system

Abstract

Provide a system. 【Solution means】 Means for inputting user information, Means for recording the user information, Communication means for a registered user to input a message, Means for analyzing the input message, Means for automatically generating a response based on the analysis result, Means for providing the response to the user, Means for automatically notifying an expert, Means for the expert to provide advice to the user, Means for storing and managing the communication history of the user, An online support system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the mental troubles and stresses of children have been increasing, but there are many cases where they are isolated without being able to consult their parents or friends. This may have a significant impact on the mental health of children. In such a situation, there is an increasing need for a safe and reliable online support system that allows children to easily talk about their inner thoughts and receive professional support quickly.

Means for Solving the Problems

[0005] This invention provides an online support system equipped with communication means that allows users to register their information and enter messages. The entered messages are automatically analyzed, a response is generated based on the results, and, if necessary, a specialist is promptly notified. Furthermore, the system securely manages the user's communication history and stores the data in encrypted form, thereby ensuring privacy and data security. This creates an environment where children can easily seek advice, contributing to the maintenance of their mental health.

[0006] A "user" is an individual who utilizes the online support system and is the entity that initiates consultations or inputs information.

[0007] "Information" refers to data necessary for a user to register, including personal information such as username, email address, and password.

[0008] "Recording" refers to saving information entered by users in a database for later reference and management.

[0009] "Communication" refers to the means by which users send messages and receive responses through a system.

[0010] "Analysis" refers to the process of automatically processing messages sent by users, evaluating their content, and determining necessary actions.

[0011] "Response" refers to a reply generated by the system in response to user input, and may include automated responses or expert advice.

[0012] A "specialist" is an individual or group that possesses specialized knowledge and can provide advice based on the content submitted by the user.

[0013] "Storage management" refers to the process of saving communication history and user information to a database and managing it securely.

[0014] "Encryption" is the process of converting data, such as communication content and user information, into an unreadable format in order to protect it. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

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

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] To implement the present invention, it is first necessary to construct an online support system and provide a user-accessible interface. This system will have a secure environment for safely handling user information and will implement key functions such as user registration, login, submission of consultation content, receipt of responses, and switching to an expert.

[0037] Users access the online platform via the internet using their devices. Upon first use, users register their information with the system and create a unique account. This registration information includes username, email address, and password. All information is securely recorded on the server and stored in a database.

[0038] Afterward, the user logs in and can easily begin a consultation via the system's chat interface. The user freely types and sends messages on the chat screen displayed on their device. Messages sent from the device are received by the server and their content is analyzed using natural language processing algorithms.

[0039] The server automatically generates an appropriate response from the analysis results and sends it to the terminal. For general inquiries, it returns a pre-programmed response, enabling a quick response. In addition, when the server detects specific keywords in the user's message, it immediately notifies an expert, prompting a detailed response from the expert.

[0040] Experts receive notifications from the server, participate in chats, and provide users with specific advice and support tailored to their individual situations. Furthermore, the server encrypts all communication history and securely stores it for future reference and data management.

[0041] This provides users with an environment where they can easily seek advice about their mental health concerns, and through professional support, effectively maintain and improve their mental health. Furthermore, this system takes great care to protect personal information, so it can be used with peace of mind.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users access the online support system registration page using their device, enter the required information, and create an account.

[0045] Step 2:

[0046] The terminal sends the entered information to the server for verification to ensure it is in the correct format.

[0047] Step 3:

[0048] The server checks the database to see if there are any duplicates in the received information, and after verification, saves the user information to the database.

[0049] Step 4:

[0050] Users log in using their account and access the system's homepage.

[0051] Step 5:

[0052] The terminal sends the user's authentication information to the server and requests the establishment of a session.

[0053] Step 6:

[0054] The server checks the authentication information from the database, generates session information if a match is found, and grants permission to log in.

[0055] Step 7:

[0056] The user opens the chat screen on the system and enters their inquiry.

[0057] Step 8:

[0058] The device sends the entered chat message to the server.

[0059] Step 9:

[0060] The server analyzes the received message using a natural language processing algorithm and generates an automated response.

[0061] Step 10:

[0062] The server sends the generated response to the terminal, where it is displayed on the user's chat screen.

[0063] Step 11:

[0064] If the server detects specific keywords based on the analysis results, it immediately notifies the experts.

[0065] Step 12:

[0066] Upon receiving notification from an expert, the server will allow the expert to participate and provide them with the ability to join the chat.

[0067] Step 13:

[0068] The server encrypts all chat history and user data and stores it in secure storage.

[0069] (Example 1)

[0070] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0071] In modern society, there is a need to create an environment where users can easily consult about psychological problems, but at the same time, protecting personal information and responding quickly and appropriately remain important challenges. Furthermore, there is a lack of mechanisms to efficiently notify experts when specialized attention is required depending on the nature of the problem. In addition, accurately analyzing user messages using natural language and generating responses based on that analysis is a technical challenge.

[0072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0073] In this invention, the server includes a device for inputting information, a device for analyzing the input message, and a device for generating a response using a generative artificial intelligence model. This enables users to securely register information, process consultation content quickly and appropriately using natural language processing, and promptly notify experts as needed. Furthermore, by encrypting and storing all communication history, it is possible to provide effective support while ensuring the protection of personal information.

[0074] A "device for inputting information" is a device that allows users to provide information such as their name and contact details to a system.

[0075] A "device for recording information" is a device for saving entered user information and accumulating it in a database.

[0076] A "communication device" is a device used by users to send messages to a system and to receive responses from the system.

[0077] A "message analysis device" is a device that uses natural language processing technology to analyze the content of a received message and understand its meaning.

[0078] A "response generation device" is a device that automatically creates an appropriate response for the user based on the analysis results.

[0079] A "device that automatically notifies experts" is a device that notifies experts in real time when specific conditions or keywords are detected.

[0080] A "device for saving and managing history" is a device that encrypts and saves all communication history for later reference and analysis.

[0081] A "natural language processing algorithm" is a technology that analyzes messages entered by users into a form that a computer can understand and then finds solutions.

[0082] A "generative artificial intelligence model" is an artificial intelligence technology that creates natural language responses based on large amounts of data.

[0083] A "device that detects and notifies specific words or phrases" is a device that detects important keywords contained in a message and prompts experts to take action based on those keywords.

[0084] In order to implement the present invention, it is first necessary to construct an online support system for users to access. This system is designed to handle user information securely and process it quickly. A specific embodiment of this system is shown below.

[0085] Users access the online platform via the internet using their own devices. First, users register by entering information such as their username, email address, and password. This information is stored in a database in a hashed state by the server.

[0086] When a user logs in, a chat interface appears on their device. Through this interface, the user can input their inquiry and send it to the server. The server uses natural language processing software (such as NLTK or SpaCy) to analyze the received message and understand its content.

[0087] Based on the analysis results, the server automatically generates a response to the user using a generative AI model (e.g., GPT-3®). In this process, the generative AI model creates a natural language response based on a large amount of data and displays it on the user's device. For example, if the user sends "I've been feeling stressed lately," the system can reply with advice on stress management.

[0088] Furthermore, the server detects specific keywords and sends real-time notifications to experts. This feature allows experts to respond quickly if the user's input contains important signs.

[0089] All communication history is encrypted for security purposes and securely stored on the server. This feature will be used later for user follow-up and data analysis.

[0090] Examples of prompt messages include, "Please tell me how to cope when I feel anxious." This allows users to easily ask for advice and receive appropriate support.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] The user accesses the online support system registration screen using their device. Here, the user enters their name, email address, and password. After entering the information, the user presses the registration button to send the information to the server. The server receives the entered information and stores it in a hashed format in its database. This creates the user's account, allowing them to access the platform.

[0094] Step 2:

[0095] The user accesses the login screen from their device and enters their registered email address and password. The entered information is sent to the server, which retrieves the corresponding user information from the database and verifies the password. If authentication is successful, the server establishes a session with the user and grants them access to the chat interface.

[0096] Step 3:

[0097] The user enters their inquiry into the chat interface on their device and presses the send button. The message is sent to the server. The server analyzes the received text using natural language processing algorithms (e.g., NLTK or SpaCy) to understand the structure and meaning of the text. The results of the analysis are output as semantic extraction and sentiment analysis.

[0098] Step 4:

[0099] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to create an appropriate response. The model leverages historical data to generate a response in natural language. The generated response is output as advice or suggestions to the user and displayed on the terminal's chat screen.

[0100] Step 5:

[0101] The server checks if specific keywords or phrases are included in user messages. If detected, a mechanism is triggered to send a notification to a specialist. This notification is sent in real time, allowing the specialist to prepare to provide expert assistance tailored to the user's situation.

[0102] Step 6:

[0103] All communication history is automatically encrypted on the server and stored in a database. This ensures user privacy and allows the data to be stored and managed in a way that makes it available for future reference and analysis.

[0104] (Application Example 1)

[0105] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0106] In recent years, the importance of mental health has increased, leading to a growing demand for online support systems that allow users to safely receive expert advice. However, current systems lack the flexibility to provide appropriate advice quickly while reducing the psychological burden on users. Furthermore, there is a lack of means to effectively analyze messages from a large volume of users and encourage the participation of experts. Effective methods to address these challenges are needed.

[0107] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0108] In this invention, the server includes means for inputting user information, means for recording the user information, and communication means for registered users to input messages. This allows users to safely and anonymously seek advice on mental health issues and receive appropriate responses quickly using a generative model algorithm. Furthermore, by providing a prompt generation means for the generative model, the accuracy and effectiveness of the responses can be improved, and experts can be automatically notified as needed. This makes it possible to provide more effective and reassuring support to users.

[0109] "Means for inputting user information" refers to input devices or software that allow users to record their own information in a system.

[0110] "Means for recording user information" refers to databases or recording media that centrally manage and securely store the user information entered by the user.

[0111] "Communication means" refers to the network and software that allows a user to input a message via an interface and send that message to a server.

[0112] "Means for analyzing input messages" refers to software that uses natural language processing algorithms to process text messages from users and understand their content.

[0113] "Means for automatically generating responses" refers to generative model algorithms that automatically create appropriate responses based on analysis results.

[0114] "Means of providing a response to the user" refers to software or hardware for displaying automatically generated responses on the user's device.

[0115] A "means of automatically notifying experts" refers to a notification system that sends alerts to experts when certain conditions are met.

[0116] "A means of providing advice" refers to a communication interface that allows experts to provide specific advice tailored to the user's situation in real time via chat.

[0117] "Means for saving and managing communication history" refers to a data management system that securely records interactions with users and makes them available for later reference.

[0118] "Methods that utilize generative model algorithms" refer to techniques that use artificial intelligence to optimize data when generating responses, thereby improving the quality of the responses.

[0119] A "prompt generation means" is a mechanism for creating inputs that provide effective instructions to the generative model and for optimizing the model's response generation.

[0120] A system for carrying out this invention is constructed to include a terminal for inputting user information and communicating, a server for processing data, and a generative model algorithm for providing responses to the user.

[0121] The user begins by entering their information. They input this information using software running on their device, and this information is transmitted to the server via the internet. The server securely records the received user information in a database and protects it using encryption technology as needed.

[0122] Users input questions and inquiries as messages through their terminals and send them to the server. The server analyzes these messages using natural language processing (NLTK) technology. Natural language processing libraries such as Python's NLTK and spaCy can be used for analysis. Based on the results of this analysis, the server automatically generates an appropriate response. Generative AI models can be used for this response generation; for example, OpenAI's GPT and Hugging Face's Transformers are available.

[0123] The response is generated by providing a prompt to the generative model. An example of a specific prompt is: "Generate an appropriate response based on the message received from the user." After generating the response, the server sends it to the user's terminal.

[0124] If the analysis results meet certain conditions, the server automatically sends a notification to a specialist. The specialist interacts with the user in real time via the terminal interface and provides specific advice. All communication history is securely stored on the server in a format that can be referenced later. Through this process, users can seek mental health advice anonymously and receive prompt and accurate support from specialists.

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] The user enters information using a terminal. This information includes usernames, email addresses, and passwords, and this data is sent to the server through the input interface. The server records the received data in a database using a secure protocol and manages it securely.

[0128] Step 2:

[0129] The user enters their inquiry as a message using their device. This message is sent from the device to the server. The server analyzes the received message using a natural language processing algorithm. This analysis examines the syntax and meaning of the message and extracts the information necessary for the next step.

[0130] Step 3:

[0131] The server generates a response using a generative AI model based on the analysis results. The generative AI model receives extracted keywords and message context as prompts and outputs a natural language response accordingly. In this case, the prompt used is "Generate an appropriate response based on the message received from the user."

[0132] Step 4:

[0133] The server sends the generated response to the user's terminal. The user can view this response on the terminal's interface. The response is displayed as text, and the user can continue with additional questions or consultations as needed.

[0134] Step 5:

[0135] After generating a response, the server automatically sends notifications to experts based on the analysis results and circumstances. If specific keywords or phrases are detected, experts are prompted to join the chat. This notification is in real time, allowing experts to immediately begin interacting with the user.

[0136] Step 6:

[0137] All communication history with users is stored on the server. This historical data is encrypted and recorded so that experts and system administrators can refer to it later. The stored data helps to provide more accurate advice by utilizing the user's past consultation history.

[0138] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0139] This invention realizes a form of online support system that combines emotion recognition functionality. This system analyzes the emotions contained in messages entered by users in real time, adjusts responses, and notifies experts as needed, thereby providing more effective support.

[0140] When a user accesses the online platform through their device, the system first registers the user's information and initiates an individual session. The user enters their consultation details into the chat interface. The device sends the entered data to the server. The server analyzes the received message and uses natural language processing technology to analyze the emotions contained in the message using an emotion engine. This emotion engine identifies emotional tones such as positive, negative, and neutral from the text.

[0141] Based on the analyzed sentiment data, the server adjusts its response. For example, if a message indicates negative emotions, the server generates a more supportive and empathetic response. The server also automatically notifies a professional if a specific emotional state is continuously recognized, as needed. This allows the professional to review the consultation and intervene quickly to provide the user with appropriate advice.

[0142] For example, if a user enters "I've been feeling sad and unmotivated lately," the server recognizes this message as a negative emotion and generates and sends a response of encouragement and acceptance to the user. Depending on the situation, this information may also be notified to a specialist, creating a system where the specialist can provide more detailed support to the user.

[0143] The server encrypts and securely stores all chat data and sentiment analysis history, fully protecting user privacy and using this data to continuously improve the system's analysis quality.

[0144] Thus, the present invention provides a support environment that improves the user experience by understanding user emotions and optimizing responses and support.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] Users access the online support system using their device and, for first-time users, create an account by entering the required information on the registration page.

[0148] Step 2:

[0149] The terminal sends the user's registration information to the server, which then securely stores that information in a database.

[0150] Step 3:

[0151] The user logs in with the account they created and accesses the system dashboard.

[0152] Step 4:

[0153] The terminal sends the user's authentication information to the server, which then verifies it against the database to perform authentication and initiate the session.

[0154] Step 5:

[0155] The user opens the chat screen and enters their question as text.

[0156] Step 6:

[0157] The terminal sends the message entered by the user to the server.

[0158] Step 7:

[0159] The server analyzes received messages using natural language processing techniques and identifies emotions from the text using an emotion engine.

[0160] Step 8:

[0161] The emotion engine classifies the emotional tone in a message as positive, negative, neutral, etc., and provides that information to the server.

[0162] Step 9:

[0163] The server adjusts its response based on emotional data. For example, if negative emotions are strong, it will generate encouraging or accepting responses.

[0164] Step 10:

[0165] The server sends the adjusted response to the terminal, which then displays it on the user's chat screen.

[0166] Step 11:

[0167] If a specific emotional state, such as persistent negative emotions, is detected, the server automatically notifies a specialist.

[0168] Step 12:

[0169] Experts receive notifications from the server, join the chat, and provide users with reliable advice in real time.

[0170] Step 13:

[0171] The server encrypts all chat history and sentiment analysis results, stores them in secure storage, and protects privacy while also providing them for future analysis.

[0172] (Example 2)

[0173] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0174] In modern society, online support systems play a crucial role. However, many existing systems struggle to adequately recognize and respond to users' emotions. In particular, there is often a lack of continuous recognition of negative emotions experienced by users and appropriate professional intervention to address them. As a result, users may feel dissatisfied and anxious because they do not receive the support they need, which can undermine their trust in the system.

[0175] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0176] In this invention, the server includes means for identifying emotions from information using natural language processing technology, means for notifying experts when a specific emotional tone is continuously recognized, and means for protecting, encrypting, and storing the user's communication history. This makes it possible to analyze the user's emotions in an advanced way and generate appropriate responses accordingly, as well as to quickly notify experts of the situation as needed and provide accurate support to the user.

[0177] "Means for inputting user information" refers to an interface that allows users to provide data about themselves to the system.

[0178] "Means of recording" refers to a function that retains the user's entered information and stores it so that it can be referenced as needed.

[0179] "Communication means" refers to a device or program for mutual communication between a user and a system, enabling the user to send information to the system and receive a response from the system.

[0180] "Means of analysis" refers to the process of analyzing received information and understanding its content and context.

[0181] "Methods for identifying emotions from information using natural language processing technology" refers to technologies that use generative AI models or emotion engines to identify emotions within text.

[0182] A "means for generating responses" is a mechanism that automatically creates appropriate responses or messages based on analyzed information.

[0183] "Means of notification" refers to a method of sending warnings or information to experts or other relevant parties when certain conditions are met.

[0184] "Means of providing advice" refers to a system for experts to provide guidance and suggestions to users.

[0185] "Means of preservation" refers to a system that securely stores communication history and sentiment analysis data and manages it so that it can be accessed later.

[0186] A "generative AI model" is an algorithm or system that uses artificial intelligence, trained on a large amount of data, to understand and manipulate natural language.

[0187] A "prompt message" is a type of text that serves as input instructions for an AI model, used to instruct the AI ​​to perform a specific task.

[0188] This system aims to analyze user emotions in real time and adjust responses within an online support platform. The following describes the configuration for implementing this system.

[0189] Users access the online platform using devices such as PCs and smartphones. There, users enter their questions into a text box. The device then transmits the entered message to the server via a communication method.

[0190] The server analyzes received messages using natural language processing techniques. Specifically, it employs a generative AI model to understand the content and context of the message, and then uses an emotion engine to identify emotional tones such as positive, negative, and neutral. In the analysis process, a general-purpose natural language processing software can be used as the emotion engine.

[0191] Based on the analysis results, the server automatically generates a response. The generated response is tailored to the user's emotions. For example, if the user's message expresses a negative emotion such as "I've been feeling sad and unmotivated lately," an empathetic and encouraging response will be generated. Furthermore, if the same negative emotion is continuously recognized, the server automatically notifies a professional. This notification allows the professional to quickly provide appropriate advice to the user.

[0192] Data security is also a consideration; the server encrypts and securely stores all communication history and sentiment analysis history. This data can be used to improve the system's analytical capabilities while ensuring user privacy.

[0193] An example of a prompt would be an instruction used for a generative AI model such as, "Detect the emotion in the user's message and provide a corresponding response. For example, if the user says, 'I've been feeling sad and unmotivated lately,' create an empathetic response."

[0194] In this way, the system can improve the user experience by taking user emotions into consideration and providing optimized support.

[0195] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0196] Step 1:

[0197] Users access the online platform using their devices and enter their inquiries via a chat interface. The entered text data is sent from the device to the server upon triggering a send action. The input is a text message, and the output is the message data that reaches the server.

[0198] Step 2:

[0199] The server analyzes message data received from the terminal. First, a generative AI model analyzes the user's text message using natural language processing techniques to understand the information and context. Here, the input is the message data, and the output is the contextual information necessary for sentiment analysis.

[0200] Step 3:

[0201] The server uses a generative AI model and an emotion engine to identify emotional tones from the analyzed contextual information. In this process, the emotion engine classifies the message as having a positive, negative, or neutral tone. The input is contextual information, and the output is the identified emotional tone.

[0202] Step 4:

[0203] The server automatically generates an appropriate response based on the identified emotional tone. If a negative emotion is detected, a generative AI model generates an empathetic and supportive response, which is then prepared as text data. The input is the emotional tone, and the output is the generated response text.

[0204] Step 5:

[0205] The server sends the generated response back to the terminal for display to the user. The response is displayed to the user via a chat interface on the terminal. The input is the generated response text, and the output is the response presented visually to the user.

[0206] Step 6:

[0207] The server notifies a professional if a specific emotional tone is continuously detected, as needed. This notification occurs when certain trigger conditions are met, enabling the professional to quickly provide appropriate advice to the user. The input is the continuously detected emotional tone, and the output is the notification to the professional.

[0208] Step 7:

[0209] The server encrypts and stores all communication history and sentiment analysis data to protect user privacy. This allows for the secure storage of data for future analysis and system improvements. The input is communication history and analysis data, and the output is encrypted data storage.

[0210] (Application Example 2)

[0211] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0212] Traditional online support systems have faced challenges in fully understanding and responding immediately to users' emotional states, as well as in anticipating and appropriately managing emotional upsets. Therefore, there has been a need to provide rapid support when users experience anxiety or stress. Furthermore, to ensure user safety, there is a demand for analyzing voice data to detect abnormal emotions.

[0213] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0214] In this invention, the server includes means for inputting user information, means for acquiring voice data and converting it to text, and means for analyzing the converted text and identifying emotions. This makes it possible to analyze the user's emotional state in real time, quickly identify emotions indicating stress or anxiety, and notify experts while issuing necessary warnings.

[0215] "Means for inputting user information" refers to an interface on an online platform that allows users to input their personal identification information and individual data into the system.

[0216] "Means for recording user information" refers to a mechanism for securely and accurately storing user data entered on a server.

[0217] "Communication means" refers to the system component that allows registered users to input messages and send and receive those messages to and from the server.

[0218] "Means for analyzing input messages" refers to a module that uses analysis techniques to process text messages sent by users and understand their content and intent.

[0219] "Means for automatically generating responses" refers to a process in which a computer generates an appropriate answer based on the content of an analyzed message and provides it to the user.

[0220] "Means of providing a response to the user" refers to a method of sending the generated response to the user's device and making it easily accessible to the user.

[0221] "Means for automatically notifying experts" refers to technology that automatically sends alerts to experts under specific conditions.

[0222] "Means for experts to provide advice to users" refers to an access mechanism for experts to contact users and provide them with professional support regarding their questions and problems.

[0223] "Means for saving and managing user communication history" refers to database technology for recording and securely storing user operation history and messages when using a system.

[0224] "Means for acquiring audio data and converting it to text" refers to speech recognition software that recognizes speech and converts it into a corresponding text format.

[0225] "Methods for analyzing converted text and identifying emotions" refers to the process of identifying emotional tones, such as positive or negative, by analyzing the emotional nuances contained in the text data.

[0226] A "warning mechanism" is an alert system designed to alert users or administrators when certain emotional states or criteria are reached.

[0227] In this invention, the server uses software to convert speech data into text data. Specifically, it can utilize speech recognition services such as Google® Cloud Speech-to-Text API. The converted text is subjected to sentiment analysis using natural language processing (NLP) techniques such as IBM Watson® Tone Analyzer API. This identifies whether the emotional tone contained in the text is positive, negative, or neutral. Based on the analysis results, the server generates a response to the user. This response will be supportive and empathetic, for example, if a negative emotion is identified. Furthermore, if an emotional state meeting specific conditions is detected, the server notifies a specialist to enable a rapid response.

[0228] When a user uses the system, they send a message using their smartphone's voice input function. This voice message is captured by the device and sent to the server. The server converts the received voice data into text format and performs sentiment analysis.

[0229] As a concrete example, if a user experiences a stressful interaction at the reception desk of an office building, this system can detect the stress level from the conversation and issue a warning to security guards in advance. This process allows reception staff and security guards to take appropriate follow-up actions.

[0230] An example of a prompt message generated using an AI model is one that can be sent to an emotion recognition system for analysis. This prompt could say something like, "Assess the emotional tone of a user complaining at the reception desk and issue a warning if necessary."

[0231] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0232] Step 1:

[0233] The user inputs a voice message through the device. The device receives this voice data and converts it into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is the converted text data.

[0234] Step 2:

[0235] The server receives text data sent from the terminal. The input is text data, and the output is text that can be used directly for analysis. Here, the data is prepared using NLP techniques.

[0236] Step 3:

[0237] The server analyzes the received text data and performs sentiment recognition using the IBM Watson Tone Analyzer API. The input is text data, and the output is data with one of the following sentiment tones identified: positive, negative, or neutral. In this process, the emotional nuances contained in the text are analyzed.

[0238] Step 4:

[0239] The server generates an automated response based on the analysis results. The input is the result of the sentiment analysis, and the output is the response message provided to the user. This response will vary depending on the emotional tone.

[0240] Step 5:

[0241] The server notifies a professional when a specific emotional tone is detected. Input is data about the emotional state and its severity, and output is an alert for the professional. When specified conditions are met, an automated notification is generated to prompt a quick response.

[0242] Step 6:

[0243] The server records all interactions and results and stores them in a database. Inputs are chat history and sentiment data, and outputs are stored in an encrypted database. This storage ensures that data is available for subsequent analysis and quality improvement.

[0244] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0245] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0246] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0247] [Second Embodiment]

[0248] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0249] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0250] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0251] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0252] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0253] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0254] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0255] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0256] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0257] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0258] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0259] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0260] To implement the present invention, it is first necessary to construct an online support system and provide a user-accessible interface. This system will have a secure environment for safely handling user information and will implement key functions such as user registration, login, submission of consultation content, receipt of responses, and switching to an expert.

[0261] Users access the online platform via the internet using their devices. Upon first use, users register their information with the system and create a unique account. This registration information includes username, email address, and password. All information is securely recorded on the server and stored in a database.

[0262] Afterward, the user logs in and can easily begin a consultation via the system's chat interface. The user freely types and sends messages on the chat screen displayed on their device. Messages sent from the device are received by the server and their content is analyzed using natural language processing algorithms.

[0263] The server automatically generates an appropriate response from the analysis results and sends it to the terminal. For general inquiries, it returns a pre-programmed response, enabling a quick response. In addition, when the server detects specific keywords in the user's message, it immediately notifies an expert, prompting a detailed response from the expert.

[0264] Experts receive notifications from the server, participate in chats, and provide users with specific advice and support tailored to their individual situations. Furthermore, the server encrypts all communication history and securely stores it for future reference and data management.

[0265] This provides users with an environment where they can easily seek advice about their mental health concerns, and through professional support, effectively maintain and improve their mental health. Furthermore, this system takes great care to protect personal information, so it can be used with peace of mind.

[0266] The following describes the processing flow.

[0267] Step 1:

[0268] Users access the online support system registration page using their device, enter the required information, and create an account.

[0269] Step 2:

[0270] The terminal sends the entered information to the server for verification to ensure it is in the correct format.

[0271] Step 3:

[0272] The server checks the database to see if there are any duplicates in the received information, and after verification, saves the user information to the database.

[0273] Step 4:

[0274] Users log in using their account and access the system's homepage.

[0275] Step 5:

[0276] The terminal sends the user's authentication information to the server and requests the establishment of a session.

[0277] Step 6:

[0278] The server checks the authentication information from the database, generates session information if a match is found, and grants permission to log in.

[0279] Step 7:

[0280] The user opens the chat screen on the system and enters their inquiry.

[0281] Step 8:

[0282] The device sends the entered chat message to the server.

[0283] Step 9:

[0284] The server analyzes the received message using a natural language processing algorithm and generates an automatic response.

[0285] Step 10:

[0286] The server sends the generated response to the terminal and displays it on the user's chat screen.

[0287] Step 11:

[0288] When the server detects a specific keyword based on the analysis result, it immediately notifies an expert.

[0289] Step 12:

[0290] Upon receiving the expert's notification, the server permits the expert to participate and provides a function to join the chat.

[0291] Step 13:

[0292] The server encrypts all chat histories and user data and stores them in a secure storage.

[0293] (Example 1)

[0294] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0295] In modern society, there is a demand to create an environment where users can easily consult about psychological problems. On the other hand, the protection of personal information and prompt and appropriate responses have become important issues. Also, when specialized responses are required according to the content of the problem, there is a lack of a mechanism to efficiently notify experts. Furthermore, the accurate analysis of users' messages using natural language and the generation of responses based on it are also technical issues.

[0296] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0297] In this invention, the server includes a device for inputting information, a device for analyzing the input message, and a device for generating a response using a generative artificial intelligence model. This enables users to securely register information, process consultation content quickly and appropriately using natural language processing, and promptly notify experts as needed. Furthermore, by encrypting and storing all communication history, it is possible to provide effective support while ensuring the protection of personal information.

[0298] A "device for inputting information" is a device that allows users to provide information such as their name and contact details to a system.

[0299] A "device for recording information" is a device for saving entered user information and accumulating it in a database.

[0300] A "communication device" is a device used by users to send messages to a system and to receive responses from the system.

[0301] A "message analysis device" is a device that uses natural language processing technology to analyze the content of a received message and understand its meaning.

[0302] A "response generation device" is a device that automatically creates an appropriate response for the user based on the analysis results.

[0303] A "device that automatically notifies experts" is a device that notifies experts in real time when specific conditions or keywords are detected.

[0304] A "device for saving and managing history" is a device that encrypts and saves all communication history for later reference and analysis.

[0305] "Natural language processing algorithm" refers to a technology that analyzes the messages input by users into a form that can be understood by computers and finds corresponding solutions.

[0306] "Generative AI model" refers to an AI technology that creates natural language responses based on large amounts of data.

[0307] "Device for detecting and notifying specific phrases" refers to a device that detects important keywords contained in messages and prompts corresponding actions to experts based on them.

[0308] To implement the present invention, first, it is necessary to construct an online support system for users to access. This system is designed to handle user information securely and process it quickly. The following shows its specific embodiments.

[0309] The user uses their terminal to access the online platform via the Internet. First, the user inputs information such as a username, email address, password, etc., and performs a registration operation. This information is stored in the database in a hashed state by the server.

[0310] When the user logs in, a chat interface is displayed on the terminal. Through this interface, the user can input the consultation content and send it to the server. The server analyzes the received message using natural language processing software (e.g., NLTK or SpaCy) and understands its content.

[0311] Based on the analysis results, the server automatically generates a response to the user using a generative AI model (e.g., GPT-3). In this process, the generative AI model creates a natural language response based on a large amount of data and displays it on the user's terminal. As a specific example, when the user sends "I've been stressed lately", the system can reply with advice on stress management.

[0312] Furthermore, the server detects specific keywords and sends real-time notifications to experts. This feature allows experts to respond quickly if the user's input contains important signs.

[0313] All communication history is encrypted for security purposes and securely stored on the server. This feature will be used later for user follow-up and data analysis.

[0314] Examples of prompt messages include, "Please tell me how to cope when I feel anxious." This allows users to easily ask for advice and receive appropriate support.

[0315] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0316] Step 1:

[0317] The user accesses the online support system registration screen using their device. Here, the user enters their name, email address, and password. After entering the information, the user presses the registration button to send the information to the server. The server receives the entered information and stores it in a hashed format in its database. This creates the user's account, allowing them to access the platform.

[0318] Step 2:

[0319] The user accesses the login screen from their device and enters their registered email address and password. The entered information is sent to the server, which retrieves the corresponding user information from the database and verifies the password. If authentication is successful, the server establishes a session with the user and grants them access to the chat interface.

[0320] Step 3:

[0321] The user enters their inquiry into the chat interface on their device and presses the send button. The message is sent to the server. The server analyzes the received text using natural language processing algorithms (e.g., NLTK or SpaCy) to understand the structure and meaning of the text. The results of the analysis are output as semantic extraction and sentiment analysis.

[0322] Step 4:

[0323] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to create an appropriate response. The model leverages historical data to generate a response in natural language. The generated response is output as advice or suggestions to the user and displayed on the terminal's chat screen.

[0324] Step 5:

[0325] The server checks if specific keywords or phrases are included in user messages. If detected, a mechanism is triggered to send a notification to a specialist. This notification is sent in real time, allowing the specialist to prepare to provide expert assistance tailored to the user's situation.

[0326] Step 6:

[0327] All communication history is automatically encrypted on the server and stored in a database. This ensures user privacy and allows the data to be stored and managed in a way that makes it available for future reference and analysis.

[0328] (Application Example 1)

[0329] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0330] In recent years, the importance of mental health has increased, leading to a growing demand for online support systems that allow users to safely receive expert advice. However, current systems lack the flexibility to provide appropriate advice quickly while reducing the psychological burden on users. Furthermore, there is a lack of means to effectively analyze messages from a large volume of users and encourage the participation of experts. Effective methods to address these challenges are needed.

[0331] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0332] In this invention, the server includes means for inputting user information, means for recording the user information, and communication means for registered users to input messages. This allows users to safely and anonymously seek advice on mental health issues and receive appropriate responses quickly using a generative model algorithm. Furthermore, by providing a prompt generation means for the generative model, the accuracy and effectiveness of the responses can be improved, and experts can be automatically notified as needed. This makes it possible to provide more effective and reassuring support to users.

[0333] "Means for inputting user information" refers to input devices or software that allow users to record their own information in a system.

[0334] "Means for recording user information" refers to databases or recording media that centrally manage and securely store the user information entered by the user.

[0335] "Communication means" refers to the network and software that allows a user to input a message via an interface and send that message to a server.

[0336] "Means for analyzing input messages" refers to software that uses natural language processing algorithms to process text messages from users and understand their content.

[0337] "Means for automatically generating responses" refers to generative model algorithms that automatically create appropriate responses based on analysis results.

[0338] "Means of providing a response to the user" refers to software or hardware for displaying automatically generated responses on the user's device.

[0339] A "means of automatically notifying experts" refers to a notification system that sends alerts to experts when certain conditions are met.

[0340] "A means of providing advice" refers to a communication interface that allows experts to provide specific advice tailored to the user's situation in real time via chat.

[0341] "Means for saving and managing communication history" refers to a data management system that securely records interactions with users and makes them available for later reference.

[0342] "Methods that utilize generative model algorithms" refer to techniques that use artificial intelligence to optimize data when generating responses, thereby improving the quality of the responses.

[0343] A "prompt generation means" is a mechanism for creating inputs that provide effective instructions to the generative model and for optimizing the model's response generation.

[0344] A system for carrying out this invention is constructed to include a terminal for inputting user information and communicating, a server for processing data, and a generative model algorithm for providing responses to the user.

[0345] The user begins by entering their information. They input this information using software running on their device, and this information is transmitted to the server via the internet. The server securely records the received user information in a database and protects it using encryption technology as needed.

[0346] Users input questions and inquiries as messages through their terminals and send them to the server. The server analyzes these messages using natural language processing (NLTK) techniques. Natural language processing libraries such as Python's NLTK and spaCy can be used for analysis. Based on the results of this analysis, the server automatically generates an appropriate response. Generative AI models can be used for this response generation; for example, OpenAI's GPT and Hugging Face's Transformers are available.

[0347] The response is generated by providing a prompt to the generative model. An example of a specific prompt is: "Generate an appropriate response based on the message received from the user." After generating the response, the server sends it to the user's terminal.

[0348] If the analysis results meet certain conditions, the server automatically sends a notification to a specialist. The specialist interacts with the user in real time via the terminal interface and provides specific advice. All communication history is securely stored on the server in a format that can be referenced later. Through this process, users can seek mental health advice anonymously and receive prompt and accurate support from specialists.

[0349] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0350] Step 1:

[0351] The user enters information using a terminal. This information includes usernames, email addresses, and passwords, and this data is sent to the server through the input interface. The server records the received data in a database using a secure protocol and manages it securely.

[0352] Step 2:

[0353] The user enters their inquiry as a message using their device. This message is sent from the device to the server. The server analyzes the received message using a natural language processing algorithm. This analysis examines the syntax and meaning of the message and extracts the information necessary for the next step.

[0354] Step 3:

[0355] The server generates a response using a generative AI model based on the analysis results. The generative AI model receives extracted keywords and message context as prompts and outputs a natural language response accordingly. In this case, the prompt used is "Generate an appropriate response based on the message received from the user."

[0356] Step 4:

[0357] The server sends the generated response to the user's terminal. The user can view this response on the terminal's interface. The response is displayed as text, and the user can continue with additional questions or consultations as needed.

[0358] Step 5:

[0359] After generating a response, the server automatically sends notifications to experts based on the analysis results and circumstances. If specific keywords or phrases are detected, experts are prompted to join the chat. This notification is in real time, allowing experts to immediately begin interacting with the user.

[0360] Step 6:

[0361] All communication history with users is stored on the server. This historical data is encrypted and recorded so that experts and system administrators can refer to it later. The stored data helps to provide more accurate advice by utilizing the user's past consultation history.

[0362] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0363] This invention realizes a form of online support system that combines emotion recognition functionality. This system analyzes the emotions contained in messages entered by users in real time, adjusts responses, and notifies experts as needed, thereby providing more effective support.

[0364] When a user accesses the online platform through their device, the system first registers the user's information and initiates an individual session. The user enters their consultation details into the chat interface. The device sends the entered data to the server. The server analyzes the received message and uses natural language processing technology to analyze the emotions contained in the message using an emotion engine. This emotion engine identifies emotional tones such as positive, negative, and neutral from the text.

[0365] Based on the analyzed sentiment data, the server adjusts its response. For example, if a message indicates negative emotions, the server generates a more supportive and empathetic response. The server also automatically notifies a professional if a specific emotional state is continuously recognized, as needed. This allows the professional to review the consultation and intervene quickly to provide the user with appropriate advice.

[0366] For example, if a user enters "I've been feeling sad and unmotivated lately," the server recognizes this message as a negative emotion and generates and sends a response of encouragement and acceptance to the user. Depending on the situation, this information may also be notified to a specialist, creating a system where the specialist can provide more detailed support to the user.

[0367] The server encrypts and securely stores all chat data and sentiment analysis history, fully protecting user privacy and using this data to continuously improve the system's analysis quality.

[0368] Thus, the present invention provides a support environment that improves the user experience by understanding user emotions and optimizing responses and support.

[0369] The following describes the processing flow.

[0370] Step 1:

[0371] Users access the online support system using their device and, for first-time users, create an account by entering the required information on the registration page.

[0372] Step 2:

[0373] The terminal sends the user's registration information to the server, which then securely stores that information in a database.

[0374] Step 3:

[0375] The user logs in with the account they created and accesses the system dashboard.

[0376] Step 4:

[0377] The terminal sends the user's authentication information to the server, which then verifies it against the database to perform authentication and initiate the session.

[0378] Step 5:

[0379] The user opens the chat screen and enters their question as text.

[0380] Step 6:

[0381] The terminal sends the message entered by the user to the server.

[0382] Step 7:

[0383] The server analyzes received messages using natural language processing techniques and identifies emotions from the text using an emotion engine.

[0384] Step 8:

[0385] The emotion engine classifies the emotional tone in a message as positive, negative, neutral, etc., and provides that information to the server.

[0386] Step 9:

[0387] The server adjusts its response based on emotional data. For example, if negative emotions are strong, it will generate encouraging or accepting responses.

[0388] Step 10:

[0389] The server sends the adjusted response to the terminal, which then displays it on the user's chat screen.

[0390] Step 11:

[0391] If a specific emotional state, such as persistent negative emotions, is detected, the server automatically notifies a specialist.

[0392] Step 12:

[0393] Experts receive notifications from the server, join the chat, and provide users with reliable advice in real time.

[0394] Step 13:

[0395] The server encrypts all chat history and sentiment analysis results, stores them in secure storage, and protects privacy while also providing them for future analysis.

[0396] (Example 2)

[0397] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0398] In modern society, online support systems play a crucial role. However, many existing systems struggle to adequately recognize and respond to users' emotions. In particular, there is often a lack of continuous recognition of negative emotions experienced by users and appropriate professional intervention to address them. As a result, users may feel dissatisfied and anxious because they do not receive the support they need, which can undermine their trust in the system.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0400] In this invention, the server includes means for identifying emotions from information using natural language processing technology, means for notifying experts when a specific emotional tone is continuously recognized, and means for protecting, encrypting, and storing the user's communication history. This makes it possible to analyze the user's emotions in an advanced way and generate appropriate responses accordingly, as well as to quickly notify experts of the situation as needed and provide accurate support to the user.

[0401] "Means for inputting user information" refers to an interface that allows users to provide data about themselves to the system.

[0402] "Means of recording" refers to a function that retains the user's entered information and stores it so that it can be referenced as needed.

[0403] "Communication means" refers to a device or program for mutual communication between a user and a system, enabling the user to send information to the system and receive a response from the system.

[0404] "Means of analysis" refers to the process of analyzing received information and understanding its content and context.

[0405] "Methods for identifying emotions from information using natural language processing technology" refers to technologies that use generative AI models or emotion engines to identify emotions within text.

[0406] A "means for generating responses" is a mechanism that automatically creates appropriate responses or messages based on analyzed information.

[0407] "Means of notification" refers to a method of sending warnings or information to experts or other relevant parties when certain conditions are met.

[0408] "Means of providing advice" refers to a system for experts to provide guidance and suggestions to users.

[0409] "Means of preservation" refers to a system that securely stores communication history and sentiment analysis data and manages it so that it can be accessed later.

[0410] A "generative AI model" is an algorithm or system that uses artificial intelligence, trained on a large amount of data, to understand and manipulate natural language.

[0411] A "prompt message" is a type of text that serves as input instructions for an AI model, used to instruct the AI ​​to perform a specific task.

[0412] This system aims to analyze user emotions in real time and adjust responses within an online support platform. The following describes the configuration for implementing this system.

[0413] Users access the online platform using devices such as PCs and smartphones. There, users enter their questions into a text box. The device then transmits the entered message to the server via a communication method.

[0414] The server analyzes received messages using natural language processing techniques. Specifically, it employs a generative AI model to understand the content and context of the message, and then uses an emotion engine to identify emotional tones such as positive, negative, and neutral. In the analysis process, a general-purpose natural language processing software can be used as the emotion engine.

[0415] Based on the analysis results, the server automatically generates a response. The generated response is tailored to the user's emotions. For example, if the user's message expresses a negative emotion such as "I've been feeling sad and unmotivated lately," an empathetic and encouraging response will be generated. Furthermore, if the same negative emotion is continuously recognized, the server automatically notifies a professional. This notification allows the professional to quickly provide appropriate advice to the user.

[0416] Data security is also a consideration; the server encrypts and securely stores all communication history and sentiment analysis history. This data can be used to improve the system's analytical capabilities while ensuring user privacy.

[0417] An example of a prompt would be an instruction used for a generative AI model such as, "Detect the emotion in the user's message and provide a corresponding response. For example, if the user says, 'I've been feeling sad and unmotivated lately,' create an empathetic response."

[0418] In this way, the system can improve the user experience by taking user emotions into consideration and providing optimized support.

[0419] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0420] Step 1:

[0421] Users access the online platform using their devices and enter their inquiries via a chat interface. The entered text data is sent from the device to the server upon triggering a send action. The input is a text message, and the output is the message data that reaches the server.

[0422] Step 2:

[0423] The server analyzes message data received from the terminal. First, a generative AI model analyzes the user's text message using natural language processing techniques to understand the information and context. Here, the input is the message data, and the output is the contextual information necessary for sentiment analysis.

[0424] Step 3:

[0425] The server uses a generative AI model and an emotion engine to identify emotional tones from the analyzed contextual information. In this process, the emotion engine classifies the message as having a positive, negative, or neutral tone. The input is contextual information, and the output is the identified emotional tone.

[0426] Step 4:

[0427] The server automatically generates an appropriate response based on the identified emotional tone. If a negative emotion is detected, a generative AI model generates an empathetic and supportive response, which is then prepared as text data. The input is the emotional tone, and the output is the generated response text.

[0428] Step 5:

[0429] The server sends the generated response back to the terminal for display to the user. The response is displayed to the user via a chat interface on the terminal. The input is the generated response text, and the output is the response presented visually to the user.

[0430] Step 6:

[0431] The server notifies a professional if a specific emotional tone is continuously detected, as needed. This notification occurs when certain trigger conditions are met, enabling the professional to quickly provide appropriate advice to the user. The input is the continuously detected emotional tone, and the output is the notification to the professional.

[0432] Step 7:

[0433] The server encrypts and stores all communication history and sentiment analysis data to protect user privacy. This allows for the secure storage of data for future analysis and system improvements. The input is communication history and analysis data, and the output is encrypted data storage.

[0434] (Application Example 2)

[0435] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0436] Traditional online support systems have faced challenges in fully understanding and responding immediately to users' emotional states, as well as in anticipating and appropriately managing emotional upsets. Therefore, there has been a need to provide rapid support when users experience anxiety or stress. Furthermore, to ensure user safety, there is a demand for analyzing voice data to detect abnormal emotions.

[0437] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0438] In this invention, the server includes means for inputting user information, means for acquiring voice data and converting it to text, and means for analyzing the converted text and identifying emotions. This makes it possible to analyze the user's emotional state in real time, quickly identify emotions indicating stress or anxiety, and notify experts while issuing necessary warnings.

[0439] "Means for inputting user information" refers to an interface on an online platform that allows users to input their personal identification information and individual data into the system.

[0440] "Means for recording user information" refers to a mechanism for securely and accurately storing user data entered on a server.

[0441] "Communication means" refers to the system component that allows registered users to input messages and send and receive those messages to and from the server.

[0442] "Means for analyzing input messages" refers to a module that uses analysis techniques to process text messages sent by users and understand their content and intent.

[0443] "Means for automatically generating responses" refers to a process in which a computer generates an appropriate answer based on the content of an analyzed message and provides it to the user.

[0444] "Means of providing a response to the user" refers to a method of sending the generated response to the user's device and making it easily accessible to the user.

[0445] "Means for automatically notifying experts" refers to technology that automatically sends alerts to experts under specific conditions.

[0446] "Means for experts to provide advice to users" refers to an access mechanism for experts to contact users and provide them with professional support regarding their questions and problems.

[0447] "Means for saving and managing user communication history" refers to database technology for recording and securely storing user operation history and messages when using a system.

[0448] "Means for acquiring audio data and converting it to text" refers to speech recognition software that recognizes speech and converts it into a corresponding text format.

[0449] "Methods for analyzing converted text and identifying emotions" refers to the process of identifying emotional tones, such as positive or negative, by analyzing the emotional nuances contained in the text data.

[0450] A "warning mechanism" is an alert system designed to alert users or administrators when certain emotional states or criteria are reached.

[0451] In this invention, the server uses software to convert speech data into text data. Specifically, it can utilize speech recognition services such as the Google Cloud Speech-to-Text API. The converted text is then subjected to sentiment analysis using natural language processing (NLP) techniques such as the IBM Watson Tone Analyzer API. This identifies whether the emotional tone contained in the text is positive, negative, or neutral. Based on the analysis results, the server generates a response to the user. This response will be supportive and empathetic, for example, if a negative emotion is identified. Furthermore, if an emotional state meeting specific conditions is detected, the server notifies a specialist to enable a rapid response.

[0452] When a user uses the system, they send a message using their smartphone's voice input function. This voice message is captured by the device and sent to the server. The server converts the received voice data into text format and performs sentiment analysis.

[0453] As a concrete example, if a user experiences a stressful interaction at the reception desk of an office building, this system can detect the stress level from the conversation and issue a warning to security guards in advance. This process allows reception staff and security guards to take appropriate follow-up actions.

[0454] An example of a prompt message generated using an AI model is one that can be sent to an emotion recognition system for analysis. This prompt could say something like, "Assess the emotional tone of a user complaining at the reception desk and issue a warning if necessary."

[0455] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0456] Step 1:

[0457] The user inputs a voice message through the device. The device receives this voice data and converts it into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is the converted text data.

[0458] Step 2:

[0459] The server receives text data sent from the terminal. The input is text data, and the output is text that can be used directly for analysis. Here, the data is prepared using NLP techniques.

[0460] Step 3:

[0461] The server analyzes the received text data and performs sentiment recognition using the IBM Watson Tone Analyzer API. The input is text data, and the output is data with one of the following sentiment tones identified: positive, negative, or neutral. In this process, the emotional nuances contained in the text are analyzed.

[0462] Step 4:

[0463] The server generates an automated response based on the analysis results. The input is the result of the sentiment analysis, and the output is the response message provided to the user. This response will vary depending on the emotional tone.

[0464] Step 5:

[0465] The server notifies a professional when a specific emotional tone is detected. Input is data about the emotional state and its severity, and output is an alert for the professional. When specified conditions are met, an automated notification is generated to prompt a quick response.

[0466] Step 6:

[0467] The server records all interactions and results and stores them in a database. Inputs are chat history and sentiment data, and outputs are stored in an encrypted database. This storage ensures that data is available for subsequent analysis and quality improvement.

[0468] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0469] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0470] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0471] [Third Embodiment]

[0472] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0473] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0474] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0475] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0476] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0477] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0478] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0479] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0480] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0481] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0482] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0483] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0484] To implement the present invention, it is first necessary to construct an online support system and provide a user-accessible interface. This system will have a secure environment for safely handling user information and will implement key functions such as user registration, login, submission of consultation content, receipt of responses, and switching to an expert.

[0485] Users access the online platform via the internet using their devices. Upon first use, users register their information with the system and create a unique account. This registration information includes username, email address, and password. All information is securely recorded on the server and stored in a database.

[0486] Afterward, the user logs in and can easily begin a consultation via the system's chat interface. The user freely types and sends messages on the chat screen displayed on their device. Messages sent from the device are received by the server and their content is analyzed using natural language processing algorithms.

[0487] The server automatically generates an appropriate response from the analysis results and sends it to the terminal. For general inquiries, it returns a pre-programmed response, enabling a quick response. In addition, when the server detects specific keywords in the user's message, it immediately notifies an expert, prompting a detailed response from the expert.

[0488] Experts receive notifications from the server, participate in chats, and provide users with specific advice and support tailored to their individual situations. Furthermore, the server encrypts all communication history and securely stores it for future reference and data management.

[0489] This provides users with an environment where they can easily seek advice about their mental health concerns, and through professional support, effectively maintain and improve their mental health. Furthermore, this system takes great care to protect personal information, so it can be used with peace of mind.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] Users access the online support system registration page using their device, enter the required information, and create an account.

[0493] Step 2:

[0494] The terminal sends the entered information to the server for verification to ensure it is in the correct format.

[0495] Step 3:

[0496] The server checks the database to see if there are any duplicates in the received information, and after verification, saves the user information to the database.

[0497] Step 4:

[0498] Users log in using their account and access the system's homepage.

[0499] Step 5:

[0500] The terminal sends the user's authentication information to the server and requests the establishment of a session.

[0501] Step 6:

[0502] The server checks the authentication information from the database, generates session information if a match is found, and grants permission to log in.

[0503] Step 7:

[0504] The user opens the chat screen on the system and enters their inquiry.

[0505] Step 8:

[0506] The device sends the entered chat message to the server.

[0507] Step 9:

[0508] The server analyzes the received message using a natural language processing algorithm and generates an automated response.

[0509] Step 10:

[0510] The server sends the generated response to the terminal, where it is displayed on the user's chat screen.

[0511] Step 11:

[0512] If the server detects specific keywords based on the analysis results, it immediately notifies the experts.

[0513] Step 12:

[0514] Upon receiving notification from an expert, the server will allow the expert to participate and provide them with the ability to join the chat.

[0515] Step 13:

[0516] The server encrypts all chat history and user data and stores it in secure storage.

[0517] (Example 1)

[0518] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] In modern society, there is a need to create an environment where users can easily consult about psychological problems, but at the same time, protecting personal information and responding quickly and appropriately remain important challenges. Furthermore, there is a lack of mechanisms to efficiently notify experts when specialized attention is required depending on the nature of the problem. In addition, accurately analyzing user messages using natural language and generating responses based on that analysis is a technical challenge.

[0520] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0521] In this invention, the server includes a device for inputting information, a device for analyzing the input message, and a device for generating a response using a generative artificial intelligence model. This enables users to securely register information, process consultation content quickly and appropriately using natural language processing, and promptly notify experts as needed. Furthermore, by encrypting and storing all communication history, it is possible to provide effective support while ensuring the protection of personal information.

[0522] A "device for inputting information" is a device that allows users to provide information such as their name and contact details to a system.

[0523] A "device for recording information" is a device for saving entered user information and accumulating it in a database.

[0524] A "communication device" is a device used by users to send messages to a system and to receive responses from the system.

[0525] A "message analysis device" is a device that uses natural language processing technology to analyze the content of a received message and understand its meaning.

[0526] A "response generation device" is a device that automatically creates an appropriate response for the user based on the analysis results.

[0527] A "device that automatically notifies experts" is a device that notifies experts in real time when specific conditions or keywords are detected.

[0528] A "device for saving and managing history" is a device that encrypts and saves all communication history for later reference and analysis.

[0529] A "natural language processing algorithm" is a technology that analyzes messages entered by users into a form that a computer can understand and then finds solutions.

[0530] A "generative artificial intelligence model" is an artificial intelligence technology that creates natural language responses based on large amounts of data.

[0531] A "device that detects and notifies specific words or phrases" is a device that detects important keywords contained in a message and prompts experts to take action based on those keywords.

[0532] In order to implement the present invention, it is first necessary to construct an online support system for users to access. This system is designed to handle user information securely and process it quickly. A specific embodiment of this system is shown below.

[0533] Users access the online platform via the internet using their own devices. First, users register by entering information such as their username, email address, and password. This information is stored in a database in a hashed state by the server.

[0534] When a user logs in, a chat interface appears on their device. Through this interface, the user can input their inquiry and send it to the server. The server uses natural language processing software (such as NLTK or SpaCy) to analyze the received message and understand its content.

[0535] Based on the analysis results, the server automatically generates a response to the user using a generative AI model (e.g., GPT-3). In this process, the generative AI model creates a natural language response based on a large amount of data and displays it on the user's device. For example, if a user sends "I've been feeling stressed lately," the system can reply with advice on stress management.

[0536] Furthermore, the server detects specific keywords and sends real-time notifications to experts. This feature allows experts to respond quickly if the user's input contains important signs.

[0537] All communication history is encrypted for security purposes and securely stored on the server. This feature will be used later for user follow-up and data analysis.

[0538] Examples of prompt messages include, "Please tell me how to cope when I feel anxious." This allows users to easily ask for advice and receive appropriate support.

[0539] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0540] Step 1:

[0541] The user accesses the online support system registration screen using their device. Here, the user enters their name, email address, and password. After entering the information, the user presses the registration button to send the information to the server. The server receives the entered information and stores it in a hashed format in its database. This creates the user's account, allowing them to access the platform.

[0542] Step 2:

[0543] The user accesses the login screen from their device and enters their registered email address and password. The entered information is sent to the server, which retrieves the corresponding user information from the database and verifies the password. If authentication is successful, the server establishes a session with the user and grants them access to the chat interface.

[0544] Step 3:

[0545] The user enters their inquiry into the chat interface on their device and presses the send button. The message is sent to the server. The server analyzes the received text using natural language processing algorithms (e.g., NLTK or SpaCy) to understand the structure and meaning of the text. The results of the analysis are output as semantic extraction and sentiment analysis.

[0546] Step 4:

[0547] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to create an appropriate response. The model leverages historical data to generate a response in natural language. The generated response is output as advice or suggestions to the user and displayed on the terminal's chat screen.

[0548] Step 5:

[0549] The server checks if specific keywords or phrases are included in user messages. If detected, a mechanism is triggered to send a notification to a specialist. This notification is sent in real time, allowing the specialist to prepare to provide expert assistance tailored to the user's situation.

[0550] Step 6:

[0551] All communication history is automatically encrypted on the server and stored in a database. This ensures user privacy and allows the data to be stored and managed in a way that makes it available for future reference and analysis.

[0552] (Application Example 1)

[0553] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] In recent years, the importance of mental health has increased, leading to a growing demand for online support systems that allow users to safely receive expert advice. However, current systems lack the flexibility to provide appropriate advice quickly while reducing the psychological burden on users. Furthermore, there is a lack of means to effectively analyze messages from a large volume of users and encourage the participation of experts. Effective methods to address these challenges are needed.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0556] In this invention, the server includes means for inputting user information, means for recording the user information, and communication means for registered users to input messages. This allows users to safely and anonymously seek advice on mental health issues and receive appropriate responses quickly using a generative model algorithm. Furthermore, by providing a prompt generation means for the generative model, the accuracy and effectiveness of the responses can be improved, and experts can be automatically notified as needed. This makes it possible to provide more effective and reassuring support to users.

[0557] "Means for inputting user information" refers to input devices or software that allow users to record their own information in a system.

[0558] "Means for recording user information" refers to databases or recording media that centrally manage and securely store the user information entered by the user.

[0559] "Communication means" refers to the network and software that allows a user to input a message via an interface and send that message to a server.

[0560] "Means for analyzing input messages" refers to software that uses natural language processing algorithms to process text messages from users and understand their content.

[0561] "Means for automatically generating responses" refers to generative model algorithms that automatically create appropriate responses based on analysis results.

[0562] "Means of providing a response to the user" refers to software or hardware for displaying automatically generated responses on the user's device.

[0563] A "means of automatically notifying experts" refers to a notification system that sends alerts to experts when certain conditions are met.

[0564] "A means of providing advice" refers to a communication interface that allows experts to provide specific advice tailored to the user's situation in real time via chat.

[0565] "Means for saving and managing communication history" refers to a data management system that securely records interactions with users and makes them available for later reference.

[0566] "Methods that utilize generative model algorithms" refer to techniques that use artificial intelligence to optimize data when generating responses, thereby improving the quality of the responses.

[0567] A "prompt generation means" is a mechanism for creating inputs that provide effective instructions to the generative model and for optimizing the model's response generation.

[0568] A system for carrying out this invention is constructed to include a terminal for inputting user information and communicating, a server for processing data, and a generative model algorithm for providing responses to the user.

[0569] The user begins by entering their information. They input this information using software running on their device, and this information is transmitted to the server via the internet. The server securely records the received user information in a database and protects it using encryption technology as needed.

[0570] Users input questions and inquiries as messages through their terminals and send them to the server. The server analyzes these messages using natural language processing (NLTK) techniques. Natural language processing libraries such as Python's NLTK and spaCy can be used for analysis. Based on the results of this analysis, the server automatically generates an appropriate response. Generative AI models can be used for this response generation; for example, OpenAI's GPT and Hugging Face's Transformers are available.

[0571] The response is generated by providing a prompt to the generative model. An example of a specific prompt is: "Generate an appropriate response based on the message received from the user." After generating the response, the server sends it to the user's terminal.

[0572] If the analysis results meet certain conditions, the server automatically sends a notification to a specialist. The specialist interacts with the user in real time via the terminal interface and provides specific advice. All communication history is securely stored on the server in a format that can be referenced later. Through this process, users can seek mental health advice anonymously and receive prompt and accurate support from specialists.

[0573] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0574] Step 1:

[0575] The user enters information using a terminal. This information includes usernames, email addresses, and passwords, and this data is sent to the server through the input interface. The server records the received data in a database using a secure protocol and manages it securely.

[0576] Step 2:

[0577] The user enters their inquiry as a message using their device. This message is sent from the device to the server. The server analyzes the received message using a natural language processing algorithm. This analysis examines the syntax and meaning of the message and extracts the information necessary for the next step.

[0578] Step 3:

[0579] The server generates a response using a generative AI model based on the analysis results. The generative AI model receives extracted keywords and message context as prompts and outputs a natural language response accordingly. In this case, the prompt used is "Generate an appropriate response based on the message received from the user."

[0580] Step 4:

[0581] The server sends the generated response to the user's terminal. The user can view this response on the terminal's interface. The response is displayed as text, and the user can continue with additional questions or consultations as needed.

[0582] Step 5:

[0583] After generating a response, the server automatically sends notifications to experts based on the analysis results and circumstances. If specific keywords or phrases are detected, experts are prompted to join the chat. This notification is in real time, allowing experts to immediately begin interacting with the user.

[0584] Step 6:

[0585] All communication history with users is stored on the server. This historical data is encrypted and recorded so that experts and system administrators can refer to it later. The stored data helps to provide more accurate advice by utilizing the user's past consultation history.

[0586] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0587] This invention realizes a form of online support system that combines emotion recognition functionality. This system analyzes the emotions contained in messages entered by users in real time, adjusts responses, and notifies experts as needed, thereby providing more effective support.

[0588] When a user accesses the online platform through their device, the system first registers the user's information and initiates an individual session. The user enters their consultation details into the chat interface. The device sends the entered data to the server. The server analyzes the received message and uses natural language processing technology to analyze the emotions contained in the message using an emotion engine. This emotion engine identifies emotional tones such as positive, negative, and neutral from the text.

[0589] Based on the analyzed sentiment data, the server adjusts its response. For example, if a message indicates negative emotions, the server generates a more supportive and empathetic response. The server also automatically notifies a professional if a specific emotional state is continuously recognized, as needed. This allows the professional to review the consultation and intervene quickly to provide the user with appropriate advice.

[0590] For example, if a user enters "I've been feeling sad and unmotivated lately," the server recognizes this message as a negative emotion and generates and sends a response of encouragement and acceptance to the user. Depending on the situation, this information may also be notified to a specialist, creating a system where the specialist can provide more detailed support to the user.

[0591] The server encrypts and securely stores all chat data and sentiment analysis history, fully protecting user privacy and using this data to continuously improve the system's analysis quality.

[0592] Thus, the present invention provides a support environment that improves the user experience by understanding user emotions and optimizing responses and support.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] Users access the online support system using their device and, for first-time users, create an account by entering the required information on the registration page.

[0596] Step 2:

[0597] The terminal sends the user's registration information to the server, which then securely stores that information in a database.

[0598] Step 3:

[0599] The user logs in with the account they created and accesses the system dashboard.

[0600] Step 4:

[0601] The terminal sends the user's authentication information to the server, which then verifies it against the database to perform authentication and initiate the session.

[0602] Step 5:

[0603] The user opens the chat screen and enters their question as text.

[0604] Step 6:

[0605] The terminal sends the message entered by the user to the server.

[0606] Step 7:

[0607] The server analyzes received messages using natural language processing techniques and identifies emotions from the text using an emotion engine.

[0608] Step 8:

[0609] The emotion engine classifies the emotional tone in a message as positive, negative, neutral, etc., and provides that information to the server.

[0610] Step 9:

[0611] The server adjusts its response based on emotional data. For example, if negative emotions are strong, it will generate encouraging or accepting responses.

[0612] Step 10:

[0613] The server sends the adjusted response to the terminal, which then displays it on the user's chat screen.

[0614] Step 11:

[0615] If a specific emotional state, such as persistent negative emotions, is detected, the server automatically notifies a specialist.

[0616] Step 12:

[0617] Experts receive notifications from the server, join the chat, and provide users with reliable advice in real time.

[0618] Step 13:

[0619] The server encrypts all chat history and sentiment analysis results, stores them in secure storage, and protects privacy while also providing them for future analysis.

[0620] (Example 2)

[0621] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0622] In modern society, online support systems play a crucial role. However, many existing systems struggle to adequately recognize and respond to users' emotions. In particular, there is often a lack of continuous recognition of negative emotions experienced by users and appropriate professional intervention to address them. As a result, users may feel dissatisfied and anxious because they do not receive the support they need, which can undermine their trust in the system.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0624] In this invention, the server includes means for identifying emotions from information using natural language processing technology, means for notifying experts when a specific emotional tone is continuously recognized, and means for protecting, encrypting, and storing the user's communication history. This makes it possible to analyze the user's emotions in an advanced way and generate appropriate responses accordingly, as well as to quickly notify experts of the situation as needed and provide accurate support to the user.

[0625] "Means for inputting user information" refers to an interface that allows users to provide data about themselves to the system.

[0626] "Means of recording" refers to a function that retains the user's entered information and stores it so that it can be referenced as needed.

[0627] "Communication means" refers to a device or program for mutual communication between a user and a system, enabling the user to send information to the system and receive a response from the system.

[0628] "Means of analysis" refers to the process of analyzing received information and understanding its content and context.

[0629] "Methods for identifying emotions from information using natural language processing technology" refers to technologies that use generative AI models or emotion engines to identify emotions within text.

[0630] A "means for generating responses" is a mechanism that automatically creates appropriate responses or messages based on analyzed information.

[0631] "Means of notification" refers to a method of sending warnings or information to experts or other relevant parties when certain conditions are met.

[0632] "Means of providing advice" refers to a system for experts to provide guidance and suggestions to users.

[0633] "Means of preservation" refers to a system that securely stores communication history and sentiment analysis data and manages it so that it can be accessed later.

[0634] A "generative AI model" is an algorithm or system that uses artificial intelligence, trained on a large amount of data, to understand and manipulate natural language.

[0635] A "prompt message" is a type of text that serves as input instructions for an AI model, used to instruct the AI ​​to perform a specific task.

[0636] This system aims to analyze user emotions in real time and adjust responses within an online support platform. The following describes the configuration for implementing this system.

[0637] Users access the online platform using devices such as PCs and smartphones. There, users enter their questions into a text box. The device then transmits the entered message to the server via a communication method.

[0638] The server analyzes received messages using natural language processing techniques. Specifically, it employs a generative AI model to understand the content and context of the message, and then uses an emotion engine to identify emotional tones such as positive, negative, and neutral. In the analysis process, a general-purpose natural language processing software can be used as the emotion engine.

[0639] Based on the analysis results, the server automatically generates a response. The generated response is tailored to the user's emotions. For example, if the user's message expresses a negative emotion such as "I've been feeling sad and unmotivated lately," an empathetic and encouraging response will be generated. Furthermore, if the same negative emotion is continuously recognized, the server automatically notifies a professional. This notification allows the professional to quickly provide appropriate advice to the user.

[0640] Data security is also a consideration; the server encrypts and securely stores all communication history and sentiment analysis history. This data can be used to improve the system's analytical capabilities while ensuring user privacy.

[0641] An example of a prompt would be an instruction used for a generative AI model such as, "Detect the emotion in the user's message and provide a corresponding response. For example, if the user says, 'I've been feeling sad and unmotivated lately,' create an empathetic response."

[0642] In this way, the system can improve the user experience by taking user emotions into consideration and providing optimized support.

[0643] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0644] Step 1:

[0645] Users access the online platform using their devices and enter their inquiries via a chat interface. The entered text data is sent from the device to the server upon triggering a send action. The input is a text message, and the output is the message data that reaches the server.

[0646] Step 2:

[0647] The server analyzes message data received from the terminal. First, a generative AI model analyzes the user's text message using natural language processing techniques to understand the information and context. Here, the input is the message data, and the output is the contextual information necessary for sentiment analysis.

[0648] Step 3:

[0649] The server uses a generative AI model and an emotion engine to identify emotional tones from the analyzed contextual information. In this process, the emotion engine classifies the message as having a positive, negative, or neutral tone. The input is contextual information, and the output is the identified emotional tone.

[0650] Step 4:

[0651] The server automatically generates an appropriate response based on the identified emotional tone. If a negative emotion is detected, a generative AI model generates an empathetic and supportive response, which is then prepared as text data. The input is the emotional tone, and the output is the generated response text.

[0652] Step 5:

[0653] The server sends the generated response back to the terminal for display to the user. The response is displayed to the user via a chat interface on the terminal. The input is the generated response text, and the output is the response presented visually to the user.

[0654] Step 6:

[0655] The server notifies a professional if a specific emotional tone is continuously detected, as needed. This notification occurs when certain trigger conditions are met, enabling the professional to quickly provide appropriate advice to the user. The input is the continuously detected emotional tone, and the output is the notification to the professional.

[0656] Step 7:

[0657] The server encrypts and stores all communication history and sentiment analysis data to protect user privacy. This allows for the secure storage of data for future analysis and system improvements. The input is communication history and analysis data, and the output is encrypted data storage.

[0658] (Application Example 2)

[0659] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0660] Traditional online support systems have faced challenges in fully understanding and responding immediately to users' emotional states, as well as in anticipating and appropriately managing emotional upsets. Therefore, there has been a need to provide rapid support when users experience anxiety or stress. Furthermore, to ensure user safety, there is a demand for analyzing voice data to detect abnormal emotions.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0662] In this invention, the server includes means for inputting user information, means for acquiring voice data and converting it to text, and means for analyzing the converted text and identifying emotions. This makes it possible to analyze the user's emotional state in real time, quickly identify emotions indicating stress or anxiety, and notify experts while issuing necessary warnings.

[0663] "Means for inputting user information" refers to an interface on an online platform that allows users to input their personal identification information and individual data into the system.

[0664] "Means for recording user information" refers to a mechanism for securely and accurately storing user data entered on a server.

[0665] "Communication means" refers to the system component that allows registered users to input messages and send and receive those messages to and from the server.

[0666] "Means for analyzing input messages" refers to a module that uses analysis techniques to process text messages sent by users and understand their content and intent.

[0667] "Means for automatically generating responses" refers to a process in which a computer generates an appropriate answer based on the content of an analyzed message and provides it to the user.

[0668] "Means of providing a response to the user" refers to a method of sending the generated response to the user's device and making it easily accessible to the user.

[0669] "Means for automatically notifying experts" refers to technology that automatically sends alerts to experts under specific conditions.

[0670] "Means for experts to provide advice to users" refers to an access mechanism for experts to contact users and provide them with professional support regarding their questions and problems.

[0671] "Means for saving and managing user communication history" refers to database technology for recording and securely storing user operation history and messages when using a system.

[0672] "Means for acquiring audio data and converting it to text" refers to speech recognition software that recognizes speech and converts it into a corresponding text format.

[0673] "Methods for analyzing converted text and identifying emotions" refers to the process of identifying emotional tones, such as positive or negative, by analyzing the emotional nuances contained in the text data.

[0674] A "warning mechanism" is an alert system designed to alert users or administrators when certain emotional states or criteria are reached.

[0675] In this invention, the server uses software to convert speech data into text data. Specifically, it can utilize speech recognition services such as the Google Cloud Speech-to-Text API. The converted text is then subjected to sentiment analysis using natural language processing (NLP) techniques such as the IBM Watson Tone Analyzer API. This identifies whether the emotional tone contained in the text is positive, negative, or neutral. Based on the analysis results, the server generates a response to the user. This response will be supportive and empathetic, for example, if a negative emotion is identified. Furthermore, if an emotional state meeting specific conditions is detected, the server notifies a specialist to enable a rapid response.

[0676] When a user uses the system, they send a message using their smartphone's voice input function. This voice message is captured by the device and sent to the server. The server converts the received voice data into text format and performs sentiment analysis.

[0677] As a concrete example, if a user experiences a stressful interaction at the reception desk of an office building, this system can detect the stress level from the conversation and issue a warning to security guards in advance. This process allows reception staff and security guards to take appropriate follow-up actions.

[0678] An example of a prompt message generated using an AI model is one that can be sent to an emotion recognition system for analysis. This prompt could say something like, "Assess the emotional tone of a user complaining at the reception desk and issue a warning if necessary."

[0679] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0680] Step 1:

[0681] The user inputs a voice message through the device. The device receives this voice data and converts it into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is the converted text data.

[0682] Step 2:

[0683] The server receives text data sent from the terminal. The input is text data, and the output is text that can be used directly for analysis. Here, the data is prepared using NLP techniques.

[0684] Step 3:

[0685] The server analyzes the received text data and performs sentiment recognition using the IBM Watson Tone Analyzer API. The input is text data, and the output is data with one of the following sentiment tones identified: positive, negative, or neutral. In this process, the emotional nuances contained in the text are analyzed.

[0686] Step 4:

[0687] The server generates an automated response based on the analysis results. The input is the result of the sentiment analysis, and the output is the response message provided to the user. This response will vary depending on the emotional tone.

[0688] Step 5:

[0689] The server notifies a professional when a specific emotional tone is detected. Input is data about the emotional state and its severity, and output is an alert for the professional. When specified conditions are met, an automated notification is generated to prompt a quick response.

[0690] Step 6:

[0691] The server records all interactions and results and stores them in a database. Inputs are chat history and sentiment data, and outputs are stored in an encrypted database. This storage ensures that data is available for subsequent analysis and quality improvement.

[0692] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0693] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0694] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0695] [Fourth Embodiment]

[0696] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0697] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0698] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0699] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0700] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0701] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0702] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0703] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0704] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0705] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0706] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0707] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0708] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] To implement the present invention, it is first necessary to construct an online support system and provide a user-accessible interface. This system will have a secure environment for safely handling user information and will implement key functions such as user registration, login, submission of consultation content, receipt of responses, and switching to an expert.

[0710] Users access the online platform via the internet using their devices. Upon first use, users register their information with the system and create a unique account. This registration information includes username, email address, and password. All information is securely recorded on the server and stored in a database.

[0711] Afterward, the user logs in and can easily begin a consultation via the system's chat interface. The user freely types and sends messages on the chat screen displayed on their device. Messages sent from the device are received by the server and their content is analyzed using natural language processing algorithms.

[0712] The server automatically generates an appropriate response from the analysis results and sends it to the terminal. For general inquiries, it returns a pre-programmed response, enabling a quick response. In addition, when the server detects specific keywords in the user's message, it immediately notifies an expert, prompting a detailed response from the expert.

[0713] Experts receive notifications from the server, participate in chats, and provide users with specific advice and support tailored to their individual situations. Furthermore, the server encrypts all communication history and securely stores it for future reference and data management.

[0714] This provides users with an environment where they can easily seek advice about their mental health concerns, and through professional support, effectively maintain and improve their mental health. Furthermore, this system takes great care to protect personal information, so it can be used with peace of mind.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] Users access the online support system registration page using their device, enter the required information, and create an account.

[0718] Step 2:

[0719] The terminal sends the entered information to the server for verification to ensure it is in the correct format.

[0720] Step 3:

[0721] The server checks the database to see if there are any duplicates in the received information, and after verification, saves the user information to the database.

[0722] Step 4:

[0723] Users log in using their account and access the system's homepage.

[0724] Step 5:

[0725] The terminal sends the user's authentication information to the server and requests the establishment of a session.

[0726] Step 6:

[0727] The server checks the authentication information from the database, generates session information if a match is found, and grants permission to log in.

[0728] Step 7:

[0729] The user opens the chat screen on the system and enters their inquiry.

[0730] Step 8:

[0731] The device sends the entered chat message to the server.

[0732] Step 9:

[0733] The server analyzes the received message using a natural language processing algorithm and generates an automated response.

[0734] Step 10:

[0735] The server sends the generated response to the terminal, where it is displayed on the user's chat screen.

[0736] Step 11:

[0737] If the server detects specific keywords based on the analysis results, it immediately notifies the experts.

[0738] Step 12:

[0739] Upon receiving notification from an expert, the server will allow the expert to participate and provide them with the ability to join the chat.

[0740] Step 13:

[0741] The server encrypts all chat history and user data and stores it in secure storage.

[0742] (Example 1)

[0743] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] In modern society, there is a need to create an environment where users can easily consult about psychological problems, but at the same time, protecting personal information and responding quickly and appropriately remain important challenges. Furthermore, there is a lack of mechanisms to efficiently notify experts when specialized attention is required depending on the nature of the problem. In addition, accurately analyzing user messages using natural language and generating responses based on that analysis is a technical challenge.

[0745] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0746] In this invention, the server includes a device for inputting information, a device for analyzing the input message, and a device for generating a response using a generative artificial intelligence model. This enables users to securely register information, process consultation content quickly and appropriately using natural language processing, and promptly notify experts as needed. Furthermore, by encrypting and storing all communication history, it is possible to provide effective support while ensuring the protection of personal information.

[0747] A "device for inputting information" is a device that allows users to provide information such as their name and contact details to a system.

[0748] A "device for recording information" is a device for saving entered user information and accumulating it in a database.

[0749] A "communication device" is a device used by users to send messages to a system and to receive responses from the system.

[0750] A "message analysis device" is a device that uses natural language processing technology to analyze the content of a received message and understand its meaning.

[0751] A "response generation device" is a device that automatically creates an appropriate response for the user based on the analysis results.

[0752] A "device that automatically notifies experts" is a device that notifies experts in real time when specific conditions or keywords are detected.

[0753] A "device for saving and managing history" is a device that encrypts and saves all communication history for later reference and analysis.

[0754] A "natural language processing algorithm" is a technology that analyzes messages entered by users into a form that a computer can understand and then finds solutions.

[0755] A "generative artificial intelligence model" is an artificial intelligence technology that creates natural language responses based on large amounts of data.

[0756] A "device that detects and notifies specific words or phrases" is a device that detects important keywords contained in a message and prompts experts to take action based on those keywords.

[0757] In order to implement the present invention, it is first necessary to construct an online support system for users to access. This system is designed to handle user information securely and process it quickly. A specific embodiment of this system is shown below.

[0758] Users access the online platform via the internet using their own devices. First, users register by entering information such as their username, email address, and password. This information is stored in a database in a hashed state by the server.

[0759] When a user logs in, a chat interface appears on their device. Through this interface, the user can input their inquiry and send it to the server. The server uses natural language processing software (such as NLTK or SpaCy) to analyze the received message and understand its content.

[0760] Based on the analysis results, the server automatically generates a response to the user using a generative AI model (e.g., GPT-3). In this process, the generative AI model creates a natural language response based on a large amount of data and displays it on the user's device. For example, if a user sends "I've been feeling stressed lately," the system can reply with advice on stress management.

[0761] Furthermore, the server detects specific keywords and sends real-time notifications to experts. This feature allows experts to respond quickly if the user's input contains important signs.

[0762] All communication history is encrypted for security purposes and securely stored on the server. This feature will be used later for user follow-up and data analysis.

[0763] Examples of prompt messages include, "Please tell me how to cope when I feel anxious." This allows users to easily ask for advice and receive appropriate support.

[0764] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0765] Step 1:

[0766] The user accesses the online support system registration screen using their device. Here, the user enters their name, email address, and password. After entering the information, the user presses the registration button to send the information to the server. The server receives the entered information and stores it in a hashed format in its database. This creates the user's account, allowing them to access the platform.

[0767] Step 2:

[0768] The user accesses the login screen from their device and enters their registered email address and password. The entered information is sent to the server, which retrieves the corresponding user information from the database and verifies the password. If authentication is successful, the server establishes a session with the user and grants them access to the chat interface.

[0769] Step 3:

[0770] The user enters their inquiry into the chat interface on their device and presses the send button. The message is sent to the server. The server analyzes the received text using natural language processing algorithms (e.g., NLTK or SpaCy) to understand the structure and meaning of the text. The results of the analysis are output as semantic extraction and sentiment analysis.

[0771] Step 4:

[0772] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to create an appropriate response. The model leverages historical data to generate a response in natural language. The generated response is output as advice or suggestions to the user and displayed on the terminal's chat screen.

[0773] Step 5:

[0774] The server checks if specific keywords or phrases are included in user messages. If detected, a mechanism is triggered to send a notification to a specialist. This notification is sent in real time, allowing the specialist to prepare to provide expert assistance tailored to the user's situation.

[0775] Step 6:

[0776] All communication history is automatically encrypted on the server and stored in a database. This ensures user privacy and allows the data to be stored and managed in a way that makes it available for future reference and analysis.

[0777] (Application Example 1)

[0778] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0779] In recent years, the importance of mental health has increased, leading to a growing demand for online support systems that allow users to safely receive expert advice. However, current systems lack the flexibility to provide appropriate advice quickly while reducing the psychological burden on users. Furthermore, there is a lack of means to effectively analyze messages from a large volume of users and encourage the participation of experts. Effective methods to address these challenges are needed.

[0780] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0781] In this invention, the server includes means for inputting user information, means for recording the user information, and communication means for registered users to input messages. This allows users to safely and anonymously seek advice on mental health issues and receive appropriate responses quickly using a generative model algorithm. Furthermore, by providing a prompt generation means for the generative model, the accuracy and effectiveness of the responses can be improved, and experts can be automatically notified as needed. This makes it possible to provide more effective and reassuring support to users.

[0782] "Means for inputting user information" refers to input devices or software that allow users to record their own information in a system.

[0783] "Means for recording user information" refers to databases or recording media that centrally manage and securely store the user information entered by the user.

[0784] "Communication means" refers to the network and software that allows a user to input a message via an interface and send that message to a server.

[0785] "Means for analyzing input messages" refers to software that uses natural language processing algorithms to process text messages from users and understand their content.

[0786] "Means for automatically generating responses" refers to generative model algorithms that automatically create appropriate responses based on analysis results.

[0787] "Means of providing a response to the user" refers to software or hardware for displaying automatically generated responses on the user's device.

[0788] A "means of automatically notifying experts" refers to a notification system that sends alerts to experts when certain conditions are met.

[0789] "A means of providing advice" refers to a communication interface that allows experts to provide specific advice tailored to the user's situation in real time via chat.

[0790] "Means for saving and managing communication history" refers to a data management system that securely records interactions with users and makes them available for later reference.

[0791] "Methods that utilize generative model algorithms" refer to techniques that use artificial intelligence to optimize data when generating responses, thereby improving the quality of the responses.

[0792] A "prompt generation means" is a mechanism for creating inputs that provide effective instructions to the generative model and for optimizing the model's response generation.

[0793] A system for carrying out this invention is constructed to include a terminal for inputting user information and communicating, a server for processing data, and a generative model algorithm for providing responses to the user.

[0794] The user begins by entering their information. They input this information using software running on their device, and this information is transmitted to the server via the internet. The server securely records the received user information in a database and protects it using encryption technology as needed.

[0795] Users input questions and inquiries as messages through their terminals and send them to the server. The server analyzes these messages using natural language processing (NLTK) techniques. Natural language processing libraries such as Python's NLTK and spaCy can be used for analysis. Based on the results of this analysis, the server automatically generates an appropriate response. Generative AI models can be used for this response generation; for example, OpenAI's GPT and Hugging Face's Transformers are available.

[0796] The response is generated by providing a prompt to the generative model. An example of a specific prompt is: "Generate an appropriate response based on the message received from the user." After generating the response, the server sends it to the user's terminal.

[0797] If the analysis results meet certain conditions, the server automatically sends a notification to a specialist. The specialist interacts with the user in real time via the terminal interface and provides specific advice. All communication history is securely stored on the server in a format that can be referenced later. Through this process, users can seek mental health advice anonymously and receive prompt and accurate support from specialists.

[0798] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0799] Step 1:

[0800] The user enters information using a terminal. This information includes usernames, email addresses, and passwords, and this data is sent to the server through the input interface. The server records the received data in a database using a secure protocol and manages it securely.

[0801] Step 2:

[0802] The user enters their inquiry as a message using their device. This message is sent from the device to the server. The server analyzes the received message using a natural language processing algorithm. This analysis examines the syntax and meaning of the message and extracts the information necessary for the next step.

[0803] Step 3:

[0804] The server generates a response using a generative AI model based on the analysis results. The generative AI model receives extracted keywords and message context as prompts and outputs a natural language response accordingly. In this case, the prompt used is "Generate an appropriate response based on the message received from the user."

[0805] Step 4:

[0806] The server sends the generated response to the user's terminal. The user can view this response on the terminal's interface. The response is displayed as text, and the user can continue with additional questions or consultations as needed.

[0807] Step 5:

[0808] After generating a response, the server automatically sends notifications to experts based on the analysis results and circumstances. If specific keywords or phrases are detected, experts are prompted to join the chat. This notification is in real time, allowing experts to immediately begin interacting with the user.

[0809] Step 6:

[0810] All communication history with users is stored on the server. This historical data is encrypted and recorded so that experts and system administrators can refer to it later. The stored data helps to provide more accurate advice by utilizing the user's past consultation history.

[0811] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0812] This invention realizes a form of online support system that combines emotion recognition functionality. This system analyzes the emotions contained in messages entered by users in real time, adjusts responses, and notifies experts as needed, thereby providing more effective support.

[0813] When a user accesses the online platform through their device, the system first registers the user's information and initiates an individual session. The user enters their consultation details into the chat interface. The device sends the entered data to the server. The server analyzes the received message and uses natural language processing technology to analyze the emotions contained in the message using an emotion engine. This emotion engine identifies emotional tones such as positive, negative, and neutral from the text.

[0814] Based on the analyzed sentiment data, the server adjusts its response. For example, if a message indicates negative emotions, the server generates a more supportive and empathetic response. The server also automatically notifies a professional if a specific emotional state is continuously recognized, as needed. This allows the professional to review the consultation and intervene quickly to provide the user with appropriate advice.

[0815] For example, if a user enters "I've been feeling sad and unmotivated lately," the server recognizes this message as a negative emotion and generates and sends a response of encouragement and acceptance to the user. Depending on the situation, this information may also be notified to a specialist, creating a system where the specialist can provide more detailed support to the user.

[0816] The server encrypts and securely stores all chat data and sentiment analysis history, fully protecting user privacy and using this data to continuously improve the system's analysis quality.

[0817] Thus, the present invention provides a support environment that improves the user experience by understanding user emotions and optimizing responses and support.

[0818] The following describes the processing flow.

[0819] Step 1:

[0820] Users access the online support system using their device and, for first-time users, create an account by entering the required information on the registration page.

[0821] Step 2:

[0822] The terminal sends the user's registration information to the server, which then securely stores that information in a database.

[0823] Step 3:

[0824] The user logs in with the account they created and accesses the system dashboard.

[0825] Step 4:

[0826] The terminal sends the user's authentication information to the server, which then verifies it against the database to perform authentication and initiate the session.

[0827] Step 5:

[0828] The user opens the chat screen and enters their question as text.

[0829] Step 6:

[0830] The terminal sends the message entered by the user to the server.

[0831] Step 7:

[0832] The server analyzes received messages using natural language processing techniques and identifies emotions from the text using an emotion engine.

[0833] Step 8:

[0834] The emotion engine classifies the emotional tone in a message as positive, negative, neutral, etc., and provides that information to the server.

[0835] Step 9:

[0836] The server adjusts its response based on emotional data. For example, if negative emotions are strong, it will generate encouraging or accepting responses.

[0837] Step 10:

[0838] The server sends the adjusted response to the terminal, which then displays it on the user's chat screen.

[0839] Step 11:

[0840] If a specific emotional state, such as persistent negative emotions, is detected, the server automatically notifies a specialist.

[0841] Step 12:

[0842] Experts receive notifications from the server, join the chat, and provide users with reliable advice in real time.

[0843] Step 13:

[0844] The server encrypts all chat history and sentiment analysis results, stores them in secure storage, and protects privacy while also providing them for future analysis.

[0845] (Example 2)

[0846] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0847] In modern society, online support systems play a crucial role. However, many existing systems struggle to adequately recognize and respond to users' emotions. In particular, there is often a lack of continuous recognition of negative emotions experienced by users and appropriate professional intervention to address them. As a result, users may feel dissatisfied and anxious because they do not receive the support they need, which can undermine their trust in the system.

[0848] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0849] In this invention, the server includes means for identifying emotions from information using natural language processing technology, means for notifying experts when a specific emotional tone is continuously recognized, and means for protecting, encrypting, and storing the user's communication history. This makes it possible to analyze the user's emotions in an advanced way and generate appropriate responses accordingly, as well as to quickly notify experts of the situation as needed and provide accurate support to the user.

[0850] "Means for inputting user information" refers to an interface that allows users to provide data about themselves to the system.

[0851] "Means of recording" refers to a function that retains the user's entered information and stores it so that it can be referenced as needed.

[0852] "Communication means" refers to a device or program for mutual communication between a user and a system, enabling the user to send information to the system and receive a response from the system.

[0853] "Means of analysis" refers to the process of analyzing received information and understanding its content and context.

[0854] "Methods for identifying emotions from information using natural language processing technology" refers to technologies that use generative AI models or emotion engines to identify emotions within text.

[0855] A "means for generating responses" is a mechanism that automatically creates appropriate responses or messages based on analyzed information.

[0856] "Means of notification" refers to a method of sending warnings or information to experts or other relevant parties when certain conditions are met.

[0857] "Means of providing advice" refers to a system for experts to provide guidance and suggestions to users.

[0858] "Means of preservation" refers to a system that securely stores communication history and sentiment analysis data and manages it so that it can be accessed later.

[0859] A "generative AI model" is an algorithm or system that uses artificial intelligence, trained on a large amount of data, to understand and manipulate natural language.

[0860] A "prompt message" is a type of text that serves as input instructions for an AI model, used to instruct the AI ​​to perform a specific task.

[0861] This system aims to analyze user emotions in real time and adjust responses within an online support platform. The following describes the configuration for implementing this system.

[0862] Users access the online platform using devices such as PCs and smartphones. There, users enter their questions into a text box. The device then transmits the entered message to the server via a communication method.

[0863] The server analyzes received messages using natural language processing techniques. Specifically, it employs a generative AI model to understand the content and context of the message, and then uses an emotion engine to identify emotional tones such as positive, negative, and neutral. In the analysis process, a general-purpose natural language processing software can be used as the emotion engine.

[0864] Based on the analysis results, the server automatically generates a response. The generated response is tailored to the user's emotions. For example, if the user's message expresses a negative emotion such as "I've been feeling sad and unmotivated lately," an empathetic and encouraging response will be generated. Furthermore, if the same negative emotion is continuously recognized, the server automatically notifies a professional. This notification allows the professional to quickly provide appropriate advice to the user.

[0865] Data security is also a consideration; the server encrypts and securely stores all communication history and sentiment analysis history. This data can be used to improve the system's analytical capabilities while ensuring user privacy.

[0866] An example of a prompt would be an instruction used for a generative AI model such as, "Detect the emotion in the user's message and provide a corresponding response. For example, if the user says, 'I've been feeling sad and unmotivated lately,' create an empathetic response."

[0867] In this way, the system can improve the user experience by taking user emotions into consideration and providing optimized support.

[0868] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0869] Step 1:

[0870] Users access the online platform using their devices and enter their inquiries via a chat interface. The entered text data is sent from the device to the server upon triggering a send action. The input is a text message, and the output is the message data that reaches the server.

[0871] Step 2:

[0872] The server analyzes message data received from the terminal. First, a generative AI model analyzes the user's text message using natural language processing techniques to understand the information and context. Here, the input is the message data, and the output is the contextual information necessary for sentiment analysis.

[0873] Step 3:

[0874] The server uses a generative AI model and an emotion engine to identify emotional tones from the analyzed contextual information. In this process, the emotion engine classifies the message as having a positive, negative, or neutral tone. The input is contextual information, and the output is the identified emotional tone.

[0875] Step 4:

[0876] The server automatically generates an appropriate response based on the identified emotional tone. If a negative emotion is detected, a generative AI model generates an empathetic and supportive response, which is then prepared as text data. The input is the emotional tone, and the output is the generated response text.

[0877] Step 5:

[0878] The server sends the generated response back to the terminal for display to the user. The response is displayed to the user via a chat interface on the terminal. The input is the generated response text, and the output is the response presented visually to the user.

[0879] Step 6:

[0880] The server notifies a professional if a specific emotional tone is continuously detected, as needed. This notification occurs when certain trigger conditions are met, enabling the professional to quickly provide appropriate advice to the user. The input is the continuously detected emotional tone, and the output is the notification to the professional.

[0881] Step 7:

[0882] The server encrypts and stores all communication history and sentiment analysis data to protect user privacy. This allows for the secure storage of data for future analysis and system improvements. The input is communication history and analysis data, and the output is encrypted data storage.

[0883] (Application Example 2)

[0884] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0885] Traditional online support systems have faced challenges in fully understanding and responding immediately to users' emotional states, as well as in anticipating and appropriately managing emotional upsets. Therefore, there has been a need to provide rapid support when users experience anxiety or stress. Furthermore, to ensure user safety, there is a demand for analyzing voice data to detect abnormal emotions.

[0886] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0887] In this invention, the server includes means for inputting user information, means for acquiring voice data and converting it to text, and means for analyzing the converted text and identifying emotions. This makes it possible to analyze the user's emotional state in real time, quickly identify emotions indicating stress or anxiety, and notify experts while issuing necessary warnings.

[0888] "Means for inputting user information" refers to an interface on an online platform that allows users to input their personal identification information and individual data into the system.

[0889] "Means for recording user information" refers to a mechanism for securely and accurately storing user data entered on a server.

[0890] "Communication means" refers to the system component that allows registered users to input messages and send and receive those messages to and from the server.

[0891] "Means for analyzing input messages" refers to a module that uses analysis techniques to process text messages sent by users and understand their content and intent.

[0892] "Means for automatically generating responses" refers to a process in which a computer generates an appropriate answer based on the content of an analyzed message and provides it to the user.

[0893] "Means of providing a response to the user" refers to a method of sending the generated response to the user's device and making it easily accessible to the user.

[0894] "Means for automatically notifying experts" refers to technology that automatically sends alerts to experts under specific conditions.

[0895] "Means for experts to provide advice to users" refers to an access mechanism for experts to contact users and provide them with professional support regarding their questions and problems.

[0896] "Means for saving and managing user communication history" refers to database technology for recording and securely storing user operation history and messages when using a system.

[0897] "Means for acquiring audio data and converting it to text" refers to speech recognition software that recognizes speech and converts it into a corresponding text format.

[0898] "Methods for analyzing converted text and identifying emotions" refers to the process of identifying emotional tones, such as positive or negative, by analyzing the emotional nuances contained in the text data.

[0899] A "warning mechanism" is an alert system designed to alert users or administrators when certain emotional states or criteria are reached.

[0900] In this invention, the server uses software to convert speech data into text data. Specifically, it can utilize speech recognition services such as the Google Cloud Speech-to-Text API. The converted text is then subjected to sentiment analysis using natural language processing (NLP) techniques such as the IBM Watson Tone Analyzer API. This identifies whether the emotional tone contained in the text is positive, negative, or neutral. Based on the analysis results, the server generates a response to the user. This response will be supportive and empathetic, for example, if a negative emotion is identified. Furthermore, if an emotional state meeting specific conditions is detected, the server notifies a specialist to enable a rapid response.

[0901] When a user uses the system, they send a message using their smartphone's voice input function. This voice message is captured by the device and sent to the server. The server converts the received voice data into text format and performs sentiment analysis.

[0902] As a concrete example, if a user experiences a stressful interaction at the reception desk of an office building, this system can detect the stress level from the conversation and issue a warning to security guards in advance. This process allows reception staff and security guards to take appropriate follow-up actions.

[0903] An example of a prompt message generated using an AI model is one that can be sent to an emotion recognition system for analysis. This prompt could say something like, "Assess the emotional tone of a user complaining at the reception desk and issue a warning if necessary."

[0904] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0905] Step 1:

[0906] The user inputs a voice message through the device. The device receives this voice data and converts it into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is the converted text data.

[0907] Step 2:

[0908] The server receives text data sent from the terminal. The input is text data, and the output is text that can be used directly for analysis. Here, the data is prepared using NLP techniques.

[0909] Step 3:

[0910] The server analyzes the received text data and performs sentiment recognition using the IBM Watson Tone Analyzer API. The input is text data, and the output is data with one of the following sentiment tones identified: positive, negative, or neutral. In this process, the emotional nuances contained in the text are analyzed.

[0911] Step 4:

[0912] The server generates an automated response based on the analysis results. The input is the result of the sentiment analysis, and the output is the response message provided to the user. This response will vary depending on the emotional tone.

[0913] Step 5:

[0914] The server notifies a professional when a specific emotional tone is detected. Input is data about the emotional state and its severity, and output is an alert for the professional. When specified conditions are met, an automated notification is generated to prompt a quick response.

[0915] Step 6:

[0916] The server records all interactions and results and stores them in a database. Inputs are chat history and sentiment data, and outputs are stored in an encrypted database. This storage ensures that data is available for subsequent analysis and quality improvement.

[0917] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0918] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0919] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0920] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0921] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0922] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0923] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0924] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0925] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0926] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0927] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0928] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0929] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0930] 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.

[0931] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0932] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0933] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0934] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0935] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0936] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0937] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0938] The following is further disclosed regarding the embodiments described above.

[0939] (Claim 1)

[0940] A means of entering user information,

[0941] means for recording the user's information,

[0942] A means of communication for registered users to enter messages,

[0943] A means for analyzing the input message,

[0944] A means for automatically generating a response based on the analysis results,

[0945] Means for providing the aforementioned response to the user,

[0946] A means of automatically notifying experts,

[0947] The means by which the aforementioned expert provides advice to the user,

[0948] A means of saving and managing the user's communication history,

[0949] An online support system including this.

[0950] (Claim 2)

[0951] The online support system according to claim 1, comprising means for notifying an expert when specific conditions based on the analysis results are met.

[0952] (Claim 3)

[0953] The online support system according to claim 1, comprising means for securely protecting the content of communications with the user and for encrypting and storing the data.

[0954] "Example 1"

[0955] (Claim 1)

[0956] A device for inputting information,

[0957] A device for recording the aforementioned information,

[0958] A communication device for users to input messages when registering information,

[0959] A device that analyzes the input message,

[0960] A device that automatically generates a response based on the analysis results,

[0961] A device that provides the aforementioned response to the user,

[0962] A device for automatically notifying experts,

[0963] The aforementioned expert provides an apparatus for giving advice to users,

[0964] A device for storing and managing communication history,

[0965] A device that analyzes content using a natural language processing algorithm,

[0966] A device that generates responses using a generative artificial intelligence model,

[0967] A device that detects specific words or phrases in a message and notifies experts,

[0968] A system that includes this.

[0969] (Claim 2)

[0970] The system according to claim 1, further comprising a device that notifies an expert when specific conditions based on the analysis results are met.

[0971] (Claim 3)

[0972] The system according to claim 1, comprising a device for securely protecting information and communication content and for encrypting and storing data.

[0973] "Application Example 1"

[0974] (Claim 1)

[0975] A means of entering user information,

[0976] means for recording the user's information,

[0977] A means of communication for registered users to enter messages,

[0978] A means for analyzing the input message,

[0979] A means for automatically generating a response based on the analysis results,

[0980] Means for providing the aforementioned response to the user,

[0981] A means of automatically notifying experts,

[0982] Means for the aforementioned expert to provide advice to the user,

[0983] A means of saving and managing the user's communication history,

[0984] A means of utilizing generative model algorithms to support the generation of responses,

[0985] A prompt generation means that provides input to the aforementioned generation model,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, further comprising means for notifying an expert when specific conditions based on the analysis results are met.

[0989] (Claim 3)

[0990] The system according to claim 1, comprising means for securely protecting the content of communications with the user and for encrypting and storing the data.

[0991] "Example 2 of combining an emotion engine"

[0992] (Claim 1)

[0993] A means of entering user information,

[0994] means for recording the user's information,

[0995] A means of communication for registered users to input information,

[0996] A means for analyzing the input information,

[0997] A means of identifying emotions from information using natural language processing technology,

[0998] A means for automatically generating a response based on the analysis results,

[0999] Means for providing the aforementioned response to the user,

[1000] A means of notifying a professional when a specific emotional tone is persistently perceived,

[1001] The means by which the aforementioned expert provides advice to the user,

[1002] A means of protecting, encrypting, and storing the user's communication history,

[1003] A system that includes this.

[1004] (Claim 2)

[1005] The system according to claim 1, further comprising means for notifying a specialist when specific emotional tone conditions based on analysis results are met.

[1006] (Claim 3)

[1007] The system according to claim 1, comprising means for identifying emotions from user input using a generative AI model.

[1008] "Application example 2 when combining with an emotional engine"

[1009] (Claim 1)

[1010] A means of entering user information,

[1011] means for recording the user's information,

[1012] A means of communication for registered users to enter messages,

[1013] A means for analyzing the input message,

[1014] A means for automatically generating a response based on the analysis results,

[1015] Means for providing the aforementioned response to the user,

[1016] A means of automatically notifying experts,

[1017] The means by which the aforementioned expert provides advice to the user,

[1018] A means of saving and managing the user's communication history,

[1019] A method for acquiring audio data and converting it to text,

[1020] A means of analyzing the converted text and identifying emotions,

[1021] When identified emotions reach a certain threshold, a means of issuing a warning,

[1022] A system that includes this.

[1023] (Claim 2)

[1024] The system according to claim 1, further comprising means for notifying an expert when specific conditions based on the analysis results are met.

[1025] (Claim 3)

[1026] The system according to claim 1, comprising means for securely protecting the content of communications with the user and for encrypting and storing the data. [Explanation of Symbols]

[1027] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of entering user information, means for recording the user's information, A means of communication for registered users to enter messages, A means for analyzing the input message, A means for automatically generating a response based on the analysis results, Means for providing the aforementioned response to the user, A means of automatically notifying experts, The means by which the aforementioned expert provides advice to the user, A means of saving and managing the user's communication history, An online support system including this.

2. The online support system according to claim 1, further comprising means for notifying an expert when specific conditions based on the analysis results are met.

3. The online support system according to claim 1, comprising means for securely protecting the content of communications with the user and for encrypting and storing the data.

Images