System

Abstract

A system is provided.SOLUTION: A system comprising: means for interacting with a user in real-time with an interactive bot using a generative AI model; means for receiving a question or consultation from the user, analyzing the question or consultation, and providing an appropriate answer; means for connecting to a specialist when the user requires specialized support; means for optimizing outreach activities; and means for ensuring continued support of the user through regular follow-up.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] The problem that this invention aims to solve is the current situation in which young people struggling with drug use are unable to obtain accurate information or appropriate support, resulting in an increase in the risks and health damage associated with drug use. In particular, there is a need to provide 24 / 7 continuous support to young people who are in a situation where it is difficult to receive support due to social prejudice and discrimination, as well as appropriate information and resources to reduce their risks. [Means for solving the problem]

[0005] The present invention provides a system that uses a conversational bot using a generative AI model to communicate with users in real time, receive questions or inquiries from users, analyze them, and provide appropriate answers. It also provides a system that connects users to experts when they need specialized support, and analyzes the user's location information and data to optimize outreach activities. It also includes a system that ensures continuous support for users through regular follow-up. This system aims to reduce the risks of drug use and build a more inclusive and understanding society.

[0006] A "generative AI model" is a model trained using an artificial intelligence-based algorithm to interact with a user.

[0007] A "conversational bot" is an automated response system equipped with artificial intelligence to receive and understand input from users and provide appropriate responses or information.

[0008] "User" refers to a person who uses this system to receive counseling or support regarding drug use.

[0009] "Real-time" refers to a state in which the system responds immediately to user input and conducts dialogue with minimal time lag.

[0010] An "expert" is someone who has knowledge and experience of drug use and is qualified to provide professional help and advice to users.

[0011] "Outreach activities" refers to external social contribution activities aimed at providing information and support to specific regions or groups.

[0012] "Location information" refers to geographic data that indicates a user's current location.

[0013] "Follow-up" refers to subsequent check-ups and support to provide ongoing assistance to the user.

[0014] "Natural language processing technology" refers to algorithms and methods for analyzing natural language input from users, understanding its meaning, and generating appropriate responses.

[0015] A "database" refers to an information system that systematically organizes and stores various information and data, allowing them to be quickly searched and retrieved. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The system of the present invention is designed to allow users to ask questions and receive advice about drug use 24 hours a day, 365 days a year. The system uses a conversational bot powered by a generative AI model to analyze users' inquiries and provide appropriate answers and support. It also connects users to experts as needed and provides information to optimize outreach activities.

[0038] This system consists of three entities: a server, a terminal, and a user. Below, we explain the program's processing in natural language.

[0039] Initial System Setup

[0040] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[0041] User Access

[0042] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password). The device then sends this information to the server.

[0043] User Authentication

[0044] The server receives the authentication information submitted by the user and matches it with existing user information in its database. If authentication is successful, the server starts a user session and launches the chatbot.

[0045] Start interactive support

[0046] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting their consultation or question to the chatbot. For example, the user can input "Please tell me how to reduce overdose."

[0047] Answers to basic questions

[0048] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "It's effective to gradually reduce the amount of medication you take at one time. Would you like me to connect you to a specialist for more detailed instructions?" and send it to the device.

[0049] Connect with experts

[0050] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[0051] Optimizing information provision

[0052] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[0053] Planning outreach activities

[0054] The server analyzes neighborhood and facility data to identify potential risk areas, plans outreach activities based on this information, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach activities for young people in City A."

[0055] Ongoing support

[0056] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0057] As described above, the present invention provides a system that utilizes generative AI models to provide a conversational bot and comprehensive support for users, enabling young people struggling with drug use to receive appropriate support and reduce their risk.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The server loads the generative AI model, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[0061] Step 2:

[0062] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0063] Step 3:

[0064] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[0065] Step 4:

[0066] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0067] Step 5:

[0068] The device sends the user's question to the server, which uses a generative AI model to analyze the question. The server then retrieves an appropriate answer from a database and sends it to the device. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[0069] Step 6:

[0070] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0071] Step 7:

[0072] The server searches for and selects an appropriate expert from the expert database, transfers the user's consultation to the selected expert, and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert.

[0073] Step 8:

[0074] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources that correspond to the user's frequently asked questions.

[0075] Step 9:

[0076] The server analyzes data from local and facility locations to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[0077] Step 10:

[0078] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[0079] Example 1

[0080] 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."

[0081] The present invention relates to a system that allows users to ask questions or seek advice about drug use, and aims to provide a system that provides support 24 hours a day, 365 days a year, connects users to experts as needed, and optimizes outreach activities. Conventional systems have prevented users from receiving appropriate support promptly and have made it difficult to maximize the effectiveness of outreach activities. For this reason, there has been a demand for a system that provides comprehensive and continuous support to users experiencing difficulties with drug use.

[0082] 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.

[0083] In this invention, the server includes means for having an interactive bot using a generative AI model interact with a user in real time, means for receiving questions or consultations from the user and analyzing the contents to provide appropriate answers, means for connecting the user to an expert when the user needs specialized support, means for analyzing the user's location information and data to optimize outreach activities, means for ensuring continuous support for the user through regular follow-ups, means for initializing the system, means for the user to access the system from a terminal, input authentication information and send it to the server, means for the server to verify the user's authentication information and authenticate the user and activate the chatbot, and means for the terminal to display a chat screen and for the user to input the consultation or question content. This allows users to ask questions or consult at any time and receive prompt and appropriate answers and support, further maximizing the effectiveness of outreach activities.

[0084] A "generative AI model" is an artificial intelligence model that analyzes user input in natural language and generates appropriate answers and actions.

[0085] An "interactive bot" is a software system that responds to users' questions and inquiries in real time and provides information.

[0086] "Server" is the central computer system that runs the generative AI model and manages access to the user and expert databases.

[0087] A "user" is an individual who asks a question or makes a request through the system.

[0088] A "terminal" is a device (such as a smartphone or PC) that a user uses to access and operate the system.

[0089] An "expert" is an individual who has knowledge and experience of drug use and is qualified to provide specialized support to users.

[0090] "Initialization" is the procedure of loading the generative AI model, initializing the database, and preparing resources to enable the system to operate normally.

[0091] "User authentication" is the process of verifying a user's identity when accessing a system.

[0092] "Natural language processing technology" is a technology for analyzing natural language text entered by a user and understanding its meaning and intent.

[0093] "Outreach activities" are activities that provide important information and support to specific areas or specific groups.

[0094] "Follow-up" is the process of tracking and checking to provide ongoing care and support to a user.

[0095] A "user database" is a database system for storing and managing information about users.

[0096] "Expert database" refers to a database system that allows the system to store and manage expert information.

[0097] The "chat screen" is an interface that allows users to input information to the chatbot.

[0098] "Question history" is a record of questions that a user has asked in the past and the answers to those questions.

[0099] The present invention provides an interactive system using a generative AI model that accepts questions and consultations about drug use 24 hours a day, 365 days a year, and provides appropriate answers and professional support in real time. Detailed embodiments of the system are described below.

[0100] System configuration

[0101] The system mainly consists of a server, terminals, and users. The server runs the generative AI model and functions as an interface with users and an expert database. Terminals are devices that users use to access and operate the system, and include smartphones and PCs. Users access the system via their terminals to ask questions or make inquiries.

[0102] Hardware and software used

[0103] Server: A high-performance computer system is used to ensure appropriate data processing and response speed. The generative AI model used is, for example, OpenAI's GPT-4.

[0104] User database: Use a relational database such as MySQL or PostgreSQL to manage user information.

[0105] Expert database: Use a non-relational database such as MongoDB to manage expert information.

[0106] Terminal: A device used by a user to access the service, such as a smartphone or computer.

[0107] Initial System Setup

[0108] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user and expert databases, and preparing resource data, so that the system can immediately interact with the user and quickly provide the required information.

[0109] User Access

[0110] The user accesses the system from their own terminal. The terminal displays a login screen, and the user enters authentication information (user ID, password). The terminal then sends this information to the server.

[0111] User Authentication

[0112] The server receives authentication information from the user and checks it against existing information in a user database. If authentication is successful, the server starts a user session and launches a chatbot.

[0113] Interactive Support

[0114] The device displays a chat screen to users who have been successfully authenticated. The user inputs their consultation or question to the chatbot, and the device sends it to the server. For example, the user might input, "Please tell me how to reduce overdose."

[0115] Answers to basic questions

[0116] The server uses a generative AI model to analyze the user's question using natural language processing technology, retrieves an appropriate answer from the database, and generates a response such as, "It is effective to gradually reduce the amount of drugs you take at one time. Would you like to be connected to a specialist for more detailed instructions?" and sends it to the device.

[0117] Connect with experts

[0118] When a user needs expert support, the device sends the request to the server, which searches for an appropriate expert from the expert database and establishes a real-time connection between the user and the expert. For example, if the user types "Yes, connect me to an expert," the server will arrange a real-time chat session with the expert and send a link to the device.

[0119] Optimizing information provision

[0120] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns. For example, it can provide information specific to the user's frequently asked questions, eliminating the need to ask the question again.

[0121] Outreach Activities

[0122] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[0123] Ongoing support

[0124] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0125] Examples of prompt statements

[0126] For example, a user might input a prompt into the system like this: "I want to reduce my drug intake. Please tell me specific ways to do this." In response, the generative AI model would provide specific advice and support.

[0127] In this way, the present invention leverages generative AI models to provide a conversational bot that provides comprehensive support to users, enabling users struggling with substance use to receive appropriate support and mitigate their risks.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1: Initial System Setup

[0130] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user database (e.g., MySQL) and expert database (e.g., MongoDB), and loading resource data (e.g., drug use guidelines and FAQs) into memory.

[0131] Input: None

[0132] Output: An initialized database and a loaded generative AI model.

[0133] Step 2: User Access

[0134] The user accesses the system from a device (smartphone or PC) and opens the login screen.

[0135] The terminal displays a login screen where the user enters their user ID and password. The entered authentication information is sent from the terminal to the server.

[0136] Input: User ID, Password

[0137] Output: Authentication information sent to the server

[0138] Step 3: User authentication

[0139] The server checks the received authentication information against existing information in a user database.

[0140] If authentication is successful, the server starts a user session and launches the chatbot, otherwise it sends an error message to the device.

[0141] Input: Submitted credentials

[0142] Output: User session started / Error message

[0143] Step 4: Start interactive support

[0144] After successful authentication, the device displays a chat screen. The user inputs their consultation or question to the chatbot, and the device sends the user's input to the server.

[0145] Input: User's consultation or question

[0146] Output: Consultation or question sent to the server

[0147] Step 5: Answer basic questions

[0148] The server uses a generative AI model to analyze the user's question using natural language processing technology. After analysis, the server retrieves the appropriate answer from the database, and the generative AI model generates the answer and sends it to the device.

[0149] For example, if a user types in "How can I reduce an overdose?", the generated answer might be "Slowly reducing the amount of drugs you take at one time is effective. Would you like me to connect you to a specialist for more information?"

[0150] Input: User's question

[0151] Output: The appropriate answer sent to the terminal

[0152] Step 6: Connect with an expert

[0153] When a user needs expert support, the terminal sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection between the user and the expert.

[0154] For example, if the user types "Yes, connect me to an expert," the server will arrange a chat session with the expert and send a link to the device.

[0155] Enter: Request for Expert Support

[0156] Output: Expert connection link sent to the device

[0157] Step 7: Optimize information delivery

[0158] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns.

[0159] For example, it provides information specific to the questions users frequently ask, eliminating the need to ask the question again.

[0160] Input: Data collected during the interaction

[0161] Output: Optimized information delivery

[0162] Step 8: Plan your outreach activities

[0163] The server analyzes local and facility data to identify potential risk areas, which are then used to plan outreach activities and notify the outreach team.

[0164] For example, you might send instructions to your outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach efforts with young people in City A."

[0165] Input: Area and facility data

[0166] Output: Plan outreach activities

[0167] Step 9: Ongoing support

[0168] The server will conduct regular follow-ups with the user and, if necessary, arrange for follow-ups by experts. It will also send follow-up reminders to the user's device to provide ongoing support for the user's situation.

[0169] Input: User data for follow-up

[0170] Output: Follow-up reminders sent to your device

[0171] The above is the specific processing flow of the system that provides an interactive bot that utilizes a generative AI model and provides comprehensive support to users.

[0172] (Application example 1)

[0173] 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."

[0174] In electronic payment services, users demand support available 24 hours a day, 365 days a year, but it is difficult to obtain prompt and appropriate answers. In particular, when users need payment troubles, emergency response, or expert advice, it often takes a long time for them to receive appropriate support. In addition, there is a demand for a system that can quickly analyze user questions and provide appropriate answers by introducing a conversational bot, but at present, it is difficult to effectively implement such a system.

[0175] 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.

[0176] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving questions or inquiries from the user and analyzing the contents to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for receiving the user's authentication information and comparing it with existing user information in a database to start a user session, a means for analyzing the user's location information and data to optimize outreach activities, and a means for ensuring continuous support for the user through regular follow-ups, thereby enabling users to receive prompt and appropriate support 24 hours a day, 365 days a year.

[0177] A "generative AI model" is a model of artificial intelligence technology used to analyze a user's question or inquiry and generate an appropriate answer.

[0178] An "interactive bot" is a system that interacts with users in real time and provides answers to their questions and inquiries.

[0179] A "user session" refers to a series of interactions and operations that occur between the time a user logs in to a system and the time they log out.

[0180] The "means for connecting with experts" is a function for connecting with experts in real time when a user needs professional support.

[0181] "Natural language processing technology" is a technology used to analyze and understand users' questions and inquiries.

[0182] "Outreach activities" are activities aimed at providing needed information and services to specific target areas or communities.

[0183] "Regular follow-up" is the process of continually monitoring the user's situation and providing additional support or information as needed.

[0184] An "expert database" is a database that stores information on experts in various fields.

[0185] This invention is a system that uses a generative AI model to provide an interactive bot that responds to user questions and problems regarding electronic payment services 24 hours a day, 365 days a year. This system consists of three components: the user's terminal, a server, and an expert database.

[0186] Initial System Setup

[0187] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[0188] User Access

[0189] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID, password). This authentication information is sent from the device to the server.

[0190] User Authentication

[0191] The server receives the authentication information submitted by the user and checks it against existing user information in a database. If authentication is successful, the server starts a user session and launches an interactive bot.

[0192] Start interactive support

[0193] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting a question or inquiry to the conversational bot. For example, the user might input, "I would like to check my credit card limit."

[0194] Answers to basic questions

[0195] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "To check your credit card limit, log in to the electronic payment service's app or website and click on the 'Account Information' section," and send it to the device.

[0196] Connect with experts

[0197] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[0198] Optimizing information provision

[0199] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[0200] Planning outreach activities

[0201] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to intensify outreach activities in certain areas of City A."

[0202] Ongoing support

[0203] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0204] For example, if a user asks a chatbot, "I want to check my credit card limit," the generative AI model will respond, "To check your credit card limit, log in to the app or website of your electronic payment service and click on the 'Account Information' section." In this way, it is possible to provide the information users need quickly and accurately.

[0205] Example prompt sentence:

[0206] After logging in with your user ID and password, you will be asked the following questions:

[0207] "I want to check my credit card limit. How can I do that?"

[0208] Example of the output response:

[0209] To check your credit card limit, log in to your payment service app or website and click on the "Account Information" section.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] (input)

[0213] Users access the system using their own devices such as smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password).

[0214] (process)

[0215] The terminal sends this authentication information to the server.

[0216] (output)

[0217] The authentication information is sent to the server.

[0218] Step 2:

[0219] (input)

[0220] The server receives the authentication information sent by the user.

[0221] (process)

[0222] The server checks the received authentication information against existing user information in its database.

[0223] (output)

[0224] If authentication is successful, the user session begins and the chatbot is launched.

[0225] Step 3:

[0226] (input)

[0227] When a user session is started, the terminal displays a chat screen.

[0228] (process)

[0229] The user inputs questions or inquiries into the chat screen.

[0230] (output)

[0231] The user's question or inquiry is entered into the terminal.

[0232] Step 4:

[0233] (input)

[0234] The terminal transmits the user's question or inquiry to the server.

[0235] (process)

[0236] The server uses a generative AI model to analyze the question, specifically, it uses natural language processing technology to understand the question and retrieves the appropriate answer from a database.

[0237] (output)

[0238] The server sends the answer generated by the generative AI model based on the analysis results to the terminal.

[0239] Step 5:

[0240] (input)

[0241] The device displays the answer of the generative AI model received from the server.

[0242] (process)

[0243] The user checks the displayed answer and, if necessary, enters additional questions or inquiries.

[0244] (output)

[0245] The user's additional questions and inquiries are input into the terminal.

[0246] Step 6:

[0247] (input)

[0248] When a user requests professional support, the request is sent from the terminal to the server.

[0249] (process)

[0250] The server searches for a suitable expert from an expert database.

[0251] (output)

[0252] Expert information obtained from an expert database is prepared on a server.

[0253] Step 7:

[0254] (input)

[0255] A request arrives at the server to connect the user with the expert in real time.

[0256] (process)

[0257] The server arranges a real-time chat session with an expert and sends a link to the device.

[0258] (output)

[0259] A chat link with the expert will appear on your device.

[0260] Step 8:

[0261] (input)

[0262] The server analyzes the data collected during the interaction.

[0263] (process)

[0264] The server provides optimal information based on the user's situation and question history, and optimizes outreach activities based on past dialogue history.

[0265] (output)

[0266] A plan for outreach activities is generated.

[0267] Step 9:

[0268] (input)

[0269] The server sends periodic follow-up reminders to the user's device.

[0270] (process)

[0271] The user is informed.

[0272] (output)

[0273] Follow-up reminders will appear on your device.

[0274] In this way, a system that provides efficient and prompt user support is configured.

[0275] 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.

[0276] The system of the present invention provides comprehensive support for users struggling with drug use, combining a generative AI model and an emotion engine to achieve deeper understanding and support. The system consists of three components: a server, a device, and a user. It includes real-time dialogue with the user, connection to experts, optimization of outreach activities, and continuous follow-up. Furthermore, the emotion engine is used to analyze the user's emotional state and provide appropriate responses.

[0277] Initial System Setup

[0278] The server loads the generative AI model, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates an emotion engine to analyze the user's emotional state.

[0279] User Access

[0280] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0281] User Authentication

[0282] The server checks the authentication information received from the device against existing user information in its database. If authentication is successful, the server starts a user session and launches a chatbot.

[0283] Start interactive support

[0284] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0285] Answers to basic questions

[0286] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server will respond, "It is effective to gradually reduce the amount of drug you take at one time."

[0287] Emotion Engine Analysis

[0288] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user is feeling anxious or scared, the emotion engine detects this and notifies the server.

[0289] Responding according to emotions

[0290] The server receives notifications from the emotion engine and instructs the chatbot on how to respond based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message such as, "Don't worry, we're here to support you."

[0291] Connect with experts

[0292] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0293] The server searches and selects an appropriate expert from the expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server then sends this information to the user's terminal and provides the user with a connection link to the expert.

[0294] Optimizing information provision

[0295] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions.

[0296] Planning outreach activities

[0297] The server analyzes data from local and facility locations to identify potential risk areas, and then uses this information to plan outreach activities and provide information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[0298] Ongoing support

[0299] The server schedules regular follow-ups for the user, sends reminders to the user's device, and arranges follow-ups by experts as needed, ensuring that the user receives continuous support.

[0300] As described above, the present invention is an interactive bot system that combines a generative AI model and an emotion engine to provide comprehensive support to users, enabling young people struggling with drug use to receive appropriate support and reduce their risk and improve their behavior.

[0301] The processing flow will be explained below.

[0302] Step 1:

[0303] The server loads the generative AI model and emotion engine, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[0304] Step 2:

[0305] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0306] Step 3:

[0307] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[0308] Step 4:

[0309] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0310] Step 5:

[0311] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[0312] Step 6:

[0313] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user inputs "I'm very anxious. Please help me," the emotion engine detects that the user is feeling anxious and notifies the server of the analysis result.

[0314] Step 7:

[0315] The server receives notifications from the emotion engine and instructs the chatbot to respond appropriately based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will generate a reassuring message such as "Don't worry, we're here to support you" and send it to the device.

[0316] Step 8:

[0317] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0318] Step 9:

[0319] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation details and emotional state to the selected expert, and arranges a chat session with the expert. The server sends this information to the user's terminal and provides the user with a connection link to the expert.

[0320] Step 10:

[0321] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources specific to the user's frequently asked questions.

[0322] Step 11:

[0323] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team. For example, it sends instructions such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[0324] Step 12:

[0325] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[0326] As described above, the server, terminals, and users work together to build a system that provides comprehensive support to users, enabling them to receive appropriate assistance, reduce the risks associated with drug use, and achieve social inclusion.

[0327] Example 2

[0328] 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."

[0329] In modern society, support for users struggling with drug use is limited, particularly in the areas of real-time dialogue and individual expert assistance. Furthermore, analysis of users' emotional states and optimization of outreach activities are lacking, making it difficult to provide efficient and effective support. This leaves users unable to receive appropriate support.

[0330] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0331] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving a question or consultation from the user and analyzing the content to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for analyzing the emotional state from the user's input and providing a response according to the emotion, a means for analyzing the user's location data and history to optimize outreach activities, and a means for conducting regular follow-ups to ensure continuous support for the user. This allows the user to interact in real time and receive appropriate information, consultation from an expert, a response according to the emotion, and continuous follow-up.

[0332] A "generative AI model" is an artificial intelligence algorithm that analyzes input data from users and generates appropriate answers and actions using natural language processing technology.

[0333] An "interactive bot" is software that communicates with users in real time, providing answers and support to questions and inquiries.

[0334] An "expert" is a person or organization that has advanced knowledge and experience in a particular field and provides users with specialized advice and assistance.

[0335] "Emotion analysis" is the process of extracting and analyzing a user's emotional state from text or voice data entered by the user.

[0336] "Outreach activities" are activities that provide assistance and information to specific areas or target groups with the aim of reducing risk and providing support.

[0337] "Follow-up" is the process of continuously checking the user's situation after the initial response or support and providing additional support as needed.

[0338] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and is used in interactive bots, etc.

[0339] A "user database" is a database that stores and manages information about users on a system.

[0340] "Expert Database" refers to a database that stores and manages information about experts, and is used to search for and select appropriate experts as needed.

[0341] The system of this invention provides comprehensive support to users struggling with drug use, and by combining a conversational bot using a generative AI model with an emotion analysis engine, it achieves deeper understanding and support. This system consists of three entities: a server, a terminal, and a user.

[0342] The server runs the system using the following software and hardware. The generative AI model uses GPT-4, which has advanced natural language processing technology. The user database and expert database are managed using MySQL, as are resource data and information related to outreach activities. For sentiment analysis, IBM Watson Tone Analyzer is integrated, which has the ability to analyze emotions from user input.

[0343] Initial System Setup

[0344] The server first loads the generative AI model, which uses GPT-4. It also initializes the user database and expert database using MySQL and integrates the sentiment analysis engine, preparing the server to analyze the user's emotional state.

[0345] User Access

[0346] Users access the system using devices such as smartphones and PCs. When a user connects to a device, they enter authentication information (user ID, password), and the device sends this information to the server.

[0347] User Authentication

[0348] The server checks the authentication information received from the user against existing user information in its database. If authentication is successful, the server starts a user session and launches a conversational bot, which allows real-time interaction with the user.

[0349] Interactive Support

[0350] After successful authentication, the device displays a chat screen. The user enters a consultation or question into the chatbot to begin a dialogue. The device then sends the user's question to the server, which analyzes it using a generative AI model. Natural language processing technology is used to retrieve an appropriate answer from a database and present it to the user.

[0351] Sentiment analysis and response

[0352] The server's emotion analysis engine analyzes the user's emotional state from their input and provides a response based on the detected emotion. For example, if the user is feeling anxious or scared, the server instructs the chatbot to deliver a reassuring message.

[0353] Connect with experts

[0354] When a user needs professional assistance, they input their request into their device and send it to the server. The server searches for an appropriate expert in the expert database and forwards the user's consultation details and emotional state to the selected expert. The server then sends this information to the device and provides the user with a connection link to the expert.

[0355] Optimizing information provision and ongoing support

[0356] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. It also plans regular follow-ups and sends reminders to the user's device. If necessary, it can also arrange for follow-ups by a specialist, ensuring the user receives ongoing support.

[0357] Specific examples

[0358] For example, if a user asks, "How can I reduce the risk of overdosing?", the server uses a generative AI model to answer, "The most effective way is to gradually reduce the amount of drugs you take at one time." If the sentiment analysis engine detects the user's anxiety, the chatbot will send a reassuring message such as, "Don't worry, we're here to help."

[0359] Prompt Sentence Examples

[0360] "If users have concerns about their drug use, we provide appropriate support messages."

[0361] In this way, the system of the present invention can provide comprehensive and ongoing support to users struggling with substance use, helping to alleviate and improve their problems.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] The server loads the generative AI model. Specifically, it uses GPT-4 and loads it into the system. MySQL is used to initialize the user database and expert database. Furthermore, it prepares resource data and integrates IBM Watson Tone Analyzer for sentiment analysis. The input is the configuration file at system startup, and the output is the system state after the initial setup is complete.

[0365] Step 2:

[0366] A user accesses the system using a smartphone or PC and enters authentication information (user ID and password) through the terminal. The terminal sends this authentication information to the server. The input is the user's authentication information, and the output is the authentication request sent to the server.

[0367] Step 3:

[0368] The server checks the authentication information received from the device against existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is the received authentication information, and the output is the authentication result and session ID.

[0369] Step 4:

[0370] After successful authentication, the device displays a chat screen. The user inputs their inquiry or question to the chatbot and begins a dialogue. The user's input is sent to the server by the device. The input is the user's inquiry or question, and the output is the data sent to the server.

[0371] Step 5:

[0372] The server receives the user's question, analyzes it using a generative AI model, and uses natural language processing technology to retrieve an appropriate answer from a database and present it to the user. The input is the user's question, and the output is the generated answer.

[0373] Step 6:

[0374] The server's emotion analysis engine analyzes the user's emotional state from the user's input. If the user is feeling anxious or scared, it detects this and notifies the server. The input is the user's input text, and the output is the emotion analysis result.

[0375] Step 7:

[0376] The server receives notifications from the emotion analysis engine and instructs the chatbot to respond according to the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message. The input is the emotion analysis result, and the output is the instructed response message.

[0377] Step 8:

[0378] When a user needs expert assistance, they input their request into their terminal and send it to the server. The server searches and selects an appropriate expert from its expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert. The input is the user's request, and the output is a connection link to the expert.

[0379] Step 9:

[0380] The server analyzes the data collected during the conversation and provides optimal information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions. The input is the collected data, and the output is optimized information.

[0381] Step 10:

[0382] The server analyzes data on local areas and facilities to identify potential risk areas. Based on this, it plans outreach activities and provides information to the outreach team. For example, it sends instructions such as, "You need to strengthen your outreach activities in City A." The input is local data, and the output is outreach plans and instructions.

[0383] Step 11:

[0384] The server plans regular follow-ups for the user and sends reminders to the user's device. If necessary, it also arranges follow-ups by experts, ensuring that the user receives continuous support. The input is a support plan, and the output is a reminder message to the user.

[0385] (Application example 2)

[0386] 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."

[0387] Providing comprehensive support to users struggling with drug use requires real-time dialogue, emotional response, connection to experts, optimization of outreach activities, and continuous follow-up. However, current systems lack the means to accurately analyze users' emotional states and provide appropriate emotional responses in real time, making it difficult for conventional systems to provide sufficient support.

[0388] 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 having an interactive bot using a generative AI model interact with a user in real time; means for receiving questions or consultations from the user and analyzing the content to provide appropriate answers; means for connecting the user to an expert when the user needs professional support; means for analyzing the user's location information and data to optimize outreach activities; means for ensuring continuous support for the user through regular follow-up; means for emotionally analyzing the user's messages and providing responses according to the emotions; and means for sending reassuring messages based on the emotional analysis. This makes it possible to provide appropriate support according to the user's emotional state and provide comprehensive, emotionally sensitive support to users struggling with drug use.

[0389] A "generative AI model" is an artificial intelligence technology designed to interact with users and analyze data. It uses natural language processing technology to analyze questions and inquiries and generate appropriate answers.

[0390] A "conversational bot" is a software application that is based on a generative AI model and allows for real-time interaction with users.

[0391] "Emotion analysis" is a technology that analyzes a user's emotional state based on messages and data obtained from the user.

[0392] An "expert" is a person who has extensive knowledge and experience in a particular field and can provide professional support to users.

[0393] "Outreach activities" are activities that are planned and implemented based on data analysis to provide support to specific areas and situations.

[0394] "Follow-up" refers to the activity of periodically checking the status of a user who has received support and providing ongoing support.

[0395] "Real-time interaction means" refers to technology or software that allows for immediate two-way communication with a user.

[0396] An "emotionally responsive means" is a technique or process that generates responses or support appropriate to a user's emotional state based on that state.

[0397] A "reassuring message" is a message that includes content intended to soothe and reassure the user.

[0398] This invention is a system for providing comprehensive support to users struggling with substance use. The system combines a generative AI model and an emotion analysis engine to provide assistance through a real-time interactive bot that responds to the user's emotional state. Specific embodiments are described below.

[0399] First, the server loads the generative AI model and the sentiment analysis engine, initializes the user database and the expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state.

[0400] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password). The terminal then sends the authentication information to the server. The server compares the authentication information received from the terminal with existing user information in the database, and if authentication is successful, it starts a user session and launches the chatbot. If authentication is successful, the terminal displays a chat screen.

[0401] The user begins a dialogue by inputting their concerns or questions into the chatbot. For example, if the user inputs, "My drug use has increased recently and I'm worried. What should I do?", the device will send this question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. At this time, the user's message is analyzed using an emotion analysis engine, and their emotional state is also evaluated at the same time. If anxiety or fear is detected, the generative AI model will take this information into account and generate a reassuring message.

[0402] For example, if the emotion analysis result is "negative," the system will provide the user with a message such as, "Let's think about specific steps to reduce your drug use. Don't worry, we're here to support you." By continuing the dialogue in this way, it is possible to provide appropriate support according to the user's emotional state.

[0403] Furthermore, if a user needs professional support, they can input and send a request for an expert on their terminal. The server searches and selects an appropriate expert from the expert database. The server then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. This allows the user to receive individual support from the expert.

[0404] Regarding outreach activities, the server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, if a particular area or facility is at high risk, outreach activities in that area will be strengthened. The server also plans regular follow-ups and sends reminders to the user's device, ensuring that the user receives ongoing support.

[0405] As a specific example, by inputting the prompt sentence "The user is feeling anxious about drug use. What should they do?" into a generative AI model, an appropriate answer can be generated. This prompt sentence is used to generate a message that takes into account the user's emotional state.

[0406] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0407] Step 1:

[0408] The server loads the generative AI model and sentiment analysis engine, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state. The input is resource information from specific configuration files and databases, and the necessary modules are launched and configured based on this. The output is an initialized server environment.

[0409] Step 2:

[0410] A user accesses the system using a smartphone or PC terminal and enters authentication information (user ID, password). The terminal sends this authentication information to the server. The input is the authentication information entered by the user, and based on this, the terminal sends an authentication request to the server. The output is an authentication request to the server.

[0411] Step 3:

[0412] The server compares the authentication information received from the device with existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is an authentication request, which is used to perform database comparison. The output is a successful authentication message and the launch of the chatbot.

[0413] Step 4:

[0414] If authentication is successful, the terminal displays a chat screen. The input is an authentication success message, and the chat screen is displayed based on this. The output is a chat screen that the user can use.

[0415] Step 5:

[0416] The user inputs their concerns or questions into the chatbot. For example, they might input, "My drug use has increased recently, and I'm worried. What should I do?" The input is the message the user sends to the chatbot, and the output is the content of their concerns, which is sent from the device to the server.

[0417] Step 6:

[0418] The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. In parallel, the sentiment analysis engine analyzes the user's message and evaluates their emotional state. The input is the user's inquiry, and based on this, answers are searched for in the database and sentiment analysis is performed. The output is the generated answer and an evaluation of the emotional state.

[0419] Step 7:

[0420] The server instructs the generative AI model to generate a reassuring message based on the results of the emotion analysis. For example, if it detects anxiety or fear, it adds a reassuring message such as "Don't worry, we're here to support you" to the generated response. The input is the result of the emotion analysis and the generated response, and the output is the final message containing reassuring content.

[0421] Step 8:

[0422] The final message is sent from the server to the terminal and provided to the user through the chatbot. The input is the final message generated by the server, and the output is the chatbot's response displayed on the user's terminal.

[0423] Step 9:

[0424] When a user needs expert support, he / she inputs the request at the terminal and sends it to the server. The input is the expert request from the user, and the output is the request sent to the server.

[0425] Step 10:

[0426] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation content and emotional state to the selected expert, and arranges a chat session with the expert. The input is the expert request and database data, and the output is the selected expert and the transferred information.

[0427] Step 11:

[0428] The server analyzes the data collected during the interaction and provides the most appropriate information based on the user's situation and question history. It also plans outreach activities and sends reminders to the user's device. The input is the data collected during the interaction, and the output is the planned outreach activities and reminder messages.

[0429] Step 12:

[0430] If regular follow-up is required, the server sends reminders to the user's device to provide ongoing support. The input is the follow-up plan, and the output is the reminder message sent to the user.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Second embodiment]

[0435] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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. 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] In the smart glasses 214, 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.

[0446] 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."

[0447] The system of the present invention is designed to allow users to ask questions and receive advice about drug use 24 hours a day, 365 days a year. The system uses a conversational bot powered by a generative AI model to analyze users' inquiries and provide appropriate answers and support. It also connects users to experts as needed and provides information to optimize outreach activities.

[0448] This system consists of three entities: a server, a terminal, and a user. Below, we explain the program's processing in natural language.

[0449] Initial System Setup

[0450] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[0451] User Access

[0452] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password). The device then sends this information to the server.

[0453] User Authentication

[0454] The server receives the authentication information submitted by the user and matches it with existing user information in its database. If authentication is successful, the server starts a user session and launches the chatbot.

[0455] Start interactive support

[0456] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting their consultation or question to the chatbot. For example, the user can input "Please tell me how to reduce overdose."

[0457] Answers to basic questions

[0458] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "It's effective to gradually reduce the amount of medication you take at one time. Would you like me to connect you to a specialist for more detailed instructions?" and send it to the device.

[0459] Connect with experts

[0460] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[0461] Optimizing information provision

[0462] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[0463] Planning outreach activities

[0464] The server analyzes neighborhood and facility data to identify potential risk areas, plans outreach activities based on this information, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach activities for young people in City A."

[0465] Ongoing support

[0466] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0467] As described above, the present invention provides a system that utilizes generative AI models to provide a conversational bot and comprehensive support for users, enabling young people struggling with drug use to receive appropriate support and reduce their risk.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] The server loads the generative AI model, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[0471] Step 2:

[0472] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0473] Step 3:

[0474] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[0475] Step 4:

[0476] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0477] Step 5:

[0478] The device sends the user's question to the server, which uses a generative AI model to analyze the question. The server then retrieves an appropriate answer from a database and sends it to the device. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[0479] Step 6:

[0480] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0481] Step 7:

[0482] The server searches for and selects an appropriate expert from the expert database, transfers the user's consultation to the selected expert, and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert.

[0483] Step 8:

[0484] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources that correspond to the user's frequently asked questions.

[0485] Step 9:

[0486] The server analyzes data from local and facility locations to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[0487] Step 10:

[0488] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[0489] Example 1

[0490] 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."

[0491] The present invention relates to a system that allows users to ask questions or seek advice about drug use, and aims to provide a system that provides support 24 hours a day, 365 days a year, connects users to experts as needed, and optimizes outreach activities. Conventional systems have prevented users from receiving appropriate support promptly and have made it difficult to maximize the effectiveness of outreach activities. For this reason, there has been a demand for a system that provides comprehensive and continuous support to users experiencing difficulties with drug use.

[0492] 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.

[0493] In this invention, the server includes means for having an interactive bot using a generative AI model interact with a user in real time, means for receiving questions or consultations from the user and analyzing the contents to provide appropriate answers, means for connecting the user to an expert when the user needs specialized support, means for analyzing the user's location information and data to optimize outreach activities, means for ensuring continuous support for the user through regular follow-ups, means for initializing the system, means for the user to access the system from a terminal, input authentication information and send it to the server, means for the server to verify the user's authentication information and authenticate the user and activate the chatbot, and means for the terminal to display a chat screen and for the user to input the consultation or question content. This allows users to ask questions or consult at any time and receive prompt and appropriate answers and support, further maximizing the effectiveness of outreach activities.

[0494] A "generative AI model" is an artificial intelligence model that analyzes user input in natural language and generates appropriate answers and actions.

[0495] An "interactive bot" is a software system that responds to users' questions and inquiries in real time and provides information.

[0496] "Server" is the central computer system that runs the generative AI model and manages access to the user and expert databases.

[0497] A "user" is an individual who asks a question or makes a request through the system.

[0498] A "terminal" is a device (such as a smartphone or PC) that a user uses to access and operate the system.

[0499] An "expert" is an individual who has knowledge and experience of drug use and is qualified to provide specialized support to users.

[0500] "Initialization" is the procedure of loading the generative AI model, initializing the database, and preparing resources to enable the system to operate normally.

[0501] "User authentication" is the process of verifying a user's identity when accessing a system.

[0502] "Natural language processing technology" is a technology for analyzing natural language text entered by a user and understanding its meaning and intent.

[0503] "Outreach activities" are activities that provide important information and support to specific areas or specific groups.

[0504] "Follow-up" is the process of tracking and checking to provide ongoing care and support to a user.

[0505] A "user database" is a database system for storing and managing information about users.

[0506] "Expert database" refers to a database system that allows the system to store and manage expert information.

[0507] The "chat screen" is an interface that allows users to input information to the chatbot.

[0508] "Question history" is a record of questions that a user has asked in the past and the answers to those questions.

[0509] The present invention provides an interactive system using a generative AI model that accepts questions and consultations about drug use 24 hours a day, 365 days a year, and provides appropriate answers and professional support in real time. Detailed embodiments of the system are described below.

[0510] System configuration

[0511] The system mainly consists of a server, terminals, and users. The server runs the generative AI model and functions as an interface with users and an expert database. Terminals are devices that users use to access and operate the system, and include smartphones and PCs. Users access the system via their terminals to ask questions or make inquiries.

[0512] Hardware and software used

[0513] Server: A high-performance computer system is used to ensure appropriate data processing and response speed. The generative AI model used is, for example, OpenAI's GPT-4.

[0514] User database: Use a relational database such as MySQL or PostgreSQL to manage user information.

[0515] Expert database: Use a non-relational database such as MongoDB to manage expert information.

[0516] Terminal: A device used by a user to access the service, such as a smartphone or computer.

[0517] Initial System Setup

[0518] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user and expert databases, and preparing resource data, so that the system can immediately interact with the user and quickly provide the required information.

[0519] User Access

[0520] The user accesses the system from their own terminal. The terminal displays a login screen, and the user enters authentication information (user ID, password). The terminal then sends this information to the server.

[0521] User Authentication

[0522] The server receives authentication information from the user and checks it against existing information in a user database. If authentication is successful, the server starts a user session and launches a chatbot.

[0523] Interactive Support

[0524] The device displays a chat screen to users who have been successfully authenticated. The user inputs their consultation or question to the chatbot, and the device sends it to the server. For example, the user might input, "Please tell me how to reduce overdose."

[0525] Answers to basic questions

[0526] The server uses a generative AI model to analyze the user's question using natural language processing technology, retrieves an appropriate answer from the database, and generates a response such as, "It is effective to gradually reduce the amount of drugs you take at one time. Would you like to be connected to a specialist for more detailed instructions?" and sends it to the device.

[0527] Connect with experts

[0528] When a user needs expert support, the device sends the request to the server, which searches for an appropriate expert from the expert database and establishes a real-time connection between the user and the expert. For example, if the user types "Yes, connect me to an expert," the server will arrange a real-time chat session with the expert and send a link to the device.

[0529] Optimizing information provision

[0530] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns. For example, it can provide information specific to the user's frequently asked questions, eliminating the need to ask the question again.

[0531] Outreach Activities

[0532] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[0533] Ongoing support

[0534] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0535] Examples of prompt statements

[0536] For example, a user might input a prompt into the system like this: "I want to reduce my drug intake. Please tell me specific ways to do this." In response, the generative AI model would provide specific advice and support.

[0537] In this way, the present invention leverages generative AI models to provide a conversational bot that provides comprehensive support to users, enabling users struggling with substance use to receive appropriate support and mitigate their risks.

[0538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0539] Step 1: Initial System Setup

[0540] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user database (e.g., MySQL) and expert database (e.g., MongoDB), and loading resource data (e.g., drug use guidelines and FAQs) into memory.

[0541] Input: None

[0542] Output: An initialized database and a loaded generative AI model.

[0543] Step 2: User Access

[0544] The user accesses the system from a device (smartphone or PC) and opens the login screen.

[0545] The terminal displays a login screen where the user enters their user ID and password. The entered authentication information is sent from the terminal to the server.

[0546] Input: User ID, Password

[0547] Output: Authentication information sent to the server

[0548] Step 3: User authentication

[0549] The server checks the received authentication information against existing information in a user database.

[0550] If authentication is successful, the server starts a user session and launches the chatbot, otherwise it sends an error message to the device.

[0551] Input: Submitted credentials

[0552] Output: User session started / Error message

[0553] Step 4: Start interactive support

[0554] After successful authentication, the device displays a chat screen. The user inputs their consultation or question to the chatbot, and the device sends the user's input to the server.

[0555] Input: User's consultation or question

[0556] Output: Consultation or question sent to the server

[0557] Step 5: Answer basic questions

[0558] The server uses a generative AI model to analyze the user's question using natural language processing technology. After analysis, the server retrieves the appropriate answer from the database, and the generative AI model generates the answer and sends it to the device.

[0559] For example, if a user types in "How can I reduce an overdose?", the generated answer might be "Slowly reducing the amount of drugs you take at one time is effective. Would you like me to connect you to a specialist for more information?"

[0560] Input: User's question

[0561] Output: The appropriate answer sent to the terminal

[0562] Step 6: Connect with an expert

[0563] When a user needs expert support, the terminal sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection between the user and the expert.

[0564] For example, if the user types "Yes, connect me to an expert," the server will arrange a chat session with the expert and send a link to the device.

[0565] Enter: Request for Expert Support

[0566] Output: Expert connection link sent to the device

[0567] Step 7: Optimize information delivery

[0568] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns.

[0569] For example, it provides information specific to the questions users frequently ask, eliminating the need to ask the question again.

[0570] Input: Data collected during the interaction

[0571] Output: Optimized information delivery

[0572] Step 8: Plan your outreach activities

[0573] The server analyzes local and facility data to identify potential risk areas, which are then used to plan outreach activities and notify the outreach team.

[0574] For example, you might send instructions to your outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach efforts with young people in City A."

[0575] Input: Area and facility data

[0576] Output: Plan outreach activities

[0577] Step 9: Ongoing support

[0578] The server will conduct regular follow-ups with the user and, if necessary, arrange for follow-ups by experts. It will also send follow-up reminders to the user's device to provide ongoing support for the user's situation.

[0579] Input: User data for follow-up

[0580] Output: Follow-up reminders sent to your device

[0581] The above is the specific processing flow of the system that provides an interactive bot that utilizes a generative AI model and provides comprehensive support to users.

[0582] (Application example 1)

[0583] 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."

[0584] In electronic payment services, users demand support available 24 hours a day, 365 days a year, but it is difficult to obtain prompt and appropriate answers. In particular, when users need payment troubles, emergency response, or expert advice, it often takes a long time for them to receive appropriate support. In addition, there is a demand for a system that can quickly analyze user questions and provide appropriate answers by introducing a conversational bot, but at present, it is difficult to effectively implement such a system.

[0585] 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.

[0586] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving questions or inquiries from the user and analyzing the contents to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for receiving the user's authentication information and comparing it with existing user information in a database to start a user session, a means for analyzing the user's location information and data to optimize outreach activities, and a means for ensuring continuous support for the user through regular follow-ups, thereby enabling users to receive prompt and appropriate support 24 hours a day, 365 days a year.

[0587] A "generative AI model" is a model of artificial intelligence technology used to analyze a user's question or inquiry and generate an appropriate answer.

[0588] An "interactive bot" is a system that interacts with users in real time and provides answers to their questions and inquiries.

[0589] A "user session" refers to a series of interactions and operations that occur between the time a user logs in to a system and the time they log out.

[0590] The "means for connecting with experts" is a function for connecting with experts in real time when a user needs professional support.

[0591] "Natural language processing technology" is a technology used to analyze and understand users' questions and inquiries.

[0592] "Outreach activities" are activities aimed at providing needed information and services to specific target areas or communities.

[0593] "Regular follow-up" is the process of continually monitoring the user's situation and providing additional support or information as needed.

[0594] An "expert database" is a database that stores information on experts in various fields.

[0595] This invention is a system that uses a generative AI model to provide an interactive bot that responds to user questions and problems regarding electronic payment services 24 hours a day, 365 days a year. This system consists of three components: the user's terminal, a server, and an expert database.

[0596] Initial System Setup

[0597] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[0598] User Access

[0599] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID, password). This authentication information is sent from the device to the server.

[0600] User Authentication

[0601] The server receives the authentication information submitted by the user and checks it against existing user information in a database. If authentication is successful, the server starts a user session and launches an interactive bot.

[0602] Start interactive support

[0603] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting a question or inquiry to the conversational bot. For example, the user might input, "I would like to check my credit card limit."

[0604] Answers to basic questions

[0605] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "To check your credit card limit, log in to the electronic payment service's app or website and click on the 'Account Information' section," and send it to the device.

[0606] Connect with experts

[0607] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[0608] Optimizing information provision

[0609] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[0610] Planning outreach activities

[0611] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to intensify outreach activities in certain areas of City A."

[0612] Ongoing support

[0613] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0614] For example, if a user asks a chatbot, "I want to check my credit card limit," the generative AI model will respond, "To check your credit card limit, log in to the app or website of your electronic payment service and click on the 'Account Information' section." In this way, it is possible to provide the information users need quickly and accurately.

[0615] Example prompt sentence:

[0616] After logging in with your user ID and password, you will be asked the following questions:

[0617] "I want to check my credit card limit. How can I do that?"

[0618] Example of the output response:

[0619] To check your credit card limit, log in to your payment service app or website and click on the "Account Information" section.

[0620] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0621] Step 1:

[0622] (input)

[0623] Users access the system using their own devices such as smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password).

[0624] (process)

[0625] The terminal sends this authentication information to the server.

[0626] (output)

[0627] The authentication information is sent to the server.

[0628] Step 2:

[0629] (input)

[0630] The server receives the authentication information sent by the user.

[0631] (process)

[0632] The server checks the received authentication information against existing user information in its database.

[0633] (output)

[0634] If authentication is successful, the user session begins and the chatbot is launched.

[0635] Step 3:

[0636] (input)

[0637] When a user session is started, the terminal displays a chat screen.

[0638] (process)

[0639] The user inputs questions or inquiries into the chat screen.

[0640] (output)

[0641] The user's question or inquiry is entered into the terminal.

[0642] Step 4:

[0643] (input)

[0644] The terminal transmits the user's question or inquiry to the server.

[0645] (process)

[0646] The server uses a generative AI model to analyze the question, specifically, it uses natural language processing technology to understand the question and retrieves the appropriate answer from a database.

[0647] (output)

[0648] The server sends the answer generated by the generative AI model based on the analysis results to the terminal.

[0649] Step 5:

[0650] (input)

[0651] The device displays the answer of the generative AI model received from the server.

[0652] (process)

[0653] The user checks the displayed answer and, if necessary, enters additional questions or inquiries.

[0654] (output)

[0655] The user's additional questions and inquiries are input into the terminal.

[0656] Step 6:

[0657] (input)

[0658] When a user requests professional support, the request is sent from the terminal to the server.

[0659] (process)

[0660] The server searches for a suitable expert from an expert database.

[0661] (output)

[0662] Expert information obtained from an expert database is prepared on a server.

[0663] Step 7:

[0664] (input)

[0665] A request arrives at the server to connect the user with the expert in real time.

[0666] (process)

[0667] The server arranges a real-time chat session with an expert and sends a link to the device.

[0668] (output)

[0669] A chat link with the expert will appear on your device.

[0670] Step 8:

[0671] (input)

[0672] The server analyzes the data collected during the interaction.

[0673] (process)

[0674] The server provides optimal information based on the user's situation and question history, and optimizes outreach activities based on past dialogue history.

[0675] (output)

[0676] A plan for outreach activities is generated.

[0677] Step 9:

[0678] (input)

[0679] The server sends periodic follow-up reminders to the user's device.

[0680] (process)

[0681] The user is informed.

[0682] (output)

[0683] Follow-up reminders will appear on your device.

[0684] In this way, a system that provides efficient and prompt user support is configured.

[0685] 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.

[0686] The system of the present invention provides comprehensive support for users struggling with drug use, combining a generative AI model and an emotion engine to achieve deeper understanding and support. The system consists of three components: a server, a device, and a user. It includes real-time dialogue with the user, connection to experts, optimization of outreach activities, and continuous follow-up. Furthermore, the emotion engine is used to analyze the user's emotional state and provide appropriate responses.

[0687] Initial System Setup

[0688] The server loads the generative AI model, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates an emotion engine to analyze the user's emotional state.

[0689] User Access

[0690] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0691] User Authentication

[0692] The server checks the authentication information received from the device against existing user information in its database. If authentication is successful, the server starts a user session and launches a chatbot.

[0693] Start interactive support

[0694] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0695] Answers to basic questions

[0696] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server will respond, "It is effective to gradually reduce the amount of drug you take at one time."

[0697] Emotion Engine Analysis

[0698] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user is feeling anxious or scared, the emotion engine detects this and notifies the server.

[0699] Responding according to emotions

[0700] The server receives notifications from the emotion engine and instructs the chatbot on how to respond based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message such as, "Don't worry, we're here to support you."

[0701] Connect with experts

[0702] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0703] The server searches and selects an appropriate expert from the expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server then sends this information to the user's terminal and provides the user with a connection link to the expert.

[0704] Optimizing information provision

[0705] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions.

[0706] Planning outreach activities

[0707] The server analyzes data from local and facility locations to identify potential risk areas, and then uses this information to plan outreach activities and provide information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[0708] Ongoing support

[0709] The server schedules regular follow-ups for the user, sends reminders to the user's device, and arranges follow-ups by experts as needed, ensuring that the user receives continuous support.

[0710] As described above, the present invention is an interactive bot system that combines a generative AI model and an emotion engine to provide comprehensive support to users, enabling young people struggling with drug use to receive appropriate support and reduce their risk and improve their behavior.

[0711] The processing flow will be explained below.

[0712] Step 1:

[0713] The server loads the generative AI model and emotion engine, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[0714] Step 2:

[0715] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0716] Step 3:

[0717] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[0718] Step 4:

[0719] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0720] Step 5:

[0721] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[0722] Step 6:

[0723] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user inputs "I'm very anxious. Please help me," the emotion engine detects that the user is feeling anxious and notifies the server of the analysis result.

[0724] Step 7:

[0725] The server receives notifications from the emotion engine and instructs the chatbot to respond appropriately based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will generate a reassuring message such as "Don't worry, we're here to support you" and send it to the device.

[0726] Step 8:

[0727] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0728] Step 9:

[0729] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation details and emotional state to the selected expert, and arranges a chat session with the expert. The server sends this information to the user's terminal and provides the user with a connection link to the expert.

[0730] Step 10:

[0731] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources specific to the user's frequently asked questions.

[0732] Step 11:

[0733] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team. For example, it sends instructions such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[0734] Step 12:

[0735] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[0736] As described above, the server, terminals, and users work together to build a system that provides comprehensive support to users, enabling them to receive appropriate assistance, reduce the risks associated with drug use, and achieve social inclusion.

[0737] Example 2

[0738] 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."

[0739] In modern society, support for users struggling with drug use is limited, particularly in the areas of real-time dialogue and individual expert assistance. Furthermore, analysis of users' emotional states and optimization of outreach activities are lacking, making it difficult to provide efficient and effective support. This leaves users unable to receive appropriate support.

[0740] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0741] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving a question or consultation from the user and analyzing the content to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for analyzing the emotional state from the user's input and providing a response according to the emotion, a means for analyzing the user's location data and history to optimize outreach activities, and a means for conducting regular follow-ups to ensure continuous support for the user. This allows the user to interact in real time and receive appropriate information, consultation from an expert, a response according to the emotion, and continuous follow-up.

[0742] A "generative AI model" is an artificial intelligence algorithm that analyzes input data from users and generates appropriate answers and actions using natural language processing technology.

[0743] An "interactive bot" is software that communicates with users in real time, providing answers and support to questions and inquiries.

[0744] An "expert" is a person or organization that has advanced knowledge and experience in a particular field and provides users with specialized advice and assistance.

[0745] "Emotion analysis" is the process of extracting and analyzing a user's emotional state from text or voice data entered by the user.

[0746] "Outreach activities" are activities that provide assistance and information to specific areas or target groups with the aim of reducing risk and providing support.

[0747] "Follow-up" is the process of continuously checking the user's situation after the initial response or support and providing additional support as needed.

[0748] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and is used in interactive bots, etc.

[0749] A "user database" is a database that stores and manages information about users on a system.

[0750] "Expert Database" refers to a database that stores and manages information about experts, and is used to search for and select appropriate experts as needed.

[0751] The system of this invention provides comprehensive support to users struggling with drug use, and by combining a conversational bot using a generative AI model with an emotion analysis engine, it achieves deeper understanding and support. This system consists of three entities: a server, a terminal, and a user.

[0752] The server runs the system using the following software and hardware. The generative AI model uses GPT-4, which has advanced natural language processing technology. The user database and expert database are managed using MySQL, as are resource data and information related to outreach activities. For sentiment analysis, IBM Watson Tone Analyzer is integrated, which has the ability to analyze emotions from user input.

[0753] Initial System Setup

[0754] The server first loads the generative AI model, which uses GPT-4. It also initializes the user database and expert database using MySQL and integrates the sentiment analysis engine, preparing the server to analyze the user's emotional state.

[0755] User Access

[0756] Users access the system using devices such as smartphones and PCs. When a user connects to a device, they enter authentication information (user ID, password), and the device sends this information to the server.

[0757] User Authentication

[0758] The server checks the authentication information received from the user against existing user information in its database. If authentication is successful, the server starts a user session and launches a conversational bot, which allows real-time interaction with the user.

[0759] Interactive Support

[0760] After successful authentication, the device displays a chat screen. The user enters a consultation or question into the chatbot to begin a dialogue. The device then sends the user's question to the server, which analyzes it using a generative AI model. Natural language processing technology is used to retrieve an appropriate answer from a database and present it to the user.

[0761] Sentiment analysis and response

[0762] The server's emotion analysis engine analyzes the user's emotional state from their input and provides a response based on the detected emotion. For example, if the user is feeling anxious or scared, the server instructs the chatbot to deliver a reassuring message.

[0763] Connect with experts

[0764] When a user needs professional assistance, they input their request into their device and send it to the server. The server searches for an appropriate expert in the expert database and forwards the user's consultation details and emotional state to the selected expert. The server then sends this information to the device and provides the user with a connection link to the expert.

[0765] Optimizing information provision and ongoing support

[0766] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. It also plans regular follow-ups and sends reminders to the user's device. If necessary, it can also arrange for follow-ups by a specialist, ensuring the user receives ongoing support.

[0767] Specific examples

[0768] For example, if a user asks, "How can I reduce the risk of overdosing?", the server uses a generative AI model to answer, "The most effective way is to gradually reduce the amount of drugs you take at one time." If the sentiment analysis engine detects the user's anxiety, the chatbot will send a reassuring message such as, "Don't worry, we're here to help."

[0769] Prompt Sentence Examples

[0770] "If users have concerns about their drug use, we provide appropriate support messages."

[0771] In this way, the system of the present invention can provide comprehensive and ongoing support to users struggling with substance use, helping to alleviate and improve their problems.

[0772] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0773] Step 1:

[0774] The server loads the generative AI model. Specifically, it uses GPT-4 and loads it into the system. MySQL is used to initialize the user database and expert database. Furthermore, it prepares resource data and integrates IBM Watson Tone Analyzer for sentiment analysis. The input is the configuration file at system startup, and the output is the system state after the initial setup is complete.

[0775] Step 2:

[0776] A user accesses the system using a smartphone or PC and enters authentication information (user ID and password) through the terminal. The terminal sends this authentication information to the server. The input is the user's authentication information, and the output is the authentication request sent to the server.

[0777] Step 3:

[0778] The server checks the authentication information received from the device against existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is the received authentication information, and the output is the authentication result and session ID.

[0779] Step 4:

[0780] After successful authentication, the device displays a chat screen. The user inputs their inquiry or question to the chatbot and begins a dialogue. The user's input is sent to the server by the device. The input is the user's inquiry or question, and the output is the data sent to the server.

[0781] Step 5:

[0782] The server receives the user's question, analyzes it using a generative AI model, and uses natural language processing technology to retrieve an appropriate answer from a database and present it to the user. The input is the user's question, and the output is the generated answer.

[0783] Step 6:

[0784] The server's emotion analysis engine analyzes the user's emotional state from the user's input. If the user is feeling anxious or scared, it detects this and notifies the server. The input is the user's input text, and the output is the emotion analysis result.

[0785] Step 7:

[0786] The server receives notifications from the emotion analysis engine and instructs the chatbot to respond according to the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message. The input is the emotion analysis result, and the output is the instructed response message.

[0787] Step 8:

[0788] When a user needs expert assistance, they input their request into their terminal and send it to the server. The server searches and selects an appropriate expert from its expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert. The input is the user's request, and the output is a connection link to the expert.

[0789] Step 9:

[0790] The server analyzes the data collected during the conversation and provides optimal information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions. The input is the collected data, and the output is optimized information.

[0791] Step 10:

[0792] The server analyzes data on local areas and facilities to identify potential risk areas. Based on this, it plans outreach activities and provides information to the outreach team. For example, it sends instructions such as, "You need to strengthen your outreach activities in City A." The input is local data, and the output is outreach plans and instructions.

[0793] Step 11:

[0794] The server plans regular follow-ups for the user and sends reminders to the user's device. If necessary, it also arranges follow-ups by experts, ensuring that the user receives continuous support. The input is a support plan, and the output is a reminder message to the user.

[0795] (Application example 2)

[0796] 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."

[0797] Providing comprehensive support to users struggling with drug use requires real-time dialogue, emotional response, connection to experts, optimization of outreach activities, and continuous follow-up. However, current systems lack the means to accurately analyze users' emotional states and provide appropriate emotional responses in real time, making it difficult for conventional systems to provide sufficient support.

[0798] 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 having an interactive bot using a generative AI model interact with a user in real time; means for receiving questions or consultations from the user and analyzing the content to provide appropriate answers; means for connecting the user to an expert when the user needs professional support; means for analyzing the user's location information and data to optimize outreach activities; means for ensuring continuous support for the user through regular follow-up; means for emotionally analyzing the user's messages and providing responses according to the emotions; and means for sending reassuring messages based on the emotional analysis. This makes it possible to provide appropriate support according to the user's emotional state and provide comprehensive, emotionally sensitive support to users struggling with drug use.

[0799] A "generative AI model" is an artificial intelligence technology designed to interact with users and analyze data. It uses natural language processing technology to analyze questions and inquiries and generate appropriate answers.

[0800] A "conversational bot" is a software application that is based on a generative AI model and allows for real-time interaction with users.

[0801] "Emotion analysis" is a technology that analyzes a user's emotional state based on messages and data obtained from the user.

[0802] An "expert" is a person who has extensive knowledge and experience in a particular field and can provide professional support to users.

[0803] "Outreach activities" are activities that are planned and implemented based on data analysis to provide support to specific areas and situations.

[0804] "Follow-up" refers to the activity of periodically checking the status of a user who has received support and providing ongoing support.

[0805] "Real-time interaction means" refers to technology or software that allows for immediate two-way communication with a user.

[0806] An "emotionally responsive means" is a technique or process that generates responses or support appropriate to a user's emotional state based on that state.

[0807] A "reassuring message" is a message that includes content intended to soothe and reassure the user.

[0808] This invention is a system for providing comprehensive support to users struggling with substance use. The system combines a generative AI model and an emotion analysis engine to provide assistance through a real-time interactive bot that responds to the user's emotional state. Specific embodiments are described below.

[0809] First, the server loads the generative AI model and the sentiment analysis engine, initializes the user database and the expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state.

[0810] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password). The terminal then sends the authentication information to the server. The server compares the authentication information received from the terminal with existing user information in the database, and if authentication is successful, it starts a user session and launches the chatbot. If authentication is successful, the terminal displays a chat screen.

[0811] The user begins a dialogue by inputting their concerns or questions into the chatbot. For example, if the user inputs, "My drug use has increased recently and I'm worried. What should I do?", the device will send this question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. At this time, the user's message is analyzed using an emotion analysis engine, and their emotional state is also evaluated at the same time. If anxiety or fear is detected, the generative AI model will take this information into account and generate a reassuring message.

[0812] For example, if the emotion analysis result is "negative," the system will provide the user with a message such as, "Let's think about specific steps to reduce your drug use. Don't worry, we're here to support you." By continuing the dialogue in this way, it is possible to provide appropriate support according to the user's emotional state.

[0813] Furthermore, if a user needs professional support, they can input and send a request for an expert on their terminal. The server searches and selects an appropriate expert from the expert database. The server then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. This allows the user to receive individual support from the expert.

[0814] Regarding outreach activities, the server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, if a particular area or facility is at high risk, outreach activities in that area will be strengthened. The server also plans regular follow-ups and sends reminders to the user's device, ensuring that the user receives ongoing support.

[0815] As a specific example, by inputting the prompt sentence "The user is feeling anxious about drug use. What should they do?" into a generative AI model, an appropriate answer can be generated. This prompt sentence is used to generate a message that takes into account the user's emotional state.

[0816] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0817] Step 1:

[0818] The server loads the generative AI model and sentiment analysis engine, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state. The input is resource information from specific configuration files and databases, and the necessary modules are launched and configured based on this. The output is an initialized server environment.

[0819] Step 2:

[0820] A user accesses the system using a smartphone or PC terminal and enters authentication information (user ID, password). The terminal sends this authentication information to the server. The input is the authentication information entered by the user, and based on this, the terminal sends an authentication request to the server. The output is an authentication request to the server.

[0821] Step 3:

[0822] The server compares the authentication information received from the device with existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is an authentication request, which is used to perform database comparison. The output is a successful authentication message and the launch of the chatbot.

[0823] Step 4:

[0824] If authentication is successful, the terminal displays a chat screen. The input is an authentication success message, and the chat screen is displayed based on this. The output is a chat screen that the user can use.

[0825] Step 5:

[0826] The user inputs their concerns or questions into the chatbot. For example, they might input, "My drug use has increased recently, and I'm worried. What should I do?" The input is the message the user sends to the chatbot, and the output is the content of their concerns, which is sent from the device to the server.

[0827] Step 6:

[0828] The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. In parallel, the sentiment analysis engine analyzes the user's message and evaluates their emotional state. The input is the user's inquiry, and based on this, answers are searched for in the database and sentiment analysis is performed. The output is the generated answer and an evaluation of the emotional state.

[0829] Step 7:

[0830] The server instructs the generative AI model to generate a reassuring message based on the results of the emotion analysis. For example, if it detects anxiety or fear, it adds a reassuring message such as "Don't worry, we're here to support you" to the generated response. The input is the result of the emotion analysis and the generated response, and the output is the final message containing reassuring content.

[0831] Step 8:

[0832] The final message is sent from the server to the terminal and provided to the user through the chatbot. The input is the final message generated by the server, and the output is the chatbot's response displayed on the user's terminal.

[0833] Step 9:

[0834] When a user needs expert support, he / she inputs the request at the terminal and sends it to the server. The input is the expert request from the user, and the output is the request sent to the server.

[0835] Step 10:

[0836] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation content and emotional state to the selected expert, and arranges a chat session with the expert. The input is the expert request and database data, and the output is the selected expert and the transferred information.

[0837] Step 11:

[0838] The server analyzes the data collected during the interaction and provides the most appropriate information based on the user's situation and question history. It also plans outreach activities and sends reminders to the user's device. The input is the data collected during the interaction, and the output is the planned outreach activities and reminder messages.

[0839] Step 12:

[0840] If regular follow-up is required, the server sends reminders to the user's device to provide ongoing support. The input is the follow-up plan, and the output is the reminder message sent to the user.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] [Third embodiment]

[0845] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0846] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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).

[0851] 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. 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] The system of the present invention is designed to allow users to ask questions and receive advice about drug use 24 hours a day, 365 days a year. The system uses a conversational bot powered by a generative AI model to analyze users' inquiries and provide appropriate answers and support. It also connects users to experts as needed and provides information to optimize outreach activities.

[0858] This system consists of three entities: a server, a terminal, and a user. Below, we explain the program's processing in natural language.

[0859] Initial System Setup

[0860] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[0861] User Access

[0862] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password). The device then sends this information to the server.

[0863] User Authentication

[0864] The server receives the authentication information submitted by the user and matches it with existing user information in its database. If authentication is successful, the server starts a user session and launches the chatbot.

[0865] Start interactive support

[0866] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting their consultation or question to the chatbot. For example, the user can input "Please tell me how to reduce overdose."

[0867] Answers to basic questions

[0868] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "It's effective to gradually reduce the amount of medication you take at one time. Would you like me to connect you to a specialist for more detailed instructions?" and send it to the device.

[0869] Connect with experts

[0870] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[0871] Optimizing information provision

[0872] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[0873] Planning outreach activities

[0874] The server analyzes neighborhood and facility data to identify potential risk areas, plans outreach activities based on this information, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach activities for young people in City A."

[0875] Ongoing support

[0876] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0877] As described above, the present invention provides a system that utilizes generative AI models to provide a conversational bot and comprehensive support for users, enabling young people struggling with drug use to receive appropriate support and reduce their risk.

[0878] The processing flow will be explained below.

[0879] Step 1:

[0880] The server loads the generative AI model, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[0881] Step 2:

[0882] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[0883] Step 3:

[0884] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[0885] Step 4:

[0886] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[0887] Step 5:

[0888] The device sends the user's question to the server, which uses a generative AI model to analyze the question. The server then retrieves an appropriate answer from a database and sends it to the device. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[0889] Step 6:

[0890] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[0891] Step 7:

[0892] The server searches for and selects an appropriate expert from the expert database, transfers the user's consultation to the selected expert, and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert.

[0893] Step 8:

[0894] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources that correspond to the user's frequently asked questions.

[0895] Step 9:

[0896] The server analyzes data from local and facility locations to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[0897] Step 10:

[0898] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[0899] Example 1

[0900] 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."

[0901] The present invention relates to a system that allows users to ask questions or seek advice about drug use, and aims to provide a system that provides support 24 hours a day, 365 days a year, connects users to experts as needed, and optimizes outreach activities. Conventional systems have prevented users from receiving appropriate support promptly and have made it difficult to maximize the effectiveness of outreach activities. For this reason, there has been a demand for a system that provides comprehensive and continuous support to users experiencing difficulties with drug use.

[0902] 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.

[0903] In this invention, the server includes means for having an interactive bot using a generative AI model interact with a user in real time, means for receiving questions or consultations from the user and analyzing the contents to provide appropriate answers, means for connecting the user to an expert when the user needs specialized support, means for analyzing the user's location information and data to optimize outreach activities, means for ensuring continuous support for the user through regular follow-ups, means for initializing the system, means for the user to access the system from a terminal, input authentication information and send it to the server, means for the server to verify the user's authentication information and authenticate the user and activate the chatbot, and means for the terminal to display a chat screen and for the user to input the consultation or question content. This allows users to ask questions or consult at any time and receive prompt and appropriate answers and support, further maximizing the effectiveness of outreach activities.

[0904] A "generative AI model" is an artificial intelligence model that analyzes user input in natural language and generates appropriate answers and actions.

[0905] An "interactive bot" is a software system that responds to users' questions and inquiries in real time and provides information.

[0906] "Server" is the central computer system that runs the generative AI model and manages access to the user and expert databases.

[0907] A "user" is an individual who asks a question or makes a request through the system.

[0908] A "terminal" is a device (such as a smartphone or PC) that a user uses to access and operate the system.

[0909] An "expert" is an individual who has knowledge and experience of drug use and is qualified to provide specialized support to users.

[0910] "Initialization" is the procedure of loading the generative AI model, initializing the database, and preparing resources to enable the system to operate normally.

[0911] "User authentication" is the process of verifying a user's identity when accessing a system.

[0912] "Natural language processing technology" is a technology for analyzing natural language text entered by a user and understanding its meaning and intent.

[0913] "Outreach activities" are activities that provide important information and support to specific areas or specific groups.

[0914] "Follow-up" is the process of tracking and checking to provide ongoing care and support to a user.

[0915] A "user database" is a database system for storing and managing information about users.

[0916] "Expert database" refers to a database system that allows the system to store and manage expert information.

[0917] The "chat screen" is an interface that allows users to input information to the chatbot.

[0918] "Question history" is a record of questions that a user has asked in the past and the answers to those questions.

[0919] The present invention provides an interactive system using a generative AI model that accepts questions and consultations about drug use 24 hours a day, 365 days a year, and provides appropriate answers and professional support in real time. Detailed embodiments of the system are described below.

[0920] System configuration

[0921] The system mainly consists of a server, terminals, and users. The server runs the generative AI model and functions as an interface with users and an expert database. Terminals are devices that users use to access and operate the system, and include smartphones and PCs. Users access the system via their terminals to ask questions or make inquiries.

[0922] Hardware and software used

[0923] Server: A high-performance computer system is used to ensure appropriate data processing and response speed. The generative AI model used is, for example, OpenAI's GPT-4.

[0924] User database: Use a relational database such as MySQL or PostgreSQL to manage user information.

[0925] Expert database: Use a non-relational database such as MongoDB to manage expert information.

[0926] Terminal: A device used by a user to access the service, such as a smartphone or computer.

[0927] Initial System Setup

[0928] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user and expert databases, and preparing resource data, so that the system can immediately interact with the user and quickly provide the required information.

[0929] User Access

[0930] The user accesses the system from their own terminal. The terminal displays a login screen, and the user enters authentication information (user ID, password). The terminal then sends this information to the server.

[0931] User Authentication

[0932] The server receives authentication information from the user and checks it against existing information in a user database. If authentication is successful, the server starts a user session and launches a chatbot.

[0933] Interactive Support

[0934] The device displays a chat screen to users who have been successfully authenticated. The user inputs their consultation or question to the chatbot, and the device sends it to the server. For example, the user might input, "Please tell me how to reduce overdose."

[0935] Answers to basic questions

[0936] The server uses a generative AI model to analyze the user's question using natural language processing technology, retrieves an appropriate answer from the database, and generates a response such as, "It is effective to gradually reduce the amount of drugs you take at one time. Would you like to be connected to a specialist for more detailed instructions?" and sends it to the device.

[0937] Connect with experts

[0938] When a user needs expert support, the device sends the request to the server, which searches for an appropriate expert from the expert database and establishes a real-time connection between the user and the expert. For example, if the user types "Yes, connect me to an expert," the server will arrange a real-time chat session with the expert and send a link to the device.

[0939] Optimizing information provision

[0940] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns. For example, it can provide information specific to the user's frequently asked questions, eliminating the need to ask the question again.

[0941] Outreach Activities

[0942] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[0943] Ongoing support

[0944] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[0945] Examples of prompt statements

[0946] For example, a user might input a prompt into the system like this: "I want to reduce my drug intake. Please tell me specific ways to do this." In response, the generative AI model would provide specific advice and support.

[0947] In this way, the present invention leverages generative AI models to provide a conversational bot that provides comprehensive support to users, enabling users struggling with substance use to receive appropriate support and mitigate their risks.

[0948] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0949] Step 1: Initial System Setup

[0950] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user database (e.g., MySQL) and expert database (e.g., MongoDB), and loading resource data (e.g., drug use guidelines and FAQs) into memory.

[0951] Input: None

[0952] Output: An initialized database and a loaded generative AI model.

[0953] Step 2: User Access

[0954] The user accesses the system from a device (smartphone or PC) and opens the login screen.

[0955] The terminal displays a login screen where the user enters their user ID and password. The entered authentication information is sent from the terminal to the server.

[0956] Input: User ID, Password

[0957] Output: Authentication information sent to the server

[0958] Step 3: User authentication

[0959] The server checks the received authentication information against existing information in a user database.

[0960] If authentication is successful, the server starts a user session and launches the chatbot, otherwise it sends an error message to the device.

[0961] Input: Submitted credentials

[0962] Output: User session started / Error message

[0963] Step 4: Start interactive support

[0964] After successful authentication, the device displays a chat screen. The user inputs their consultation or question to the chatbot, and the device sends the user's input to the server.

[0965] Input: User's consultation or question

[0966] Output: Consultation or question sent to the server

[0967] Step 5: Answer basic questions

[0968] The server uses a generative AI model to analyze the user's question using natural language processing technology. After analysis, the server retrieves the appropriate answer from the database, and the generative AI model generates the answer and sends it to the device.

[0969] For example, if a user types in "How can I reduce an overdose?", the generated answer might be "Slowly reducing the amount of drugs you take at one time is effective. Would you like me to connect you to a specialist for more information?"

[0970] Input: User's question

[0971] Output: The appropriate answer sent to the terminal

[0972] Step 6: Connect with an expert

[0973] When a user needs expert support, the terminal sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection between the user and the expert.

[0974] For example, if the user types "Yes, connect me to an expert," the server will arrange a chat session with the expert and send a link to the device.

[0975] Enter: Request for Expert Support

[0976] Output: Expert connection link sent to the device

[0977] Step 7: Optimize information delivery

[0978] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns.

[0979] For example, it provides information specific to the questions users frequently ask, eliminating the need to ask the question again.

[0980] Input: Data collected during the interaction

[0981] Output: Optimized information delivery

[0982] Step 8: Plan your outreach activities

[0983] The server analyzes local and facility data to identify potential risk areas, which are then used to plan outreach activities and notify the outreach team.

[0984] For example, you might send instructions to your outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach efforts with young people in City A."

[0985] Input: Area and facility data

[0986] Output: Plan outreach activities

[0987] Step 9: Ongoing support

[0988] The server will conduct regular follow-ups with the user and, if necessary, arrange for follow-ups by experts. It will also send follow-up reminders to the user's device to provide ongoing support for the user's situation.

[0989] Input: User data for follow-up

[0990] Output: Follow-up reminders sent to your device

[0991] The above is the specific processing flow of the system that provides an interactive bot that utilizes a generative AI model and provides comprehensive support to users.

[0992] (Application example 1)

[0993] 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."

[0994] In electronic payment services, users demand support available 24 hours a day, 365 days a year, but it is difficult to obtain prompt and appropriate answers. In particular, when users need payment troubles, emergency response, or expert advice, it often takes a long time for them to receive appropriate support. In addition, there is a demand for a system that can quickly analyze user questions and provide appropriate answers by introducing a conversational bot, but at present, it is difficult to effectively implement such a system.

[0995] 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.

[0996] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving questions or inquiries from the user and analyzing the contents to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for receiving the user's authentication information and comparing it with existing user information in a database to start a user session, a means for analyzing the user's location information and data to optimize outreach activities, and a means for ensuring continuous support for the user through regular follow-ups, thereby enabling users to receive prompt and appropriate support 24 hours a day, 365 days a year.

[0997] A "generative AI model" is a model of artificial intelligence technology used to analyze a user's question or inquiry and generate an appropriate answer.

[0998] An "interactive bot" is a system that interacts with users in real time and provides answers to their questions and inquiries.

[0999] A "user session" refers to a series of interactions and operations that occur between the time a user logs in to a system and the time they log out.

[1000] The "means for connecting with experts" is a function for connecting with experts in real time when a user needs professional support.

[1001] "Natural language processing technology" is a technology used to analyze and understand users' questions and inquiries.

[1002] "Outreach activities" are activities aimed at providing needed information and services to specific target areas or communities.

[1003] "Regular follow-up" is the process of continually monitoring the user's situation and providing additional support or information as needed.

[1004] An "expert database" is a database that stores information on experts in various fields.

[1005] This invention is a system that uses a generative AI model to provide an interactive bot that responds to user questions and problems regarding electronic payment services 24 hours a day, 365 days a year. This system consists of three components: the user's terminal, a server, and an expert database.

[1006] Initial System Setup

[1007] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[1008] User Access

[1009] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID, password). This authentication information is sent from the device to the server.

[1010] User Authentication

[1011] The server receives the authentication information submitted by the user and checks it against existing user information in a database. If authentication is successful, the server starts a user session and launches an interactive bot.

[1012] Start interactive support

[1013] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting a question or inquiry to the conversational bot. For example, the user might input, "I would like to check my credit card limit."

[1014] Answers to basic questions

[1015] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "To check your credit card limit, log in to the electronic payment service's app or website and click on the 'Account Information' section," and send it to the device.

[1016] Connect with experts

[1017] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[1018] Optimizing information provision

[1019] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[1020] Planning outreach activities

[1021] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to intensify outreach activities in certain areas of City A."

[1022] Ongoing support

[1023] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[1024] For example, if a user asks a chatbot, "I want to check my credit card limit," the generative AI model will respond, "To check your credit card limit, log in to the app or website of your electronic payment service and click on the 'Account Information' section." In this way, it is possible to provide the information users need quickly and accurately.

[1025] Example prompt sentence:

[1026] After logging in with your user ID and password, you will be asked the following questions:

[1027] "I want to check my credit card limit. How can I do that?"

[1028] Example of the output response:

[1029] To check your credit card limit, log in to your payment service app or website and click on the "Account Information" section.

[1030] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1031] Step 1:

[1032] (input)

[1033] Users access the system using their own devices such as smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password).

[1034] (process)

[1035] The terminal sends this authentication information to the server.

[1036] (output)

[1037] The authentication information is sent to the server.

[1038] Step 2:

[1039] (input)

[1040] The server receives the authentication information sent by the user.

[1041] (process)

[1042] The server checks the received authentication information against existing user information in its database.

[1043] (output)

[1044] If authentication is successful, the user session begins and the chatbot is launched.

[1045] Step 3:

[1046] (input)

[1047] When a user session is started, the terminal displays a chat screen.

[1048] (process)

[1049] The user inputs questions or inquiries into the chat screen.

[1050] (output)

[1051] The user's question or inquiry is entered into the terminal.

[1052] Step 4:

[1053] (input)

[1054] The terminal transmits the user's question or inquiry to the server.

[1055] (process)

[1056] The server uses a generative AI model to analyze the question, specifically, it uses natural language processing technology to understand the question and retrieves the appropriate answer from a database.

[1057] (output)

[1058] The server sends the answer generated by the generative AI model based on the analysis results to the terminal.

[1059] Step 5:

[1060] (input)

[1061] The device displays the answer of the generative AI model received from the server.

[1062] (process)

[1063] The user checks the displayed answer and, if necessary, enters additional questions or inquiries.

[1064] (output)

[1065] The user's additional questions and inquiries are input into the terminal.

[1066] Step 6:

[1067] (input)

[1068] When a user requests professional support, the request is sent from the terminal to the server.

[1069] (process)

[1070] The server searches for a suitable expert from an expert database.

[1071] (output)

[1072] Expert information obtained from an expert database is prepared on a server.

[1073] Step 7:

[1074] (input)

[1075] A request arrives at the server to connect the user with the expert in real time.

[1076] (process)

[1077] The server arranges a real-time chat session with an expert and sends a link to the device.

[1078] (output)

[1079] A chat link with the expert will appear on your device.

[1080] Step 8:

[1081] (input)

[1082] The server analyzes the data collected during the interaction.

[1083] (process)

[1084] The server provides optimal information based on the user's situation and question history, and optimizes outreach activities based on past dialogue history.

[1085] (output)

[1086] A plan for outreach activities is generated.

[1087] Step 9:

[1088] (input)

[1089] The server sends periodic follow-up reminders to the user's device.

[1090] (process)

[1091] The user is informed.

[1092] (output)

[1093] Follow-up reminders will appear on your device.

[1094] In this way, a system that provides efficient and prompt user support is configured.

[1095] 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.

[1096] The system of the present invention provides comprehensive support for users struggling with drug use, combining a generative AI model and an emotion engine to achieve deeper understanding and support. The system consists of three components: a server, a device, and a user. It includes real-time dialogue with the user, connection to experts, optimization of outreach activities, and continuous follow-up. Furthermore, the emotion engine is used to analyze the user's emotional state and provide appropriate responses.

[1097] Initial System Setup

[1098] The server loads the generative AI model, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates an emotion engine to analyze the user's emotional state.

[1099] User Access

[1100] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[1101] User Authentication

[1102] The server checks the authentication information received from the device against existing user information in its database. If authentication is successful, the server starts a user session and launches a chatbot.

[1103] Start interactive support

[1104] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[1105] Answers to basic questions

[1106] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server will respond, "It is effective to gradually reduce the amount of drug you take at one time."

[1107] Emotion Engine Analysis

[1108] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user is feeling anxious or scared, the emotion engine detects this and notifies the server.

[1109] Responding according to emotions

[1110] The server receives notifications from the emotion engine and instructs the chatbot on how to respond based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message such as, "Don't worry, we're here to support you."

[1111] Connect with experts

[1112] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[1113] The server searches and selects an appropriate expert from the expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server then sends this information to the user's terminal and provides the user with a connection link to the expert.

[1114] Optimizing information provision

[1115] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions.

[1116] Planning outreach activities

[1117] The server analyzes data from local and facility locations to identify potential risk areas, and then uses this information to plan outreach activities and provide information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[1118] Ongoing support

[1119] The server schedules regular follow-ups for the user, sends reminders to the user's device, and arranges follow-ups by experts as needed, ensuring that the user receives continuous support.

[1120] As described above, the present invention is an interactive bot system that combines a generative AI model and an emotion engine to provide comprehensive support to users, enabling young people struggling with drug use to receive appropriate support and reduce their risk and improve their behavior.

[1121] The processing flow will be explained below.

[1122] Step 1:

[1123] The server loads the generative AI model and emotion engine, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[1124] Step 2:

[1125] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[1126] Step 3:

[1127] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[1128] Step 4:

[1129] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[1130] Step 5:

[1131] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[1132] Step 6:

[1133] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user inputs "I'm very anxious. Please help me," the emotion engine detects that the user is feeling anxious and notifies the server of the analysis result.

[1134] Step 7:

[1135] The server receives notifications from the emotion engine and instructs the chatbot to respond appropriately based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will generate a reassuring message such as "Don't worry, we're here to support you" and send it to the device.

[1136] Step 8:

[1137] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[1138] Step 9:

[1139] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation details and emotional state to the selected expert, and arranges a chat session with the expert. The server sends this information to the user's terminal and provides the user with a connection link to the expert.

[1140] Step 10:

[1141] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources specific to the user's frequently asked questions.

[1142] Step 11:

[1143] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team. For example, it sends instructions such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[1144] Step 12:

[1145] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[1146] As described above, the server, terminals, and users work together to build a system that provides comprehensive support to users, enabling them to receive appropriate assistance, reduce the risks associated with drug use, and achieve social inclusion.

[1147] Example 2

[1148] 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."

[1149] In modern society, support for users struggling with drug use is limited, particularly in the areas of real-time dialogue and individual expert assistance. Furthermore, analysis of users' emotional states and optimization of outreach activities are lacking, making it difficult to provide efficient and effective support. This leaves users unable to receive appropriate support.

[1150] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1151] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving a question or consultation from the user and analyzing the content to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for analyzing the emotional state from the user's input and providing a response according to the emotion, a means for analyzing the user's location data and history to optimize outreach activities, and a means for conducting regular follow-ups to ensure continuous support for the user. This allows the user to interact in real time and receive appropriate information, consultation from an expert, a response according to the emotion, and continuous follow-up.

[1152] A "generative AI model" is an artificial intelligence algorithm that analyzes input data from users and generates appropriate answers and actions using natural language processing technology.

[1153] An "interactive bot" is software that communicates with users in real time, providing answers and support to questions and inquiries.

[1154] An "expert" is a person or organization that has advanced knowledge and experience in a particular field and provides users with specialized advice and assistance.

[1155] "Emotion analysis" is the process of extracting and analyzing a user's emotional state from text or voice data entered by the user.

[1156] "Outreach activities" are activities that provide assistance and information to specific areas or target groups with the aim of reducing risk and providing support.

[1157] "Follow-up" is the process of continuously checking the user's situation after the initial response or support and providing additional support as needed.

[1158] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and is used in interactive bots, etc.

[1159] A "user database" is a database that stores and manages information about users on a system.

[1160] "Expert Database" refers to a database that stores and manages information about experts, and is used to search for and select appropriate experts as needed.

[1161] The system of this invention provides comprehensive support to users struggling with drug use, and by combining a conversational bot using a generative AI model with an emotion analysis engine, it achieves deeper understanding and support. This system consists of three entities: a server, a terminal, and a user.

[1162] The server runs the system using the following software and hardware. The generative AI model uses GPT-4, which has advanced natural language processing technology. The user database and expert database are managed using MySQL, as are resource data and information related to outreach activities. For sentiment analysis, IBM Watson Tone Analyzer is integrated, which has the ability to analyze emotions from user input.

[1163] Initial System Setup

[1164] The server first loads the generative AI model, which uses GPT-4. It also initializes the user database and expert database using MySQL and integrates the sentiment analysis engine, preparing the server to analyze the user's emotional state.

[1165] User Access

[1166] Users access the system using devices such as smartphones and PCs. When a user connects to a device, they enter authentication information (user ID, password), and the device sends this information to the server.

[1167] User Authentication

[1168] The server checks the authentication information received from the user against existing user information in its database. If authentication is successful, the server starts a user session and launches a conversational bot, which allows real-time interaction with the user.

[1169] Interactive Support

[1170] After successful authentication, the device displays a chat screen. The user enters a consultation or question into the chatbot to begin a dialogue. The device then sends the user's question to the server, which analyzes it using a generative AI model. Natural language processing technology is used to retrieve an appropriate answer from a database and present it to the user.

[1171] Sentiment analysis and response

[1172] The server's emotion analysis engine analyzes the user's emotional state from their input and provides a response based on the detected emotion. For example, if the user is feeling anxious or scared, the server instructs the chatbot to deliver a reassuring message.

[1173] Connect with experts

[1174] When a user needs professional assistance, they input their request into their device and send it to the server. The server searches for an appropriate expert in the expert database and forwards the user's consultation details and emotional state to the selected expert. The server then sends this information to the device and provides the user with a connection link to the expert.

[1175] Optimizing information provision and ongoing support

[1176] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. It also plans regular follow-ups and sends reminders to the user's device. If necessary, it can also arrange for follow-ups by a specialist, ensuring the user receives ongoing support.

[1177] Specific examples

[1178] For example, if a user asks, "How can I reduce the risk of overdosing?", the server uses a generative AI model to answer, "The most effective way is to gradually reduce the amount of drugs you take at one time." If the sentiment analysis engine detects the user's anxiety, the chatbot will send a reassuring message such as, "Don't worry, we're here to help."

[1179] Prompt Sentence Examples

[1180] "If users have concerns about their drug use, we provide appropriate support messages."

[1181] In this way, the system of the present invention can provide comprehensive and ongoing support to users struggling with substance use, helping to alleviate and improve their problems.

[1182] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1183] Step 1:

[1184] The server loads the generative AI model. Specifically, it uses GPT-4 and loads it into the system. MySQL is used to initialize the user database and expert database. Furthermore, it prepares resource data and integrates IBM Watson Tone Analyzer for sentiment analysis. The input is the configuration file at system startup, and the output is the system state after the initial setup is complete.

[1185] Step 2:

[1186] A user accesses the system using a smartphone or PC and enters authentication information (user ID and password) through the terminal. The terminal sends this authentication information to the server. The input is the user's authentication information, and the output is the authentication request sent to the server.

[1187] Step 3:

[1188] The server checks the authentication information received from the device against existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is the received authentication information, and the output is the authentication result and session ID.

[1189] Step 4:

[1190] After successful authentication, the device displays a chat screen. The user inputs their inquiry or question to the chatbot and begins a dialogue. The user's input is sent to the server by the device. The input is the user's inquiry or question, and the output is the data sent to the server.

[1191] Step 5:

[1192] The server receives the user's question, analyzes it using a generative AI model, and uses natural language processing technology to retrieve an appropriate answer from a database and present it to the user. The input is the user's question, and the output is the generated answer.

[1193] Step 6:

[1194] The server's emotion analysis engine analyzes the user's emotional state from the user's input. If the user is feeling anxious or scared, it detects this and notifies the server. The input is the user's input text, and the output is the emotion analysis result.

[1195] Step 7:

[1196] The server receives notifications from the emotion analysis engine and instructs the chatbot to respond according to the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message. The input is the emotion analysis result, and the output is the instructed response message.

[1197] Step 8:

[1198] When a user needs expert assistance, they input their request into their terminal and send it to the server. The server searches and selects an appropriate expert from its expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert. The input is the user's request, and the output is a connection link to the expert.

[1199] Step 9:

[1200] The server analyzes the data collected during the conversation and provides optimal information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions. The input is the collected data, and the output is optimized information.

[1201] Step 10:

[1202] The server analyzes data on local areas and facilities to identify potential risk areas. Based on this, it plans outreach activities and provides information to the outreach team. For example, it sends instructions such as, "You need to strengthen your outreach activities in City A." The input is local data, and the output is outreach plans and instructions.

[1203] Step 11:

[1204] The server plans regular follow-ups for the user and sends reminders to the user's device. If necessary, it also arranges follow-ups by experts, ensuring that the user receives continuous support. The input is a support plan, and the output is a reminder message to the user.

[1205] (Application example 2)

[1206] 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."

[1207] Providing comprehensive support to users struggling with drug use requires real-time dialogue, emotional response, connection to experts, optimization of outreach activities, and continuous follow-up. However, current systems lack the means to accurately analyze users' emotional states and provide appropriate emotional responses in real time, making it difficult for conventional systems to provide sufficient support.

[1208] 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 having an interactive bot using a generative AI model interact with a user in real time; means for receiving questions or consultations from the user and analyzing the content to provide appropriate answers; means for connecting the user to an expert when the user needs professional support; means for analyzing the user's location information and data to optimize outreach activities; means for ensuring continuous support for the user through regular follow-up; means for emotionally analyzing the user's messages and providing responses according to the emotions; and means for sending reassuring messages based on the emotional analysis. This makes it possible to provide appropriate support according to the user's emotional state and provide comprehensive, emotionally sensitive support to users struggling with drug use.

[1209] A "generative AI model" is an artificial intelligence technology designed to interact with users and analyze data. It uses natural language processing technology to analyze questions and inquiries and generate appropriate answers.

[1210] A "conversational bot" is a software application that is based on a generative AI model and allows for real-time interaction with users.

[1211] "Emotion analysis" is a technology that analyzes a user's emotional state based on messages and data obtained from the user.

[1212] An "expert" is a person who has extensive knowledge and experience in a particular field and can provide professional support to users.

[1213] "Outreach activities" are activities that are planned and implemented based on data analysis to provide support to specific areas and situations.

[1214] "Follow-up" refers to the activity of periodically checking the status of a user who has received support and providing ongoing support.

[1215] "Real-time interaction means" refers to technology or software that allows for immediate two-way communication with a user.

[1216] An "emotionally responsive means" is a technique or process that generates responses or support appropriate to a user's emotional state based on that state.

[1217] A "reassuring message" is a message that includes content intended to soothe and reassure the user.

[1218] This invention is a system for providing comprehensive support to users struggling with substance use. The system combines a generative AI model and an emotion analysis engine to provide assistance through a real-time interactive bot that responds to the user's emotional state. Specific embodiments are described below.

[1219] First, the server loads the generative AI model and the sentiment analysis engine, initializes the user database and the expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state.

[1220] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password). The terminal then sends the authentication information to the server. The server compares the authentication information received from the terminal with existing user information in the database, and if authentication is successful, it starts a user session and launches the chatbot. If authentication is successful, the terminal displays a chat screen.

[1221] The user begins a dialogue by inputting their concerns or questions into the chatbot. For example, if the user inputs, "My drug use has increased recently and I'm worried. What should I do?", the device will send this question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. At this time, the user's message is analyzed using an emotion analysis engine, and their emotional state is also evaluated at the same time. If anxiety or fear is detected, the generative AI model will take this information into account and generate a reassuring message.

[1222] For example, if the emotion analysis result is "negative," the system will provide the user with a message such as, "Let's think about specific steps to reduce your drug use. Don't worry, we're here to support you." By continuing the dialogue in this way, it is possible to provide appropriate support according to the user's emotional state.

[1223] Furthermore, if a user needs professional support, they can input and send a request for an expert on their terminal. The server searches and selects an appropriate expert from the expert database. The server then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. This allows the user to receive individual support from the expert.

[1224] Regarding outreach activities, the server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, if a particular area or facility is at high risk, outreach activities in that area will be strengthened. The server also plans regular follow-ups and sends reminders to the user's device, ensuring that the user receives ongoing support.

[1225] As a specific example, by inputting the prompt sentence "The user is feeling anxious about drug use. What should they do?" into a generative AI model, an appropriate answer can be generated. This prompt sentence is used to generate a message that takes into account the user's emotional state.

[1226] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1227] Step 1:

[1228] The server loads the generative AI model and sentiment analysis engine, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state. The input is resource information from specific configuration files and databases, and the necessary modules are launched and configured based on this. The output is an initialized server environment.

[1229] Step 2:

[1230] A user accesses the system using a smartphone or PC terminal and enters authentication information (user ID, password). The terminal sends this authentication information to the server. The input is the authentication information entered by the user, and based on this, the terminal sends an authentication request to the server. The output is an authentication request to the server.

[1231] Step 3:

[1232] The server compares the authentication information received from the device with existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is an authentication request, which is used to perform database comparison. The output is a successful authentication message and the launch of the chatbot.

[1233] Step 4:

[1234] If authentication is successful, the terminal displays a chat screen. The input is an authentication success message, and the chat screen is displayed based on this. The output is a chat screen that the user can use.

[1235] Step 5:

[1236] The user inputs their concerns or questions into the chatbot. For example, they might input, "My drug use has increased recently, and I'm worried. What should I do?" The input is the message the user sends to the chatbot, and the output is the content of their concerns, which is sent from the device to the server.

[1237] Step 6:

[1238] The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. In parallel, the sentiment analysis engine analyzes the user's message and evaluates their emotional state. The input is the user's inquiry, and based on this, answers are searched for in the database and sentiment analysis is performed. The output is the generated answer and an evaluation of the emotional state.

[1239] Step 7:

[1240] The server instructs the generative AI model to generate a reassuring message based on the results of the emotion analysis. For example, if it detects anxiety or fear, it adds a reassuring message such as "Don't worry, we're here to support you" to the generated response. The input is the result of the emotion analysis and the generated response, and the output is the final message containing reassuring content.

[1241] Step 8:

[1242] The final message is sent from the server to the terminal and provided to the user through the chatbot. The input is the final message generated by the server, and the output is the chatbot's response displayed on the user's terminal.

[1243] Step 9:

[1244] When a user needs expert support, he / she inputs the request at the terminal and sends it to the server. The input is the expert request from the user, and the output is the request sent to the server.

[1245] Step 10:

[1246] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation content and emotional state to the selected expert, and arranges a chat session with the expert. The input is the expert request and database data, and the output is the selected expert and the transferred information.

[1247] Step 11:

[1248] The server analyzes the data collected during the interaction and provides the most appropriate information based on the user's situation and question history. It also plans outreach activities and sends reminders to the user's device. The input is the data collected during the interaction, and the output is the planned outreach activities and reminder messages.

[1249] Step 12:

[1250] If regular follow-up is required, the server sends reminders to the user's device to provide ongoing support. The input is the follow-up plan, and the output is the reminder message sent to the user.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] [Fourth embodiment]

[1255] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1256] 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.

[1257] 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).

[1258] 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.

[1259] 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.

[1260] 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).

[1261] 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. 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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."

[1268] The system of the present invention is designed to allow users to ask questions and receive advice about drug use 24 hours a day, 365 days a year. The system uses a conversational bot powered by a generative AI model to analyze users' inquiries and provide appropriate answers and support. It also connects users to experts as needed and provides information to optimize outreach activities.

[1269] This system consists of three entities: a server, a terminal, and a user. Below, we explain the program's processing in natural language.

[1270] Initial System Setup

[1271] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[1272] User Access

[1273] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password). The device then sends this information to the server.

[1274] User Authentication

[1275] The server receives the authentication information submitted by the user and matches it with existing user information in its database. If authentication is successful, the server starts a user session and launches the chatbot.

[1276] Start interactive support

[1277] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting their consultation or question to the chatbot. For example, the user can input "Please tell me how to reduce overdose."

[1278] Answers to basic questions

[1279] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "It's effective to gradually reduce the amount of medication you take at one time. Would you like me to connect you to a specialist for more detailed instructions?" and send it to the device.

[1280] Connect with experts

[1281] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[1282] Optimizing information provision

[1283] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[1284] Planning outreach activities

[1285] The server analyzes neighborhood and facility data to identify potential risk areas, plans outreach activities based on this information, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach activities for young people in City A."

[1286] Ongoing support

[1287] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[1288] As described above, the present invention provides a system that utilizes generative AI models to provide a conversational bot and comprehensive support for users, enabling young people struggling with drug use to receive appropriate support and reduce their risk.

[1289] The processing flow will be explained below.

[1290] Step 1:

[1291] The server loads the generative AI model, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[1292] Step 2:

[1293] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[1294] Step 3:

[1295] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[1296] Step 4:

[1297] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[1298] Step 5:

[1299] The device sends the user's question to the server, which uses a generative AI model to analyze the question. The server then retrieves an appropriate answer from a database and sends it to the device. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[1300] Step 6:

[1301] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[1302] Step 7:

[1303] The server searches for and selects an appropriate expert from the expert database, transfers the user's consultation to the selected expert, and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert.

[1304] Step 8:

[1305] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources that correspond to the user's frequently asked questions.

[1306] Step 9:

[1307] The server analyzes data from local and facility locations to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[1308] Step 10:

[1309] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[1310] Example 1

[1311] 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."

[1312] The present invention relates to a system that allows users to ask questions or seek advice about drug use, and aims to provide a system that provides support 24 hours a day, 365 days a year, connects users to experts as needed, and optimizes outreach activities. Conventional systems have prevented users from receiving appropriate support promptly and have made it difficult to maximize the effectiveness of outreach activities. For this reason, there has been a demand for a system that provides comprehensive and continuous support to users experiencing difficulties with drug use.

[1313] 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.

[1314] In this invention, the server includes means for having an interactive bot using a generative AI model interact with a user in real time, means for receiving questions or consultations from the user and analyzing the contents to provide appropriate answers, means for connecting the user to an expert when the user needs specialized support, means for analyzing the user's location information and data to optimize outreach activities, means for ensuring continuous support for the user through regular follow-ups, means for initializing the system, means for the user to access the system from a terminal, input authentication information and send it to the server, means for the server to verify the user's authentication information and authenticate the user and activate the chatbot, and means for the terminal to display a chat screen and for the user to input the consultation or question content. This allows users to ask questions or consult at any time and receive prompt and appropriate answers and support, further maximizing the effectiveness of outreach activities.

[1315] A "generative AI model" is an artificial intelligence model that analyzes user input in natural language and generates appropriate answers and actions.

[1316] An "interactive bot" is a software system that responds to users' questions and inquiries in real time and provides information.

[1317] "Server" is the central computer system that runs the generative AI model and manages access to the user and expert databases.

[1318] A "user" is an individual who asks a question or makes a request through the system.

[1319] A "terminal" is a device (such as a smartphone or PC) that a user uses to access and operate the system.

[1320] An "expert" is an individual who has knowledge and experience of drug use and is qualified to provide specialized support to users.

[1321] "Initialization" is the procedure of loading the generative AI model, initializing the database, and preparing resources to enable the system to operate normally.

[1322] "User authentication" is the process of verifying a user's identity when accessing a system.

[1323] "Natural language processing technology" is a technology for analyzing natural language text entered by a user and understanding its meaning and intent.

[1324] "Outreach activities" are activities that provide important information and support to specific areas or specific groups.

[1325] "Follow-up" is the process of tracking and checking to provide ongoing care and support to a user.

[1326] A "user database" is a database system for storing and managing information about users.

[1327] "Expert database" refers to a database system that allows the system to store and manage expert information.

[1328] The "chat screen" is an interface that allows users to input information to the chatbot.

[1329] "Question history" is a record of questions that a user has asked in the past and the answers to those questions.

[1330] The present invention provides an interactive system using a generative AI model that accepts questions and consultations about drug use 24 hours a day, 365 days a year, and provides appropriate answers and professional support in real time. Detailed embodiments of the system are described below.

[1331] System configuration

[1332] The system mainly consists of a server, terminals, and users. The server runs the generative AI model and functions as an interface with users and an expert database. Terminals are devices that users use to access and operate the system, and include smartphones and PCs. Users access the system via their terminals to ask questions or make inquiries.

[1333] Hardware and software used

[1334] Server: A high-performance computer system is used to ensure appropriate data processing and response speed. The generative AI model used is, for example, OpenAI's GPT-4.

[1335] User database: Use a relational database such as MySQL or PostgreSQL to manage user information.

[1336] Expert database: Use a non-relational database such as MongoDB to manage expert information.

[1337] Terminal: A device used by a user to access the service, such as a smartphone or computer.

[1338] Initial System Setup

[1339] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user and expert databases, and preparing resource data, so that the system can immediately interact with the user and quickly provide the required information.

[1340] User Access

[1341] The user accesses the system from their own terminal. The terminal displays a login screen, and the user enters authentication information (user ID, password). The terminal then sends this information to the server.

[1342] User Authentication

[1343] The server receives authentication information from the user and checks it against existing information in a user database. If authentication is successful, the server starts a user session and launches a chatbot.

[1344] Interactive Support

[1345] The device displays a chat screen to users who have been successfully authenticated. The user inputs their consultation or question to the chatbot, and the device sends it to the server. For example, the user might input, "Please tell me how to reduce overdose."

[1346] Answers to basic questions

[1347] The server uses a generative AI model to analyze the user's question using natural language processing technology, retrieves an appropriate answer from the database, and generates a response such as, "It is effective to gradually reduce the amount of drugs you take at one time. Would you like to be connected to a specialist for more detailed instructions?" and sends it to the device.

[1348] Connect with experts

[1349] When a user needs expert support, the device sends the request to the server, which searches for an appropriate expert from the expert database and establishes a real-time connection between the user and the expert. For example, if the user types "Yes, connect me to an expert," the server will arrange a real-time chat session with the expert and send a link to the device.

[1350] Optimizing information provision

[1351] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns. For example, it can provide information specific to the user's frequently asked questions, eliminating the need to ask the question again.

[1352] Outreach Activities

[1353] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[1354] Ongoing support

[1355] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[1356] Examples of prompt statements

[1357] For example, a user might input a prompt into the system like this: "I want to reduce my drug intake. Please tell me specific ways to do this." In response, the generative AI model would provide specific advice and support.

[1358] In this way, the present invention leverages generative AI models to provide a conversational bot that provides comprehensive support to users, enabling users struggling with substance use to receive appropriate support and mitigate their risks.

[1359] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1360] Step 1: Initial System Setup

[1361] The server performs the initial setup of the system, specifically loading the generative AI model, establishing connections to the user database (e.g., MySQL) and expert database (e.g., MongoDB), and loading resource data (e.g., drug use guidelines and FAQs) into memory.

[1362] Input: None

[1363] Output: An initialized database and a loaded generative AI model.

[1364] Step 2: User Access

[1365] The user accesses the system from a device (smartphone or PC) and opens the login screen.

[1366] The terminal displays a login screen where the user enters their user ID and password. The entered authentication information is sent from the terminal to the server.

[1367] Input: User ID, Password

[1368] Output: Authentication information sent to the server

[1369] Step 3: User authentication

[1370] The server checks the received authentication information against existing information in a user database.

[1371] If authentication is successful, the server starts a user session and launches the chatbot, otherwise it sends an error message to the device.

[1372] Input: Submitted credentials

[1373] Output: User session started / Error message

[1374] Step 4: Start interactive support

[1375] After successful authentication, the device displays a chat screen. The user inputs their consultation or question to the chatbot, and the device sends the user's input to the server.

[1376] Input: User's consultation or question

[1377] Output: Consultation or question sent to the server

[1378] Step 5: Answer basic questions

[1379] The server uses a generative AI model to analyze the user's question using natural language processing technology. After analysis, the server retrieves the appropriate answer from the database, and the generative AI model generates the answer and sends it to the device.

[1380] For example, if a user types in "How can I reduce an overdose?", the generated answer might be "Slowly reducing the amount of drugs you take at one time is effective. Would you like me to connect you to a specialist for more information?"

[1381] Input: User's question

[1382] Output: The appropriate answer sent to the terminal

[1383] Step 6: Connect with an expert

[1384] When a user needs expert support, the terminal sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection between the user and the expert.

[1385] For example, if the user types "Yes, connect me to an expert," the server will arrange a chat session with the expert and send a link to the device.

[1386] Enter: Request for Expert Support

[1387] Output: Expert connection link sent to the device

[1388] Step 7: Optimize information delivery

[1389] The server analyzes the data collected during the conversation and provides optimal information based on the user's question history and behavioral patterns.

[1390] For example, it provides information specific to the questions users frequently ask, eliminating the need to ask the question again.

[1391] Input: Data collected during the interaction

[1392] Output: Optimized information delivery

[1393] Step 8: Plan your outreach activities

[1394] The server analyzes local and facility data to identify potential risk areas, which are then used to plan outreach activities and notify the outreach team.

[1395] For example, you might send instructions to your outreach team such as, "Based on the latest data analysis, we need to strengthen our outreach efforts with young people in City A."

[1396] Input: Area and facility data

[1397] Output: Plan outreach activities

[1398] Step 9: Ongoing support

[1399] The server will conduct regular follow-ups with the user and, if necessary, arrange for follow-ups by experts. It will also send follow-up reminders to the user's device to provide ongoing support for the user's situation.

[1400] Input: User data for follow-up

[1401] Output: Follow-up reminders sent to your device

[1402] The above is the specific processing flow of the system that provides an interactive bot that utilizes a generative AI model and provides comprehensive support to users.

[1403] (Application example 1)

[1404] 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."

[1405] In electronic payment services, users demand support available 24 hours a day, 365 days a year, but it is difficult to obtain prompt and appropriate answers. In particular, when users need payment troubles, emergency response, or expert advice, it often takes a long time for them to receive appropriate support. In addition, there is a demand for a system that can quickly analyze user questions and provide appropriate answers by introducing a conversational bot, but at present, it is difficult to effectively implement such a system.

[1406] 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.

[1407] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving questions or inquiries from the user and analyzing the contents to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for receiving the user's authentication information and comparing it with existing user information in a database to start a user session, a means for analyzing the user's location information and data to optimize outreach activities, and a means for ensuring continuous support for the user through regular follow-ups, thereby enabling users to receive prompt and appropriate support 24 hours a day, 365 days a year.

[1408] A "generative AI model" is a model of artificial intelligence technology used to analyze a user's question or inquiry and generate an appropriate answer.

[1409] An "interactive bot" is a system that interacts with users in real time and provides answers to their questions and inquiries.

[1410] A "user session" refers to a series of interactions and operations that occur between the time a user logs in to a system and the time they log out.

[1411] The "means for connecting with experts" is a function for connecting with experts in real time when a user needs professional support.

[1412] "Natural language processing technology" is a technology used to analyze and understand users' questions and inquiries.

[1413] "Outreach activities" are activities aimed at providing needed information and services to specific target areas or communities.

[1414] "Regular follow-up" is the process of continually monitoring the user's situation and providing additional support or information as needed.

[1415] An "expert database" is a database that stores information on experts in various fields.

[1416] This invention is a system that uses a generative AI model to provide an interactive bot that responds to user questions and problems regarding electronic payment services 24 hours a day, 365 days a year. This system consists of three components: the user's terminal, a server, and an expert database.

[1417] Initial System Setup

[1418] The server performs the initial setup of the system, specifically loading the generative AI model, initializing the user database, connecting to the expert database, and preparing resource data, so that the system can immediately interact with users and quickly provide them with the information they need.

[1419] User Access

[1420] Users access the system using their own devices, such as their smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID, password). This authentication information is sent from the device to the server.

[1421] User Authentication

[1422] The server receives the authentication information submitted by the user and checks it against existing user information in a database. If authentication is successful, the server starts a user session and launches an interactive bot.

[1423] Start interactive support

[1424] After successful authentication, the device displays a chat screen. The user can start a conversation by inputting a question or inquiry to the conversational bot. For example, the user might input, "I would like to check my credit card limit."

[1425] Answers to basic questions

[1426] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, the AI ​​chatbot might generate an answer such as, "To check your credit card limit, log in to the electronic payment service's app or website and click on the 'Account Information' section," and send it to the device.

[1427] Connect with experts

[1428] When a user requests expert support, the device sends the request to the server, which searches for a suitable expert from the expert database and establishes a real-time connection. For example, if the user types "Yes, connect me to an expert," the server arranges a real-time chat session with the expert and sends a link to the device.

[1429] Optimizing information provision

[1430] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it provides information specific to the user's frequently asked questions, eliminating the need for the user to ask the question again.

[1431] Planning outreach activities

[1432] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this, and notifies the outreach team of the plan. For example, it might send instructions to the outreach team such as, "Based on the latest data analysis, we need to intensify outreach activities in certain areas of City A."

[1433] Ongoing support

[1434] The server will periodically follow up with the user to ensure ongoing support, sending follow-up reminders to the user's device and arranging for follow-up by a specialist if necessary.

[1435] For example, if a user asks a chatbot, "I want to check my credit card limit," the generative AI model will respond, "To check your credit card limit, log in to the app or website of your electronic payment service and click on the 'Account Information' section." In this way, it is possible to provide the information users need quickly and accurately.

[1436] Example prompt sentence:

[1437] After logging in with your user ID and password, you will be asked the following questions:

[1438] "I want to check my credit card limit. How can I do that?"

[1439] Example of the output response:

[1440] To check your credit card limit, log in to your payment service app or website and click on the "Account Information" section.

[1441] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1442] Step 1:

[1443] (input)

[1444] Users access the system using their own devices such as smartphones or PCs. The device displays a login screen for user authentication, and the user enters authentication information (user ID and password).

[1445] (process)

[1446] The terminal sends this authentication information to the server.

[1447] (output)

[1448] The authentication information is sent to the server.

[1449] Step 2:

[1450] (input)

[1451] The server receives the authentication information sent by the user.

[1452] (process)

[1453] The server checks the received authentication information against existing user information in its database.

[1454] (output)

[1455] If authentication is successful, the user session begins and the chatbot is launched.

[1456] Step 3:

[1457] (input)

[1458] When a user session is started, the terminal displays a chat screen.

[1459] (process)

[1460] The user inputs questions or inquiries into the chat screen.

[1461] (output)

[1462] The user's question or inquiry is entered into the terminal.

[1463] Step 4:

[1464] (input)

[1465] The terminal transmits the user's question or inquiry to the server.

[1466] (process)

[1467] The server uses a generative AI model to analyze the question, specifically, it uses natural language processing technology to understand the question and retrieves the appropriate answer from a database.

[1468] (output)

[1469] The server sends the answer generated by the generative AI model based on the analysis results to the terminal.

[1470] Step 5:

[1471] (input)

[1472] The device displays the answer of the generative AI model received from the server.

[1473] (process)

[1474] The user checks the displayed answer and, if necessary, enters additional questions or inquiries.

[1475] (output)

[1476] The user's additional questions and inquiries are input into the terminal.

[1477] Step 6:

[1478] (input)

[1479] When a user requests professional support, the request is sent from the terminal to the server.

[1480] (process)

[1481] The server searches for a suitable expert from an expert database.

[1482] (output)

[1483] Expert information obtained from an expert database is prepared on a server.

[1484] Step 7:

[1485] (input)

[1486] A request arrives at the server to connect the user with the expert in real time.

[1487] (process)

[1488] The server arranges a real-time chat session with an expert and sends a link to the device.

[1489] (output)

[1490] A chat link with the expert will appear on your device.

[1491] Step 8:

[1492] (input)

[1493] The server analyzes the data collected during the interaction.

[1494] (process)

[1495] The server provides optimal information based on the user's situation and question history, and optimizes outreach activities based on past dialogue history.

[1496] (output)

[1497] A plan for outreach activities is generated.

[1498] Step 9:

[1499] (input)

[1500] The server sends periodic follow-up reminders to the user's device.

[1501] (process)

[1502] The user is informed.

[1503] (output)

[1504] Follow-up reminders will appear on your device.

[1505] In this way, a system that provides efficient and prompt user support is configured.

[1506] 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.

[1507] The system of the present invention provides comprehensive support for users struggling with drug use, combining a generative AI model and an emotion engine to achieve deeper understanding and support. The system consists of three components: a server, a device, and a user. It includes real-time dialogue with the user, connection to experts, optimization of outreach activities, and continuous follow-up. Furthermore, the emotion engine is used to analyze the user's emotional state and provide appropriate responses.

[1508] Initial System Setup

[1509] The server loads the generative AI model, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates an emotion engine to analyze the user's emotional state.

[1510] User Access

[1511] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[1512] User Authentication

[1513] The server checks the authentication information received from the device against existing user information in its database. If authentication is successful, the server starts a user session and launches a chatbot.

[1514] Start interactive support

[1515] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[1516] Answers to basic questions

[1517] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server will respond, "It is effective to gradually reduce the amount of drug you take at one time."

[1518] Emotion Engine Analysis

[1519] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user is feeling anxious or scared, the emotion engine detects this and notifies the server.

[1520] Responding according to emotions

[1521] The server receives notifications from the emotion engine and instructs the chatbot on how to respond based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message such as, "Don't worry, we're here to support you."

[1522] Connect with experts

[1523] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[1524] The server searches and selects an appropriate expert from the expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server then sends this information to the user's terminal and provides the user with a connection link to the expert.

[1525] Optimizing information provision

[1526] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions.

[1527] Planning outreach activities

[1528] The server analyzes data from local and facility locations to identify potential risk areas, and then uses this information to plan outreach activities and provide information to the outreach team, such as "We need to strengthen our outreach activities in City A."

[1529] Ongoing support

[1530] The server schedules regular follow-ups for the user, sends reminders to the user's device, and arranges follow-ups by experts as needed, ensuring that the user receives continuous support.

[1531] As described above, the present invention is an interactive bot system that combines a generative AI model and an emotion engine to provide comprehensive support to users, enabling young people struggling with drug use to receive appropriate support and reduce their risk and improve their behavior.

[1532] The processing flow will be explained below.

[1533] Step 1:

[1534] The server loads the generative AI model and emotion engine, initializes the user and expert databases, and prepares resource data related to outreach activities, so the system is ready to immediately start dialogue and provide appropriate information.

[1535] Step 2:

[1536] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password), which is then sent to the server.

[1537] Step 3:

[1538] The server checks the authentication information received from the device against existing user information in its database. If the check is successful, the server starts a user session and launches a chatbot.

[1539] Step 4:

[1540] After successful authentication, the device displays a chat screen, and the user can start a conversation by inputting their inquiry or question to the chatbot.

[1541] Step 5:

[1542] The device sends the user's question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. For example, if the user asks, "How can I reduce an overdose?" the server might respond, "It's effective to gradually reduce the amount of drug you take at one time."

[1543] Step 6:

[1544] The server's emotion engine analyzes the user's emotional state from the text and voice input. For example, if the user inputs "I'm very anxious. Please help me," the emotion engine detects that the user is feeling anxious and notifies the server of the analysis result.

[1545] Step 7:

[1546] The server receives notifications from the emotion engine and instructs the chatbot to respond appropriately based on the user's emotional state. For example, if the user is feeling anxious, the chatbot will generate a reassuring message such as "Don't worry, we're here to support you" and send it to the device.

[1547] Step 8:

[1548] When a user needs expert support, he / she inputs and sends a request for expert support at the terminal, which then sends the expert request to the server.

[1549] Step 9:

[1550] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation details and emotional state to the selected expert, and arranges a chat session with the expert. The server sends this information to the user's terminal and provides the user with a connection link to the expert.

[1551] Step 10:

[1552] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, it prioritizes the presentation of information and resources specific to the user's frequently asked questions.

[1553] Step 11:

[1554] The server analyzes data from neighborhoods and facilities to identify potential risk areas, plans outreach activities based on this information, and provides information to the outreach team. For example, it sends instructions such as, "Based on the latest data analysis, we need to strengthen outreach activities for young people in City A."

[1555] Step 12:

[1556] The server schedules regular follow-ups for the user and sends reminders to the user's device, and if necessary, arranges follow-ups by experts, ensuring that the user receives continuous support.

[1557] As described above, the server, terminals, and users work together to build a system that provides comprehensive support to users, enabling them to receive appropriate assistance, reduce the risks associated with drug use, and achieve social inclusion.

[1558] Example 2

[1559] 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."

[1560] In modern society, support for users struggling with drug use is limited, particularly in the areas of real-time dialogue and individual expert assistance. Furthermore, analysis of users' emotional states and optimization of outreach activities are lacking, making it difficult to provide efficient and effective support. This leaves users unable to receive appropriate support.

[1561] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1562] In this invention, the server includes a means for having an interactive bot using a generative AI model interact with a user in real time, a means for receiving a question or consultation from the user and analyzing the content to provide an appropriate answer, a means for connecting the user to an expert when the user needs professional support, a means for analyzing the emotional state from the user's input and providing a response according to the emotion, a means for analyzing the user's location data and history to optimize outreach activities, and a means for conducting regular follow-ups to ensure continuous support for the user. This allows the user to interact in real time and receive appropriate information, consultation from an expert, a response according to the emotion, and continuous follow-up.

[1563] A "generative AI model" is an artificial intelligence algorithm that analyzes input data from users and generates appropriate answers and actions using natural language processing technology.

[1564] An "interactive bot" is software that communicates with users in real time, providing answers and support to questions and inquiries.

[1565] An "expert" is a person or organization that has advanced knowledge and experience in a particular field and provides users with specialized advice and assistance.

[1566] "Emotion analysis" is the process of extracting and analyzing a user's emotional state from text or voice data entered by the user.

[1567] "Outreach activities" are activities that provide assistance and information to specific areas or target groups with the aim of reducing risk and providing support.

[1568] "Follow-up" is the process of continuously checking the user's situation after the initial response or support and providing additional support as needed.

[1569] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and is used in interactive bots, etc.

[1570] A "user database" is a database that stores and manages information about users on a system.

[1571] "Expert Database" refers to a database that stores and manages information about experts, and is used to search for and select appropriate experts as needed.

[1572] The system of this invention provides comprehensive support to users struggling with drug use, and by combining a conversational bot using a generative AI model with an emotion analysis engine, it achieves deeper understanding and support. This system consists of three entities: a server, a terminal, and a user.

[1573] The server runs the system using the following software and hardware. The generative AI model uses GPT-4, which has advanced natural language processing technology. The user database and expert database are managed using MySQL, as are resource data and information related to outreach activities. For sentiment analysis, IBM Watson Tone Analyzer is integrated, which has the ability to analyze emotions from user input.

[1574] Initial System Setup

[1575] The server first loads the generative AI model, which uses GPT-4. It also initializes the user database and expert database using MySQL and integrates the sentiment analysis engine, preparing the server to analyze the user's emotional state.

[1576] User Access

[1577] Users access the system using devices such as smartphones and PCs. When a user connects to a device, they enter authentication information (user ID, password), and the device sends this information to the server.

[1578] User Authentication

[1579] The server checks the authentication information received from the user against existing user information in its database. If authentication is successful, the server starts a user session and launches a conversational bot, which allows real-time interaction with the user.

[1580] Interactive Support

[1581] After successful authentication, the device displays a chat screen. The user enters a consultation or question into the chatbot to begin a dialogue. The device then sends the user's question to the server, which analyzes it using a generative AI model. Natural language processing technology is used to retrieve an appropriate answer from a database and present it to the user.

[1582] Sentiment analysis and response

[1583] The server's emotion analysis engine analyzes the user's emotional state from their input and provides a response based on the detected emotion. For example, if the user is feeling anxious or scared, the server instructs the chatbot to deliver a reassuring message.

[1584] Connect with experts

[1585] When a user needs professional assistance, they input their request into their device and send it to the server. The server searches for an appropriate expert in the expert database and forwards the user's consultation details and emotional state to the selected expert. The server then sends this information to the device and provides the user with a connection link to the expert.

[1586] Optimizing information provision and ongoing support

[1587] The server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. It also plans regular follow-ups and sends reminders to the user's device. If necessary, it can also arrange for follow-ups by a specialist, ensuring the user receives ongoing support.

[1588] Specific examples

[1589] For example, if a user asks, "How can I reduce the risk of overdosing?", the server uses a generative AI model to answer, "The most effective way is to gradually reduce the amount of drugs you take at one time." If the sentiment analysis engine detects the user's anxiety, the chatbot will send a reassuring message such as, "Don't worry, we're here to help."

[1590] Prompt Sentence Examples

[1591] "If users have concerns about their drug use, we provide appropriate support messages."

[1592] In this way, the system of the present invention can provide comprehensive and ongoing support to users struggling with substance use, helping to alleviate and improve their problems.

[1593] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1594] Step 1:

[1595] The server loads the generative AI model. Specifically, it uses GPT-4 and loads it into the system. MySQL is used to initialize the user database and expert database. Furthermore, it prepares resource data and integrates IBM Watson Tone Analyzer for sentiment analysis. The input is the configuration file at system startup, and the output is the system state after the initial setup is complete.

[1596] Step 2:

[1597] A user accesses the system using a smartphone or PC and enters authentication information (user ID and password) through the terminal. The terminal sends this authentication information to the server. The input is the user's authentication information, and the output is the authentication request sent to the server.

[1598] Step 3:

[1599] The server checks the authentication information received from the device against existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is the received authentication information, and the output is the authentication result and session ID.

[1600] Step 4:

[1601] After successful authentication, the device displays a chat screen. The user inputs their inquiry or question to the chatbot and begins a dialogue. The user's input is sent to the server by the device. The input is the user's inquiry or question, and the output is the data sent to the server.

[1602] Step 5:

[1603] The server receives the user's question, analyzes it using a generative AI model, and uses natural language processing technology to retrieve an appropriate answer from a database and present it to the user. The input is the user's question, and the output is the generated answer.

[1604] Step 6:

[1605] The server's emotion analysis engine analyzes the user's emotional state from the user's input. If the user is feeling anxious or scared, it detects this and notifies the server. The input is the user's input text, and the output is the emotion analysis result.

[1606] Step 7:

[1607] The server receives notifications from the emotion analysis engine and instructs the chatbot to respond according to the user's emotional state. For example, if the user is feeling anxious, the chatbot will send a reassuring message. The input is the emotion analysis result, and the output is the instructed response message.

[1608] Step 8:

[1609] When a user needs expert assistance, they input their request into their terminal and send it to the server. The server searches and selects an appropriate expert from its expert database. It then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. The server sends this information to the terminal and provides the user with a connection link to the expert. The input is the user's request, and the output is a connection link to the expert.

[1610] Step 9:

[1611] The server analyzes the data collected during the conversation and provides optimal information based on the user's situation and question history. For example, it prioritizes information specific to the user's frequently asked questions. The input is the collected data, and the output is optimized information.

[1612] Step 10:

[1613] The server analyzes data on local areas and facilities to identify potential risk areas. Based on this, it plans outreach activities and provides information to the outreach team. For example, it sends instructions such as, "You need to strengthen your outreach activities in City A." The input is local data, and the output is outreach plans and instructions.

[1614] Step 11:

[1615] The server plans regular follow-ups for the user and sends reminders to the user's device. If necessary, it also arranges follow-ups by experts, ensuring that the user receives continuous support. The input is a support plan, and the output is a reminder message to the user.

[1616] (Application example 2)

[1617] 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."

[1618] Providing comprehensive support to users struggling with drug use requires real-time dialogue, emotional response, connection to experts, optimization of outreach activities, and continuous follow-up. However, current systems lack the means to accurately analyze users' emotional states and provide appropriate emotional responses in real time, making it difficult for conventional systems to provide sufficient support.

[1619] 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 having an interactive bot using a generative AI model interact with a user in real time; means for receiving questions or consultations from the user and analyzing the content to provide appropriate answers; means for connecting the user to an expert when the user needs professional support; means for analyzing the user's location information and data to optimize outreach activities; means for ensuring continuous support for the user through regular follow-up; means for emotionally analyzing the user's messages and providing responses according to the emotions; and means for sending reassuring messages based on the emotional analysis. This makes it possible to provide appropriate support according to the user's emotional state and provide comprehensive, emotionally sensitive support to users struggling with drug use.

[1620] A "generative AI model" is an artificial intelligence technology designed to interact with users and analyze data. It uses natural language processing technology to analyze questions and inquiries and generate appropriate answers.

[1621] A "conversational bot" is a software application that is based on a generative AI model and allows for real-time interaction with users.

[1622] "Emotion analysis" is a technology that analyzes a user's emotional state based on messages and data obtained from the user.

[1623] An "expert" is a person who has extensive knowledge and experience in a particular field and can provide professional support to users.

[1624] "Outreach activities" are activities that are planned and implemented based on data analysis to provide support to specific areas and situations.

[1625] "Follow-up" refers to the activity of periodically checking the status of a user who has received support and providing ongoing support.

[1626] "Real-time interaction means" refers to technology or software that allows for immediate two-way communication with a user.

[1627] An "emotionally responsive means" is a technique or process that generates responses or support appropriate to a user's emotional state based on that state.

[1628] A "reassuring message" is a message that includes content intended to soothe and reassure the user.

[1629] This invention is a system for providing comprehensive support to users struggling with substance use. The system combines a generative AI model and an emotion analysis engine to provide assistance through a real-time interactive bot that responds to the user's emotional state. Specific embodiments are described below.

[1630] First, the server loads the generative AI model and the sentiment analysis engine, initializes the user database and the expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state.

[1631] Users access the system using a smartphone or PC terminal and enter authentication information (user ID, password). The terminal then sends the authentication information to the server. The server compares the authentication information received from the terminal with existing user information in the database, and if authentication is successful, it starts a user session and launches the chatbot. If authentication is successful, the terminal displays a chat screen.

[1632] The user begins a dialogue by inputting their concerns or questions into the chatbot. For example, if the user inputs, "My drug use has increased recently and I'm worried. What should I do?", the device will send this question to the server. The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. At this time, the user's message is analyzed using an emotion analysis engine, and their emotional state is also evaluated at the same time. If anxiety or fear is detected, the generative AI model will take this information into account and generate a reassuring message.

[1633] For example, if the emotion analysis result is "negative," the system will provide the user with a message such as, "Let's think about specific steps to reduce your drug use. Don't worry, we're here to support you." By continuing the dialogue in this way, it is possible to provide appropriate support according to the user's emotional state.

[1634] Furthermore, if a user needs professional support, they can input and send a request for an expert on their terminal. The server searches and selects an appropriate expert from the expert database. The server then transfers the user's consultation details and emotional state to the selected expert and arranges a chat session with the expert. This allows the user to receive individual support from the expert.

[1635] Regarding outreach activities, the server analyzes the data collected during the conversation and provides the most appropriate information based on the user's situation and question history. For example, if a particular area or facility is at high risk, outreach activities in that area will be strengthened. The server also plans regular follow-ups and sends reminders to the user's device, ensuring that the user receives ongoing support.

[1636] As a specific example, by inputting the prompt sentence "The user is feeling anxious about drug use. What should they do?" into a generative AI model, an appropriate answer can be generated. This prompt sentence is used to generate a message that takes into account the user's emotional state.

[1637] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1638] Step 1:

[1639] The server loads the generative AI model and sentiment analysis engine, initializes the user database and expert database, prepares resource data related to outreach activities, and integrates the sentiment analysis engine to analyze the user's emotional state. The input is resource information from specific configuration files and databases, and the necessary modules are launched and configured based on this. The output is an initialized server environment.

[1640] Step 2:

[1641] A user accesses the system using a smartphone or PC terminal and enters authentication information (user ID, password). The terminal sends this authentication information to the server. The input is the authentication information entered by the user, and based on this, the terminal sends an authentication request to the server. The output is an authentication request to the server.

[1642] Step 3:

[1643] The server compares the authentication information received from the device with existing user information in the database. If authentication is successful, the server starts a user session and launches a chatbot. The input is an authentication request, which is used to perform database comparison. The output is a successful authentication message and the launch of the chatbot.

[1644] Step 4:

[1645] If authentication is successful, the terminal displays a chat screen. The input is an authentication success message, and the chat screen is displayed based on this. The output is a chat screen that the user can use.

[1646] Step 5:

[1647] The user inputs their concerns or questions into the chatbot. For example, they might input, "My drug use has increased recently, and I'm worried. What should I do?" The input is the message the user sends to the chatbot, and the output is the content of their concerns, which is sent from the device to the server.

[1648] Step 6:

[1649] The server's generative AI model analyzes the user's question and uses natural language processing technology to retrieve an appropriate answer from a database. In parallel, the sentiment analysis engine analyzes the user's message and evaluates their emotional state. The input is the user's inquiry, and based on this, answers are searched for in the database and sentiment analysis is performed. The output is the generated answer and an evaluation of the emotional state.

[1650] Step 7:

[1651] The server instructs the generative AI model to generate a reassuring message based on the results of the emotion analysis. For example, if it detects anxiety or fear, it adds a reassuring message such as "Don't worry, we're here to support you" to the generated response. The input is the result of the emotion analysis and the generated response, and the output is the final message containing reassuring content.

[1652] Step 8:

[1653] The final message is sent from the server to the terminal and provided to the user through the chatbot. The input is the final message generated by the server, and the output is the chatbot's response displayed on the user's terminal.

[1654] Step 9:

[1655] When a user needs expert support, he / she inputs the request at the terminal and sends it to the server. The input is the expert request from the user, and the output is the request sent to the server.

[1656] Step 10:

[1657] The server searches and selects an appropriate expert from the expert database, transfers the user's consultation content and emotional state to the selected expert, and arranges a chat session with the expert. The input is the expert request and database data, and the output is the selected expert and the transferred information.

[1658] Step 11:

[1659] The server analyzes the data collected during the interaction and provides the most appropriate information based on the user's situation and question history. It also plans outreach activities and sends reminders to the user's device. The input is the data collected during the interaction, and the output is the planned outreach activities and reminder messages.

[1660] Step 12:

[1661] If regular follow-up is required, the server sends reminders to the user's device to provide ongoing support. The input is the follow-up plan, and the output is the reminder message sent to the user.

[1662] 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.

[1663] 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.

[1664] 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 robot 414.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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).

[1669] 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.

[1670] 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."

[1671] 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.

[1672] 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).

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] The following is further disclosed regarding the above embodiment.

[1684] (Claim 1)

[1685] [A means for real-time interaction between a conversational bot using a generative AI model and a user,

[1686] A means for receiving a question or inquiry from a user, analyzing the content of the question or inquiry, and providing an appropriate answer;

[1687] A means to connect users with experts if they need professional support;

[1688] A means for analyzing user location information and data to optimize outreach activities;

[1689] A means of ensuring ongoing support for users through regular follow-up]

[1690] A system including:

[1691] (Claim 2)

[1692] The system described in claim 1 [uses a generative AI model to analyze the content of the user's question using natural language processing technology and retrieve an appropriate answer from a database].

[1693] (Claim 3)

[1694] The system of claim 1 [searches for appropriate experts from an expert database and connects users with experts in real time].

[1695] "Example 1"

[1696] (Claim 1)

[1697] [A means for real-time interaction between a conversational bot using a generative AI model and a user,

[1698] A means for receiving a question or inquiry from a user, analyzing the content of the question or inquiry, and providing an appropriate answer;

[1699] A means to connect users with experts if they need professional support;

[1700] A means for analyzing user location information and data to optimize outreach activities;

[1701] A means of ensuring ongoing support for users through regular follow-up; and

[1702] A means for the server to perform system initialization;

[1703] A means for a user to access from a terminal, enter authentication information, and send it to a server;

[1704] A means for the server to verify the user's authentication information, perform authentication, and activate the chatbot;

[1705] The device displays a chat screen and users input their inquiries or questions.

[1706] A system including:

[1707] (Claim 2)

[1708] The system described in claim 1 [uses a generative AI model to analyze the content of the user's question using natural language processing technology and retrieve an appropriate answer from a database].

[1709] (Claim 3)

[1710] The system of claim 1 [searches for appropriate experts from an expert database and connects users with experts in real time].

[1711] "Application Example 1"

[1712] (Claim 1)

[1713] [A means for real-time interaction between a conversational bot using a generative AI model and a user,

[1714] A means for receiving a question or inquiry from a user, analyzing the content of the question or inquiry, and providing an appropriate answer;

[1715] A means to connect users with experts if they need professional support;

[1716] means for receiving a user's authentication information and matching it with existing user information in a database to initiate a user session;

[1717] A means for analyzing user location information and data to optimize outreach activities;

[1718] A means of ensuring ongoing support for users through regular follow-up]

[1719] A system including:

[1720] (Claim 2)

[1721] The system described in claim 1 [uses a generative AI model to analyze the content of the user's question using natural language processing technology and retrieve an appropriate answer from a database].

[1722] (Claim 3)

[1723] The system of claim 1 [searches for appropriate experts from an expert database and connects users with experts in real time].

[1724] "Example 2: Combining Emotion Engines"

[1725] (Claim 1)

[1726] [A means for enabling interactive bots using generative AI models to interact with users in real time]

[1727] [Means for receiving questions or inquiries from users, analyzing the content, and providing appropriate answers;]

[1728] [Means to connect users with experts if they need professional assistance; and]

[1729] [Means for analyzing the emotional state of the user from their input and providing a response according to the emotion; and]

[1730] [Means of analyzing user location data and history to optimize outreach activities; and]

[1731] [Means of conducting regular follow-up and ensuring ongoing support for users]

[1732] A system including:

[1733] (Claim 2)

[1734] The system described in claim 1 [uses a generative AI model to analyze the content of a user's question using natural language processing technology and obtain an appropriate answer].

[1735] (Claim 3)

[1736] The system of claim 1 [searches for appropriate experts from an expert database and connects users with experts in real time].

[1737] "Application example 2 when combining emotion engines"

[1738] (Claim 1)

[1739] [A means for real-time interaction between a conversational bot using a generative AI model and a user,

[1740] A means for receiving a question or inquiry from a user, analyzing the content of the question or inquiry, and providing an appropriate answer;

[1741] A means to connect users with experts if they need professional support;

[1742] A means for analyzing user location information and data to optimize outreach activities;

[1743] A means of ensuring ongoing support for users through regular follow-up; and

[1744] A means for analyzing the emotions of a user's message and providing a response according to the emotions;

[1745] A means of sending reassuring messages based on sentiment analysis]

[1746] A system including:

[1747] (Claim 2)

[1748] The system described in claim 1 [uses a generative AI model to analyze the content of the user's question using natural language processing technology and retrieve an appropriate answer from a database].

[1749] (Claim 3)

[1750] The system of claim 1 [searches for appropriate experts from an expert database and connects users with experts in real time]. [Explanation of symbols]

[1751] 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 a conversational bot using a generative AI model to interact with users in real time; A means for receiving a question or inquiry from a user, analyzing the content of the question or inquiry, and providing an appropriate answer; A means to connect users with experts if they need professional support; A means for analyzing user location information and data to optimize outreach activities; A means of ensuring ongoing support for users through regular follow-up A system including:

2. The system according to claim 1, wherein the system uses a generative AI model to analyze the content of a user's question using natural language processing technology and retrieve an appropriate answer from a database.

3. 10. The system of claim 1, further comprising: searching for an appropriate expert from an expert database; and connecting the user with the expert in real time.

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