system

Abstract

We provide the system. [Solution] A system including means for receiving inquiries from users, means for AI learning to analyze compliance issues in society based on the received inquiries, means for presenting general opinions based on the analysis results, and means for suggesting where to seek advice based on the analysis results.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that the psychological hurdle for employees to report to their superiors or the departments in charge is high, and as a result, it is difficult to detect and solve problems at an early stage.

Means for Solving the Problems

[0005] As a means for solving this problem, a system is provided that accepts consultations from users, analyzes compliance problems in the world by AI based on the consultations, presents general opinions based on the analysis results, and suggests the parties to be consulted. With this system, employees can easily consult and attempt to detect and solve problems at an early stage.

Brief Description of the Drawings

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16]It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Example 1 of Form Example 1 when combined with an emotion engine. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Example 2 of Form Example 2 when combined with an emotion engine. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Example 3 of Form Example 3 when combined with an emotion engine. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. [Figure 23] It is a sequence diagram showing the processing flow of the data processing system in other embodiments.

Embodiments for Carrying Out the Invention

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0012] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc. The system accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] One embodiment of the present invention provides an AI consultation and reporting system. This system has a means of receiving consultations from users. Specifically, users can access the system and submit a consultation by entering their problem or concern as text.

[0029] "Example of form 2"

[0030] Furthermore, this system has a mechanism for AI to analyze compliance issues in society based on the consultations it receives. Specifically, the AI ​​uses a pre-trained database to generate general opinions and solutions to the user's consultation. For example, if a user consults about "being troubled by harassment from their boss," the AI ​​will analyze laws and cases related to harassment and generate appropriate advice.

[0031] "Example of form 3"

[0032] Furthermore, this system has a mechanism to suggest who to consult based on the analysis results. Specifically, the AI ​​will instruct the user on the department or person to consult based on the analysis results. For example, if a user consults about suspected irregularities in company expense reports, the AI ​​can give specific instructions such as "We recommend consulting the internal audit department."

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The user accesses the AI ​​consultation and reporting system.

[0036] Step 2: The user enters their problem or concern as text and submits it to the system for consultation.

[0037] "Example of form 2"

[0038] Step 1: The system receives the user's inquiry.

[0039] Step 2: The AI ​​uses a pre-trained database to generate general opinions and solutions regarding the consultation.

[0040] Step 3: Present the AI-generated views and solutions to the user.

[0041] "Example of form 3"

[0042] Step 1: Based on the user's inquiry and the opinions it generates, the AI ​​determines which department or person should be consulted.

[0043] Step 2: The AI ​​suggests to the user which department or person they should consult.

[0044] (Example 1)

[0045] Next, we will describe Example 1 of Form 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."

[0046] Traditional consultation systems faced challenges in providing prompt and appropriate responses to user inquiries. In particular, when inquiries covered a wide range of topics or required specialized knowledge, responses were often delayed or insufficient.

[0047] 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.

[0048] In this invention, the server includes means for receiving inquiries from users, means for passing the received inquiries to a generation AI model, and means for the generation AI model to analyze the content of the inquiries and generate a response. This makes it possible to provide a quick and appropriate response to user inquiries.

[0049] A "user" refers to an individual or organization that accesses the system and enters their inquiry details.

[0050] "Consultation" refers to the content of problems or concerns that users input into the system.

[0051] A "generative AI model" refers to artificial intelligence technology that analyzes the content of inquiries received from users and generates appropriate responses.

[0052] "Response" refers to the answer or advice generated by the generative AI model as a result of analyzing the content of the consultation.

[0053] A "server" refers to a computer system that receives inquiries from users, passes data to a generation AI model, and provides the generated response to the user.

[0054] A "terminal" refers to a device used by a user to access the system, input their inquiries, and receive responses.

[0055] As an embodiment for carrying out this invention, an AI consultation and reporting system will be specifically described.

[0056] The server provides a web interface for receiving inquiries from users. This interface is accessed through a device accessible to the user (such as a PC or smartphone). Users can access the system and input their inquiries in text format. The entered text data is sent to a generative AI model running on the server. This generative AI model uses natural language processing technology to analyze the user's inquiries and generate appropriate responses.

[0057] For the generative AI model, advanced natural language processing models such as OpenAI's GPT-4 (registered trademark) can be used. This makes it possible to understand the user's intent and construct the optimal response based on past training data. The generated response is then sent back to the user's device and displayed to the user.

[0058] As a concrete example, consider a scenario where a user enters a question such as, "My motivation at work has been low lately. What should I do?" This prompt is sent to a generative AI model, which generates a response such as, "I recommend setting new goals and accumulating small achievements." The server sends this response back to the terminal, which then displays it to the user. The user can then use this advice to guide their daily work.

[0059] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0060] Step 1:

[0061] The user accesses the AI ​​consultation reporting system using their device. The user enters their consultation details into the text box and clicks the "Submit" button. The user's consultation details are provided as input in text format.

[0062] Step 2:

[0063] The terminal sends the text data entered by the user to the server as an HTTP request. The input is the user's inquiry, and the output is the request data sent to the server. This request contains the user's inquiry.

[0064] Step 3:

[0065] The server passes the received text data to the generating AI model. The input is the consultation content received from the terminal, and the output is the input data for the generating AI model. The server converts the data into a format that the generating AI model can access.

[0066] Step 4:

[0067] The generative AI model analyzes data received from the server and generates an appropriate response. The input is the inquiry from the server, and the output is the generated response. The model uses natural language processing techniques to understand the user's intent and construct the optimal answer.

[0068] Step 5:

[0069] The server sends the response received from the generative AI model back to the terminal as an HTTP response. The input is the response from the generative AI model, and the output is the response data sent to the terminal.

[0070] Step 6:

[0071] The terminal displays the response received from the server to the user. The input is the response data from the server, and the output is the response displayed to the user. The user can review the advice generated on the screen and consider their next course of action.

[0072] (Application Example 1)

[0073] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0074] In modern society, individuals and businesses face a wide range of security issues, but obtaining appropriate advice and information quickly is difficult. In particular, it is challenging for ordinary users to determine how to respond to threats such as phishing scams and data breaches. In this situation, there is a need for a system that allows users to confidently seek security advice and take appropriate measures.

[0075] 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.

[0076] In this invention, the server includes means for receiving inquiries from users, means for artificial intelligence learning for analyzing security issues based on the received inquiries, and means for providing appropriate advice based on the analysis results. This enables users to resolve their security concerns and questions and take quick and appropriate measures.

[0077] "A means of receiving inquiries from users" refers to an interface that allows users to access the system and input their security concerns and questions in text.

[0078] "An artificial intelligence learning method for analyzing security issues" is a processing device that uses artificial intelligence technology to analyze the content of inquiries received, identify security-related problems, and derive appropriate countermeasures.

[0079] "Means of providing appropriate advice" refers to a function that presents users with specific countermeasures and information based on analysis results, and provides information to alleviate security concerns.

[0080] "Means of providing security information" refers to information provision devices that provide users with the latest information on security incidents and regulations to support appropriate decision-making.

[0081] The system for implementing this invention enables users to consult about security using a device such as a smartphone. Users input their consultation details as text through an application on their device. The server utilizes an artificial intelligence learning method using a generative AI model to analyze the received consultation details. This artificial intelligence learning method analyzes the text using a natural language processing library (e.g., spaCy) and extracts important keywords and context.

[0082] Based on the extracted information, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate advice for the user. Furthermore, by cross-referencing with a security database, it can provide the latest security information. This allows users to resolve their security concerns and questions and take quick and appropriate action.

[0083] For example, if a user enters "I've been receiving more emails from my bank lately and I'm worried. What should I do?", the server will analyze the content and provide advice such as, "To verify whether the email is genuine, we recommend checking the sender's email address and logging in directly from the official website. Also, please be careful not to enter any personal information, as it may be a phishing scam."

[0084] An example of a prompt message is: "User inquiry: I've been receiving more emails from my bank lately and I'm worried. What should I do? As an AI, what advice would you offer the user?"

[0085] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0086] Step 1:

[0087] The user launches an application on their device and enters their security-related questions as text. The entered text is sent to the server. The input data is text information that specifically expresses the user's concerns and questions.

[0088] Step 2:

[0089] The server analyzes the received text data using a natural language processing library (e.g., spaCy). During the analysis process, important keywords and context are extracted from the text. The input is the user's inquiry, and the output is the extracted keywords and contextual information.

[0090] Step 3:

[0091] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the extracted information to generate appropriate advice for the user. The AI ​​model uses prompts to generate answers to the user's inquiries. The input consists of extracted keywords and contextual information, and the output is the generated advice.

[0092] Step 4:

[0093] The server compares the generated advice against the security database and adds the latest security information. This ensures that the information provided to the user is up-to-date and accurate. The input is the generated advice, and the output is the final advice information after comparison.

[0094] Step 5:

[0095] The server sends the final advice information to the user's terminal. The user can then review the advice on their terminal and take specific actions to resolve any security concerns or questions. The input is the final advice information, and the output is the user's understanding and actions.

[0096] (Example 2)

[0097] Next, we will describe Example 2 of Form 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".

[0098] In modern society, users face a wide range of compliance issues, and resolving them requires specialized knowledge. However, it is not easy for ordinary users to obtain appropriate views and solutions to these problems. Therefore, there is a need for a system that allows users to receive quick and accurate advice on compliance issues.

[0099] 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.

[0100] In this invention, the server includes means for receiving information from a user, means for processing information to analyze the received information, means for searching for relevant information from a knowledge base based on the analyzed information, means for generating an opinion using a generative AI model based on the search results, and means for providing the generated opinion to the user. This makes it possible for users to quickly obtain appropriate advice on compliance issues even without specialized knowledge.

[0101] "Means of receiving information from users" refers to the function by which the system receives information entered by the user and incorporates it for processing.

[0102] "Information processing means" refers to the technologies and processes used to analyze received information and extract necessary data.

[0103] "Means of searching for relevant information from a knowledge base" refers to a function that finds relevant information from a pre-stored database based on the analyzed information.

[0104] "Means of generating opinions using generative AI models" refers to a function that utilizes AI technology to create opinions and advice to provide to users based on search results.

[0105] "Means of providing generated views to users" refers to a function that communicates views generated by an AI model to users and presents them in an accessible format.

[0106] This invention is a system that provides prompt and accurate advice to users regarding compliance issues they face. The following describes a specific implementation of this system.

[0107] The server connects to the user's terminal via the internet to receive information from the user. The user uses their terminal to input their inquiry and send it to the server. For example, they can input specific details such as, "I'm suffering from harassment from my boss."

[0108] The server uses natural language processing techniques to analyze the information it receives. In this process, the server uses natural language processing libraries such as "spaCy" and "NLTK" to extract keywords and context from the consultation content.

[0109] Based on the analyzed information, the server searches its knowledge base for relevant information. The knowledge base contains laws and regulations related to compliance and past case studies. The server refers to this data and extracts information relevant to the user's inquiry.

[0110] Next, the server uses a generative AI model such as "GPT-4" to generate opinions based on the search results. At this time, the AI ​​model is given instructions such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[0111] Finally, the server provides the generated insights to the user's terminal. The user can then review the specific advice on their terminal and use it as a reference for resolving the problem. In this way, users can quickly obtain appropriate advice on compliance issues, even without specialized knowledge.

[0112] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0113] Step 1:

[0114] The user uses a terminal to input their consultation details and sends them to the server. The input could be specific details such as, "I'm suffering from harassment from my boss." The terminal then sends this input as digital data to the server.

[0115] Step 2:

[0116] The server analyzes the received consultation content using natural language processing technology. The input is the consultation content from the user, and the output is the analyzed keywords and contextual information. The server uses libraries such as "spaCy" and "NLTK" to tokenize the text data and extract important keywords.

[0117] Step 3:

[0118] The server searches for relevant information from its knowledge base based on the analysis results. The input is the analyzed keywords, and the output is data on relevant laws and cases. The server executes database queries to extract the relevant information.

[0119] Step 4:

[0120] The server inputs the search results into a generative AI model such as "GPT-4" to generate an opinion. The input is data containing relevant information, and the output is an opinion or advice to be provided to the user. The server gives the AI ​​model instructions as a prompt, such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[0121] Step 5:

[0122] The server sends the generated opinion to the user's terminal. The input is the generated opinion, and the output is advice that the user can view on their terminal. The user can receive specific advice through their terminal and use it as a reference for solving problems.

[0123] (Application Example 2)

[0124] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0125] In modern society, individuals and businesses face a wide range of compliance issues, but obtaining appropriate advice quickly is difficult. Furthermore, the lack of systems that provide concrete solutions in real time based on the consultation content can lead to delays in problem resolution.

[0126] 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.

[0127] In this invention, the server includes means for receiving inquiries from users, machine learning means for analyzing social norm issues based on the received inquiries, and means for providing specific countermeasures based on the analysis results. This enables users to receive quick and appropriate advice in real time.

[0128] "Means for receiving inquiries from users" refers to an interface for receiving inquiries or problem reports from individuals or organizations.

[0129] "Machine learning methods for analyzing social norm issues" are algorithms that use databases to analyze legal and ethical issues and derive appropriate solutions.

[0130] "Means of presenting general views" refers to a function that presents widely accepted opinions or solutions based on the analysis results.

[0131] "Means of suggesting who to consult" refers to a function that suggests which department or position a user should consult in order to resolve a problem.

[0132] "Means of providing specific countermeasures" refers to a function that provides specific action guidelines for the problems faced by users, based on the analysis results.

[0133] "A means of generating advice in real time" refers to a function that immediately analyzes user inquiries and provides advice quickly.

[0134] The system for implementing this invention is designed to receive inquiries from users, analyze social norms, and provide specific countermeasures. The system is configured as follows:

[0135] The server provides an interface for receiving inquiries from users. Users can input their inquiries in text format using their smartphones or computers. The entered inquiries are then sent to the server.

[0136] The server uses a machine learning model to analyze the content of the inquiries it receives. This model utilizes OpenAI's GPT, which analyzes social norm issues by referencing a pre-trained database. The database contains information on laws and past cases.

[0137] Based on the analysis results, the server provides users with general insights and specific countermeasures in real time. This allows users to receive timely and appropriate advice.

[0138] For example, if a user enters "I would like to consult about the risk of information leakage at work," the server will analyze relevant information security laws and past cases and provide advice such as, "To prevent information leakage, it is important to conduct regular security training and properly manage access rights."

[0139] An example of a prompt message is: "User inquiry: I would like to discuss the risk of information leakage in the workplace. Please provide appropriate advice based on relevant laws and case examples."

[0140] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0141] Step 1:

[0142] Users input their consultation details in text format via a smartphone or computer interface. The entered text is sent to the server. The input data consists of the user's consultation details.

[0143] Step 2:

[0144] The server sends the received consultation content as a prompt message to OpenAI's GPT, a generative AI model, for analysis. The prompt message is structured to include the user's consultation content. The input data is the prompt message containing the user's consultation content, and the output data is the analysis result from the AI ​​model.

[0145] Step 3:

[0146] The server uses the analysis results obtained from the AI ​​model to reference relevant laws and cases from a database and generate specific countermeasures. The input data is the analysis results from the AI ​​model, and the output data is advice including specific countermeasures.

[0147] Step 4:

[0148] The server provides the generated advice to the user in real time. The user can view the advice on their terminal screen. The input data is advice that includes specific countermeasures, and the output data is the advice presented to the user.

[0149] (Example 3)

[0150] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In today's information society, it is difficult for users to quickly find the right resource for addressing problems and questions they face. This is especially true when specialized knowledge is required or when dealing with complex issues, making it difficult to determine which department or person to consult. In this situation, there is a need for a system that allows users to quickly identify the appropriate resource and efficiently resolve their problems.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0153] In this invention, the server includes means for receiving information from users, data processing means for analyzing the received information, and means for analyzing the information using a generative AI model. This makes it possible to quickly identify the appropriate contact for advice regarding the problems faced by the user and to solve the problems efficiently.

[0154] A "user" is an individual or organization that uses the system to input information and seeks to identify a contact point for consultation.

[0155] "Information" refers to data about the consultation content and problems that users input into the system.

[0156] A "server" is a computer system that receives and processes information from users.

[0157] "Data processing means" refers to functions that perform the necessary preprocessing and transformations to analyze the received information.

[0158] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze information and identify the appropriate source of advice.

[0159] "Analysis" is the process of understanding the meaning of information using generative AI models and extracting relevant keywords and contexts.

[0160] A "consultation point" refers to the appropriate organization or person that a user should consult in order to resolve a problem.

[0161] "Presentation" refers to the act of informing the user of the contact point identified based on the analysis results.

[0162] This invention is a system for quickly identifying the appropriate resource for addressing a user's problem. The system begins with the user inputting information using a terminal. The user enters the consultation details in text format and sends them to the server.

[0163] The server uses data processing tools to process the information received from the user. Specifically, it performs text preprocessing and tokenization to convert the data into a format suitable for generative AI models. These generative AI models utilize natural language processing techniques. For example, OpenAI's GPT-4 is one such model.

[0164] The generative AI model analyzes information provided by the server and extracts important keywords and context. This analysis makes it possible to identify the appropriate resources for the user to consult. Based on the analysis results, the server presents the user with specific resources to consult.

[0165] For example, if a user enters a question such as "We don't have enough budget for a new project," the server sends this information to a generating AI model, which then analyzes it and generates an instruction recommending that the user consult with the finance department, which is then sent back to the user.

[0166] An example of a prompt message is: "Based on the following issue, indicate the appropriate department or person the user should contact: 'The project is behind schedule.'"

[0167] This system allows users to efficiently find the appropriate resources to consult in order to solve their problems. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0168] Step 1:

[0169] The user enters their inquiry details using a terminal. The entered information is sent to the server in text format. Specifically, when a user enters an inquiry such as "the project is behind schedule" and presses the send button, the data is sent to the server. The input is text data, and the output is data sent to the server.

[0170] Step 2:

[0171] The server uses data processing tools to analyze the text data received from the user. Specifically, it performs preprocessing of the text, removing unnecessary spaces and special characters, and then tokenizing the text. This process converts the text into a format suitable for the generative AI model. The input is text data from the user, and the output is preprocessed text data.

[0172] Step 3:

[0173] The server inputs pre-processed text data into a generative AI model. The generative AI model analyzes the text using natural language processing techniques and extracts important keywords and context. Specifically, it uses models such as GPT-4 to understand the meaning of the consultation content and identify relevant information. The input is pre-processed text data, and the output is keyword and contextual information as a result of the analysis.

[0174] Step 4:

[0175] The server identifies the appropriate person the user should consult based on the analysis results obtained from the generated AI model. Specifically, based on the analysis results, it generates concrete instructions for the user, such as "We recommend consulting the finance department." The input is the analysis results, and the output is the instruction on where to consult.

[0176] Step 5:

[0177] The server sends the generated instructions back to the user. The user can view the instructions from the server on their terminal. Specifically, instructions such as "We recommend consulting with the finance department" will be displayed on the terminal screen. The input is the instruction to consult, and the output is the instruction displayed to the user.

[0178] (Application Example 3)

[0179] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] In today's business environment, there is a demand for swift and appropriate responses to security issues and suspected misconduct. However, determining which department or person to consult can be difficult, sometimes leading to delays in response. Improving this situation and enabling rapid responses to security issues is a key challenge.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0182] In this invention, the server includes means for receiving information from users, means for artificial intelligence learning for analyzing social security issues based on the received information, and means for suggesting appropriate resources for consultation based on the analysis results. This makes it possible to quickly and appropriately identify and respond to resources for consultation when a user has a security-related problem.

[0183] "Means for receiving information from users" refers to devices or software that have the function of receiving security-related information provided by users as input.

[0184] "An artificial intelligence learning tool for analyzing social security issues" refers to a device or software that uses artificial intelligence technology to collect data on security-related events in society and analyze it.

[0185] "Means for presenting general opinions based on analysis results" refers to a device or software that has the function of providing general advice or opinions to users based on the results of analysis by artificial intelligence.

[0186] "Means of suggesting appropriate consultation channels" refers to devices or software that, based on analysis results, identify the appropriate department or person the user should consult and present this information to the user.

[0187] "Means for analyzing information using natural language processing technology and selecting the appropriate department or person in charge" refers to a device or software that has the function of analyzing information from users using natural language processing technology and selecting the appropriate department or person in charge based on the results.

[0188] "Means for generating prompt sentences using a generative AI model" refers to a device or software that has the function of automatically generating prompt sentences in response to user input by utilizing a generative AI model.

[0189] The system for implementing this invention mainly consists of a server and a user terminal. The server provides an interface for receiving information from the user and analyzes social security issues based on the received information. The analysis uses artificial intelligence learning methods using Python and TENSORFLOW®. Based on the analysis results, the server presents a general opinion and suggests appropriate resources for consultation.

[0190] The user terminal is a device such as a smartphone or computer, and it provides a means for the user to input security-related information. The input information is analyzed using natural language processing technology, and the appropriate department or person in charge is selected. Specifically, the backend of the application, which uses Flask, processes the user input and generates prompt sentences using a generative AI model.

[0191] For example, if a user enters "There has been an increase in suspicious emails within the company recently," the server will issue specific instructions such as "We recommend consulting with the information security department." In this case, the generation AI model generates a prompt asking "If there is an increase in suspicious emails, which department should I consult?" and performs analysis.

[0192] In this way, users can obtain information to respond quickly and appropriately when security issues arise.

[0193] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0194] Step 1:

[0195] The user enters security-related information using their device. The entered information is sent to the server in text format.

[0196] Step 2:

[0197] The server analyzes the received information using natural language processing techniques. Specifically, it tokenizes the text data and extracts important keywords. This process generates foundational data necessary to understand the meaning of the input information.

[0198] Step 3:

[0199] The server uses a generative AI model to generate prompt messages based on the analyzed data. For example, it might generate a prompt message such as, "If there is an increase in suspicious emails, which department should I contact?" This prompt message serves as the basis for the AI ​​model to identify the appropriate department to contact.

[0200] Step 4:

[0201] The server inputs the generated prompt message into the AI ​​model, which then performs analysis to suggest the appropriate contact point. Based on pre-trained data, the AI ​​model selects the most appropriate department or person in charge. This process outputs specific contact information.

[0202] Step 5:

[0203] Based on the analysis results, the server provides the user with a general opinion and specific resources for consultation. The results are sent to the user's terminal, where the user can view the recommended resources on the screen.

[0204] This series of processes allows users to obtain information that enables them to respond quickly and appropriately when security issues arise.

[0205] 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.

[0206] "Example of form 1"

[0207] One embodiment of the present invention involves a system for receiving inquiries from users that includes an emotion engine that recognizes the user's emotions. In this system, when a user inputs their inquiry as text, the emotion engine analyzes the user's emotional state from that text. For example, if a user uses the expression "I'm in trouble," the emotion engine recognizes that the user is confused. This emotional information is taken into consideration when the AI ​​generates general opinions or suggests who the user should consult.

[0208] "Example of form 2"

[0209] Another embodiment of the present invention is a system in which an AI learning means learns user emotion data. In this system, the AI ​​learns not only the content of the user's consultation but also emotion data obtained from an emotion engine. This enables the AI ​​to generate more appropriate views and solutions based on emotion. For example, if the user is showing anger, the AI ​​can take that emotion into consideration and suggest a more courteous response.

[0210] "Example of form 3"

[0211] In a further embodiment of the present invention, there is a system that suggests a place to seek advice, which takes into account the user's emotional state to specify a particular department or person. In this system, the AI ​​considers the user's emotional state and suggests the most appropriate place to seek advice. For example, if the user is showing strong anxiety, the AI ​​may suggest a psychological counselor as a place to seek advice.

[0212] The following describes the processing flow for each example of the form.

[0213] "Example of form 1"

[0214] Step 1: The user enters their inquiry into the system as text.

[0215] Step 2: The emotion engine analyzes the user's emotional state from the text.

[0216] Step 3: The AI ​​considers emotional information and generates a general opinion.

[0217] Step 4: The AI ​​considers emotional information and suggests who to consult.

[0218] "Example of form 2"

[0219] Step 1: The user enters their inquiry into the system as text.

[0220] Step 2: The emotion engine analyzes the user's emotional state from the text.

[0221] Step 3: The AI ​​learns from user inquiries and emotional data.

[0222] Step 4: The AI ​​generates more appropriate viewpoints and solutions based on emotions.

[0223] "Example of form 3"

[0224] Step 1: The user enters their inquiry into the system as text.

[0225] Step 2: The emotion engine analyzes the user's emotional state from the text.

[0226] Step 3: The AI ​​considers the user's emotional state and suggests the most appropriate person to consult.

[0227] (Example 1)

[0228] Next, we will describe Example 1 of Form 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."

[0229] Traditional consultation systems have struggled to accurately grasp users' emotional states and provide appropriate advice based on those states. Furthermore, they lacked features to suggest specific resources based on the nature of the consultation, resulting in users being unable to receive appropriate support.

[0230] 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.

[0231] In this invention, the server includes means for receiving inquiries from users, means for analyzing the content of the received inquiries and recognizing the emotional state, means for generating a general opinion based on the analyzed emotional state, means for suggesting where to seek advice based on the generated opinion, and means for generating advice using a generative AI model. This makes it possible to provide appropriate advice that takes into account the user's emotional state and to suggest specific places to seek advice.

[0232] "Means of receiving inquiries from users" refers to an interface that allows users to access the system and input their inquiries in text format.

[0233] "Means of analyzing received consultation content to recognize emotional state" refers to a function that uses natural language processing technology to analyze emotions from text entered by the user and identify the user's emotional state.

[0234] "Means for generating general opinions based on analyzed emotional states" refers to a function that considers the user's emotional state and uses a generative AI model to generate appropriate advice and opinions.

[0235] "Means of suggesting who to consult based on generated opinions" refers to a function that suggests specific organizations or personnel that the user should consult based on the generated advice and opinions.

[0236] "Methods for generating advice using generative AI models" refers to a function that utilizes AI technology to automatically generate advice tailored to the user's consultation content and emotional state.

[0237] A description of the embodiment for carrying out the invention will be provided.

[0238] This system begins with the user entering their consultation details via a terminal. The user sends the consultation details to the system in text format using a web browser or a dedicated application. The terminal then transmits the entered text data to the server via the internet. The data is encrypted during transmission, ensuring security.

[0239] The server passes the received text to the emotion engine, which analyzes the user's emotional state. The emotion engine uses software that employs natural language processing technology. Specifically, Google's Cloud Natural Language API is one such example. For example, the emotion engine might determine that the user is feeling tired from an expression like "tired."

[0240] Next, the server uses a generative AI model to generate advice based on the analyzed sentiment information. This generative AI model may include OpenAI's GPT-4, for example. The generative AI model generates appropriate advice tailored to the user's situation.

[0241] For example, if a user inputs "I've been really busy with work lately and I'm exhausted," the server uses its emotion engine to recognize "fatigue" as an emotion. It then prompts the generative AI model with "Please provide advice for a user who is feeling fatigued," and generates advice such as "It's important to take regular breaks and make time to relax." The server returns this advice to the user, who can then view it on their device.

[0242] In this way, it becomes possible to provide appropriate advice that takes the user's emotional state into consideration, and to suggest specific places to seek advice.

[0243] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0244] Step 1:

[0245] The user enters their consultation details in text format using a terminal. The entered text specifically describes the user's concerns and problems. This text data becomes the input for the next processing step.

[0246] Step 2:

[0247] The terminal sends the entered text data to the server. Security is ensured because the data is encrypted during transmission. The output of this step is the encrypted text data received by the server.

[0248] Step 3:

[0249] The server decodes the received text data and passes it to the emotion engine. The emotion engine uses natural language processing techniques to analyze the text and identify the user's emotional state. For example, from the expression "tired," it might determine that the user is feeling tired. The output of this step is the analyzed emotion information.

[0250] Step 4:

[0251] The server inputs a prompt message into the generative AI model based on the analyzed sentiment information. The generative AI model generates advice tailored to the user's situation. For example, if the prompt message is "Provide advice for a user who is feeling fatigued," it might generate advice such as "It is important to take regular breaks and make time to relax." The output of this step is the generated advice.

[0252] Step 5:

[0253] The server sends the generated advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and consider their next course of action. The output of this step is the advice that the user reviews.

[0254] (Application Example 1)

[0255] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0256] In modern society, there is a demand for quick and accurate solutions to users' problems and anxieties. However, conventional systems have difficulty considering users' emotions and are unable to propose appropriate solutions.

[0257] 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.

[0258] In this invention, the server includes means for receiving inquiries from users, means for AI learning to analyze compliance issues in society based on the received inquiries, means for presenting general opinions based on the analysis results, means for sentiment analysis to analyze the user's emotions, and means for proposing appropriate countermeasures based on sentiment information. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[0259] "A means of receiving inquiries from users" refers to an interface that allows users to input their problems and concerns into the system and receive the content of their inquiries.

[0260] "AI learning methods" refer to artificial intelligence technology that analyzes and learns about public compliance issues and related information based on the content of inquiries received.

[0261] "Means of presenting general views" refers to a function that provides users with general solutions or opinions based on analysis results obtained through AI learning methods.

[0262] "Means of suggesting who to consult" refers to a function that suggests the appropriate department or expert the user should consult based on the analysis results.

[0263] "Emotional analysis means" refers to technology that analyzes emotions from the consultation content entered by the user and identifies the user's emotional state.

[0264] "Means for proposing appropriate countermeasures" refers to a function that proposes optimal solutions and actions to users based on emotional information obtained through emotion analysis.

[0265] The system for implementing this invention can be accessed by users using smartphones or computer terminals. Users input their inquiries in text format and send them to the system. The server uses software such as Python or TensorFlow and utilizes natural language processing libraries (such as NLTK) to analyze the received inquiries.

[0266] The server first receives the user's inquiry and uses sentiment analysis to identify the user's emotions. This involves analyzing text data to extract the user's emotional state (e.g., anxiety, fear). Next, AI learning tools are used to analyze relevant societal compliance issues and related information concerning the inquiry. This allows the server to present the user with a general perspective.

[0267] Furthermore, a system that suggests appropriate measures based on emotional information is activated, proposing the best solutions and actions to the user. For example, if a user inputs "I've recently seen suspicious people around my house," the emotional analysis system identifies "anxiety," and the AI ​​suggestion engine makes suggestions such as "consult the local police station" or "consider installing security cameras."

[0268] By utilizing a generative AI model, it is possible to generate prompt messages that are tailored to the user's emotions and the content of their inquiry, enabling appropriate responses. An example of a prompt message would be, "If the user is feeling anxious, what security measures should be suggested?"

[0269] The flow of the specific process in Application Example 1 will be described using FIG. 18.

[0270] Step 1:

[0271] The user accesses the system using a smartphone or a computer terminal and enters the consultation content in text form. The input text data is sent to the server.

[0272] Step 2:

[0273] The server passes the received text data to the sentiment analysis means. The sentiment analysis means analyzes the text using a natural language processing library (such as NLTK) to identify the user's emotional state. For example, emotions such as "uneasy" or "fear" are extracted. The analysis result is passed to the next step as sentiment information.

[0274] Step 3:

[0275] The server inputs the sentiment information and the consultation content into the AI learning means. The AI learning means analyzes compliance issues and related information in the world using a machine learning framework such as TensorFlow. As an analysis result, general opinions and related information are generated.

[0276] Step 4:

[0277] The server activates the means for proposing appropriate countermeasures based on the sentiment information and the analysis result of the AI learning means. This means utilizes a generative AI model to generate prompt sentences that propose optimal solutions or actions to the user. For example, specific proposals such as "Consult the nearby police station" or "Consider installing a security camera" are made.

[0278] Step 5:

[0279] The server sends the generated prompt sentences to the user's terminal and displays the proposed content to the user. The user can select appropriate actions by referring to the proposed countermeasures.

[0280] (Example 2)

[0281] Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0282] In modern society, the problems related to social norms faced by individuals are diverse, but it is not easy to obtain appropriate views and solutions for these problems. In addition, a response considering the feelings of the counselor is required, but there is a problem that it is difficult to provide advice considering feelings in the conventional system.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0284] In this invention, the server includes means for receiving information from a user, machine learning means for analyzing problems related to social norms based on the received information, and means for acquiring the user's emotional information and reflecting it in the analysis result. Thereby, it becomes possible to present a general opinion on the consultation content and make an adjustment considering feelings.

[0285] The "means for receiving information from a user" is an interface for the system to receive the consultation content and questions provided by the user.

[0286] The "machine learning means for analyzing problems related to social norms" is an algorithm for analyzing problems related to social norms by utilizing pre-learned data based on the received information.

[0287] The "means for presenting a general opinion" is a function for providing a standard or general opinion to the user based on the analysis result.

[0288] "Means of suggesting who to consult" refers to a function that suggests appropriate organizations or positions for the user to consult based on the analysis results.

[0289] "Means for acquiring user emotional information and reflecting it in analysis results" refers to a function that analyzes the user's emotional state and incorporates that information into the analysis results in order to provide more appropriate responses.

[0290] "Means for adjusting generated opinions" refers to functions that appropriately modify or adjust generated opinions by taking into account the user's emotional information and other factors.

[0291] One embodiment of this invention is a system that receives inquiries from users, analyzes them, and provides appropriate opinions and solutions. A specific embodiment is shown below.

[0292] The server provides a web interface accessible via the internet to receive information from users. Through this interface, users can input their concerns in text format. For example, a user might input a specific concern such as, "I'm suffering from harassment from my boss."

[0293] The terminal sends the user's inputted consultation details to the server. The consultation details are sent to the server as text data. The server inputs the received consultation details into a generative AI model. The generative AI model then refers to a pre-trained database to search for relevant laws and cases related to the consultation details.

[0294] Furthermore, the server uses an emotion engine to acquire user emotion data. For example, it analyzes emotions such as anger and sadness from the text entered by the user. This emotion data is then reflected in the analysis results of the generative AI model.

[0295] The server uses a generative AI model to generate general opinions and solutions for the consultation content. For example, based on laws and regulations and cases related to power harassment, appropriate advice is created. The generated opinions and solutions are adjusted considering the user's emotional information. For example, when the user shows anger, advice is provided with more polite language.

[0296] As a specific example, when the user inputs "I want to consult about sexual harassment in the workplace", the server generates the following prompt sentence for the generative AI model: "The user is consulting about sexual harassment in the workplace. Please provide appropriate advice based on relevant laws and regulations and cases." Based on this prompt sentence, the generative AI model generates appropriate opinions and solutions, and the server provides them to the user.

[0297] The flow of the specific process in Example 2 will be described using FIG. 19.

[0298] Step 1:

[0299] The user inputs the consultation content through the terminal. For example, a specific consultation such as "I'm troubled by power harassment from my boss" is input. The input text data is sent from the terminal to the server.

[0300] Step 2:

[0301] The server inputs the received consultation content into the generative AI model. The server refers to the pre-learned database and searches for laws and regulations and cases related to the consultation content. By analyzing the input text data and extracting relevant information, basic data for generating general opinions and solutions is obtained.

[0302] Step 3:

[0303] The server uses an emotion engine to acquire user emotion data. It analyzes the user's emotional state from the input text data, identifying emotions such as anger and sadness. This emotion data is then incorporated into the analysis results of a generative AI model.

[0304] Step 4:

[0305] The server uses a generative AI model to generate general opinions and solutions to the consultation content. For example, it creates appropriate advice based on laws and cases related to power harassment. The generated opinions and solutions are adjusted to take into account the user's emotional information.

[0306] Step 5:

[0307] The server sends the generated views and solutions to the terminal. The terminal displays the results to the user. For example, if the user is expressing anger, the server will offer advice in more polite language.

[0308] (Application Example 2)

[0309] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0310] In today's workplace, employees face a wide range of compliance issues and workplace troubles. A system is needed that allows employees to receive appropriate advice regarding these issues. However, conventional systems often struggle to consider user emotions and lack features to suggest specific resources for consultation, which can delay problem resolution.

[0311] 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.

[0312] In this invention, the server includes means for receiving inquiries from users, information processing means for analyzing compliance issues in society based on the received inquiries, and means for analyzing the user's emotional data and generating emotionally-based opinions. As a result, users can receive appropriate advice tailored to their emotions and, by suggesting specific resources for consultation, can quickly resolve problems.

[0313] "A means of receiving inquiries from users" refers to an interface that allows users to anonymously input problems and concerns they face at work, and for the system to receive them.

[0314] "Information processing means" refers to a processing device that uses AI technology to analyze the content of inquiries received and generate general opinions and solutions based on relevant laws and cases.

[0315] "Means for analyzing emotional data and generating emotion-based viewpoints" refers to an analytical device that extracts emotions from user input and provides appropriate responses and advice based on those emotions.

[0316] "A means of generating advice on consultation content using a generative AI model" refers to a system that utilizes AI technology to automatically generate specific advice and solutions to users' consultation content.

[0317] "Means of suggesting who to consult" refers to a function that suggests appropriate organizations or positions for the problems a user is facing, and indicates specific contact points for consultation.

[0318] The system for implementing this invention provides a platform where users can anonymously consult about compliance issues and troubles they face in the workplace. The server receives consultations from users and analyzes the content of the consultations using information processing means. Specifically, the server uses software such as Python and TensorFlow, and utilizes natural language processing libraries (e.g., spaCy) to analyze the content of the consultations.

[0319] The server uses sentiment analysis engines such as the Google Cloud Natural Language API to acquire user sentiment data. Based on this, it generates opinions that are relevant to the user's emotions, and then uses a generative AI model (e.g., OpenAI's GPT) to generate specific advice regarding the consultation.

[0320] The terminal sends the user's inputted consultation details to the server and receives analysis results and advice from the server. Based on this information, the user can obtain specific resources for consultation and solutions.

[0321] For example, if a user consults the server about suffering from harassment from their boss, the server will suggest appropriate countermeasures based on laws and cases related to harassment. Furthermore, if the user expresses anger, the server will suggest a more courteous approach.

[0322] An example of a prompt message would be: "The user is seeking advice about workplace harassment. Generate appropriate advice based on relevant laws and cases. Also, adjust the tone of the advice considering the user's emotional data."

[0323] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0324] Step 1:

[0325] Users anonymously input workplace problems and concerns using a terminal. The entered information is sent from the terminal to the server. The input data is in text format and includes the user's specific concerns.

[0326] Step 2:

[0327] The server analyzes the received consultation content using a natural language processing library (e.g., spaCy). It tokenizes the received text data and extracts important keywords and phrases. This allows the server to identify the subject of the consultation and relevant laws and regulations.

[0328] Step 3:

[0329] The server uses the Google Cloud Natural Language API to retrieve user emotion data from the consultation content. The parsed text data is passed to an emotion analysis engine as input, and emotion scores and emotion categories are obtained as output. This allows for an understanding of the user's emotional state.

[0330] Step 4:

[0331] The server uses a generative AI model (e.g., OpenAI's GPT) to generate advice for the consultation. The analyzed consultation content and sentiment data are incorporated into prompt sentences as input and passed to the generative AI model. The output is text containing specific advice and solutions.

[0332] Step 5:

[0333] The server sends the generated advice to the terminal. The terminal displays the advice to the user, who can then consider specific actions based on it. The output includes the text of the advice provided to the user.

[0334] (Example 3)

[0335] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0336] Conventional consultation systems have difficulty suggesting appropriate resources based on the user's concerns, and in particular, they lack consideration for the user's emotional state. Furthermore, the accuracy of natural language processing in analyzing consultation content is low, making it difficult to provide users with specific and useful guidance.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0338] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing the data based on the received information, and means for suggesting appropriate consultation services based on the analysis results. This makes it possible to suggest appropriate consultation services that take into account the emotional state of the user in response to the content of their consultation.

[0339] A "user" refers to an individual or group that uses the system to input information and seek advice.

[0340] "Means of receiving information" refers to the function of receiving input from users and converting it into a format that can be processed within the system.

[0341] "Machine learning methods for analyzing data" refers to a function that uses machine learning algorithms to analyze data based on received information and understand the user's intentions and emotions.

[0342] "Means of providing general guidelines" refers to a function that provides users with general advice or guidelines based on the analysis results.

[0343] "Means of suggesting appropriate resources for consultation" refers to a function that, based on the analysis results, suggests specific organizations or individuals that the user should consult.

[0344] "A means of determining who to consult based on emotional state" refers to a function that analyzes the user's emotional state and determines the most suitable consultation service accordingly.

[0345] A "generative AI model" is an artificial intelligence model used for natural language processing, referring to a technology that analyzes user input and generates appropriate output.

[0346] This invention begins with a user inputting consultation details via a terminal, and a server receiving that information. The server is equipped with means for receiving information from the user and uses a generative AI model to analyze the input information. Specifically, it uses a generative AI model such as OpenAI's GPT model to perform natural language processing.

[0347] The server uses machine learning techniques to analyze the received information as data. These techniques aim to understand the user's inquiry and analyze their intentions and emotions. Based on the analysis results, the server uses methods to suggest appropriate resources, indicating specific organizations or individuals to the user.

[0348] Furthermore, the server has a mechanism to determine who to consult based on the user's emotional state. This makes it possible to suggest the most suitable consultation service based on the user's emotions. For example, if a user consults the server saying, "I've been having trouble with relationships at work lately and I'm feeling stressed," the server can suggest a psychological counselor as a consultation option.

[0349] An example of a prompt message is, "If the user is experiencing stress due to interpersonal relationships at work, please suggest which department or person they should consult." By inputting this prompt message into the AI ​​generation model, the server can determine the appropriate contact point and notify the user. The specific processing flow in Example 3 is explained using Figure 21.

[0350] Step 1:

[0351] The user enters their consultation details through their device. The entered information is saved on the device as text data. This data includes the user's intentions and emotions.

[0352] Step 2:

[0353] The terminal sends the text data entered by the user to the server. A secure communication protocol is used for transmission to ensure data security. The server prepares the received data for analysis.

[0354] Step 3:

[0355] The server inputs the received text data into a generative AI model. Specifically, it uses the generative AI model to perform natural language processing. The server analyzes the data and processes it to understand the user's intent and emotions.

[0356] Step 4:

[0357] The server determines the appropriate contact point based on the analysis results from the generated AI model. The analysis results include information related to the user's inquiry, and the server uses this to suggest specific organizations or individuals.

[0358] Step 5:

[0359] The server considers the user's emotional state to determine the most suitable consultation service. The generative AI model infers emotions from the user's input and suggests consultation services such as psychological counselors as needed.

[0360] Step 6:

[0361] The server sends the information of the chosen contact person to the terminal. The terminal notifies the user of the information received from the server, and the user can check the recommended contact person on the screen.

[0362] (Application Example 3)

[0363] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0364] In today's business environment, there is a demand for swift and appropriate responses when security incidents or compliance issues arise. However, finding the right resources and solutions for these problems is not easy, and there is a particular lack of responses that take into account the emotional state of users. Therefore, a system is needed that allows users to resolve problems with peace of mind.

[0365] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0366] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing social norm issues based on the received information, means for presenting general opinions based on the analysis results, and means for analyzing the user's emotional state and proposing appropriate countermeasures. This enables the user to quickly and appropriately find someone to consult and receive support that is appropriate to their emotional state.

[0367] "Means for receiving information from users" refers to an interface that allows the system to receive and process data and inquiries provided by users.

[0368] "Machine learning methods for analyzing social norm issues" refers to the process of analyzing and understanding issues related to social norms and laws using machine learning techniques.

[0369] "Means of presenting general opinions" refers to a function that provides users with general and objective views based on analyzed data.

[0370] "Means of suggesting who to consult" refers to a function that suggests the appropriate department or person to consult based on the analysis results.

[0371] "A means of analyzing a user's emotional state and proposing appropriate countermeasures" refers to a process for analyzing a user's emotions and proposing the optimal countermeasures based on that state.

[0372] The system for implementing this invention mainly consists of a server and a user terminal. The server receives information from the user and is equipped with machine learning means for analyzing social norm issues. Specifically, the server uses the Google Cloud Natural Language API to process the user's inquiries in natural language and IBM Watson® Tone Analyzer to analyze the user's emotional state.

[0373] The server provides general opinions based on the analysis results and suggests who to consult. This allows users to quickly access the appropriate department or person in charge. Furthermore, by suggesting solutions tailored to the user's emotional state, it reduces user anxiety and assists in problem-solving.

[0374] The user terminal is a device such as a smartphone or smart glasses, which provides an interface for the user to input their inquiry. When a user inputs an inquiry such as "There is a possibility of data leakage," the server analyzes the content and returns specific instructions such as "We recommend that you contact the IT security department."

[0375] For example, if a user enters a prompt such as, "If there is a possibility of a data breach, which department should I contact?", the server can suggest appropriate countermeasures. This allows the user to address the problem quickly and appropriately.

[0376] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0377] Step 1:

[0378] The user enters their inquiry details using a terminal. The entered text data is sent to the server.

[0379] Step 2:

[0380] The server performs natural language processing on the received text data using the Google Cloud Natural Language API. This process analyzes the structure and meaning of the text and extracts important keywords and phrases. The output is the analyzed text data.

[0381] Step 3:

[0382] The server inputs the analyzed text data into IBM Watson Tone Analyzer to analyze the user's emotional state. This process determines the user's emotional state (e.g., anxiety, reassurance, etc.). The output is data on the emotional state.

[0383] Step 4:

[0384] The server uses a generative AI model to generate general opinions based on the analysis results. This model has learned from past data and cases, and generates appropriate opinions regarding the user's inquiry. The output is a general opinion.

[0385] Step 5:

[0386] The server considers the analysis results and emotional state to suggest who to consult. Specifically, it generates prompt messages to suggest the appropriate department or person in charge. The output provides a list of suggested consultation locations.

[0387] Step 6:

[0388] The server sends generated general opinions and suggested contacts to the user's terminal. The user can receive this information through their terminal and take appropriate action.

[0389] (Other examples)

[0390] Next, other embodiments 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".

[0391] In today's business environment, it is crucial to respond quickly and appropriately to user inquiries and consultations. However, when the content of the consultations is diverse or the user's emotional state is complex, traditional systems struggle to provide appropriate responses. In particular, consultations regarding compliance issues require specialized knowledge and a rapid response, which limits the capabilities of traditional methods.

[0392] The identification processing performed by the identification processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0393] In this invention, the server includes means for receiving inquiries from users; sentiment analysis means that analyze the received inquiries and use natural language processing technology to recognize the user's emotional state; generative AI means that use a generative AI model to analyze compliance issues in society based on the analyzed emotional state and inquiries; means for inputting prompts to instruct the generative AI model to generate a general opinion based on the analysis results; and means for identifying specific departments or individuals to suggest where to seek advice based on the generated opinion and the user's emotional state. This enables a quick and appropriate response to the user's inquiries.

[0394] "A means of receiving inquiries from users" refers to an interface that allows a server to receive inquiry details entered by users via their terminals and save them in a database.

[0395] "Emotional analysis means" refers to software or hardware that uses natural language processing technology to analyze the content of a user's consultation and recognize their emotional state, such as positive, negative, or neutral.

[0396] "Generative AI methods" refer to methods that use generative AI models to analyze compliance issues based on the user's consultation content and emotional state.

[0397] A "generative AI model" is a pre-trained artificial intelligence model that has the ability to generate appropriate responses and opinions based on input data.

[0398] A "prompt" is an input sentence used to instruct a generative AI model to perform a specific task, and is used to generate general insights based on the analysis results.

[0399] "Means of suggesting who to consult" refers to means of identifying specific departments or individuals that the user should consult, based on the generated views and the user's emotional state.

[0400] This invention is a system that receives inquiries from users, performs sentiment analysis, analyzes compliance issues using a generative AI model, and presents appropriate opinions and referrals. A specific embodiment of this system is described below.

[0401] The server receives consultation details sent from the user's terminal. Users input their consultation details through a dedicated application or web interface, and this information is sent to the server. The server stores the received consultation details in a database.

[0402] Next, the server uses a natural language processing library (e.g., NLTK or spaCy) to analyze the consultation content and recognize the user's emotional state. The results of the emotional analysis are classified as positive, negative, or neutral and stored in a database.

[0403] Subsequently, the server uses a generative AI model (e.g., OpenAI's GPT-3®) to analyze compliance issues using the consultation content and sentiment analysis results as input data. Prompts are input to the generative AI model instructing it to generate general opinions based on the analysis results. Examples of prompts include:

[0404] Example prompt: "Generate a general opinion on compliance based on the following inquiry: Inquiry: I would like to discuss workplace harassment. Emotional state: Negative"

[0405] Based on the analysis results obtained from the generative AI model, the server generates a general opinion and presents it to the user. Furthermore, considering the generated opinion and the user's emotional state, it identifies who the user should consult. To identify specific departments or individuals, the server refers to organizational information stored in the database.

[0406] The user's terminal receives general opinions and contact information sent from the server and displays them on the screen. Based on the information presented, the user can then decide on their next course of action.

[0407] In this way, it becomes possible to respond quickly and appropriately to user inquiries.

[0408] The flow of specific processing in other embodiments will be explained using Figure 23.

[0409] Step 1:

[0410] Users enter their consultation details through a dedicated application on their terminal or a web interface. The entered consultation details are sent to the server in text format. The server stores the received consultation details in a database. The input is the user's consultation details, and the output is the consultation details stored in the database.

[0411] Step 2:

[0412] The server analyzes the received consultation content using natural language processing libraries (e.g., NLTK or spaCy). Specifically, it tokenizes the text and applies an emotion analysis model to recognize one of three emotional states: positive, negative, or neutral. The input is the consultation content stored in the database, and the output is the result of the emotion analysis. This result is stored in the database.

[0413] Step 3:

[0414] The server uses a generative AI model (e.g., OpenAI's GPT-3) to analyze compliance issues using the consultation content and sentiment analysis results as input data. The generative AI model is prompted to generate a general opinion based on the analysis results. The input is the consultation content and sentiment analysis results, and the output is the analysis results obtained from the generative AI model. Example prompt: "Please generate a general opinion on compliance based on the following consultation content. Consultation content: I would like to consult about workplace harassment. Sentimental state: Negative"

[0415] Step 4:

[0416] The server generates a general opinion based on the analysis results obtained from the generative AI model. This opinion is stored in a database for presentation to the user. The input is the analysis results from the generative AI model, and the output is the generated general opinion.

[0417] Step 5:

[0418] The server identifies who to consult, taking into account the generated views and the user's emotional state. It references organizational information stored in the database to identify specific departments or individuals. The input is the generated views and emotional state, and the output is the identified consultation contact information.

[0419] Step 6:

[0420] The user terminal receives general opinions and contact information from the server and displays them on the screen. Based on the presented information, the user can decide on their next action. The input is the opinions and contact information from the server, and the output is the information presented to the user.

[0421] 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.

[0422] Data generation model 58 is a form 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> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0424] 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.

[0425] [Second Embodiment]

[0426] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0438] "Example of form 1"

[0439] One embodiment of the present invention provides an AI consultation and reporting system. This system has a means of receiving consultations from users. Specifically, users can access the system and submit a consultation by entering their problem or concern as text.

[0440] "Example of form 2"

[0441] Furthermore, this system has a mechanism for AI to analyze compliance issues in society based on the consultations it receives. Specifically, the AI ​​uses a pre-trained database to generate general opinions and solutions to the user's consultation. For example, if a user consults about "being troubled by harassment from their boss," the AI ​​will analyze laws and cases related to harassment and generate appropriate advice.

[0442] "Example of form 3"

[0443] Furthermore, this system has a mechanism to suggest who to consult based on the analysis results. Specifically, the AI ​​will instruct the user on the department or person to consult based on the analysis results. For example, if a user consults about suspected irregularities in company expense reports, the AI ​​can give specific instructions such as "We recommend consulting the internal audit department."

[0444] The following describes the processing flow for each example of the form.

[0445] "Example of form 1"

[0446] Step 1: The user accesses the AI ​​consultation and reporting system.

[0447] Step 2: The user enters their problem or concern as text and submits it to the system for consultation.

[0448] "Example of form 2"

[0449] Step 1: The system receives the user's inquiry.

[0450] Step 2: The AI ​​uses a pre-trained database to generate general opinions and solutions regarding the consultation.

[0451] Step 3: Present the AI-generated views and solutions to the user.

[0452] "Example of form 3"

[0453] Step 1: Based on the user's inquiry and the opinions it generates, the AI ​​determines which department or person should be consulted.

[0454] Step 2: The AI ​​suggests to the user which department or person they should consult.

[0455] (Example 1)

[0456] Next, we will describe Example 1 of Form 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".

[0457] Traditional consultation systems faced challenges in providing prompt and appropriate responses to user inquiries. In particular, when inquiries covered a wide range of topics or required specialized knowledge, responses were often delayed or insufficient.

[0458] 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.

[0459] In this invention, the server includes means for receiving inquiries from users, means for passing the received inquiries to a generation AI model, and means for the generation AI model to analyze the content of the inquiries and generate a response. This makes it possible to provide a quick and appropriate response to user inquiries.

[0460] A "user" refers to an individual or organization that accesses the system and enters their inquiry details.

[0461] "Consultation" refers to the content of problems or concerns that users input into the system.

[0462] A "generative AI model" refers to artificial intelligence technology that analyzes the content of inquiries received from users and generates appropriate responses.

[0463] "Response" refers to the answer or advice generated by the generative AI model as a result of analyzing the content of the consultation.

[0464] A "server" refers to a computer system that receives inquiries from users, passes data to a generation AI model, and provides the generated response to the user.

[0465] A "terminal" refers to a device used by a user to access the system, input their inquiries, and receive responses.

[0466] As an embodiment for carrying out this invention, an AI consultation and reporting system will be specifically described.

[0467] The server provides a web interface for receiving inquiries from users. This interface is accessed through a device accessible to the user (such as a PC or smartphone). Users can access the system and input their inquiries in text format. The entered text data is sent to a generative AI model running on the server. This generative AI model uses natural language processing technology to analyze the user's inquiries and generate appropriate responses.

[0468] For the generative AI model, advanced natural language processing models such as OpenAI's GPT-4 can be used. This makes it possible to understand the user's intent and construct the optimal response based on past training data. The generated response is then sent back to the user's device and displayed to the user.

[0469] As a concrete example, consider a scenario where a user enters a question such as, "My motivation at work has been low lately. What should I do?" This prompt is sent to a generative AI model, which generates a response such as, "I recommend setting new goals and accumulating small achievements." The server sends this response back to the terminal, which then displays it to the user. The user can then use this advice to guide their daily work.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The user accesses the AI ​​consultation reporting system using their device. The user enters their consultation details into the text box and clicks the "Submit" button. The user's consultation details are provided as input in text format.

[0473] Step 2:

[0474] The terminal sends the text data entered by the user to the server as an HTTP request. The input is the user's inquiry, and the output is the request data sent to the server. This request contains the user's inquiry.

[0475] Step 3:

[0476] The server passes the received text data to the generating AI model. The input is the consultation content received from the terminal, and the output is the input data for the generating AI model. The server converts the data into a format that the generating AI model can access.

[0477] Step 4:

[0478] The generative AI model analyzes data received from the server and generates an appropriate response. The input is the inquiry from the server, and the output is the generated response. The model uses natural language processing techniques to understand the user's intent and construct the optimal answer.

[0479] Step 5:

[0480] The server sends the response received from the generative AI model back to the terminal as an HTTP response. The input is the response from the generative AI model, and the output is the response data sent to the terminal.

[0481] Step 6:

[0482] The terminal displays the response received from the server to the user. The input is the response data from the server, and the output is the response displayed to the user. The user can review the advice generated on the screen and consider their next course of action.

[0483] (Application Example 1)

[0484] Next, we will describe Application Example 1 of Form 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."

[0485] In modern society, individuals and businesses face a wide range of security issues, but obtaining appropriate advice and information quickly is difficult. In particular, it is challenging for ordinary users to determine how to respond to threats such as phishing scams and data breaches. In this situation, there is a need for a system that allows users to confidently seek security advice and take appropriate measures.

[0486] 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.

[0487] In this invention, the server includes means for receiving inquiries from users, means for artificial intelligence learning for analyzing security issues based on the received inquiries, and means for providing appropriate advice based on the analysis results. This enables users to resolve their security concerns and questions and take quick and appropriate measures.

[0488] "A means of receiving inquiries from users" refers to an interface that allows users to access the system and input their security concerns and questions in text.

[0489] "An artificial intelligence learning method for analyzing security issues" is a processing device that uses artificial intelligence technology to analyze the content of inquiries received, identify security-related problems, and derive appropriate countermeasures.

[0490] "Means of providing appropriate advice" refers to a function that presents users with specific countermeasures and information based on analysis results, and provides information to alleviate security concerns.

[0491] "Means of providing security information" refers to information provision devices that provide users with the latest information on security incidents and regulations to support appropriate decision-making.

[0492] The system for implementing this invention enables users to consult about security using a device such as a smartphone. Users input their consultation details as text through an application on their device. The server utilizes an artificial intelligence learning method using a generative AI model to analyze the received consultation details. This artificial intelligence learning method analyzes the text using a natural language processing library (e.g., spaCy) and extracts important keywords and context.

[0493] Based on the extracted information, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate advice for the user. Furthermore, by cross-referencing with a security database, it can provide the latest security information. This allows users to resolve their security concerns and questions and take quick and appropriate action.

[0494] For example, if a user enters "I've been receiving more emails from my bank lately and I'm worried. What should I do?", the server will analyze the content and provide advice such as, "To verify whether the email is genuine, we recommend checking the sender's email address and logging in directly from the official website. Also, please be careful not to enter any personal information, as it may be a phishing scam."

[0495] An example of a prompt message is: "User inquiry: I've been receiving more emails from my bank lately and I'm worried. What should I do? As an AI, what advice would you offer the user?"

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The user launches an application on their device and enters their security-related questions as text. The entered text is sent to the server. The input data is text information that specifically expresses the user's concerns and questions.

[0499] Step 2:

[0500] The server analyzes the received text data using a natural language processing library (e.g., spaCy). During the analysis process, important keywords and context are extracted from the text. The input is the user's inquiry, and the output is the extracted keywords and contextual information.

[0501] Step 3:

[0502] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the extracted information to generate appropriate advice for the user. The AI ​​model uses prompts to generate answers to the user's inquiries. The input consists of extracted keywords and contextual information, and the output is the generated advice.

[0503] Step 4:

[0504] The server compares the generated advice against the security database and adds the latest security information. This ensures that the information provided to the user is up-to-date and accurate. The input is the generated advice, and the output is the final advice information after comparison.

[0505] Step 5:

[0506] The server sends the final advice information to the user's terminal. The user can then review the advice on their terminal and take specific actions to resolve any security concerns or questions. The input is the final advice information, and the output is the user's understanding and actions.

[0507] (Example 2)

[0508] Next, we will describe Example 2 of Form 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".

[0509] In modern society, users face a wide range of compliance issues, and resolving them requires specialized knowledge. However, it is not easy for ordinary users to obtain appropriate views and solutions to these problems. Therefore, there is a need for a system that allows users to receive quick and accurate advice on compliance issues.

[0510] 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.

[0511] In this invention, the server includes means for receiving information from a user, means for processing information to analyze the received information, means for searching for relevant information from a knowledge base based on the analyzed information, means for generating an opinion using a generative AI model based on the search results, and means for providing the generated opinion to the user. This makes it possible for users to quickly obtain appropriate advice on compliance issues even without specialized knowledge.

[0512] "Means of receiving information from users" refers to the function by which the system receives information entered by the user and incorporates it for processing.

[0513] "Information processing means" refers to the technologies and processes used to analyze received information and extract necessary data.

[0514] "Means of searching for relevant information from a knowledge base" refers to a function that finds relevant information from a pre-stored database based on the analyzed information.

[0515] "Means of generating opinions using generative AI models" refers to a function that utilizes AI technology to create opinions and advice to provide to users based on search results.

[0516] "Means of providing generated views to users" refers to a function that communicates views generated by an AI model to users and presents them in an accessible format.

[0517] This invention is a system that provides prompt and accurate advice to users regarding compliance issues they face. The following describes a specific implementation of this system.

[0518] The server connects to the user's terminal via the internet to receive information from the user. The user uses their terminal to input their inquiry and send it to the server. For example, they can input specific details such as, "I'm suffering from harassment from my boss."

[0519] The server uses natural language processing techniques to analyze the information it receives. In this process, the server uses natural language processing libraries such as "spaCy" and "NLTK" to extract keywords and context from the consultation content.

[0520] Based on the analyzed information, the server searches its knowledge base for relevant information. The knowledge base contains laws and regulations related to compliance and past case studies. The server refers to this data and extracts information relevant to the user's inquiry.

[0521] Next, the server uses a generative AI model such as "GPT-4" to generate opinions based on the search results. At this time, the AI ​​model is given instructions such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[0522] Finally, the server provides the generated insights to the user's terminal. The user can then review the specific advice on their terminal and use it as a reference for resolving the problem. In this way, users can quickly obtain appropriate advice on compliance issues, even without specialized knowledge.

[0523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0524] Step 1:

[0525] The user uses a terminal to input their consultation details and sends them to the server. The input could be specific details such as, "I'm suffering from harassment from my boss." The terminal then sends this input as digital data to the server.

[0526] Step 2:

[0527] The server analyzes the received consultation content using natural language processing technology. The input is the consultation content from the user, and the output is the analyzed keywords and contextual information. The server uses libraries such as "spaCy" and "NLTK" to tokenize the text data and extract important keywords.

[0528] Step 3:

[0529] The server searches for relevant information from its knowledge base based on the analysis results. The input is the analyzed keywords, and the output is data on relevant laws and cases. The server executes database queries to extract the relevant information.

[0530] Step 4:

[0531] The server inputs the search results into a generative AI model such as "GPT-4" to generate an opinion. The input is data containing relevant information, and the output is an opinion or advice to be provided to the user. The server gives the AI ​​model instructions as a prompt, such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[0532] Step 5:

[0533] The server sends the generated opinion to the user's terminal. The input is the generated opinion, and the output is advice that the user can view on their terminal. The user can receive specific advice through their terminal and use it as a reference for solving problems.

[0534] (Application Example 2)

[0535] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0536] In modern society, individuals and businesses face a wide range of compliance issues, but obtaining appropriate advice quickly is difficult. Furthermore, the lack of systems that provide concrete solutions in real time based on the consultation content can lead to delays in problem resolution.

[0537] 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.

[0538] In this invention, the server includes means for receiving inquiries from users, machine learning means for analyzing social norm issues based on the received inquiries, and means for providing specific countermeasures based on the analysis results. This enables users to receive quick and appropriate advice in real time.

[0539] "Means for receiving inquiries from users" refers to an interface for receiving inquiries or problem reports from individuals or organizations.

[0540] "Machine learning methods for analyzing social norm issues" are algorithms that use databases to analyze legal and ethical issues and derive appropriate solutions.

[0541] "Means of presenting general views" refers to a function that presents widely accepted opinions or solutions based on the analysis results.

[0542] "Means of suggesting who to consult" refers to a function that suggests which department or position a user should consult in order to resolve a problem.

[0543] "Means of providing specific countermeasures" refers to a function that provides specific action guidelines for the problems faced by users, based on the analysis results.

[0544] "A means of generating advice in real time" refers to a function that immediately analyzes user inquiries and provides advice quickly.

[0545] The system for implementing this invention is designed to receive inquiries from users, analyze social norms, and provide specific countermeasures. The system is configured as follows:

[0546] The server provides an interface for receiving inquiries from users. Users can input their inquiries in text format using their smartphones or computers. The entered inquiries are then sent to the server.

[0547] The server uses a machine learning model to analyze the content of the inquiries it receives. This model utilizes OpenAI's GPT, which analyzes social norm issues by referencing a pre-trained database. The database contains information on laws and past cases.

[0548] Based on the analysis results, the server provides users with general insights and specific countermeasures in real time. This allows users to receive timely and appropriate advice.

[0549] For example, if a user enters "I would like to consult about the risk of information leakage at work," the server will analyze relevant information security laws and past cases and provide advice such as, "To prevent information leakage, it is important to conduct regular security training and properly manage access rights."

[0550] An example of a prompt message is: "User inquiry: I would like to discuss the risk of information leakage in the workplace. Please provide appropriate advice based on relevant laws and case examples."

[0551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0552] Step 1:

[0553] Users input their consultation details in text format via a smartphone or computer interface. The entered text is sent to the server. The input data consists of the user's consultation details.

[0554] Step 2:

[0555] The server sends the received consultation content as a prompt message to OpenAI's GPT, a generative AI model, for analysis. The prompt message is structured to include the user's consultation content. The input data is the prompt message containing the user's consultation content, and the output data is the analysis result from the AI ​​model.

[0556] Step 3:

[0557] The server uses the analysis results obtained from the AI ​​model to reference relevant laws and cases from a database and generate specific countermeasures. The input data is the analysis results from the AI ​​model, and the output data is advice including specific countermeasures.

[0558] Step 4:

[0559] The server provides the generated advice to the user in real time. The user can view the advice on their terminal screen. The input data is advice that includes specific countermeasures, and the output data is the advice presented to the user.

[0560] (Example 3)

[0561] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0562] In today's information society, it is difficult for users to quickly find the right resource for addressing problems and questions they face. This is especially true when specialized knowledge is required or when dealing with complex issues, making it difficult to determine which department or person to consult. In this situation, there is a need for a system that allows users to quickly identify the appropriate resource and efficiently resolve their problems.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0564] In this invention, the server includes means for receiving information from users, data processing means for analyzing the received information, and means for analyzing the information using a generative AI model. This makes it possible to quickly identify the appropriate contact for advice regarding the problems faced by the user and to solve the problems efficiently.

[0565] A "user" is an individual or organization that uses the system to input information and seeks to identify a contact point for consultation.

[0566] "Information" refers to data about the consultation content and problems that users input into the system.

[0567] A "server" is a computer system that receives and processes information from users.

[0568] "Data processing means" refers to functions that perform the necessary preprocessing and transformations to analyze the received information.

[0569] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze information and identify the appropriate source of advice.

[0570] "Analysis" is the process of understanding the meaning of information using generative AI models and extracting relevant keywords and contexts.

[0571] A "consultation point" refers to the appropriate organization or person that a user should consult in order to resolve a problem.

[0572] "Presentation" refers to the act of informing the user of the contact point identified based on the analysis results.

[0573] This invention is a system for quickly identifying the appropriate resource for addressing a user's problem. The system begins with the user inputting information using a terminal. The user enters the consultation details in text format and sends them to the server.

[0574] The server uses data processing tools to process the information received from the user. Specifically, it performs text preprocessing and tokenization to convert the data into a format suitable for generative AI models. These generative AI models utilize natural language processing techniques. For example, OpenAI's GPT-4 is one such model.

[0575] The generative AI model analyzes information provided by the server and extracts important keywords and context. This analysis makes it possible to identify the appropriate resources for the user to consult. Based on the analysis results, the server presents the user with specific resources to consult.

[0576] For example, if a user enters a question such as "We don't have enough budget for a new project," the server sends this information to a generating AI model, which then analyzes it and generates an instruction recommending that the user consult with the finance department, which is then sent back to the user.

[0577] An example of a prompt message is: "Based on the following issue, indicate the appropriate department or person the user should contact: 'The project is behind schedule.'"

[0578] This system allows users to efficiently find the appropriate resources to consult in order to solve their problems. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0579] Step 1:

[0580] The user enters their inquiry details using a terminal. The entered information is sent to the server in text format. Specifically, when a user enters an inquiry such as "the project is behind schedule" and presses the send button, the data is sent to the server. The input is text data, and the output is data sent to the server.

[0581] Step 2:

[0582] The server uses data processing tools to analyze the text data received from the user. Specifically, it performs preprocessing of the text, removing unnecessary spaces and special characters, and then tokenizing the text. This process converts the text into a format suitable for the generative AI model. The input is text data from the user, and the output is preprocessed text data.

[0583] Step 3:

[0584] The server inputs pre-processed text data into a generative AI model. The generative AI model analyzes the text using natural language processing techniques and extracts important keywords and context. Specifically, it uses models such as GPT-4 to understand the meaning of the consultation content and identify relevant information. The input is pre-processed text data, and the output is keyword and contextual information as a result of the analysis.

[0585] Step 4:

[0586] The server identifies the appropriate person the user should consult based on the analysis results obtained from the generated AI model. Specifically, based on the analysis results, it generates concrete instructions for the user, such as "We recommend consulting the finance department." The input is the analysis results, and the output is the instruction on where to consult.

[0587] Step 5:

[0588] The server sends the generated instructions back to the user. The user can view the instructions from the server on their terminal. Specifically, instructions such as "We recommend consulting with the finance department" will be displayed on the terminal screen. The input is the instruction to consult, and the output is the instruction displayed to the user.

[0589] (Application Example 3)

[0590] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0591] In today's business environment, there is a demand for swift and appropriate responses to security issues and suspected misconduct. However, determining which department or person to consult can be difficult, sometimes leading to delays in response. Improving this situation and enabling rapid responses to security issues is a key challenge.

[0592] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0593] In this invention, the server includes means for receiving information from users, means for artificial intelligence learning for analyzing social security issues based on the received information, and means for suggesting appropriate resources for consultation based on the analysis results. This makes it possible to quickly and appropriately identify and respond to resources for consultation when a user has a security-related problem.

[0594] "Means for receiving information from users" refers to devices or software that have the function of receiving security-related information provided by users as input.

[0595] "An artificial intelligence learning tool for analyzing social security issues" refers to a device or software that uses artificial intelligence technology to collect data on security-related events in society and analyze it.

[0596] "Means for presenting general opinions based on analysis results" refers to a device or software that has the function of providing general advice or opinions to users based on the results of analysis by artificial intelligence.

[0597] "Means of suggesting appropriate consultation channels" refers to devices or software that, based on analysis results, identify the appropriate department or person the user should consult and present this information to the user.

[0598] "Means for analyzing information using natural language processing technology and selecting the appropriate department or person in charge" refers to a device or software that has the function of analyzing information from users using natural language processing technology and selecting the appropriate department or person in charge based on the results.

[0599] "Means for generating prompt sentences using a generative AI model" refers to a device or software that has the function of automatically generating prompt sentences in response to user input by utilizing a generative AI model.

[0600] The system for implementing this invention mainly consists of a server and a user terminal. The server provides an interface for receiving information from the user and analyzes social security issues based on the received information. Artificial intelligence learning methods using Python and TensorFlow are used for the analysis. Based on the analysis results, the server presents a general opinion and suggests appropriate resources for consultation.

[0601] The user terminal is a device such as a smartphone or computer, and it provides a means for the user to input security-related information. The input information is analyzed using natural language processing technology, and the appropriate department or person in charge is selected. Specifically, the backend of the application, which uses Flask, processes the user input and generates prompt sentences using a generative AI model.

[0602] For example, if a user enters "There has been an increase in suspicious emails within the company recently," the server will issue specific instructions such as "We recommend consulting with the information security department." In this case, the generation AI model generates a prompt asking "If there is an increase in suspicious emails, which department should I consult?" and performs analysis.

[0603] In this way, users can obtain information to respond quickly and appropriately when security issues arise.

[0604] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0605] Step 1:

[0606] The user enters security-related information using their device. The entered information is sent to the server in text format.

[0607] Step 2:

[0608] The server analyzes the received information using natural language processing techniques. Specifically, it tokenizes the text data and extracts important keywords. This process generates foundational data necessary to understand the meaning of the input information.

[0609] Step 3:

[0610] The server uses a generative AI model to generate prompt messages based on the analyzed data. For example, it might generate a prompt message such as, "If there is an increase in suspicious emails, which department should I contact?" This prompt message serves as the basis for the AI ​​model to identify the appropriate department to contact.

[0611] Step 4:

[0612] The server inputs the generated prompt message into the AI ​​model, which then performs analysis to suggest the appropriate contact point. Based on pre-trained data, the AI ​​model selects the most appropriate department or person in charge. This process outputs specific contact information.

[0613] Step 5:

[0614] Based on the analysis results, the server provides the user with a general opinion and specific resources for consultation. The results are sent to the user's terminal, where the user can view the recommended resources on the screen.

[0615] This series of processes allows users to obtain information that enables them to respond quickly and appropriately when security issues arise.

[0616] 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.

[0617] "Example of form 1"

[0618] One embodiment of the present invention involves a system for receiving inquiries from users that includes an emotion engine that recognizes the user's emotions. In this system, when a user inputs their inquiry as text, the emotion engine analyzes the user's emotional state from that text. For example, if a user uses the expression "I'm in trouble," the emotion engine recognizes that the user is confused. This emotional information is taken into consideration when the AI ​​generates general opinions or suggests who the user should consult.

[0619] "Example of form 2"

[0620] Another embodiment of the present invention is a system in which an AI learning means learns user emotion data. In this system, the AI ​​learns not only the content of the user's consultation but also emotion data obtained from an emotion engine. This enables the AI ​​to generate more appropriate views and solutions based on emotion. For example, if the user is showing anger, the AI ​​can take that emotion into consideration and suggest a more courteous response.

[0621] "Example of form 3"

[0622] In a further embodiment of the present invention, there is a system that suggests a place to seek advice, which takes into account the user's emotional state to specify a particular department or person. In this system, the AI ​​considers the user's emotional state and suggests the most appropriate place to seek advice. For example, if the user is showing strong anxiety, the AI ​​may suggest a psychological counselor as a place to seek advice.

[0623] The following describes the processing flow for each example of the form.

[0624] "Example of form 1"

[0625] Step 1: The user enters their inquiry into the system as text.

[0626] Step 2: The emotion engine analyzes the user's emotional state from the text.

[0627] Step 3: The AI ​​considers emotional information and generates a general opinion.

[0628] Step 4: The AI ​​considers emotional information and suggests who to consult.

[0629] "Example of form 2"

[0630] Step 1: The user enters their inquiry into the system as text.

[0631] Step 2: The emotion engine analyzes the user's emotional state from the text.

[0632] Step 3: The AI ​​learns from user inquiries and emotional data.

[0633] Step 4: The AI ​​generates more appropriate viewpoints and solutions based on emotions.

[0634] "Example of form 3"

[0635] Step 1: The user enters their inquiry into the system as text.

[0636] Step 2: The emotion engine analyzes the user's emotional state from the text.

[0637] Step 3: The AI ​​considers the user's emotional state and suggests the most appropriate person to consult.

[0638] (Example 1)

[0639] Next, we will describe Example 1 of Form 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".

[0640] Traditional consultation systems have struggled to accurately grasp users' emotional states and provide appropriate advice based on those states. Furthermore, they lacked features to suggest specific resources based on the nature of the consultation, resulting in users being unable to receive appropriate support.

[0641] 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.

[0642] In this invention, the server includes means for receiving inquiries from users, means for analyzing the content of the received inquiries and recognizing the emotional state, means for generating a general opinion based on the analyzed emotional state, means for suggesting where to seek advice based on the generated opinion, and means for generating advice using a generative AI model. This makes it possible to provide appropriate advice that takes into account the user's emotional state and to suggest specific places to seek advice.

[0643] "Means of receiving inquiries from users" refers to an interface that allows users to access the system and input their inquiries in text format.

[0644] "Means of analyzing received consultation content to recognize emotional state" refers to a function that uses natural language processing technology to analyze emotions from text entered by the user and identify the user's emotional state.

[0645] "Means for generating general opinions based on analyzed emotional states" refers to a function that considers the user's emotional state and uses a generative AI model to generate appropriate advice and opinions.

[0646] "Means of suggesting who to consult based on generated opinions" refers to a function that suggests specific organizations or personnel that the user should consult based on the generated advice and opinions.

[0647] "Methods for generating advice using generative AI models" refers to a function that utilizes AI technology to automatically generate advice tailored to the user's consultation content and emotional state.

[0648] A description of the embodiment for carrying out the invention will be provided.

[0649] This system begins with the user entering their consultation details via a terminal. The user sends the consultation details to the system in text format using a web browser or a dedicated application. The terminal then transmits the entered text data to the server via the internet. The data is encrypted during transmission, ensuring security.

[0650] The server passes the received text to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses software that employs natural language processing technology. Specifically, Google Cloud Natural Language API is one such example. For instance, the sentiment engine might determine that the user is feeling tired from an expression like "tired."

[0651] Next, the server uses a generative AI model to generate advice based on the analyzed sentiment information. This generative AI model may include OpenAI's GPT-4, for example. The generative AI model generates appropriate advice tailored to the user's situation.

[0652] For example, if a user inputs "I've been really busy with work lately and I'm exhausted," the server uses its emotion engine to recognize "fatigue" as an emotion. It then prompts the generative AI model with "Please provide advice for a user who is feeling fatigued," and generates advice such as "It's important to take regular breaks and make time to relax." The server returns this advice to the user, who can then view it on their device.

[0653] In this way, it becomes possible to provide appropriate advice that takes the user's emotional state into consideration, and to suggest specific places to seek advice.

[0654] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0655] Step 1:

[0656] The user enters their consultation details in text format using a terminal. The entered text specifically describes the user's concerns and problems. This text data becomes the input for the next processing step.

[0657] Step 2:

[0658] The terminal sends the entered text data to the server. Security is ensured because the data is encrypted during transmission. The output of this step is the encrypted text data received by the server.

[0659] Step 3:

[0660] The server decodes the received text data and passes it to the emotion engine. The emotion engine uses natural language processing techniques to analyze the text and identify the user's emotional state. For example, from the expression "tired," it might determine that the user is feeling tired. The output of this step is the analyzed emotion information.

[0661] Step 4:

[0662] The server inputs a prompt message into the generative AI model based on the analyzed sentiment information. The generative AI model generates advice tailored to the user's situation. For example, if the prompt message is "Provide advice for a user who is feeling fatigued," it might generate advice such as "It is important to take regular breaks and make time to relax." The output of this step is the generated advice.

[0663] Step 5:

[0664] The server sends the generated advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and consider their next course of action. The output of this step is the advice that the user reviews.

[0665] (Application Example 1)

[0666] Next, we will describe Application Example 1 of Form 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."

[0667] In modern society, there is a demand for quick and accurate solutions to users' problems and anxieties. However, conventional systems have difficulty considering users' emotions and are unable to propose appropriate solutions.

[0668] 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.

[0669] In this invention, the server includes means for receiving inquiries from users, means for AI learning to analyze compliance issues in society based on the received inquiries, means for presenting general opinions based on the analysis results, means for sentiment analysis to analyze the user's emotions, and means for proposing appropriate countermeasures based on sentiment information. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[0670] "A means of receiving inquiries from users" refers to an interface that allows users to input their problems and concerns into the system and receive the content of their inquiries.

[0671] "AI learning methods" refer to artificial intelligence technology that analyzes and learns about public compliance issues and related information based on the content of inquiries received.

[0672] "Means of presenting general views" refers to a function that provides users with general solutions or opinions based on analysis results obtained through AI learning methods.

[0673] "Means of suggesting who to consult" refers to a function that suggests the appropriate department or expert the user should consult based on the analysis results.

[0674] "Emotional analysis means" refers to technology that analyzes emotions from the consultation content entered by the user and identifies the user's emotional state.

[0675] "Means for proposing appropriate countermeasures" refers to a function that proposes optimal solutions and actions to users based on emotional information obtained through emotion analysis.

[0676] The system for implementing this invention can be accessed by users using smartphones or computer terminals. Users input their inquiries in text format and send them to the system. The server uses software such as Python or TensorFlow and utilizes natural language processing libraries (such as NLTK) to analyze the received inquiries.

[0677] The server first receives the user's inquiry and uses sentiment analysis to identify the user's emotions. This involves analyzing text data to extract the user's emotional state (e.g., anxiety, fear). Next, AI learning tools are used to analyze relevant societal compliance issues and related information concerning the inquiry. This allows the server to present the user with a general perspective.

[0678] Furthermore, a system that suggests appropriate measures based on emotional information is activated, proposing the best solutions and actions to the user. For example, if a user inputs "I've recently seen suspicious people around my house," the emotional analysis system identifies "anxiety," and the AI ​​suggestion engine makes suggestions such as "consult the local police station" or "consider installing security cameras."

[0679] By utilizing a generative AI model, it is possible to generate prompt messages that are tailored to the user's emotions and the content of their inquiry, enabling appropriate responses. An example of a prompt message would be, "If the user is feeling anxious, what security measures should be suggested?"

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0681] Step 1:

[0682] Users access the system using a smartphone or computer terminal and input their inquiry details in text format. The entered text data is then sent to the server.

[0683] Step 2:

[0684] The server passes the received text data to the sentiment analysis tool. The sentiment analysis tool analyzes the text using a natural language processing library (such as NLTK) to identify the user's emotional state. For example, it extracts emotions such as "anxiety" or "fear." The analysis results are then passed on as emotional information to the next step.

[0685] Step 3:

[0686] The server inputs emotional information and consultation details into an AI learning tool. The AI ​​learning tool uses machine learning frameworks such as TensorFlow to analyze public compliance issues and related information. As a result of the analysis, general opinions and related information are generated.

[0687] Step 4:

[0688] The server activates a mechanism to suggest appropriate countermeasures based on emotional information and the results of AI learning analysis. This mechanism utilizes a generative AI model to generate prompt messages that suggest the optimal solution or action for the user. For example, specific suggestions such as "consult the nearest police station" or "consider installing security cameras" may be made.

[0689] Step 5:

[0690] The server sends the generated prompt to the user's terminal and displays the suggested actions to the user. The user can then choose the appropriate action based on the suggested solutions.

[0691] (Example 2)

[0692] Next, we will describe Example 2 of Form 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".

[0693] In modern society, individuals face a wide range of issues related to social norms, but obtaining appropriate views and solutions to these problems is not easy. Furthermore, while it is necessary to respond in a way that takes the client's feelings into consideration, conventional systems have the challenge of making it difficult to provide advice that takes these feelings into account.

[0694] 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.

[0695] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing social norms based on the received information, and means for acquiring the user's emotional information and reflecting it in the analysis results. This makes it possible to present general opinions on the consultation content and make adjustments that take emotions into consideration.

[0696] "Means for receiving information from users" refers to the interface through which the system receives inquiries and questions provided by users.

[0697] A "machine learning method for analyzing issues related to social norms" is an algorithm that uses pre-trained data based on received information to analyze issues related to social norms.

[0698] A "means of presenting general opinions" refers to a function that provides users with standard or general views based on analysis results.

[0699] "Means of suggesting who to consult" refers to a function that suggests appropriate organizations or positions for the user to consult based on the analysis results.

[0700] "Means for acquiring user emotional information and reflecting it in analysis results" refers to a function that analyzes the user's emotional state and incorporates that information into the analysis results in order to provide more appropriate responses.

[0701] "Means for adjusting generated opinions" refers to functions that appropriately modify or adjust generated opinions by taking into account the user's emotional information and other factors.

[0702] One embodiment of this invention is a system that receives inquiries from users, analyzes them, and provides appropriate opinions and solutions. A specific embodiment is shown below.

[0703] The server provides a web interface accessible via the internet to receive information from users. Through this interface, users can input their concerns in text format. For example, a user might input a specific concern such as, "I'm suffering from harassment from my boss."

[0704] The terminal sends the user's inputted consultation details to the server. The consultation details are sent to the server as text data. The server inputs the received consultation details into a generative AI model. The generative AI model then refers to a pre-trained database to search for relevant laws and cases related to the consultation details.

[0705] Furthermore, the server uses an emotion engine to acquire user emotion data. For example, it analyzes emotions such as anger and sadness from the text entered by the user. This emotion data is then reflected in the analysis results of the generative AI model.

[0706] The server uses a generative AI model to generate general opinions and solutions to the consultation content. For example, it creates appropriate advice based on laws and cases related to power harassment. The generated opinions and solutions are adjusted to take into account the user's emotional information. For example, if the user is expressing anger, the advice will be provided in more polite language.

[0707] For example, if a user enters "I would like to discuss sexual harassment at work," the server will generate the following prompt for the AI ​​model: "The user is seeking advice about sexual harassment at work. Please provide appropriate advice based on relevant laws and cases." Based on this prompt, the AI ​​model will generate appropriate opinions and solutions, which the server will then provide to the user.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0709] Step 1:

[0710] The user enters their inquiry details via their device. For example, they might enter a specific inquiry such as, "I'm suffering from harassment from my boss." The entered text data is then sent from the device to the server.

[0711] Step 2:

[0712] The server inputs the received consultation details into a generating AI model. The server then refers to a pre-trained database to search for laws and cases related to the consultation details. By analyzing the input text data and extracting relevant information, it obtains the basic data needed to generate general opinions and solutions.

[0713] Step 3:

[0714] The server uses an emotion engine to acquire user emotion data. It analyzes the user's emotional state from the input text data, identifying emotions such as anger and sadness. This emotion data is then incorporated into the analysis results of a generative AI model.

[0715] Step 4:

[0716] The server uses a generative AI model to generate general opinions and solutions to the consultation content. For example, it creates appropriate advice based on laws and cases related to power harassment. The generated opinions and solutions are adjusted to take into account the user's emotional information.

[0717] Step 5:

[0718] The server sends the generated views and solutions to the terminal. The terminal displays the results to the user. For example, if the user is expressing anger, the server will offer advice in more polite language.

[0719] (Application Example 2)

[0720] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0721] In today's workplace, employees face a wide range of compliance issues and workplace troubles. A system is needed that allows employees to receive appropriate advice regarding these issues. However, conventional systems often struggle to consider user emotions and lack features to suggest specific resources for consultation, which can delay problem resolution.

[0722] 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.

[0723] In this invention, the server includes means for receiving inquiries from users, information processing means for analyzing compliance issues in society based on the received inquiries, and means for analyzing the user's emotional data and generating emotionally-based opinions. As a result, users can receive appropriate advice tailored to their emotions and, by suggesting specific resources for consultation, can quickly resolve problems.

[0724] "A means of receiving inquiries from users" refers to an interface that allows users to anonymously input problems and concerns they face at work, and for the system to receive them.

[0725] "Information processing means" refers to a processing device that uses AI technology to analyze the content of inquiries received and generate general opinions and solutions based on relevant laws and cases.

[0726] "Means for analyzing emotional data and generating emotion-based viewpoints" refers to an analytical device that extracts emotions from user input and provides appropriate responses and advice based on those emotions.

[0727] "A means of generating advice on consultation content using a generative AI model" refers to a system that utilizes AI technology to automatically generate specific advice and solutions to users' consultation content.

[0728] "Means of suggesting who to consult" refers to a function that suggests appropriate organizations or positions for the problems a user is facing, and indicates specific contact points for consultation.

[0729] The system for implementing this invention provides a platform where users can anonymously consult about compliance issues and troubles they face in the workplace. The server receives consultations from users and analyzes the content of the consultations using information processing means. Specifically, the server uses software such as Python and TensorFlow, and utilizes natural language processing libraries (e.g., spaCy) to analyze the content of the consultations.

[0730] The server uses sentiment analysis engines such as the Google Cloud Natural Language API to acquire user sentiment data. Based on this, it generates opinions that are relevant to the user's emotions, and then uses a generative AI model (e.g., OpenAI's GPT) to generate specific advice regarding the consultation.

[0731] The terminal sends the user's inputted consultation details to the server and receives analysis results and advice from the server. Based on this information, the user can obtain specific resources for consultation and solutions.

[0732] For example, if a user consults the server about suffering from harassment from their boss, the server will suggest appropriate countermeasures based on laws and cases related to harassment. Furthermore, if the user expresses anger, the server will suggest a more courteous approach.

[0733] An example of a prompt message would be: "The user is seeking advice about workplace harassment. Generate appropriate advice based on relevant laws and cases. Also, adjust the tone of the advice considering the user's emotional data."

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0735] Step 1:

[0736] Users anonymously input workplace problems and concerns using a terminal. The entered information is sent from the terminal to the server. The input data is in text format and includes the user's specific concerns.

[0737] Step 2:

[0738] The server analyzes the received consultation content using a natural language processing library (e.g., spaCy). It tokenizes the received text data and extracts important keywords and phrases. This allows the server to identify the subject of the consultation and relevant laws and regulations.

[0739] Step 3:

[0740] The server uses the Google Cloud Natural Language API to retrieve user emotion data from the consultation content. The parsed text data is passed to an emotion analysis engine as input, and emotion scores and emotion categories are obtained as output. This allows for an understanding of the user's emotional state.

[0741] Step 4:

[0742] The server uses a generative AI model (e.g., OpenAI's GPT) to generate advice for the consultation. The analyzed consultation content and sentiment data are incorporated into prompt sentences as input and passed to the generative AI model. The output is text containing specific advice and solutions.

[0743] Step 5:

[0744] The server sends the generated advice to the terminal. The terminal displays the advice to the user, who can then consider specific actions based on it. The output includes the text of the advice provided to the user.

[0745] (Example 3)

[0746] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0747] Conventional consultation systems have difficulty suggesting appropriate resources based on the user's concerns, and in particular, they lack consideration for the user's emotional state. Furthermore, the accuracy of natural language processing in analyzing consultation content is low, making it difficult to provide users with specific and useful guidance.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0749] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing the data based on the received information, and means for suggesting appropriate consultation services based on the analysis results. This makes it possible to suggest appropriate consultation services that take into account the emotional state of the user in response to the content of their consultation.

[0750] A "user" refers to an individual or group that uses the system to input information and seek advice.

[0751] "Means of receiving information" refers to the function of receiving input from users and converting it into a format that can be processed within the system.

[0752] "Machine learning methods for analyzing data" refers to a function that uses machine learning algorithms to analyze data based on received information and understand the user's intentions and emotions.

[0753] "Means of providing general guidelines" refers to a function that provides users with general advice or guidelines based on the analysis results.

[0754] "Means of suggesting appropriate resources for consultation" refers to a function that, based on the analysis results, suggests specific organizations or individuals that the user should consult.

[0755] "A means of determining who to consult based on emotional state" refers to a function that analyzes the user's emotional state and determines the most suitable consultation service accordingly.

[0756] A "generative AI model" is an artificial intelligence model used for natural language processing, referring to a technology that analyzes user input and generates appropriate output.

[0757] This invention begins with a user inputting consultation details via a terminal, and a server receiving that information. The server is equipped with means for receiving information from the user and uses a generative AI model to analyze the input information. Specifically, it uses a generative AI model such as OpenAI's GPT model to perform natural language processing.

[0758] The server uses machine learning techniques to analyze the received information as data. These techniques aim to understand the user's inquiry and analyze their intentions and emotions. Based on the analysis results, the server uses methods to suggest appropriate resources, indicating specific organizations or individuals to the user.

[0759] Furthermore, the server has a mechanism to determine who to consult based on the user's emotional state. This makes it possible to suggest the most suitable consultation service based on the user's emotions. For example, if a user consults the server saying, "I've been having trouble with relationships at work lately and I'm feeling stressed," the server can suggest a psychological counselor as a consultation option.

[0760] An example of a prompt message is, "If the user is experiencing stress due to interpersonal relationships at work, please suggest which department or person they should consult." By inputting this prompt message into the AI ​​generation model, the server can determine the appropriate contact point and notify the user. The specific processing flow in Example 3 is explained using Figure 21.

[0761] Step 1:

[0762] The user enters their consultation details through their device. The entered information is saved on the device as text data. This data includes the user's intentions and emotions.

[0763] Step 2:

[0764] The terminal sends the text data entered by the user to the server. A secure communication protocol is used for transmission to ensure data security. The server prepares the received data for analysis.

[0765] Step 3:

[0766] The server inputs the received text data into a generative AI model. Specifically, it uses the generative AI model to perform natural language processing. The server analyzes the data and processes it to understand the user's intent and emotions.

[0767] Step 4:

[0768] The server determines the appropriate contact point based on the analysis results from the generated AI model. The analysis results include information related to the user's inquiry, and the server uses this to suggest specific organizations or individuals.

[0769] Step 5:

[0770] The server considers the user's emotional state to determine the most suitable consultation service. The generative AI model infers emotions from the user's input and suggests consultation services such as psychological counselors as needed.

[0771] Step 6:

[0772] The server sends the information of the chosen contact person to the terminal. The terminal notifies the user of the information received from the server, and the user can check the recommended contact person on the screen.

[0773] (Application Example 3)

[0774] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0775] In today's business environment, there is a demand for swift and appropriate responses when security incidents or compliance issues arise. However, finding the right resources and solutions for these problems is not easy, and there is a particular lack of responses that take into account the emotional state of users. Therefore, a system is needed that allows users to resolve problems with peace of mind.

[0776] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0777] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing social norm issues based on the received information, means for presenting general opinions based on the analysis results, and means for analyzing the user's emotional state and proposing appropriate countermeasures. This enables the user to quickly and appropriately find someone to consult and receive support that is appropriate to their emotional state.

[0778] "Means for receiving information from users" refers to an interface that allows the system to receive and process data and inquiries provided by users.

[0779] "Machine learning methods for analyzing social norm issues" refers to the process of analyzing and understanding issues related to social norms and laws using machine learning techniques.

[0780] "Means of presenting general opinions" refers to a function that provides users with general and objective views based on analyzed data.

[0781] "Means of suggesting who to consult" refers to a function that suggests the appropriate department or person to consult based on the analysis results.

[0782] "A means of analyzing a user's emotional state and proposing appropriate countermeasures" refers to a process for analyzing a user's emotions and proposing the optimal countermeasures based on that state.

[0783] The system for implementing this invention mainly consists of a server and a user terminal. The server receives information from the user and is equipped with machine learning means for analyzing social norm issues. Specifically, the server uses the Google Cloud Natural Language API to process the user's inquiries in natural language and IBM Watson Tone Analyzer to analyze the user's emotional state.

[0784] The server provides general opinions based on the analysis results and suggests who to consult. This allows users to quickly access the appropriate department or person in charge. Furthermore, by suggesting solutions tailored to the user's emotional state, it reduces user anxiety and assists in problem-solving.

[0785] The user terminal is a device such as a smartphone or smart glasses, which provides an interface for the user to input their inquiry. When a user inputs an inquiry such as "There is a possibility of data leakage," the server analyzes the content and returns specific instructions such as "We recommend that you contact the IT security department."

[0786] For example, if a user enters a prompt such as, "If there is a possibility of a data breach, which department should I contact?", the server can suggest appropriate countermeasures. This allows the user to address the problem quickly and appropriately.

[0787] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0788] Step 1:

[0789] The user enters their inquiry details using a terminal. The entered text data is sent to the server.

[0790] Step 2:

[0791] The server performs natural language processing on the received text data using the Google Cloud Natural Language API. This process analyzes the structure and meaning of the text and extracts important keywords and phrases. The output is the analyzed text data.

[0792] Step 3:

[0793] The server inputs the analyzed text data into IBM Watson Tone Analyzer to analyze the user's emotional state. This process determines the user's emotional state (e.g., anxiety, reassurance, etc.). The output is data on the emotional state.

[0794] Step 4:

[0795] The server uses a generative AI model to generate general opinions based on the analysis results. This model has learned from past data and cases, and generates appropriate opinions regarding the user's inquiry. The output is a general opinion.

[0796] Step 5:

[0797] The server considers the analysis results and emotional state to suggest who to consult. Specifically, it generates prompt messages to suggest the appropriate department or person in charge. The output provides a list of suggested consultation locations.

[0798] Step 6:

[0799] The server sends generated general opinions and suggested contacts to the user's terminal. The user can receive this information through their terminal and take appropriate action.

[0800] (Other examples)

[0801] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0802] 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.

[0803] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0805] 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.

[0806] [Third Embodiment]

[0807] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0808] 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.

[0809] The data processing device 12 includes 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 includes a processor 28, RAM 30, and storage 32.

[0810] The processor 28, RAM 30, and storage 32 are connected to the bus 34. The database 24 and communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to the network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0811] 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.

[0812] 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.

[0813] 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).

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0820] "Example of form 1"

[0821] One embodiment of the present invention provides an AI consultation and reporting system. This system has a means of receiving consultations from users. Specifically, users can access the system and submit a consultation by entering their problem or concern as text.

[0822] "Example of form 2"

[0823] Furthermore, this system has a mechanism for AI to analyze compliance issues in society based on the consultations it receives. Specifically, the AI ​​uses a pre-trained database to generate general opinions and solutions to the user's consultation. For example, if a user consults about "being troubled by harassment from their boss," the AI ​​will analyze laws and cases related to harassment and generate appropriate advice.

[0824] "Example of form 3"

[0825] Furthermore, this system has a mechanism to suggest who to consult based on the analysis results. Specifically, the AI ​​will instruct the user on the department or person to consult based on the analysis results. For example, if a user consults about suspected irregularities in company expense reports, the AI ​​can give specific instructions such as "We recommend consulting the internal audit department."

[0826] The following describes the processing flow for each example of the form.

[0827] "Example of form 1"

[0828] Step 1: The user accesses the AI ​​consultation and reporting system.

[0829] Step 2: The user enters their problem or concern as text and submits it to the system for consultation.

[0830] "Example of form 2"

[0831] Step 1: The system receives the user's inquiry.

[0832] Step 2: The AI ​​uses a pre-trained database to generate general opinions and solutions regarding the consultation.

[0833] Step 3: Present the AI-generated views and solutions to the user.

[0834] "Example of form 3"

[0835] Step 1: Based on the user's inquiry and the opinions it generates, the AI ​​determines which department or person should be consulted.

[0836] Step 2: The AI ​​suggests to the user which department or person they should consult.

[0837] (Example 1)

[0838] Next, we will describe Embodiment 1 of 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."

[0839] Traditional consultation systems faced challenges in providing prompt and appropriate responses to user inquiries. In particular, when inquiries covered a wide range of topics or required specialized knowledge, responses were often delayed or insufficient.

[0840] 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.

[0841] In this invention, the server includes means for receiving inquiries from users, means for passing the received inquiries to a generation AI model, and means for the generation AI model to analyze the content of the inquiries and generate a response. This makes it possible to provide a quick and appropriate response to user inquiries.

[0842] A "user" refers to an individual or organization that accesses the system and enters their inquiry details.

[0843] "Consultation" refers to the content of problems or concerns that users input into the system.

[0844] A "generative AI model" refers to artificial intelligence technology that analyzes the content of inquiries received from users and generates appropriate responses.

[0845] "Response" refers to the answer or advice generated by the generative AI model as a result of analyzing the content of the consultation.

[0846] A "server" refers to a computer system that receives inquiries from users, passes data to a generation AI model, and provides the generated response to the user.

[0847] A "terminal" refers to a device used by a user to access the system, input their inquiries, and receive responses.

[0848] As an embodiment for carrying out this invention, an AI consultation and reporting system will be specifically described.

[0849] The server provides a web interface for receiving inquiries from users. This interface is accessed through a device accessible to the user (such as a PC or smartphone). Users can access the system and input their inquiries in text format. The entered text data is sent to a generative AI model running on the server. This generative AI model uses natural language processing technology to analyze the user's inquiries and generate appropriate responses.

[0850] For the generative AI model, advanced natural language processing models such as OpenAI's GPT-4 can be used. This makes it possible to understand the user's intent and construct the optimal response based on past training data. The generated response is then sent back to the user's device and displayed to the user.

[0851] As a concrete example, consider a scenario where a user enters a question such as, "My motivation at work has been low lately. What should I do?" This prompt is sent to a generative AI model, which generates a response such as, "I recommend setting new goals and accumulating small achievements." The server sends this response back to the terminal, which then displays it to the user. The user can then use this advice to guide their daily work.

[0852] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0853] Step 1:

[0854] The user accesses the AI ​​consultation reporting system using their device. The user enters their consultation details into the text box and clicks the "Submit" button. The user's consultation details are provided as input in text format.

[0855] Step 2:

[0856] The terminal sends the text data entered by the user to the server as an HTTP request. The input is the user's inquiry, and the output is the request data sent to the server. This request contains the user's inquiry.

[0857] Step 3:

[0858] The server passes the received text data to the generating AI model. The input is the consultation content received from the terminal, and the output is the input data for the generating AI model. The server converts the data into a format that the generating AI model can access.

[0859] Step 4:

[0860] The generative AI model analyzes data received from the server and generates an appropriate response. The input is the inquiry from the server, and the output is the generated response. The model uses natural language processing techniques to understand the user's intent and construct the optimal answer.

[0861] Step 5:

[0862] The server sends the response received from the generative AI model back to the terminal as an HTTP response. The input is the response from the generative AI model, and the output is the response data sent to the terminal.

[0863] Step 6:

[0864] The terminal displays the response received from the server to the user. The input is the response data from the server, and the output is the response displayed to the user. The user can review the advice generated on the screen and consider their next course of action.

[0865] (Application Example 1)

[0866] Next, we will describe Application Example 1 of Form 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."

[0867] In modern society, individuals and businesses face a wide range of security issues, but obtaining appropriate advice and information quickly is difficult. In particular, it is challenging for ordinary users to determine how to respond to threats such as phishing scams and data breaches. In this situation, there is a need for a system that allows users to confidently seek security advice and take appropriate measures.

[0868] 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.

[0869] In this invention, the server includes means for receiving inquiries from users, means for artificial intelligence learning for analyzing security issues based on the received inquiries, and means for providing appropriate advice based on the analysis results. This enables users to resolve their security concerns and questions and take quick and appropriate measures.

[0870] "A means of receiving inquiries from users" refers to an interface that allows users to access the system and input their security concerns and questions in text.

[0871] "An artificial intelligence learning method for analyzing security issues" is a processing device that uses artificial intelligence technology to analyze the content of inquiries received, identify security-related problems, and derive appropriate countermeasures.

[0872] "Means of providing appropriate advice" refers to a function that presents users with specific countermeasures and information based on analysis results, and provides information to alleviate security concerns.

[0873] "Means of providing security information" refers to information provision devices that provide users with the latest information on security incidents and regulations to support appropriate decision-making.

[0874] The system for implementing this invention enables users to consult about security using a device such as a smartphone. Users input their consultation details as text through an application on their device. The server utilizes an artificial intelligence learning method using a generative AI model to analyze the received consultation details. This artificial intelligence learning method analyzes the text using a natural language processing library (e.g., spaCy) and extracts important keywords and context.

[0875] Based on the extracted information, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate advice for the user. Furthermore, by cross-referencing with a security database, it can provide the latest security information. This allows users to resolve their security concerns and questions and take quick and appropriate action.

[0876] For example, if a user enters "I've been receiving more emails from my bank lately and I'm worried. What should I do?", the server will analyze the content and provide advice such as, "To verify whether the email is genuine, we recommend checking the sender's email address and logging in directly from the official website. Also, please be careful not to enter any personal information, as it may be a phishing scam."

[0877] An example of a prompt message is: "User inquiry: I've been receiving more emails from my bank lately and I'm worried. What should I do? As an AI, what advice would you offer the user?"

[0878] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0879] Step 1:

[0880] The user launches an application on their device and enters their security-related questions as text. The entered text is sent to the server. The input data is text information that specifically expresses the user's concerns and questions.

[0881] Step 2:

[0882] The server analyzes the received text data using a natural language processing library (e.g., spaCy). During the analysis process, important keywords and context are extracted from the text. The input is the user's inquiry, and the output is the extracted keywords and contextual information.

[0883] Step 3:

[0884] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the extracted information to generate appropriate advice for the user. The AI ​​model uses prompts to generate answers to the user's inquiries. The input consists of extracted keywords and contextual information, and the output is the generated advice.

[0885] Step 4:

[0886] The server compares the generated advice against the security database and adds the latest security information. This ensures that the information provided to the user is up-to-date and accurate. The input is the generated advice, and the output is the final advice information after comparison.

[0887] Step 5:

[0888] The server sends the final advice information to the user's terminal. The user can then review the advice on their terminal and take specific actions to resolve any security concerns or questions. The input is the final advice information, and the output is the user's understanding and actions.

[0889] (Example 2)

[0890] Next, we will describe Example 2 of the morphological example. 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."

[0891] In modern society, users face a wide range of compliance issues, and resolving them requires specialized knowledge. However, it is not easy for ordinary users to obtain appropriate views and solutions to these problems. Therefore, there is a need for a system that allows users to receive quick and accurate advice on compliance issues.

[0892] 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.

[0893] In this invention, the server includes means for receiving information from a user, means for processing information to analyze the received information, means for searching for relevant information from a knowledge base based on the analyzed information, means for generating an opinion using a generative AI model based on the search results, and means for providing the generated opinion to the user. This makes it possible for users to quickly obtain appropriate advice on compliance issues even without specialized knowledge.

[0894] "Means of receiving information from users" refers to the function by which the system receives information entered by the user and incorporates it for processing.

[0895] "Information processing means" refers to the technologies and processes used to analyze received information and extract necessary data.

[0896] "Means of searching for relevant information from a knowledge base" refers to a function that finds relevant information from a pre-stored database based on the analyzed information.

[0897] "Means of generating opinions using generative AI models" refers to a function that utilizes AI technology to create opinions and advice to provide to users based on search results.

[0898] "Means of providing generated views to users" refers to a function that communicates views generated by an AI model to users and presents them in an accessible format.

[0899] This invention is a system that provides prompt and accurate advice to users regarding compliance issues they face. The following describes a specific implementation of this system.

[0900] The server connects to the user's terminal via the internet to receive information from the user. The user uses their terminal to input their inquiry and send it to the server. For example, they can input specific details such as, "I'm suffering from harassment from my boss."

[0901] The server uses natural language processing techniques to analyze the information it receives. In this process, the server uses natural language processing libraries such as "spaCy" and "NLTK" to extract keywords and context from the consultation content.

[0902] Based on the analyzed information, the server searches its knowledge base for relevant information. The knowledge base contains laws and regulations related to compliance and past case studies. The server refers to this data and extracts information relevant to the user's inquiry.

[0903] Next, the server uses a generative AI model such as "GPT-4" to generate opinions based on the search results. At this time, the AI ​​model is given instructions such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[0904] Finally, the server provides the generated insights to the user's terminal. The user can then review the specific advice on their terminal and use it as a reference for resolving the problem. In this way, users can quickly obtain appropriate advice on compliance issues, even without specialized knowledge.

[0905] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0906] Step 1:

[0907] The user uses a terminal to input their consultation details and sends them to the server. The input could be specific details such as, "I'm suffering from harassment from my boss." The terminal then sends this input as digital data to the server.

[0908] Step 2:

[0909] The server analyzes the received consultation content using natural language processing technology. The input is the consultation content from the user, and the output is the analyzed keywords and contextual information. The server uses libraries such as "spaCy" and "NLTK" to tokenize the text data and extract important keywords.

[0910] Step 3:

[0911] The server searches for relevant information from its knowledge base based on the analysis results. The input is the analyzed keywords, and the output is data on relevant laws and cases. The server executes database queries to extract the relevant information.

[0912] Step 4:

[0913] The server inputs the search results into a generative AI model such as "GPT-4" to generate an opinion. The input is data containing relevant information, and the output is an opinion or advice to be provided to the user. The server gives the AI ​​model instructions as a prompt, such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[0914] Step 5:

[0915] The server sends the generated opinion to the user's terminal. The input is the generated opinion, and the output is advice that the user can view on their terminal. The user can receive specific advice through their terminal and use it as a reference for solving problems.

[0916] (Application Example 2)

[0917] Next, we will describe application example 2 of form 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."

[0918] In modern society, individuals and businesses face a wide range of compliance issues, but obtaining appropriate advice quickly is difficult. Furthermore, the lack of systems that provide concrete solutions in real time based on the consultation content can lead to delays in problem resolution.

[0919] 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.

[0920] In this invention, the server includes means for receiving inquiries from users, machine learning means for analyzing social norm issues based on the received inquiries, and means for providing specific countermeasures based on the analysis results. This enables users to receive quick and appropriate advice in real time.

[0921] "Means for receiving inquiries from users" refers to an interface for receiving inquiries or problem reports from individuals or organizations.

[0922] "Machine learning methods for analyzing social norm issues" are algorithms that use databases to analyze legal and ethical issues and derive appropriate solutions.

[0923] "Means of presenting general views" refers to a function that presents widely accepted opinions or solutions based on the analysis results.

[0924] "Means of suggesting who to consult" refers to a function that suggests which department or position a user should consult in order to resolve a problem.

[0925] "Means of providing specific countermeasures" refers to a function that provides specific action guidelines for the problems faced by users, based on the analysis results.

[0926] "A means of generating advice in real time" refers to a function that immediately analyzes user inquiries and provides advice quickly.

[0927] The system for implementing this invention is designed to receive inquiries from users, analyze social norms, and provide specific countermeasures. The system is configured as follows:

[0928] The server provides an interface for receiving inquiries from users. Users can input their inquiries in text format using their smartphones or computers. The entered inquiries are then sent to the server.

[0929] The server uses a machine learning model to analyze the content of the inquiries it receives. This model utilizes OpenAI's GPT, which analyzes social norm issues by referencing a pre-trained database. The database contains information on laws and past cases.

[0930] Based on the analysis results, the server provides users with general insights and specific countermeasures in real time. This allows users to receive timely and appropriate advice.

[0931] For example, if a user enters "I would like to consult about the risk of information leakage at work," the server will analyze relevant information security laws and past cases and provide advice such as, "To prevent information leakage, it is important to conduct regular security training and properly manage access rights."

[0932] An example of a prompt message is: "User inquiry: I would like to discuss the risk of information leakage in the workplace. Please provide appropriate advice based on relevant laws and case examples."

[0933] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0934] Step 1:

[0935] Users input their consultation details in text format via a smartphone or computer interface. The entered text is sent to the server. The input data consists of the user's consultation details.

[0936] Step 2:

[0937] The server sends the received consultation content as a prompt message to OpenAI's GPT, a generative AI model, for analysis. The prompt message is structured to include the user's consultation content. The input data is the prompt message containing the user's consultation content, and the output data is the analysis result from the AI ​​model.

[0938] Step 3:

[0939] The server uses the analysis results obtained from the AI ​​model to reference relevant laws and cases from a database and generate specific countermeasures. The input data is the analysis results from the AI ​​model, and the output data is advice including specific countermeasures.

[0940] Step 4:

[0941] The server provides the generated advice to the user in real time. The user can view the advice on their terminal screen. The input data is advice that includes specific countermeasures, and the output data is the advice presented to the user.

[0942] (Example 3)

[0943] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[0944] In today's information society, it is difficult for users to quickly find the right resource for addressing problems and questions they face. This is especially true when specialized knowledge is required or when dealing with complex issues, making it difficult to determine which department or person to consult. In this situation, there is a need for a system that allows users to quickly identify the appropriate resource and efficiently resolve their problems.

[0945] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0946] In this invention, the server includes means for receiving information from users, data processing means for analyzing the received information, and means for analyzing the information using a generative AI model. This makes it possible to quickly identify the appropriate contact for advice regarding the problems faced by the user and to solve the problems efficiently.

[0947] A "user" is an individual or organization that uses the system to input information and seeks to identify a contact point for consultation.

[0948] "Information" refers to data about the consultation content and problems that users input into the system.

[0949] A "server" is a computer system that receives and processes information from users.

[0950] "Data processing means" refers to functions that perform the necessary preprocessing and transformations to analyze the received information.

[0951] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze information and identify the appropriate source of advice.

[0952] "Analysis" is the process of understanding the meaning of information using generative AI models and extracting relevant keywords and contexts.

[0953] A "consultation point" refers to the appropriate organization or person that a user should consult in order to resolve a problem.

[0954] "Presentation" refers to the act of informing the user of the contact point identified based on the analysis results.

[0955] This invention is a system for quickly identifying the appropriate resource for addressing a user's problem. The system begins with the user inputting information using a terminal. The user enters the consultation details in text format and sends them to the server.

[0956] The server uses data processing tools to process the information received from the user. Specifically, it performs text preprocessing and tokenization to convert the data into a format suitable for generative AI models. These generative AI models utilize natural language processing techniques. For example, OpenAI's GPT-4 is one such model.

[0957] The generative AI model analyzes information provided by the server and extracts important keywords and context. This analysis makes it possible to identify the appropriate resources for the user to consult. Based on the analysis results, the server presents the user with specific resources to consult.

[0958] For example, if a user enters a question such as "We don't have enough budget for a new project," the server sends this information to a generating AI model, which then analyzes it and generates an instruction recommending that the user consult with the finance department, which is then sent back to the user.

[0959] An example of a prompt message is: "Based on the following issue, indicate the appropriate department or person the user should contact: 'The project is behind schedule.'"

[0960] This system allows users to efficiently find the appropriate resources to consult in order to solve their problems. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0961] Step 1:

[0962] The user enters their inquiry details using a terminal. The entered information is sent to the server in text format. Specifically, when a user enters an inquiry such as "the project is behind schedule" and presses the send button, the data is sent to the server. The input is text data, and the output is data sent to the server.

[0963] Step 2:

[0964] The server uses data processing tools to analyze the text data received from the user. Specifically, it performs preprocessing of the text, removing unnecessary spaces and special characters, and then tokenizing the text. This process converts the text into a format suitable for the generative AI model. The input is text data from the user, and the output is preprocessed text data.

[0965] Step 3:

[0966] The server inputs pre-processed text data into a generative AI model. The generative AI model analyzes the text using natural language processing techniques and extracts important keywords and context. Specifically, it uses models such as GPT-4 to understand the meaning of the consultation content and identify relevant information. The input is pre-processed text data, and the output is keyword and contextual information as a result of the analysis.

[0967] Step 4:

[0968] The server identifies the appropriate person the user should consult based on the analysis results obtained from the generated AI model. Specifically, based on the analysis results, it generates concrete instructions for the user, such as "We recommend consulting the finance department." The input is the analysis results, and the output is the instruction on where to consult.

[0969] Step 5:

[0970] The server sends the generated instructions back to the user. The user can view the instructions from the server on their terminal. Specifically, instructions such as "We recommend consulting with the finance department" will be displayed on the terminal screen. The input is the instruction to consult, and the output is the instruction displayed to the user.

[0971] (Application Example 3)

[0972] Next, we will describe application example 3 of form example 3. 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."

[0973] In today's business environment, there is a demand for swift and appropriate responses to security issues and suspected misconduct. However, determining which department or person to consult can be difficult, sometimes leading to delays in response. Improving this situation and enabling rapid responses to security issues is a key challenge.

[0974] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0975] In this invention, the server includes means for receiving information from users, means for artificial intelligence learning for analyzing social security issues based on the received information, and means for suggesting appropriate resources for consultation based on the analysis results. This makes it possible to quickly and appropriately identify and respond to resources for consultation when a user has a security-related problem.

[0976] "Means for receiving information from users" refers to devices or software that have the function of receiving security-related information provided by users as input.

[0977] "An artificial intelligence learning tool for analyzing social security issues" refers to a device or software that uses artificial intelligence technology to collect data on security-related events in society and analyze it.

[0978] "Means for presenting general opinions based on analysis results" refers to a device or software that has the function of providing general advice or opinions to users based on the results of analysis by artificial intelligence.

[0979] "Means of suggesting appropriate consultation channels" refers to devices or software that, based on analysis results, identify the appropriate department or person the user should consult and present this information to the user.

[0980] "Means for analyzing information using natural language processing technology and selecting the appropriate department or person in charge" refers to a device or software that has the function of analyzing information from users using natural language processing technology and selecting the appropriate department or person in charge based on the results.

[0981] "Means for generating prompt sentences using a generative AI model" refers to a device or software that has the function of automatically generating prompt sentences in response to user input by utilizing a generative AI model.

[0982] The system for implementing this invention mainly consists of a server and a user terminal. The server provides an interface for receiving information from the user and analyzes social security issues based on the received information. Artificial intelligence learning methods using Python and TensorFlow are used for the analysis. Based on the analysis results, the server presents a general opinion and suggests appropriate resources for consultation.

[0983] The user terminal is a device such as a smartphone or computer, and it provides a means for the user to input security-related information. The input information is analyzed using natural language processing technology, and the appropriate department or person in charge is selected. Specifically, the backend of the application, which uses Flask, processes the user input and generates prompt sentences using a generative AI model.

[0984] For example, if a user enters "There has been an increase in suspicious emails within the company recently," the server will issue specific instructions such as "We recommend consulting with the information security department." In this case, the generation AI model generates a prompt asking "If there is an increase in suspicious emails, which department should I consult?" and performs analysis.

[0985] In this way, users can obtain information to respond quickly and appropriately when security issues arise.

[0986] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0987] Step 1:

[0988] The user enters security-related information using their device. The entered information is sent to the server in text format.

[0989] Step 2:

[0990] The server analyzes the received information using natural language processing techniques. Specifically, it tokenizes the text data and extracts important keywords. This process generates foundational data necessary to understand the meaning of the input information.

[0991] Step 3:

[0992] The server uses a generative AI model to generate prompt messages based on the analyzed data. For example, it might generate a prompt message such as, "If there is an increase in suspicious emails, which department should I contact?" This prompt message serves as the basis for the AI ​​model to identify the appropriate department to contact.

[0993] Step 4:

[0994] The server inputs the generated prompt message into the AI ​​model, which then performs analysis to suggest the appropriate contact point. Based on pre-trained data, the AI ​​model selects the most appropriate department or person in charge. This process outputs specific contact information.

[0995] Step 5:

[0996] Based on the analysis results, the server provides the user with a general opinion and specific resources for consultation. The results are sent to the user's terminal, where the user can view the recommended resources on the screen.

[0997] This series of processes allows users to obtain information that enables them to respond quickly and appropriately when security issues arise.

[0998] 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.

[0999] "Example of form 1"

[1000] One embodiment of the present invention involves a system for receiving inquiries from users that includes an emotion engine that recognizes the user's emotions. In this system, when a user inputs their inquiry as text, the emotion engine analyzes the user's emotional state from that text. For example, if a user uses the expression "I'm in trouble," the emotion engine recognizes that the user is confused. This emotional information is taken into consideration when the AI ​​generates general opinions or suggests who the user should consult.

[1001] "Example of form 2"

[1002] Another embodiment of the present invention is a system in which an AI learning means learns user emotion data. In this system, the AI ​​learns not only the content of the user's consultation but also emotion data obtained from an emotion engine. This enables the AI ​​to generate more appropriate views and solutions based on emotion. For example, if the user is showing anger, the AI ​​can take that emotion into consideration and suggest a more courteous response.

[1003] "Example of form 3"

[1004] In a further embodiment of the present invention, there is a system that suggests a place to seek advice, which takes into account the user's emotional state to specify a particular department or person. In this system, the AI ​​considers the user's emotional state and suggests the most appropriate place to seek advice. For example, if the user is showing strong anxiety, the AI ​​may suggest a psychological counselor as a place to seek advice.

[1005] The following describes the processing flow for each example of the form.

[1006] "Example of form 1"

[1007] Step 1: The user enters their inquiry into the system as text.

[1008] Step 2: The emotion engine analyzes the user's emotional state from the text.

[1009] Step 3: The AI ​​considers emotional information and generates a general opinion.

[1010] Step 4: The AI ​​considers emotional information and suggests who to consult.

[1011] "Example of form 2"

[1012] Step 1: The user enters their inquiry into the system as text.

[1013] Step 2: The emotion engine analyzes the user's emotional state from the text.

[1014] Step 3: The AI ​​learns from user inquiries and emotional data.

[1015] Step 4: The AI ​​generates more appropriate viewpoints and solutions based on emotions.

[1016] "Example of form 3"

[1017] Step 1: The user enters their inquiry into the system as text.

[1018] Step 2: The emotion engine analyzes the user's emotional state from the text.

[1019] Step 3: The AI ​​considers the user's emotional state and suggests the most appropriate person to consult.

[1020] (Example 1)

[1021] Next, we will describe Embodiment 1 of 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."

[1022] Traditional consultation systems have struggled to accurately grasp users' emotional states and provide appropriate advice based on those states. Furthermore, they lacked features to suggest specific resources based on the nature of the consultation, resulting in users being unable to receive appropriate support.

[1023] 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.

[1024] In this invention, the server includes means for receiving inquiries from users, means for analyzing the content of the received inquiries and recognizing the emotional state, means for generating a general opinion based on the analyzed emotional state, means for suggesting where to seek advice based on the generated opinion, and means for generating advice using a generative AI model. This makes it possible to provide appropriate advice that takes into account the user's emotional state and to suggest specific places to seek advice.

[1025] "Means of receiving inquiries from users" refers to an interface that allows users to access the system and input their inquiries in text format.

[1026] "Means of analyzing received consultation content to recognize emotional state" refers to a function that uses natural language processing technology to analyze emotions from text entered by the user and identify the user's emotional state.

[1027] "Means for generating general opinions based on analyzed emotional states" refers to a function that considers the user's emotional state and uses a generative AI model to generate appropriate advice and opinions.

[1028] "Means of suggesting who to consult based on generated opinions" refers to a function that suggests specific organizations or personnel that the user should consult based on the generated advice and opinions.

[1029] "Methods for generating advice using generative AI models" refers to a function that utilizes AI technology to automatically generate advice tailored to the user's consultation content and emotional state.

[1030] A description of the embodiment for carrying out the invention will be provided.

[1031] This system begins with the user entering their consultation details via a terminal. The user sends the consultation details to the system in text format using a web browser or a dedicated application. The terminal then transmits the entered text data to the server via the internet. The data is encrypted during transmission, ensuring security.

[1032] The server passes the received text to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses software that employs natural language processing technology. Specifically, Google Cloud Natural Language API is one such example. For instance, the sentiment engine might determine that the user is feeling tired from an expression like "tired."

[1033] Next, the server uses a generative AI model to generate advice based on the analyzed sentiment information. This generative AI model may include OpenAI's GPT-4, for example. The generative AI model generates appropriate advice tailored to the user's situation.

[1034] For example, if a user inputs "I've been really busy with work lately and I'm exhausted," the server uses its emotion engine to recognize "fatigue" as an emotion. It then prompts the generative AI model with "Please provide advice for a user who is feeling fatigued," and generates advice such as "It's important to take regular breaks and make time to relax." The server returns this advice to the user, who can then view it on their device.

[1035] In this way, it becomes possible to provide appropriate advice that takes the user's emotional state into consideration, and to suggest specific places to seek advice.

[1036] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1037] Step 1:

[1038] The user enters their consultation details in text format using a terminal. The entered text specifically describes the user's concerns and problems. This text data becomes the input for the next processing step.

[1039] Step 2:

[1040] The terminal sends the entered text data to the server. Security is ensured because the data is encrypted during transmission. The output of this step is the encrypted text data received by the server.

[1041] Step 3:

[1042] The server decodes the received text data and passes it to the emotion engine. The emotion engine uses natural language processing techniques to analyze the text and identify the user's emotional state. For example, from the expression "tired," it might determine that the user is feeling tired. The output of this step is the analyzed emotion information.

[1043] Step 4:

[1044] The server inputs a prompt message into the generative AI model based on the analyzed sentiment information. The generative AI model generates advice tailored to the user's situation. For example, if the prompt message is "Provide advice for a user who is feeling fatigued," it might generate advice such as "It is important to take regular breaks and make time to relax." The output of this step is the generated advice.

[1045] Step 5:

[1046] The server sends the generated advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and consider their next course of action. The output of this step is the advice that the user reviews.

[1047] (Application Example 1)

[1048] Next, we will describe Application Example 1 of Form 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."

[1049] In modern society, there is a demand for quick and accurate solutions to users' problems and anxieties. However, conventional systems have difficulty considering users' emotions and are unable to propose appropriate solutions.

[1050] 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.

[1051] In this invention, the server includes means for receiving inquiries from users, means for AI learning to analyze compliance issues in society based on the received inquiries, means for presenting general opinions based on the analysis results, means for sentiment analysis to analyze the user's emotions, and means for proposing appropriate countermeasures based on sentiment information. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[1052] "A means of receiving inquiries from users" refers to an interface that allows users to input their problems and concerns into the system and receive the content of their inquiries.

[1053] "AI learning methods" refer to artificial intelligence technology that analyzes and learns about public compliance issues and related information based on the content of inquiries received.

[1054] "Means of presenting general views" refers to a function that provides users with general solutions or opinions based on analysis results obtained through AI learning methods.

[1055] "Means of suggesting who to consult" refers to a function that suggests the appropriate department or expert the user should consult based on the analysis results.

[1056] "Emotional analysis means" refers to technology that analyzes emotions from the consultation content entered by the user and identifies the user's emotional state.

[1057] "Means for proposing appropriate countermeasures" refers to a function that proposes optimal solutions and actions to users based on emotional information obtained through emotion analysis.

[1058] The system for implementing this invention can be accessed by users using smartphones or computer terminals. Users input their inquiries in text format and send them to the system. The server uses software such as Python or TensorFlow and utilizes natural language processing libraries (such as NLTK) to analyze the received inquiries.

[1059] The server first receives the user's inquiry and uses sentiment analysis to identify the user's emotions. This involves analyzing text data to extract the user's emotional state (e.g., anxiety, fear). Next, AI learning tools are used to analyze relevant societal compliance issues and related information concerning the inquiry. This allows the server to present the user with a general perspective.

[1060] Furthermore, a system that suggests appropriate measures based on emotional information is activated, proposing the best solutions and actions to the user. For example, if a user inputs "I've recently seen suspicious people around my house," the emotional analysis system identifies "anxiety," and the AI ​​suggestion engine makes suggestions such as "consult the local police station" or "consider installing security cameras."

[1061] By utilizing a generative AI model, it is possible to generate prompt messages that are tailored to the user's emotions and the content of their inquiry, enabling appropriate responses. An example of a prompt message would be, "If the user is feeling anxious, what security measures should be suggested?"

[1062] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1063] Step 1:

[1064] Users access the system using a smartphone or computer terminal and input their inquiry details in text format. The entered text data is then sent to the server.

[1065] Step 2:

[1066] The server passes the received text data to the sentiment analysis tool. The sentiment analysis tool analyzes the text using a natural language processing library (such as NLTK) to identify the user's emotional state. For example, it extracts emotions such as "anxiety" or "fear." The analysis results are then passed on as emotional information to the next step.

[1067] Step 3:

[1068] The server inputs emotional information and consultation details into an AI learning tool. The AI ​​learning tool uses machine learning frameworks such as TensorFlow to analyze public compliance issues and related information. As a result of the analysis, general opinions and related information are generated.

[1069] Step 4:

[1070] The server activates a mechanism to suggest appropriate countermeasures based on emotional information and the results of AI learning analysis. This mechanism utilizes a generative AI model to generate prompt messages that suggest the optimal solution or action for the user. For example, specific suggestions such as "consult the nearest police station" or "consider installing security cameras" may be made.

[1071] Step 5:

[1072] The server sends the generated prompt to the user's terminal and displays the suggested actions to the user. The user can then choose the appropriate action based on the suggested solutions.

[1073] (Example 2)

[1074] Next, we will describe Example 2 of the morphological example. 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."

[1075] In modern society, individuals face a wide range of issues related to social norms, but obtaining appropriate views and solutions to these problems is not easy. Furthermore, while it is necessary to respond in a way that takes the client's feelings into consideration, conventional systems have the challenge of making it difficult to provide advice that takes these feelings into account.

[1076] 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.

[1077] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing social norms based on the received information, and means for acquiring the user's emotional information and reflecting it in the analysis results. This makes it possible to present general opinions on the consultation content and make adjustments that take emotions into consideration.

[1078] "Means for receiving information from users" refers to the interface through which the system receives inquiries and questions provided by users.

[1079] A "machine learning method for analyzing issues related to social norms" is an algorithm that uses pre-trained data based on received information to analyze issues related to social norms.

[1080] A "means of presenting general opinions" refers to a function that provides users with standard or general views based on analysis results.

[1081] "Means of suggesting who to consult" refers to a function that suggests appropriate organizations or positions for the user to consult based on the analysis results.

[1082] "Means for acquiring user emotional information and reflecting it in analysis results" refers to a function that analyzes the user's emotional state and incorporates that information into the analysis results in order to provide more appropriate responses.

[1083] "Means for adjusting generated opinions" refers to functions that appropriately modify or adjust generated opinions by taking into account the user's emotional information and other factors.

[1084] One embodiment of this invention is a system that receives inquiries from users, analyzes them, and provides appropriate opinions and solutions. A specific embodiment is shown below.

[1085] The server provides a web interface accessible via the internet to receive information from users. Through this interface, users can input their concerns in text format. For example, a user might input a specific concern such as, "I'm suffering from harassment from my boss."

[1086] The terminal sends the user's inputted consultation details to the server. The consultation details are sent to the server as text data. The server inputs the received consultation details into a generative AI model. The generative AI model then refers to a pre-trained database to search for relevant laws and cases related to the consultation details.

[1087] Furthermore, the server uses an emotion engine to acquire user emotion data. For example, it analyzes emotions such as anger and sadness from the text entered by the user. This emotion data is then reflected in the analysis results of the generative AI model.

[1088] The server uses a generative AI model to generate general opinions and solutions to the consultation content. For example, it creates appropriate advice based on laws and cases related to power harassment. The generated opinions and solutions are adjusted to take into account the user's emotional information. For example, if the user is expressing anger, the advice will be provided in more polite language.

[1089] For example, if a user enters "I would like to discuss sexual harassment at work," the server will generate the following prompt for the AI ​​model: "The user is seeking advice about sexual harassment at work. Please provide appropriate advice based on relevant laws and cases." Based on this prompt, the AI ​​model will generate appropriate opinions and solutions, which the server will then provide to the user.

[1090] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1091] Step 1:

[1092] The user enters their inquiry details via their device. For example, they might enter a specific inquiry such as, "I'm suffering from harassment from my boss." The entered text data is then sent from the device to the server.

[1093] Step 2:

[1094] The server inputs the received consultation details into a generating AI model. The server then refers to a pre-trained database to search for laws and cases related to the consultation details. By analyzing the input text data and extracting relevant information, it obtains the basic data needed to generate general opinions and solutions.

[1095] Step 3:

[1096] The server uses an emotion engine to acquire user emotion data. It analyzes the user's emotional state from the input text data, identifying emotions such as anger and sadness. This emotion data is then incorporated into the analysis results of a generative AI model.

[1097] Step 4:

[1098] The server uses a generative AI model to generate general opinions and solutions to the consultation content. For example, it creates appropriate advice based on laws and cases related to power harassment. The generated opinions and solutions are adjusted to take into account the user's emotional information.

[1099] Step 5:

[1100] The server sends the generated views and solutions to the terminal. The terminal displays the results to the user. For example, if the user is expressing anger, the server will offer advice in more polite language.

[1101] (Application Example 2)

[1102] Next, we will describe application example 2 of form 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."

[1103] In today's workplace, employees face a wide range of compliance issues and workplace troubles. A system is needed that allows employees to receive appropriate advice regarding these issues. However, conventional systems often struggle to consider user emotions and lack features to suggest specific resources for consultation, which can delay problem resolution.

[1104] 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.

[1105] In this invention, the server includes means for receiving inquiries from users, information processing means for analyzing compliance issues in society based on the received inquiries, and means for analyzing the user's emotional data and generating emotionally-based opinions. As a result, users can receive appropriate advice tailored to their emotions and, by suggesting specific resources for consultation, can quickly resolve problems.

[1106] "A means of receiving inquiries from users" refers to an interface that allows users to anonymously input problems and concerns they face at work, and for the system to receive them.

[1107] "Information processing means" refers to a processing device that uses AI technology to analyze the content of inquiries received and generate general opinions and solutions based on relevant laws and cases.

[1108] "Means for analyzing emotional data and generating emotion-based viewpoints" refers to an analytical device that extracts emotions from user input and provides appropriate responses and advice based on those emotions.

[1109] "A means of generating advice on consultation content using a generative AI model" refers to a system that utilizes AI technology to automatically generate specific advice and solutions to users' consultation content.

[1110] "Means of suggesting who to consult" refers to a function that suggests appropriate organizations or positions for the problems a user is facing, and indicates specific contact points for consultation.

[1111] The system for implementing this invention provides a platform where users can anonymously consult about compliance issues and troubles they face in the workplace. The server receives consultations from users and analyzes the content of the consultations using information processing means. Specifically, the server uses software such as Python and TensorFlow, and utilizes natural language processing libraries (e.g., spaCy) to analyze the content of the consultations.

[1112] The server uses sentiment analysis engines such as the Google Cloud Natural Language API to acquire user sentiment data. Based on this, it generates opinions that are relevant to the user's emotions, and then uses a generative AI model (e.g., OpenAI's GPT) to generate specific advice regarding the consultation.

[1113] The terminal sends the user's inputted consultation details to the server and receives analysis results and advice from the server. Based on this information, the user can obtain specific resources for consultation and solutions.

[1114] For example, if a user consults the server about suffering from harassment from their boss, the server will suggest appropriate countermeasures based on laws and cases related to harassment. Furthermore, if the user expresses anger, the server will suggest a more courteous approach.

[1115] An example of a prompt message would be: "The user is seeking advice about workplace harassment. Generate appropriate advice based on relevant laws and cases. Also, adjust the tone of the advice considering the user's emotional data."

[1116] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1117] Step 1:

[1118] Users anonymously input workplace problems and concerns using a terminal. The entered information is sent from the terminal to the server. The input data is in text format and includes the user's specific concerns.

[1119] Step 2:

[1120] The server analyzes the received consultation content using a natural language processing library (e.g., spaCy). It tokenizes the received text data and extracts important keywords and phrases. This allows the server to identify the subject of the consultation and relevant laws and regulations.

[1121] Step 3:

[1122] The server uses the Google Cloud Natural Language API to retrieve user emotion data from the consultation content. The parsed text data is passed to an emotion analysis engine as input, and emotion scores and emotion categories are obtained as output. This allows for an understanding of the user's emotional state.

[1123] Step 4:

[1124] The server uses a generative AI model (e.g., OpenAI's GPT) to generate advice for the consultation. The analyzed consultation content and sentiment data are incorporated into prompt sentences as input and passed to the generative AI model. The output is text containing specific advice and solutions.

[1125] Step 5:

[1126] The server sends the generated advice to the terminal. The terminal displays the advice to the user, who can then consider specific actions based on it. The output includes the text of the advice provided to the user.

[1127] (Example 3)

[1128] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1129] Conventional consultation systems have difficulty suggesting appropriate resources based on the user's concerns, and in particular, they lack consideration for the user's emotional state. Furthermore, the accuracy of natural language processing in analyzing consultation content is low, making it difficult to provide users with specific and useful guidance.

[1130] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1131] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing the data based on the received information, and means for suggesting appropriate consultation services based on the analysis results. This makes it possible to suggest appropriate consultation services that take into account the emotional state of the user in response to the content of their consultation.

[1132] A "user" refers to an individual or group that uses the system to input information and seek advice.

[1133] "Means of receiving information" refers to the function of receiving input from users and converting it into a format that can be processed within the system.

[1134] "Machine learning methods for analyzing data" refers to a function that uses machine learning algorithms to analyze data based on received information and understand the user's intentions and emotions.

[1135] "Means of providing general guidelines" refers to a function that provides users with general advice or guidelines based on the analysis results.

[1136] "Means of suggesting appropriate resources for consultation" refers to a function that, based on the analysis results, suggests specific organizations or individuals that the user should consult.

[1137] "A means of determining who to consult based on emotional state" refers to a function that analyzes the user's emotional state and determines the most suitable consultation service accordingly.

[1138] A "generative AI model" is an artificial intelligence model used for natural language processing, referring to a technology that analyzes user input and generates appropriate output.

[1139] This invention begins with a user inputting consultation details via a terminal, and a server receiving that information. The server is equipped with means for receiving information from the user and uses a generative AI model to analyze the input information. Specifically, it uses a generative AI model such as OpenAI's GPT model to perform natural language processing.

[1140] The server uses machine learning techniques to analyze the received information as data. These techniques aim to understand the user's inquiry and analyze their intentions and emotions. Based on the analysis results, the server uses methods to suggest appropriate resources, indicating specific organizations or individuals to the user.

[1141] Furthermore, the server has a mechanism to determine who to consult based on the user's emotional state. This makes it possible to suggest the most suitable consultation service based on the user's emotions. For example, if a user consults the server saying, "I've been having trouble with relationships at work lately and I'm feeling stressed," the server can suggest a psychological counselor as a consultation option.

[1142] An example of a prompt message is, "If the user is experiencing stress due to interpersonal relationships at work, please suggest which department or person they should consult." By inputting this prompt message into the AI ​​generation model, the server can determine the appropriate contact point and notify the user. The specific processing flow in Example 3 is explained using Figure 21.

[1143] Step 1:

[1144] The user enters their consultation details through their device. The entered information is saved on the device as text data. This data includes the user's intentions and emotions.

[1145] Step 2:

[1146] The terminal sends the text data entered by the user to the server. A secure communication protocol is used for transmission to ensure data security. The server prepares the received data for analysis.

[1147] Step 3:

[1148] The server inputs the received text data into a generative AI model. Specifically, it uses the generative AI model to perform natural language processing. The server analyzes the data and processes it to understand the user's intent and emotions.

[1149] Step 4:

[1150] The server determines the appropriate contact point based on the analysis results from the generated AI model. The analysis results include information related to the user's inquiry, and the server uses this to suggest specific organizations or individuals.

[1151] Step 5:

[1152] The server considers the user's emotional state to determine the most suitable consultation service. The generative AI model infers emotions from the user's input and suggests consultation services such as psychological counselors as needed.

[1153] Step 6:

[1154] The server sends the information of the chosen contact person to the terminal. The terminal notifies the user of the information received from the server, and the user can check the recommended contact person on the screen.

[1155] (Application Example 3)

[1156] Next, we will describe application example 3 of form example 3. 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."

[1157] In today's business environment, there is a demand for swift and appropriate responses when security incidents or compliance issues arise. However, finding the right resources and solutions for these problems is not easy, and there is a particular lack of responses that take into account the emotional state of users. Therefore, a system is needed that allows users to resolve problems with peace of mind.

[1158] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1159] In this invention, the server includes means for receiving information from the user, machine learning means for analyzing social norm issues based on the received information, means for presenting general opinions based on the analysis results, and means for analyzing the user's emotional state and proposing appropriate countermeasures. This enables the user to quickly and appropriately find someone to consult and receive support that is appropriate to their emotional state.

[1160] "Means for receiving information from users" refers to an interface that allows the system to receive and process data and inquiries provided by users.

[1161] "Machine learning methods for analyzing social norm issues" refers to the process of analyzing and understanding issues related to social norms and laws using machine learning techniques.

[1162] "Means of presenting general opinions" refers to a function that provides users with general and objective views based on analyzed data.

[1163] "Means of suggesting who to consult" refers to a function that suggests the appropriate department or person to consult based on the analysis results.

[1164] "A means of analyzing a user's emotional state and proposing appropriate countermeasures" refers to a process for analyzing a user's emotions and proposing the optimal countermeasures based on that state.

[1165] The system for implementing this invention mainly consists of a server and a user terminal. The server receives information from the user and is equipped with machine learning means for analyzing social norm issues. Specifically, the server uses the Google Cloud Natural Language API to process the user's inquiries in natural language and IBM Watson Tone Analyzer to analyze the user's emotional state.

[1166] The server provides general opinions based on the analysis results and suggests who to consult. This allows users to quickly access the appropriate department or person in charge. Furthermore, by suggesting solutions tailored to the user's emotional state, it reduces user anxiety and assists in problem-solving.

[1167] The user terminal is a device such as a smartphone or smart glasses, which provides an interface for the user to input their inquiry. When a user inputs an inquiry such as "There is a possibility of data leakage," the server analyzes the content and returns specific instructions such as "We recommend that you contact the IT security department."

[1168] For example, if a user enters a prompt such as, "If there is a possibility of a data breach, which department should I contact?", the server can suggest appropriate countermeasures. This allows the user to address the problem quickly and appropriately.

[1169] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1170] Step 1:

[1171] The user enters their inquiry details using a terminal. The entered text data is sent to the server.

[1172] Step 2:

[1173] The server performs natural language processing on the received text data using the Google Cloud Natural Language API. This process analyzes the structure and meaning of the text and extracts important keywords and phrases. The output is the analyzed text data.

[1174] Step 3:

[1175] The server inputs the analyzed text data into IBM Watson Tone Analyzer to analyze the user's emotional state. This process determines the user's emotional state (e.g., anxiety, reassurance, etc.). The output is data on the emotional state.

[1176] Step 4:

[1177] The server uses a generative AI model to generate general opinions based on the analysis results. This model has learned from past data and cases, and generates appropriate opinions regarding the user's inquiry. The output is a general opinion.

[1178] Step 5:

[1179] The server considers the analysis results and emotional state to suggest who to consult. Specifically, it generates prompt messages to suggest the appropriate department or person in charge. The output provides a list of suggested consultation locations.

[1180] Step 6:

[1181] The server sends generated general opinions and suggested contacts to the user's terminal. The user can receive this information through their terminal and take appropriate action.

[1182] (Other examples)

[1183] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1184] 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.

[1185] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1186] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1187] 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.

[1188] [Fourth Embodiment]

[1189] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1190] 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.

[1191] 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).

[1192] 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.

[1193] 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.

[1194] 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).

[1195] 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.

[1196] 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.

[1197] 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.

[1198] 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.

[1199] 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.

[1200] 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.

[1201] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1202] "Example of form 1"

[1203] One embodiment of the present invention provides an AI consultation and reporting system. This system has a means of receiving consultations from users. Specifically, users can access the system and submit a consultation by entering their problem or concern as text.

[1204] "Example of form 2"

[1205] Furthermore, this system has a mechanism for AI to analyze compliance issues in society based on the consultations it receives. Specifically, the AI ​​uses a pre-trained database to generate general opinions and solutions to the user's consultation. For example, if a user consults about "being troubled by harassment from their boss," the AI ​​will analyze laws and cases related to harassment and generate appropriate advice.

[1206] "Example of form 3"

[1207] Furthermore, this system has a mechanism to suggest who to consult based on the analysis results. Specifically, the AI ​​will instruct the user on the department or person to consult based on the analysis results. For example, if a user consults about suspected irregularities in company expense reports, the AI ​​can give specific instructions such as "We recommend consulting the internal audit department."

[1208] The following describes the processing flow for each example of the form.

[1209] "Example of form 1"

[1210] Step 1: The user accesses the AI ​​consultation and reporting system.

[1211] Step 2: The user enters their problem or concern as text and submits it to the system for consultation.

[1212] "Example of form 2"

[1213] Step 1: The system receives the user's inquiry.

[1214] Step 2: The AI ​​uses a pre-trained database to generate general opinions and solutions regarding the consultation.

[1215] Step 3: Present the AI-generated views and solutions to the user.

[1216] "Example of form 3"

[1217] Step 1: Based on the user's inquiry and the opinions it generates, the AI ​​determines which department or person should be consulted.

[1218] Step 2: The AI ​​suggests to the user which department or person they should consult.

[1219] (Example 1)

[1220] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1221] Traditional consultation systems faced challenges in providing prompt and appropriate responses to user inquiries. In particular, when inquiries covered a wide range of topics or required specialized knowledge, responses were often delayed or insufficient.

[1222] 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.

[1223] In this invention, the server includes means for receiving inquiries from users, means for passing the received inquiries to a generation AI model, and means for the generation AI model to analyze the content of the inquiries and generate a response. This makes it possible to provide a quick and appropriate response to user inquiries.

[1224] A "user" refers to an individual or organization that accesses the system and enters their inquiry details.

[1225] "Consultation" refers to the content of problems or concerns that users input into the system.

[1226] A "generative AI model" refers to artificial intelligence technology that analyzes the content of inquiries received from users and generates appropriate responses.

[1227] "Response" refers to the answer or advice generated by the generative AI model as a result of analyzing the content of the consultation.

[1228] A "server" refers to a computer system that receives inquiries from users, passes data to a generation AI model, and provides the generated response to the user.

[1229] A "terminal" refers to a device used by a user to access the system, input their inquiries, and receive responses.

[1230] As an embodiment for carrying out this invention, an AI consultation and reporting system will be specifically described.

[1231] The server provides a web interface for receiving inquiries from users. This interface is accessed through a device accessible to the user (such as a PC or smartphone). Users can access the system and input their inquiries in text format. The entered text data is sent to a generative AI model running on the server. This generative AI model uses natural language processing technology to analyze the user's inquiries and generate appropriate responses.

[1232] For the generative AI model, advanced natural language processing models such as OpenAI's GPT-4 can be used. This makes it possible to understand the user's intent and construct the optimal response based on past training data. The generated response is then sent back to the user's device and displayed to the user.

[1233] As a concrete example, consider a scenario where a user enters a question such as, "My motivation at work has been low lately. What should I do?" This prompt is sent to a generative AI model, which generates a response such as, "I recommend setting new goals and accumulating small achievements." The server sends this response back to the terminal, which then displays it to the user. The user can then use this advice to guide their daily work.

[1234] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1235] Step 1:

[1236] The user accesses the AI ​​consultation reporting system using their device. The user enters their consultation details into the text box and clicks the "Submit" button. The user's consultation details are provided as input in text format.

[1237] Step 2:

[1238] The terminal sends the text data entered by the user to the server as an HTTP request. The input is the user's inquiry, and the output is the request data sent to the server. This request contains the user's inquiry.

[1239] Step 3:

[1240] The server passes the received text data to the generating AI model. The input is the consultation content received from the terminal, and the output is the input data for the generating AI model. The server converts the data into a format that the generating AI model can access.

[1241] Step 4:

[1242] The generative AI model analyzes data received from the server and generates an appropriate response. The input is the inquiry from the server, and the output is the generated response. The model uses natural language processing techniques to understand the user's intent and construct the optimal answer.

[1243] Step 5:

[1244] The server sends the response received from the generative AI model back to the terminal as an HTTP response. The input is the response from the generative AI model, and the output is the response data sent to the terminal.

[1245] Step 6:

[1246] The terminal displays the response received from the server to the user. The input is the response data from the server, and the output is the response displayed to the user. The user can review the advice generated on the screen and consider their next course of action.

[1247] (Application Example 1)

[1248] Next, we will describe Application Example 1 of Form 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".

[1249] In modern society, individuals and businesses face a wide range of security issues, but obtaining appropriate advice and information quickly is difficult. In particular, it is challenging for ordinary users to determine how to respond to threats such as phishing scams and data breaches. In this situation, there is a need for a system that allows users to confidently seek security advice and take appropriate measures.

[1250] 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.

[1251] In this invention, the server includes means for receiving inquiries from users, means for artificial intelligence learning for analyzing security issues based on the received inquiries, and means for providing appropriate advice based on the analysis results. This enables users to resolve their security concerns and questions and take quick and appropriate measures.

[1252] "A means of receiving inquiries from users" refers to an interface that allows users to access the system and input their security concerns and questions in text.

[1253] "An artificial intelligence learning method for analyzing security issues" is a processing device that uses artificial intelligence technology to analyze the content of inquiries received, identify security-related problems, and derive appropriate countermeasures.

[1254] "Means of providing appropriate advice" refers to a function that presents users with specific countermeasures and information based on analysis results, and provides information to alleviate security concerns.

[1255] "Means of providing security information" refers to information provision devices that provide users with the latest information on security incidents and regulations to support appropriate decision-making.

[1256] The system for implementing this invention enables users to consult about security using a device such as a smartphone. Users input their consultation details as text through an application on their device. The server utilizes an artificial intelligence learning method using a generative AI model to analyze the received consultation details. This artificial intelligence learning method analyzes the text using a natural language processing library (e.g., spaCy) and extracts important keywords and context.

[1257] Based on the extracted information, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate advice for the user. Furthermore, by cross-referencing with a security database, it can provide the latest security information. This allows users to resolve their security concerns and questions and take quick and appropriate action.

[1258] For example, if a user enters "I've been receiving more emails from my bank lately and I'm worried. What should I do?", the server will analyze the content and provide advice such as, "To verify whether the email is genuine, we recommend checking the sender's email address and logging in directly from the official website. Also, please be careful not to enter any personal information, as it may be a phishing scam."

[1259] An example of a prompt message is: "User inquiry: I've been receiving more emails from my bank lately and I'm worried. What should I do? As an AI, what advice would you offer the user?"

[1260] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1261] Step 1:

[1262] The user launches an application on their device and enters their security-related questions as text. The entered text is sent to the server. The input data is text information that specifically expresses the user's concerns and questions.

[1263] Step 2:

[1264] The server analyzes the received text data using a natural language processing library (e.g., spaCy). During the analysis process, important keywords and context are extracted from the text. The input is the user's inquiry, and the output is the extracted keywords and contextual information.

[1265] Step 3:

[1266] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the extracted information to generate appropriate advice for the user. The AI ​​model uses prompts to generate answers to the user's inquiries. The input consists of extracted keywords and contextual information, and the output is the generated advice.

[1267] Step 4:

[1268] The server compares the generated advice against the security database and adds the latest security information. This ensures that the information provided to the user is up-to-date and accurate. The input is the generated advice, and the output is the final advice information after comparison.

[1269] Step 5:

[1270] The server sends the final advice information to the user's terminal. The user can then review the advice on their terminal and take specific actions to resolve any security concerns or questions. The input is the final advice information, and the output is the user's understanding and actions.

[1271] (Example 2)

[1272] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1273] In modern society, users face a wide range of compliance issues, and resolving them requires specialized knowledge. However, it is not easy for ordinary users to obtain appropriate views and solutions to these problems. Therefore, there is a need for a system that allows users to receive quick and accurate advice on compliance issues.

[1274] 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.

[1275] In this invention, the server includes means for receiving information from a user, means for processing information to analyze the received information, means for searching for relevant information from a knowledge base based on the analyzed information, means for generating an opinion using a generative AI model based on the search results, and means for providing the generated opinion to the user. This makes it possible for users to quickly obtain appropriate advice on compliance issues even without specialized knowledge.

[1276] "Means of receiving information from users" refers to the function by which the system receives information entered by the user and incorporates it for processing.

[1277] "Information processing means" refers to the technologies and processes used to analyze received information and extract necessary data.

[1278] "Means of searching for relevant information from a knowledge base" refers to a function that finds relevant information from a pre-stored database based on the analyzed information.

[1279] "Means of generating opinions using generative AI models" refers to a function that utilizes AI technology to create opinions and advice to provide to users based on search results.

[1280] "Means of providing generated views to users" refers to a function that communicates views generated by an AI model to users and presents them in an accessible format.

[1281] This invention is a system that provides prompt and accurate advice to users regarding compliance issues they face. The following describes a specific implementation of this system.

[1282] The server connects to the user's terminal via the internet to receive information from the user. The user uses their terminal to input their inquiry and send it to the server. For example, they can input specific details such as, "I'm suffering from harassment from my boss."

[1283] The server uses natural language processing techniques to analyze the information it receives. In this process, the server uses natural language processing libraries such as "spaCy" and "NLTK" to extract keywords and context from the consultation content.

[1284] Based on the analyzed information, the server searches its knowledge base for relevant information. The knowledge base contains laws and regulations related to compliance and past case studies. The server refers to this data and extracts information relevant to the user's inquiry.

[1285] Next, the server uses a generative AI model such as "GPT-4" to generate opinions based on the search results. At this time, the AI ​​model is given instructions such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[1286] Finally, the server provides the generated insights to the user's terminal. The user can then review the specific advice on their terminal and use it as a reference for resolving the problem. In this way, users can quickly obtain appropriate advice on compliance issues, even without specialized knowledge.

[1287] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1288] Step 1:

[1289] The user uses a terminal to input their consultation details and sends them to the server. The input could be specific details such as, "I'm suffering from harassment from my boss." The terminal then sends this input as digital data to the server.

[1290] Step 2:

[1291] The server analyzes the received consultation content using natural language processing technology. The input is the consultation content from the user, and the output is the analyzed keywords and contextual information. The server uses libraries such as "spaCy" and "NLTK" to tokenize the text data and extract important keywords.

[1292] Step 3:

[1293] The server searches for relevant information from its knowledge base based on the analysis results. The input is the analyzed keywords, and the output is data on relevant laws and cases. The server executes database queries to extract the relevant information.

[1294] Step 4:

[1295] The server inputs the search results into a generative AI model such as "GPT-4" to generate an opinion. The input is data containing relevant information, and the output is an opinion or advice to be provided to the user. The server gives the AI ​​model instructions as a prompt, such as, "The user is suffering from harassment from their boss. Please generate appropriate advice based on relevant laws and cases."

[1296] Step 5:

[1297] The server sends the generated opinion to the user's terminal. The input is the generated opinion, and the output is advice that the user can view on their terminal. The user can receive specific advice through their terminal and use it as a reference for solving problems.

[1298] (Application Example 2)

[1299] Next, we will describe application example 2 of form 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".

[1300] In modern society, individuals and businesses face a wide range of compliance issues, but obtaining appropriate advice quickly is difficult. Furthermore, the lack of systems that provide concrete solutions in real time based on the consultation content can lead to delays in problem resolution.

[1301] 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.

[1302] In this invention, the server includes means for receiving inquiries from users, machine learning means for analyzing social norm issues based on the received inquiries, and means for providing specific countermeasures based on the analysis results. This enables users to receive quick and appropriate advice in real time.

[1303] "Means for receiving inquiries from users" refers to an interface for receiving inquiries or problem reports from individuals or organizations.

[1304] "Machine learning methods for analyzing social norm issues" are algorithms that use databases to analyze legal and ethical issues and derive appropriate solutions.

[1305] "Means of presenting general views" refers to a function that presents widely accepted opinions or solutions based on the analysis results.

[1306] "Means of suggesting who to consult" refers to a function that suggests which department or position a user should consult in order to resolve a problem.

[1307] "Means of providing specific countermeasures" refers to a function that provides specific action guidelines for the problems faced by users, based on the analysis results.

[1308] "A means of generating advice in real time" refers to a function that immediately analyzes user inquiries and provides advice quickly.

[1309] The system for implementing this invention is designed to receive inquiries from users, analyze social norms, and provide specific countermeasures. The system is configured as follows:

[1310] The server provides an interface for receiving inquiries from users. Users can input their inquiries in text format using their smartphones or computers. The entered inquiries are then sent to the server.

[1311] The server uses a machine learning model to analyze the content of the inquiries it receives. This model utilizes OpenAI's GPT, which analyzes social norm issues by referencing a pre-trained database. The database contains information on laws and past cases.

[1312] Based on the analysis results, the server provides users with general insights and specific countermeasures in real time. This allows users to receive timely and appropriate advice.

[1313] For example, if a user enters "I would like to consult about the risk of information leakage at work," the server will analyze relevant information security laws and past cases and provide advice such as, "To prevent information leakage, it is important to conduct regular security training and properly manage access rights."

[1314] An example of a prompt message is: "User inquiry: I would like to discuss the risk of information leakage in the workplace. Please provide appropriate advice based on relevant laws and case examples."

[1315] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1316] Step 1:

[1317] Users input their consultation details in text format via a smartphone or computer interface. The entered text is sent to the server. The input data consists of the user's consultation details.

[1318] Step 2:

[1319] The server sends the received consultation content as a prompt message to OpenAI's GPT, a generative AI model, for analysis. The prompt message is structured to include the user's consultation content. The input data is the prompt message containing the user's consultation content, and the output data is the analysis result from the AI ​​model.

[1320] Step 3:

[1321] The server uses the analysis results obtained from the AI ​​model to reference relevant laws and cases from a database and generate specific countermeasures. The input data is the analysis results from the AI ​​model, and the output data is advice including specific countermeasures.

[1322] Step 4:

[1323] The server provides the generated advice to the user in real time. The user can view the advice on their terminal screen. The input data is advice that includes specific countermeasures, and the output data is the advice presented to the user.

[1324] (Example 3)

[1325] Next, we will describe Embodiment 3 of Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1326] In today's information society, it is difficult for users to quickly find the right resource for addressing problems and questions they face. This is especially true when specialized knowledge is required or when dealing with complex issues, making it difficult to determine which department or person to consult. In this situation, there is a need for a system that allows users to quickly identify the appropriate resource and efficiently resolve their problems.

[1327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1328] In this invention, the server includes means for receiving information from users, data processing means for analyzing the received information, and means for analyzing the information using a generative AI model. This makes it possible to quickly identify the appropriate contact for advice regarding the problems faced by the user and to solve the problems efficiently.

[1329] A "user" is an individual or organization that uses the system to input information and seeks to identify a contact point for consultation.

[1330] "Information" refers to data about the consultation content and problems that users input into the system.

[1331] A "server" is a computer system that receives and processes information from users.

[1332] "Data processing means" refers to functions that perform the necessary preprocessing and transformations to analyze the received information.

[1333] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze information and identify the appropriate source of advice.

[1334] "Analysis" is the process of understanding the meaning of information using generative AI models and extracting relevant keywords and contexts.

[1335] A "consultation point" refers to the appropriate organization or person that a user should consult in order to resolve a problem.

[1336] "Presentation" refers to the act of informing the user of the contact point identified based on the analysis results.

[1337] This invention is a system for quickly identifying the appropriate resource for addressing a user's problem. The system begins with the user inputting information using a terminal. The user enters the consultation details in text format and sends them to the server.

[1338] The server uses data processing tools to process the information received from the user. Specifically, it performs text preprocessing and tokenization to convert the data into a format suitable for generative AI models. These generative AI models utilize natural language processing techniques. For example, OpenAI's GPT-4 is one such model.

[1339] The generative AI model analyzes information provided by the server and extracts important keywords and context. This analysis makes it possible to identify the appropriate resources for the user to consult. Based on the analysis results, the server presents the user with specific resources to consult.

[1340] For example, if a user enters a question such as "We don't have enough budget for a new project," the server sends this information to a generating AI model, which then analyzes it and generates an instruction recommending that the user consult with the finance department, which is then sent back to the user.

[1341] An example of a prompt message is: "Based on the following issue, indicate the appropriate department or person the user should contact: 'The project is behind schedule.'"

[1342] This system allows users to efficiently find the appropriate resources to consult in order to solve their problems. The flow of the specific processing in Example 3 will be explained using Figure 15.

[1343] Step 1:

[1344] The user enters their inquiry details using a terminal. The entered information is sent to the server in text format. Specifically, when a user enters an inquiry such as "the project is behind schedule" and presses the send button, the data is sent to the server. The input is text data, and the output is data sent to the server.

[1345] Step 2:

[1346] The server uses data processing tools to analyze the text data received from the user. Specifically, it performs preprocessing of the text, removing unnecessary spaces and special characters, and then tokenizing the text. This process converts the text into a format suitable for the generative AI model. The input is text data from the user, and the output is preprocessed text data.

[1347] Step 3:

[1348] The server inputs pre-processed text data into a generative AI model. The generative AI model analyzes the text using natural language processing techniques and extracts important keywords and context. Specifically, it uses models such as GPT-4 to understand the meaning of the consultation content and identify relevant information. The input is pre-processed text data, and the output is keyword and contextual information as a result of the analysis.

[1349] Step 4:

[1350] The server identifies the appropriate person the user should consult based on the analysis results obtained from the generated AI model. Specifically, based on the analysis results, it generates concrete instructions for the user, such as "We recommend consulting the finance department." The input is the analysis results, and the output is the instruction on where to consult.

[1351] Step 5:

[1352] The server sends the generated instructions back to the user. The user can view the instructions from the server on their terminal. Specifically, instructions such as "We recommend consulting with the finance department" will be displayed on the terminal screen. The input is the instruction to consult, and the output is the instruction displayed to the user.

[1353] (Application Example 3)

[1354] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1355] In today's business environment, there is a demand for swift and appropriate responses to security issues and suspected misconduct. However, determining which department or person to consult can be difficult, sometimes leading to delays in response. Improving this situation and enabling rapid responses to security issues is a key challenge.

[1356] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1357] In this invention, the server includes means for receiving information from users, means for artificial intelligence learning for analyzing social security issues based on the received information, and means for suggesting appropriate resources for consultation based on the analysis results. This makes it possible to quickly and appropriately identify and respond to resources for consultation when a user has a security-related problem.

[1358] "Means for receiving information from users" refers to devices or software that have the function of receiving security-related information provided by users as input.

[1359] "An artificial intelligence learning tool for analyzing social security issues" refers to a device or software that uses artificial intelligence technology to collect data on security-related events in society and analyze it.

[1360] "Means for presenting general opinions based on analysis results" refers to a device or software that has the function of providing general advice or opinions to users based on the results of analysis by artificial intelligence.

[1361] "Means of suggesting appropriate consultation channels" refers to devices or software that, based on analysis results, identify the appropriate department or person the user should consult and present this information to the user.

[1362] "Means for analyzing information using natural language processing technology and selecting the appropriate department or person in charge" refers to a device or software that has the function of analyzing information from users using natural language processing technology and selecting the appropriate department or person in charge based on the results.

[1363] "Means for generating prompt sentences using a generative AI model" refers to a device or software that has the function of automatically generating prompt sentences in response to user input by utilizing a generative AI model.

[1364] The system for implementing this invention mainly consists of a server and a user terminal. The server provides an interface for receiving information from the user and analyzes social security issues based on the received information. Artificial intelligence learning methods using Python and TensorFlow are used for the analysis. Based on the analysis results, the server presents a general opinion and suggests appropriate resources for consultation.

[1365] The user terminal is a device such as a smartphone or computer, and it provides a means for the user to input security-related information. The input information is analyzed using natural language processing technology, and the appropriate department or person in charge is selected. Specifically, the backend of the application, which uses Flask, processes the user input and generates prompt sentences using a generative AI model.

[1366] For example, if a user enters "There has been an increase in suspicious emails within the company recently," the server will issue specific instructions such as "We recommend consulting with the information security department." In this case, the generation AI model generates a prompt asking "If there is an increase in suspicious emails, which department should I consult?" and performs analysis.

[1367] In this way, users can obtain information to respond quickly and appropriately when security issues arise.

[1368] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1369] Step 1:

[1370] The user enters security-related information using their device. The entered information is sent to the server in text format.

[1371] Step 2:

[1372] The server analyzes the received information using natural language processing techniques. Specifically, it tokenizes the text data and extracts important keywords. This process generates foundational data necessary to understand the meaning of the input information.

[1373] Step 3:

[1374] The server uses a generative AI model to generate prompt messages based on the analyzed data. For example, it might generate a prompt message such as, "If there is an increase in suspicious emails, which department should I contact?" This prompt message serves as the basis for the AI ​​model to identify the appropriate department to contact.

[1375] Step 4:

[1376] The server inputs the generated prompt message into the AI ​​model, which then performs analysis to suggest the appropriate contact point. Based on pre-trained data, the AI ​​model selects the most appropriate department or person in charge. This process outputs specific contact information.

[1377] Step 5:

[1378] Based on the analysis results, the server provides the user with a general opinion and specific resources for consultation. The results are sent to the user's terminal, where the user can view the recommended resources on the screen.

[1379] This series of processes allows users to obtain information that enables them to respond quickly and appropriately when security issues arise.

[1380] 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.

[1381] "Example of form 1"

[1382] One embodiment of the present invention involves a system for receiving inquiries from users that includes an emotion engine that recognizes the user's emotions. In this system, when a user inputs their inquiry as text, the emotion engine analyzes the user's emotional state from that text. For example, if a user uses the expression "I'm in trouble," the emotion engine recognizes that the user is confused. This emotional information is taken into consideration when the AI ​​generates general opinions or suggests who the user should consult.

[1383] "Example of form 2"

[1384] Another embodiment of the present invention is a system in which an AI learning means learns user emotion data. In this system, the AI ​​learns not only the content of the user's consultation but also emotion data obtained from an emotion engine. This enables the AI ​​to generate more appropriate views and solutions based on emotion. For example, if the user is showing anger, the AI ​​can take that emotion into consideration and suggest a more courteous response.

[1385] "Example of form 3"

[1386] In a further embodiment of the present invention, there is a system that suggests a place to seek advice, which takes into account the user's emotional state to specify a particular department or person. In this system, the AI ​​considers the user's emotional state and suggests the most appropriate place to seek advice. For example, if the user is showing strong anxiety, the AI ​​may suggest a psychological counselor as a place to seek advice.

[1387] The following describes the processing flow for each example of the form.

[1388] "Example of form 1"

[1389] Step 1: The user enters their inquiry into the system as text.

[1390] Step 2: The emotion engine analyzes the user's emotional state from the text.

[1391] Step 3: The AI ​​considers emotional information and generates a general opinion.

[1392] Step 4: The AI ​​considers emotional information and suggests who to consult.

[1393] "Example of form 2"

[1394] Step 1: The user enters their inquiry into the system as text.

[1395] Step 2: The emotion engine analyzes the user's emotional state from the text.

[1396] Step 3: The AI ​​learns from user inquiries and emotional data.

[1397] Step 4: The AI ​​generates more appropriate viewpoints and solutions based on emotions.

[1398] "Example of form 3"

[1399] Step 1: The user enters their inquiry into the system as text.

[1400] Step 2: The emotion engine analyzes the user's emotional state from the text.

[1401] Step 3: The AI ​​considers the user's emotional state and suggests the most appropriate person to consult.

[1402] (Example 1)

[1403] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1404] Traditional consultation systems have struggled to accurately grasp users' emotional states and provide appropriate advice based on those states. Furthermore, they lacked features to suggest specific resources based on the nature of the consultation, resulting in users being unable to receive appropriate support.

[1405] 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.

[1406] In this invention, the server includes means for receiving inquiries from users, means for analyzing the content of the received inquiries and recognizing the emotional state, means for generating a general opinion based on the analyzed emotional state, means for suggesting where to seek advice based on the generated opinion, and means for generating advice using a generative AI model. This makes it possible to provide appropriate advice that takes into account the user's emotional state and to suggest specific places to seek advice.

[1407] "Means of receiving inquiries from users" refers to an interface that allows users to access the system and input their inquiries in text format.

[1408] "Means of analyzing received consultation content to recognize emotional state" refers to a function that uses natural language processing technology to analyze emotions from text entered by the user and identify the user's emotional state.

[1409] "Means for generating general opinions based on analyzed emotional states" refers to a function that considers the user's emotional state and uses a generative AI model to generate appropriate advice and opinions.

[1410] "Means of suggesting who to consult based on generated opinions" refers to a function that suggests specific organizations or personnel that the user should consult based on the generated advice and opinions.

[1411] "Methods for generating advice using generative AI models" refers to a function that utilizes AI technology to automatically generate advice tailored to the user's consultation content and emotional state.

[1412] A description of the embodiment for carrying out the invention will be provided.

[1413] This system begins with the user entering their consultation details via a terminal. The user sends the consultation details to the system in text format using a web browser or a dedicated application. The terminal then transmits the entered text data to the server via the internet. The data is encrypted during transmission, ensuring security.

[1414] The server passes the received text to the sentiment engine, which analyzes the user's emotional state. The sentiment engine uses software that employs natural language processing technology. Specifically, Google Cloud Natural Language API is one such example. For instance, the sentiment engine might determine that the user is feeling tired from an expression like "tired."

[1415] Next, the server uses a generative AI model to generate advice based on the analyzed sentiment information. This generative AI model may include OpenAI's GPT-4, for example. The generative AI model generates appropriate advice tailored to the user's situation.

[1416] For example, if a user inputs "I've been really busy with work lately and I'm exhausted," the server uses its emotion engine to recognize "fatigue" as an emotion. It then prompts the generative AI model with "Please provide advice for a user who is feeling fatigued," and generates advice such as "It's important to take regular breaks and make time to relax." The server returns this advice to the user, who can then view it on their device.

[1417] In this way, it becomes possible to provide appropriate advice that takes the user's emotional state into consideration, and to suggest specific places to seek advice.

[1418] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1419] Step 1:

[1420] The user enters their consultation details in text format using a terminal. The entered text specifically describes the user's concerns and problems. This text data becomes the input for the next processing step.

[1421] Step 2:

[1422] The terminal sends the entered text data to the server. Security is ensured because the data is encrypted during transmission. The output of this step is the encrypted text data received by the server.

[1423] Step 3:

[1424] The server decodes the received text data and passes it to the emotion engine. The emotion engine uses natural language processing techniques to analyze the text and identify the user's emotional state. For example, from the expression "tired," it might determine that the user is feeling tired. The output of this step is the analyzed emotion information.

[1425] Step 4:

[1426] The server inputs a prompt message into the generative AI model based on the analyzed sentiment information. The generative AI model generates advice tailored to the user's situation. For example, if the prompt message is "Provide advice for a user who is feeling fatigued," it might generate advice such as "It is important to take regular breaks and make time to relax." The output of this step is the generated advice.

[1427] Step 5:

[1428] The server sends the generated advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and consider their next course of action. The output of this s...

Claims

[Claim 1] Equipped with a processor, database, and communication interface, The aforementioned processor, The system receives inquiries from users, receives text data of the inquiry content via the communication interface, and stores it in the database. The text data stored in the database is tokenized and analyzed, an emotion analysis model is applied to recognize the user's emotional state, which is classified as either positive, negative, or neutral, and natural language processing techniques are used to store the analysis results of the recognized emotional state in the database. Based on the information obtained by analyzing the aforementioned text data, information related to the consultation content is searched and extracted from a knowledge base containing laws and regulations concerning compliance and past cases. Using the analysis results of the emotional state stored in the database and the text data as input data, prompts are input to a generative AI model, pre-trained to analyze compliance issues, to generate a general opinion on compliance based on the consultation content and the related information, including the text data, extracted information related to the consultation content, and the analysis results of the emotional state classified as positive, negative, or neutral. Based on the opinions generated by the generation AI model in response to the input of the prompt and the analysis results of the emotional state, the system refers to organizational information stored in the database to identify a specific department or person to consult, in order to suggest a suitable contact. system.

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