System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving consultation contents from users; means for generating answers suitable for the users based on the received consultation contents using a generative AI model; means for providing the generated answers to the users; means for recording the past consultation contents and answers and using them as learned data; and means for linking the consultation contents with manned windows and medical institutions as necessary.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, many people have mental health issues and want to seek advice. However, existing telephone counseling services, social media counseling services, and medical institutions are difficult to access and the hurdles to seeking advice are high, meaning users are unable to receive adequate support. To solve this problem, a more convenient and personalized consultation service is needed. [Means for solving the problem]

[0005] The present invention utilizes a generative AI model to provide an environment where users can consult at their own convenience, 24 hours a day, 365 days a year. Specifically, the system includes a means for receiving consultation content from users, a means for generating appropriate answers for users based on the received consultation content using a generative AI model, a means for providing the generated answers to users, a means for recording past consultation content and answers and using them as learning data, and a means for sharing the consultation content with manned counters or medical institutions as needed. The system also includes a means for conducting a personality assessment of users and saving the results as a profile, and a means for automatically generating and updating a digital medical record based on the consultation content and its answers. This provides easy and appropriate psychological support, realizing an environment where users can receive professional assistance at the appropriate time.

[0006] "User" refers to an individual who receives consultation or support through this system.

[0007] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate appropriate answers to inquiries.

[0008] A "personality assessment" refers to a series of questions or tests designed to understand a user's personality and behavioral characteristics.

[0009] A "profile" refers to data that indicates an individual's characteristics and condition, created based on the results of a personality test and the user's consultation history.

[0010] A "digital medical record" refers to electronic data that records the user's consultation details and responses, and centrally manages the user's condition.

[0011] "Learning data" refers to data that the generative AI model uses to learn from past consultation content and response history and improve accuracy.

[0012] A "staffed desk" refers to a department or service within the system that is staffed by human counselors or support staff.

[0013] "Medical institution" refers to an organization or facility that provides medical services and employs doctors and specialized staff.

[0014] "Risk level" refers to the degree of mental or health risk that the generative AI model determines based on the user's consultation content. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single 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), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] 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.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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.

[0029] 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 (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" 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 process 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output 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.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0036] This invention relates to a system that provides an environment where users with concerns can seek advice 24 hours a day, 365 days a year, anywhere, at their own pace. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records.

[0037] The server first receives a consultation message from the user. For example, if a user sends a message through the LINE app saying, "I've been feeling stressed at work lately," this triggers the server to start working.

[0038] The server then passes the consultation message to a generative AI model, which generates the optimal answer based on the consultation content. Specifically, the AI ​​analyzes past data and the user's personality profile to generate a specific and appropriate response such as, "That's tough. What specifically is causing you stress?"

[0039] The generated answer is provided to the user via the server and displayed on the user's device. The user can continue asking questions or seeking advice based on this answer. For example, the user can add, "I'm having trouble communicating with my boss," and the AI ​​will analyze that message as well and generate the next answer.

[0040] The system also has the ability to diagnose users' personalities. Users answer a personality assessment form sent from the server, asking questions such as, "When do you feel stressed?" The results are saved as a profile and used for subsequent analysis.

[0041] Another important feature of this invention is the automatic generation and updating of digital medical records. The server records and updates the details of inquiries sent by users and the responses to those inquiries as digital medical records. This allows for centralized management of users' conditions and, if necessary, provides that data to staffed counters or medical institutions.

[0042] The generative AI model also determines risk levels. It detects mental or health risks from the user's consultation content, and if a high risk is detected, the server immediately escalates the situation to a manned help desk or appropriate medical institution. For example, if a serious message such as "I'm thinking about suicide" is received, the server immediately contacts a counselor or doctor and arranges for appropriate support.

[0043] In this way, the present invention is a system that provides personalized psychological support to users who are troubled, allowing them to receive professional assistance at the appropriate time.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The server receives a "Register" message from the user via the LINE app.

[0047] Step 2:

[0048] The server generates a user ID based on the received "Register" message and registers the user information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[0049] Step 3:

[0050] The server sends a personality test form to users who have completed registration via the LINE app.

[0051] Step 4:

[0052] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[0053] Step 5:

[0054] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[0055] Step 6:

[0056] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[0057] Step 7:

[0058] The server receives a consultation message from the user as a trigger and passes the message to the generative AI model.

[0059] Step 8:

[0060] The generative AI model generates the optimal answer based on the received consultation content, the user's personality profile, and past consultation history.

[0061] Step 9:

[0062] The server then sends the generated answer to the user via the LINE app, sending a message such as, "That's tough. What specifically is causing you stress?"

[0063] Step 10:

[0064] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record.

[0065] Step 11:

[0066] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take. For example, if the user adds, "I'm having trouble communicating with my boss," the message is sent to the server again.

[0067] Step 12:

[0068] The server resends any additional messages from the user to the generative AI model, generates a new answer, and sends it to the user.

[0069] Step 13:

[0070] The server uses the generative AI model to determine the risk level based on the user's consultation and response. If the risk is deemed high, the server escalates the situation to a manned consultation desk or an appropriate medical institution.

[0071] Step 14:

[0072] If escalation is necessary, the server will send the necessary digital medical records and chat summaries to a manned desk or medical institution and arrange for appropriate response.

[0073] Example 1

[0074] Next, a description will be given of 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."

[0075] Conventional systems were unable to respond immediately to users' concerns and stress, making it difficult to provide 24 / 7 support. Furthermore, they did not provide sufficient personalized support based on the content of the user's consultation, making it difficult to provide appropriate answers or assess risks according to the user's situation. Furthermore, there was a lack of a means to comprehensively manage users' consultation history and personality assessment results, making it difficult to effectively create and update digital medical records. New technology is needed to solve these issues.

[0076] 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.

[0077] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for transmitting the consultation content using the user's terminal and displaying the generated answer, means for diagnosing the user's personality, means for saving the results of the personality diagnosis as the user's profile, means for recording past consultation content and answers and using them as learning data, means for automatically generating and updating the user's digital medical record based on the consultation content and its answers, means for determining mental or health risks from the user's consultation content, and means for sharing the consultation content with a manned counter or medical institution as needed. This allows users to continue consultation anywhere, 24 hours a day, 365 days a year, and provides personalized support, enabling them to quickly receive appropriate professional assistance as needed.

[0078] "User" refers to a person who uses the system to input and send consultation details.

[0079] "Consultation content" refers to the concerns or questions that users input and send through the system.

[0080] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the content of the consultation received.

[0081] A "prompt" is a text sentence that is input to a generative AI model to generate the optimal answer.

[0082] A "server" is a computer system that manages and controls the entire system and processes and transmits and receives various data.

[0083] An "answer" is a response generated by the generative AI model based on the content of the consultation.

[0084] "Terminal" refers to the device (smartphone, PC, etc.) used by the user to input and send the inquiry content and receive and display the response.

[0085] A "personality test" is a test conducted to analyze a user's personality and tendencies.

[0086] A "profile" is data that includes a user's personality and personality traits, created based on the results of a personality test and other information.

[0087] "Learning data" refers to data that records past consultation content and responses to those consultations and is used to improve the performance of the generative AI model.

[0088] A "digital medical record" is a centralized data management system that is automatically generated and updated based on the user's consultation details, responses, and personality test results.

[0089] "Risk assessment" refers to the generative AI model analyzing the user's consultation content and assessing and determining mental or health risks.

[0090] A "staffed counter" is a counter where specialized staff or counselors are always on hand to respond as needed.

[0091] A "medical institution" is a facility staffed by professionals such as doctors and counselors that provides medical and psychological support to users.

[0092] The present invention is a system that provides an environment where users with concerns can receive consultation at their own pace, 24 hours a day, 365 days a year. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records. A specific embodiment of this system is described below.

[0093] Hardware and Software Configuration

[0094] The entire system consists of three components: a server, a terminal, and a user. Specifically, the following hardware and software are used:

[0095] Server: A cloud-based system is used for data processing and management. In this case, the server infrastructure of Amazon Web Services (AWS) is used.

[0096] Device: A device such as a smartphone or computer used by a user, on which the LINE app or other chat tools are installed.

[0097] Generative AI models: Use advanced generative AI models such as OpenAI's GPT-4 to generate appropriate answers based on the content of the consultation.

[0098] Data processing and calculation flow

[0099] 1. Message received:

[0100] The user inputs the content of their problem using an app such as LINE and sends it. For example, they can send a message saying, "I've been feeling stressed at work lately."

[0101] The server receives the message via the API of the chat tool, such as the LINE API.

[0102] 2. Generate and send a prompt:

[0103] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt such as, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[0104] Send the prompt to the generative AI model.

[0105] 3. Generate answers:

[0106] The generative AI model generates an appropriate response based on the prompt it receives, such as, "That's tough. What specifically is causing you stress?"

[0107] 4. Providing answers:

[0108] The server receives the generated response and transmits it to the user's terminal.

[0109] The answer will be displayed on the user's device using the LINE app or other similar app.

[0110] Personality assessment and digital medical record creation / update

[0111] 1. Personality Test:

[0112] The server generates a questionnaire to assess the user's personality and sends it to the user. For example, it sends a question such as "When do you feel stressed?"

[0113] The user answers the personality test form and sends it to the server, which stores the received answers in a database.

[0114] 2. Digital medical record generation and updating:

[0115] The server automatically generates a digital medical record based on the user's consultation, responses, and personality test results.

[0116] The generated digital medical records are updated regularly, and the user's status is managed centrally.

[0117] Risk Level Determination and Escalation

[0118] 1. Determine the risk level:

[0119] The generative AI model analyzes the content of the user's consultation and determines mental or health risks. For example, if a serious message such as "I'm thinking about suicide" is received, the generative AI model will determine that there is a high risk.

[0120] 2. Escalation:

[0121] If a person is deemed to be at high risk, the server will immediately escalate the situation to a manned help desk or medical institution, contacting a counselor or doctor and arranging for appropriate assistance.

[0122] Examples of prompt statements

[0123] "A user submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[0124] "To create a personality profile for your users, generate questions like: 'When do you feel stressed?'"

[0125] This completes the detailed description of the present invention, which allows the system to provide 24 / 7 support, automatically generate personalized answers, and provide users with fast, professional assistance when needed.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] The server receives a consultation message from the user. When the user sends a message such as "I've been feeling stressed at work lately" via a chat tool such as the LINE app, the server receives this message via the LINE API. The input is the user's message, which is temporarily stored in a database. The output is the received message data.

[0129] Step 2:

[0130] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt like, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer." The input is the message received from the user, and the output is the generated prompt.

[0131] Step 3:

[0132] The server sends the generated prompt to a generative AI model. For example, it sends the prompt to OpenAI's GPT-4 through an API. The input is the generated prompt, and the output is the operation of sending the prompt to the generative AI model.

[0133] Step 4:

[0134] The generative AI model generates the optimal answer based on the prompt it receives. The input is the prompt sent from the server, and the output is the generated answer (e.g., "That's tough. What exactly is causing you stress?").

[0135] Step 5:

[0136] The server receives the answer returned from the generative AI model. The input is the answer from the generative AI model, which is temporarily stored in a database. The output is the received answer data.

[0137] Step 6:

[0138] The server sends the received answer to the user's device. The answer is displayed on the user's chat screen using the LINE API or similar. The inputs are the answer received from the generative AI model and the user's device information, and the output is the answer displayed on the user's device.

[0139] Step 7:

[0140] The user checks the answer sent from the server, then inputs and sends a new message to continue the consultation. For example, the user inputs "I'm having trouble communicating with my boss" and sends it. The input is the answer from the server, and the output is a new message from the user.

[0141] Step 8:

[0142] The server generates a question form to diagnose the user's personality and sends it via the LINE app, etc. For example, it generates a question such as "When do you feel stressed?" The input is the user's profile information and past consultation details, and the output is the generated question form.

[0143] Step 9:

[0144] The user answers the personality test form and sends it to the server. The input is the question form sent from the server, and the user enters the answers. The output is the user's answer data.

[0145] Step 10:

[0146] The server receives the user's answers and stores them in a database. The input is the answer data sent by the user, and the output is the answers stored in the database.

[0147] Step 11:

[0148] The server automatically generates and updates a digital medical record based on the collected consultation details, answers, and personality test results. The input is all data from the user, and the output is the digital medical record.

[0149] Step 12:

[0150] Using a generative AI model, the server determines mental or health risks based on the user's consultation. The input is all consultation details and responses from the user, and the data is analyzed. The output is a risk assessment result.

[0151] Step 13:

[0152] If necessary, the server will share the consultation details with a manned help desk or a medical institution. If the consultation is judged to be high risk, the server will perform an emergency escalation and arrange for appropriate professional support. The input is the risk assessment result, and the output is the linked professional support information.

[0153] These are the specific processing steps of this system. This system provides an environment where users can consult with us 24 hours a day, 365 days a year, and can provide personalized support quickly.

[0154] (Application example 1)

[0155] Next, a description will be given of Application 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."

[0156] In today's information society, security-related worries and anxieties are increasing day by day. In particular, the number of inquiries about password management and phishing scams is rapidly increasing, creating a growing demand for systems that can quickly and appropriately respond to such problems. Conventional consultation systems have difficulty providing personalized advice to individual users, and few systems are available 24 hours a day, 365 days a year. Therefore, from a security perspective, there is an increasing need for systems that can quickly respond to users' worries and questions.

[0157] 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.

[0158] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with manned help desks or medical institutions as necessary, and means for providing personalized advice on security-related concerns, thereby enabling users to quickly resolve their security-related concerns and questions 24 hours a day, 365 days a year.

[0159] "User" refers to an individual who uses the system to receive consultation or support.

[0160] The "means for receiving consultation content" is a system function for receiving messages sent by users.

[0161] A "generative AI model" is a model that uses artificial intelligence (AI) technology to analyze the content of inquiries from users and generate optimal answers.

[0162] The "means for generating an answer" is a system function that uses a generative AI model to create the optimal answer based on the user's inquiry.

[0163] The "means for providing an answer" is a system function for displaying or notifying the user of the generated answer.

[0164] "Means for recording past consultation content and responses and using them as learning data" refers to a system function that saves users' consultation history and responses to those requests and uses them for future responses.

[0165] "Means for communicating consultation details to manned counters or medical institutions" is a system function for transmitting the user's consultation details to a human counselor or medical institution as needed.

[0166] "Means for providing personalized advice on security-related concerns" refers to a system function that uses generative AI models to provide personalized advice on security-related issues faced by users.

[0167] This invention is implemented primarily through an application on a smartphone. The components and their operations will be described in detail below.

[0168] The server receives the content of the inquiry from the user. Specifically, the content of the inquiry entered through a smartphone application is sent to the server. For example, a user may send a message saying, "I'm having a hard time managing my passwords these days."

[0169] The server then uses a generative AI model to analyze the consultation and generate an appropriate response. The generative AI model is an artificial intelligence that has learned from training data in advance, and generates the optimal response based on the user's consultation and past data. For example, the AI ​​might generate a specific response such as, "That's a difficult problem. Why don't you consider using a password management tool?"

[0170] The generated answer is provided to the smartphone application via the server and displayed on the user's device. The user can continue to ask questions or receive advice based on this answer. For example, they can enter additional information such as, "What kind of password management tool is best?"

[0171] In addition, the server records the consultation content and the generated answers and uses them as learning data. This information is used to generate more accurate answers for future consultations. More personalized answers are provided based on past history.

[0172] If necessary, the server will also have the ability to share the consultation details with a manned helpline or medical institution. For example, if a user sends a serious message such as "I'm thinking about suicide," the server can immediately contact a counselor or doctor and arrange for appropriate support.

[0173] The system also provides advice on security-related concerns, leveraging generative AI models to provide personalized advice to users regarding their security issues, enabling users to quickly resolve their security concerns anywhere, 24 hours a day, 365 days a year.

[0174] The hardware used includes smartphones and servers, and the software uses the OpenAI API for generative AI models and Flask (a Python microweb framework) for data exchange.

[0175] For example, if a user sends a message saying, "I'm worried about my security these days. What can I do?" the app will respond with, "That sounds worrying. It's important to create a strong password and change it regularly."

[0176] As a further example, let's consider the case where a user sends a message saying, "I received a phishing email, what should I do?" In this case, the application responds with, "Do not open the email. It is important not to click on suspicious links and to verify the sender before acting."

[0177] Here are some example prompts:

[0178] "Message from a user: I'm worried that my password has been leaked recently. What should I do?

[0179] Good AI response: First, change your password and, if possible, set up two-factor authentication.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] The user inputs and sends the content of their inquiry through a smartphone application. For example, the input may include a message such as, "I'm having a hard time managing my passwords recently." This is then sent to the server.

[0183] Step 2:

[0184] The server receives the user's inquiry. In order to analyze the received message, it processes it as input data for the generative AI model. Specifically, it stores the received message in text format and prepares it for analysis.

[0185] Step 3:

[0186] The server analyzes the received consultation content using a generative AI model. In this step, the consultation content (input) is passed to the generative AI model, and the model generates the optimal answer. For example, in response to the consultation content "I've been having trouble managing my passwords lately," the server generates an answer (output) such as "That's tough. Why don't you consider using a password management tool?"

[0187] Step 4:

[0188] The server receives the generated answer and provides it to the user. The generated answer is then passed to a smartphone application and displayed on the user's device, allowing the user to receive appropriate advice in real time.

[0189] Step 5:

[0190] The server records the user's inquiry and the generated answer, and saves it as learning data. The recorded data is used as a database to provide more accurate answers to future inquiries.

[0191] Step 6:

[0192] If necessary, the server will contact a staffed help desk or medical institution to discuss the issue. For example, if the issue is serious, such as "considering suicide," the server will immediately contact a counselor or doctor and arrange for appropriate support.

[0193] Step 7:

[0194] The server uses a generative AI model to generate personalized advice for security-related concerns and provides it to users. When a user asks, "I've been worried about security lately. What are some good measures?", the server handles everything from receiving the message to generating and providing an answer, providing individually tailored advice.

[0195] Furthermore, an emotion engine that estimates the user's emotion may be 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.

[0196] This invention relates to a system that receives inquiries from users and provides more adaptive and personalized answers by combining a generative AI model and an emotion engine. This system provides an environment where users can consult at their own pace, 24 hours a day, 365 days a year.

[0197] The server first receives a "Register" message from the user via the LINE app. After receiving the message, the server generates a user ID and registers the user's information in a database. After registration is complete, the server sends the user a personality assessment form via the LINE app, and the user enters and submits their answers. Based on the answers, the server generates a personality profile for the user and stores it in a database.

[0198] When a consultation begins, the user enters the content of the consultation through the LINE app and sends it to the server. For example, they can send a message such as, "I've been feeling stressed at work lately." The server then passes this message to the generative AI model and emotion engine.

[0199] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it determines emotional states such as "anxiety" or "anger" from emotional expressions and keywords in the text. The results are provided to the generative AI model.

[0200] The generative AI model generates the optimal answer based on the user's personality profile, past consultation history, and emotional state analyzed by the emotion engine. For example, it might generate an answer along the lines of, "That's tough. What specifically is causing you stress?" The server then sends this answer to the user via the LINE app.

[0201] The server records the consultation details and the answers generated, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[0202] The system, which uses an emotion engine, can analyze the user's emotional state in real time and respond accordingly. For example, if a user sends a serious message such as "I'm thinking about suicide," the emotion engine will determine this as "high risk," and the server will immediately escalate the situation to a manned help desk or an appropriate medical institution.

[0203] If necessary, the server sends the digital medical record and chat summary to a staffed reception desk or medical institution and arranges for appropriate response.In this way, the present invention is a system that combines an emotion engine and a generative AI model to provide personalized psychological support to users and enable them to receive professional assistance at the appropriate time.

[0204] The processing flow will be explained below.

[0205] Step 1:

[0206] The server receives a "Register" message from the user via the LINE app.

[0207] Step 2:

[0208] The server generates a user ID based on the received "Register" message and registers the user's information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[0209] Step 3:

[0210] The server sends a personality test form to users who have completed registration via the LINE app.

[0211] Step 4:

[0212] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[0213] Step 5:

[0214] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[0215] Step 6:

[0216] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[0217] Step 7:

[0218] The server receives consultation messages from users and passes them to the emotion engine and generative AI model.

[0219] Step 8:

[0220] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it detects emotions such as "anxiety" or "stress." The results are provided to the generative AI model.

[0221] Step 9:

[0222] The generative AI model generates the optimal answer based on the emotional state analyzed by the emotion engine, the user's personality profile, and past consultation history. For example, it might generate a response such as, "That's tough. What specifically is causing you stress?"

[0223] Step 10:

[0224] The server sends the generated answer to the user through the LINE app, and the user can receive the answer and decide what to do next.

[0225] Step 11:

[0226] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take, for example, sending an additional question such as "I'm having trouble communicating with my boss" to the server again.

[0227] Step 12:

[0228] The server then passes any additional messages from the user back to the emotion engine and generative AI model to generate new answers.

[0229] Step 13:

[0230] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[0231] Step 14:

[0232] The emotion engine analyzes the user's emotional state in real time, and if it determines that the risk is high, it flags the user as "high risk" and notifies the server of the result.

[0233] Step 15:

[0234] If the emotion engine determines that the situation is "high risk," the server immediately escalates the situation to a manned reception desk or an appropriate medical institution. The server then sends the necessary digital medical records and chat summaries to the manned reception desk or medical institution, and arranges for the appropriate response.

[0235] Example 2

[0236] Next, a description will be given of 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."

[0237] Conventional consultation systems have had difficulty providing personalized answers that fully consider the user's emotional state. Furthermore, they lacked mechanisms for providing prompt and appropriate responses when the user was in a serious emotional state. Furthermore, they were unable to effectively utilize past consultations and their responses as learning data.

[0238] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the user, a means for generating an answer appropriate for the user based on the received content of the consultation using a generation AI model, a means for determining the emotional state of the user from the content of the consultation using an emotion analysis engine, a means for providing the generated answer to the user, a means for recording past consultation contents and answers and using them as learning data, and a means for sharing the consultation contents with a manned counter or a medical institution as necessary. This makes it possible to analyze the emotional state of the user in real time and provide a personalized answer based on the analysis. It also enables rapid escalation for users in serious emotional states.

[0239] "Consultation content" refers to text data such as questions, concerns, and opinions provided by users through the system.

[0240] A "generative AI model" is an algorithm or engine that uses machine learning and artificial intelligence techniques to generate appropriate outputs for input data.

[0241] An "emotion analysis engine" is software or algorithms that analyze and determine emotions and emotional states from text data.

[0242] A "profile" is a data set that centrally compiles information such as a user's personality, characteristics, and past consultation details.

[0243] A "digital medical record" is an electronic information file that records and centrally manages various digital data about users, specifically consultation details and response history.

[0244] "Escalation" is the process by which the system communicates and reports to a higher level responder or expert agency when it recognizes a serious problem or high-risk consultation.

[0245] This invention relates to a consultation system and describes a specific method for providing personalized answers to users. By combining a generative AI model and an emotion analysis engine, the system generates adaptive answers according to the user's emotional state and can respond quickly when necessary.

[0246] The main components of the system include:

[0247] 1. Means of receiving inquiries from users

[0248] 2. A means of generating appropriate answers based on the received consultation content using a generative AI model

[0249] 3. A means of determining the user's emotional state from the content of the consultation using an emotion analysis engine

[0250] 4. How to provide generated answers to users

[0251] 5. Recording past consultations and responses and using them as learning data

[0252] 6. A means of communicating consultation details to a staffed help desk or medical institution as needed

[0253] Hardware and Software Use

[0254] Hardware: Server (a server machine with a powerful CPU, GPU, and sufficient memory)

[0255] Software: LINE Platform, database system, generative AI model, sentiment analysis engine

[0256] Program processing

[0257] User Registration:

[0258] The user sends a "Register" message through the LINE app. The server receives this message, generates a user ID, and registers the user's information in the database. It also sends a personality assessment form, receives the user's answers, analyzes them, generates a personality profile, and saves it in the database.

[0259] Receiving and analyzing consultation content:

[0260] The user inputs and sends the content of their problem through the LINE app. For example, they send a message saying, "I've been feeling stressed at work lately." The server receives this message and passes it to the generative AI model and emotion analysis engine. The emotion analysis engine analyzes the content of the problem and determines the user's emotional state. The result of this determination is provided to the generative AI model, which generates the optimal answer.

[0261] Submit and save your answers:

[0262] The server then sends the generated response to the user via the LINE app. The consultation details and responses are recorded in a database and saved and updated as a digital medical record. If a serious message is included, the emotion analysis engine will determine it as "high risk," and the server will immediately escalate the call to a manned help desk or medical institution.

[0263] Specific examples

[0264] User: Sends a message on LINE saying, "I'm feeling stressed at work."

[0265] Server: Receives the consultation content and passes the "stress" text to the generative AI model and sentiment analysis engine.

[0266] Sentiment analysis engine: Determined as "anxiety."

[0267] Generative AI model: Generates answers to questions such as "What specifically is causing you stress?" based on the user's personality profile and past history.

[0268] Server: Sends the answer to the user.

[0269] Prompt Sentence Examples

[0270] "My work has been getting more stressful lately. What should I do?"

[0271] "I had a fight with a friend, what should I do?"

[0272] This invention allows users to consult at their own pace 24 hours a day, 365 days a year, and by combining an emotion analysis engine with a generative AI model, they can receive more adaptive and personalized answers, while also achieving a rapid response to serious emotional states.

[0273] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0274] Step 1:

[0275] Receiving a registration message

[0276] Input: User sends "Register" message via LINE app.

[0277] Behavior: The server receives a "Register" message from the LINE Platform.

[0278] Output: The registration request has been accepted.

[0279] Step 2:

[0280] User ID generation and information registration

[0281] Input: The "register" message received by the server.

[0282] How it works: The server generates a unique identifier (such as a UUID) and creates a user ID. It also registers the user's basic information in a database.

[0283] Output: A user ID is generated and the user information is registered in the database.

[0284] Step 3:

[0285] Submit personality test form

[0286] Input: User information after registration is complete.

[0287] Operation: The server uses the LINE API to push a personality test form to the user.

[0288] Output: The personality test form has been sent to the user.

[0289] Step 4:

[0290] Receiving and analyzing personality tests

[0291] Input: The user enters answers into the personality test form through the LINE app and submits it.

[0292] How it works: The server receives the responses, analyzes them using natural language processing algorithms, and generates a personality profile of the user.

[0293] Output: A personality profile is generated and stored in a database.

[0294] Step 5:

[0295] Receiving consultation details

[0296] Input: The user inputs the consultation details through the LINE app and sends it.

[0297] Operation: The server receives the consultation content from the LINE platform.

[0298] Output: The received consultation content is saved.

[0299] Step 6:

[0300] Emotional state analysis

[0301] Input: Received consultation content.

[0302] How it works: The server passes the consultation to a sentiment analysis engine, which performs text analysis to identify the emotional state (e.g., anxiety, anger, happiness).

[0303] Output: The sentiment analysis results are output.

[0304] Step 7:

[0305] Generating optimal answers

[0306] Input: Sentiment analysis results, user's personality profile, and past consultation history.

[0307] How it works: The server inputs this data into a generative AI model to generate the optimal answer. The AI ​​model creates answers based on prompts, questions, and answer patterns.

[0308] Output: A personalized answer is generated.

[0309] Step 8:

[0310] Submit your answer

[0311] Input: The generated answer.

[0312] Behavior: The server uses the LINE API to push the generated answer to the user.

[0313] Output: The answer is sent to the user.

[0314] Step 9:

[0315] Data storage

[0316] Input: Consultation details and generated answers.

[0317] How it works: The server stores this information in a database as a digital medical record. The stored data will be used as learning data for the future.

[0318] Output: The consultation history and response history are saved in a database.

[0319] Step 10:

[0320] escalation

[0321] Input: If the sentiment analysis result is determined to be "high risk."

[0322] Operation: The server uses the emergency contact system and email notification system to immediately escalate the case to a staffed reception desk or medical institution, and sends a digital medical record or chat summary as necessary.

[0323] Output: Appropriate information is provided to manned counters and medical institutions.

[0324] (Application example 2)

[0325] Next, a description will be given of Application 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."

[0326] Conventional security services have not adequately considered the emotional state of the user or emergency responses, making it difficult to quickly take appropriate measures when the user is in a dangerous situation. The present invention aims to improve this situation by providing a security service system that analyzes the emotional state of the user in real time and can respond quickly and appropriately in an emergency.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the content of the consultation from the user, means for generating an answer suitable for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with a manned counter or a medical institution as necessary, and means for analyzing the emotional state of the user in real time and taking appropriate action in an emergency. This makes it possible to monitor the emotional state of the user in real time and take prompt and appropriate action.

[0328] "User" refers to an individual or organization who uses the system to input their inquiry and receive a response.

[0329] "Consultation content" refers to information such as worries, questions, and explanations of situations that users enter through the system.

[0330] A "generative AI model" is an artificial intelligence model that automatically generates the optimal answer based on the user's inquiry.

[0331] "Emotional state" refers to the emotional situation or state analyzed from the content of the user's consultation.

[0332] "Appropriate response" refers to the optimized response and support provided according to the user's emotional state and the content of their consultation.

[0333] "Real-time analysis" means that the content of a user's inquiry is processed immediately upon receipt and a result is generated.

[0334] An "emergency" is when a user is deemed to be in a dangerous or high-risk situation.

[0335] A "digital medical record" is an electronic database that records and updates the contents of a user's consultation and the responses to them.

[0336] A "manned contact point" is a human operator or counselor with whom you can interact directly within the system.

[0337] "Medical institution" refers to a facility that provides medical services, such as a hospital, clinic, or psychiatric clinic.

[0338] "Collaboration" means sharing information and working together to address different systems and institutions.

[0339] A "profile" is a collection of data that analyzes a user's personality and tendencies and saves them.

[0340] The system of this invention receives inquiries from users, generates optimal answers using a generative AI model and an emotion engine, and takes emergency measures as needed. Specific implementation methods of this system are described below.

[0341] System Configuration

[0342] 1. User terminal

[0343] Users use a device such as a smartphone to input their inquiry details into the system, and consultations are mainly conducted via chat apps such as LINE.

[0344] 2. Server

[0345] The server receives messages from users and performs various processes. The server is equipped with a generative AI model and an emotion engine, which are used to analyze messages.

[0346] 3. Database

[0347] The server stores user information, personality profiles, past consultation history, and other information in a database, and the AI ​​model learns based on this.

[0348] Operation flow

[0349] 1. Registration

[0350] Users first enter their information and answer a personality questionnaire, based on which the server generates a personality profile and stores it in a database.

[0351] 2. Message reception and analysis

[0352] When users have a daily consultation, they input a text message, such as "I saw a suspicious person recently."

[0353] When the server receives this message, it analyzes it with an emotion engine and determines the user's emotional state, such as "anxiety" or "danger."

[0354] 3. Answer generation

[0355] Based on the results of the emotion engine and the user's profile, the generative AI model generates optimal advice and warnings. For example, in response to a message such as "I saw a suspicious person recently," the model generates a response such as "Are you worried because you saw a suspicious person? Please ensure a safe environment."

[0356] 4. Emergency Response

[0357] If the user's emotional state is determined to be high risk, the server will immediately contact security services or medical institutions.

[0358] For example, if a message is sent saying "a stranger is following me," it will be deemed a high risk and an emergency call will be made.

[0359] Hardware and software used

[0360] Hardware: Smartphones, servers

[0361] Software: LINE app, generative AI model, emotion engine, database management system

[0362] Example prompt

[0363] For example, the following prompts are used:

[0364] User ID: 123

[0365] Message: A stranger is following you

[0366] Emotional state: Anxious

[0367] Using this prompt, the generative AI model can generate "response measures to take when receiving a message about a suspicious person," enabling immediate appropriate responses and emergency calls. For example, specific responses such as "To ensure your safety, immediately ask for help from someone nearby."

[0368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0369] Step 1:

[0370] The user operates the user device (smartphone) and inputs the content of the consultation into the LINE app. For example, the user inputs a message such as "I saw a suspicious person recently." This message becomes the input data.

[0371] Step 2:

[0372] The device sends the input consultation details to the server, along with metadata such as the user ID. The input data consists of a text message and related user information.

[0373] Step 3:

[0374] The server passes the received message to the emotion engine, which analyzes the emotional state. The emotion engine analyzes the input data and determines the emotional state, such as "anxiety" or "danger." The output is the emotional state resulting from the analysis.

[0375] Step 4:

[0376] The server passes the analysis results of the emotion engine and the user's personality profile to the generative AI model. The generative AI model refers to past consultation details and answer history to generate an appropriate answer. The input data are the analysis results and the user profile, and the output is the generated answer.

[0377] Step 5:

[0378] The server sends the generated answer to the user's device via the LINE app. The user confirms the generated answer on their device. The input data is the generated answer, and the output is a message that the user confirms.

[0379] Step 6:

[0380] The server records the consultation details and the generated answers in a digital medical record and saves them in a database. The input data are the consultation details and the generated answers, and the output is an updated digital medical record.

[0381] Step 7:

[0382] If the emotion engine analyzes a high-risk emotional state, the server immediately takes emergency action, such as notifying security services or medical institutions. The input data is the high-risk emotional state, and the output is a notification message.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0384] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[0385] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 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.

[0389] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] 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.

[0391] 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 audio in accordance with instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0393] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0394] Fig. 4 shows an example of the 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0399] This invention relates to a system that provides an environment where users with concerns can seek advice 24 hours a day, 365 days a year, anywhere, at their own pace. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records.

[0400] The server first receives a consultation message from the user. For example, if a user sends a message through the LINE app saying, "I've been feeling stressed at work lately," this triggers the server to start working.

[0401] The server then passes the consultation message to a generative AI model, which generates the optimal answer based on the consultation content. Specifically, the AI ​​analyzes past data and the user's personality profile to generate a specific and appropriate response such as, "That's tough. What specifically is causing you stress?"

[0402] The generated answer is provided to the user via the server and displayed on the user's device. The user can continue asking questions or seeking advice based on this answer. For example, the user can add, "I'm having trouble communicating with my boss," and the AI ​​will analyze that message as well and generate the next answer.

[0403] The system also has the ability to diagnose users' personalities. Users answer a personality assessment form sent from the server, asking questions such as, "When do you feel stressed?" The results are saved as a profile and used for subsequent analysis.

[0404] Another important feature of this invention is the automatic generation and updating of digital medical records. The server records and updates the details of inquiries sent by users and the responses to those inquiries as digital medical records. This allows for centralized management of users' conditions and, if necessary, provides that data to staffed counters or medical institutions.

[0405] The generative AI model also determines risk levels. It detects mental or health risks from the user's consultation content, and if a high risk is detected, the server immediately escalates the situation to a manned help desk or appropriate medical institution. For example, if a serious message such as "I'm thinking about suicide" is received, the server immediately contacts a counselor or doctor and arranges for appropriate support.

[0406] In this way, the present invention is a system that provides personalized psychological support to users who are troubled, allowing them to receive professional assistance at the appropriate time.

[0407] The processing flow will be explained below.

[0408] Step 1:

[0409] The server receives a "Register" message from the user via the LINE app.

[0410] Step 2:

[0411] The server generates a user ID based on the received "Register" message and registers the user information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[0412] Step 3:

[0413] The server sends a personality test form to users who have completed registration via the LINE app.

[0414] Step 4:

[0415] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[0416] Step 5:

[0417] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[0418] Step 6:

[0419] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[0420] Step 7:

[0421] The server receives a consultation message from the user as a trigger and passes the message to the generative AI model.

[0422] Step 8:

[0423] The generative AI model generates the optimal answer based on the received consultation content, the user's personality profile, and past consultation history.

[0424] Step 9:

[0425] The server then sends the generated answer to the user via the LINE app, sending a message such as, "That's tough. What specifically is causing you stress?"

[0426] Step 10:

[0427] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record.

[0428] Step 11:

[0429] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take. For example, if the user adds, "I'm having trouble communicating with my boss," the message is sent to the server again.

[0430] Step 12:

[0431] The server resends any additional messages from the user to the generative AI model, generates a new answer, and sends it to the user.

[0432] Step 13:

[0433] The server uses the generative AI model to determine the risk level based on the user's consultation and response. If the risk is deemed high, the server escalates the situation to a manned consultation desk or an appropriate medical institution.

[0434] Step 14:

[0435] If escalation is necessary, the server will send the necessary digital medical records and chat summaries to a manned desk or medical institution and arrange for appropriate response.

[0436] Example 1

[0437] Next, a description will be given of 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."

[0438] Conventional systems were unable to respond immediately to users' concerns and stress, making it difficult to provide 24 / 7 support. Furthermore, they did not provide sufficient personalized support based on the content of the user's consultation, making it difficult to provide appropriate answers or assess risks according to the user's situation. Furthermore, there was a lack of a means to comprehensively manage users' consultation history and personality assessment results, making it difficult to effectively create and update digital medical records. New technology is needed to solve these issues.

[0439] 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.

[0440] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for transmitting the consultation content using the user's terminal and displaying the generated answer, means for diagnosing the user's personality, means for saving the results of the personality diagnosis as the user's profile, means for recording past consultation content and answers and using them as learning data, means for automatically generating and updating the user's digital medical record based on the consultation content and its answers, means for determining mental or health risks from the user's consultation content, and means for sharing the consultation content with a manned counter or medical institution as needed. This allows users to continue consultation anywhere, 24 hours a day, 365 days a year, and provides personalized support, enabling them to quickly receive appropriate professional assistance as needed.

[0441] "User" refers to a person who uses the system to input and send consultation details.

[0442] "Consultation content" refers to the concerns or questions that users input and send through the system.

[0443] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the content of the consultation received.

[0444] A "prompt" is a text sentence that is input to a generative AI model to generate the optimal answer.

[0445] A "server" is a computer system that manages and controls the entire system and processes and transmits and receives various data.

[0446] An "answer" is a response generated by the generative AI model based on the content of the consultation.

[0447] "Terminal" refers to the device (smartphone, PC, etc.) used by the user to input and send the inquiry content and receive and display the response.

[0448] A "personality test" is a test conducted to analyze a user's personality and tendencies.

[0449] A "profile" is data that includes a user's personality and personality traits, created based on the results of a personality test and other information.

[0450] "Learning data" refers to data that records past consultation content and responses to those consultations and is used to improve the performance of the generative AI model.

[0451] A "digital medical record" is a centralized data management system that is automatically generated and updated based on the user's consultation details, responses, and personality test results.

[0452] "Risk assessment" refers to the generative AI model analyzing the user's consultation content and assessing and determining mental or health risks.

[0453] A "staffed counter" is a counter where specialized staff or counselors are always on hand to respond as needed.

[0454] A "medical institution" is a facility staffed by professionals such as doctors and counselors that provides medical and psychological support to users.

[0455] The present invention is a system that provides an environment where users with concerns can receive consultation at their own pace, 24 hours a day, 365 days a year. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records. A specific embodiment of this system is described below.

[0456] Hardware and Software Configuration

[0457] The entire system consists of three components: a server, a terminal, and a user. Specifically, the following hardware and software are used:

[0458] Server: A cloud-based system is used for data processing and management. In this case, the server infrastructure of Amazon Web Services (AWS) is used.

[0459] Device: A device such as a smartphone or computer used by a user, on which the LINE app or other chat tools are installed.

[0460] Generative AI models: Use advanced generative AI models such as OpenAI's GPT-4 to generate appropriate answers based on the content of the consultation.

[0461] Data processing and calculation flow

[0462] 1. Message received:

[0463] The user inputs the content of their problem using an app such as LINE and sends it. For example, they can send a message saying, "I've been feeling stressed at work lately."

[0464] The server receives the message via the API of the chat tool, such as the LINE API.

[0465] 2. Generate and send a prompt:

[0466] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt such as, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[0467] Send the prompt to the generative AI model.

[0468] 3. Generate answers:

[0469] The generative AI model generates an appropriate response based on the prompt it receives, such as, "That's tough. What specifically is causing you stress?"

[0470] 4. Providing answers:

[0471] The server receives the generated response and transmits it to the user's terminal.

[0472] The answer will be displayed on the user's device using the LINE app or other similar app.

[0473] Personality assessment and digital medical record creation / update

[0474] 1. Personality Test:

[0475] The server generates a questionnaire to assess the user's personality and sends it to the user. For example, it sends a question such as "When do you feel stressed?"

[0476] The user answers the personality test form and sends it to the server, which stores the received answers in a database.

[0477] 2. Digital medical record generation and updating:

[0478] The server automatically generates a digital medical record based on the user's consultation, responses, and personality test results.

[0479] The generated digital medical records are updated regularly, and the user's status is managed centrally.

[0480] Risk Level Determination and Escalation

[0481] 1. Determine the risk level:

[0482] The generative AI model analyzes the content of the user's consultation and determines mental or health risks. For example, if a serious message such as "I'm thinking about suicide" is received, the generative AI model will determine that there is a high risk.

[0483] 2. Escalation:

[0484] If a person is deemed to be at high risk, the server will immediately escalate the situation to a manned help desk or medical institution, contacting a counselor or doctor and arranging for appropriate assistance.

[0485] Examples of prompt statements

[0486] "A user submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[0487] "To create a personality profile for your users, generate questions like: 'When do you feel stressed?'"

[0488] This completes the detailed description of the present invention, which allows the system to provide 24 / 7 support, automatically generate personalized answers, and provide users with fast, professional assistance when needed.

[0489] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0490] Step 1:

[0491] The server receives a consultation message from the user. When the user sends a message such as "I've been feeling stressed at work lately" via a chat tool such as the LINE app, the server receives this message via the LINE API. The input is the user's message, which is temporarily stored in a database. The output is the received message data.

[0492] Step 2:

[0493] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt like, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer." The input is the message received from the user, and the output is the generated prompt.

[0494] Step 3:

[0495] The server sends the generated prompt to a generative AI model. For example, it sends the prompt to OpenAI's GPT-4 through an API. The input is the generated prompt, and the output is the operation of sending the prompt to the generative AI model.

[0496] Step 4:

[0497] The generative AI model generates the optimal answer based on the prompt it receives. The input is the prompt sent from the server, and the output is the generated answer (e.g., "That's tough. What exactly is causing you stress?").

[0498] Step 5:

[0499] The server receives the answer returned from the generative AI model. The input is the answer from the generative AI model, which is temporarily stored in a database. The output is the received answer data.

[0500] Step 6:

[0501] The server sends the received answer to the user's device. The answer is displayed on the user's chat screen using the LINE API or similar. The inputs are the answer received from the generative AI model and the user's device information, and the output is the answer displayed on the user's device.

[0502] Step 7:

[0503] The user checks the answer sent from the server, then inputs and sends a new message to continue the consultation. For example, the user inputs "I'm having trouble communicating with my boss" and sends it. The input is the answer from the server, and the output is a new message from the user.

[0504] Step 8:

[0505] The server generates a question form to diagnose the user's personality and sends it via the LINE app, etc. For example, it generates a question such as "When do you feel stressed?" The input is the user's profile information and past consultation details, and the output is the generated question form.

[0506] Step 9:

[0507] The user answers the personality test form and sends it to the server. The input is the question form sent from the server, and the user enters the answers. The output is the user's answer data.

[0508] Step 10:

[0509] The server receives the user's answers and stores them in a database. The input is the answer data sent by the user, and the output is the answers stored in the database.

[0510] Step 11:

[0511] The server automatically generates and updates a digital medical record based on the collected consultation details, answers, and personality test results. The input is all data from the user, and the output is the digital medical record.

[0512] Step 12:

[0513] Using a generative AI model, the server determines mental or health risks based on the user's consultation. The input is all consultation details and responses from the user, and the data is analyzed. The output is a risk assessment result.

[0514] Step 13:

[0515] If necessary, the server will share the consultation details with a manned help desk or a medical institution. If the consultation is judged to be high risk, the server will perform an emergency escalation and arrange for appropriate professional support. The input is the risk assessment result, and the output is the linked professional support information.

[0516] These are the specific processing steps of this system. This system provides an environment where users can consult with us 24 hours a day, 365 days a year, and can provide personalized support quickly.

[0517] (Application example 1)

[0518] Next, a description will be given of Application 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."

[0519] In today's information society, security-related worries and anxieties are increasing day by day. In particular, the number of inquiries about password management and phishing scams is rapidly increasing, creating a growing demand for systems that can quickly and appropriately respond to such problems. Conventional consultation systems have difficulty providing personalized advice to individual users, and few systems are available 24 hours a day, 365 days a year. Therefore, from a security perspective, there is an increasing need for systems that can quickly respond to users' worries and questions.

[0520] 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.

[0521] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with manned help desks or medical institutions as necessary, and means for providing personalized advice on security-related concerns, thereby enabling users to quickly resolve their security-related concerns and questions 24 hours a day, 365 days a year.

[0522] "User" refers to an individual who uses the system to receive consultation or support.

[0523] The "means for receiving consultation content" is a system function for receiving messages sent by users.

[0524] A "generative AI model" is a model that uses artificial intelligence (AI) technology to analyze the content of inquiries from users and generate optimal answers.

[0525] The "means for generating an answer" is a system function that uses a generative AI model to create the optimal answer based on the user's inquiry.

[0526] The "means for providing an answer" is a system function for displaying or notifying the user of the generated answer.

[0527] "Means for recording past consultation content and responses and using them as learning data" refers to a system function that saves users' consultation history and responses to those requests and uses them for future responses.

[0528] "Means for communicating consultation details to manned counters or medical institutions" is a system function for transmitting the user's consultation details to a human counselor or medical institution as needed.

[0529] "Means for providing personalized advice on security-related concerns" refers to a system function that uses generative AI models to provide personalized advice on security-related issues faced by users.

[0530] This invention is implemented primarily through an application on a smartphone. The components and their operations will be described in detail below.

[0531] The server receives the content of the inquiry from the user. Specifically, the content of the inquiry entered through a smartphone application is sent to the server. For example, a user may send a message saying, "I'm having a hard time managing my passwords these days."

[0532] The server then uses a generative AI model to analyze the consultation and generate an appropriate response. The generative AI model is an artificial intelligence that has learned from training data in advance, and generates the optimal response based on the user's consultation and past data. For example, the AI ​​might generate a specific response such as, "That's a difficult problem. Why don't you consider using a password management tool?"

[0533] The generated answer is provided to the smartphone application via the server and displayed on the user's device. The user can continue to ask questions or receive advice based on this answer. For example, they can enter additional information such as, "What kind of password management tool is best?"

[0534] In addition, the server records the consultation content and the generated answers and uses them as learning data. This information is used to generate more accurate answers for future consultations. More personalized answers are provided based on past history.

[0535] If necessary, the server will also have the ability to share the consultation details with a manned helpline or medical institution. For example, if a user sends a serious message such as "I'm thinking about suicide," the server can immediately contact a counselor or doctor and arrange for appropriate support.

[0536] The system also provides advice on security-related concerns, leveraging generative AI models to provide personalized advice to users regarding their security issues, enabling users to quickly resolve their security concerns anywhere, 24 hours a day, 365 days a year.

[0537] The hardware used includes smartphones and servers, and the software uses the OpenAI API for generative AI models and Flask (a Python microweb framework) for data exchange.

[0538] For example, if a user sends a message saying, "I'm worried about my security these days. What can I do?" the app will respond with, "That sounds worrying. It's important to create a strong password and change it regularly."

[0539] As a further example, let's consider the case where a user sends a message saying, "I received a phishing email, what should I do?" In this case, the application responds with, "Do not open the email. It is important not to click on suspicious links and to verify the sender before acting."

[0540] Here are some example prompts:

[0541] "Message from a user: I'm worried that my password has been leaked recently. What should I do?

[0542] Good AI response: First, change your password and, if possible, set up two-factor authentication.

[0543] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0544] Step 1:

[0545] The user inputs and sends the content of their inquiry through a smartphone application. For example, the input may include a message such as, "I'm having a hard time managing my passwords recently." This is then sent to the server.

[0546] Step 2:

[0547] The server receives the user's inquiry. In order to analyze the received message, it processes it as input data for the generative AI model. Specifically, it stores the received message in text format and prepares it for analysis.

[0548] Step 3:

[0549] The server analyzes the received consultation content using a generative AI model. In this step, the consultation content (input) is passed to the generative AI model, and the model generates the optimal answer. For example, in response to the consultation content "I've been having trouble managing my passwords lately," the server generates an answer (output) such as "That's tough. Why don't you consider using a password management tool?"

[0550] Step 4:

[0551] The server receives the generated answer and provides it to the user. The generated answer is then passed to a smartphone application and displayed on the user's device, allowing the user to receive appropriate advice in real time.

[0552] Step 5:

[0553] The server records the user's inquiry and the generated answer, and saves it as learning data. The recorded data is used as a database to provide more accurate answers to future inquiries.

[0554] Step 6:

[0555] If necessary, the server will contact a staffed help desk or medical institution to discuss the issue. For example, if the issue is serious, such as "considering suicide," the server will immediately contact a counselor or doctor and arrange for appropriate support.

[0556] Step 7:

[0557] The server uses a generative AI model to generate personalized advice for security-related concerns and provides it to users. When a user asks, "I've been worried about security lately. What are some good measures?", the server handles everything from receiving the message to generating and providing an answer, providing individually tailored advice.

[0558] Furthermore, 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.

[0559] This invention relates to a system that receives inquiries from users and provides more adaptive and personalized answers by combining a generative AI model and an emotion engine. This system provides an environment where users can consult at their own pace, 24 hours a day, 365 days a year.

[0560] The server first receives a "Register" message from the user via the LINE app. After receiving the message, the server generates a user ID and registers the user's information in a database. After registration is complete, the server sends the user a personality assessment form via the LINE app, and the user enters and submits their answers. Based on the answers, the server generates a personality profile for the user and stores it in a database.

[0561] When a consultation begins, the user enters the content of the consultation through the LINE app and sends it to the server. For example, they can send a message such as, "I've been feeling stressed at work lately." The server then passes this message to the generative AI model and emotion engine.

[0562] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it determines emotional states such as "anxiety" or "anger" from emotional expressions and keywords in the text. The results are provided to the generative AI model.

[0563] The generative AI model generates the optimal answer based on the user's personality profile, past consultation history, and emotional state analyzed by the emotion engine. For example, it might generate an answer along the lines of, "That's tough. What specifically is causing you stress?" The server then sends this answer to the user via the LINE app.

[0564] The server records the consultation details and the answers generated, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[0565] The system, which uses an emotion engine, can analyze the user's emotional state in real time and respond accordingly. For example, if a user sends a serious message such as "I'm thinking about suicide," the emotion engine will determine this as "high risk," and the server will immediately escalate the situation to a manned help desk or an appropriate medical institution.

[0566] If necessary, the server sends the digital medical record and chat summary to a staffed reception desk or medical institution and arranges for appropriate response.In this way, the present invention is a system that combines an emotion engine and a generative AI model to provide personalized psychological support to users and enable them to receive professional assistance at the appropriate time.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] The server receives a "Register" message from the user via the LINE app.

[0570] Step 2:

[0571] The server generates a user ID based on the received "Register" message and registers the user's information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[0572] Step 3:

[0573] The server sends a personality test form to users who have completed registration via the LINE app.

[0574] Step 4:

[0575] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[0576] Step 5:

[0577] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[0578] Step 6:

[0579] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[0580] Step 7:

[0581] The server receives consultation messages from users and passes them to the emotion engine and generative AI model.

[0582] Step 8:

[0583] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it detects emotions such as "anxiety" or "stress." The results are provided to the generative AI model.

[0584] Step 9:

[0585] The generative AI model generates the optimal answer based on the emotional state analyzed by the emotion engine, the user's personality profile, and past consultation history. For example, it might generate a response such as, "That's tough. What specifically is causing you stress?"

[0586] Step 10:

[0587] The server sends the generated answer to the user through the LINE app, and the user can receive the answer and decide what to do next.

[0588] Step 11:

[0589] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take, for example, sending an additional question such as "I'm having trouble communicating with my boss" to the server again.

[0590] Step 12:

[0591] The server then passes any additional messages from the user back to the emotion engine and generative AI model to generate new answers.

[0592] Step 13:

[0593] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[0594] Step 14:

[0595] The emotion engine analyzes the user's emotional state in real time, and if it determines that the risk is high, it flags the user as "high risk" and notifies the server of the result.

[0596] Step 15:

[0597] If the emotion engine determines that the situation is "high risk," the server immediately escalates the situation to a manned reception desk or an appropriate medical institution. The server then sends the necessary digital medical records and chat summaries to the manned reception desk or medical institution, and arranges for the appropriate response.

[0598] Example 2

[0599] Next, a description will be given of 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."

[0600] Conventional consultation systems have had difficulty providing personalized answers that fully consider the user's emotional state. Furthermore, they lacked mechanisms for providing prompt and appropriate responses when the user was in a serious emotional state. Furthermore, they were unable to effectively utilize past consultations and their responses as learning data.

[0601] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the user, a means for generating an answer appropriate for the user based on the received content of the consultation using a generation AI model, a means for determining the emotional state of the user from the content of the consultation using an emotion analysis engine, a means for providing the generated answer to the user, a means for recording past consultation contents and answers and using them as learning data, and a means for sharing the consultation contents with a manned counter or a medical institution as necessary. This makes it possible to analyze the emotional state of the user in real time and provide a personalized answer based on the analysis. It also enables rapid escalation for users in serious emotional states.

[0602] "Consultation content" refers to text data such as questions, concerns, and opinions provided by users through the system.

[0603] A "generative AI model" is an algorithm or engine that uses machine learning and artificial intelligence techniques to generate appropriate outputs for input data.

[0604] An "emotion analysis engine" is software or algorithms that analyze and determine emotions and emotional states from text data.

[0605] A "profile" is a data set that centrally compiles information such as a user's personality, characteristics, and past consultation details.

[0606] A "digital medical record" is an electronic information file that records and centrally manages various digital data about users, specifically consultation details and response history.

[0607] "Escalation" is the process by which the system communicates and reports to a higher level responder or expert agency when it recognizes a serious problem or high-risk consultation.

[0608] This invention relates to a consultation system and describes a specific method for providing personalized answers to users. By combining a generative AI model and an emotion analysis engine, the system generates adaptive answers according to the user's emotional state and can respond quickly when necessary.

[0609] The main components of the system include:

[0610] 1. Means of receiving inquiries from users

[0611] 2. A means of generating appropriate answers based on the received consultation content using a generative AI model

[0612] 3. A means of determining the user's emotional state from the content of the consultation using an emotion analysis engine

[0613] 4. How to provide generated answers to users

[0614] 5. Recording past consultations and responses and using them as learning data

[0615] 6. A means of communicating consultation details to a staffed help desk or medical institution as needed

[0616] Hardware and Software Use

[0617] Hardware: Server (a server machine with a powerful CPU, GPU, and sufficient memory)

[0618] Software: LINE Platform, database system, generative AI model, sentiment analysis engine

[0619] Program processing

[0620] User Registration:

[0621] The user sends a "Register" message through the LINE app. The server receives this message, generates a user ID, and registers the user's information in the database. It also sends a personality assessment form, receives the user's answers, analyzes them, generates a personality profile, and saves it in the database.

[0622] Receiving and analyzing consultation content:

[0623] The user inputs and sends the content of their problem through the LINE app. For example, they send a message saying, "I've been feeling stressed at work lately." The server receives this message and passes it to the generative AI model and emotion analysis engine. The emotion analysis engine analyzes the content of the problem and determines the user's emotional state. The result of this determination is provided to the generative AI model, which generates the optimal answer.

[0624] Submit and save your answers:

[0625] The server then sends the generated response to the user via the LINE app. The consultation details and responses are recorded in a database and saved and updated as a digital medical record. If a serious message is included, the emotion analysis engine will determine it as "high risk," and the server will immediately escalate the call to a manned help desk or medical institution.

[0626] Specific examples

[0627] User: Sends a message on LINE saying, "I'm feeling stressed at work."

[0628] Server: Receives the consultation content and passes the "stress" text to the generative AI model and sentiment analysis engine.

[0629] Sentiment analysis engine: Determined as "anxiety."

[0630] Generative AI model: Generates answers to questions such as "What specifically is causing you stress?" based on the user's personality profile and past history.

[0631] Server: Sends the answer to the user.

[0632] Prompt Sentence Examples

[0633] "My work has been getting more stressful lately. What should I do?"

[0634] "I had a fight with a friend, what should I do?"

[0635] This invention allows users to consult at their own pace 24 hours a day, 365 days a year, and by combining an emotion analysis engine with a generative AI model, they can receive more adaptive and personalized answers, while also achieving a rapid response to serious emotional states.

[0636] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] Receiving a registration message

[0639] Input: User sends "Register" message via LINE app.

[0640] Behavior: The server receives a "Register" message from the LINE Platform.

[0641] Output: The registration request has been accepted.

[0642] Step 2:

[0643] User ID generation and information registration

[0644] Input: The "register" message received by the server.

[0645] How it works: The server generates a unique identifier (such as a UUID) and creates a user ID. It also registers the user's basic information in a database.

[0646] Output: A user ID is generated and the user information is registered in the database.

[0647] Step 3:

[0648] Submit personality test form

[0649] Input: User information after registration is complete.

[0650] Operation: The server uses the LINE API to push a personality test form to the user.

[0651] Output: The personality test form has been sent to the user.

[0652] Step 4:

[0653] Receiving and analyzing personality tests

[0654] Input: The user enters answers into the personality test form through the LINE app and submits it.

[0655] How it works: The server receives the responses, analyzes them using natural language processing algorithms, and generates a personality profile of the user.

[0656] Output: A personality profile is generated and stored in a database.

[0657] Step 5:

[0658] Receiving consultation details

[0659] Input: The user inputs the consultation details through the LINE app and sends it.

[0660] Operation: The server receives the consultation content from the LINE platform.

[0661] Output: The received consultation content is saved.

[0662] Step 6:

[0663] Emotional state analysis

[0664] Input: Received consultation content.

[0665] How it works: The server passes the consultation to a sentiment analysis engine, which performs text analysis to identify the emotional state (e.g., anxiety, anger, happiness).

[0666] Output: The sentiment analysis results are output.

[0667] Step 7:

[0668] Generating optimal answers

[0669] Input: Sentiment analysis results, user's personality profile, and past consultation history.

[0670] How it works: The server inputs this data into a generative AI model to generate the optimal answer. The AI ​​model creates answers based on prompts, questions, and answer patterns.

[0671] Output: A personalized answer is generated.

[0672] Step 8:

[0673] Submit your answer

[0674] Input: The generated answer.

[0675] Behavior: The server uses the LINE API to push the generated answer to the user.

[0676] Output: The answer is sent to the user.

[0677] Step 9:

[0678] Data storage

[0679] Input: Consultation details and generated answers.

[0680] How it works: The server stores this information in a database as a digital medical record. The stored data will be used as learning data for the future.

[0681] Output: The consultation history and response history are saved in a database.

[0682] Step 10:

[0683] escalation

[0684] Input: If the sentiment analysis result is determined to be "high risk."

[0685] Operation: The server uses the emergency contact system and email notification system to immediately escalate the case to a staffed reception desk or medical institution, and sends a digital medical record or chat summary as necessary.

[0686] Output: Appropriate information is provided to manned counters and medical institutions.

[0687] (Application example 2)

[0688] Next, a description will be given of Application 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."

[0689] Conventional security services have not adequately considered the emotional state of the user or emergency responses, making it difficult to quickly take appropriate measures when the user is in a dangerous situation. The present invention aims to improve this situation by providing a security service system that analyzes the emotional state of the user in real time and can respond quickly and appropriately in an emergency.

[0690] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the content of the consultation from the user, means for generating an answer suitable for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with a manned counter or a medical institution as necessary, and means for analyzing the emotional state of the user in real time and taking appropriate action in an emergency. This makes it possible to monitor the emotional state of the user in real time and take prompt and appropriate action.

[0691] "User" refers to an individual or organization who uses the system to input their inquiry and receive a response.

[0692] "Consultation content" refers to information such as worries, questions, and explanations of situations that users enter through the system.

[0693] A "generative AI model" is an artificial intelligence model that automatically generates the optimal answer based on the user's inquiry.

[0694] "Emotional state" refers to the emotional situation or state analyzed from the content of the user's consultation.

[0695] "Appropriate response" refers to the optimized response and support provided according to the user's emotional state and the content of their consultation.

[0696] "Real-time analysis" means that the content of a user's inquiry is processed immediately upon receipt and a result is generated.

[0697] An "emergency" is when a user is deemed to be in a dangerous or high-risk situation.

[0698] A "digital medical record" is an electronic database that records and updates the contents of a user's consultation and the responses to them.

[0699] A "manned contact point" is a human operator or counselor with whom you can interact directly within the system.

[0700] "Medical institution" refers to a facility that provides medical services, such as a hospital, clinic, or psychiatric clinic.

[0701] "Collaboration" means sharing information and working together to address different systems and institutions.

[0702] A "profile" is a collection of data that analyzes a user's personality and tendencies and saves them.

[0703] The system of this invention receives inquiries from users, generates optimal answers using a generative AI model and an emotion engine, and takes emergency measures as needed. Specific implementation methods of this system are described below.

[0704] System Configuration

[0705] 1. User terminal

[0706] Users use a device such as a smartphone to input their inquiry details into the system, and consultations are mainly conducted via chat apps such as LINE.

[0707] 2. Server

[0708] The server receives messages from users and performs various processes. The server is equipped with a generative AI model and an emotion engine, which are used to analyze messages.

[0709] 3. Database

[0710] The server stores user information, personality profiles, past consultation history, and other information in a database, and the AI ​​model learns based on this.

[0711] Operation flow

[0712] 1. Registration

[0713] Users first enter their information and answer a personality questionnaire, based on which the server generates a personality profile and stores it in a database.

[0714] 2. Message reception and analysis

[0715] When users have a daily consultation, they input a text message, such as "I saw a suspicious person recently."

[0716] When the server receives this message, it analyzes it with an emotion engine and determines the user's emotional state, such as "anxiety" or "danger."

[0717] 3. Answer generation

[0718] Based on the results of the emotion engine and the user's profile, the generative AI model generates optimal advice and warnings. For example, in response to a message such as "I saw a suspicious person recently," the model generates a response such as "Are you worried because you saw a suspicious person? Please ensure a safe environment."

[0719] 4. Emergency Response

[0720] If the user's emotional state is determined to be high risk, the server will immediately contact security services or medical institutions.

[0721] For example, if a message is sent saying "a stranger is following me," it will be deemed a high risk and an emergency call will be made.

[0722] Hardware and software used

[0723] Hardware: Smartphones, servers

[0724] Software: LINE app, generative AI model, emotion engine, database management system

[0725] Example prompt

[0726] For example, the following prompts are used:

[0727] User ID: 123

[0728] Message: A stranger is following you

[0729] Emotional state: Anxious

[0730] Using this prompt, the generative AI model can generate "response measures to take when receiving a message about a suspicious person," enabling immediate appropriate responses and emergency calls. For example, specific responses such as "To ensure your safety, immediately ask for help from someone nearby."

[0731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0732] Step 1:

[0733] The user operates the user device (smartphone) and inputs the content of the consultation into the LINE app. For example, the user inputs a message such as "I saw a suspicious person recently." This message becomes the input data.

[0734] Step 2:

[0735] The device sends the input consultation details to the server, along with metadata such as the user ID. The input data consists of a text message and related user information.

[0736] Step 3:

[0737] The server passes the received message to the emotion engine, which analyzes the emotional state. The emotion engine analyzes the input data and determines the emotional state, such as "anxiety" or "danger." The output is the emotional state resulting from the analysis.

[0738] Step 4:

[0739] The server passes the analysis results of the emotion engine and the user's personality profile to the generative AI model. The generative AI model refers to past consultation details and answer history to generate an appropriate answer. The input data are the analysis results and the user profile, and the output is the generated answer.

[0740] Step 5:

[0741] The server sends the generated answer to the user's device via the LINE app. The user confirms the generated answer on their device. The input data is the generated answer, and the output is a message that the user confirms.

[0742] Step 6:

[0743] The server records the consultation details and the generated answers in a digital medical record and saves them in a database. The input data are the consultation details and the generated answers, and the output is an updated digital medical record.

[0744] Step 7:

[0745] If the emotion engine analyzes a high-risk emotional state, the server immediately takes emergency action, such as notifying security services or medical institutions. The input data is the high-risk emotional state, and the output is a notification message.

[0746] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0747] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[0748] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0749] [Third embodiment]

[0750] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0751] 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.

[0752] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0753] 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.

[0754] 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 audio in accordance with instructions from the processor 46.

[0755] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0756] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0757] Fig. 6 shows an example of the 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.

[0758] 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.

[0759] 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.

[0760] In the headset type terminal 314, a 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.

[0761] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0762] This invention relates to a system that provides an environment where users with concerns can seek advice 24 hours a day, 365 days a year, anywhere, at their own pace. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records.

[0763] The server first receives a consultation message from the user. For example, if a user sends a message through the LINE app saying, "I've been feeling stressed at work lately," this triggers the server to start working.

[0764] The server then passes the consultation message to a generative AI model, which generates the optimal answer based on the consultation content. Specifically, the AI ​​analyzes past data and the user's personality profile to generate a specific and appropriate response such as, "That's tough. What specifically is causing you stress?"

[0765] The generated answer is provided to the user via the server and displayed on the user's device. The user can continue asking questions or seeking advice based on this answer. For example, the user can add, "I'm having trouble communicating with my boss," and the AI ​​will analyze that message as well and generate the next answer.

[0766] The system also has the ability to diagnose users' personalities. Users answer a personality assessment form sent from the server, asking questions such as, "When do you feel stressed?" The results are saved as a profile and used for subsequent analysis.

[0767] Another important feature of this invention is the automatic generation and updating of digital medical records. The server records and updates the details of inquiries sent by users and the responses to those inquiries as digital medical records. This allows for centralized management of users' conditions and, if necessary, provides that data to staffed counters or medical institutions.

[0768] The generative AI model also determines risk levels. It detects mental or health risks from the user's consultation content, and if a high risk is detected, the server immediately escalates the situation to a manned help desk or appropriate medical institution. For example, if a serious message such as "I'm thinking about suicide" is received, the server immediately contacts a counselor or doctor and arranges for appropriate support.

[0769] In this way, the present invention is a system that provides personalized psychological support to users who are troubled, allowing them to receive professional assistance at the appropriate time.

[0770] The processing flow will be explained below.

[0771] Step 1:

[0772] The server receives a "Register" message from the user via the LINE app.

[0773] Step 2:

[0774] The server generates a user ID based on the received "Register" message and registers the user information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[0775] Step 3:

[0776] The server sends a personality test form to users who have completed registration via the LINE app.

[0777] Step 4:

[0778] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[0779] Step 5:

[0780] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[0781] Step 6:

[0782] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[0783] Step 7:

[0784] The server receives a consultation message from the user as a trigger and passes the message to the generative AI model.

[0785] Step 8:

[0786] The generative AI model generates the optimal answer based on the received consultation content, the user's personality profile, and past consultation history.

[0787] Step 9:

[0788] The server then sends the generated answer to the user via the LINE app, sending a message such as, "That's tough. What specifically is causing you stress?"

[0789] Step 10:

[0790] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record.

[0791] Step 11:

[0792] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take. For example, if the user adds, "I'm having trouble communicating with my boss," the message is sent to the server again.

[0793] Step 12:

[0794] The server resends any additional messages from the user to the generative AI model, generates a new answer, and sends it to the user.

[0795] Step 13:

[0796] The server uses the generative AI model to determine the risk level based on the user's consultation and response. If the risk is deemed high, the server escalates the situation to a manned consultation desk or an appropriate medical institution.

[0797] Step 14:

[0798] If escalation is necessary, the server will send the necessary digital medical records and chat summaries to a manned desk or medical institution and arrange for appropriate response.

[0799] Example 1

[0800] Next, a description will be given of 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."

[0801] Conventional systems were unable to respond immediately to users' concerns and stress, making it difficult to provide 24 / 7 support. Furthermore, they did not provide sufficient personalized support based on the content of the user's consultation, making it difficult to provide appropriate answers or assess risks according to the user's situation. Furthermore, there was a lack of a means to comprehensively manage users' consultation history and personality assessment results, making it difficult to effectively create and update digital medical records. New technology is needed to solve these issues.

[0802] 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.

[0803] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for transmitting the consultation content using the user's terminal and displaying the generated answer, means for diagnosing the user's personality, means for saving the results of the personality diagnosis as the user's profile, means for recording past consultation content and answers and using them as learning data, means for automatically generating and updating the user's digital medical record based on the consultation content and its answers, means for determining mental or health risks from the user's consultation content, and means for sharing the consultation content with a manned counter or medical institution as needed. This allows users to continue consultation anywhere, 24 hours a day, 365 days a year, and provides personalized support, enabling them to quickly receive appropriate professional assistance as needed.

[0804] "User" refers to a person who uses the system to input and send consultation details.

[0805] "Consultation content" refers to the concerns or questions that users input and send through the system.

[0806] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the content of the consultation received.

[0807] A "prompt" is a text sentence that is input to a generative AI model to generate the optimal answer.

[0808] A "server" is a computer system that manages and controls the entire system and processes and transmits and receives various data.

[0809] An "answer" is a response generated by the generative AI model based on the content of the consultation.

[0810] "Terminal" refers to the device (smartphone, PC, etc.) used by the user to input and send the inquiry content and receive and display the response.

[0811] A "personality test" is a test conducted to analyze a user's personality and tendencies.

[0812] A "profile" is data that includes a user's personality and personality traits, created based on the results of a personality test and other information.

[0813] "Learning data" refers to data that records past consultation content and responses to those consultations and is used to improve the performance of the generative AI model.

[0814] A "digital medical record" is a centralized data management system that is automatically generated and updated based on the user's consultation details, responses, and personality test results.

[0815] "Risk assessment" refers to the generative AI model analyzing the user's consultation content and assessing and determining mental or health risks.

[0816] A "staffed counter" is a counter where specialized staff or counselors are always on hand to respond as needed.

[0817] A "medical institution" is a facility staffed by professionals such as doctors and counselors that provides medical and psychological support to users.

[0818] The present invention is a system that provides an environment where users with concerns can receive consultation at their own pace, 24 hours a day, 365 days a year. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records. A specific embodiment of this system is described below.

[0819] Hardware and Software Configuration

[0820] The entire system consists of three components: a server, a terminal, and a user. Specifically, the following hardware and software are used:

[0821] Server: A cloud-based system is used for data processing and management. In this case, the server infrastructure of Amazon Web Services (AWS) is used.

[0822] Device: A device such as a smartphone or computer used by a user, on which the LINE app or other chat tools are installed.

[0823] Generative AI models: Use advanced generative AI models such as OpenAI's GPT-4 to generate appropriate answers based on the content of the consultation.

[0824] Data processing and calculation flow

[0825] 1. Message received:

[0826] The user inputs the content of their problem using an app such as LINE and sends it. For example, they can send a message saying, "I've been feeling stressed at work lately."

[0827] The server receives the message via the API of the chat tool, such as the LINE API.

[0828] 2. Generate and send a prompt:

[0829] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt such as, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[0830] Send the prompt to the generative AI model.

[0831] 3. Generate answers:

[0832] The generative AI model generates an appropriate response based on the prompt it receives, such as, "That's tough. What specifically is causing you stress?"

[0833] 4. Providing answers:

[0834] The server receives the generated response and transmits it to the user's terminal.

[0835] The answer will be displayed on the user's device using the LINE app or other similar app.

[0836] Personality assessment and digital medical record creation / update

[0837] 1. Personality Test:

[0838] The server generates a questionnaire to assess the user's personality and sends it to the user. For example, it sends a question such as "When do you feel stressed?"

[0839] The user answers the personality test form and sends it to the server, which stores the received answers in a database.

[0840] 2. Digital medical record generation and updating:

[0841] The server automatically generates a digital medical record based on the user's consultation, responses, and personality test results.

[0842] The generated digital medical records are updated regularly, and the user's status is managed centrally.

[0843] Risk Level Determination and Escalation

[0844] 1. Determine the risk level:

[0845] The generative AI model analyzes the content of the user's consultation and determines mental or health risks. For example, if a serious message such as "I'm thinking about suicide" is received, the generative AI model will determine that there is a high risk.

[0846] 2. Escalation:

[0847] If a person is deemed to be at high risk, the server will immediately escalate the situation to a manned help desk or medical institution, contacting a counselor or doctor and arranging for appropriate assistance.

[0848] Examples of prompt statements

[0849] "A user submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[0850] "To create a personality profile for your users, generate questions like: 'When do you feel stressed?'"

[0851] This completes the detailed description of the present invention, which allows the system to provide 24 / 7 support, automatically generate personalized answers, and provide users with fast, professional assistance when needed.

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The server receives a consultation message from the user. When the user sends a message such as "I've been feeling stressed at work lately" via a chat tool such as the LINE app, the server receives this message via the LINE API. The input is the user's message, which is temporarily stored in a database. The output is the received message data.

[0855] Step 2:

[0856] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt like, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer." The input is the message received from the user, and the output is the generated prompt.

[0857] Step 3:

[0858] The server sends the generated prompt to a generative AI model. For example, it sends the prompt to OpenAI's GPT-4 through an API. The input is the generated prompt, and the output is the operation of sending the prompt to the generative AI model.

[0859] Step 4:

[0860] The generative AI model generates the optimal answer based on the prompt it receives. The input is the prompt sent from the server, and the output is the generated answer (e.g., "That's tough. What exactly is causing you stress?").

[0861] Step 5:

[0862] The server receives the answer returned from the generative AI model. The input is the answer from the generative AI model, which is temporarily stored in a database. The output is the received answer data.

[0863] Step 6:

[0864] The server sends the received answer to the user's device. The answer is displayed on the user's chat screen using the LINE API or similar. The inputs are the answer received from the generative AI model and the user's device information, and the output is the answer displayed on the user's device.

[0865] Step 7:

[0866] The user checks the answer sent from the server, then inputs and sends a new message to continue the consultation. For example, the user inputs "I'm having trouble communicating with my boss" and sends it. The input is the answer from the server, and the output is a new message from the user.

[0867] Step 8:

[0868] The server generates a question form to diagnose the user's personality and sends it via the LINE app, etc. For example, it generates a question such as "When do you feel stressed?" The input is the user's profile information and past consultation details, and the output is the generated question form.

[0869] Step 9:

[0870] The user answers the personality test form and sends it to the server. The input is the question form sent from the server, and the user enters the answers. The output is the user's answer data.

[0871] Step 10:

[0872] The server receives the user's answers and stores them in a database. The input is the answer data sent by the user, and the output is the answers stored in the database.

[0873] Step 11:

[0874] The server automatically generates and updates a digital medical record based on the collected consultation details, answers, and personality test results. The input is all data from the user, and the output is the digital medical record.

[0875] Step 12:

[0876] Using a generative AI model, the server determines mental or health risks based on the user's consultation. The input is all consultation details and responses from the user, and the data is analyzed. The output is a risk assessment result.

[0877] Step 13:

[0878] If necessary, the server will share the consultation details with a manned help desk or a medical institution. If the consultation is judged to be high risk, the server will perform an emergency escalation and arrange for appropriate professional support. The input is the risk assessment result, and the output is the linked professional support information.

[0879] These are the specific processing steps of this system. This system provides an environment where users can consult with us 24 hours a day, 365 days a year, and can provide personalized support quickly.

[0880] (Application example 1)

[0881] Next, a description will be given of Application 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."

[0882] In today's information society, security-related worries and anxieties are increasing day by day. In particular, the number of inquiries about password management and phishing scams is rapidly increasing, creating a growing demand for systems that can quickly and appropriately respond to such problems. Conventional consultation systems have difficulty providing personalized advice to individual users, and few systems are available 24 hours a day, 365 days a year. Therefore, from a security perspective, there is an increasing need for systems that can quickly respond to users' worries and questions.

[0883] 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.

[0884] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with manned help desks or medical institutions as necessary, and means for providing personalized advice on security-related concerns, thereby enabling users to quickly resolve their security-related concerns and questions 24 hours a day, 365 days a year.

[0885] "User" refers to an individual who uses the system to receive consultation or support.

[0886] The "means for receiving consultation content" is a system function for receiving messages sent by users.

[0887] A "generative AI model" is a model that uses artificial intelligence (AI) technology to analyze the content of inquiries from users and generate optimal answers.

[0888] The "means for generating an answer" is a system function that uses a generative AI model to create the optimal answer based on the user's inquiry.

[0889] The "means for providing an answer" is a system function for displaying or notifying the user of the generated answer.

[0890] "Means for recording past consultation content and responses and using them as learning data" refers to a system function that saves users' consultation history and responses to those requests and uses them for future responses.

[0891] "Means for communicating consultation details to manned counters or medical institutions" is a system function for transmitting the user's consultation details to a human counselor or medical institution as needed.

[0892] "Means for providing personalized advice on security-related concerns" refers to a system function that uses generative AI models to provide personalized advice on security-related issues faced by users.

[0893] This invention is implemented primarily through an application on a smartphone. The components and their operations will be described in detail below.

[0894] The server receives the content of the inquiry from the user. Specifically, the content of the inquiry entered through a smartphone application is sent to the server. For example, a user may send a message saying, "I'm having a hard time managing my passwords these days."

[0895] The server then uses a generative AI model to analyze the consultation and generate an appropriate response. The generative AI model is an artificial intelligence that has learned from training data in advance, and generates the optimal response based on the user's consultation and past data. For example, the AI ​​might generate a specific response such as, "That's a difficult problem. Why don't you consider using a password management tool?"

[0896] The generated answer is provided to the smartphone application via the server and displayed on the user's device. The user can continue to ask questions or receive advice based on this answer. For example, they can enter additional information such as, "What kind of password management tool is best?"

[0897] In addition, the server records the consultation content and the generated answers and uses them as learning data. This information is used to generate more accurate answers for future consultations. More personalized answers are provided based on past history.

[0898] If necessary, the server will also have the ability to share the consultation details with a manned helpline or medical institution. For example, if a user sends a serious message such as "I'm thinking about suicide," the server can immediately contact a counselor or doctor and arrange for appropriate support.

[0899] The system also provides advice on security-related concerns, leveraging generative AI models to provide personalized advice to users regarding their security issues, enabling users to quickly resolve their security concerns anywhere, 24 hours a day, 365 days a year.

[0900] The hardware used includes smartphones and servers, and the software uses the OpenAI API for generative AI models and Flask (a Python microweb framework) for data exchange.

[0901] For example, if a user sends a message saying, "I'm worried about my security these days. What can I do?" the app will respond with, "That sounds worrying. It's important to create a strong password and change it regularly."

[0902] As a further example, let's consider the case where a user sends a message saying, "I received a phishing email, what should I do?" In this case, the application responds with, "Do not open the email. It is important not to click on suspicious links and to verify the sender before acting."

[0903] Here are some example prompts:

[0904] "Message from a user: I'm worried that my password has been leaked recently. What should I do?

[0905] Good AI response: First, change your password and, if possible, set up two-factor authentication.

[0906] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0907] Step 1:

[0908] The user inputs and sends the content of their inquiry through a smartphone application. For example, the input may include a message such as, "I'm having a hard time managing my passwords recently." This is then sent to the server.

[0909] Step 2:

[0910] The server receives the user's inquiry. In order to analyze the received message, it processes it as input data for the generative AI model. Specifically, it stores the received message in text format and prepares it for analysis.

[0911] Step 3:

[0912] The server analyzes the received consultation content using a generative AI model. In this step, the consultation content (input) is passed to the generative AI model, and the model generates the optimal answer. For example, in response to the consultation content "I've been having trouble managing my passwords lately," the server generates an answer (output) such as "That's tough. Why don't you consider using a password management tool?"

[0913] Step 4:

[0914] The server receives the generated answer and provides it to the user. The generated answer is then passed to a smartphone application and displayed on the user's device, allowing the user to receive appropriate advice in real time.

[0915] Step 5:

[0916] The server records the user's inquiry and the generated answer, and saves it as learning data. The recorded data is used as a database to provide more accurate answers to future inquiries.

[0917] Step 6:

[0918] If necessary, the server will contact a staffed help desk or medical institution to discuss the issue. For example, if the issue is serious, such as "considering suicide," the server will immediately contact a counselor or doctor and arrange for appropriate support.

[0919] Step 7:

[0920] The server uses a generative AI model to generate personalized advice for security-related concerns and provides it to users. When a user asks, "I've been worried about security lately. What are some good measures?", the server handles everything from receiving the message to generating and providing an answer, providing individually tailored advice.

[0921] Furthermore, 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.

[0922] This invention relates to a system that receives inquiries from users and provides more adaptive and personalized answers by combining a generative AI model and an emotion engine. This system provides an environment where users can consult at their own pace, 24 hours a day, 365 days a year.

[0923] The server first receives a "Register" message from the user via the LINE app. After receiving the message, the server generates a user ID and registers the user's information in a database. After registration is complete, the server sends the user a personality assessment form via the LINE app, and the user enters and submits their answers. Based on the answers, the server generates a personality profile for the user and stores it in a database.

[0924] When a consultation begins, the user enters the content of the consultation through the LINE app and sends it to the server. For example, they can send a message such as, "I've been feeling stressed at work lately." The server then passes this message to the generative AI model and emotion engine.

[0925] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it determines emotional states such as "anxiety" or "anger" from emotional expressions and keywords in the text. The results are provided to the generative AI model.

[0926] The generative AI model generates the optimal answer based on the user's personality profile, past consultation history, and emotional state analyzed by the emotion engine. For example, it might generate an answer along the lines of, "That's tough. What specifically is causing you stress?" The server then sends this answer to the user via the LINE app.

[0927] The server records the consultation details and the answers generated, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[0928] The system, which uses an emotion engine, can analyze the user's emotional state in real time and respond accordingly. For example, if a user sends a serious message such as "I'm thinking about suicide," the emotion engine will determine this as "high risk," and the server will immediately escalate the situation to a manned help desk or an appropriate medical institution.

[0929] If necessary, the server sends the digital medical record and chat summary to a staffed reception desk or medical institution and arranges for appropriate response.In this way, the present invention is a system that combines an emotion engine and a generative AI model to provide personalized psychological support to users and enable them to receive professional assistance at the appropriate time.

[0930] The processing flow will be explained below.

[0931] Step 1:

[0932] The server receives a "Register" message from the user via the LINE app.

[0933] Step 2:

[0934] The server generates a user ID based on the received "Register" message and registers the user's information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[0935] Step 3:

[0936] The server sends a personality test form to users who have completed registration via the LINE app.

[0937] Step 4:

[0938] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[0939] Step 5:

[0940] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[0941] Step 6:

[0942] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[0943] Step 7:

[0944] The server receives consultation messages from users and passes them to the emotion engine and generative AI model.

[0945] Step 8:

[0946] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it detects emotions such as "anxiety" or "stress." The results are provided to the generative AI model.

[0947] Step 9:

[0948] The generative AI model generates the optimal answer based on the emotional state analyzed by the emotion engine, the user's personality profile, and past consultation history. For example, it might generate a response such as, "That's tough. What specifically is causing you stress?"

[0949] Step 10:

[0950] The server sends the generated answer to the user through the LINE app, and the user can receive the answer and decide what to do next.

[0951] Step 11:

[0952] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take, for example, sending an additional question such as "I'm having trouble communicating with my boss" to the server again.

[0953] Step 12:

[0954] The server then passes any additional messages from the user back to the emotion engine and generative AI model to generate new answers.

[0955] Step 13:

[0956] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[0957] Step 14:

[0958] The emotion engine analyzes the user's emotional state in real time, and if it determines that the risk is high, it flags the user as "high risk" and notifies the server of the result.

[0959] Step 15:

[0960] If the emotion engine determines that the situation is "high risk," the server immediately escalates the situation to a manned reception desk or an appropriate medical institution. The server then sends the necessary digital medical records and chat summaries to the manned reception desk or medical institution, and arranges for the appropriate response.

[0961] Example 2

[0962] Next, a description will be given of 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."

[0963] Conventional consultation systems have had difficulty providing personalized answers that fully consider the user's emotional state. Furthermore, they lacked mechanisms for providing prompt and appropriate responses when the user was in a serious emotional state. Furthermore, they were unable to effectively utilize past consultations and their responses as learning data.

[0964] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the user, a means for generating an answer appropriate for the user based on the received content of the consultation using a generation AI model, a means for determining the emotional state of the user from the content of the consultation using an emotion analysis engine, a means for providing the generated answer to the user, a means for recording past consultation contents and answers and using them as learning data, and a means for sharing the consultation contents with a manned counter or a medical institution as necessary. This makes it possible to analyze the emotional state of the user in real time and provide a personalized answer based on the analysis. It also enables rapid escalation for users in serious emotional states.

[0965] "Consultation content" refers to text data such as questions, concerns, and opinions provided by users through the system.

[0966] A "generative AI model" is an algorithm or engine that uses machine learning and artificial intelligence techniques to generate appropriate outputs for input data.

[0967] An "emotion analysis engine" is software or algorithms that analyze and determine emotions and emotional states from text data.

[0968] A "profile" is a data set that centrally compiles information such as a user's personality, characteristics, and past consultation details.

[0969] A "digital medical record" is an electronic information file that records and centrally manages various digital data about users, specifically consultation details and response history.

[0970] "Escalation" is the process by which the system communicates and reports to a higher level responder or expert agency when it recognizes a serious problem or high-risk consultation.

[0971] This invention relates to a consultation system and describes a specific method for providing personalized answers to users. By combining a generative AI model and an emotion analysis engine, the system generates adaptive answers according to the user's emotional state and can respond quickly when necessary.

[0972] The main components of the system include:

[0973] 1. Means of receiving inquiries from users

[0974] 2. A means of generating appropriate answers based on the received consultation content using a generative AI model

[0975] 3. A means of determining the user's emotional state from the content of the consultation using an emotion analysis engine

[0976] 4. How to provide generated answers to users

[0977] 5. Recording past consultations and responses and using them as learning data

[0978] 6. A means of communicating consultation details to a staffed help desk or medical institution as needed

[0979] Hardware and Software Use

[0980] Hardware: Server (a server machine with a powerful CPU, GPU, and sufficient memory)

[0981] Software: LINE Platform, database system, generative AI model, sentiment analysis engine

[0982] Program processing

[0983] User Registration:

[0984] The user sends a "Register" message through the LINE app. The server receives this message, generates a user ID, and registers the user's information in the database. It also sends a personality assessment form, receives the user's answers, analyzes them, generates a personality profile, and saves it in the database.

[0985] Receiving and analyzing consultation content:

[0986] The user inputs and sends the content of their problem through the LINE app. For example, they send a message saying, "I've been feeling stressed at work lately." The server receives this message and passes it to the generative AI model and emotion analysis engine. The emotion analysis engine analyzes the content of the problem and determines the user's emotional state. The result of this determination is provided to the generative AI model, which generates the optimal answer.

[0987] Submit and save your answers:

[0988] The server then sends the generated response to the user via the LINE app. The consultation details and responses are recorded in a database and saved and updated as a digital medical record. If a serious message is included, the emotion analysis engine will determine it as "high risk," and the server will immediately escalate the call to a manned help desk or medical institution.

[0989] Specific examples

[0990] User: Sends a message on LINE saying, "I'm feeling stressed at work."

[0991] Server: Receives the consultation content and passes the "stress" text to the generative AI model and sentiment analysis engine.

[0992] Sentiment analysis engine: Determined as "anxiety."

[0993] Generative AI model: Generates answers to questions such as "What specifically is causing you stress?" based on the user's personality profile and past history.

[0994] Server: Sends the answer to the user.

[0995] Prompt Sentence Examples

[0996] "My work has been getting more stressful lately. What should I do?"

[0997] "I had a fight with a friend, what should I do?"

[0998] This invention allows users to consult at their own pace 24 hours a day, 365 days a year, and by combining an emotion analysis engine with a generative AI model, they can receive more adaptive and personalized answers, while also achieving a rapid response to serious emotional states.

[0999] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] Receiving a registration message

[1002] Input: User sends "Register" message via LINE app.

[1003] Behavior: The server receives a "Register" message from the LINE Platform.

[1004] Output: The registration request has been accepted.

[1005] Step 2:

[1006] User ID generation and information registration

[1007] Input: The "register" message received by the server.

[1008] How it works: The server generates a unique identifier (such as a UUID) and creates a user ID. It also registers the user's basic information in a database.

[1009] Output: A user ID is generated and the user information is registered in the database.

[1010] Step 3:

[1011] Submit personality test form

[1012] Input: User information after registration is complete.

[1013] Operation: The server uses the LINE API to push a personality test form to the user.

[1014] Output: The personality test form has been sent to the user.

[1015] Step 4:

[1016] Receiving and analyzing personality tests

[1017] Input: The user enters answers into the personality test form through the LINE app and submits it.

[1018] How it works: The server receives the responses, analyzes them using natural language processing algorithms, and generates a personality profile of the user.

[1019] Output: A personality profile is generated and stored in a database.

[1020] Step 5:

[1021] Receiving consultation details

[1022] Input: The user inputs the consultation details through the LINE app and sends it.

[1023] Operation: The server receives the consultation content from the LINE platform.

[1024] Output: The received consultation content is saved.

[1025] Step 6:

[1026] Emotional state analysis

[1027] Input: Received consultation content.

[1028] How it works: The server passes the consultation to a sentiment analysis engine, which performs text analysis to identify the emotional state (e.g., anxiety, anger, happiness).

[1029] Output: The sentiment analysis results are output.

[1030] Step 7:

[1031] Generating optimal answers

[1032] Input: Sentiment analysis results, user's personality profile, and past consultation history.

[1033] How it works: The server inputs this data into a generative AI model to generate the optimal answer. The AI ​​model creates answers based on prompts, questions, and answer patterns.

[1034] Output: A personalized answer is generated.

[1035] Step 8:

[1036] Submit your answer

[1037] Input: The generated answer.

[1038] Behavior: The server uses the LINE API to push the generated answer to the user.

[1039] Output: The answer is sent to the user.

[1040] Step 9:

[1041] Data storage

[1042] Input: Consultation details and generated answers.

[1043] How it works: The server stores this information in a database as a digital medical record. The stored data will be used as learning data for the future.

[1044] Output: The consultation history and response history are saved in a database.

[1045] Step 10:

[1046] escalation

[1047] Input: If the sentiment analysis result is determined to be "high risk."

[1048] Operation: The server uses the emergency contact system and email notification system to immediately escalate the case to a staffed reception desk or medical institution, and sends a digital medical record or chat summary as necessary.

[1049] Output: Appropriate information is provided to manned counters and medical institutions.

[1050] (Application example 2)

[1051] Next, a description will be given of Application 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."

[1052] Conventional security services have not adequately considered the emotional state of the user or emergency responses, making it difficult to quickly take appropriate measures when the user is in a dangerous situation. The present invention aims to improve this situation by providing a security service system that analyzes the emotional state of the user in real time and can respond quickly and appropriately in an emergency.

[1053] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the content of the consultation from the user, means for generating an answer suitable for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with a manned counter or a medical institution as necessary, and means for analyzing the emotional state of the user in real time and taking appropriate action in an emergency. This makes it possible to monitor the emotional state of the user in real time and take prompt and appropriate action.

[1054] "User" refers to an individual or organization who uses the system to input their inquiry and receive a response.

[1055] "Consultation content" refers to information such as worries, questions, and explanations of situations that users enter through the system.

[1056] A "generative AI model" is an artificial intelligence model that automatically generates the optimal answer based on the user's inquiry.

[1057] "Emotional state" refers to the emotional situation or state analyzed from the content of the user's consultation.

[1058] "Appropriate response" refers to the optimized response and support provided according to the user's emotional state and the content of their consultation.

[1059] "Real-time analysis" means that the content of a user's inquiry is processed immediately upon receipt and a result is generated.

[1060] An "emergency" is when a user is deemed to be in a dangerous or high-risk situation.

[1061] A "digital medical record" is an electronic database that records and updates the contents of a user's consultation and the responses to them.

[1062] A "manned contact point" is a human operator or counselor with whom you can interact directly within the system.

[1063] "Medical institution" refers to a facility that provides medical services, such as a hospital, clinic, or psychiatric clinic.

[1064] "Collaboration" means sharing information and working together to address different systems and institutions.

[1065] A "profile" is a collection of data that analyzes a user's personality and tendencies and saves them.

[1066] The system of this invention receives inquiries from users, generates optimal answers using a generative AI model and an emotion engine, and takes emergency measures as needed. Specific implementation methods of this system are described below.

[1067] System Configuration

[1068] 1. User terminal

[1069] Users use a device such as a smartphone to input their inquiry details into the system, and consultations are mainly conducted via chat apps such as LINE.

[1070] 2. Server

[1071] The server receives messages from users and performs various processes. The server is equipped with a generative AI model and an emotion engine, which are used to analyze messages.

[1072] 3. Database

[1073] The server stores user information, personality profiles, past consultation history, and other information in a database, and the AI ​​model learns based on this.

[1074] Operation flow

[1075] 1. Registration

[1076] Users first enter their information and answer a personality questionnaire, based on which the server generates a personality profile and stores it in a database.

[1077] 2. Message reception and analysis

[1078] When users have a daily consultation, they input a text message, such as "I saw a suspicious person recently."

[1079] When the server receives this message, it analyzes it with an emotion engine and determines the user's emotional state, such as "anxiety" or "danger."

[1080] 3. Answer generation

[1081] Based on the results of the emotion engine and the user's profile, the generative AI model generates optimal advice and warnings. For example, in response to a message such as "I saw a suspicious person recently," the model generates a response such as "Are you worried because you saw a suspicious person? Please ensure a safe environment."

[1082] 4. Emergency Response

[1083] If the user's emotional state is determined to be high risk, the server will immediately contact security services or medical institutions.

[1084] For example, if a message is sent saying "a stranger is following me," it will be deemed a high risk and an emergency call will be made.

[1085] Hardware and software used

[1086] Hardware: Smartphones, servers

[1087] Software: LINE app, generative AI model, emotion engine, database management system

[1088] Example prompt

[1089] For example, the following prompts are used:

[1090] User ID: 123

[1091] Message: A stranger is following you

[1092] Emotional state: Anxious

[1093] Using this prompt, the generative AI model can generate "response measures to take when receiving a message about a suspicious person," enabling immediate appropriate responses and emergency calls. For example, specific responses such as "To ensure your safety, immediately ask for help from someone nearby."

[1094] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1095] Step 1:

[1096] The user operates the user device (smartphone) and inputs the content of the consultation into the LINE app. For example, the user inputs a message such as "I saw a suspicious person recently." This message becomes the input data.

[1097] Step 2:

[1098] The device sends the input consultation details to the server, along with metadata such as the user ID. The input data consists of a text message and related user information.

[1099] Step 3:

[1100] The server passes the received message to the emotion engine, which analyzes the emotional state. The emotion engine analyzes the input data and determines the emotional state, such as "anxiety" or "danger." The output is the emotional state resulting from the analysis.

[1101] Step 4:

[1102] The server passes the analysis results of the emotion engine and the user's personality profile to the generative AI model. The generative AI model refers to past consultation details and answer history to generate an appropriate answer. The input data are the analysis results and the user profile, and the output is the generated answer.

[1103] Step 5:

[1104] The server sends the generated answer to the user's device via the LINE app. The user confirms the generated answer on their device. The input data is the generated answer, and the output is a message that the user confirms.

[1105] Step 6:

[1106] The server records the consultation details and the generated answers in a digital medical record and saves them in a database. The input data are the consultation details and the generated answers, and the output is an updated digital medical record.

[1107] Step 7:

[1108] If the emotion engine analyzes a high-risk emotional state, the server immediately takes emergency action, such as notifying security services or medical institutions. The input data is the high-risk emotional state, and the output is a notification message.

[1109] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[1110] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[1111] 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.

[1112] [Fourth embodiment]

[1113] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1114] 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.

[1115] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1116] 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1117] 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 audio in accordance with instructions from the processor 46.

[1118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1120] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[1121] Fig. 8 shows an example of the 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.

[1122] 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.

[1123] 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.

[1124] In the robot 414, the processor 46 performs the reception output process. 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.

[1125] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[1126] This invention relates to a system that provides an environment where users with concerns can seek advice 24 hours a day, 365 days a year, anywhere, at their own pace. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records.

[1127] The server first receives a consultation message from the user. For example, if a user sends a message through the LINE app saying, "I've been feeling stressed at work lately," this triggers the server to start working.

[1128] The server then passes the consultation message to a generative AI model, which generates the optimal answer based on the consultation content. Specifically, the AI ​​analyzes past data and the user's personality profile to generate a specific and appropriate response such as, "That's tough. What specifically is causing you stress?"

[1129] The generated answer is provided to the user via the server and displayed on the user's device. The user can continue asking questions or seeking advice based on this answer. For example, the user can add, "I'm having trouble communicating with my boss," and the AI ​​will analyze that message as well and generate the next answer.

[1130] The system also has the ability to diagnose users' personalities. Users answer a personality assessment form sent from the server, asking questions such as, "When do you feel stressed?" The results are saved as a profile and used for subsequent analysis.

[1131] Another important feature of this invention is the automatic generation and updating of digital medical records. The server records and updates the details of inquiries sent by users and the responses to those inquiries as digital medical records. This allows for centralized management of users' conditions and, if necessary, provides that data to staffed counters or medical institutions.

[1132] The generative AI model also determines risk levels. It detects mental or health risks from the user's consultation content, and if a high risk is detected, the server immediately escalates the situation to a manned help desk or appropriate medical institution. For example, if a serious message such as "I'm thinking about suicide" is received, the server immediately contacts a counselor or doctor and arranges for appropriate support.

[1133] In this way, the present invention is a system that provides personalized psychological support to users who are troubled, allowing them to receive professional assistance at the appropriate time.

[1134] The processing flow will be explained below.

[1135] Step 1:

[1136] The server receives a "Register" message from the user via the LINE app.

[1137] Step 2:

[1138] The server generates a user ID based on the received "Register" message and registers the user information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[1139] Step 3:

[1140] The server sends a personality test form to users who have completed registration via the LINE app.

[1141] Step 4:

[1142] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[1143] Step 5:

[1144] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[1145] Step 6:

[1146] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[1147] Step 7:

[1148] The server receives a consultation message from the user as a trigger and passes the message to the generative AI model.

[1149] Step 8:

[1150] The generative AI model generates the optimal answer based on the received consultation content, the user's personality profile, and past consultation history.

[1151] Step 9:

[1152] The server then sends the generated answer to the user via the LINE app, sending a message such as, "That's tough. What specifically is causing you stress?"

[1153] Step 10:

[1154] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record.

[1155] Step 11:

[1156] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take. For example, if the user adds, "I'm having trouble communicating with my boss," the message is sent to the server again.

[1157] Step 12:

[1158] The server resends any additional messages from the user to the generative AI model, generates a new answer, and sends it to the user.

[1159] Step 13:

[1160] The server uses the generative AI model to determine the risk level based on the user's consultation and response. If the risk is deemed high, the server escalates the situation to a manned consultation desk or an appropriate medical institution.

[1161] Step 14:

[1162] If escalation is necessary, the server will send the necessary digital medical records and chat summaries to a manned desk or medical institution and arrange for appropriate response.

[1163] Example 1

[1164] Next, a description will be given of 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."

[1165] Conventional systems were unable to respond immediately to users' concerns and stress, making it difficult to provide 24 / 7 support. Furthermore, they did not provide sufficient personalized support based on the content of the user's consultation, making it difficult to provide appropriate answers or assess risks according to the user's situation. Furthermore, there was a lack of a means to comprehensively manage users' consultation history and personality assessment results, making it difficult to effectively create and update digital medical records. New technology is needed to solve these issues.

[1166] 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.

[1167] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for transmitting the consultation content using the user's terminal and displaying the generated answer, means for diagnosing the user's personality, means for saving the results of the personality diagnosis as the user's profile, means for recording past consultation content and answers and using them as learning data, means for automatically generating and updating the user's digital medical record based on the consultation content and its answers, means for determining mental or health risks from the user's consultation content, and means for sharing the consultation content with a manned counter or medical institution as needed. This allows users to continue consultation anywhere, 24 hours a day, 365 days a year, and provides personalized support, enabling them to quickly receive appropriate professional assistance as needed.

[1168] "User" refers to a person who uses the system to input and send consultation details.

[1169] "Consultation content" refers to the concerns or questions that users input and send through the system.

[1170] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the content of the consultation received.

[1171] A "prompt" is a text sentence that is input to a generative AI model to generate the optimal answer.

[1172] A "server" is a computer system that manages and controls the entire system and processes and transmits and receives various data.

[1173] An "answer" is a response generated by the generative AI model based on the content of the consultation.

[1174] "Terminal" refers to the device (smartphone, PC, etc.) used by the user to input and send the inquiry content and receive and display the response.

[1175] A "personality test" is a test conducted to analyze a user's personality and tendencies.

[1176] A "profile" is data that includes a user's personality and personality traits, created based on the results of a personality test and other information.

[1177] "Learning data" refers to data that records past consultation content and responses to those consultations and is used to improve the performance of the generative AI model.

[1178] A "digital medical record" is a centralized data management system that is automatically generated and updated based on the user's consultation details, responses, and personality test results.

[1179] "Risk assessment" refers to the generative AI model analyzing the user's consultation content and assessing and determining mental or health risks.

[1180] A "staffed counter" is a counter where specialized staff or counselors are always on hand to respond as needed.

[1181] A "medical institution" is a facility staffed by professionals such as doctors and counselors that provides medical and psychological support to users.

[1182] The present invention is a system that provides an environment where users with concerns can receive consultation at their own pace, 24 hours a day, 365 days a year. This system utilizes a generative AI model to provide users with personalized answers and has the ability to automatically generate and update digital medical records. A specific embodiment of this system is described below.

[1183] Hardware and Software Configuration

[1184] The entire system consists of three components: a server, a terminal, and a user. Specifically, the following hardware and software are used:

[1185] Server: A cloud-based system is used for data processing and management. In this case, the server infrastructure of Amazon Web Services (AWS) is used.

[1186] Device: A device such as a smartphone or computer used by a user, on which the LINE app or other chat tools are installed.

[1187] Generative AI models: Use advanced generative AI models such as OpenAI's GPT-4 to generate appropriate answers based on the content of the consultation.

[1188] Data processing and calculation flow

[1189] 1. Message received:

[1190] The user inputs the content of their problem using an app such as LINE and sends it. For example, they can send a message saying, "I've been feeling stressed at work lately."

[1191] The server receives the message via the API of the chat tool, such as the LINE API.

[1192] 2. Generate and send a prompt:

[1193] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt such as, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[1194] Send the prompt to the generative AI model.

[1195] 3. Generate answers:

[1196] The generative AI model generates an appropriate response based on the prompt it receives, such as, "That's tough. What specifically is causing you stress?"

[1197] 4. Providing answers:

[1198] The server receives the generated response and transmits it to the user's terminal.

[1199] The answer will be displayed on the user's device using the LINE app or other similar app.

[1200] Personality assessment and digital medical record creation / update

[1201] 1. Personality Test:

[1202] The server generates a questionnaire to assess the user's personality and sends it to the user. For example, it sends a question such as "When do you feel stressed?"

[1203] The user answers the personality test form and sends it to the server, which stores the received answers in a database.

[1204] 2. Digital medical record generation and updating:

[1205] The server automatically generates a digital medical record based on the user's consultation, responses, and personality test results.

[1206] The generated digital medical records are updated regularly, and the user's status is managed centrally.

[1207] Risk Level Determination and Escalation

[1208] 1. Determine the risk level:

[1209] The generative AI model analyzes the content of the user's consultation and determines mental or health risks. For example, if a serious message such as "I'm thinking about suicide" is received, the generative AI model will determine that there is a high risk.

[1210] 2. Escalation:

[1211] If a person is deemed to be at high risk, the server will immediately escalate the situation to a manned help desk or medical institution, contacting a counselor or doctor and arranging for appropriate assistance.

[1212] Examples of prompt statements

[1213] "A user submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer."

[1214] "To create a personality profile for your users, generate questions like: 'When do you feel stressed?'"

[1215] This completes the detailed description of the present invention, which allows the system to provide 24 / 7 support, automatically generate personalized answers, and provide users with fast, professional assistance when needed.

[1216] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1217] Step 1:

[1218] The server receives a consultation message from the user. When the user sends a message such as "I've been feeling stressed at work lately" via a chat tool such as the LINE app, the server receives this message via the LINE API. The input is the user's message, which is temporarily stored in a database. The output is the received message data.

[1219] Step 2:

[1220] The server analyzes the received message and generates a prompt to send to the generative AI model. For example, it generates a prompt like, "A user has submitted the following question: 'I've been feeling stressed at work lately.' Please generate the best answer." The input is the message received from the user, and the output is the generated prompt.

[1221] Step 3:

[1222] The server sends the generated prompt to a generative AI model. For example, it sends the prompt to OpenAI's GPT-4 through an API. The input is the generated prompt, and the output is the operation of sending the prompt to the generative AI model.

[1223] Step 4:

[1224] The generative AI model generates the optimal answer based on the prompt it receives. The input is the prompt sent from the server, and the output is the generated answer (e.g., "That's tough. What exactly is causing you stress?").

[1225] Step 5:

[1226] The server receives the answer returned from the generative AI model. The input is the answer from the generative AI model, which is temporarily stored in a database. The output is the received answer data.

[1227] Step 6:

[1228] The server sends the received answer to the user's device. The answer is displayed on the user's chat screen using the LINE API or similar. The inputs are the answer received from the generative AI model and the user's device information, and the output is the answer displayed on the user's device.

[1229] Step 7:

[1230] The user checks the answer sent from the server, then inputs and sends a new message to continue the consultation. For example, the user inputs "I'm having trouble communicating with my boss" and sends it. The input is the answer from the server, and the output is a new message from the user.

[1231] Step 8:

[1232] The server generates a question form to diagnose the user's personality and sends it via the LINE app, etc. For example, it generates a question such as "When do you feel stressed?" The input is the user's profile information and past consultation details, and the output is the generated question form.

[1233] Step 9:

[1234] The user answers the personality test form and sends it to the server. The input is the question form sent from the server, and the user enters the answers. The output is the user's answer data.

[1235] Step 10:

[1236] The server receives the user's answers and stores them in a database. The input is the answer data sent by the user, and the output is the answers stored in the database.

[1237] Step 11:

[1238] The server automatically generates and updates a digital medical record based on the collected consultation details, answers, and personality test results. The input is all data from the user, and the output is the digital medical record.

[1239] Step 12:

[1240] Using a generative AI model, the server determines mental or health risks based on the user's consultation. The input is all consultation details and responses from the user, and the data is analyzed. The output is a risk assessment result.

[1241] Step 13:

[1242] If necessary, the server will share the consultation details with a manned help desk or a medical institution. If the consultation is judged to be high risk, the server will perform an emergency escalation and arrange for appropriate professional support. The input is the risk assessment result, and the output is the linked professional support information.

[1243] These are the specific processing steps of this system. This system provides an environment where users can consult with us 24 hours a day, 365 days a year, and can provide personalized support quickly.

[1244] (Application example 1)

[1245] Next, a description will be given of Application 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."

[1246] In today's information society, security-related worries and anxieties are increasing day by day. In particular, the number of inquiries about password management and phishing scams is rapidly increasing, creating a growing demand for systems that can quickly and appropriately respond to such problems. Conventional consultation systems have difficulty providing personalized advice to individual users, and few systems are available 24 hours a day, 365 days a year. Therefore, from a security perspective, there is an increasing need for systems that can quickly respond to users' worries and questions.

[1247] 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.

[1248] In this invention, the server includes means for receiving consultation content from a user, means for generating an answer appropriate for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with manned help desks or medical institutions as necessary, and means for providing personalized advice on security-related concerns, thereby enabling users to quickly resolve their security-related concerns and questions 24 hours a day, 365 days a year.

[1249] "User" refers to an individual who uses the system to receive consultation or support.

[1250] The "means for receiving consultation content" is a system function for receiving messages sent by users.

[1251] A "generative AI model" is a model that uses artificial intelligence (AI) technology to analyze the content of inquiries from users and generate optimal answers.

[1252] The "means for generating an answer" is a system function that uses a generative AI model to create the optimal answer based on the user's inquiry.

[1253] The "means for providing an answer" is a system function for displaying or notifying the user of the generated answer.

[1254] "Means for recording past consultation content and responses and using them as learning data" refers to a system function that saves users' consultation history and responses to those requests and uses them for future responses.

[1255] "Means for communicating consultation details to manned counters or medical institutions" is a system function for transmitting the user's consultation details to a human counselor or medical institution as needed.

[1256] "Means for providing personalized advice on security-related concerns" refers to a system function that uses generative AI models to provide personalized advice on security-related issues faced by users.

[1257] This invention is implemented primarily through an application on a smartphone. The components and their operations will be described in detail below.

[1258] The server receives the content of the inquiry from the user. Specifically, the content of the inquiry entered through a smartphone application is sent to the server. For example, a user may send a message saying, "I'm having a hard time managing my passwords these days."

[1259] The server then uses a generative AI model to analyze the consultation and generate an appropriate response. The generative AI model is an artificial intelligence that has learned from training data in advance, and generates the optimal response based on the user's consultation and past data. For example, the AI ​​might generate a specific response such as, "That's a difficult problem. Why don't you consider using a password management tool?"

[1260] The generated answer is provided to the smartphone application via the server and displayed on the user's device. The user can continue to ask questions or receive advice based on this answer. For example, they can enter additional information such as, "What kind of password management tool is best?"

[1261] In addition, the server records the consultation content and the generated answers and uses them as learning data. This information is used to generate more accurate answers for future consultations. More personalized answers are provided based on past history.

[1262] If necessary, the server will also have the ability to share the consultation details with a manned helpline or medical institution. For example, if a user sends a serious message such as "I'm thinking about suicide," the server can immediately contact a counselor or doctor and arrange for appropriate support.

[1263] The system also provides advice on security-related concerns, leveraging generative AI models to provide personalized advice to users regarding their security issues, enabling users to quickly resolve their security concerns anywhere, 24 hours a day, 365 days a year.

[1264] The hardware used includes smartphones and servers, and the software uses the OpenAI API for generative AI models and Flask (a Python microweb framework) for data exchange.

[1265] For example, if a user sends a message saying, "I'm worried about my security these days. What can I do?" the app will respond with, "That sounds worrying. It's important to create a strong password and change it regularly."

[1266] As a further example, let's consider the case where a user sends a message saying, "I received a phishing email, what should I do?" In this case, the application responds with, "Do not open the email. It is important not to click on suspicious links and to verify the sender before acting."

[1267] Here are some example prompts:

[1268] "Message from a user: I'm worried that my password has been leaked recently. What should I do?

[1269] Good AI response: First, change your password and, if possible, set up two-factor authentication.

[1270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1271] Step 1:

[1272] The user inputs and sends the content of their inquiry through a smartphone application. For example, the input may include a message such as, "I'm having a hard time managing my passwords recently." This is then sent to the server.

[1273] Step 2:

[1274] The server receives the user's inquiry. In order to analyze the received message, it processes it as input data for the generative AI model. Specifically, it stores the received message in text format and prepares it for analysis.

[1275] Step 3:

[1276] The server analyzes the received consultation content using a generative AI model. In this step, the consultation content (input) is passed to the generative AI model, and the model generates the optimal answer. For example, in response to the consultation content "I've been having trouble managing my passwords lately," the server generates an answer (output) such as "That's tough. Why don't you consider using a password management tool?"

[1277] Step 4:

[1278] The server receives the generated answer and provides it to the user. The generated answer is then passed to a smartphone application and displayed on the user's device, allowing the user to receive appropriate advice in real time.

[1279] Step 5:

[1280] The server records the user's inquiry and the generated answer, and saves it as learning data. The recorded data is used as a database to provide more accurate answers to future inquiries.

[1281] Step 6:

[1282] If necessary, the server will contact a staffed help desk or medical institution to discuss the issue. For example, if the issue is serious, such as "considering suicide," the server will immediately contact a counselor or doctor and arrange for appropriate support.

[1283] Step 7:

[1284] The server uses a generative AI model to generate personalized advice for security-related concerns and provides it to users. When a user asks, "I've been worried about security lately. What are some good measures?", the server handles everything from receiving the message to generating and providing an answer, providing individually tailored advice.

[1285] Furthermore, 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.

[1286] This invention relates to a system that receives inquiries from users and provides more adaptive and personalized answers by combining a generative AI model and an emotion engine. This system provides an environment where users can consult at their own pace, 24 hours a day, 365 days a year.

[1287] The server first receives a "Register" message from the user via the LINE app. After receiving the message, the server generates a user ID and registers the user's information in a database. After registration is complete, the server sends the user a personality assessment form via the LINE app, and the user enters and submits their answers. Based on the answers, the server generates a personality profile for the user and stores it in a database.

[1288] When a consultation begins, the user enters the content of the consultation through the LINE app and sends it to the server. For example, they can send a message such as, "I've been feeling stressed at work lately." The server then passes this message to the generative AI model and emotion engine.

[1289] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it determines emotional states such as "anxiety" or "anger" from emotional expressions and keywords in the text. The results are provided to the generative AI model.

[1290] The generative AI model generates the optimal answer based on the user's personality profile, past consultation history, and emotional state analyzed by the emotion engine. For example, it might generate an answer along the lines of, "That's tough. What specifically is causing you stress?" The server then sends this answer to the user via the LINE app.

[1291] The server records the consultation details and the answers generated, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[1292] The system, which uses an emotion engine, can analyze the user's emotional state in real time and respond accordingly. For example, if a user sends a serious message such as "I'm thinking about suicide," the emotion engine will determine this as "high risk," and the server will immediately escalate the situation to a manned help desk or an appropriate medical institution.

[1293] If necessary, the server sends the digital medical record and chat summary to a staffed reception desk or medical institution and arranges for appropriate response.In this way, the present invention is a system that combines an emotion engine and a generative AI model to provide personalized psychological support to users and enable them to receive professional assistance at the appropriate time.

[1294] The processing flow will be explained below.

[1295] Step 1:

[1296] The server receives a "Register" message from the user via the LINE app.

[1297] Step 2:

[1298] The server generates a user ID based on the received "Register" message and registers the user's information in the database. Specifically, it stores the user ID, registration date and time, LINE ID, etc.

[1299] Step 3:

[1300] The server sends a personality test form to users who have completed registration via the LINE app.

[1301] Step 4:

[1302] Users enter answers to each question on the personality test form and send them to the server via the LINE app.

[1303] Step 5:

[1304] The server receives and analyzes the answers to the personality test form sent by the user to generate a personality profile for the user, which is then stored in a database.

[1305] Step 6:

[1306] The user enters the content of their problem into the LINE app and sends it to the server. For example, they could enter a message such as, "I've been feeling stressed at work lately."

[1307] Step 7:

[1308] The server receives consultation messages from users and passes them to the emotion engine and generative AI model.

[1309] Step 8:

[1310] The emotion engine analyzes the user's emotional state based on the received consultation content. For example, it detects emotions such as "anxiety" or "stress." The results are provided to the generative AI model.

[1311] Step 9:

[1312] The generative AI model generates the optimal answer based on the emotional state analyzed by the emotion engine, the user's personality profile, and past consultation history. For example, it might generate a response such as, "That's tough. What specifically is causing you stress?"

[1313] Step 10:

[1314] The server sends the generated answer to the user through the LINE app, and the user can receive the answer and decide what to do next.

[1315] Step 11:

[1316] The user receives the AI-generated answer sent from the server in the LINE app and decides on the next action to take, for example, sending an additional question such as "I'm having trouble communicating with my boss" to the server again.

[1317] Step 12:

[1318] The server then passes any additional messages from the user back to the emotion engine and generative AI model to generate new answers.

[1319] Step 13:

[1320] The server records the received consultation details and responses as a conversation history, and automatically generates and updates a digital medical record, allowing for centralized management of the user's condition.

[1321] Step 14:

[1322] The emotion engine analyzes the user's emotional state in real time, and if it determines that the risk is high, it flags the user as "high risk" and notifies the server of the result.

[1323] Step 15:

[1324] If the emotion engine determines that the situation is "high risk," the server immediately escalates the situation to a manned reception desk or an appropriate medical institution. The server then sends the necessary digital medical records and chat summaries to the manned reception desk or medical institution, and arranges for the appropriate response.

[1325] Example 2

[1326] Next, a description will be given of 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."

[1327] Conventional consultation systems have had difficulty providing personalized answers that fully consider the user's emotional state. Furthermore, they lacked mechanisms for providing prompt and appropriate responses when the user was in a serious emotional state. Furthermore, they were unable to effectively utilize past consultations and their responses as learning data.

[1328] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving the content of the consultation from the user, a means for generating an answer appropriate for the user based on the received content of the consultation using a generation AI model, a means for determining the emotional state of the user from the content of the consultation using an emotion analysis engine, a means for providing the generated answer to the user, a means for recording past consultation contents and answers and using them as learning data, and a means for sharing the consultation contents with a manned counter or a medical institution as necessary. This makes it possible to analyze the emotional state of the user in real time and provide a personalized answer based on the analysis. It also enables rapid escalation for users in serious emotional states.

[1329] "Consultation content" refers to text data such as questions, concerns, and opinions provided by users through the system.

[1330] A "generative AI model" is an algorithm or engine that uses machine learning and artificial intelligence techniques to generate appropriate outputs for input data.

[1331] An "emotion analysis engine" is software or algorithms that analyze and determine emotions and emotional states from text data.

[1332] A "profile" is a data set that centrally compiles information such as a user's personality, characteristics, and past consultation details.

[1333] A "digital medical record" is an electronic information file that records and centrally manages various digital data about users, specifically consultation details and response history.

[1334] "Escalation" is the process by which the system communicates and reports to a higher level responder or expert agency when it recognizes a serious problem or high-risk consultation.

[1335] This invention relates to a consultation system and describes a specific method for providing personalized answers to users. By combining a generative AI model and an emotion analysis engine, the system generates adaptive answers according to the user's emotional state and can respond quickly when necessary.

[1336] The main components of the system include:

[1337] 1. Means of receiving inquiries from users

[1338] 2. A means of generating appropriate answers based on the received consultation content using a generative AI model

[1339] 3. A means of determining the user's emotional state from the content of the consultation using an emotion analysis engine

[1340] 4. How to provide generated answers to users

[1341] 5. Recording past consultations and responses and using them as learning data

[1342] 6. A means of communicating consultation details to a staffed help desk or medical institution as needed

[1343] Hardware and Software Use

[1344] Hardware: Server (a server machine with a powerful CPU, GPU, and sufficient memory)

[1345] Software: LINE Platform, database system, generative AI model, sentiment analysis engine

[1346] Program processing

[1347] User Registration:

[1348] The user sends a "Register" message through the LINE app. The server receives this message, generates a user ID, and registers the user's information in the database. It also sends a personality assessment form, receives the user's answers, analyzes them, generates a personality profile, and saves it in the database.

[1349] Receiving and analyzing consultation content:

[1350] The user inputs and sends the content of their problem through the LINE app. For example, they send a message saying, "I've been feeling stressed at work lately." The server receives this message and passes it to the generative AI model and emotion analysis engine. The emotion analysis engine analyzes the content of the problem and determines the user's emotional state. The result of this determination is provided to the generative AI model, which generates the optimal answer.

[1351] Submit and save your answers:

[1352] The server then sends the generated response to the user via the LINE app. The consultation details and responses are recorded in a database and saved and updated as a digital medical record. If a serious message is included, the emotion analysis engine will determine it as "high risk," and the server will immediately escalate the call to a manned help desk or medical institution.

[1353] Specific examples

[1354] User: Sends a message on LINE saying, "I'm feeling stressed at work."

[1355] Server: Receives the consultation content and passes the "stress" text to the generative AI model and sentiment analysis engine.

[1356] Sentiment analysis engine: Determined as "anxiety."

[1357] Generative AI model: Generates answers to questions such as "What specifically is causing you stress?" based on the user's personality profile and past history.

[1358] Server: Sends the answer to the user.

[1359] Prompt Sentence Examples

[1360] "My work has been getting more stressful lately. What should I do?"

[1361] "I had a fight with a friend, what should I do?"

[1362] This invention allows users to consult at their own pace 24 hours a day, 365 days a year, and by combining an emotion analysis engine with a generative AI model, they can receive more adaptive and personalized answers, while also achieving a rapid response to serious emotional states.

[1363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1364] Step 1:

[1365] Receiving a registration message

[1366] Input: User sends "Register" message via LINE app.

[1367] Behavior: The server receives a "Register" message from the LINE Platform.

[1368] Output: The registration request has been accepted.

[1369] Step 2:

[1370] User ID generation and information registration

[1371] Input: The "register" message received by the server.

[1372] How it works: The server generates a unique identifier (such as a UUID) and creates a user ID. It also registers the user's basic information in a database.

[1373] Output: A user ID is generated and the user information is registered in the database.

[1374] Step 3:

[1375] Submit personality test form

[1376] Input: User information after registration is complete.

[1377] Operation: The server uses the LINE API to push a personality test form to the user.

[1378] Output: The personality test form has been sent to the user.

[1379] Step 4:

[1380] Receiving and analyzing personality tests

[1381] Input: The user enters answers into the personality test form through the LINE app and submits it.

[1382] How it works: The server receives the responses, analyzes them using natural language processing algorithms, and generates a personality profile of the user.

[1383] Output: A personality profile is generated and stored in a database.

[1384] Step 5:

[1385] Receiving consultation details

[1386] Input: The user inputs the consultation details through the LINE app and sends it.

[1387] Operation: The server receives the consultation content from the LINE platform.

[1388] Output: The received consultation content is saved.

[1389] Step 6:

[1390] Emotional state analysis

[1391] Input: Received consultation content.

[1392] How it works: The server passes the consultation to a sentiment analysis engine, which performs text analysis to identify the emotional state (e.g., anxiety, anger, happiness).

[1393] Output: The sentiment analysis results are output.

[1394] Step 7:

[1395] Generating optimal answers

[1396] Input: Sentiment analysis results, user's personality profile, and past consultation history.

[1397] How it works: The server inputs this data into a generative AI model to generate the optimal answer. The AI ​​model creates answers based on prompts, questions, and answer patterns.

[1398] Output: A personalized answer is generated.

[1399] Step 8:

[1400] Submit your answer

[1401] Input: The generated answer.

[1402] Behavior: The server uses the LINE API to push the generated answer to the user.

[1403] Output: The answer is sent to the user.

[1404] Step 9:

[1405] Data storage

[1406] Input: Consultation details and generated answers.

[1407] How it works: The server stores this information in a database as a digital medical record. The stored data will be used as learning data for the future.

[1408] Output: The consultation history and response history are saved in a database.

[1409] Step 10:

[1410] escalation

[1411] Input: If the sentiment analysis result is determined to be "high risk."

[1412] Operation: The server uses the emergency contact system and email notification system to immediately escalate the case to a staffed reception desk or medical institution, and sends a digital medical record or chat summary as necessary.

[1413] Output: Appropriate information is provided to manned counters and medical institutions.

[1414] (Application example 2)

[1415] Next, a description will be given of Application 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."

[1416] Conventional security services have not adequately considered the emotional state of the user or emergency responses, making it difficult to quickly take appropriate measures when the user is in a dangerous situation. The present invention aims to improve this situation by providing a security service system that analyzes the emotional state of the user in real time and can respond quickly and appropriately in an emergency.

[1417] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the content of the consultation from the user, means for generating an answer suitable for the user based on the received consultation content using a generative AI model, means for providing the generated answer to the user, means for recording past consultation content and answers and using them as learning data, means for sharing the consultation content with a manned counter or a medical institution as necessary, and means for analyzing the emotional state of the user in real time and taking appropriate action in an emergency. This makes it possible to monitor the emotional state of the user in real time and take prompt and appropriate action.

[1418] "User" refers to an individual or organization who uses the system to input their inquiry and receive a response.

[1419] "Consultation content" refers to information such as worries, questions, and explanations of situations that users enter through the system.

[1420] A "generative AI model" is an artificial intelligence model that automatically generates the optimal answer based on the user's inquiry.

[1421] "Emotional state" refers to the emotional situation or state analyzed from the content of the user's consultation.

[1422] "Appropriate response" refers to the optimized response and support provided according to the user's emotional state and the content of their consultation.

[1423] "Real-time analysis" means that the content of a user's inquiry is processed immediately upon receipt and a result is generated.

[1424] An "emergency" is when a user is deemed to be in a dangerous or high-risk situation.

[1425] A "digital medical record" is an electronic database that records and updates the contents of a user's consultation and the responses to them.

[1426] A "manned contact point" is a human operator or counselor with whom you can interact directly within the system.

[1427] "Medical institution" refers to a facility that provides medical services, such as a hospital, clinic, or psychiatric clinic.

[1428] "Collaboration" means sharing information and working together to address different systems and institutions.

[1429] A "profile" is a collection of data that analyzes a user's personality and tendencies and saves them.

[1430] The system of this invention receives inquiries from users, generates optimal answers using a generative AI model and an emotion engine, and takes emergency measures as needed. Specific implementation methods of this system are described below.

[1431] System Configuration

[1432] 1. User terminal

[1433] Users use a device such as a smartphone to input their inquiry details into the system, and consultations are mainly conducted via chat apps such as LINE.

[1434] 2. Server

[1435] The server receives messages from users and performs various processes. The server is equipped with a generative AI model and an emotion engine, which are used to analyze messages.

[1436] 3. Database

[1437] The server stores user information, personality profiles, past consultation history, and other information in a database, and the AI ​​model learns based on this.

[1438] Operation flow

[1439] 1. Registration

[1440] Users first enter their information and answer a personality questionnaire, based on which the server generates a personality profile and stores it in a database.

[1441] 2. Message reception and analysis

[1442] When users have a daily consultation, they input a text message, such as "I saw a suspicious person recently."

[1443] When the server receives this message, it analyzes it with an emotion engine and determines the user's emotional state, such as "anxiety" or "danger."

[1444] 3. Answer generation

[1445] Based on the results of the emotion engine and the user's profile, the generative AI model generates optimal advice and warnings. For example, in response to a message such as "I saw a suspicious person recently," the model generates a response such as "Are you worried because you saw a suspicious person? Please ensure a safe environment."

[1446] 4. Emergency Response

[1447] If the user's emotional state is determined to be high risk, the server will immediately contact security services or medical institutions.

[1448] For example, if a message is sent saying "a stranger is following me," it will be deemed a high risk and an emergency call will be made.

[1449] Hardware and software used

[1450] Hardware: Smartphones, servers

[1451] Software: LINE app, generative AI model, emotion engine, database management system

[1452] Example prompt

[1453] For example, the following prompts are used:

[1454] User ID: 123

[1455] Message: A stranger is following you

[1456] Emotional state: Anxious

[1457] Using this prompt, the generative AI model can generate "response measures to take when receiving a message about a suspicious person," enabling immediate appropriate responses and emergency calls. For example, specific responses such as "To ensure your safety, immediately ask for help from someone nearby."

[1458] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1459] Step 1:

[1460] The user operates the user device (smartphone) and inputs the content of the consultation into the LINE app. For example, the user inputs a message such as "I saw a suspicious person recently." This message becomes the input data.

[1461] Step 2:

[1462] The device sends the input consultation details to the server, along with metadata such as the user ID. The input data consists of a text message and related user information.

[1463] Step 3:

[1464] The server passes the received message to the emotion engine, which analyzes the emotional state. The emotion engine analyzes the input data and determines the emotional state, such as "anxiety" or "danger." The output is the emotional state resulting from the analysis.

[1465] Step 4:

[1466] The server passes the analysis results of the emotion engine and the user's personality profile to the generative AI model. The generative AI model refers to past consultation details and answer history to generate an appropriate answer. The input data are the analysis results and the user profile, and the output is the generated answer.

[1467] Step 5:

[1468] The server sends the generated answer to the user's device via the LINE app. The user confirms the generated answer on their device. The input data is the generated answer, and the output is a message that the user confirms.

[1469] Step 6:

[1470] The server records the consultation details and the generated answers in a digital medical record and saves them in a database. The input data are the consultation details and the generated answers, and the output is an updated digital medical record.

[1471] Step 7:

[1472] If the emotion engine analyzes a high-risk emotional state, the server immediately takes emergency action, such as notifying security services or medical institutions. The input data is the high-risk emotional state, and the output is a notification message.

[1473] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding 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.

[1474] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[1475] 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 robot 414.

[1476] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1477] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1478] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1479] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1480] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1481] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1482] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1483] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1484] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1485] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1486] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1487] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1488] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1489] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1490] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1491] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1492] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1493] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1494] The following is further disclosed regarding the above embodiment.

[1495] (Claim 1)

[1496] A means for receiving consultation content from a user;

[1497] A means for generating an answer appropriate for the user based on the received consultation content using a generative AI model;

[1498] a means for providing the generated answer to a user;

[1499] A means of recording past consultation details and responses and using them as learning data;

[1500] A means of communicating consultation details to manned help desks or medical institutions as necessary,

[1501] A system including:

[1502] (Claim 2)

[1503] 10. The system according to claim 1, further comprising means for conducting a personality assessment of the user and saving the results as a profile of the user.

[1504] (Claim 3)

[1505] The system according to claim 1, further comprising means for automatically generating and updating a digital medical record of the user based on the consultation content and the response thereto.

[1506] "Example 1"

[1507] (Claim 1)

[1508] A means for receiving consultation content from a user;

[1509] A means for generating an answer appropriate for the user based on the received consultation content using a generative AI model;

[1510] a means for providing the generated answer to a user;

[1511] means for transmitting the content of the consultation using a user's terminal and displaying the generated answer;

[1512] A means for conducting a personality diagnosis of a user;

[1513] A means for storing the results of the personality test as a user profile;

[1514] A means of recording past consultation details and responses and using them as learning data;

[1515] A means to automatically generate and update the user's digital medical record based on the content of the consultation and the response;

[1516] A means of determining mental or health risks based on the content of users' consultations, and

[1517] A means of communicating consultation details to manned help desks or medical institutions as necessary,

[1518] A system including:

[1519] (Claim 2)

[1520] The system of claim 1 generates appropriate answers using a generative AI model and prompt sentences.

[1521] (Claim 3)

[1522] The system according to claim 1, wherein the consultation details sent from the user's terminal and the generated answers are recorded in a database, and digital medical records are managed in a unified manner.

[1523] "Application Example 1"

[1524] (Claim 1)

[1525] A means for receiving consultation content from a user;

[1526] A means for generating an answer appropriate for the user based on the received consultation content using a generative AI model;

[1527] a means for providing the generated answer to a user;

[1528] A means of recording past consultation details and responses and using them as learning data;

[1529] A means of communicating consultation details to manned help desks or medical institutions as necessary,

[1530] A means to provide personalized advice on security-related concerns;

[1531] A system including:

[1532] (Claim 2)

[1533] 10. The system according to claim 1, further comprising means for conducting a personality assessment of the user and saving the results as a profile of the user.

[1534] (Claim 3)

[1535] The system according to claim 1, further comprising means for automatically generating and updating a digital medical record of the user based on the consultation content and the response thereto.

[1536] "Example 2: Combining Emotion Engines"

[1537] (Claim 1)

[1538] A means for receiving consultation content from a user;

[1539] A means for generating an answer appropriate for the user based on the received consultation content using a generative AI model;

[1540] A means for determining the emotional state of a user from the content of the consultation using an emotion analysis engine;

[1541] a means for providing the generated answer to a user;

[1542] A means of recording past consultation details and responses and using them as learning data;

[1543] A means of communicating consultation details to manned help desks or medical institutions as necessary,

[1544] A system including:

[1545] (Claim 2)

[1546] A means for conducting a personality test on a user and storing the result as a user profile;

[1547] The system further includes a means for determining the emotional state of the user from the consultation content using an emotion analysis engine.

[1548] 10. The system of claim 1.

[1549] (Claim 3)

[1550] A means to automatically generate and update the user's digital medical record based on the content of the consultation and the response;

[1551] It also includes a means to monitor the user's emotional state in real time using an emotion analysis engine and escalate to a manned help desk or medical institution in the event of an emergency.

[1552] 10. The system of claim 1.

[1553] "Application example 2 when combining emotion engines"

[1554] (Claim 1)

[1555] A means for receiving consultation content from a user;

[1556] A means for generating an answer appropriate for the user based on the received consultation content using a generative AI model;

[1557] a means for providing the generated answer to a user;

[1558] A means of recording past consultation details and responses and using them as learning data;

[1559] A means of communicating consultation details to manned help desks or medical institutions as necessary,

[1560] A means of analyzing the user's emotional state in real time and taking appropriate action in an emergency;

[1561] A system including:

[1562] (Claim 2)

[1563] 10. The system according to claim 1, further comprising means for conducting a personality assessment of the user and saving the results as a profile of the user.

[1564] (Claim 3)

[1565] The system according to claim 1, further comprising means for automatically generating and updating a digital medical record of the user based on the consultation content and the response thereto.

[1566] (Claim 4)

[1567] 10. The system of claim 1, further comprising means for contacting a security service in an emergency based on the user's emotional state. [Explanation of symbols]

[1568] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving consultation content from a user; A means for generating an answer appropriate for the user based on the received consultation content using a generative AI model; a means for providing the generated answer to a user; A means of recording past consultation details and responses and using them as learning data; A means of communicating consultation details to manned help desks or medical institutions as necessary, A system including:

2. 2. The system according to claim 1, further comprising means for conducting a personality assessment of the user and saving the results as a user profile.

3. The system according to claim 1, further comprising means for automatically generating and updating a digital medical record of the user based on the content of the consultation and the response thereto.

Images