System

Abstract

To provide a system.SOLUTION: A system includes: input means for inputting a user's concern about security; means for analyzing the input and generating a security measure; a feeling engine for recognizing a feeling included in the input of the user and adjusting the generated security measure on the basis of the feeling; means for providing the user with the adjusted security measure; and means for accumulating the concern about the security and the adjusted security measure as learning data.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the past, there were limited ways to share minor worries, complaints, and anxieties within a company, and it was difficult to share them anonymously. Furthermore, there was no way to aggregate this information in real time, learn from it, and use it to solve problems across the entire organization. [Means for solving the problem]

[0005] As a means for solving this problem, the system of the present invention includes an input means for inputting a user's security concerns, a means for analyzing the input and generating security measures, an emotion engine that recognizes emotions contained in the user's input and adjusts the generated security measures based on the emotions, a means for providing the adjusted security measures to the user, and a means for accumulating the security concerns and the adjusted security measures as learning data. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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)).

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

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

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

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

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

[0016] 1, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[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 of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an 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 a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0027] "Example 1"

[0028] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[0029] "Example 2"

[0030] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his or her own worries, dissatisfaction, and anxiety through this interface. The terminal device

[0031] to the natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a concern that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides this advice to the employee. These concerns and advice are also accumulated as learning data and used for training the natural language processing engine.

[0032] The process flow of each embodiment will be described below.

[0033] "Example 1"

[0034] Step 1: The user inputs his / her worries, dissatisfaction, and anxiety using a terminal device connected to the company's network. This input is done through the interface of the terminal device.

[0035] Step 2: The terminal device receives input from the user and sends the information to the natural language processing engine.

[0036] Step 3: The natural language processing engine generates advice based on the received input information, and the generated advice is sent to the terminal device.

[0037] Step 4: The terminal device provides the advice received from the natural language processing engine to the user.

[0038] Step 5: The natural language processing engine accumulates the input information from the user and the generated advice as training data. This data is used to train the natural language processing engine.

[0039] Example 1

[0040] Next, a description will be given of Example 1 of the embodiment 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".

[0041] Conventional systems have difficulty providing appropriate advice based on user input, and are unable to accumulate the advice provided as learning data to improve the accuracy of the system. Furthermore, there is a lack of anonymization measures to protect user privacy.

[0042] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0043] In this invention, the server includes a means for accepting input from a user, a means for transmitting the input information to a natural language processing engine, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and a means for training the natural language processing engine using the learning data. This makes it possible to provide appropriate advice based on the input from the user, to store the advice and the input information as learning data, and to improve the accuracy of the system. In addition, an anonymization means for protecting the privacy of the user is included, so that a system that can be used with peace of mind can be provided.

[0044] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input his or her own worries, dissatisfaction, and anxieties.

[0045] The "means for transmitting the input information to the natural language processing engine" refers to a communication means or protocol for transferring information input by a user to the natural language processing engine.

[0046] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[0047] The "means for providing the user with advice generated by the engine" refers to a device or software for displaying or notifying the user of the advice generated by the natural language processing engine.

[0048] The "means for storing the input and the advice as learning data" refers to a device or software for saving the user's input information and the generated advice in a database or the like.

[0049] "Means for training the natural language processing engine using the training data" refers to an algorithm or process for improving the performance of the natural language processing engine using accumulated training data.

[0050] A "means for anonymizing user input" is a device or software for removing or converting personally identifiable information from the input information in order to protect the user's personal information.

[0051] The present invention is implemented using a terminal device connected to an in-house network. The terminal device has an interface that accepts input from a user. The user inputs his or her own worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine.

[0052] The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine.

[0053] A terminal device connected to an in-house network is used as the specific hardware, and a natural language processing engine is used as the specific software. The natural language processing engine is software for analyzing input information from a user and generating appropriate advice.

[0054] For example, if an employee inputs a concern that "there are not enough seats in the office," the natural language processing engine will generate advice such as "consider introducing flexible working" and provide that advice to the employee. In addition, these concerns and advice will be accumulated as learning data and used to train the natural language processing engine.

[0055] An example of a prompt sentence is "Please tell me what measures should be taken if there are not enough seats in the office." By inputting this prompt sentence, the natural language processing engine can generate appropriate advice and provide it to the user.

[0056] In this way, the system allows users to input their concerns and complaints through their terminal devices, and the server generates and provides advice based on the input. The generated advice and input information are stored as learning data, which contributes to improving the accuracy of the system.

[0057] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0058] Step 1:

[0059] Accepting user input

[0060] User: Uses a terminal device connected to the company network.

[0061] User: Enters his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[0062] Input: For example, a user types, "There aren't enough seats in the office."

[0063] Output: The input information is saved on the terminal device.

[0064] Specific action: A user inputs text using the keyboard or touch screen of a terminal device.

[0065] Step 2:

[0066] Sending input information

[0067] Terminal: Receives information entered by the user and sends it to the natural language processing engine.

[0068] Input: Text data entered by the user.

[0069] Output: The text data that is sent to the natural language processing engine.

[0070] Specific operations: The terminal device calls an API for transmitting the input text data to the server.

[0071] Step 3:

[0072] Generating Advice

[0073] Server: Analyzes the received input using a natural language processing engine.

[0074] Input: Text data sent from the terminal device.

[0075] Output: Advice generated based on the analysis results.

[0076] Specific operation: In response to the input "There aren't enough seats in the office," the natural language processing engine generates the advice "Consider introducing flexible working."

[0077] Step 4:

[0078] Providing advice

[0079] Server: Sends the generated advice to the terminal device.

[0080] Terminal: Displays the received advice to the user.

[0081] Input: Advice generated by a natural language processing engine.

[0082] Output: The advice that is displayed to the user.

[0083] Specific actions: The advice "Consider introducing flexible working" is displayed on the terminal screen.

[0084] Step 5:

[0085] Accumulating and learning data

[0086] Server: Stores user input information and generated advice as learning data.

[0087] Server: Uses the accumulated data to train the natural language processing engine.

[0088] Input: User input and generated advice.

[0089] Output: Information stored as training data.

[0090] What happens: New inputs and advice are stored in the database and the model is periodically retrained.

[0091] (Application example 1)

[0092] Next, a description will be given of 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."

[0093] In today's digital society, users face many security risks. In particular, there is growing concern about threats such as phishing emails and unauthorized access. However, there are only a limited number of systems that can quickly provide specific advice on these security risks. Therefore, there is a need for a system that allows users to easily input their security concerns and worries, and provides appropriate advice based on that input.

[0094] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for accumulating the input and the advice as learning data, a means for providing an interface for inputting worries and anxieties about security, and a means for generating security advice based on information input through the interface. This enables the user to quickly receive specific advice about security.

[0095] "Means for accepting input from a user" refers to a device or software that provides an interface for a user to input their own worries and anxieties into the system.

[0096] A "natural language processing engine that generates advice" is an engine that uses artificial intelligence technology to analyze information entered by a user and generate appropriate advice.

[0097] The "means for providing the generated advice to the user" refers to a device or software for conveying the advice generated by the natural language processing engine to the user.

[0098] The "means for storing input and advice as learning data" refers to a device or software for storing the input information from the user and the generated advice in a database or the like and using it for later learning.

[0099] "Means for providing an interface for inputting security-related worries and anxieties" refers to a device or software that provides a dedicated interface for a user to input specific security-related worries and anxieties.

[0100] The "means for generating security advice" refers to a device or software for generating appropriate security measures and advice based on security information input by a user.

[0101] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the generated advice to the user, means for storing the input and advice as learning data, means for providing an interface for inputting security-related concerns and worries, and means for generating security advice based on information input through the interface.

[0102] System Programming

[0103] This system is realized by the following program. First, the user inputs their security-related worries and concerns using a smartphone. The input information is sent to a server through a web application using Flask. The server uses a natural language processing engine such as GPT-3 (registered trademark) from OpenAI (registered trademark) to generate appropriate advice based on the input information. The generated advice is returned to the user again through the Flask application.

[0104] Hardware and software used

[0105] Hardware: Smartphone

[0106] Software: Flask (Python web framework), OpenAI API

[0107] Data processing and calculation

[0108] 1. Accepting user input: The user inputs security-related worries or concerns using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried."

[0109] 2. Sending to the natural language processing engine: The Flask application accepts input from the user and sends it to OpenAI's GPT-3.

[0110] 3. Advice Generation: GPT-3 generates advice based on the input, for example, "Learn the characteristics of phishing emails and avoid opening suspicious emails."

[0111] 4. Return the advice: The generated advice is returned to the user via the Flask application.

[0112] Examples

[0113] If a user enters, "I'm worried because I've been receiving a lot of phishing emails lately," the system will generate advice like the following:

[0114] Examples of prompt statements

[0115] User's concern: I'm worried because there have been a lot of phishing emails lately.

[0116] advice:

[0117] By sending this prompt to GPT-3, appropriate advice is generated. The advice is returned to the user, allowing the user to quickly obtain specific security measures.

[0118] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0119] Step 1:

[0120] A user inputs security-related worries and anxieties using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried." This input is sent to the server through the Flask application interface. The input data is in text format, and specifically describes the user's worries and anxieties.

[0121] Step 2:

[0122] The server sends the user's input received through the Flask application to OpenAI's GPT-3. Specifically, it formats the user's input as a prompt sentence and sends a request to the GPT-3 API. An example of a prompt sentence is "User's concern: I've been getting a lot of phishing emails lately, so I'm worried\nAdvice:". Based on this prompt sentence, GPT-3 generates appropriate advice.

[0123] Step 3:

[0124] The server receives the advice returned by GPT-3. GPT-3 performs natural language processing based on the user's input and generates appropriate advice. For example, advice such as "Learn the characteristics of phishing emails and avoid opening suspicious emails" is generated. This advice is returned to the server in text format.

[0125] Step 4:

[0126] The server returns the received advice to the user through the Flask application. Specifically, it generates a response to display the generated advice on the user's smartphone. This response includes the generated advice, and the user can check the advice on the screen of their smartphone.

[0127] Step 5:

[0128] The server accumulates the user's input and the generated advice as training data. Specifically, it stores the input data and the advice data in a database. This data is later used for training the natural language processing engine. The database stores the user's input and the corresponding advice as a pair.

[0129] Step 6:

[0130] The server uses the accumulated learning data to train the natural language processing engine. Specifically, it periodically extracts learning data from the database and feeds it back to the natural language processing engine. This feedback improves the engine's accuracy, enabling it to generate more appropriate advice.

[0131] Example 2

[0132] Next, a second embodiment of the second embodiment will be described. 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."

[0133] Conventional systems have difficulty in providing prompt and appropriate advice to users on their concerns and complaints, and are unable to effectively accumulate that data and use it for learning. For this reason, a system that can improve user satisfaction is needed.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0135] In this invention, the server includes a means for accepting input information from a user, a means for transmitting the input information to the server, a natural language processing engine for generating advice based on the input, a means for transmitting the advice generated by the engine from the server to a terminal, a means for providing the advice received by the terminal to the user, and a means for storing the input and the advice as learning data, thereby making it possible to quickly provide appropriate advice for the user's worries and dissatisfaction, and to effectively store the data and use it for learning.

[0136] A "user" is an individual or group that uses the system to input their own worries, complaints, and anxieties.

[0137] "Input information" refers to text data or other types of information that a user provides to the system through a terminal device.

[0138] A "server" is a computer system that receives input information from a user and passes it to a natural language processing engine.

[0139] A "terminal" is a device that a user uses to provide input information and receive advice from a server.

[0140] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[0141] "Advice" is a suggestion or solution that the natural language processing engine generates based on user input.

[0142] "Training Data" means a dataset containing user input and generated advice used to improve the performance of a natural language processing engine.

[0143] "Anonymization" is the process of removing personally identifiable information from input information in order to protect the user's personal information.

[0144] The present invention is a system that provides appropriate advice to users regarding their worries and dissatisfaction, accumulates the data, and uses it for learning. A specific embodiment of this system will be described below.

[0145] Hardware and Software Used

[0146] Terminal device: A computer or tablet connected to the company network

[0147] Server: A computer system that receives input information from the user and passes it to the natural language processing engine.

[0148] Natural language processing engine: For example, a generative AI model (such as GPT-4 (registered trademark))

[0149] Data processing and calculation flow

[0150] 1. Accepting user input

[0151] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[0152] Example: A user uses a keyboard or touch screen on a terminal device to type, "There are not enough seats in the office."

[0153] 2. Sending input information

[0154] The terminal transmits the information entered by the user to the server.

[0155] Specific operation: The terminal sends the entered text data to the server as an HTTP request.

[0156] 3. Generating Advice

[0157] The server passes the received input information to a natural language processing engine to generate advice.

[0158] Specific operation: The text data received by the server is input into a generative AI model (e.g., GPT-4) and advice such as "Consider introducing flexible working" is generated.

[0159] 4. Submitting Advice

[0160] The server transmits the generated advice to the terminal.

[0161] Specific operation: The server sends the generated advice to the terminal as an HTTP response.

[0162] 5. Providing advice

[0163] The terminal provides the received advice to the user.

[0164] Specific operation: The device displays the received advice on the screen and the user confirms it.

[0165] 6. Data accumulation and learning

[0166] The server accumulates the user's input information and the generated advice as learning data.

[0167] What it does: The server stores the input information and advice in a database and later uses it to train the natural language processing engine.

[0168] Examples

[0169] User input: "There aren't enough seats in the office."

[0170] Generated advice: "Consider implementing flexible working."

[0171] Examples of prompt statements

[0172] Prompt: "What advice should you offer to employees if they feel there aren't enough seats in their office?"

[0173] The system provides users with advice to quickly resolve their concerns and complaints, and at the same time accumulates that data to help train the natural language processing engine.

[0174] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] Accepting user input

[0177] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. Specifically, the user uses the keyboard or touch screen of the terminal device to input "There are not enough seats in the office." The input information is stored in the terminal as text data.

[0178] Step 2:

[0179] Sending input information

[0180] The terminal transmits the information input by the user to the server. In concrete terms, the terminal transmits the input text data to the server as an HTTP request. The input is text data, and the output is the transmission of a request to the server.

[0181] Step 3:

[0182] Generating Advice

[0183] The server passes the received input information to a natural language processing engine and generates advice. Specifically, the server inputs the received text data into a generative AI model (e.g., GPT-4) and generates advice such as "Consider introducing flexible work." The input is text data, and the output is the generated advice.

[0184] Step 4:

[0185] Sending Advice

[0186] The server sends the generated advice to the terminal. In concrete terms, the server sends the generated advice to the terminal as an HTTP response. The input is the generated advice, and the output is the response sent to the terminal.

[0187] Step 5:

[0188] Providing advice

[0189] The terminal provides the received advice to the user. In concrete terms, the terminal displays the advice received on the screen and the user confirms it. The input is the advice received from the server, and the output is the display to the user.

[0190] Step 6:

[0191] Accumulating and learning data

[0192] The server accumulates the user's input information and the generated advice as learning data. Specifically, the server saves the input information and advice in a database and later uses them to train the natural language processing engine. The input is the user's input information and the generated advice, and the output is saved in the database.

[0193] (Application example 2)

[0194] Next, a description will be given of 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 will be referred to as a "terminal."

[0195] In today's information society, individuals and companies face a wide range of security risks. In particular, cyber attacks such as phishing emails and unauthorized access have been increasing, and appropriate measures against these attacks are required. However, many users do not have specialized knowledge about security, making it difficult for them to take appropriate measures. For this reason, a system is needed that allows users to easily input their security concerns and receive appropriate advice.

[0196] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0197] In this invention, the server includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the advice generated by the engine to the user, means for storing the input and the advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for storing the input information and the generated security measures as learning data. This enables a user to easily input security concerns and receive appropriate advice.

[0198] The "means for accepting input from the user" is an interface through which the user provides information to the system.

[0199] The "natural language processing engine for generating advice" is software or hardware for analyzing input information from a user and generating appropriate advice.

[0200] The "means for providing the generated advice to the user" is an interface for conveying the advice generated by the natural language processing engine to the user.

[0201] The "means for storing input and advice as learning data" refers to a system for storing input information from a user and generated advice in a database or the like, and using the information for future learning.

[0202] The "means for inputting user's security concerns" is an interface that allows the user to input security issues and fears into the system.

[0203] The "means for analyzing input information and generating appropriate security measures" refers to software or hardware for analyzing security-related input information from a user and proposing appropriate measures.

[0204] The "means for providing the generated security measures to the user" refers to an interface for communicating the security measures generated by the analysis to the user.

[0205] The "means for storing input information and generated security measures as learning data" refers to a system for storing input information from a user and generated security measures in a database or the like, and using them for future learning.

[0206] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice, means for providing the generated advice to the user, means for accumulating the input and advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for accumulating the input information and the generated security measures as learning data.

[0207] Program processing explanation

[0208] Hardware used

[0209] Smartphone

[0210] Software used

[0211] Python

[0212] OpenAI API

[0213] SQLite (database)

[0214] Processing flow

[0215] 1. User Input

[0216] The user starts the smartphone application and inputs a security concern. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me."

[0217] 2. Sending input information

[0218] The input information is sent from the application to the natural language processing engine via OpenAI's API.

[0219] 3. Generating Advice

[0220] The natural language processing engine analyzes the input information and generates appropriate advice, such as "Points to identify phishing emails, and we recommend that you avoid clicking on links in emails."

[0221] 4. Providing advice

[0222] The generated advice is provided to the user through the application.

[0223] 5. Data accumulation

[0224] The input information and generated advice are stored in a SQLite database and used as learning data for future use.

[0225] Examples

[0226] If a user types, "I've been receiving a lot of phishing emails lately," the application will generate advice for the user, such as, "Points to identify phishing emails and how we recommend that you avoid clicking on links in emails," and provide it to the user.

[0227] Examples of prompt statements

[0228] User security concerns: I've been getting a lot of phishing emails lately.

[0229] Please provide appropriate advice.

[0230] In this way, a system is realized that allows users to easily input security concerns and receive appropriate advice.

[0231] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0232] Step 1:

[0233] A user starts a smartphone application and inputs a security concern. The input information is sent to the application in text format. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me." The input data is text data that specifically describes the user's concern.

[0234] Step 2:

[0235] The device sends the input information to the natural language processing engine through OpenAI's API. The input data is sent as an API request. Specifically, the user's input text is sent to the API endpoint to request analysis. The input data is text data that includes the user's concerns, and the output data is the analysis result by the natural language processing engine.

[0236] Step 3:

[0237] The server analyzes the input information using a natural language processing engine and generates appropriate advice, such as "We recommend that you learn how to identify phishing emails and not click on links in emails." The input data is text data that includes the user's concerns, and the output data is text data of the generated advice.

[0238] Step 4:

[0239] The terminal provides the generated advice to the user. Specifically, the generated advice is displayed through an interface of the application. The input data is text data of the generated advice, and the output data is the text data of the advice displayed to the user.

[0240] Step 5:

[0241] The server stores the input information and the generated advice in a SQLite database. Specifically, it executes SQL queries to insert the user's input text and the generated advice into the database. The input data are the text data of the user's concerns and the generated advice, and the output data are the records stored in the database.

[0242] Step 6:

[0243] The server uses the accumulated data to train the natural language processing engine. Specifically, it analyzes the stored data and uses it as training data to improve the accuracy of the model. The input data is the text data of past input information and advice stored in the database, and the output data is the updated natural language processing model.

[0244] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0245] "Example 1"

[0246] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[0247] "Example 2"

[0248] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[0249] The process flow of each embodiment will be described below.

[0250] "Example 1"

[0251] Step 1: A user inputs information through a terminal device. This input is text data including the user's concerns and problems.

[0252] Step 2: The entered text data is sent to the natural language processing engine via the internal network.

[0253] Step 3: The natural language processing engine analyzes the submitted text data and generates advice for the user's problem or concern.

[0254] Step 4: At the same time, the emotion engine recognizes the user's emotion from the transmitted text data.

[0255] Step 5: The emotion engine adjusts the advice generated by the natural language processing engine based on the recognized emotion.

[0256] Step 6: The tailored advice is sent to the terminal device through the internal network and presented to the user.

[0257] Step 7: Finally, the input from the user, the advice given to the user, and the recognized emotions are stored as learning data. This allows the system to learn the changes in the user's emotions and provide more appropriate advice.

[0258] Example 1

[0259] Next, a description will be given of Example 1 of the embodiment 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".

[0260] In conventional systems, it can be difficult to provide appropriate advice in response to user input. In addition, since the system cannot provide advice that takes into account the user's feelings, the user's satisfaction may decrease. Furthermore, there is a need to effectively accumulate input information and advice as learning data to improve the accuracy of the system.

[0261] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0262] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing emotions contained in the user's input and adjusting the advice based on the emotions, thereby making it possible to provide appropriate advice that takes the user's emotions into consideration and improve the accuracy of the system.

[0263] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input information such as their worries, dissatisfaction, and anxiety.

[0264] A "natural language processing engine" is software that analyzes information input by a user and generates appropriate advice based on that information.

[0265] The "means for providing advice to a user" refers to a device or software for displaying or notifying a user of advice generated by a natural language processing engine.

[0266] The "means for storing as learning data" refers to a device or software that stores the input information from the user and the generated advice in a database or the like for use in subsequent learning processes.

[0267] An "emotion engine" is software that analyzes emotions contained in a user's input and tailors the advice it generates based on those emotions.

[0268] An "anonymization means" is a device or software that converts information input by a user into a form that cannot identify an individual.

[0269] A "learning means" is a device or software that uses accumulated learning data to improve the performance of a natural language processing engine or an emotion engine.

[0270] The present invention is a system for accepting input from a user and providing appropriate advice. The system includes a terminal device for accepting input from a user, a natural language processing engine for analyzing the input information and generating advice, an emotion engine for analyzing the user's emotions, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[0271] Hardware and Software Used

[0272] Hardware: Terminal devices connected to the company network

[0273] Software: Natural language processing engine, emotion engine

[0274] Data processing and calculation flow

[0275] 1. Accepting user input

[0276] Terminal: The user inputs his / her worries, dissatisfaction, and anxiety through the terminal device interface. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[0277] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[0278] 2. Sending input information

[0279] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[0280] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[0281] 3. Generating Advice

[0282] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[0283] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[0284] 4. Providing advice

[0285] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[0286] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[0287] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[0288] 5. Accumulation of learning data

[0289] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[0290] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[0291] Examples

[0292] Sample prompt 1: "There aren't enough seats in the office."

[0293] Expected advice: "Consider implementing flexible working."

[0294] Specific operation: The user inputs "There are not enough seats in the office" and the terminal device sends the information to the server. The server calls the natural language processing engine, generates advice, and returns it to the terminal device. The terminal device displays the advice to the user.

[0295] Sample prompt 2: "My job isn't going well."

[0296] Expected advice: "I know this is tough. I think it might be time to take a breather and reevaluate how you're doing your job."

[0297] Specific operation: The user inputs "My work is not going well," and the terminal device sends the information to the server. The server calls the emotion engine to perform emotion analysis, and the natural language processing engine generates advice based on the results. The generated advice is returned to the terminal device and displayed to the user.

[0298] The system aims to reduce users' worries and anxieties by processing user input in real time and providing appropriate advice.

[0299] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0300] Step 1:

[0301] Accepting user input

[0302] User: The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[0303] Input: Text data entered by the user.

[0304] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[0305] Step 2:

[0306] Sending input information

[0307] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[0308] Input: Text data entered by the user.

[0309] Output: Text data sent to the server

[0310] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[0311] Step 3:

[0312] Generating Advice

[0313] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[0314] Input: Text data sent to the server

[0315] Output: Generated advice

[0316] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[0317] Step 4:

[0318] Providing advice

[0319] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[0320] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[0321] Input: Generated advice

[0322] Output: Advice displayed to the user

[0323] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[0324] Step 5:

[0325] Accumulation of learning data

[0326] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[0327] Input: User input and generated advice

[0328] Output: Training data stored in a database

[0329] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[0330] (Application example 1)

[0331] Next, a description will be given of 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."

[0332] In modern society, users often have concerns or questions about security, but there is a lack of means to quickly provide appropriate advice on these issues. In addition, it is difficult to provide advice that takes into account the user's feelings, which makes it difficult to fully alleviate the user's concerns.

[0333] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0334] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing the user's emotions and adjusting the advice based on the emotions, thereby making it possible to quickly provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions.

[0335] A "means for accepting input from a user" is an interface through which a user can input security concerns or questions in the form of text, voice, or the like.

[0336] A "natural language processing engine" is a software engine that analyzes input from a user and generates appropriate advice.

[0337] The "means for providing advice to a user" refers to a means for displaying or notifying the user of the generated advice.

[0338] The "means for storing as learning data" refers to a means for storing the user's input and the generated advice in a database or the like and using it for future learning.

[0339] An "emotion engine" is a software engine for recognizing emotions in a user's input and tailoring advice based on those emotions.

[0340] To implement this invention, a terminal device for users to input security concerns and questions, and a server for processing the input information are required. The terminal device can be a device such as a smartphone or smart glasses.

[0341] The server includes the following means:

[0342] 1. A way to accept input from the user:

[0343] The user inputs security concerns or questions in text or voice format through the terminal device, which is then transmitted to the server via the terminal device interface.

[0344] 2. Natural Language Processing Engine:

[0345] The server uses a natural language processing engine to analyze the input from the user and generate appropriate advice. The engine is implemented using the OpenAI API. It analyzes the input text and generates relevant advice.

[0346] 3. Means of providing advice to users:

[0347] The server transmits the generated advice to the terminal device and displays or notifies the user, thereby enabling the user to receive the advice in real time.

[0348] 4. Methods for accumulating training data:

[0349] The server stores the user's input and the generated advice in a database for future learning, helping to improve the accuracy of the natural language processing engine.

[0350] 5. Emotion Engine:

[0351] The server uses an emotion engine to recognize emotions in the user's input and adjust the advice based on the emotion. The engine is implemented using emotion analysis tools such as TextBlob. The emotion engine analyzes the emotion of the user's input text and determines whether it is positive or negative.

[0352] As a concrete example, if a user enters, "I've been seeing a lot of suspicious people around my house lately and it makes me feel anxious," the emotion engine will recognize the anxiety and generate advice like the following:

[0353] Example prompt:

[0354] User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy.

[0355] Our advice: Don't worry - first check with your local police and consider installing security cameras.

[0356] In this way, advice that is in tune with the user's emotions can be provided.

[0357] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0358] Step 1:

[0359] A user inputs security concerns or questions using a terminal device. The input is in the form of text or voice and is transmitted to a server via an interface of the terminal device. The input data is text data including the user's concerns or questions.

[0360] Step 2:

[0361] The server sends the received user input data to the natural language processing engine. The natural language processing engine uses OpenAI's API to analyze the input data and generate appropriate advice. Based on the input data, data processing and data calculations are performed to generate relevant advice. The output is text data of the generated advice.

[0362] Step 3:

[0363] The server sends the generated advice to the emotion engine, which uses emotion analysis tools such as TextBlob to analyze the emotion of the user's input text. The emotion engine determines the emotion of the input text as positive or negative, and adjusts the advice based on the result. The output is the text data of the adjusted advice.

[0364] Step 4:

[0365] The server transmits the adjusted advice to the terminal device. The terminal device displays or notifies the received advice to the user. This allows the user to receive the advice in real time. The output is text data of the advice that is displayed to the user.

[0366] Step 5:

[0367] The server stores the user's input data and the generated advice in a database. The stored data is used for future learning, which improves the accuracy of the natural language processing engine and the emotion engine. The output is the learning data stored in the database.

[0368] In this way, it is possible to quickly provide appropriate advice that takes into account the user's emotions in response to their security concerns or questions.

[0369] Example 2

[0370] Next, a second embodiment of the second embodiment will be described. 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."

[0371] In conventional systems, it was difficult to provide appropriate advice based on user input, and it was particularly difficult to generate advice that took the user's emotions into account. Another challenge was to effectively accumulate the user's input information and the generated advice as learning data to improve the accuracy of the system.

[0372] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0373] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing emotions contained in the input and adjusting the advice. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, and furthermore, by storing the input information and advice as learning data, it becomes possible to improve the accuracy of the system.

[0374] The "means for accepting input from a user" is a device that provides an interface for a user to input their own worries, dissatisfaction, and anxieties in text form.

[0375] A "natural language processing engine" is an engine that analyzes information input by users and generates appropriate advice, and uses a generative AI model.

[0376] The "means for providing advice to a user" is a device for displaying advice generated by the natural language processing engine to a user.

[0377] The "means for storing as learning data" is a device for storing the user's input information and the generated advice in a database and using it for future learning.

[0378] An "emotion engine" is an engine that analyzes emotions contained in input from a user and adjusts advice based on those emotions.

[0379] An "anonymization means" is a device for converting information input by a user into a form that does not identify an individual.

[0380] The "learning means" is a device for improving the performance of the natural language processing engine using accumulated training data.

[0381] This invention is a system for providing appropriate advice for a user's worries, dissatisfaction, and anxiety. The system includes a terminal device that accepts input from a user, a natural language processing engine that analyzes the input information and generates advice, an emotion engine that analyzes emotions and adjusts the advice, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[0382] Hardware and Software Used

[0383] Terminal equipment

[0384] The terminal device provides an interface for receiving input from the user. The user inputs his / her worries, dissatisfaction, and anxiety in text format through this interface. For example, an employee inputs a worry that "there are not enough seats in the office."

[0385] server

[0386] The server receives input information sent from the terminal device and passes it to the natural language processing engine and the emotion engine. The server also transmits generated advice to the terminal device and accumulates the input information and advice as learning data.

[0387] Natural Language Processing Engine

[0388] The natural language processing engine uses a generative AI model (e.g., GPT-4) to analyze input information from the user and generate appropriate advice, such as "consider introducing flexible working."

[0389] Emotion Engine

[0390] The emotion engine analyzes the emotions contained in the user's input and adjusts advice based on those emotions. For example, if a user types, "I'm not doing well at work," the emotion engine will recognize the frustration and adjust the advice to, "I understand that this is tough. Why don't you take a breather and reconsider how you're going about your work."

[0391] Accumulation of learning data

[0392] The server stores the user's input information and the generated advice in a database and uses it for future learning, which can improve the accuracy of the natural language processing engine and the emotion engine.

[0393] Examples of concrete examples and prompts

[0394] Examples

[0395] A user inputs a concern such as "There aren't enough seats in the office" into a terminal device. The terminal device sends this information to a server, which passes it to a natural language processing engine. The natural language processing engine generates advice such as "Consider introducing flexible working." The emotion engine adjusts this advice, and the server sends it to the terminal device. The terminal device displays the advice to the user, and the server accumulates the input information and advice as learning data.

[0396] Examples of prompt statements

[0397] "If a user types in 'I don't have enough seats in my office,' what advice do you generate and how do you adjust it to take sentiment into account?"

[0398] The system aims to provide users with appropriate advice in response to their worries, complaints, and anxieties, and to facilitate smooth communication within the company.

[0399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] The user inputs his / her worries, dissatisfaction, and anxiety into the terminal device.

[0402] Input: User's concerns, complaints, and worries (e.g., "There aren't enough seats in the office.")

[0403] Output: Text data entered into the terminal device

[0404] Specific operation: The user inputs his / her worries, dissatisfaction, and anxiety in text format using the interface of the terminal device. The terminal device accepts this input and stores it as text data.

[0405] Step 2:

[0406] The terminal device transmits the input information to the server.

[0407] Input: Text data entered into a terminal device

[0408] Output: Text data sent to the server

[0409] Specific operation: The terminal device transmits the text data entered by the user to the server over the network, where the data is appropriately formatted.

[0410] Step 3:

[0411] The server passes the input information to a natural language processing engine.

[0412] Input: Text data sent to the server

[0413] Output: Text data passed to the natural language processing engine

[0414] How it works: The server passes the received text data to a natural language processing engine, which uses a generative AI model (e.g. GPT-4).

[0415] Step 4:

[0416] A natural language processing engine analyzes the input information and generates advice.

[0417] Input: Text data passed to the natural language processing engine

[0418] Output: Generated advice (e.g. "Consider implementing flexible working")

[0419] How it works: The natural language processing engine analyzes the input text data and generates appropriate advice. It uses generative AI models to suggest solutions to the user's concerns.

[0420] Step 5:

[0421] The emotion engine analyzes the sentiment of the input information and tailors advice.

[0422] Input: Advice generated by the natural language processing engine, user input information

[0423] Output: Tailored advice (e.g., "I understand how hard this is for you. Maybe you should take a break and consider implementing flexible working.")

[0424] How it works: The emotion engine analyzes the emotions contained in the user's input. For example, if the user types "I'm not doing well at work," the emotion engine will recognize the frustration and adjust its advice.

[0425] Step 6:

[0426] The server transmits the generated advice to the terminal device.

[0427] Input: Tailored advice

[0428] Output: Advice sent to the terminal

[0429] Specific operation: The server transmits the advice adjusted by the emotion engine to the terminal device via the network.

[0430] Step 7:

[0431] The terminal device displays the advice to the user.

[0432] Input: Advice sent to terminal

[0433] Output: The advice displayed to the user

[0434] Specific operation: The terminal device displays the received advice to the user, who can check the advice on the screen of the terminal device.

[0435] Step 8:

[0436] The server accumulates the input information and the generated advice as learning data.

[0437] Input: User input information, generated advice

[0438] Output: Accumulated training data

[0439] How it works: The server stores the user's input information and generated advice in a database. This data is used to train the natural language processing engine and the emotion engine.

[0440] (Application example 2)

[0441] Next, a description will be given of 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 will be referred to as a "terminal."

[0442] Conventional security services have the problem that it is difficult to provide appropriate advice to users regarding their anxieties and questions. In addition, it is not possible to provide advice that takes into account the user's feelings, making it difficult to improve user satisfaction. Furthermore, there is a lack of a mechanism to accumulate the provided advice as learning data and improve the accuracy of the system.

[0443] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0444] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing the user's emotions and adjusting the advice based on the emotions. This makes it possible to provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions, and also improves the accuracy of the system.

[0445] A "means for accepting input from a user" is a device or software that provides an interface for a user to input security concerns or questions.

[0446] A "natural language processing engine" is a system that uses artificial intelligence technology to analyze input from users and generate appropriate advice.

[0447] The "means for providing advice to a user" refers to a device or software for displaying or notifying the generated advice to a user.

[0448] The "means for storing as learning data" refers to a device or software that stores the user's input and the generated advice in a database or the like, and uses it for later analysis or to improve the accuracy of the system.

[0449] An "emotion engine" is a system that uses artificial intelligence techniques to analyze emotions contained in user input and tailor advice based on those emotions.

[0450] As an embodiment of the present invention, a security advisor system is constructed. The system includes a means for accepting input from a user, a natural language processing engine, an emotion engine, a means for providing advice to the user, and a means for accumulating the input as learning data.

[0451] System Programming

[0452] The system is realized using the following hardware and software.

[0453] Hardware: Smartphones, smart glasses

[0454] Software: Python, OpenAI API

[0455] Processing Description

[0456] A means of accepting input from the user

[0457] Users input their security concerns or questions via their smartphone or smart glasses, and the data is sent to the server through the system's interface.

[0458] Natural Language Processing Engine

[0459] The server uses the OpenAI API to analyze the user's input and generate appropriate advice that is specific and actionable based on the user's input.

[0460] Emotion Engine

[0461] The server uses an emotion engine to analyze the emotions contained in the user's input. Based on the analysis results, the server adjusts the generated advice to take the user's emotions into consideration. This allows the user to receive advice that resonates with them more.

[0462] A means of providing advice to users

[0463] The tailored advice is then provided to the user via their smartphone or smart glasses, where the user can review the advice and take any necessary measures.

[0464] A means of accumulating learning data

[0465] The server stores the user's input and the generated advice in a database. These data are used for later analysis and to improve the accuracy of the system. The information accumulated as training data is used to train the natural language processing engine and the emotion engine, improving the performance of the system.

[0466] Examples

[0467] User input: "I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy."

[0468] Generated advice: "If you see anyone acting suspiciously, we encourage you to contact the police immediately. Also, consider installing security cameras."

[0469] Tailored advice: "We understand that you are feeling anxious. If you see anyone acting suspiciously, we encourage you to contact police immediately. Also, consider installing security cameras."

[0470] Examples of prompt statements

[0471] For natural language processing engines: "User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy. Please provide some appropriate security advice."

[0472] For the emotion engine: "User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy. Analyze the emotion and tailor your advice to show empathy. Advice: If you see anyone suspicious, I recommend contacting the police immediately. Also, consider installing security cameras."

[0473] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0474] Step 1:

[0475] The user inputs their security concerns and questions using a smartphone or smart glasses. The input data is sent to the server through the device's interface. The input data is in text format, and specifically describes the user's concerns and questions.

[0476] Step 2:

[0477] The server sends the received user input data to the natural language processing engine, which uses the OpenAI API to analyze the input data and generate appropriate advice. Based on the input data, specific security advice is output in text format.

[0478] Step 3:

[0479] The server sends the generated advice to the emotion engine. The emotion engine analyzes the emotion contained in the user's input data and adjusts the advice in a manner that shows empathy. The emotion engine reanalyzes the input data to analyze the user's emotion and generates advice that takes the emotion into consideration. The adjusted advice is output in text format.

[0480] Step 4:

[0481] The server sends the adjusted advice to the terminal, and the terminal displays the adjusted advice to the user, so that the user can check the displayed advice and take necessary measures.

[0482] Step 5:

[0483] The server saves the user's input data and the generated advice in a database. The saved data is accumulated as learning data and used for later analysis and to improve the accuracy of the system. The user's input data and the generated advice are saved in pairs in the database.

[0484] Step 6:

[0485] The server uses the accumulated learning data to train the natural language processing engine and emotion engine. By analyzing the learning data and discovering new patterns and trends, the accuracy of the engines is improved, which makes it possible to provide more appropriate advice in the future.

[0486] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0487] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0488] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0489] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0490] [Second embodiment]

[0491] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0492] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0493] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0494] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0495] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0496] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0497] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0498] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0499] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0500] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0501] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0502] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0503] "Example 1"

[0504] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[0505] "Example 2"

[0506] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[0507] The process flow of each embodiment will be described below.

[0508] "Example 1"

[0509] Step 1: The user inputs his / her worries, dissatisfaction, and anxiety using a terminal device connected to the company's network. This input is done through the interface of the terminal device.

[0510] Step 2: The terminal device receives input from the user and sends the information to the natural language processing engine.

[0511] Step 3: The natural language processing engine generates advice based on the received input information, and the generated advice is sent to the terminal device.

[0512] Step 4: The terminal device provides the advice received from the natural language processing engine to the user.

[0513] Step 5: The natural language processing engine accumulates the input information from the user and the generated advice as training data. This data is used to train the natural language processing engine.

[0514] Example 1

[0515] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal".

[0516] Conventional systems have difficulty providing appropriate advice based on user input, and are unable to accumulate the advice provided as learning data to improve the accuracy of the system. Furthermore, there is a lack of anonymization measures to protect user privacy.

[0517] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0518] In this invention, the server includes a means for accepting input from a user, a means for transmitting the input information to a natural language processing engine, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and a means for training the natural language processing engine using the learning data. This makes it possible to provide appropriate advice based on the input from the user, to store the advice and the input information as learning data, and to improve the accuracy of the system. In addition, an anonymization means for protecting the privacy of the user is included, so that a system that can be used with peace of mind can be provided.

[0519] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input his or her own worries, dissatisfaction, and anxieties.

[0520] The "means for transmitting the input information to the natural language processing engine" refers to a communication means or protocol for transferring information input by a user to the natural language processing engine.

[0521] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[0522] The "means for providing the user with advice generated by the engine" refers to a device or software for displaying or notifying the user of the advice generated by the natural language processing engine.

[0523] The "means for storing the input and the advice as learning data" refers to a device or software for saving the user's input information and the generated advice in a database or the like.

[0524] "Means for training the natural language processing engine using the training data" refers to an algorithm or process for improving the performance of the natural language processing engine using accumulated training data.

[0525] A "means for anonymizing user input" is a device or software for removing or converting personally identifiable information from the input information in order to protect the user's personal information.

[0526] The present invention is implemented using a terminal device connected to an in-house network. The terminal device has an interface that accepts input from a user. The user inputs his or her own worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine.

[0527] The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine.

[0528] A terminal device connected to an in-house network is used as the specific hardware, and a natural language processing engine is used as the specific software. The natural language processing engine is software for analyzing input information from a user and generating appropriate advice.

[0529] For example, if an employee inputs a concern that "there are not enough seats in the office," the natural language processing engine will generate advice such as "consider introducing flexible working" and provide that advice to the employee. In addition, these concerns and advice will be accumulated as learning data and used to train the natural language processing engine.

[0530] An example of a prompt sentence is "Please tell me what measures should be taken if there are not enough seats in the office." By inputting this prompt sentence, the natural language processing engine can generate appropriate advice and provide it to the user.

[0531] In this way, the system allows users to input their concerns and complaints through their terminal devices, and the server generates and provides advice based on the input. The generated advice and input information are stored as learning data, which contributes to improving the accuracy of the system.

[0532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0533] Step 1:

[0534] Accepting user input

[0535] User: Uses a terminal device connected to the company network.

[0536] User: Enters his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[0537] Input: For example, a user types, "There aren't enough seats in the office."

[0538] Output: The input information is saved on the terminal device.

[0539] Specific action: A user inputs text using the keyboard or touch screen of a terminal device.

[0540] Step 2:

[0541] Sending input information

[0542] Terminal: Receives information entered by the user and sends it to the natural language processing engine.

[0543] Input: Text data entered by the user.

[0544] Output: The text data that is sent to the natural language processing engine.

[0545] Specific operation: The terminal device calls an API for transmitting the input text data to the server.

[0546] Step 3:

[0547] Generating Advice

[0548] Server: Analyzes the received input using a natural language processing engine.

[0549] Input: Text data sent from the terminal device.

[0550] Output: Advice generated based on the analysis results.

[0551] Specific operation: In response to the input "There aren't enough seats in the office," the natural language processing engine generates the advice "Consider introducing flexible working."

[0552] Step 4:

[0553] Providing advice

[0554] Server: Sends the generated advice to the terminal device.

[0555] Terminal: Displays the received advice to the user.

[0556] Input: Advice generated by a natural language processing engine.

[0557] Output: The advice that is displayed to the user.

[0558] Specific actions: The advice "Consider introducing flexible working" is displayed on the terminal screen.

[0559] Step 5:

[0560] Accumulating and learning data

[0561] Server: Stores user input information and generated advice as learning data.

[0562] Server: Uses the accumulated data to train the natural language processing engine.

[0563] Input: User input and generated advice.

[0564] Output: Information stored as training data.

[0565] What happens: New inputs and advice are stored in the database and the model is periodically retrained.

[0566] (Application example 1)

[0567] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal".

[0568] In today's digital society, users face many security risks. In particular, there is growing concern about threats such as phishing emails and unauthorized access. However, there are only a limited number of systems that can quickly provide specific advice on these security risks. Therefore, there is a need for a system that allows users to easily input their security concerns and worries, and provides appropriate advice based on that input.

[0569] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for accumulating the input and the advice as learning data, a means for providing an interface for inputting worries and anxieties about security, and a means for generating security advice based on information input through the interface. This enables the user to quickly receive specific advice about security.

[0570] "Means for accepting input from a user" refers to a device or software that provides an interface for a user to input their own worries and anxieties into the system.

[0571] A "natural language processing engine that generates advice" is an engine that uses artificial intelligence technology to analyze information entered by a user and generate appropriate advice.

[0572] The "means for providing the generated advice to the user" refers to a device or software for conveying the advice generated by the natural language processing engine to the user.

[0573] The "means for storing input and advice as learning data" refers to a device or software for storing the input information from the user and the generated advice in a database or the like and using it for later learning.

[0574] "Means for providing an interface for inputting security-related worries and anxieties" refers to a device or software that provides a dedicated interface for a user to input specific security-related worries and anxieties.

[0575] The "means for generating security advice" refers to a device or software for generating appropriate security measures and advice based on security information input by a user.

[0576] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the generated advice to the user, means for storing the input and advice as learning data, means for providing an interface for inputting security-related concerns and worries, and means for generating security advice based on information input through the interface.

[0577] System Programming

[0578] This system is realized by the following program. First, the user inputs their security-related worries and concerns using their smartphone. The input information is sent to the server through a web application using Flask. The server uses a natural language processing engine such as OpenAI's GPT-3 to generate appropriate advice based on the input information. The generated advice is returned to the user again through the Flask application.

[0579] Hardware and software used

[0580] Hardware: Smartphone

[0581] Software: Flask (Python web framework), OpenAI API

[0582] Data processing and calculation

[0583] 1. Accepting user input: The user inputs security-related worries or concerns using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried."

[0584] 2. Sending to the natural language processing engine: The Flask application accepts input from the user and sends it to OpenAI's GPT-3.

[0585] 3. Advice Generation: GPT-3 generates advice based on the input, for example, "Learn the characteristics of phishing emails and avoid opening suspicious emails."

[0586] 4. Return the advice: The generated advice is returned to the user via the Flask application.

[0587] Examples

[0588] If a user enters, "I'm worried because I've been receiving a lot of phishing emails lately," the system will generate advice like the following:

[0589] Examples of prompt statements

[0590] User's concern: I'm worried because there have been a lot of phishing emails lately.

[0591] advice:

[0592] By sending this prompt to GPT-3, appropriate advice is generated. The advice is returned to the user, allowing the user to quickly obtain specific security measures.

[0593] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0594] Step 1:

[0595] A user inputs security-related worries and anxieties using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried." This input is sent to the server through the Flask application interface. The input data is in text format, and specifically describes the user's worries and anxieties.

[0596] Step 2:

[0597] The server sends the user's input received through the Flask application to OpenAI's GPT-3. Specifically, it formats the user's input as a prompt sentence and sends a request to the GPT-3 API. An example of a prompt sentence is "User's concern: I've been getting a lot of phishing emails lately, so I'm worried\nAdvice:". Based on this prompt sentence, GPT-3 generates appropriate advice.

[0598] Step 3:

[0599] The server receives the advice returned by GPT-3. GPT-3 performs natural language processing based on the user's input and generates appropriate advice. For example, advice such as "Learn the characteristics of phishing emails and avoid opening suspicious emails" is generated. This advice is returned to the server in text format.

[0600] Step 4:

[0601] The server returns the received advice to the user through the Flask application. Specifically, it generates a response to display the generated advice on the user's smartphone. This response includes the generated advice, and the user can check the advice on the screen of their smartphone.

[0602] Step 5:

[0603] The server accumulates the user's input and the generated advice as training data. Specifically, it stores the input data and the advice data in a database. This data is later used for training the natural language processing engine. The database stores the user's input and the corresponding advice as a pair.

[0604] Step 6:

[0605] The server uses the accumulated learning data to train the natural language processing engine. Specifically, it periodically extracts learning data from the database and feeds it back to the natural language processing engine. This feedback improves the engine's accuracy, enabling it to generate more appropriate advice.

[0606] Example 2

[0607] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0608] Conventional systems have difficulty in providing prompt and appropriate advice to users on their concerns and complaints, and are unable to effectively accumulate that data and use it for learning. For this reason, a system that can improve user satisfaction is needed.

[0609] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0610] In this invention, the server includes a means for accepting input information from a user, a means for transmitting the input information to the server, a natural language processing engine for generating advice based on the input, a means for transmitting the advice generated by the engine from the server to a terminal, a means for providing the advice received by the terminal to the user, and a means for storing the input and the advice as learning data, thereby making it possible to quickly provide appropriate advice for the user's worries and dissatisfaction, and to effectively store the data and use it for learning.

[0611] A "user" is an individual or group that uses the system to input their own worries, complaints, and anxieties.

[0612] "Input information" refers to text data or other types of information that a user provides to the system through a terminal device.

[0613] A "server" is a computer system that receives input information from a user and passes it to a natural language processing engine.

[0614] A "terminal" is a device that a user uses to provide input information and receive advice from a server.

[0615] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[0616] "Advice" is a suggestion or solution that the natural language processing engine generates based on user input.

[0617] "Training Data" means a dataset containing user input and generated advice used to improve the performance of a natural language processing engine.

[0618] "Anonymization" is the process of removing personally identifiable information from input information in order to protect the user's personal information.

[0619] The present invention is a system that provides appropriate advice to users regarding their worries and dissatisfaction, accumulates the data, and uses it for learning. A specific embodiment of this system will be described below.

[0620] Hardware and Software Used

[0621] Terminal device: A computer or tablet connected to the company network

[0622] Server: A computer system that receives input information from the user and passes it to the natural language processing engine.

[0623] Natural language processing engines: For example, generative AI models (such as GPT-4)

[0624] Data processing and calculation flow

[0625] 1. Accepting user input

[0626] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[0627] Example: A user uses a keyboard or touch screen on a terminal device to type, "There are not enough seats in the office."

[0628] 2. Sending input information

[0629] The terminal transmits the information entered by the user to the server.

[0630] Specific operation: The terminal sends the entered text data to the server as an HTTP request.

[0631] 3. Generating Advice

[0632] The server passes the received input information to a natural language processing engine to generate advice.

[0633] Specific operation: The text data received by the server is input into a generative AI model (e.g., GPT-4) and advice such as "Consider introducing flexible working" is generated.

[0634] 4. Submitting Advice

[0635] The server transmits the generated advice to the terminal.

[0636] Specific operation: The server sends the generated advice to the terminal as an HTTP response.

[0637] 5. Providing advice

[0638] The terminal provides the received advice to the user.

[0639] Specific operation: The device displays the received advice on the screen and the user confirms it.

[0640] 6. Data accumulation and learning

[0641] The server accumulates the user's input information and the generated advice as learning data.

[0642] What it does: The server stores the input information and advice in a database and later uses it to train the natural language processing engine.

[0643] Examples

[0644] User input: "There aren't enough seats in the office."

[0645] Generated advice: "Consider implementing flexible working."

[0646] Examples of prompt statements

[0647] Prompt: "What advice should you offer to employees if they feel there aren't enough seats in their office?"

[0648] The system provides users with advice to quickly resolve their concerns and complaints, and at the same time accumulates that data to help train the natural language processing engine.

[0649] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0650] Step 1:

[0651] Accepting user input

[0652] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. Specifically, the user uses the keyboard or touch screen of the terminal device to input "There are not enough seats in the office." The input information is stored in the terminal as text data.

[0653] Step 2:

[0654] Sending input information

[0655] The terminal transmits the information input by the user to the server. In concrete terms, the terminal transmits the input text data to the server as an HTTP request. The input is text data, and the output is the transmission of a request to the server.

[0656] Step 3:

[0657] Generating Advice

[0658] The server passes the received input information to a natural language processing engine and generates advice. Specifically, the server inputs the received text data into a generative AI model (e.g., GPT-4) and generates advice such as "Consider introducing flexible work." The input is text data, and the output is the generated advice.

[0659] Step 4:

[0660] Sending Advice

[0661] The server sends the generated advice to the terminal. In concrete terms, the server sends the generated advice to the terminal as an HTTP response. The input is the generated advice, and the output is the response sent to the terminal.

[0662] Step 5:

[0663] Providing advice

[0664] The terminal provides the received advice to the user. In concrete terms, the terminal displays the advice received on the screen and the user confirms it. The input is the advice received from the server, and the output is the display to the user.

[0665] Step 6:

[0666] Accumulating and learning data

[0667] The server accumulates the user's input information and the generated advice as learning data. Specifically, the server saves the input information and advice in a database and later uses them to train the natural language processing engine. The input is the user's input information and the generated advice, and the output is saved in the database.

[0668] (Application example 2)

[0669] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal".

[0670] In today's information society, individuals and companies face a wide range of security risks. In particular, cyber attacks such as phishing emails and unauthorized access have been increasing, and appropriate measures against these attacks are required. However, many users do not have specialized knowledge about security, making it difficult for them to take appropriate measures. For this reason, a system is needed that allows users to easily input their security concerns and receive appropriate advice.

[0671] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0672] In this invention, the server includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the advice generated by the engine to the user, means for storing the input and the advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for storing the input information and the generated security measures as learning data. This enables a user to easily input security concerns and receive appropriate advice.

[0673] The "means for accepting input from the user" is an interface through which the user provides information to the system.

[0674] The "natural language processing engine for generating advice" is software or hardware for analyzing input information from a user and generating appropriate advice.

[0675] The "means for providing the generated advice to the user" is an interface for conveying the advice generated by the natural language processing engine to the user.

[0676] The "means for storing input and advice as learning data" refers to a system for storing input information from a user and generated advice in a database or the like, and using the information for future learning.

[0677] The "means for inputting user's security concerns" is an interface that allows the user to input security issues and fears into the system.

[0678] The "means for analyzing input information and generating appropriate security measures" refers to software or hardware for analyzing security-related input information from a user and proposing appropriate measures.

[0679] The "means for providing the generated security measures to the user" refers to an interface for communicating the security measures generated by the analysis to the user.

[0680] The "means for storing input information and generated security measures as learning data" refers to a system for storing input information from a user and generated security measures in a database or the like, and using them for future learning.

[0681] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice, means for providing the generated advice to the user, means for accumulating the input and advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for accumulating the input information and the generated security measures as learning data.

[0682] Program processing explanation

[0683] Hardware used

[0684] Smartphone

[0685] Software used

[0686] Python

[0687] OpenAI API

[0688] SQLite (database)

[0689] Processing flow

[0690] 1. User Input

[0691] The user starts the smartphone application and inputs a security concern. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me."

[0692] 2. Sending input information

[0693] The input information is sent from the application to the natural language processing engine via OpenAI's API.

[0694] 3. Generating Advice

[0695] The natural language processing engine analyzes the input information and generates appropriate advice, such as "Points to identify phishing emails, and we recommend that you avoid clicking on links in emails."

[0696] 4. Providing advice

[0697] The generated advice is provided to the user through the application.

[0698] 5. Data accumulation

[0699] The input information and generated advice are stored in a SQLite database and used as learning data for future use.

[0700] Examples

[0701] If a user types, "I've been receiving a lot of phishing emails lately," the application will generate advice for the user, such as, "Points to identify phishing emails and how we recommend that you avoid clicking on links in emails," and provide it to the user.

[0702] Examples of prompt statements

[0703] User security concerns: I've been getting a lot of phishing emails lately.

[0704] Please provide appropriate advice.

[0705] In this way, a system is realized that allows users to easily input security concerns and receive appropriate advice.

[0706] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0707] Step 1:

[0708] A user starts a smartphone application and inputs a security concern. The input information is sent to the application in text format. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me." The input data is text data that specifically describes the user's concern.

[0709] Step 2:

[0710] The device sends the input information to the natural language processing engine through OpenAI's API. The input data is sent as an API request. Specifically, the user's input text is sent to the API endpoint to request analysis. The input data is text data that includes the user's concerns, and the output data is the analysis result by the natural language processing engine.

[0711] Step 3:

[0712] The server analyzes the input information using a natural language processing engine and generates appropriate advice, such as "We recommend that you learn how to identify phishing emails and not click on links in emails." The input data is text data that includes the user's concerns, and the output data is text data of the generated advice.

[0713] Step 4:

[0714] The terminal provides the generated advice to the user. Specifically, the generated advice is displayed through an interface of the application. The input data is text data of the generated advice, and the output data is the text data of the advice displayed to the user.

[0715] Step 5:

[0716] The server stores the input information and the generated advice in a SQLite database. Specifically, it executes SQL queries to insert the user's input text and the generated advice into the database. The input data are the text data of the user's concerns and the generated advice, and the output data are the records stored in the database.

[0717] Step 6:

[0718] The server uses the accumulated data to train the natural language processing engine. Specifically, it analyzes the stored data and uses it as training data to improve the accuracy of the model. The input data is the text data of past input information and advice stored in the database, and the output data is the updated natural language processing model.

[0719] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0720] "Example 1"

[0721] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[0722] "Example 2"

[0723] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[0724] The process flow of each embodiment will be described below.

[0725] "Example 1"

[0726] Step 1: A user inputs information through a terminal device. This input is text data including the user's concerns and problems.

[0727] Step 2: The entered text data is sent to the natural language processing engine via the internal network.

[0728] Step 3: The natural language processing engine analyzes the submitted text data and generates advice for the user's problem or concern.

[0729] Step 4: At the same time, the emotion engine recognizes the user's emotion from the transmitted text data.

[0730] Step 5: The emotion engine adjusts the advice generated by the natural language processing engine based on the recognized emotion.

[0731] Step 6: The tailored advice is sent to the terminal device through the internal network and presented to the user.

[0732] Step 7: Finally, the user input, corresponding advice, and recognized emotions are accumulated as learning data.

[0733] This enables the system to learn changes in the user's emotions and provide more appropriate advice.

[0734] Example 1

[0735] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal".

[0736] In conventional systems, it can be difficult to provide appropriate advice in response to user input. In addition, since the system cannot provide advice that takes into account the user's feelings, the user's satisfaction may decrease. Furthermore, there is a need to effectively accumulate input information and advice as learning data to improve the accuracy of the system.

[0737] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0738] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing emotions contained in the user's input and adjusting the advice based on the emotions, thereby making it possible to provide appropriate advice that takes the user's emotions into consideration and improve the accuracy of the system.

[0739] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input information such as their worries, dissatisfaction, and anxiety.

[0740] A "natural language processing engine" is software that analyzes information input by a user and generates appropriate advice based on that information.

[0741] The "means for providing advice to a user" refers to a device or software for displaying or notifying a user of advice generated by a natural language processing engine.

[0742] The "means for storing as learning data" refers to a device or software that stores the input information from the user and the generated advice in a database or the like for use in subsequent learning processes.

[0743] An "emotion engine" is software that analyzes emotions contained in a user's input and tailors the advice it generates based on those emotions.

[0744] An "anonymization means" is a device or software that converts information input by a user into a form that cannot identify an individual.

[0745] A "learning means" is a device or software that uses accumulated learning data to improve the performance of a natural language processing engine or an emotion engine.

[0746] The present invention is a system for accepting input from a user and providing appropriate advice. The system includes a terminal device for accepting input from a user, a natural language processing engine for analyzing the input information and generating advice, an emotion engine for analyzing the user's emotions, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[0747] Hardware and Software Used

[0748] Hardware: Terminal devices connected to the company network

[0749] Software: Natural language processing engine, emotion engine

[0750] Data processing and calculation flow

[0751] 1. Accepting user input

[0752] Terminal: The user inputs his / her worries, dissatisfaction, and anxiety through the terminal device interface. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[0753] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[0754] 2. Sending input information

[0755] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[0756] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[0757] 3. Generating Advice

[0758] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[0759] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[0760] 4. Providing advice

[0761] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[0762] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[0763] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[0764] 5. Accumulation of learning data

[0765] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[0766] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[0767] Examples

[0768] Sample prompt 1: "There aren't enough seats in the office."

[0769] Expected advice: "Consider implementing flexible working."

[0770] Specific operation: The user inputs "There are not enough seats in the office" and the terminal device sends the information to the server. The server calls the natural language processing engine, generates advice, and returns it to the terminal device. The terminal device displays the advice to the user.

[0771] Sample prompt 2: "My job isn't going well."

[0772] Expected advice: "I know this is tough. I think it might be time to take a breather and reevaluate how you're doing your job."

[0773] Specific operation: The user inputs "My work is not going well," and the terminal device sends the information to the server. The server calls the emotion engine to perform emotion analysis, and the natural language processing engine generates advice based on the results. The generated advice is returned to the terminal device and displayed to the user.

[0774] The system aims to reduce users' worries and anxieties by processing user input in real time and providing appropriate advice.

[0775] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0776] Step 1:

[0777] Accepting user input

[0778] User: The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[0779] Input: Text data entered by the user.

[0780] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[0781] Step 2:

[0782] Sending input information

[0783] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[0784] Input: Text data entered by the user.

[0785] Output: Text data sent to the server

[0786] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[0787] Step 3:

[0788] Generating Advice

[0789] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[0790] Input: Text data sent to the server

[0791] Output: Generated advice

[0792] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[0793] Step 4:

[0794] Providing advice

[0795] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[0796] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[0797] Input: Generated advice

[0798] Output: Advice displayed to the user

[0799] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[0800] Step 5:

[0801] Accumulation of learning data

[0802] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[0803] Input: User input and generated advice

[0804] Output: Training data stored in a database

[0805] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[0806] (Application example 1)

[0807] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal".

[0808] In modern society, users often have concerns or questions about security, but there is a lack of means to quickly provide appropriate advice on these issues. In addition, it is difficult to provide advice that takes into account the user's feelings, which makes it difficult to fully alleviate the user's concerns.

[0809] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0810] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing the user's emotions and adjusting the advice based on the emotions, thereby making it possible to quickly provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions.

[0811] A "means for accepting input from a user" is an interface through which a user can input security concerns or questions in the form of text, voice, or the like.

[0812] A "natural language processing engine" is a software engine that analyzes input from a user and generates appropriate advice.

[0813] The "means for providing advice to a user" refers to a means for displaying or notifying the user of the generated advice.

[0814] The "means for storing as learning data" refers to a means for storing the user's input and the generated advice in a database or the like and using it for future learning.

[0815] An "emotion engine" is a software engine for recognizing emotions in a user's input and tailoring advice based on those emotions.

[0816] To implement this invention, a terminal device for users to input security concerns and questions, and a server for processing the input information are required. The terminal device can be a device such as a smartphone or smart glasses.

[0817] The server includes the following means:

[0818] 1. A way to accept input from the user:

[0819] The user inputs security concerns or questions in text or voice format through the terminal device, which is then transmitted to the server via the terminal device interface.

[0820] 2. Natural Language Processing Engine:

[0821] The server uses a natural language processing engine to analyze the input from the user and generate appropriate advice. The engine is implemented using the OpenAI API. It analyzes the input text and generates relevant advice.

[0822] 3. Means of providing advice to users:

[0823] The server transmits the generated advice to the terminal device and displays or notifies the user, thereby enabling the user to receive the advice in real time.

[0824] 4. Methods for accumulating training data:

[0825] The server stores the user's input and the generated advice in a database for future learning, helping to improve the accuracy of the natural language processing engine.

[0826] 5. Emotion Engine:

[0827] The server uses an emotion engine to recognize emotions in the user's input and adjust the advice based on the emotion. The engine is implemented using emotion analysis tools such as TextBlob. The emotion engine analyzes the emotion of the user's input text and determines whether it is positive or negative.

[0828] As a concrete example, if a user enters, "I've been seeing a lot of suspicious people around my house lately and it makes me feel anxious," the emotion engine will recognize the anxiety and generate advice like the following:

[0829] Example prompt:

[0830] User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy.

[0831] Our advice: Don't worry - first check with your local police and consider installing security cameras.

[0832] In this way, advice that is in tune with the user's emotions can be provided.

[0833] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0834] Step 1:

[0835] A user inputs security concerns or questions using a terminal device. The input is in the form of text or voice and is transmitted to a server via an interface of the terminal device. The input data is text data including the user's concerns or questions.

[0836] Step 2:

[0837] The server sends the received user input data to the natural language processing engine. The natural language processing engine uses OpenAI's API to analyze the input data and generate appropriate advice. Based on the input data, data processing and data calculations are performed to generate relevant advice. The output is text data of the generated advice.

[0838] Step 3:

[0839] The server sends the generated advice to the emotion engine, which uses emotion analysis tools such as TextBlob to analyze the emotion of the user's input text. The emotion engine determines the emotion of the input text as positive or negative, and adjusts the advice based on the result. The output is the text data of the adjusted advice.

[0840] Step 4:

[0841] The server transmits the adjusted advice to the terminal device. The terminal device displays or notifies the received advice to the user. This allows the user to receive the advice in real time. The output is text data of the advice that is displayed to the user.

[0842] Step 5:

[0843] The server stores the user's input data and the generated advice in a database. The stored data is used for future learning, which improves the accuracy of the natural language processing engine and the emotion engine. The output is the learning data stored in the database.

[0844] In this way, it is possible to quickly provide appropriate advice that takes into account the user's emotions in response to their security concerns or questions.

[0845] Example 2

[0846] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0847] In conventional systems, it was difficult to provide appropriate advice based on user input, and it was particularly difficult to generate advice that took the user's emotions into account. Another challenge was to effectively accumulate the user's input information and the generated advice as learning data to improve the accuracy of the system.

[0848] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0849] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing emotions contained in the input and adjusting the advice. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, and furthermore, by storing the input information and advice as learning data, it becomes possible to improve the accuracy of the system.

[0850] The "means for accepting input from a user" is a device that provides an interface for a user to input their own worries, dissatisfaction, and anxieties in text form.

[0851] A "natural language processing engine" is an engine that analyzes information input by users and generates appropriate advice, and uses a generative AI model.

[0852] The "means for providing advice to a user" is a device for displaying advice generated by the natural language processing engine to a user.

[0853] The "means for storing as learning data" is a device for storing the user's input information and the generated advice in a database and using it for future learning.

[0854] An "emotion engine" is an engine that analyzes emotions contained in input from a user and adjusts advice based on those emotions.

[0855] An "anonymization means" is a device for converting information input by a user into a form that does not identify an individual.

[0856] The "learning means" is a device for improving the performance of the natural language processing engine using accumulated training data.

[0857] This invention is a system for providing appropriate advice for a user's worries, dissatisfaction, and anxiety. The system includes a terminal device that accepts input from a user, a natural language processing engine that analyzes the input information and generates advice, an emotion engine that analyzes emotions and adjusts the advice, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[0858] Hardware and Software Used

[0859] Terminal equipment

[0860] The terminal device provides an interface for receiving input from the user. The user inputs his / her worries, dissatisfaction, and anxiety in text format through this interface. For example, an employee inputs a worry that "there are not enough seats in the office."

[0861] server

[0862] The server receives input information sent from the terminal device and passes it to the natural language processing engine and the emotion engine. The server also transmits generated advice to the terminal device and accumulates the input information and advice as learning data.

[0863] Natural Language Processing Engine

[0864] The natural language processing engine uses a generative AI model (e.g., GPT-4) to analyze input information from the user and generate appropriate advice, such as "consider introducing flexible working."

[0865] Emotion Engine

[0866] The emotion engine analyzes the emotions contained in the user's input and adjusts advice based on those emotions. For example, if a user types, "I'm not doing well at work," the emotion engine will recognize the frustration and adjust the advice to, "I understand that this is tough. Why don't you take a breather and reconsider how you're going about your work."

[0867] Accumulation of learning data

[0868] The server stores the user's input information and the generated advice in a database and uses it for future learning, which can improve the accuracy of the natural language processing engine and the emotion engine.

[0869] Examples of concrete examples and prompts

[0870] Examples

[0871] A user inputs a concern such as "There aren't enough seats in the office" into a terminal device. The terminal device sends this information to a server, which passes it to a natural language processing engine. The natural language processing engine generates advice such as "Consider introducing flexible working." The emotion engine adjusts this advice, and the server sends it to the terminal device. The terminal device displays the advice to the user, and the server accumulates the input information and advice as learning data.

[0872] Examples of prompt statements

[0873] "If a user types in 'I don't have enough seats in my office,' what advice do you generate and how do you adjust it to take sentiment into account?"

[0874] The system aims to provide users with appropriate advice in response to their worries, complaints, and anxieties, and to facilitate smooth communication within the company.

[0875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0876] Step 1:

[0877] The user inputs his / her worries, dissatisfaction, and anxiety into the terminal device.

[0878] Input: User's concerns, complaints, and worries (e.g., "There aren't enough seats in the office.")

[0879] Output: Text data entered into the terminal device

[0880] Specific operation: The user inputs his / her worries, dissatisfaction, and anxiety in text format using the interface of the terminal device. The terminal device accepts this input and stores it as text data.

[0881] Step 2:

[0882] The terminal device transmits the input information to the server.

[0883] Input: Text data entered into a terminal device

[0884] Output: Text data sent to the server

[0885] Specific operation: The terminal device transmits the text data entered by the user to the server over the network, where the data is appropriately formatted.

[0886] Step 3:

[0887] The server passes the input information to a natural language processing engine.

[0888] Input: Text data sent to the server

[0889] Output: Text data passed to the natural language processing engine

[0890] How it works: The server passes the received text data to a natural language processing engine, which uses a generative AI model (e.g. GPT-4).

[0891] Step 4:

[0892] A natural language processing engine analyzes the input information and generates advice.

[0893] Input: Text data passed to the natural language processing engine

[0894] Output: Generated advice (e.g. "Consider implementing flexible working")

[0895] How it works: The natural language processing engine analyzes the input text data and generates appropriate advice. It uses generative AI models to suggest solutions to the user's concerns.

[0896] Step 5:

[0897] The emotion engine analyzes the sentiment of the input information and tailors advice.

[0898] Input: Advice generated by the natural language processing engine, user input information

[0899] Output: Tailored advice (e.g., "I understand how hard this is for you. Maybe you should take a break and consider implementing flexible working.")

[0900] How it works: The emotion engine analyzes the emotions contained in the user's input. For example, if the user types "I'm not doing well at work," the emotion engine will recognize the frustration and adjust its advice.

[0901] Step 6:

[0902] The server transmits the generated advice to the terminal device.

[0903] Input: Tailored advice

[0904] Output: Advice sent to the terminal

[0905] Specific operation: The server transmits the advice adjusted by the emotion engine to the terminal device via the network.

[0906] Step 7:

[0907] The terminal device displays the advice to the user.

[0908] Input: Advice sent to terminal

[0909] Output: The advice displayed to the user

[0910] Specific operation: The terminal device displays the received advice to the user, who can check the advice on the screen of the terminal device.

[0911] Step 8:

[0912] The server accumulates the input information and the generated advice as learning data.

[0913] Input: User input information, generated advice

[0914] Output: Accumulated training data

[0915] How it works: The server stores the user's input information and generated advice in a database. This data is used to train the natural language processing engine and the emotion engine.

[0916] (Application example 2)

[0917] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal".

[0918] Conventional security services have the problem that it is difficult to provide appropriate advice to users regarding their anxieties and questions. In addition, it is not possible to provide advice that takes into account the user's feelings, making it difficult to improve user satisfaction. Furthermore, there is a lack of a mechanism to accumulate the provided advice as learning data and improve the accuracy of the system.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0920] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing the user's emotions and adjusting the advice based on the emotions. This makes it possible to provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions, and also improves the accuracy of the system.

[0921] A "means for accepting input from a user" is a device or software that provides an interface for a user to input security concerns or questions.

[0922] A "natural language processing engine" is a system that uses artificial intelligence technology to analyze input from users and generate appropriate advice.

[0923] The "means for providing advice to a user" refers to a device or software for displaying or notifying the generated advice to a user.

[0924] The "means for storing as learning data" refers to a device or software that stores the user's input and the generated advice in a database or the like, and uses it for later analysis or to improve the accuracy of the system.

[0925] An "emotion engine" is a system that uses artificial intelligence techniques to analyze emotions contained in user input and tailor advice based on those emotions.

[0926] As an embodiment of the present invention, a security advisor system is constructed. The system includes a means for accepting input from a user, a natural language processing engine, an emotion engine, a means for providing advice to the user, and a means for accumulating the input as learning data.

[0927] System Programming

[0928] The system is realized using the following hardware and software.

[0929] Hardware: Smartphones, smart glasses

[0930] Software: Python, OpenAI API

[0931] Processing Description

[0932] A means of accepting input from the user

[0933] Users input their security concerns or questions via their smartphone or smart glasses, and the data is sent to the server through the system's interface.

[0934] Natural Language Processing Engine

[0935] The server uses the OpenAI API to analyze the user's input and generate appropriate advice that is specific and actionable based on the user's input.

[0936] Emotion Engine

[0937] The server uses an emotion engine to analyze the emotions contained in the user's input. Based on the analysis results, the server adjusts the generated advice to take the user's emotions into consideration. This allows the user to receive advice that resonates with them more.

[0938] A means of providing advice to users

[0939] The tailored advice is then provided to the user via their smartphone or smart glasses, where the user can review the advice and take any necessary measures.

[0940] A means of accumulating learning data

[0941] The server stores the user's input and the generated advice in a database. These data are used for later analysis and to improve the accuracy of the system. The information accumulated as training data is used to train the natural language processing engine and the emotion engine, improving the performance of the system.

[0942] Examples

[0943] User input: "I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy."

[0944] Generated advice: "If you see anyone acting suspiciously, we encourage you to contact the police immediately. Also, consider installing security cameras."

[0945] Tailored advice: "We understand that you are feeling anxious. If you see anyone acting suspiciously, we encourage you to contact police immediately. Also, consider installing security cameras."

[0946] Examples of prompt statements

[0947] For natural language processing engines: "User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy. Please provide some appropriate security advice."

[0948] For the emotion engine: "User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy. Analyze the emotion and tailor your advice to show empathy. Advice: If you see anyone suspicious, I recommend contacting the police immediately. Also, consider installing security cameras."

[0949] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0950] Step 1:

[0951] The user inputs their security concerns and questions using a smartphone or smart glasses. The input data is sent to the server through the device's interface. The input data is in text format, and specifically describes the user's concerns and questions.

[0952] Step 2:

[0953] The server sends the received user input data to the natural language processing engine, which uses the OpenAI API to analyze the input data and generate appropriate advice. Based on the input data, specific security advice is output in text format.

[0954] Step 3:

[0955] The server sends the generated advice to the emotion engine. The emotion engine analyzes the emotion contained in the user's input data and adjusts the advice in a manner that shows empathy. The emotion engine reanalyzes the input data to analyze the user's emotion and generates advice that takes the emotion into consideration. The adjusted advice is output in text format.

[0956] Step 4:

[0957] The server sends the adjusted advice to the terminal, and the terminal displays the adjusted advice to the user, so that the user can check the displayed advice and take necessary measures.

[0958] Step 5:

[0959] The server saves the user's input data and the generated advice in a database. The saved data is accumulated as learning data and used for later analysis and to improve the accuracy of the system. The user's input data and the generated advice are saved in pairs in the database.

[0960] Step 6:

[0961] The server uses the accumulated learning data to train the natural language processing engine and emotion engine. By analyzing the learning data and discovering new patterns and trends, the accuracy of the engines is improved, which makes it possible to provide more appropriate advice in the future.

[0962] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0963] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0964] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0965] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0966] [Third embodiment]

[0967] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0968] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0969] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0970] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0971] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0972] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0973] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0974] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0975] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0976] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0977] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0978] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0979] "Example 1"

[0980] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[0981] "Example 2"

[0982] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[0983] The process flow of each embodiment will be described below.

[0984] "Example 1"

[0985] Step 1: The user inputs his / her worries, dissatisfaction, and anxiety using a terminal device connected to the company's network. This input is done through the interface of the terminal device.

[0986] Step 2: The terminal device receives input from the user and sends the information to the natural language processing engine.

[0987] Step 3: The natural language processing engine generates advice based on the received input information, and the generated advice is sent to the terminal device.

[0988] Step 4: The terminal device provides the advice received from the natural language processing engine to the user.

[0989] Step 5: The natural language processing engine accumulates the input information from the user and the generated advice as training data. This data is used to train the natural language processing engine.

[0990] Example 1

[0991] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[0992] Conventional systems have difficulty providing appropriate advice based on user input, and are unable to accumulate the advice provided as learning data to improve the accuracy of the system. Furthermore, there is a lack of anonymization measures to protect user privacy.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0994] In this invention, the server includes a means for accepting input from a user, a means for transmitting the input information to a natural language processing engine, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and a means for training the natural language processing engine using the learning data. This makes it possible to provide appropriate advice based on the input from the user, to store the advice and the input information as learning data, and to improve the accuracy of the system. In addition, an anonymization means for protecting the privacy of the user is included, so that a system that can be used with peace of mind can be provided.

[0995] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input his or her own worries, dissatisfaction, and anxieties.

[0996] The "means for transmitting the input information to the natural language processing engine" refers to a communication means or protocol for transferring information input by a user to the natural language processing engine.

[0997] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[0998] The "means for providing the user with advice generated by the engine" refers to a device or software for displaying or notifying the user of the advice generated by the natural language processing engine.

[0999] The "means for storing the input and the advice as learning data" refers to a device or software for saving the user's input information and the generated advice in a database or the like.

[1000] "Means for training the natural language processing engine using the training data" refers to an algorithm or process for improving the performance of the natural language processing engine using accumulated training data.

[1001] A "means for anonymizing user input" is a device or software for removing or converting personally identifiable information from the input information in order to protect the user's personal information.

[1002] The present invention is implemented using a terminal device connected to an in-house network. The terminal device has an interface that accepts input from a user. The user inputs his or her own worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine.

[1003] The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine.

[1004] A terminal device connected to an in-house network is used as the specific hardware, and a natural language processing engine is used as the specific software. The natural language processing engine is software for analyzing input information from a user and generating appropriate advice.

[1005] For example, if an employee inputs a concern that "there are not enough seats in the office," the natural language processing engine will generate advice such as "consider introducing flexible working" and provide that advice to the employee. In addition, these concerns and advice will be accumulated as learning data and used to train the natural language processing engine.

[1006] An example of a prompt sentence is "Please tell me what measures should be taken if there are not enough seats in the office." By inputting this prompt sentence, the natural language processing engine can generate appropriate advice and provide it to the user.

[1007] In this way, the system allows users to input their concerns and complaints through their terminal devices, and the server generates and provides advice based on the input. The generated advice and input information are stored as learning data, which contributes to improving the accuracy of the system.

[1008] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1009] Step 1:

[1010] Accepting user input

[1011] User: Uses a terminal device connected to the company network.

[1012] User: Enters his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[1013] Input: For example, a user types, "There aren't enough seats in the office."

[1014] Output: The input information is saved on the terminal device.

[1015] Specific action: A user inputs text using the keyboard or touch screen of a terminal device.

[1016] Step 2:

[1017] Sending input information

[1018] Terminal: Receives information entered by the user and sends it to the natural language processing engine.

[1019] Input: Text data entered by the user.

[1020] Output: The text data that is sent to the natural language processing engine.

[1021] Specific operations: The terminal device calls an API for transmitting the input text data to the server.

[1022] Step 3:

[1023] Generating Advice

[1024] Server: Analyzes the received input using a natural language processing engine.

[1025] Input: Text data sent from the terminal device.

[1026] Output: Advice generated based on the analysis results.

[1027] Specific operation: In response to the input "There aren't enough seats in the office," the natural language processing engine generates the advice "Consider introducing flexible working."

[1028] Step 4:

[1029] Providing advice

[1030] Server: Sends the generated advice to the terminal device.

[1031] Terminal: Displays the received advice to the user.

[1032] Input: Advice generated by a natural language processing engine.

[1033] Output: The advice that is displayed to the user.

[1034] Specific actions: The advice "Consider introducing flexible working" is displayed on the terminal screen.

[1035] Step 5:

[1036] Accumulating and learning data

[1037] Server: Stores user input information and generated advice as learning data.

[1038] Server: Uses the accumulated data to train the natural language processing engine.

[1039] Input: User input and generated advice.

[1040] Output: Information stored as training data.

[1041] What happens: New inputs and advice are stored in the database and the model is periodically retrained.

[1042] (Application example 1)

[1043] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal".

[1044] In today's digital society, users face many security risks. In particular, there is growing concern about threats such as phishing emails and unauthorized access. However, there are only a limited number of systems that can quickly provide specific advice on these security risks. Therefore, there is a need for a system that allows users to easily input their security concerns and worries, and provides appropriate advice based on that input.

[1045] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for accumulating the input and the advice as learning data, a means for providing an interface for inputting worries and anxieties about security, and a means for generating security advice based on information input through the interface. This enables the user to quickly receive specific advice about security.

[1046] "Means for accepting input from a user" refers to a device or software that provides an interface for a user to input their own worries and anxieties into the system.

[1047] A "natural language processing engine that generates advice" is an engine that uses artificial intelligence technology to analyze information entered by a user and generate appropriate advice.

[1048] The "means for providing the generated advice to the user" refers to a device or software for conveying the advice generated by the natural language processing engine to the user.

[1049] The "means for storing input and advice as learning data" refers to a device or software for storing the input information from the user and the generated advice in a database or the like and using it for later learning.

[1050] "Means for providing an interface for inputting security-related worries and anxieties" refers to a device or software that provides a dedicated interface for a user to input specific security-related worries and anxieties.

[1051] The "means for generating security advice" refers to a device or software for generating appropriate security measures and advice based on security information input by a user.

[1052] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the generated advice to the user, means for storing the input and advice as learning data, means for providing an interface for inputting security-related concerns and worries, and means for generating security advice based on information input through the interface.

[1053] System Programming

[1054] This system is realized by the following program. First, the user inputs their security-related worries and concerns using their smartphone. The input information is sent to the server through a web application using Flask. The server uses a natural language processing engine such as OpenAI's GPT-3 to generate appropriate advice based on the input information. The generated advice is returned to the user again through the Flask application.

[1055] Hardware and software used

[1056] Hardware: Smartphone

[1057] Software: Flask (Python web framework), OpenAI API

[1058] Data processing and calculation

[1059] 1. Accepting user input: The user inputs security-related worries or concerns using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried."

[1060] 2. Sending to the natural language processing engine: The Flask application accepts input from the user and sends it to OpenAI's GPT-3.

[1061] 3. Advice Generation: GPT-3 generates advice based on the input, for example, "Learn the characteristics of phishing emails and avoid opening suspicious emails."

[1062] 4. Return the advice: The generated advice is returned to the user via the Flask application.

[1063] Examples

[1064] If a user enters, "I'm worried because I've been receiving a lot of phishing emails lately," the system will generate advice like the following:

[1065] Examples of prompt statements

[1066] User's concern: I'm worried because there have been a lot of phishing emails lately.

[1067] advice:

[1068] By sending this prompt to GPT-3, appropriate advice is generated. The advice is returned to the user, allowing the user to quickly obtain specific security measures.

[1069] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1070] Step 1:

[1071] A user inputs security-related worries and anxieties using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried." This input is sent to the server through the Flask application interface. The input data is in text format, and specifically describes the user's worries and anxieties.

[1072] Step 2:

[1073] The server sends the user's input received through the Flask application to OpenAI's GPT-3. Specifically, it formats the user's input as a prompt sentence and sends a request to the GPT-3 API. An example of a prompt sentence is "User's concern: I've been getting a lot of phishing emails lately, so I'm worried\nAdvice:". Based on this prompt sentence, GPT-3 generates appropriate advice.

[1074] Step 3:

[1075] The server receives the advice returned by GPT-3. GPT-3 performs natural language processing based on the user's input and generates appropriate advice. For example, advice such as "Learn the characteristics of phishing emails and avoid opening suspicious emails" is generated. This advice is returned to the server in text format.

[1076] Step 4:

[1077] The server returns the received advice to the user through the Flask application. Specifically, it generates a response to display the generated advice on the user's smartphone. This response includes the generated advice, and the user can check the advice on the screen of their smartphone.

[1078] Step 5:

[1079] The server accumulates the user's input and the generated advice as training data. Specifically, it stores the input data and the advice data in a database. This data is later used for training the natural language processing engine. The database stores the user's input and the corresponding advice as a pair.

[1080] Step 6:

[1081] The server uses the accumulated learning data to train the natural language processing engine. Specifically, it periodically extracts learning data from the database and feeds it back to the natural language processing engine. This feedback improves the engine's accuracy, enabling it to generate more appropriate advice.

[1082] Example 2

[1083] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1084] Conventional systems have difficulty in providing prompt and appropriate advice to users on their concerns and complaints, and are unable to effectively accumulate that data and use it for learning. For this reason, a system that can improve user satisfaction is needed.

[1085] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1086] In this invention, the server includes a means for accepting input information from a user, a means for transmitting the input information to the server, a natural language processing engine for generating advice based on the input, a means for transmitting the advice generated by the engine from the server to a terminal, a means for providing the advice received by the terminal to the user, and a means for storing the input and the advice as learning data, thereby making it possible to quickly provide appropriate advice for the user's worries and dissatisfaction, and to effectively store the data and use it for learning.

[1087] A "user" is an individual or group that uses the system to input their own worries, complaints, and anxieties.

[1088] "Input information" refers to text data or other types of information that a user provides to the system through a terminal device.

[1089] A "server" is a computer system that receives input information from a user and passes it to a natural language processing engine.

[1090] A "terminal" is a device that a user uses to provide input information and receive advice from a server.

[1091] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[1092] "Advice" is a suggestion or solution that the natural language processing engine generates based on user input.

[1093] "Training Data" means a dataset containing user input and generated advice used to improve the performance of a natural language processing engine.

[1094] "Anonymization" is the process of removing personally identifiable information from input information in order to protect the user's personal information.

[1095] The present invention is a system that provides appropriate advice to users regarding their worries and dissatisfaction, accumulates the data, and uses it for learning. A specific embodiment of this system will be described below.

[1096] Hardware and Software Used

[1097] Terminal device: A computer or tablet connected to the company network

[1098] Server: A computer system that receives input information from the user and passes it to the natural language processing engine.

[1099] Natural language processing engines: For example, generative AI models (such as GPT-4)

[1100] Data processing and calculation flow

[1101] 1. Accepting user input

[1102] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[1103] Example: A user uses a keyboard or touch screen on a terminal device to type, "There are not enough seats in the office."

[1104] 2. Sending input information

[1105] The terminal transmits the information entered by the user to the server.

[1106] Specific operation: The terminal sends the entered text data to the server as an HTTP request.

[1107] 3. Generating Advice

[1108] The server passes the received input information to a natural language processing engine to generate advice.

[1109] Specific operation: The text data received by the server is input into a generative AI model (e.g., GPT-4) and advice such as "Consider introducing flexible working" is generated.

[1110] 4. Submitting Advice

[1111] The server transmits the generated advice to the terminal.

[1112] Specific operation: The server sends the generated advice to the terminal as an HTTP response.

[1113] 5. Providing advice

[1114] The terminal provides the received advice to the user.

[1115] Specific operation: The device displays the received advice on the screen and the user confirms it.

[1116] 6. Data accumulation and learning

[1117] The server accumulates the user's input information and the generated advice as learning data.

[1118] What it does: The server stores the input information and advice in a database and later uses it to train the natural language processing engine.

[1119] Examples

[1120] User input: "There aren't enough seats in the office."

[1121] Generated advice: "Consider implementing flexible working."

[1122] Examples of prompt statements

[1123] Prompt: "What advice should you offer to employees if they feel there aren't enough seats in their office?"

[1124] The system provides users with advice to quickly resolve their concerns and complaints, and at the same time accumulates that data to help train the natural language processing engine.

[1125] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1126] Step 1:

[1127] Accepting user input

[1128] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. Specifically, the user uses the keyboard or touch screen of the terminal device to input "There are not enough seats in the office." The input information is stored in the terminal as text data.

[1129] Step 2:

[1130] Sending input information

[1131] The terminal transmits the information input by the user to the server. In concrete terms, the terminal transmits the input text data to the server as an HTTP request. The input is text data, and the output is the transmission of a request to the server.

[1132] Step 3:

[1133] Generating Advice

[1134] The server passes the received input information to a natural language processing engine and generates advice. Specifically, the server inputs the received text data into a generative AI model (e.g., GPT-4) and generates advice such as "Consider introducing flexible work." The input is text data, and the output is the generated advice.

[1135] Step 4:

[1136] Sending Advice

[1137] The server sends the generated advice to the terminal. In concrete terms, the server sends the generated advice to the terminal as an HTTP response. The input is the generated advice, and the output is the response sent to the terminal.

[1138] Step 5:

[1139] Providing advice

[1140] The terminal provides the received advice to the user. In concrete terms, the terminal displays the advice received on the screen and the user confirms it. The input is the advice received from the server, and the output is the display to the user.

[1141] Step 6:

[1142] Accumulating and learning data

[1143] The server accumulates the user's input information and the generated advice as learning data. Specifically, the server saves the input information and advice in a database and later uses them to train the natural language processing engine. The input is the user's input information and the generated advice, and the output is saved in the database.

[1144] (Application example 2)

[1145] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal".

[1146] In today's information society, individuals and companies face a wide range of security risks. In particular, cyber attacks such as phishing emails and unauthorized access have been increasing, and appropriate measures against these attacks are required. However, many users do not have specialized knowledge about security, making it difficult for them to take appropriate measures. For this reason, a system is needed that allows users to easily input their security concerns and receive appropriate advice.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1148] In this invention, the server includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the advice generated by the engine to the user, means for storing the input and the advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for storing the input information and the generated security measures as learning data. This enables a user to easily input security concerns and receive appropriate advice.

[1149] The "means for accepting input from the user" is an interface through which the user provides information to the system.

[1150] The "natural language processing engine for generating advice" is software or hardware for analyzing input information from a user and generating appropriate advice.

[1151] The "means for providing the generated advice to the user" is an interface for conveying the advice generated by the natural language processing engine to the user.

[1152] The "means for storing input and advice as learning data" refers to a system for storing input information from a user and generated advice in a database or the like, and using the information for future learning.

[1153] The "means for inputting user's security concerns" is an interface that allows the user to input security issues and fears into the system.

[1154] The "means for analyzing input information and generating appropriate security measures" refers to software or hardware for analyzing security-related input information from a user and proposing appropriate measures.

[1155] The "means for providing the generated security measures to the user" refers to an interface for communicating the security measures generated by the analysis to the user.

[1156] The "means for storing input information and generated security measures as learning data" refers to a system for storing input information from a user and generated security measures in a database or the like, and using them for future learning.

[1157] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice, means for providing the generated advice to the user, means for accumulating the input and advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for accumulating the input information and the generated security measures as learning data.

[1158] Program processing explanation

[1159] Hardware used

[1160] Smartphone

[1161] Software used

[1162] Python

[1163] OpenAI API

[1164] SQLite (database)

[1165] Processing flow

[1166] 1. User Input

[1167] The user starts the smartphone application and inputs a security concern. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me."

[1168] 2. Sending input information

[1169] The input information is sent from the application to the natural language processing engine via OpenAI's API.

[1170] 3. Generating Advice

[1171] The natural language processing engine analyzes the input information and generates appropriate advice, such as "Points to identify phishing emails, and we recommend that you avoid clicking on links in emails."

[1172] 4. Providing advice

[1173] The generated advice is provided to the user through the application.

[1174] 5. Data accumulation

[1175] The input information and generated advice are stored in a SQLite database and used as learning data for future use.

[1176] Examples

[1177] If a user types, "I've been receiving a lot of phishing emails lately," the application will generate advice for the user, such as, "Points to identify phishing emails and how we recommend that you avoid clicking on links in emails," and provide it to the user.

[1178] Examples of prompt statements

[1179] User security concerns: I've been getting a lot of phishing emails lately.

[1180] Please provide appropriate advice.

[1181] In this way, a system is realized that allows users to easily input security concerns and receive appropriate advice.

[1182] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1183] Step 1:

[1184] A user starts a smartphone application and inputs a security concern. The input information is sent to the application in text format. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me." The input data is text data that specifically describes the user's concern.

[1185] Step 2:

[1186] The device sends the input information to the natural language processing engine through OpenAI's API. The input data is sent as an API request. Specifically, the user's input text is sent to the API endpoint to request analysis. The input data is text data that includes the user's concerns, and the output data is the analysis result by the natural language processing engine.

[1187] Step 3:

[1188] The server analyzes the input information using a natural language processing engine and generates appropriate advice, such as "We recommend that you learn how to identify phishing emails and not click on links in emails." The input data is text data that includes the user's concerns, and the output data is text data of the generated advice.

[1189] Step 4:

[1190] The terminal provides the generated advice to the user. Specifically, the generated advice is displayed through an interface of the application. The input data is text data of the generated advice, and the output data is the text data of the advice displayed to the user.

[1191] Step 5:

[1192] The server stores the input information and the generated advice in a SQLite database. Specifically, it executes SQL queries to insert the user's input text and the generated advice into the database. The input data are the text data of the user's concerns and the generated advice, and the output data are the records stored in the database.

[1193] Step 6:

[1194] The server uses the accumulated data to train the natural language processing engine. Specifically, it analyzes the stored data and uses it as training data to improve the accuracy of the model. The input data is the text data of past input information and advice stored in the database, and the output data is the updated natural language processing model.

[1195] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1196] "Example 1"

[1197] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[1198] "Example 2"

[1199] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[1200] The process flow of each embodiment will be described below.

[1201] "Example 1"

[1202] Step 1: A user inputs information through a terminal device. This input is text data including the user's concerns and problems.

[1203] Step 2: The entered text data is sent to the natural language processing engine via the internal network.

[1204] Step 3: The natural language processing engine analyzes the submitted text data and generates advice for the user's problem or concern.

[1205] Step 4: At the same time, the emotion engine recognizes the user's emotion from the transmitted text data.

[1206] Step 5: The emotion engine adjusts the advice generated by the natural language processing engine based on the recognized emotion.

[1207] Step 6: The tailored advice is sent to the terminal device through the internal network and presented to the user.

[1208] Step 7: Finally, the user input and advice, and the recognized

[1209] Emotions are accumulated as learning data, which allows the system to learn changes in the user's emotions and provide more appropriate advice.

[1210] Example 1

[1211] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1212] In conventional systems, it can be difficult to provide appropriate advice in response to user input. In addition, since the system cannot provide advice that takes into account the user's feelings, the user's satisfaction may decrease. Furthermore, there is a need to effectively accumulate input information and advice as learning data to improve the accuracy of the system.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1214] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing emotions contained in the user's input and adjusting the advice based on the emotions, thereby making it possible to provide appropriate advice that takes the user's emotions into consideration and improve the accuracy of the system.

[1215] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input information such as their worries, dissatisfaction, and anxiety.

[1216] A "natural language processing engine" is software that analyzes information input by a user and generates appropriate advice based on that information.

[1217] The "means for providing advice to a user" refers to a device or software for displaying or notifying a user of advice generated by a natural language processing engine.

[1218] The "means for storing as learning data" refers to a device or software that stores the input information from the user and the generated advice in a database or the like for use in subsequent learning processes.

[1219] An "emotion engine" is software that analyzes emotions contained in a user's input and tailors the advice it generates based on those emotions.

[1220] An "anonymization means" is a device or software that converts information input by a user into a form that does not identify the individual.

[1221] A "learning means" is a device or software that uses accumulated learning data to improve the performance of a natural language processing engine or an emotion engine.

[1222] The present invention is a system for accepting input from a user and providing appropriate advice. The system includes a terminal device for accepting input from a user, a natural language processing engine for analyzing the input information and generating advice, an emotion engine for analyzing the user's emotions, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[1223] Hardware and Software Used

[1224] Hardware: Terminal devices connected to the company network

[1225] Software: Natural language processing engine, emotion engine

[1226] Data processing and calculation flow

[1227] 1. Accepting user input

[1228] Terminal: The user inputs his / her worries, dissatisfaction, and anxiety through the terminal device interface. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[1229] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[1230] 2. Sending input information

[1231] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[1232] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[1233] 3. Generating Advice

[1234] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[1235] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[1236] 4. Providing advice

[1237] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[1238] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[1239] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[1240] 5. Accumulation of learning data

[1241] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[1242] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[1243] Examples

[1244] Sample prompt 1: "There aren't enough seats in the office."

[1245] Expected advice: "Consider implementing flexible working."

[1246] Specific operation: The user inputs "There are not enough seats in the office" and the terminal device sends the information to the server. The server calls the natural language processing engine, generates advice, and returns it to the terminal device. The terminal device displays the advice to the user.

[1247] Sample prompt 2: "My job isn't going well."

[1248] Expected advice: "I know this is tough. I think it might be time to take a breather and reevaluate how you're doing your job."

[1249] Specific operation: The user inputs "My work is not going well," and the terminal device sends the information to the server. The server calls the emotion engine to perform emotion analysis, and the natural language processing engine generates advice based on the results. The generated advice is returned to the terminal device and displayed to the user.

[1250] The system aims to reduce users' worries and anxieties by processing user input in real time and providing appropriate advice.

[1251] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1252] Step 1:

[1253] Accepting user input

[1254] User: The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[1255] Input: Text data entered by the user.

[1256] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[1257] Step 2:

[1258] Sending input information

[1259] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[1260] Input: Text data entered by the user.

[1261] Output: Text data sent to the server

[1262] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[1263] Step 3:

[1264] Generating Advice

[1265] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[1266] Input: Text data sent to the server

[1267] Output: The generated advice.

[1268] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[1269] Step 4:

[1270] Providing advice

[1271] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[1272] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[1273] Input: Generated advice

[1274] Output: Advice displayed to the user

[1275] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[1276] Step 5:

[1277] Accumulation of learning data

[1278] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[1279] Input: User input and generated advice

[1280] Output: Training data stored in a database

[1281] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[1282] (Application example 1)

[1283] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal".

[1284] In modern society, users often have concerns or questions about security, but there is a lack of means to quickly provide appropriate advice on these issues. In addition, it is difficult to provide advice that takes into account the user's feelings, which makes it difficult to fully alleviate the user's concerns.

[1285] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1286] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing the user's emotions and adjusting the advice based on the emotions, thereby making it possible to quickly provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions.

[1287] A "means for accepting input from a user" is an interface through which a user can input security concerns or questions in the form of text, voice, or the like.

[1288] A "natural language processing engine" is a software engine that analyzes input from a user and generates appropriate advice.

[1289] The "means for providing advice to a user" refers to a means for displaying or notifying the user of the generated advice.

[1290] The "means for storing as learning data" refers to a means for storing the user's input and the generated advice in a database or the like and using it for future learning.

[1291] An "emotion engine" is a software engine for recognizing emotions in a user's input and tailoring advice based on those emotions.

[1292] To implement this invention, a terminal device for users to input security concerns and questions, and a server for processing the input information are required. The terminal device can be a device such as a smartphone or smart glasses.

[1293] The server includes the following means:

[1294] 1. A way to accept input from the user:

[1295] The user inputs security concerns or questions in text or voice format through the terminal device, which is then transmitted to the server via the terminal device interface.

[1296] 2. Natural Language Processing Engine:

[1297] The server uses a natural language processing engine to analyze the input from the user and generate appropriate advice. The engine is implemented using the OpenAI API. It analyzes the input text and generates relevant advice.

[1298] 3. Means of providing advice to users:

[1299] The server transmits the generated advice to the terminal device and displays or notifies the user, thereby enabling the user to receive the advice in real time.

[1300] 4. Methods for accumulating training data:

[1301] The server stores the user's input and the generated advice in a database for future learning, helping to improve the accuracy of the natural language processing engine.

[1302] 5. Emotion Engine:

[1303] The server uses an emotion engine to recognize emotions in the user's input and adjust the advice based on the emotion. The engine is implemented using emotion analysis tools such as TextBlob. The emotion engine analyzes the emotion of the user's input text and determines whether it is positive or negative.

[1304] As a concrete example, if a user enters, "I've been seeing a lot of suspicious people around my house lately and it makes me feel anxious," the emotion engine will recognize the anxiety and generate advice like the following:

[1305] Example prompt:

[1306] User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy.

[1307] Our advice: Don't worry - first check with your local police and consider installing security cameras.

[1308] In this way, advice that is in tune with the user's emotions can be provided.

[1309] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1310] Step 1:

[1311] A user inputs security concerns or questions using a terminal device. The input is in the form of text or voice and is transmitted to a server via an interface of the terminal device. The input data is text data including the user's concerns or questions.

[1312] Step 2:

[1313] The server sends the received user input data to the natural language processing engine. The natural language processing engine uses OpenAI's API to analyze the input data and generate appropriate advice. Based on the input data, data processing and data calculations are performed to generate relevant advice. The output is text data of the generated advice.

[1314] Step 3:

[1315] The server sends the generated advice to the emotion engine, which uses emotion analysis tools such as TextBlob to analyze the emotion of the user's input text. The emotion engine determines the emotion of the input text as positive or negative, and adjusts the advice based on the result. The output is the text data of the adjusted advice.

[1316] Step 4:

[1317] The server transmits the adjusted advice to the terminal device. The terminal device displays or notifies the received advice to the user. This allows the user to receive the advice in real time. The output is text data of the advice that is displayed to the user.

[1318] Step 5:

[1319] The server stores the user's input data and the generated advice in a database. The stored data is used for future learning, which improves the accuracy of the natural language processing engine and the emotion engine. The output is the learning data stored in the database.

[1320] In this way, it is possible to quickly provide appropriate advice that takes into account the user's emotions in response to their security concerns or questions.

[1321] Example 2

[1322] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1323] In conventional systems, it was difficult to provide appropriate advice based on user input, and it was particularly difficult to generate advice that took the user's emotions into account. Another challenge was to effectively accumulate the user's input information and the generated advice as learning data to improve the accuracy of the system.

[1324] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1325] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing emotions contained in the input and adjusting the advice. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, and furthermore, by storing the input information and advice as learning data, it becomes possible to improve the accuracy of the system.

[1326] The "means for accepting input from a user" is a device that provides an interface for a user to input their own worries, dissatisfaction, and anxieties in text form.

[1327] A "natural language processing engine" is an engine that analyzes information input by users and generates appropriate advice, and uses a generative AI model.

[1328] The "means for providing advice to a user" is a device for displaying advice generated by the natural language processing engine to a user.

[1329] The "means for storing as learning data" is a device for storing the user's input information and the generated advice in a database and using it for future learning.

[1330] An "emotion engine" is an engine that analyzes emotions contained in input from a user and adjusts advice based on those emotions.

[1331] An "anonymization means" is a device for converting information input by a user into a form that does not identify an individual.

[1332] The "learning means" is a device for improving the performance of the natural language processing engine using accumulated training data.

[1333] This invention is a system for providing appropriate advice for a user's worries, dissatisfaction, and anxiety. The system includes a terminal device that accepts input from a user, a natural language processing engine that analyzes the input information and generates advice, an emotion engine that analyzes emotions and adjusts the advice, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[1334] Hardware and Software Used

[1335] Terminal equipment

[1336] The terminal device provides an interface for receiving input from the user. The user inputs his / her worries, dissatisfaction, and anxiety in text format through this interface. For example, an employee inputs a worry that "there are not enough seats in the office."

[1337] server

[1338] The server receives input information sent from the terminal device and passes it to the natural language processing engine and the emotion engine. The server also transmits generated advice to the terminal device and accumulates the input information and advice as learning data.

[1339] Natural Language Processing Engine

[1340] The natural language processing engine uses a generative AI model (e.g., GPT-4) to analyze input information from the user and generate appropriate advice, such as "consider introducing flexible working."

[1341] Emotion Engine

[1342] The emotion engine analyzes the emotions contained in the user's input and adjusts advice based on those emotions. For example, if a user types, "I'm not doing well at work," the emotion engine will recognize the frustration and adjust the advice to, "I understand that this is tough. Why don't you take a breather and reconsider how you're going about your work."

[1343] Accumulation of learning data

[1344] The server stores the user's input information and the generated advice in a database and uses it for future learning, which can improve the accuracy of the natural language processing engine and the emotion engine.

[1345] Examples of concrete examples and prompts

[1346] Examples

[1347] A user inputs a concern such as "There aren't enough seats in the office" into a terminal device. The terminal device sends this information to a server, which passes it to a natural language processing engine. The natural language processing engine generates advice such as "Consider introducing flexible working." The emotion engine adjusts this advice, and the server sends it to the terminal device. The terminal device displays the advice to the user, and the server accumulates the input information and advice as learning data.

[1348] Examples of prompt statements

[1349] "If a user types in 'I don't have enough seats in my office,' what advice do you generate and how do you adjust it to take sentiment into account?"

[1350] The system aims to provide users with appropriate advice in response to their worries, complaints, and anxieties, and to facilitate smooth communication within the company.

[1351] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1352] Step 1:

[1353] The user inputs his / her worries, dissatisfaction, and anxiety into the terminal device.

[1354] Input: User's concerns, complaints, and worries (e.g., "There aren't enough seats in the office.")

[1355] Output: Text data entered into the terminal device

[1356] Specific operation: The user inputs his / her worries, dissatisfaction, and anxiety in text format using the interface of the terminal device. The terminal device accepts this input and stores it as text data.

[1357] Step 2:

[1358] The terminal device transmits the input information to the server.

[1359] Input: Text data entered into a terminal device

[1360] Output: Text data sent to the server

[1361] Specific operation: The terminal device transmits the text data entered by the user to the server over the network, where the data is appropriately formatted.

[1362] Step 3:

[1363] The server passes the input information to a natural language processing engine.

[1364] Input: Text data sent to the server

[1365] Output: Text data passed to the natural language processing engine

[1366] How it works: The server passes the received text data to a natural language processing engine, which uses a generative AI model (e.g. GPT-4).

[1367] Step 4:

[1368] A natural language processing engine analyzes the input information and generates advice.

[1369] Input: Text data passed to the natural language processing engine

[1370] Output: Generated advice (e.g. "Consider implementing flexible working")

[1371] How it works: The natural language processing engine analyzes the input text data and generates appropriate advice. It uses generative AI models to suggest solutions to the user's concerns.

[1372] Step 5:

[1373] The emotion engine analyzes the sentiment of the input information and tailors advice.

[1374] Input: Advice generated by the natural language processing engine, user input information

[1375] Output: Tailored advice (e.g., "I understand how hard this is for you. Maybe you should take a break and consider implementing flexible working.")

[1376] How it works: The emotion engine analyzes the emotions contained in the user's input. For example, if the user types "I'm not doing well at work," the emotion engine will recognize the frustration and adjust its advice.

[1377] Step 6:

[1378] The server transmits the generated advice to the terminal device.

[1379] Input: Tailored advice

[1380] Output: Advice sent to the terminal

[1381] Specific operation: The server transmits the advice adjusted by the emotion engine to the terminal device via the network.

[1382] Step 7:

[1383] The terminal device displays the advice to the user.

[1384] Input: Advice sent to terminal

[1385] Output: The advice displayed to the user

[1386] Specific operation: The terminal device displays the received advice to the user, who can check the advice on the screen of the terminal device.

[1387] Step 8:

[1388] The server accumulates the input information and the generated advice as learning data.

[1389] Input: User input information, generated advice

[1390] Output: Accumulated training data

[1391] How it works: The server stores the user's input information and generated advice in a database. This data is used to train the natural language processing engine and the emotion engine.

[1392] (Application example 2)

[1393] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal".

[1394] Conventional security services have the problem that it is difficult to provide appropriate advice to users regarding their anxieties and questions. In addition, it is not possible to provide advice that takes into account the user's feelings, making it difficult to improve user satisfaction. Furthermore, there is a lack of a mechanism to accumulate the provided advice as learning data and improve the accuracy of the system.

[1395] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1396] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing the user's emotions and adjusting the advice based on the emotions. This makes it possible to provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions, and also improves the accuracy of the system.

[1397] A "means for accepting input from a user" is a device or software that provides an interface for a user to input security concerns or questions.

[1398] A "natural language processing engine" is a system that uses artificial intelligence technology to analyze input from users and generate appropriate advice.

[1399] The "means for providing advice to a user" refers to a device or software for displaying or notifying the generated advice to a user.

[1400] The "means for storing as learning data" refers to a device or software that stores the user's input and the generated advice in a database or the like, and uses it for later analysis or to improve the accuracy of the system.

[1401] An "emotion engine" is a system that uses artificial intelligence techniques to analyze emotions contained in user input and tailor advice based on those emotions.

[1402] As an embodiment of the present invention, a security advisor system is constructed. The system includes a means for accepting input from a user, a natural language processing engine, an emotion engine, a means for providing advice to the user, and a means for accumulating the input as learning data.

[1403] System Programming

[1404] The system is realized using the following hardware and software.

[1405] Hardware: Smartphones, smart glasses

[1406] Software: Python, OpenAI API

[1407] Processing Description

[1408] A means of accepting input from the user

[1409] Users input their security concerns or questions via their smartphone or smart glasses, and the data is sent to the server through the system's interface.

[1410] Natural Language Processing Engine

[1411] The server uses the OpenAI API to analyze the user's input and generate appropriate advice that is specific and actionable based on the user's input.

[1412] Emotion Engine

[1413] The server uses an emotion engine to analyze the emotions contained in the user's input. Based on the analysis results, the server adjusts the generated advice to take the user's emotions into consideration. This allows the user to receive advice that resonates with them more.

[1414] A means of providing advice to users

[1415] The tailored advice is then provided to the user via their smartphone or smart glasses, where the user can review the advice and take any necessary measures.

[1416] A means of accumulating learning data

[1417] The server stores the user's input and the generated advice in a database. These data are used for later analysis and to improve the accuracy of the system. The information accumulated as training data is used to train the natural language processing engine and the emotion engine, improving the performance of the system.

[1418] Examples

[1419] User input: "I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy."

[1420] Generated advice: "If you see anyone acting suspiciously, we encourage you to contact the police immediately. Also, consider installing security cameras."

[1421] Tailored advice: "We understand that you are feeling anxious. If you see anyone acting suspiciously, we encourage you to contact police immediately. Also, consider installing security cameras."

[1422] Examples of prompt statements

[1423] For natural language processing engines: "User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy. Please provide some appropriate security advice."

[1424] For the emotion engine: "User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy. Analyze the emotion and tailor your advice to show empathy. Advice: If you see anyone suspicious, I recommend contacting the police immediately. Also, consider installing security cameras."

[1425] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1426] Step 1:

[1427] The user inputs their security concerns and questions using a smartphone or smart glasses. The input data is sent to the server through the device's interface. The input data is in text format, and specifically describes the user's concerns and questions.

[1428] Step 2:

[1429] The server sends the received user input data to the natural language processing engine, which uses the OpenAI API to analyze the input data and generate appropriate advice. Based on the input data, specific security advice is output in text format.

[1430] Step 3:

[1431] The server sends the generated advice to the emotion engine. The emotion engine analyzes the emotion contained in the user's input data and adjusts the advice in a manner that shows empathy. The emotion engine reanalyzes the input data to analyze the user's emotion and generates advice that takes the emotion into consideration. The adjusted advice is output in text format.

[1432] Step 4:

[1433] The server sends the adjusted advice to the terminal, and the terminal displays the adjusted advice to the user, so that the user can check the displayed advice and take necessary measures.

[1434] Step 5:

[1435] The server saves the user's input data and the generated advice in a database. The saved data is accumulated as learning data and used for later analysis and to improve the accuracy of the system. The user's input data and the generated advice are saved in pairs in the database.

[1436] Step 6:

[1437] The server uses the accumulated learning data to train the natural language processing engine and emotion engine. By analyzing the learning data and discovering new patterns and trends, the accuracy of the engines is improved, which makes it possible to provide more appropriate advice in the future.

[1438] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1440] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1441] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1442] [Fourth embodiment]

[1443] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1444] 7, a 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.

[1445] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1446] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1447] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1448] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1449] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1450] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1451] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1452] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1453] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1454] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1455] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1456] "Example 1"

[1457] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[1458] "Example 2"

[1459] In one embodiment of the present invention, a terminal device connected to an in-house network is used. The terminal device has an interface that accepts input from a user. The user inputs his / her worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine. The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine. As a specific example, if an employee inputs a worry that "there are not enough seats in the office," the natural language processing engine generates advice such as "consider introducing flexible work," and provides the advice to the employee. These worries and advice are also accumulated as learning data and used for training the natural language processing engine.

[1460] The process flow of each embodiment will be described below.

[1461] "Example 1"

[1462] Step 1: The user inputs his / her worries, dissatisfaction, and anxiety using a terminal device connected to the company's network. This input is done through the interface of the terminal device.

[1463] Step 2: The terminal device receives input from the user and sends the information to the natural language processing engine.

[1464] Step 3: The natural language processing engine generates advice based on the received input information, and the generated advice is sent to the terminal device.

[1465] Step 4: The terminal device provides the advice received from the natural language processing engine to the user.

[1466] Step 5: The natural language processing engine accumulates the input information from the user and the generated advice as training data. This data is used to train the natural language processing engine.

[1467] Example 1

[1468] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1469] Conventional systems have difficulty providing appropriate advice based on user input, and are unable to accumulate the advice provided as learning data to improve the accuracy of the system. Furthermore, there is a lack of anonymization measures to protect user privacy.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1471] In this invention, the server includes a means for accepting input from a user, a means for transmitting the input information to a natural language processing engine, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and a means for training the natural language processing engine using the learning data. This makes it possible to provide appropriate advice based on the input from the user, to store the advice and the input information as learning data, and to improve the accuracy of the system. In addition, an anonymization means for protecting the privacy of the user is included, so that a system that can be used with peace of mind can be provided.

[1472] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input his or her own worries, dissatisfaction, and anxieties.

[1473] The "means for transmitting the input information to the natural language processing engine" refers to a communication means or protocol for transferring information input by a user to the natural language processing engine.

[1474] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[1475] The "means for providing the user with advice generated by the engine" refers to a device or software for displaying or notifying the user of the advice generated by the natural language processing engine.

[1476] The "means for storing the input and the advice as learning data" refers to a device or software for saving the user's input information and the generated advice in a database or the like.

[1477] "Means for training the natural language processing engine using the training data" refers to an algorithm or process for improving the performance of the natural language processing engine using accumulated training data.

[1478] A "means for anonymizing user input" is a device or software for removing or converting personally identifiable information from the input information in order to protect the user's personal information.

[1479] The present invention is implemented using a terminal device connected to an in-house network. The terminal device has an interface that accepts input from a user. The user inputs his or her own worries, dissatisfaction, and anxiety through this interface. The terminal device transmits the input information to a natural language processing engine.

[1480] The natural language processing engine generates advice based on the input information and transmits the generated advice to the terminal device. The terminal device provides the received advice to the user. The natural language processing engine also accumulates the input information and the generated advice as learning data. These data are used for training the natural language processing engine.

[1481] A terminal device connected to an in-house network is used as the specific hardware, and a natural language processing engine is used as the specific software. The natural language processing engine is software for analyzing input information from a user and generating appropriate advice.

[1482] For example, if an employee inputs a concern that "there are not enough seats in the office," the natural language processing engine will generate advice such as "consider introducing flexible working" and provide that advice to the employee. In addition, these concerns and advice will be accumulated as learning data and used to train the natural language processing engine.

[1483] An example of a prompt sentence is "Please tell me what measures should be taken if there are not enough seats in the office." By inputting this prompt sentence, the natural language processing engine can generate appropriate advice and provide it to the user.

[1484] In this way, the system allows users to input their concerns and complaints through their terminal devices, and the server generates and provides advice based on the input. The generated advice and input information are stored as learning data, which contributes to improving the accuracy of the system.

[1485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1486] Step 1:

[1487] Accepting user input

[1488] User: Uses a terminal device connected to the company network.

[1489] User: Enters his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[1490] Input: For example, a user types, "There aren't enough seats in the office."

[1491] Output: The input information is saved on the terminal device.

[1492] Specific action: A user inputs text using the keyboard or touch screen of a terminal device.

[1493] Step 2:

[1494] Sending input information

[1495] Terminal: Receives information entered by the user and sends it to the natural language processing engine.

[1496] Input: Text data entered by the user.

[1497] Output: The text data that is sent to the natural language processing engine.

[1498] Specific operations: The terminal device calls an API for transmitting the input text data to the server.

[1499] Step 3:

[1500] Generating Advice

[1501] Server: Analyzes the received input using a natural language processing engine.

[1502] Input: Text data sent from the terminal device.

[1503] Output: Advice generated based on the analysis results.

[1504] Specific operation: In response to the input "There aren't enough seats in the office," the natural language processing engine generates the advice "Consider introducing flexible working."

[1505] Step 4:

[1506] Providing advice

[1507] Server: Sends the generated advice to the terminal device.

[1508] Terminal: Displays the received advice to the user.

[1509] Input: Advice generated by a natural language processing engine.

[1510] Output: The advice that is displayed to the user.

[1511] Specific actions: The advice "Consider introducing flexible working" is displayed on the terminal screen.

[1512] Step 5:

[1513] Accumulating and learning data

[1514] Server: Stores user input information and generated advice as learning data.

[1515] Server: Uses the accumulated data to train the natural language processing engine.

[1516] Input: User input and generated advice.

[1517] Output: Information stored as training data.

[1518] What happens: New inputs and advice are stored in the database and the model is periodically retrained.

[1519] (Application example 1)

[1520] Next, a description will be given of 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 robot 414 will be referred to as a "terminal".

[1521] In today's digital society, users face many security risks. In particular, there is growing concern about threats such as phishing emails and unauthorized access. However, there are only a limited number of systems that can quickly provide specific advice on these security risks. Therefore, there is a need for a system that allows users to easily input their security concerns and worries, and provides appropriate advice based on that input.

[1522] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for accumulating the input and the advice as learning data, a means for providing an interface for inputting worries and anxieties about security, and a means for generating security advice based on information input through the interface. This enables the user to quickly receive specific advice about security.

[1523] "Means for accepting input from a user" refers to a device or software that provides an interface for a user to input their own worries and anxieties into the system.

[1524] A "natural language processing engine that generates advice" is an engine that uses artificial intelligence technology to analyze information entered by a user and generate appropriate advice.

[1525] The "means for providing the generated advice to the user" refers to a device or software for conveying the advice generated by the natural language processing engine to the user.

[1526] The "means for storing input and advice as learning data" refers to a device or software for storing the input information from the user and the generated advice in a database or the like and using it for later learning.

[1527] "Means for providing an interface for inputting security-related worries and anxieties" refers to a device or software that provides a dedicated interface for a user to input specific security-related worries and anxieties.

[1528] The "means for generating security advice" refers to a device or software for generating appropriate security measures and advice based on security information input by a user.

[1529] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the generated advice to the user, means for storing the input and advice as learning data, means for providing an interface for inputting security-related concerns and worries, and means for generating security advice based on information input through the interface.

[1530] System Programming

[1531] This system is realized by the following program. First, the user inputs their security-related worries and concerns using their smartphone. The input information is sent to the server through a web application using Flask. The server uses a natural language processing engine such as OpenAI's GPT-3 to generate appropriate advice based on the input information. The generated advice is returned to the user again through the Flask application.

[1532] Hardware and software used

[1533] Hardware: Smartphone

[1534] Software: Flask (Python web framework), OpenAI API

[1535] Data processing and calculation

[1536] 1. Accepting user input: The user inputs security-related worries or concerns using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried."

[1537] 2. Sending to the natural language processing engine: The Flask application accepts input from the user and sends it to OpenAI's GPT-3.

[1538] 3. Advice Generation: GPT-3 generates advice based on the input, for example, "Learn the characteristics of phishing emails and avoid opening suspicious emails."

[1539] 4. Return the advice: The generated advice is returned to the user via the Flask application.

[1540] Examples

[1541] If a user enters, "I'm worried because I've been receiving a lot of phishing emails lately," the system will generate advice like the following:

[1542] Examples of prompt statements

[1543] User's concern: I'm worried because there have been a lot of phishing emails lately.

[1544] advice:

[1545] By sending this prompt to GPT-3, appropriate advice is generated. The advice is returned to the user, allowing the user to quickly obtain specific security measures.

[1546] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1547] Step 1:

[1548] A user inputs security-related worries and anxieties using a smartphone. For example, the user might input, "I've been getting a lot of phishing emails lately, and I'm worried." This input is sent to the server through the Flask application interface. The input data is in text format, and specifically describes the user's worries and anxieties.

[1549] Step 2:

[1550] The server sends the user's input received through the Flask application to OpenAI's GPT-3. Specifically, it formats the user's input as a prompt sentence and sends a request to the GPT-3 API. An example of a prompt sentence is "User's concern: I've been getting a lot of phishing emails lately, so I'm worried\nAdvice:". Based on this prompt sentence, GPT-3 generates appropriate advice.

[1551] Step 3:

[1552] The server receives the advice returned by GPT-3. GPT-3 performs natural language processing based on the user's input and generates appropriate advice. For example, advice such as "Learn the characteristics of phishing emails and avoid opening suspicious emails" is generated. This advice is returned to the server in text format.

[1553] Step 4:

[1554] The server returns the received advice to the user through the Flask application. Specifically, it generates a response to display the generated advice on the user's smartphone. This response includes the generated advice, and the user can check the advice on the screen of their smartphone.

[1555] Step 5:

[1556] The server accumulates the user's input and the generated advice as training data. Specifically, it stores the input data and the advice data in a database. This data is later used for training the natural language processing engine. The database stores the user's input and the corresponding advice as a pair.

[1557] Step 6:

[1558] The server uses the accumulated learning data to train the natural language processing engine. Specifically, it periodically extracts learning data from the database and feeds it back to the natural language processing engine. This feedback improves the engine's accuracy, enabling it to generate more appropriate advice.

[1559] Example 2

[1560] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1561] Conventional systems have difficulty in providing prompt and appropriate advice to users on their concerns and complaints, and are unable to effectively accumulate that data and use it for learning. For this reason, a system that can improve user satisfaction is needed.

[1562] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1563] In this invention, the server includes a means for accepting input information from a user, a means for transmitting the input information to the server, a natural language processing engine for generating advice based on the input, a means for transmitting the advice generated by the engine from the server to a terminal, a means for providing the advice received by the terminal to the user, and a means for storing the input and the advice as learning data, thereby making it possible to quickly provide appropriate advice for the user's worries and dissatisfaction, and to effectively store the data and use it for learning.

[1564] A "user" is an individual or group that uses the system to input their own worries, complaints, and anxieties.

[1565] "Input information" refers to text data or other types of information that a user provides to the system through a terminal device.

[1566] A "server" is a computer system that receives input information from a user and passes it to a natural language processing engine.

[1567] A "terminal" is a device that a user uses to provide input information and receive advice from a server.

[1568] A "natural language processing engine" is software or algorithms that analyze input information from a user and generate appropriate advice.

[1569] "Advice" is a suggestion or solution that the natural language processing engine generates based on user input.

[1570] "Training Data" means a dataset containing user input and generated advice used to improve the performance of a natural language processing engine.

[1571] "Anonymization" is the process of removing personally identifiable information from input information in order to protect the user's personal information.

[1572] The present invention is a system that provides appropriate advice to users regarding their worries and dissatisfaction, accumulates the data, and uses it for learning. A specific embodiment of this system will be described below.

[1573] Hardware and Software Used

[1574] Terminal device: A computer or tablet connected to the company network

[1575] Server: A computer system that receives input information from the user and passes it to the natural language processing engine.

[1576] Natural language processing engines: For example, generative AI models (such as GPT-4)

[1577] Data processing and calculation flow

[1578] 1. Accepting user input

[1579] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device.

[1580] Example: A user uses a keyboard or touch screen on a terminal device to type, "There are not enough seats in the office."

[1581] 2. Sending input information

[1582] The terminal transmits the information entered by the user to the server.

[1583] Specific operation: The terminal sends the entered text data to the server as an HTTP request.

[1584] 3. Generating Advice

[1585] The server passes the received input information to a natural language processing engine to generate advice.

[1586] Specific operation: The text data received by the server is input into a generative AI model (e.g., GPT-4) and advice such as "Consider introducing flexible working" is generated.

[1587] 4. Submitting Advice

[1588] The server transmits the generated advice to the terminal.

[1589] Specific operation: The server sends the generated advice to the terminal as an HTTP response.

[1590] 5. Providing advice

[1591] The terminal provides the received advice to the user.

[1592] Specific operation: The device displays the received advice on the screen and the user confirms it.

[1593] 6. Data accumulation and learning

[1594] The server accumulates the user's input information and the generated advice as learning data.

[1595] What it does: The server stores the input information and advice in a database and later uses it to train the natural language processing engine.

[1596] Examples

[1597] User input: "There aren't enough seats in the office."

[1598] Generated advice: "Consider implementing flexible working."

[1599] Examples of prompt statements

[1600] Prompt: "What advice should you offer to employees if they feel there aren't enough seats in their office?"

[1601] The system provides users with advice to quickly resolve their concerns and complaints, and at the same time accumulates that data to help train the natural language processing engine.

[1602] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1603] Step 1:

[1604] Accepting user input

[1605] The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. Specifically, the user uses the keyboard or touch screen of the terminal device to input "There are not enough seats in the office." The input information is stored in the terminal as text data.

[1606] Step 2:

[1607] Sending input information

[1608] The terminal transmits the information input by the user to the server. In concrete terms, the terminal transmits the input text data to the server as an HTTP request. The input is text data, and the output is the transmission of a request to the server.

[1609] Step 3:

[1610] Generating Advice

[1611] The server passes the received input information to a natural language processing engine and generates advice. Specifically, the server inputs the received text data into a generative AI model (e.g., GPT-4) and generates advice such as "Consider introducing flexible work." The input is text data, and the output is the generated advice.

[1612] Step 4:

[1613] Sending Advice

[1614] The server sends the generated advice to the terminal. In concrete terms, the server sends the generated advice to the terminal as an HTTP response. The input is the generated advice, and the output is the response sent to the terminal.

[1615] Step 5:

[1616] Providing advice

[1617] The terminal provides the received advice to the user. In concrete terms, the terminal displays the advice received on the screen and the user confirms it. The input is the advice received from the server, and the output is the display to the user.

[1618] Step 6:

[1619] Accumulating and learning data

[1620] The server accumulates the user's input information and the generated advice as learning data. Specifically, the server saves the input information and advice in a database and later uses them to train the natural language processing engine. The input is the user's input information and the generated advice, and the output is saved in the database.

[1621] (Application example 2)

[1622] Next, a description will be given of 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 robot 414 will be referred to as a "terminal".

[1623] In today's information society, individuals and companies face a wide range of security risks. In particular, cyber attacks such as phishing emails and unauthorized access have been increasing, and appropriate measures against these attacks are required. However, many users do not have specialized knowledge about security, making it difficult for them to take appropriate measures. For this reason, a system is needed that allows users to easily input their security concerns and receive appropriate advice.

[1624] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1625] In this invention, the server includes means for accepting input from a user, a natural language processing engine for generating advice based on the input, means for providing the advice generated by the engine to the user, means for storing the input and the advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for storing the input information and the generated security measures as learning data. This enables a user to easily input security concerns and receive appropriate advice.

[1626] The "means for accepting input from the user" is an interface through which the user provides information to the system.

[1627] The "natural language processing engine for generating advice" is software or hardware for analyzing input information from a user and generating appropriate advice.

[1628] The "means for providing the generated advice to the user" is an interface for conveying the advice generated by the natural language processing engine to the user.

[1629] The "means for storing input and advice as learning data" refers to a system for storing input information from a user and generated advice in a database or the like, and using the information for future learning.

[1630] The "means for inputting user's security concerns" is an interface that allows the user to input security issues and fears into the system.

[1631] The "means for analyzing input information and generating appropriate security measures" refers to software or hardware for analyzing security-related input information from a user and proposing appropriate measures.

[1632] The "means for providing the generated security measures to the user" refers to an interface for communicating the security measures generated by the analysis to the user.

[1633] The "means for storing input information and generated security measures as learning data" refers to a system for storing input information from a user and generated security measures in a database or the like, and using them for future learning.

[1634] A system for implementing the present invention includes means for accepting input from a user, a natural language processing engine for generating advice, means for providing the generated advice to the user, means for accumulating the input and advice as learning data, means for inputting a user's security concerns, means for analyzing the input information and generating appropriate security measures, means for providing the generated security measures to the user, and means for accumulating the input information and the generated security measures as learning data.

[1635] Program processing explanation

[1636] Hardware used

[1637] Smartphone

[1638] Software used

[1639] Python

[1640] OpenAI API

[1641] SQLite (database)

[1642] Processing flow

[1643] 1. User Input

[1644] The user starts the smartphone application and inputs a security concern. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me."

[1645] 2. Sending input information

[1646] The input information is sent from the application to the natural language processing engine via OpenAI's API.

[1647] 3. Generating Advice

[1648] The natural language processing engine analyzes the input information and generates appropriate advice, such as "Points to identify phishing emails, and we recommend that you avoid clicking on links in emails."

[1649] 4. Providing advice

[1650] The generated advice is provided to the user through the application.

[1651] 5. Data accumulation

[1652] The input information and generated advice are stored in a SQLite database and used as learning data for future use.

[1653] Examples

[1654] If a user types, "I've been receiving a lot of phishing emails lately," the application will generate advice for the user, such as, "Points to identify phishing emails and how we recommend that you avoid clicking on links in emails," and provide it to the user.

[1655] Examples of prompt statements

[1656] User security concerns: I've been getting a lot of phishing emails lately.

[1657] Please provide appropriate advice.

[1658] In this way, a system is realized that allows users to easily input security concerns and receive appropriate advice.

[1659] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1660] Step 1:

[1661] A user starts a smartphone application and inputs a security concern. The input information is sent to the application in text format. For example, the user might input, "I've been getting a lot of phishing emails lately, and it's bothering me." The input data is text data that specifically describes the user's concern.

[1662] Step 2:

[1663] The device sends the input information to the natural language processing engine through OpenAI's API. The input data is sent as an API request. Specifically, the user's input text is sent to the API endpoint to request analysis. The input data is text data that includes the user's concerns, and the output data is the analysis result by the natural language processing engine.

[1664] Step 3:

[1665] The server analyzes the input information using a natural language processing engine and generates appropriate advice, such as "We recommend that you learn how to identify phishing emails and not click on links in emails." The input data is text data that includes the user's concerns, and the output data is text data of the generated advice.

[1666] Step 4:

[1667] The terminal provides the generated advice to the user. Specifically, the generated advice is displayed through an interface of the application. The input data is text data of the generated advice, and the output data is the text data of the advice displayed to the user.

[1668] Step 5:

[1669] The server stores the input information and the generated advice in a SQLite database. Specifically, it executes SQL queries to insert the user's input text and the generated advice into the database. The input data are the text data of the user's concerns and the generated advice, and the output data are the records stored in the database.

[1670] Step 6:

[1671] The server uses the accumulated data to train the natural language processing engine. Specifically, it analyzes the stored data and uses it as training data to improve the accuracy of the model. The input data is the text data of past input information and advice stored in the database, and the output data is the updated natural language processing model.

[1672] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1673] "Example 1"

[1674] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[1675] "Example 2"

[1676] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions contained in the input from the user and adjusts advice based on the emotions. Specifically, if the user inputs "the work is not going well," the emotion engine recognizes from the input that the user is feeling frustrated. Then, based on this recognition, the advice generated by the natural language processing engine is adjusted. For example, the advice "Why don't you reconsider how you are doing your work?" is adjusted to a form that shows empathy for the user's emotions, such as "I understand that you are feeling difficult. Why don't you take a breather and reconsider how you are doing your work." In addition, the emotion engine accumulates emotions contained in the input from the user as learning data. This enables the emotion engine to learn changes in the user's emotions and provide more appropriate advice.

[1677] The process flow of each embodiment will be described below.

[1678] "Example 1"

[1679] Step 1: A user inputs information through a terminal device. This input is text data including the user's concerns and problems.

[1680] Step 2: The entered text data is sent to the natural language processing engine via the internal network.

[1681] Step 3: The natural language processing engine analyzes the submitted text data and generates advice for the user's problem or concern.

[1682] Step 4: At the same time, the emotion engine recognizes the user's emotion from the transmitted text data.

[1683] Step 5: The emotion engine adjusts the advice generated by the natural language processing engine based on the recognized emotion.

[1684] Step 6: The tailored advice is sent to the terminal device through the internal network and presented to the user.

[1685] Step 7: Finally, the input from the user, the advice given to the user, and the recognized emotions are stored as learning data. This allows the system to learn the changes in the user's emotions and provide more appropriate advice.

[1686] Example 1

[1687] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1688] In conventional systems, it can be difficult to provide appropriate advice in response to user input. In addition, since the system cannot provide advice that takes into account the user's feelings, the user's satisfaction may decrease. Furthermore, there is a need to effectively accumulate input information and advice as learning data to improve the accuracy of the system.

[1689] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1690] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing emotions contained in the user's input and adjusting the advice based on the emotions, thereby making it possible to provide appropriate advice that takes the user's emotions into consideration and improve the accuracy of the system.

[1691] The "means for accepting input from the user" refers to a device or software that provides an interface for the user to input information such as their worries, dissatisfaction, and anxiety.

[1692] A "natural language processing engine" is software that analyzes information input by a user and generates appropriate advice based on that information.

[1693] The "means for providing advice to a user" refers to a device or software for displaying or notifying a user of advice generated by a natural language processing engine.

[1694] The "means for storing as learning data" refers to a device or software that stores the input information from the user and the generated advice in a database or the like for use in subsequent learning processes.

[1695] An "emotion engine" is software that analyzes emotions contained in a user's input and tailors the advice it generates based on those emotions.

[1696] An "anonymization means" is a device or software that converts information input by a user into a form that cannot identify an individual.

[1697] A "learning means" is a device or software that uses accumulated learning data to improve the performance of a natural language processing engine or an emotion engine.

[1698] The present invention is a system for accepting input from a user and providing appropriate advice. The system includes a terminal device for accepting input from a user, a natural language processing engine for analyzing the input information and generating advice, an emotion engine for analyzing the user's emotions, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[1699] Hardware and Software Used

[1700] Hardware: Terminal devices connected to the company network

[1701] Software: Natural language processing engine, emotion engine

[1702] Data processing and calculation flow

[1703] 1. Accepting user input

[1704] Terminal: The user inputs his / her worries, dissatisfaction, and anxiety through the terminal device interface. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[1705] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[1706] 2. Sending input information

[1707] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[1708] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[1709] 3. Generating Advice

[1710] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[1711] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[1712] 4. Providing advice

[1713] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[1714] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[1715] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[1716] 5. Accumulation of learning data

[1717] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[1718] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[1719] Examples

[1720] Sample prompt 1: "There aren't enough seats in the office."

[1721] Expected advice: "Consider implementing flexible working."

[1722] Specific operation: The user inputs "There are not enough seats in the office" and the terminal device sends the information to the server. The server calls the natural language processing engine, generates advice, and returns it to the terminal device. The terminal device displays the advice to the user.

[1723] Sample prompt 2: "My job isn't going well."

[1724] Expected advice: "I know this is tough. I think it might be time to take a breather and reevaluate how you're doing your job."

[1725] Specific operation: The user inputs "My work is not going well," and the terminal device sends the information to the server. The server calls the emotion engine to perform emotion analysis, and the natural language processing engine generates advice based on the results. The generated advice is returned to the terminal device and displayed to the user.

[1726] The system aims to reduce users' worries and anxieties by processing user input in real time and providing appropriate advice.

[1727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1728] Step 1:

[1729] Accepting user input

[1730] User: The user inputs his / her worries, dissatisfaction, and anxiety through the interface of the terminal device. For example, the user types "There aren't enough seats in the office" into a text box displayed on the terminal screen.

[1731] Input: Text data entered by the user.

[1732] Specific operation: The terminal device receives the text entered by the user in real time and temporarily stores it in its internal memory.

[1733] Step 2:

[1734] Sending input information

[1735] Terminal: The terminal device sends the information input by the user to the server. Specifically, it sends the input information to the server using an HTTP request.

[1736] Input: Text data entered by the user.

[1737] Output: Text data sent to the server

[1738] Specific operation: The terminal device converts the input text data into JSON format and sends a POST request to the server's API endpoint.

[1739] Step 3:

[1740] Generating Advice

[1741] Server: The server passes the received input information to the natural language processing engine, which generates advice based on the input information. If an emotion engine is included, it analyzes the emotions contained in the input information and adjusts the advice.

[1742] Input: Text data sent to the server

[1743] Output: Generated advice

[1744] Specific operation: The server calls the emotion engine to perform emotion analysis before passing the input information to the natural language processing engine. Based on the results of the emotion analysis, the natural language processing engine generates advice such as "Consider introducing flexible working."

[1745] Step 4:

[1746] Providing advice

[1747] Server: The server sends the generated advice to the terminal device. Specifically, the server returns the advice as an HTTP response.

[1748] Terminal: The terminal device displays the received advice to the user. For example, a message such as "Consider introducing flexible working" is displayed on the screen.

[1749] Input: Generated advice

[1750] Output: Advice displayed to the user

[1751] Specific operation: The server returns the generated advice in JSON format to the terminal device, which parses the data and displays it in the user interface.

[1752] Step 5:

[1753] Accumulation of learning data

[1754] Server: The server stores the input information and generated advice in a database, which is then used as learning data for the natural language processing engine and emotion engine.

[1755] Input: User input and generated advice

[1756] Output: Training data stored in a database

[1757] What happens: The server inserts the input and advice pair into a database for future use in the learning process.

[1758] (Application example 1)

[1759] Next, a description will be given of 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 robot 414 will be referred to as a "terminal".

[1760] In modern society, users often have concerns or questions about security, but there is a lack of means to quickly provide appropriate advice on these issues. In addition, it is difficult to provide advice that takes into account the user's feelings, which makes it difficult to fully alleviate the user's concerns.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1762] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for recognizing the user's emotions and adjusting the advice based on the emotions, thereby making it possible to quickly provide appropriate advice that takes emotions into consideration in response to the user's security concerns and questions.

[1763] A "means for accepting input from a user" is an interface through which a user can input security concerns or questions in the form of text, voice, or the like.

[1764] A "natural language processing engine" is a software engine that analyzes input from a user and generates appropriate advice.

[1765] The "means for providing advice to a user" refers to a means for displaying or notifying the user of the generated advice.

[1766] The "means for storing as learning data" refers to a means for storing the user's input and the generated advice in a database or the like and using it for future learning.

[1767] An "emotion engine" is a software engine for recognizing emotions in a user's input and tailoring advice based on those emotions.

[1768] To implement this invention, a terminal device for users to input security concerns and questions, and a server for processing the input information are required. The terminal device can be a device such as a smartphone or smart glasses.

[1769] The server includes the following means:

[1770] 1. A way to accept input from the user:

[1771] The user inputs security concerns or questions in text or voice format through the terminal device, which is then transmitted to the server via the terminal device interface.

[1772] 2. Natural Language Processing Engine:

[1773] The server uses a natural language processing engine to analyze the input from the user and generate appropriate advice. The engine is implemented using the OpenAI API. It analyzes the input text and generates relevant advice.

[1774] 3. Means of providing advice to users:

[1775] The server transmits the generated advice to the terminal device and displays or notifies the user, thereby enabling the user to receive the advice in real time.

[1776] 4. Methods for accumulating training data:

[1777] The server stores the user's input and the generated advice in a database for future learning, helping to improve the accuracy of the natural language processing engine.

[1778] 5. Emotion Engine:

[1779] The server uses an emotion engine to recognize emotions in the user's input and adjust the advice based on the emotion. The engine is implemented using emotion analysis tools such as TextBlob. The emotion engine analyzes the emotion of the user's input text and determines whether it is positive or negative.

[1780] As a concrete example, if a user enters, "I've been seeing a lot of suspicious people around my house lately and it makes me feel anxious," the emotion engine will recognize the anxiety and generate advice like the following:

[1781] Example prompt:

[1782] User input: I've been seeing a lot of suspicious people around my house lately and it's making me feel uneasy.

[1783] Our advice: Don't worry - first check with your local police and consider installing security cameras.

[1784] In this way, advice that is in tune with the user's emotions can be provided.

[1785] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1786] Step 1:

[1787] A user inputs security concerns or questions using a terminal device. The input is in the form of text or voice and is transmitted to a server via an interface of the terminal device. The input data is text data including the user's concerns or questions.

[1788] Step 2:

[1789] The server sends the received user input data to the natural language processing engine. The natural language processing engine uses OpenAI's API to analyze the input data and generate appropriate advice. Based on the input data, data processing and data calculations are performed to generate relevant advice. The output is text data of the generated advice.

[1790] Step 3:

[1791] The server sends the generated advice to the emotion engine, which uses emotion analysis tools such as TextBlob to analyze the emotion of the user's input text. The emotion engine determines the emotion of the input text as positive or negative, and adjusts the advice based on the result. The output is the text data of the adjusted advice.

[1792] Step 4:

[1793] The server transmits the adjusted advice to the terminal device. The terminal device displays or notifies the received advice to the user. This allows the user to receive the advice in real time. The output is text data of the advice that is displayed to the user.

[1794] Step 5:

[1795] The server stores the user's input data and the generated advice in a database. The stored data is used for future learning, which improves the accuracy of the natural language processing engine and the emotion engine. The output is the learning data stored in the database.

[1796] In this way, it is possible to quickly provide appropriate advice that takes into account the user's emotions in response to their security concerns or questions.

[1797] Example 2

[1798] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1799] In conventional systems, it was difficult to provide appropriate advice based on user input, and it was particularly difficult to generate advice that took the user's emotions into account. Another challenge was to effectively accumulate the user's input information and the generated advice as learning data to improve the accuracy of the system.

[1800] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1801] In this invention, the server includes a means for accepting input from a user, a natural language processing engine for generating advice based on the input, a means for providing the advice generated by the engine to the user, a means for storing the input and the advice as learning data, and an emotion engine for analyzing emotions contained in the input and adjusting the advice. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, and furthermore, by storing the input information and advice as learning data, it becomes possible to improve the accuracy of the system.

[1802] The "means for accepting input from a user" is a device that provides an interface for a user to input their own worries, dissatisfaction, and anxieties in text form.

[1803] A "natural language processing engine" is an engine that analyzes information input by users and generates appropriate advice, and uses a generative AI model.

[1804] The "means for providing advice to a user" is a device for displaying advice generated by the natural language processing engine to a user.

[1805] The "means for storing as learning data" is a device for storing the user's input information and the generated advice in a database and using it for future learning.

[1806] An "emotion engine" is an engine that analyzes emotions contained in input from a user and adjusts advice based on those emotions.

[1807] An "anonymization means" is a device for converting information input by a user into a form that does not identify an individual.

[1808] The "learning means" is a device for improving the performance of the natural language processing engine using accumulated training data.

[1809] This invention is a system for providing appropriate advice for a user's worries, dissatisfaction, and anxiety. The system includes a terminal device that accepts input from a user, a natural language processing engine that analyzes the input information and generates advice, an emotion engine that analyzes emotions and adjusts the advice, a means for providing the generated advice to the user, and a means for accumulating the input information and advice as learning data.

[1810] Hardware and Software Used

[1811] Terminal equipment

[1812] The terminal device provides an interface for receiving input from the user. The user inputs his / her worries, dissatisfaction, and anxiety in text format through this interface. For example, an employee inputs a worry that "there are not enough seats in the office."

[1813] server

[1814] The server receives input information sent from the terminal device and passes it to the natural language processing engine and the emotion engine. The server also transmits generated advice to the terminal device and accumulates the input information and advice as learning data.

[1815] Natural Language Processing Engine

[1816] The natural language processing engine uses a generative AI model (e.g., GPT-4) to analyze input information from the user and generate appropriate advice, such as "consider introducing flexible working."

[1817] Emotion Engine

[1818] The emotion engine analyzes the emotions contained in the user's input and adjusts advice based on those emotions. For example, if a user types, "I'm not doing well at work," the emotion engine will recognize the frustration and adjust the advice to, "I understand that this is tough. Why don't you take a breather and reconsider how you're going about your work."

[1819] Accumulation of learning data

[1820] The server stores the user's input information and the generated advice in a database and uses it for future learning, which can improve the accuracy of the natural language processing engine and the emotion engine.

[1821] Examples of concrete examples and prompts

[1822] Examples

[1823] A user inputs a concern such as "There aren't enough seats in the office" into a terminal device. The terminal device sends this information to a server, which passes it to a natural language processing engine. The natural language processing engine generates advice such as "Consider introducing flexible working." The emotion engine adjusts this advice, and the server sends it to the terminal device. The terminal device ...

Claims

1. an input means for inputting a user's security concerns; means for analyzing the input and generating security countermeasures; an emotion engine that recognizes emotions in the user input and adjusts generated security countermeasures based on the emotions; means for providing coordinated security measures to said user; A means for accumulating the security concerns and the adjusted security measures as learning data; A system including:

2. The system of claim 1 further comprising means for removing said user-identifiable elements.

3. The system of claim 1 , further comprising means for training the emotion engine using the training data.

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