completion of the consultation process.

[0534] (Example 2)

[0535] 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."

[0536] Conventional information processing systems responded to user inquiries mechanically, making it difficult to provide flexible and appropriate responses that reflected the user's emotions. This resulted in decreased user satisfaction and reduced efficiency in problem-solving. Furthermore, the lack of consideration for user emotions during the selection of personnel sometimes led to inappropriate responses.

[0537] 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.

[0538] In this invention, the server includes a device for receiving information input from a user, including emotions; an analysis means for analyzing the information received from the user and extracting relevant information and emotional information; and an assignment means for selecting a person in charge who needs to respond based on the extracted relevant information and emotional information. This enables a quick and appropriate response that is in line with the user's emotions.

[0539] A "user" refers to someone who inputs information into the system and requests support or consultation.

[0540] "Information input containing emotions" refers to information provided by users through their devices, which includes elements that express emotions in addition to problems and questions.

[0541] "Device" refers to equipment or software configurations used to receive input from users.

[0542] "Analysis means" refers to a system component that has the function of analyzing received information and extracting relevant data and emotional information.

[0543] "Related information" refers to data extracted from user input that is useful for solving problems.

[0544] "Emotional information" refers to data that indicates the emotional state of a user, analyzed from their input.

[0545] An "assignment mechanism" is a system component that has the function of selecting the appropriate person in charge and assigning them the task based on the analysis results.

[0546] "Responsible person" refers to a specialist staff member or support person selected to address users' inquiries and problems.

[0547] "Display means" refers to a device or software function for visually displaying the analyzed results or information.

[0548] "Management tools" are components of a system for tracking the progress of a case and notifying stakeholders of that information.

[0549] An "emotion engine" refers to an algorithm that analyzes the user's emotions from the input information and makes a positive or negative judgment.

[0550] "Urgency" is an indicator that shows whether a problem or consultation requires a prompt response.

[0551] "Past response history" refers to records of responses taken to date, and is data that is referenced when selecting a person in charge.

[0552] This invention is a system aimed at providing prompt and appropriate responses based on the user's emotions in information security consultation services. The system mainly consists of a server, terminals, and users.

[0553] The user first enters their inquiry into the terminal. The terminal receives this information and identifies it as input containing words or phrases that express emotions. Next, the terminal sends the received data to the server. For example, if the user makes an inquiry such as "I'm worried about data breaches," the terminal will send this sentence along with the inquiry to the server.

[0554] The server receives input from the terminal and analyzes emotional information using an emotion engine. This emotion engine has the function of determining whether the emotion is positive or negative. If the user's expression is negative, the server sets the urgency level to high. In addition, the server tokenizes the consultation content using an analysis method and generates relevant information by extracting keywords. The server's analysis information is input into an algorithm that uses past interaction history to select the most suitable person in charge. The selection of a person in charge is based on their area of ​​expertise and emotional information.

[0555] The selected personnel are provided with visualized analysis results and emotional information, enabling them to respond quickly and accurately. The server also includes a progress management function, periodically notifying the personnel and stakeholders of the project's progress and any changes in emotional responses.

[0556] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Given a situation where a user is concerned about a data breach, how can we determine the urgency and optimize the response process?"

[0557] Through the above operations, this invention enables flexible responses that are tailored to the user's emotions, thereby improving the quality of information security consultation services.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The user enters their information security concerns into the terminal. This input includes expressions of emotion, such as "I'm worried about data leaks." Text data corresponding to the user's questions and concerns is generated as input data. This text data then enters a processing state within the terminal.

[0561] Step 2:

[0562] The device performs a preliminary analysis of the input data received from the user. Here, it detects emotional keywords within the text data and tags them as metadata. The input is the user's raw text, and the output is text data with emotional tags attached. This process prepares data enhanced with emotional information.

[0563] Step 3:

[0564] The device sends tagged text data to the server. The transmitted data includes text information containing the user's inquiry and sentiment tags, which is then input to the server. Specifically, the server enters a data waiting state due to the packetization and transmission protocol processing of the data.

[0565] Step 4:

[0566] The server analyzes the received data in detail using analytical tools. The input is data transmitted from the terminal, which is tokenized and keywords are extracted. Furthermore, a built-in emotion engine determines the emotional information (positive or negative). This analysis generates a dataset for evaluating the user's situation.

[0567] Step 5:

[0568] The server selects the most suitable person in charge based on the analysis results and sentiment information. The input is the analyzed dataset, and the algorithm is used to search for the most suitable person in charge. The output generates information about the selected person in charge. Specifically, it sets high-priority responses by referring to past response history and the person in charge's area of ​​expertise.

[0569] Step 6:

[0570] The server provides the selection results to the person in charge through a visualization mechanism. The visualized data is provided as input displayed on the person in charge's terminal, and the prioritized tasks are output as visual data. This visualization operation allows the person in charge to quickly and accurately formulate countermeasures.

[0571] Step 7:

[0572] The server tracks project progress and periodically notifies project managers and stakeholders of changes in sentiment and progress. This process receives project data to be managed as input, the progress management system processes the data, and status changes are output as notification data. This enables proactive responses tailored to the situation.

[0573] (Application Example 2)

[0574] 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."

[0575] The problem that this invention aims to solve is to provide a more effective information analysis and personnel assignment system in an information security consultation system, in order to accurately analyze the emotions of users and enable a swift and appropriate response based on the results. Furthermore, it aims to realize more user-friendly support for daily life by extending the functions of consumer robots that provide support according to the emotional state of family members and users.

[0576] 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.

[0577] In this invention, the server includes communication means for receiving information input from users, analysis means for analyzing the information and extracting relevant information and emotional information, and assignment means for selecting a responder based on the extracted information. This makes it possible to understand the user's emotional state and provide a quick and appropriate response. Furthermore, it enables life support tailored to the user's emotional information, providing more flexible and appropriate support.

[0578] "Communication means" refers to a function that provides an interface for receiving information input from users and transmitting it to a server.

[0579] "Analysis means" refers to a function that performs a process to analyze the received user information and extract relevant information and emotional information.

[0580] "Emotional information" refers to data about positive or negative emotions extracted from user input.

[0581] The "assignment method" is a function that selects the most suitable person to handle a situation based on the analysis results.

[0582] "Visualization means" refers to a function for visually presenting analyzed emotional information and related information.

[0583] A "response mechanism" is a function that takes action to provide support in accordance with the user's emotional state.

[0584] "Management measures" refer to functions for tracking and monitoring the progress of assigned cases and notifying relevant parties of that information.

[0585] This invention makes it possible to realize a system in which a consumer robot for home use analyzes emotions and provides life support based on that analysis. This system includes support to help users live comfortably in their daily lives in a home environment. Specifically, it consists of a server, a robotic device, and dedicated software.

[0586] The server has a communication method to analyze voice input from users and uses speech recognition software (such as a speech recognition API) to convert the voice data into text. The converted text data is then analyzed through an emotion engine (e.g., Microsoft's Text Analytics API) to extract sentiment information.

[0587] The robotic device uses this emotional information to provide situation-appropriate support. For example, if a positive emotion is detected, it displays a congratulatory message; if a negative emotion is detected, it plays relaxing music or offers words of encouragement. These actions are performed by response means assigned based on information provided by the analysis means.

[0588] For example, if the robot detects signs of stress while a family is eating, it can suggest, "Why don't you take it easy for a bit?" By combining the emotion analysis technology and robot motion control technology shown in these application examples, it is possible to create a more harmonious home environment.

[0589] An example of a prompt for a generative AI model is, "Design a function for a home robot that analyzes the emotions of a user's conversation and suggests relaxation techniques based on the results." This is expected to allow the generative AI model to provide information that complements the design of an appropriate function.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] When a user speaks to a home robot, the robot receives the voice. The input is the user's voice data. The robot takes this voice data and prepares to send it to the server.

[0593] Step 2:

[0594] The server converts the received audio data into text data using a speech recognition API. The output of this step is the text converted from the audio data. During the conversion process, a language model analyzes phonemes and identifies corresponding words.

[0595] Step 3:

[0596] The server passes the converted text data to the sentiment analysis engine. The input is the text data obtained in step 2, and the analysis engine extracts sentiment information. In the data calculation, the nuances of the words and phrases in the text are calculated to determine whether the sentiment is positive, negative, or neutral.

[0597] Step 4:

[0598] Upon receiving the emotional information, the server generates instructions for the robot. The input is the emotional information obtained in step 3, and the output is the specific action the robot should perform. The server selects a response appropriate to the situation and sends instructions to the robot, such as a message of joy for positive emotions or guidance to relax for negative emotions.

[0599] Step 5:

[0600] The robot performs actions based on instructions from the server. The input is an action instruction generated in step 4, and the specific actions include responding to the user's emotions, such as playing an audio message, playing music, or displaying a message on the screen.

[0601] 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.

[0602] 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 those described above. 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 shown 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.

[0603] 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.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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".

[0618] This invention is a system for efficiently and effectively providing information security consultation services. The system includes functions to receive information input from users, analyze it, and automatically perform appropriate processing.

[0619] Specifically, users input their questions or concerns via their device. The device then transmits this input information to a server using a secure communication method. The server analyzes the received information in detail using analytical tools. This analysis process includes tokenizing the information using natural language processing technology, extracting keywords, and referencing past databases related to the consultation content. Ambiguous expressions are automatically extracted, and suggestions for improvement are provided to help users identify their consultation content more specifically.

[0620] Based on these analysis results, the server uses an assignment mechanism to assign the most suitable person to the inquiry. The selected person will use past cases and relevant materials to respond to the inquiry quickly and effectively. The server also visualizes risk information and presents important information to users and assigned personnel in an easily understandable format.

[0621] Furthermore, the server manages the progress of projects and notifies stakeholders of updates in real time. This ensures that users and personnel can always proceed with their work based on the latest information.

[0622] As a concrete example, consider a case where a user consults about "measures to prevent information leakage when introducing a new cloud service." When the user enters this consultation request, the terminal sends it to the server. The server identifies keywords such as "cloud service" and "measures to prevent information leakage," and by referring to relevant past consultation history and laws, provides the user with appropriate risk information and countermeasures. Based on this, the server automatically assigns an information security officer with specialized knowledge, then manages the progress, and sends feedback to the user.

[0623] Thus, the present invention provides a concrete form for achieving increased efficiency and consistent quality improvement in information security consultations.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The user enters their questions and concerns about information security into the terminal. These entries include specific questions and points of confusion.

[0627] Step 2:

[0628] The terminal encrypts the entered consultation content and transmits it to the server via a secure communication channel. This process maintains the integrity and confidentiality of the data.

[0629] Step 3:

[0630] The server processes the received consultation content using analytical tools and tokenizes it using natural language processing. It also extracts important keywords and performs contextual semantic analysis.

[0631] Step 4:

[0632] The server searches its internal database for relevant past consultation cases based on the analyzed information, and uses this to suggest similar consultations and identify risk points.

[0633] Step 5:

[0634] The server automatically generates specific mitigation measures for identified risk points and presents ambiguous statements as suggested improvements. At this point, the user may be asked to provide additional information.

[0635] Step 6:

[0636] Based on the analysis results, the server automatically selects the most suitable consultant using an assignment mechanism. The selection criteria include past response history and expertise.

[0637] Step 7:

[0638] The server notifies assigned personnel of the case information and related documents to be handled, enabling them to begin responding quickly.

[0639] Step 8:

[0640] The server monitors the progress of consultations in real time, and if the monitoring system detects any changes in the progress status, it notifies users and stakeholders as necessary.

[0641] Step 9:

[0642] The user receives final feedback from the server and confirms the problem resolution and future course of action. This completes the resolution of the inquiry.

[0643] (Example 1)

[0644] 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".

[0645] In information security consulting services, the diverse nature of user questions and ambiguous language make it difficult to respond quickly and accurately. Furthermore, effectively utilizing past response records and quickly assigning the most suitable personnel is also a challenge. Additionally, visually presenting risk information and managing case progress in real time are difficult. Therefore, a system is needed to enable appropriate information analysis and efficient operational management.

[0646] 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.

[0647] In this invention, the server includes communication means via a terminal that receives information data, analysis means using a generative AI model that analyzes the information and extracts important terms, and visualization means that visually displays the risks based on the analysis results. This makes it possible to concretize the user's vague questions and quickly present the optimal countermeasures. Furthermore, it is possible to select the most suitable personnel by utilizing past response records and improve the quality and effectiveness of consultations.

[0648] "Communication means" refers to devices and methods for sending and receiving information, and in particular, those that have the function of securely transmitting user input data to a server.

[0649] "Analysis means" refers to systems and methods for analyzing input information and extracting important words and relevant data, and utilizes generative AI models.

[0650] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze text data and extract specific patterns or phrases.

[0651] "Assignment means" refers to a method or system for automatically selecting the most suitable person based on the results of the analysis, and includes the ability to refer to past history.

[0652] "Visualization methods" refer to technologies that visually display analyzed data and present it in a way that is easy for users and personnel to understand.

[0653] "Management means" refers to a method or system that has the function of tracking the progress of a project and notifying relevant parties of that information.

[0654] This invention provides a system for efficiently conducting information security consultations. Users input their questions and concerns regarding information security into a terminal. This terminal transmits the user input information to a server using a secure communication method. The server then analyzes the received information using a generation AI model.

[0655] The server uses natural language processing technology to tokenize user input and extract key terms. This analysis identifies keywords in the user's inquiry and references relevant historical databases. The server identifies ambiguous expressions and, through a generative AI model, presents improvement suggestions to the user. For example, if a user enters a question about "measures to prevent information leakage when introducing a new cloud service," the server extracts keywords such as "cloud service" and "measures to prevent information leakage."

[0656] Based on this, the server automatically selects the most suitable expert to handle the consultation using an assignment mechanism. This selection process utilizes past response history to ensure the assignment of a more appropriate person. The server also visually displays risk information along with the analysis results, providing information to users and staff in a dashboard format.

[0657] Furthermore, the server tracks the progress of the project and notifies stakeholders of updates as they occur. This ensures that users and staff always have the latest information to guide their actions. As users progress through the consultation process, the server updates information in real time, supporting efficient business operations.

[0658] A concrete example of a prompt is, "When introducing a new cloud service, I would like to know the best practices for preventing information leaks. I would also like to know about relevant laws and past cases." Based on this prompt, the system can provide accurate information and countermeasures.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user enters their information security inquiry into the terminal. They type a prompt such as "Measures to prevent information leakage when introducing a new cloud service" into the terminal's input field. Once input is complete, the terminal sends this data to the server via a secure communication method. In this step, the input is the user's inquiry text, and the output is the inquiry data sent to the server.

[0662] Step 2:

[0663] The server analyzes the received consultation data using a generation AI model. Specifically, a natural language processing module within the server tokenizes the text and extracts important keywords. Data processing involves operations such as decomposing strings into tokens and identifying keywords to grasp the core of the consultation content entered by the user. The input for this step is the consultation data sent to the server, and the output is the extracted keywords and related database entries.

[0664] Step 3:

[0665] The server identifies ambiguous expressions identified through analysis and uses a generative AI model to present specific improvement suggestions to the user. This clarifies the user's inquiry and facilitates the next process. The data calculations involve automatic detection of ambiguous expressions and generation of appropriate explanatory texts. The input for this step is extracted keywords, and the output is improvement suggestions for the user.

[0666] Step 4:

[0667] Based on the analysis results, the server automatically selects an appropriate information security expert using an assignment mechanism. This selection takes into account past response history and expertise data. The server refers to the database to determine the most suitable person and configures that information within the system. The input for this step is the analysis results and database information, and the output is the information of the selected person.

[0668] Step 5:

[0669] The server visualizes risk information and provides it to users and personnel. This process generates and displays dashboards and reports based on the analysis results. The server visualizes important information as diagrams and text, displaying it on the system screen in a way that is easy for users and personnel to understand. The input for this step is the analysis results, and the output is visualized risk information.

[0670] Step 6:

[0671] The server continuously monitors project progress and notifies users and relevant organizational members in real time. The server collects progress data and immediately notifies users via email or chat tools when changes occur. This ensures that stakeholders can always act based on the latest information. The input for this step is the progress status, and the output is the notification information for stakeholders.

[0672] (Application Example 1)

[0673] 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".

[0674] In information security consulting services, it is essential to quickly and accurately resolve the diverse questions and problems that users have. However, traditional methods often involve manual information analysis and appropriate assignment of personnel, making it difficult to maintain efficiency and consistent quality. Furthermore, progress management and visualization of risk information are prone to information scattering, making it difficult for stakeholders to always keep up-to-date. There is a need to provide new methods to solve these challenges.

[0675] 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.

[0676] In this invention, the server includes support means for receiving user inquiries, analyzing them in detail using a generated AI model, and providing appropriate advice; display means for visualizing the specific content of the user's inquiry in real time; and assignment means for selecting the most suitable person in charge based on past response history and the generated AI model. This makes it possible to perform information security consultation services efficiently and with high quality.

[0677] "A communication method for receiving information input from users" refers to a method for securely transmitting consultation information from users to a server via a terminal.

[0678] "Analysis methods" refer to techniques for analyzing received information in detail and extracting relevant keywords and problems.

[0679] "Assignment method" refers to a method for automatically selecting the most suitable person based on the results of the analysis.

[0680] "Visualization methods" are techniques for visually representing information and risks and providing them in a format that is easily understandable to users and those in charge.

[0681] "Management measures" refer to methods for tracking the progress of a project and notifying stakeholders of the latest information.

[0682] "A support method that analyzes the specific content of a user's consultation and provides advice using a generative AI model" refers to a method that utilizes AI technology to analyze the content of a user's consultation and present appropriate advice and solutions.

[0683] "A display method that visualizes the progress of consultations in real time via a terminal" refers to a method for displaying the progress of consultations and related information on the user's terminal so that they can instantly check it.

[0684] The system for implementing this invention consists of a server, a terminal, and software including a generative AI model. First, the user inputs information security-related inquiries using a terminal such as a smartphone. This input information is transmitted from the terminal to the server via a secure communication means.

[0685] Upon receiving information, the server uses analysis tools and natural language processing techniques to analyze the input text. Specifically, it uses natural language processing libraries such as spaCy to tokenize the information and extract important keywords and related information. Based on these analysis results, a generative AI model is used to provide the user with appropriate advice. By utilizing AI technology, even ambiguous expressions from the user are transformed into concrete solutions.

[0686] Furthermore, the server references a historical database via an assignment mechanism to select the most suitable person in charge. The case is assigned to the selected person, and its progress is notified to stakeholders in real time through a management mechanism. Risk information and progress status are displayed on the terminal through a visualization mechanism, allowing users to always check the latest information.

[0687] For example, if a user enters "Information leakage prevention measures when introducing a new cloud service" as a consultation request, the server identifies keywords such as "cloud service" and "information leakage prevention measures," and then refers to past cases to suggest appropriate countermeasures. Based on this process, the AI ​​model quickly selects the appropriate information security officer.

[0688] An example of a prompt message to the generating AI model would be, "I would like to consult about the security risks when purchasing new software. What is the best way to implement security measures?" The system will function correctly when a user inputs this kind of message.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The user uses a terminal to input their information security-related inquiries. The entered information is sent to the server via a secure communication method from the terminal. The input here is the user's text message, and the output is the data sent to the server.

[0692] Step 2:

[0693] The server passes the received data to the analysis tool. The analysis tool uses natural language processing techniques to tokenize the received data and extract important keywords and related information. This process uses the spaCy library to parse the text and generate a keyword list. The input here is text data sent by the user, and the output is the analyzed keyword list.

[0694] Step 3:

[0695] Based on the analysis results, the server uses a generative AI model to analyze the user's inquiry in detail and generate specific advice. The input is a keyword list, which is passed to the generative AI model, and the output is suggested advice and solutions. In this process, the AI ​​transforms the user's vague inquiries into specific answers.

[0696] Step 4:

[0697] The server uses an assignment mechanism to reference historical databases and select the most suitable information security officer. This selection includes searches based on past history and current keywords. The input is a keyword list and a database query, and the output is information about the selected officer.

[0698] Step 5:

[0699] The server uses visualization tools to visualize risk information and the progress of consultations, displaying them on the user's terminal. This allows the user to check the latest information. Inputs are analysis results and risk information, and output is visualized information on the user's terminal.

[0700] Step 6:

[0701] The user's terminal receives project progress and update information through a progress management system. This includes a real-time notification system from the server, ensuring the user always receives the latest information. The input is the server's progress information, and the output is the information notified to the user.

[0702] 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.

[0703] This invention is a system for improving the convenience and efficiency of information security consultation services, and in particular aims to further improve the quality of consultation responses by integrating an emotion engine that recognizes the user's emotions. This system receives consultation content from users as input and has means for analyzing that content, as well as an emotion engine that analyzes the user's emotions.

[0704] Specifically, users input their consultation details into the terminal. This input may include not only specific questions and problems, but also words and phrases that suggest the user's emotions. Before sending this input to the server, the terminal performs a preliminary analysis to determine if emotional elements are present.

[0705] The server analyzes the received consultation content in detail. The analysis system has the function of tokenizing the content, extracting keywords, and extracting relevant information. In addition, an emotion engine is incorporated, which makes a judgment of positive or negative emotion based on the user's input. This emotion information is used to adjust the response to the consultation content. For example, if a negative emotion is detected, the system increases the urgency of the response and sends an immediate notification to the person in charge.

[0706] Based on this analysis and emotional information, the server selects the most suitable counselor. The selection process uses an algorithm that considers past interaction history, the counselor's area of ​​expertise, and the user's emotional information. Furthermore, visualization tools present these results and emotional information visually, allowing counselors to easily understand the priority of their involvement and the appropriate course of action.

[0707] For example, if a user expresses concern about the risk of data leakage, the terminal transfers this information to the server, where content analysis and sentiment analysis are performed. The sentiment engine detects expressions indicating the user's anxiety, and the server determines the urgency and sets a top-level response priority. This prepares the support staff to respond quickly and appropriately.

[0708] By utilizing an emotion engine in this way, the present invention can respond more closely to user needs compared to conventional information security consultation systems.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] Users enter the content of their consultation into the device. They may also enter words or phrases that express their emotions.

[0712] Step 2:

[0713] The device temporarily stores the user's input and filters it to detect emotional elements. This process utilizes an emotion recognition algorithm.

[0714] Step 3:

[0715] The terminal transmits the consultation content, including emotional information, to the server via a secure communication channel. An encryption protocol is used for this transmission.

[0716] Step 4:

[0717] The server tokenizes the received consultation content using an analysis tool, extracts keywords, and identifies relevant information.

[0718] Step 5:

[0719] The server uses an emotion engine to determine the user's emotional state from their input. Based on this, an emotion label (e.g., positive, negative, neutral) is assigned.

[0720] Step 6:

[0721] The server selects the most appropriate counselor using an assignment mechanism based on the analyzed information and emotion labels. Past interaction history and the counselor's expertise are also taken into consideration.

[0722] Step 7:

[0723] The server sends a notification to the selected agent containing the details of the inquiry, relevant information, and an emotion label. This allows the agent to determine the urgency of the response and the resources required.

[0724] Step 8:

[0725] The server monitors the progress of consultations and immediately notifies stakeholders of any significant changes or updates. This ensures that responses are always based on the latest information.

[0726] Step 9:

[0727] The user receives feedback from the server and decides on their course of action based on the proposed solutions and response plans. This marks the successful completion of the consultation process.

[0728] (Example 2)

[0729] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] Conventional information processing systems responded to user inquiries mechanically, making it difficult to provide flexible and appropriate responses that reflected the user's emotions. This resulted in decreased user satisfaction and reduced efficiency in problem-solving. Furthermore, the lack of consideration for user emotions during the selection of personnel sometimes led to inappropriate responses.

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

[0732] In this invention, the server includes a device for receiving information input from a user, including emotions; an analysis means for analyzing the information received from the user and extracting relevant information and emotional information; and an assignment means for selecting a person in charge who needs to respond based on the extracted relevant information and emotional information. This enables a quick and appropriate response that is in line with the user's emotions.

[0733] A "user" refers to someone who inputs information into the system and requests support or consultation.

[0734] "Information input containing emotions" refers to information provided by users through their devices, which includes elements that express emotions in addition to problems and questions.

[0735] "Device" refers to equipment or software configurations used to receive input from users.

[0736] "Analysis means" refers to a system component that has the function of analyzing received information and extracting relevant data and emotional information.

[0737] "Related information" refers to data extracted from user input that is useful for solving problems.

[0738] "Emotional information" refers to data that indicates the emotional state of a user, analyzed from their input.

[0739] An "assignment mechanism" is a system component that has the function of selecting the appropriate person in charge and assigning them the task based on the analysis results.

[0740] "Responsible person" refers to a specialist staff member or support person selected to address users' inquiries and problems.

[0741] "Display means" refers to a device or software function for visually displaying the analyzed results or information.

[0742] "Management tools" are components of a system for tracking the progress of a case and notifying stakeholders of that information.

[0743] An "emotion engine" refers to an algorithm that analyzes the user's emotions from the input information and makes a positive or negative judgment.

[0744] "Urgency" is an indicator that shows whether a problem or consultation requires a prompt response.

[0745] "Past response history" refers to records of responses taken to date, and is data that is referenced when selecting a person in charge.

[0746] This invention is a system aimed at providing prompt and appropriate responses based on the user's emotions in information security consultation services. The system mainly consists of a server, terminals, and users.

[0747] The user first enters their inquiry into the terminal. The terminal receives this information and identifies it as input containing words or phrases that express emotions. Next, the terminal sends the received data to the server. For example, if the user makes an inquiry such as "I'm worried about data breaches," the terminal will send this sentence along with the inquiry to the server.

[0748] The server receives input from the terminal and analyzes emotional information using an emotion engine. This emotion engine has the function of determining whether the emotion is positive or negative. If the user's expression is negative, the server sets the urgency level to high. In addition, the server tokenizes the consultation content using an analysis method and generates relevant information by extracting keywords. The server's analysis information is input into an algorithm that uses past interaction history to select the most suitable person in charge. The selection of a person in charge is based on their area of ​​expertise and emotional information.

[0749] The selected personnel are provided with visualized analysis results and emotional information, enabling them to respond quickly and accurately. The server also includes a progress management function, periodically notifying the personnel and stakeholders of the project's progress and any changes in emotional responses.

[0750] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Given a situation where a user is concerned about a data breach, how can we determine the urgency and optimize the response process?"

[0751] Through the above operations, this invention enables flexible responses that are tailored to the user's emotions, thereby improving the quality of information security consultation services.

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] The user enters their information security concerns into the terminal. This input includes expressions of emotion, such as "I'm worried about data leaks." Text data corresponding to the user's questions and concerns is generated as input data. This text data then enters a processing state within the terminal.

[0755] Step 2:

[0756] The device performs a preliminary analysis of the input data received from the user. Here, it detects emotional keywords within the text data and tags them as metadata. The input is the user's raw text, and the output is text data with emotional tags attached. This process prepares data enhanced with emotional information.

[0757] Step 3:

[0758] The device sends tagged text data to the server. The transmitted data includes text information containing the user's inquiry and sentiment tags, which is then input to the server. Specifically, the server enters a data waiting state due to the packetization and transmission protocol processing of the data.

[0759] Step 4:

[0760] The server analyzes the received data in detail using analytical tools. The input is data transmitted from the terminal, which is tokenized and keywords are extracted. Furthermore, a built-in emotion engine determines the emotional information (positive or negative). This analysis generates a dataset for evaluating the user's situation.

[0761] Step 5:

[0762] The server selects the most suitable person in charge based on the analysis results and sentiment information. The input is the analyzed dataset, and the algorithm is used to search for the most suitable person in charge. The output generates information about the selected person in charge. Specifically, it sets high-priority responses by referring to past response history and the person in charge's area of ​​expertise.

[0763] Step 6:

[0764] The server provides the selection results to the person in charge through a visualization mechanism. The visualized data is provided as input displayed on the person in charge's terminal, and the prioritized tasks are output as visual data. This visualization operation allows the person in charge to quickly and accurately formulate countermeasures.

[0765] Step 7:

[0766] The server tracks project progress and periodically notifies project managers and stakeholders of changes in sentiment and progress. This process receives project data to be managed as input, the progress management system processes the data, and status changes are output as notification data. This enables proactive responses tailored to the situation.

[0767] (Application Example 2)

[0768] 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".

[0769] The problem that this invention aims to solve is to provide a more effective information analysis and personnel assignment system in an information security consultation system, in order to accurately analyze the emotions of users and enable a swift and appropriate response based on the results. Furthermore, it aims to realize more user-friendly support for daily life by extending the functions of consumer robots that provide support according to the emotional state of family members and users.

[0770] 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.

[0771] In this invention, the server includes communication means for receiving information input from users, analysis means for analyzing the information and extracting relevant information and emotional information, and assignment means for selecting a responder based on the extracted information. This makes it possible to understand the user's emotional state and provide a quick and appropriate response. Furthermore, it enables life support tailored to the user's emotional information, providing more flexible and appropriate support.

[0772] "Communication means" refers to a function that provides an interface for receiving information input from users and transmitting it to a server.

[0773] "Analysis means" refers to a function that performs a process to analyze the received user information and extract relevant information and emotional information.

[0774] "Emotional information" refers to data about positive or negative emotions extracted from user input.

[0775] The "assignment method" is a function that selects the most suitable person to handle a situation based on the analysis results.

[0776] "Visualization means" refers to a function for visually presenting analyzed emotional information and related information.

[0777] A "response mechanism" is a function that takes action to provide support in accordance with the user's emotional state.

[0778] "Management measures" refer to functions for tracking and monitoring the progress of assigned cases and notifying relevant parties of that information.

[0779] This invention makes it possible to realize a system in which a consumer robot for home use analyzes emotions and provides life support based on that analysis. This system includes support to help users live comfortably in their daily lives in a home environment. Specifically, it consists of a server, a robotic device, and dedicated software.

[0780] The server has a communication method to analyze voice input from users and uses speech recognition software (such as a speech recognition API) to convert the voice data into text. The converted text data is then analyzed through an emotion engine (e.g., Microsoft's Text Analytics API) to extract sentiment information.

[0781] The robotic device uses this emotional information to provide situation-appropriate support. For example, if a positive emotion is detected, it displays a congratulatory message; if a negative emotion is detected, it plays relaxing music or offers words of encouragement. These actions are performed by response means assigned based on information provided by the analysis means.

[0782] For example, if the robot detects signs of stress while a family is eating, it can suggest, "Why don't you take it easy for a bit?" By combining the emotion analysis technology and robot motion control technology shown in these application examples, it is possible to create a more harmonious home environment.

[0783] An example of a prompt for a generative AI model is, "Design a function for a home robot that analyzes the emotions of a user's conversation and suggests relaxation techniques based on the results." This is expected to allow the generative AI model to provide information that complements the design of an appropriate function.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] When a user speaks to a home robot, the robot receives the voice. The input is the user's voice data. The robot takes this voice data and prepares to send it to the server.

[0787] Step 2:

[0788] The server converts the received audio data into text data using a speech recognition API. The output of this step is the text converted from the audio data. During the conversion process, a language model analyzes phonemes and identifies corresponding words.

[0789] Step 3:

[0790] The server passes the converted text data to the sentiment analysis engine. The input is the text data obtained in step 2, and the analysis engine extracts sentiment information. In the data calculation, the nuances of the words and phrases in the text are calculated to determine whether the sentiment is positive, negative, or neutral.

[0791] Step 4:

[0792] Upon receiving the emotional information, the server generates instructions for the robot. The input is the emotional information obtained in step 3, and the output is the specific action the robot should perform. The server selects a response appropriate to the situation and sends instructions to the robot, such as a message of joy for positive emotions or guidance to relax for negative emotions.

[0793] Step 5:

[0794] The robot performs actions based on instructions from the server. The input is an action instruction generated in step 4, and the specific actions include responding to the user's emotions, such as playing an audio message, playing music, or displaying a message on the screen.

[0795] 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.

[0796] 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 those described above. 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 shown 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] A communication method for receiving information input from users,

[0819] An analysis means for analyzing the information received from the user and extracting relevant information,

[0820] A means of assigning personnel who need to take action based on the extracted relevant information,

[0821] A visualization means that presents risk information identified by the aforementioned analysis means,

[0822] A management system for tracking and monitoring the progress of assigned cases and notifying relevant stakeholders of the progress information,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, characterized in that the analysis means has the function of automatically identifying ambiguous expressions and proposing specific improvement plans.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the assignment means selects the most suitable person in charge based on past response history.

[0828] "Example 1"

[0829] (Claim 1)

[0830] A means of communication via a terminal that receives information data,

[0831] An analysis means using a generative AI model that analyzes the aforementioned information and extracts important words and phrases,

[0832] A means of assigning personnel with the necessary expertise based on the extracted key terms,

[0833] A visualization method that visually displays the risk based on the analysis results,

[0834] A management system for tracking the progress of a project and notifying relevant organizational members of the results,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, characterized in that the analysis means identifies ambiguous expressions and proposes specific improvements using a generative AI model.

[0838] (Claim 3)

[0839] The system according to claim 1, characterized in that the assignment means automatically selects the most suitable personnel by referring to past response records.

[0840] "Application Example 1"

[0841] (Claim 1)

[0842] A communication method for receiving information input from users,

[0843] An analysis means for analyzing the information received from the user and extracting relevant information,

[0844] A means of assigning personnel who need to take action based on the extracted relevant information,

[0845] A visualization means that presents risk information identified by the aforementioned analysis means,

[0846] A management system for tracking and monitoring the progress of assigned cases and notifying relevant stakeholders of the progress information,

[0847] A support method that uses a generative AI model to analyze the specific content of a user's consultation and provide advice,

[0848] A display means that visualizes the progress of the consultation in real time via a terminal,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized in that the analysis means has a function to automatically identify ambiguous expressions and propose specific improvement plans using an AI model.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the assignment means selects the most suitable person in charge based on past correspondence history and generated AI model.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A device that accepts information input from users, including emotions,

[0857] An analysis means for analyzing information received from the user and extracting relevant information and emotional information,

[0858] A means for assigning personnel who need to take action based on extracted relevant information and emotional information,

[0859] A display means that presents risk information and sentiment information identified by the analysis means,

[0860] A management system for tracking and monitoring the progress of assigned cases and notifying relevant stakeholders of progress information and changes in sentiment,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that the analysis means uses an emotion engine to determine positive and negative emotions and set the urgency of the response.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the assignment means selects the most suitable person in charge based on past interaction history and emotional information.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A communication method for receiving information input from users,

[0869] An analysis means for analyzing information received from the user and extracting relevant information and emotional information,

[0870] A means for assigning personnel who need to take action based on extracted relevant information and emotional information,

[0871] A visualization means that presents emotional information identified by the aforementioned analysis means,

[0872] A means of providing support tailored to the emotional state of the user,

[0873] A management system for tracking and monitoring the progress of assigned cases and notifying relevant stakeholders of the progress information,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the analysis means has a function to automatically identify ambiguous expressions and propose specific improvement plans, and further performs positive and negative emotional judgments through emotional analysis.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the assignment means is characterized by selecting the most suitable person in charge based on past interaction history, as well as selecting the person in charge while taking into consideration the user's emotional information. [Explanation of symbols]

[0879] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A communication method for receiving information input from users, An analysis means for analyzing the information received from the user and extracting relevant information, A means of assigning personnel who need to take action based on the extracted relevant information, A visualization means that presents risk information identified by the aforementioned analysis means, A management system for tracking and monitoring the progress of assigned cases and notifying relevant stakeholders of the progress information, A support method that uses a generative AI model to analyze the specific content of a user's consultation and provide advice, A display means that visualizes the progress of the consultation in real time via a terminal, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means has a function to automatically identify ambiguous expressions and propose specific improvement plans using an AI model.

3. The system according to claim 1, wherein the assignment means selects the most suitable person in charge based on past correspondence history and generated AI model.

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