system

Abstract

We provide the system. [Solution] A means of receiving inquiries from users, An analysis means for analyzing the aforementioned consultation content, A response generation means that generates an appropriate response based on the analysis results, A notification means for notifying the user of the aforementioned response, A support provision method that provides additional support when a specific keyword is detected, A notification means for issuing a notification when the aforementioned consultation content requires the intervention of a professional, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: 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 as a 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, mental health problems among young people and students have been increasing, and in particular, the risk of suicide has been rising, which has become a social issue. Conventional counseling services and mental support often take a long time for users to access, and there are many cases where necessary support cannot be received at an appropriate timing. The present invention aims to solve this problem by providing a system that enables users to receive rapid and continuous support.

Means for Solving the Problems

[0005] This invention provides a system that receives inquiries from users, analyzes them using natural language processing technology, generates appropriate responses, and notifies the users. This system has a function to provide additional support when specific keywords are detected and can issue notifications prompting professional intervention as needed. This enables users to receive prompt and appropriate mental health support, preventing serious situations arising from mental health problems.

[0006] "Communication means" refers to the interface and protocol used to transmit user inquiries to the system.

[0007] "Analysis method" refers to the process of analyzing the content of consultations received from users and identifying specific patterns or emotions.

[0008] "Response generation means" refers to a function that creates messages to provide appropriate feedback and information to the user based on the results of the analysis means.

[0009] "Notification means" refers to the system's function of sending the generated response to the user's terminal and displaying it to the user.

[0010] "Support provision method" refers to a function that provides additional information or resources when a user enters a specific keyword.

[0011] "Notification transmission method" refers to a function that sends alerts to the appropriate parties when a user's inquiry is urgent or requires expert intervention. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It 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 the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention adopts the following configuration to implement a system aimed at supporting users' mental health. Users input their consultation content and concerns into the system through a dedicated application. This input is transmitted to a server via a terminal. The server analyzes the received content using natural language processing technology and generates an appropriate response based on the results.

[0034] This generated response is sent from the server to the terminal and notified to the user. If the server determines that the consultation contains specific keywords, it prepares to provide additional support. For example, if the consultation contains expressions of stress or anxiety, it may provide information on stress management methods or access to relaxation resources.

[0035] Furthermore, if a user's inquiry requires expert judgment or intervention, the server will send a notification to a pre-configured expert. This notification will include part of the user's inquiry and analysis results, enabling the expert to respond quickly.

[0036] For example, if a user enters "I'm having trouble with relationships at school," the server analyzes this content, generates "tips for improving relationships" as a response, and notifies the user via their device. Furthermore, if the content contains certain keywords indicating the potential for the situation to escalate, it sends a notification to an expert along with detailed analysis results.

[0037] Thus, this system aims to provide more effective and timely mental health support by interacting with users in real time.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the application and enters their inquiry details. After entering the details, they press the send button to prepare the data for processing on their device.

[0041] Step 2:

[0042] The terminal performs the procedure of sending the consultation details entered by the user to the server. Once the transmission is complete, a notification of completion is displayed to the user on the terminal.

[0043] Step 3:

[0044] The server stores the data received from the terminal in a database for analysis and passes the contents to a natural language processing (NLP) analysis module. The analysis module processes the text data and performs sentiment analysis and keyword extraction.

[0045] Step 4:

[0046] The server's analysis module generates an appropriate response based on the extracted information. This response may include specific advice or suggestions for psychological support regarding the user's concerns.

[0047] Step 5:

[0048] The server performs communication processing to send the generated response to the terminal. As a result of the communication, the response message arrives at the user's terminal.

[0049] Step 6:

[0050] The terminal notifies the user of the response message sent from the server and indicates that a reply to the inquiry is available.

[0051] Step 7:

[0052] When the server detects specific keywords during analysis, it determines whether additional assistance is needed. If it determines that assistance is needed, it prepares to push notifications to the user with additional information or resources.

[0053] Step 8:

[0054] If the server determines that the consultation is urgent, it will send a notification to registered experts. This notification will include a summary of the user's consultation and the results of the analysis.

[0055] Step 9:

[0056] If a specialist receives notification, they will investigate the details and contact the user to provide direct assistance if necessary.

[0057] (Example 1)

[0058] 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."

[0059] In modern society, mental health problems are steadily increasing, yet there is still insufficient access to resources where individuals can easily seek advice. Furthermore, mechanisms for appropriately connecting individuals with professionals when support is needed are underdeveloped. In this situation, there is a need for a system that allows users to consult with professionals in real time with peace of mind, and to receive prompt intervention from experts when necessary.

[0060] 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.

[0061] In this invention, the server includes a device for acquiring consultation content, a device for analyzing the consultation content using language processing technology, and a device for generating a response based on the analyzed information. This enables users to quickly and effectively seek advice on mental health issues and receive support from experts as needed.

[0062] A "device for acquiring consultation content" is a device that collects the mental health consultation content entered by the user and sends it to the next processing stage.

[0063] "Language processing technology" refers to techniques for analyzing natural language and extracting meaning and emotion, and includes means of analyzing information based on text data.

[0064] A "device for analysis" is a device that uses language processing technology to analyze the acquired consultation content and extract emotions and important elements.

[0065] A "response generation device" is a device that automatically creates an appropriate response for the user based on analyzed information and prepares feedback for the user.

[0066] A "device that provides assistance when specific words are identified" is a device that provides additional information and support when specific keywords are included in the content of a consultation.

[0067] A "device that sends a warning when professional intervention is required" is a device that immediately sends a notification to a professional when it is determined that the user's consultation requires the attention of a professional.

[0068] Embodiments for carrying out this invention are described below.

[0069] Users input their mental health consultation details using a dedicated application. The terminal receives this input and sends it to the server using the secure communication protocol HTTPS. The server uses natural language processing (NLP) techniques to analyze the received consultation details. This technique utilizes open-source libraries such as "spaCy".

[0070] The server processes the consultation content using language processing technology, extracting emotions and important keywords. This allows for an analysis of the sentiment behind the consultation. Based on the analysis results, the server generates an appropriate response using a generative AI model. The GPT model, one of the generative AI models, is used for this response generation.

[0071] The generated response is sent back to the terminal via a secure communication protocol, and the terminal notifies the user of the response. If the consultation content contains specific keywords, the server will provide additional support information, such as materials on stress management or information on support services.

[0072] Furthermore, if the consultation requires professional intervention, the server will send a warning to a designated specialist. This warning includes a summary of the consultation and analysis results, allowing the specialist to respond quickly.

[0073] For example, if a user inputs "I have concerns about relationships at school," the server could analyze this inquiry, generate a response offering "hints and advice for improving relationships," and notify the user. An example of a prompt for the generating AI model would be, "When a user asks for advice about relationships at school, output the corresponding advice."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] Users input their mental health consultation details using a dedicated application. The entered consultation details are saved on the device in text format. The user's input data is prepared for the next processing step using a secure communication protocol.

[0077] Step 2:

[0078] The device sends the saved consultation details to the server. This transmission uses the HTTPS protocol, and the data is transmitted to the server in an encrypted state. This protects user privacy and ensures secure data transfer.

[0079] Step 3:

[0080] The server receives the consultation content and analyzes the data using a natural language processing engine. This analysis includes sentiment analysis and keyword extraction of the input text. Specifically, a natural language processing library is used to analyze the sentence structure and classify the sentiment. The output consists of word-for-word analysis results and sentiment information.

[0081] Step 4:

[0082] The server sends a prompt message to the generating AI model based on the analysis results. The prompt message reflects solutions and advice for the user's problem derived from the analysis results. The generating AI model, such as a GPT model, receives this prompt and automatically generates an appropriate response. The response text is output, and the process proceeds to the next step.

[0083] Step 5:

[0084] The server receives the generated response text and prepares to send it to the terminal. This outputted response is then securely sent to the terminal again using the HTTPS protocol.

[0085] Step 6:

[0086] The terminal notifies the user of the response text received from the server. The user can then review the response through the application and receive any necessary support information. The notified response allows the user to consider further actions.

[0087] Step 7:

[0088] If the server determines that the consultation content contains specific keywords, it will prepare additional support information. For example, if keywords such as "stress" or "anxiety" are included, information on stress management methods and support organizations will be generated, and alerts will be sent to professionals as needed. This allows users to receive appropriate support tailored to their situation.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, it is crucial to appropriately manage users' psychological states and provide necessary support promptly. However, many mental health support systems tend to rely heavily on user input, lacking continuous monitoring and coordination with professional support. Furthermore, there is a need for a system that can monitor users' emotional states in real time and respond quickly when abnormalities are detected.

[0092] 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.

[0093] In this invention, the server includes data acquisition means for receiving content from a user, information analysis means for analyzing the content, and data generation means for generating appropriate feedback based on the analysis results. This enables continuous monitoring of the user's emotional state and allows for the rapid generation and notification of warnings if an anomaly is detected.

[0094] "Data acquisition means" refers to a device or function for receiving content from a user and converting it into a format that can be processed within the system.

[0095] "Information analysis means" refers to a device or function that uses natural language processing technology to analyze acquired content and understand its content and meaning.

[0096] "Data generation means" refers to a device or function that generates appropriate feedback for the user based on the analysis results obtained by the information analysis means.

[0097] "Notification means" refers to a device or function for communicating generated feedback or warnings to the user.

[0098] "Support provision means" refers to a device or function that provides assistance or related information to the user when a specific discriminant word is detected.

[0099] "Communication transmission means" refers to a device or function for sending a notification to an expert when content from a user requires expert advice.

[0100] "State monitoring means" refers to a device or function for continuously monitoring a user's emotional state and detecting any abnormal state.

[0101] An "anomaly detection means" is a device or function for recognizing an anomaly in the user's emotional state detected by a state monitoring means and generating a warning.

[0102] In this invention, a server and a user terminal are the main components of the system. The server receives user content using data acquisition means, and then analyzes the content using information analysis means, which utilizes natural language processing technology.

[0103] Based on the analysis results, the server generates appropriate feedback using data generation means and notifies the user via notification means. This feedback takes into account the user's mental health status and, in some cases, includes alerts to professionals.

[0104] Furthermore, the server utilizes state monitoring mechanisms to continuously monitor the user's emotional state. This allows anomaly detection mechanisms to detect unusual emotional changes early and generate warnings for the user.

[0105] The server is expected to use "Transformers," a natural language processing library developed using the Python language. A concrete example of a prompt would be, "When a user enters 'I'm feeling stressed because I'm having trouble with relationships at work,' determine whether that emotion is negative." This is expected to fully utilize the characteristics of mental health support and provide better support to users.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives user input from the terminal. This input is text data in which the user freely describes their feelings and worries. This input is then passed directly to the next processing step.

[0109] Step 2:

[0110] The server uses information analysis tools to analyze the received user input using the natural language processing library "Transformers." Specifically, it analyzes the sentiment of the input text and generates labels such as positive, negative, and neutral. This provides data to understand the emotional tendencies of the user's input.

[0111] Step 3:

[0112] The server generates feedback for the user through data generation means based on the analysis results. Using a generative AI model, it creates appropriate messages corresponding to the analyzed emotions. The messages generated by this prompt must contain helpful and supportive content for the user.

[0113] Step 4:

[0114] The server uses a notification mechanism to inform the user of the generated feedback. The feedback message is sent to the user's device as output, and the user checks it on their device.

[0115] Step 5:

[0116] The server uses state monitoring measures to continuously monitor the user's emotional state. If the anomaly detection measures detect an anomaly based on the trends in the analysis results, they generate a warning and determine whether it is necessary to contact an expert.

[0117] Step 6:

[0118] The server will use communication methods as needed to send alerts to experts. The transmitted data will include user input and the results of its analysis, enabling experts to respond quickly. This output is the alert notification received by the experts.

[0119] 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.

[0120] This invention is a system that analyzes user inquiries and provides appropriate support, and in particular, it implements a form that incorporates an emotion engine that recognizes the user's emotions. First, the user inputs their inquiry through a dedicated application. This input is sent from the terminal to the server.

[0121] When the server receives input, it analyzes its content using natural language processing technology. The analysis includes an emotion engine that extracts emotions from the text data of the consultation and classifies them into categories such as positive, negative, and neutral. This emotion identification result influences the response generation mechanism, adjusting the tone and content of the generated response.

[0122] For example, if a user enters "I've been feeling really down lately," the emotion engine recognizes this expression as a negative emotion. The response generation system then prepares a message of comfort or encouragement, which is then sent to the user via the device.

[0123] Furthermore, the emotion engine has a learning function that references the user's emotional history, improving the accuracy of emotion analysis by learning from previous consultation data. This function enables more personalized support through ongoing consultations.

[0124] Furthermore, if the server determines that the content of the consultation and the perceived emotions are serious enough to require professional intervention, it will send a notification to registered professionals. This notification will include a summary of the user's consultation and the results of the sentiment analysis, enabling professionals to respond promptly.

[0125] Notifications to users are delivered in real time via their devices, providing a fast interface, and users receive additional support and hotline information when needed. In this way, this system, which incorporates an emotion engine, enables users to receive rapid and accurate mental health support.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user launches a dedicated application and enters their inquiry details. Once the input is complete, pressing the send button causes the terminal to process the data and begin sending it to the server.

[0129] Step 2:

[0130] The terminal uses a communication protocol to send data entered by the user to the server, ensuring the secure transfer of data.

[0131] Step 3:

[0132] The server saves the consultation details received from the terminal to a database. Afterward, it prepares to pass the data to an analysis module that uses natural language processing technology.

[0133] Step 4:

[0134] The server's analysis module analyzes the received consultation content and uses an emotion engine to recognize emotions along with extracting keywords. This includes the process of identifying emotions such as positive, negative, and neutral from the consultation content.

[0135] Step 5:

[0136] The emotion engine, based on the analysis results, further refines the analysis by referring to the user's emotional history. Through this process, it understands the user's unique emotional patterns and utilizes them to generate responses.

[0137] Step 6:

[0138] The server operates a response generation module based on the analysis results to create the optimal response message. This message is then adjusted to match the user's emotional state.

[0139] Step 7:

[0140] The generated response message is ready to be sent to the terminal via a notification mechanism, and the message is sent from the server to the terminal.

[0141] Step 8:

[0142] The terminal displays a response message sent from the server to the user. The user receives this message and receives feedback regarding the consultation.

[0143] Step 9:

[0144] The server also automatically sends notifications to registered professionals if the consultation content and sentiment analysis results indicate that urgent or professional intervention is required. This notification includes a summary of the user's consultation content and the results of the sentiment analysis.

[0145] Step 10:

[0146] Experts will receive notifications and, if necessary, take steps to communicate with users to provide additional support or intervention.

[0147] (Example 2)

[0148] 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".

[0149] Conventional systems had problems such as insufficient individualization of sentiment analysis for user inquiries, and difficulty in providing prompt expert intervention or additional support as needed. Furthermore, personalization of responses that took sentiment history into account was not sufficiently achieved, and user satisfaction was not sufficiently improved.

[0150] 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.

[0151] In this invention, the server includes communication means for receiving text data from a user, analysis means including an emotion engine for analyzing the text data and extracting emotions, and response generation means for generating a response using a generation AI model based on the analysis results. This makes it possible to generate personalized responses tailored to the content of the consultation and quickly notify the user, thereby providing appropriate support or expert intervention as needed.

[0152] "Communication means" refers to the technology and methods for receiving text data from a user and transmitting it to a server.

[0153] The "analysis method" is a mechanism that analyzes received text data using natural language processing technology, extracts emotions through an emotion engine, and classifies them into categories.

[0154] An "emotion engine" is a specialized technology for detecting emotions from text data and classifying them into categories such as positive, negative, and neutral.

[0155] A "response generation means" is a mechanism that uses a generative AI model to generate a response suitable for the user based on emotional information obtained by the analysis means.

[0156] A "notification method" is a method for transmitting the generated response to the user's terminal in real time.

[0157] A "means of providing support" refers to a method for providing additional support or assistance to a user when specific information is detected.

[0158] A "notification system" is a mechanism for sending notifications to experts when text data is deemed to require professional intervention.

[0159] "Learning methods" are techniques for analyzing past data to improve the accuracy of analysis and response generation.

[0160] "Personalization means" refers to methods for managing a user's emotional history and providing personalized responses tailored to each individual user.

[0161] This invention is a system aimed at supporting users' mental health, utilizing emotion analysis technology to provide appropriate support to users. Specifically, the server analyzes the content of the consultation received from the user and generates a response using a generative AI model.

[0162] The server receives text data from the user's terminal via a communication method. This text data is entered by the user using a dedicated application and sent from the terminal. The application used runs on iOS and Android® platforms and has a user-friendly interface.

[0163] The received text data is analyzed using natural language processing techniques by an analysis system on the server. The analysis utilizes commercially available natural language processing libraries, and emotions are extracted from the text data through an emotion engine. This emotion engine has the ability to classify emotions into positive, negative, and neutral categories.

[0164] Based on the analyzed sentiment data, the server utilizes a generative AI model to generate appropriate responses. For example, the generative AI model automatically generates an appropriate response based on a prompt such as, "The user is feeling tired because they have been having trouble sleeping. Please provide a message of encouragement."

[0165] The generated response is delivered to the user's device in real time via a notification system. This allows the user to receive support quickly, and if necessary, hotline information and access information to resources from additional support channels are also provided.

[0166] Furthermore, the emotion engine learns from past consultations and improves its analytical accuracy, enabling more personalized support through ongoing consultations. In addition, if a serious consultation is detected, a notification system transmits the information to a specialist, allowing for prompt expert intervention. This overall system mechanism provides comprehensive and personalized support to the user.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] Users input their consultation details using a dedicated application. The information entered by the user is saved as text data on the device and sent to the server when the send button is pressed. The input data includes the user's consultation details and a timestamp. The server receives this data using a secure communication protocol.

[0170] Step 2:

[0171] The server analyzes the received text data. Using natural language processing technology as the analysis method, it activates an emotion engine to extract emotions from the text content. The received text data is output as emotion data categorized into positive, negative, and neutral. This emotion data is evaluated by the emotion engine, and a log of the analysis results is recorded on the server.

[0172] Step 3:

[0173] The server generates a response based on the analysis results. Using a generative AI model, it generates a response based on the prompt "Consider the user's emotion to be XX and generate an appropriate response." This prompt prompt causes the model to construct a response based on the input emotion data. The generated response has a tone and content appropriate for the user. The generated response message is saved on the server as output.

[0174] Step 4:

[0175] The server notifies the user of the generated response. Using a notification method, the generated response is sent to the user's device in real time. Here, the server uses push notification functionality to display the message on the device. The user can check the received message within the application and view the details.

[0176] Step 5:

[0177] If necessary, the server will provide additional support. This support system detects specific information from the emotion engine and analysis results, providing users with registered hotline information and access to resources. This information is personalized according to the user's situation, enabling them to be guided to the appropriate resources.

[0178] Step 6:

[0179] If the server determines that professional intervention is necessary, it will notify a specialist using a notification system. This notification will include analyzed sentiment data and a summary of the consultation. Based on this information, the specialist can respond quickly. The server will securely send notifications while maintaining the accuracy of the information.

[0180] (Application Example 2)

[0181] 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".

[0182] Current information and communication systems struggle to accurately understand users' emotional states and respond promptly based on that understanding. This makes it difficult to efficiently manage users' mental health and safety, and there is a problem of insufficient support, especially when users are experiencing high levels of stress and anxiety.

[0183] 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.

[0184] In this invention, the server includes communication means for receiving information from the user, analysis means for analyzing the information, and monitoring means for monitoring the user's emotional state in real time. This makes it possible to grasp the user's emotional state in real time and provide appropriate responses and support.

[0185] A "means of communication for receiving information" is a mechanism for receiving information entered by a user as data and transferring it to a system.

[0186] "Information analysis tools" are technologies for processing received information and evaluating its content and characteristics.

[0187] A "response-providing means" is a means of returning appropriate information or instructions to the user based on the analysis results.

[0188] "Transmission means" refers to the means by which generated responses or information are delivered to the user.

[0189] A "monitoring method that monitors emotional states in real time" is a technology that continuously observes and analyzes the user's psychological state.

[0190] A "warning mechanism that issues a warning" is a system that alerts the user when observed data indicates a dangerous or abnormal condition.

[0191] "Means of communication for sending out information" refers to means of reporting the situation to relevant parties and experts as needed and prompting them to take action.

[0192] A system that implements an application example of this invention has the function of analyzing the user's emotional state in real time and issuing warnings or contacting experts as needed.

[0193] First, the terminal acquires data through a communication method that receives information from the user. This data mainly consists of entered text information, but may also include detailed feedback indicating emotions. The received information is transferred to a server and processed by analytical means that analyze the information.

[0194] The server extracts the user's emotional state from the information using analysis methods that employ natural language processing techniques. Natural language processing typically utilizes machine learning models as sentiment analysis engines. These models evaluate the input text and classify it into emotional categories such as positive, negative, and neutral.

[0195] The server quantitatively evaluates stress levels and anxiety through a monitoring system that monitors the user's emotional state in real time. The evaluation results are compared to pre-set criteria, and if the criteria are exceeded, a warning system is activated to issue a warning. This system sends the user a message encouraging relaxation.

[0196] Furthermore, if a serious situation is identified, the server can send notifications to registered specialists through its designated communication channels. This allows for a swift response, protecting the user's safety and health.

[0197] For example, if a user enters "I've been suffering from excessive stress lately" into the device, the system immediately assesses that emotion as negative, and a warning system sends a message encouraging relaxation. If necessary, it also notifies registered professionals for further action.

[0198] Examples of prompt statements for a generative AI model are as follows:

[0199] "User input: 'I've been suffering from excessive stress lately.' Analyze this and generate appropriate emotion labels and responses."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The terminal receives information from the user. This information is text data indicating the content of the consultation and emotions entered by the user. After receiving this data, the terminal uses a communication method to send it to the server.

[0203] Step 2:

[0204] The server analyzes data through natural language processing techniques using analytical means to parse information sent from the terminal. Using the text data received as input, the server extracts emotions using a machine learning model and classifies them into positive, negative, and neutral emotional categories. These analysis results are then used in the next step.

[0205] Step 3:

[0206] The server monitors the user's emotional state in real time based on the analyzed emotional data. Using monitoring tools, it evaluates whether the emotional score exceeds a set threshold. Based on the evaluation results, if the emotional score reaches an abnormal level, preparations are made to activate warning mechanisms.

[0207] Step 4:

[0208] If the emotion score exceeds a certain threshold, the server will send a message to the user via a warning system, encouraging relaxation. Specifically, it utilizes a generative AI model to create appropriate wording tailored to the situation and delivers it to the user via the transmission system.

[0209] Step 5:

[0210] If the server detects a more serious emotional state, it will use a communication channel to send a notification to a pre-registered professional. This notification will include the user's emotional score and a summary of the situation, which will be used by the professional to respond quickly. In this step, the analysis results and emotional state are provided as prompt messages, generating information to encourage the professional to take action.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention adopts the following configuration to implement a system aimed at supporting users' mental health. Users input their consultation content and concerns into the system through a dedicated application. This input is transmitted to a server via a terminal. The server analyzes the received content using natural language processing technology and generates an appropriate response based on the results.

[0228] This generated response is sent from the server to the terminal and notified to the user. If the server determines that the consultation contains specific keywords, it prepares to provide additional support. For example, if the consultation contains expressions of stress or anxiety, it may provide information on stress management methods or access to relaxation resources.

[0229] Furthermore, if a user's inquiry requires expert judgment or intervention, the server will send a notification to a pre-configured expert. This notification will include part of the user's inquiry and analysis results, enabling the expert to respond quickly.

[0230] For example, if a user enters "I'm having trouble with relationships at school," the server analyzes this content, generates "tips for improving relationships" as a response, and notifies the user via their device. Furthermore, if the content contains certain keywords indicating the potential for the situation to escalate, it sends a notification to an expert along with detailed analysis results.

[0231] Thus, this system aims to provide more effective and timely mental health support by interacting with users in real time.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user launches the application and enters their inquiry details. After entering the details, they press the send button to prepare the data for processing on their device.

[0235] Step 2:

[0236] The terminal performs the procedure of sending the consultation details entered by the user to the server. Once the transmission is complete, a notification of completion is displayed to the user on the terminal.

[0237] Step 3:

[0238] The server stores the data received from the terminal in a database for analysis and passes the contents to a natural language processing (NLP) analysis module. The analysis module processes the text data and performs sentiment analysis and keyword extraction.

[0239] Step 4:

[0240] The server's analysis module generates an appropriate response based on the extracted information. This response may include specific advice or suggestions for psychological support regarding the user's concerns.

[0241] Step 5:

[0242] The server performs communication processing to send the generated response to the terminal. As a result of the communication, the response message arrives at the user's terminal.

[0243] Step 6:

[0244] The terminal notifies the user of the response message sent from the server and indicates that a reply to the inquiry is available.

[0245] Step 7:

[0246] When the server detects specific keywords during analysis, it determines whether additional assistance is needed. If it determines that assistance is needed, it prepares to push notifications to the user with additional information or resources.

[0247] Step 8:

[0248] If the server determines that the consultation is urgent, it will send a notification to registered experts. This notification will include a summary of the user's consultation and the results of the analysis.

[0249] Step 9:

[0250] If a specialist receives notification, they will investigate the details and contact the user to provide direct assistance if necessary.

[0251] (Example 1)

[0252] 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 glasses 214 will be referred to as the "terminal."

[0253] In modern society, mental health problems are steadily increasing, yet there is still insufficient access to resources where individuals can easily seek advice. Furthermore, mechanisms for appropriately connecting individuals with professionals when support is needed are underdeveloped. In this situation, there is a need for a system that allows users to consult with professionals in real time with peace of mind, and to receive prompt intervention from experts when necessary.

[0254] 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.

[0255] In this invention, the server includes a device for acquiring consultation content, a device for analyzing the consultation content using language processing technology, and a device for generating a response based on the analyzed information. This enables users to quickly and effectively seek advice on mental health issues and receive support from experts as needed.

[0256] A "device for acquiring consultation content" is a device that collects the mental health consultation content entered by the user and sends it to the next processing stage.

[0257] "Language processing technology" refers to techniques for analyzing natural language and extracting meaning and emotion, and includes means of analyzing information based on text data.

[0258] A "device for analysis" is a device that uses language processing technology to analyze the acquired consultation content and extract emotions and important elements.

[0259] A "response generation device" is a device that automatically creates an appropriate response for the user based on analyzed information and prepares feedback for the user.

[0260] A "device that provides assistance when specific words are identified" is a device that provides additional information and support when specific keywords are included in the content of a consultation.

[0261] A "device that sends a warning when professional intervention is required" is a device that immediately sends a notification to a professional when it is determined that the user's consultation requires the attention of a professional.

[0262] Embodiments for carrying out this invention are described below.

[0263] Users input their mental health consultation details using a dedicated application. The terminal receives this input and sends it to the server using the secure communication protocol HTTPS. The server uses natural language processing (NLP) techniques to analyze the received consultation details. This technique utilizes open-source libraries such as "spaCy".

[0264] The server processes the consultation content using language processing technology, extracting emotions and important keywords. This allows for an analysis of the sentiment behind the consultation. Based on the analysis results, the server generates an appropriate response using a generative AI model. The GPT model, one of the generative AI models, is used for this response generation.

[0265] The generated response is sent back to the terminal via a secure communication protocol, and the terminal notifies the user of the response. If the consultation content contains specific keywords, the server will provide additional support information, such as materials on stress management or information on support services.

[0266] Furthermore, if the consultation requires professional intervention, the server will send a warning to a designated specialist. This warning includes a summary of the consultation and analysis results, allowing the specialist to respond quickly.

[0267] For example, if a user inputs "I have concerns about relationships at school," the server could analyze this inquiry, generate a response offering "hints and advice for improving relationships," and notify the user. An example of a prompt for the generating AI model would be, "When a user asks for advice about relationships at school, output the corresponding advice."

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users input their mental health consultation details using a dedicated application. The entered consultation details are saved on the device in text format. The user's input data is prepared for the next processing step using a secure communication protocol.

[0271] Step 2:

[0272] The device sends the saved consultation details to the server. This transmission uses the HTTPS protocol, and the data is transmitted to the server in an encrypted state. This protects user privacy and ensures secure data transfer.

[0273] Step 3:

[0274] The server receives the consultation content and analyzes the data using a natural language processing engine. This analysis includes sentiment analysis and keyword extraction of the input text. Specifically, a natural language processing library is used to analyze the sentence structure and classify the sentiment. The output consists of word-for-word analysis results and sentiment information.

[0275] Step 4:

[0276] The server sends a prompt sentence to the generated AI model based on the analysis result. The prompt sentence reflects the solution and advice content for the user's problem derived from the analysis result. The generated AI model, such as the GPT model, receives this prompt and automatically generates an appropriate response. The response text is output and the next step can be advanced.

[0277] Step 5:

[0278] The server receives the generated response text and prepares to send it to the terminal. This output response is securely sent to the terminal again using the HTTPS protocol.

[0279] Step 6:

[0280] The terminal notifies the user of the response text received from the server. The user can confirm the response through the application and receive the necessary support information. With the notified response, the user can consider new actions.

[0281] Step 7:

[0282] If the server determines that the consultation content contains specific keywords, it prepares additional support information. For example, if keywords such as "stress" or "anxiety" are included, stress management methods and information on support institutions are generated, and alerts are sent to experts as necessary. As a result, the user can receive appropriate support according to the situation.

[0283] (Application Example 1)

[0284] Next, Application 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".

[0285] In modern society, it is extremely important to appropriately manage the psychological state of users and promptly provide necessary support. However, many mental health support systems strongly rely on input information from users and lack sufficient continuous monitoring and cooperation with professional support. Additionally, there is a demand for a mechanism that can monitor the emotional state of users in real time and quickly respond when an abnormality is detected.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0287] In this invention, the server includes a data acquisition means for receiving content from a user, an information analysis means for analyzing the content, and a data generation means for generating appropriate feedback based on the analysis result. As a result, continuous monitoring of the emotional state of the user becomes possible, and when an abnormality is detected, a warning can be quickly generated and notified.

[0288] The "data acquisition means" is a device or function for receiving content from a user and converting it into a form that can be processed within the system.

[0289] The "information analysis means" is a device or function that analyzes the acquired content and uses natural language processing technology to understand its content and meaning.

[0290] The "data generation means" is a device or function that generates appropriate feedback to the user based on the analysis result obtained by the information analysis means.

[0291] The "notification means" is a device or function for transmitting the generated feedback and warning to the user.

[0292] The "support providing means" is a device or function for providing assistance and related information to the user when a specific discriminant word is detected.

[0293] "Communication transmission means" refers to a device or function for sending a notification to an expert when content from a user requires expert advice.

[0294] "State monitoring means" refers to a device or function for continuously monitoring a user's emotional state and detecting any abnormal state.

[0295] An "anomaly detection means" is a device or function for recognizing an anomaly in the user's emotional state detected by a state monitoring means and generating a warning.

[0296] In this invention, a server and a user terminal are the main components of the system. The server receives user content using data acquisition means, and then analyzes the content using information analysis means, which utilizes natural language processing technology.

[0297] Based on the analysis results, the server generates appropriate feedback using data generation means and notifies the user via notification means. This feedback takes into account the user's mental health status and, in some cases, includes alerts to professionals.

[0298] Furthermore, the server utilizes state monitoring mechanisms to continuously monitor the user's emotional state. This allows anomaly detection mechanisms to detect unusual emotional changes early and generate warnings for the user.

[0299] The server is expected to use "Transformers," a natural language processing library developed using the Python language. A concrete example of a prompt would be, "When a user enters 'I'm feeling stressed because I'm having trouble with relationships at work,' determine whether that emotion is negative." This is expected to fully utilize the characteristics of mental health support and provide better support to users.

[0300] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0301] Step 1:

[0302] The server receives the input content of the user from the terminal. The input is text data in which the user freely describes their feelings and worries. This input is directly passed to the next processing step.

[0303] Step 2:

[0304] The server uses information analysis means to analyze the received input content of the user using the natural language processing library "Transformers". Specifically, it analyzes the sentiment of the input text and generates labels such as positive, negative, and neutral. Thereby, data for grasping the sentiment tendency of the user's input content is obtained.

[0305] Step 3:

[0306] Based on the analysis results, the server generates feedback for the user through data generation means. Using a generation AI model, an appropriate message corresponding to the analyzed sentiment is created. It is required that the message generated by this prompt text includes content beneficial to the user.

[0307] Step 4:

[0308] The server uses notification means to notify the user of the generated feedback. As output, a feedback message is sent to the user's terminal, and the user checks it on the terminal.

[0309] Step 5:

[0310] The server continues to monitor the user's emotional state using state monitoring means. When the anomaly detection means detects an anomaly from the tendency of the analysis results, it generates a warning and determines whether communication to an expert is necessary.

[0311] Step 6:

[0312] The server will use communication methods as needed to send alerts to experts. The transmitted data will include user input and the results of its analysis, enabling experts to respond quickly. This output is the alert notification received by the experts.

[0313] 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.

[0314] This invention is a system that analyzes user inquiries and provides appropriate support, and in particular, it implements a form that incorporates an emotion engine that recognizes the user's emotions. First, the user inputs their inquiry through a dedicated application. This input is sent from the terminal to the server.

[0315] When the server receives input, it analyzes its content using natural language processing technology. The analysis includes an emotion engine that extracts emotions from the text data of the consultation and classifies them into categories such as positive, negative, and neutral. This emotion identification result influences the response generation mechanism, adjusting the tone and content of the generated response.

[0316] For example, if a user enters "I've been feeling really down lately," the emotion engine recognizes this expression as a negative emotion. The response generation system then prepares a message of comfort or encouragement, which is then sent to the user via the device.

[0317] Furthermore, the emotion engine has a learning function that references the user's emotional history, improving the accuracy of emotion analysis by learning from previous consultation data. This function enables more personalized support through ongoing consultations.

[0318] Furthermore, if the server determines that the content of the consultation and the perceived emotions are serious enough to require professional intervention, it will send a notification to registered professionals. This notification will include a summary of the user's consultation and the results of the sentiment analysis, enabling professionals to respond promptly.

[0319] Notifications to users are delivered in real time via their devices, providing a fast interface, and users receive additional support and hotline information when needed. In this way, this system, which incorporates an emotion engine, enables users to receive rapid and accurate mental health support.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user launches a dedicated application and enters their inquiry details. Once the input is complete, pressing the send button causes the terminal to process the data and begin sending it to the server.

[0323] Step 2:

[0324] The terminal uses a communication protocol to send data entered by the user to the server, ensuring the secure transfer of data.

[0325] Step 3:

[0326] The server saves the consultation details received from the terminal to a database. Afterward, it prepares to pass the data to an analysis module that uses natural language processing technology.

[0327] Step 4:

[0328] The server's analysis module analyzes the received consultation content and uses an emotion engine to recognize emotions along with extracting keywords. This includes the process of identifying emotions such as positive, negative, and neutral from the consultation content.

[0329] Step 5:

[0330] The emotion engine, based on the analysis results, further refines the analysis by referring to the user's emotional history. Through this process, it understands the user's unique emotional patterns and utilizes them to generate responses.

[0331] Step 6:

[0332] The server operates a response generation module based on the analysis results to create the optimal response message. This message is then adjusted to match the user's emotional state.

[0333] Step 7:

[0334] The generated response message is ready to be sent to the terminal via a notification mechanism, and the message is sent from the server to the terminal.

[0335] Step 8:

[0336] The terminal displays a response message sent from the server to the user. The user receives this message and receives feedback regarding the consultation.

[0337] Step 9:

[0338] The server also automatically sends notifications to registered professionals if the consultation content and sentiment analysis results indicate that urgent or professional intervention is required. This notification includes a summary of the user's consultation content and the results of the sentiment analysis.

[0339] Step 10:

[0340] Experts will receive notifications and, if necessary, take steps to communicate with users to provide additional support or intervention.

[0341] (Example 2)

[0342] 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".

[0343] Conventional systems had problems such as insufficient individualization of sentiment analysis for user inquiries, and difficulty in providing prompt expert intervention or additional support as needed. Furthermore, personalization of responses that took sentiment history into account was not sufficiently achieved, and user satisfaction was not sufficiently improved.

[0344] 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.

[0345] In this invention, the server includes communication means for receiving text data from a user, analysis means including an emotion engine for analyzing the text data and extracting emotions, and response generation means for generating a response using a generation AI model based on the analysis results. This makes it possible to generate personalized responses tailored to the content of the consultation and quickly notify the user, thereby providing appropriate support or expert intervention as needed.

[0346] "Communication means" refers to the technology and methods for receiving text data from a user and transmitting it to a server.

[0347] The "analysis method" is a mechanism that analyzes received text data using natural language processing technology, extracts emotions through an emotion engine, and classifies them into categories.

[0348] An "emotion engine" is a specialized technology for detecting emotions from text data and classifying them into categories such as positive, negative, and neutral.

[0349] A "response generation means" is a mechanism that uses a generative AI model to generate a response suitable for the user based on emotional information obtained by the analysis means.

[0350] A "notification method" is a method for transmitting the generated response to the user's terminal in real time.

[0351] A "means of providing support" refers to a method for providing additional support or assistance to a user when specific information is detected.

[0352] A "notification system" is a mechanism for sending notifications to experts when text data is deemed to require professional intervention.

[0353] "Learning methods" are techniques for analyzing past data to improve the accuracy of analysis and response generation.

[0354] "Personalization means" refers to methods for managing a user's emotional history and providing personalized responses tailored to each individual user.

[0355] This invention is a system aimed at supporting users' mental health, utilizing emotion analysis technology to provide appropriate support to users. Specifically, the server analyzes the content of the consultation received from the user and generates a response using a generative AI model.

[0356] The server receives text data from the user's terminal via a communication method. This text data is entered by the user using a dedicated application and sent from the terminal. The application used runs on iOS and Android platforms and has a user-friendly interface.

[0357] The received text data is analyzed using natural language processing techniques by an analysis system on the server. The analysis utilizes commercially available natural language processing libraries, and emotions are extracted from the text data through an emotion engine. This emotion engine has the ability to classify emotions into positive, negative, and neutral categories.

[0358] Based on the analyzed sentiment data, the server utilizes a generative AI model to generate appropriate responses. For example, the generative AI model automatically generates an appropriate response based on a prompt such as, "The user is feeling tired because they have been having trouble sleeping. Please provide a message of encouragement."

[0359] The generated response is delivered to the user's device in real time via a notification system. This allows the user to receive support quickly, and if necessary, hotline information and access information to resources from additional support channels are also provided.

[0360] Furthermore, the emotion engine learns from past consultations and improves its analytical accuracy, enabling more personalized support through ongoing consultations. In addition, if a serious consultation is detected, a notification system transmits the information to a specialist, allowing for prompt expert intervention. This overall system mechanism provides comprehensive and personalized support to the user.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] Users input their consultation details using a dedicated application. The information entered by the user is saved as text data on the device and sent to the server when the send button is pressed. The input data includes the user's consultation details and a timestamp. The server receives this data using a secure communication protocol.

[0364] Step 2:

[0365] The server analyzes the received text data. Using natural language processing technology as the analysis method, it activates an emotion engine to extract emotions from the text content. The received text data is output as emotion data categorized into positive, negative, and neutral. This emotion data is evaluated by the emotion engine, and a log of the analysis results is recorded on the server.

[0366] Step 3:

[0367] The server generates a response based on the analysis results. Using a generative AI model, it generates a response based on the prompt "Consider the user's emotion to be XX and generate an appropriate response." This prompt prompt causes the model to construct a response based on the input emotion data. The generated response has a tone and content appropriate for the user. The generated response message is saved on the server as output.

[0368] Step 4:

[0369] The server notifies the user of the generated response. Using a notification method, the generated response is sent to the user's device in real time. Here, the server uses push notification functionality to display the message on the device. The user can check the received message within the application and view the details.

[0370] Step 5:

[0371] If necessary, the server will provide additional support. This support system detects specific information from the emotion engine and analysis results, providing users with registered hotline information and access to resources. This information is personalized according to the user's situation, enabling them to be guided to the appropriate resources.

[0372] Step 6:

[0373] If the server determines that professional intervention is necessary, it will notify a specialist using a notification system. This notification will include analyzed sentiment data and a summary of the consultation. Based on this information, the specialist can respond quickly. The server will securely send notifications while maintaining the accuracy of the information.

[0374] (Application Example 2)

[0375] 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."

[0376] Current information and communication systems struggle to accurately understand users' emotional states and respond promptly based on that understanding. This makes it difficult to efficiently manage users' mental health and safety, and there is a problem of insufficient support, especially when users are experiencing high levels of stress and anxiety.

[0377] 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.

[0378] In this invention, the server includes communication means for receiving information from the user, analysis means for analyzing the information, and monitoring means for monitoring the user's emotional state in real time. This makes it possible to grasp the user's emotional state in real time and provide appropriate responses and support.

[0379] A "means of communication for receiving information" is a mechanism for receiving information entered by a user as data and transferring it to a system.

[0380] "Information analysis tools" are technologies for processing received information and evaluating its content and characteristics.

[0381] A "response-providing means" is a means of returning appropriate information or instructions to the user based on the analysis results.

[0382] "Transmission means" refers to the means by which generated responses or information are delivered to the user.

[0383] A "monitoring method that monitors emotional states in real time" is a technology that continuously observes and analyzes the user's psychological state.

[0384] A "warning mechanism that issues a warning" is a system that alerts the user when observed data indicates a dangerous or abnormal condition.

[0385] "Means of communication for sending out information" refers to means of reporting the situation to relevant parties and experts as needed and prompting them to take action.

[0386] A system that implements an application example of this invention has the function of analyzing the user's emotional state in real time and issuing warnings or contacting experts as needed.

[0387] First, the terminal acquires data through a communication method that receives information from the user. This data mainly consists of entered text information, but may also include detailed feedback indicating emotions. The received information is transferred to a server and processed by analytical means that analyze the information.

[0388] The server extracts the user's emotional state from the information using analysis methods that employ natural language processing techniques. Natural language processing typically utilizes machine learning models as sentiment analysis engines. These models evaluate the input text and classify it into emotional categories such as positive, negative, and neutral.

[0389] The server quantitatively evaluates stress levels and anxiety through a monitoring system that monitors the user's emotional state in real time. The evaluation results are compared to pre-set criteria, and if the criteria are exceeded, a warning system is activated to issue a warning. This system sends the user a message encouraging relaxation.

[0390] Furthermore, if a serious situation is identified, the server can send notifications to registered specialists through its designated communication channels. This allows for a swift response, protecting the user's safety and health.

[0391] For example, if a user enters "I've been suffering from excessive stress lately" into the device, the system immediately assesses that emotion as negative, and a warning system sends a message encouraging relaxation. If necessary, it also notifies registered professionals for further action.

[0392] Examples of prompt statements for a generative AI model are as follows:

[0393] "User input: 'I've been suffering from excessive stress lately.' Analyze this and generate appropriate emotion labels and responses."

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The terminal receives information from the user. This information is text data indicating the content of the consultation and emotions entered by the user. After receiving this data, the terminal uses a communication method to send it to the server.

[0397] Step 2:

[0398] The server analyzes data through natural language processing techniques using analytical means to parse information sent from the terminal. Using the text data received as input, the server extracts emotions using a machine learning model and classifies them into positive, negative, and neutral emotional categories. These analysis results are then used in the next step.

[0399] Step 3:

[0400] The server monitors the user's emotional state in real time based on the analyzed emotional data. Using monitoring tools, it evaluates whether the emotional score exceeds a set threshold. Based on the evaluation results, if the emotional score reaches an abnormal level, preparations are made to activate warning mechanisms.

[0401] Step 4:

[0402] If the emotion score exceeds a certain threshold, the server will send a message to the user via a warning system, encouraging relaxation. Specifically, it utilizes a generative AI model to create appropriate wording tailored to the situation and delivers it to the user via the transmission system.

[0403] Step 5:

[0404] If the server detects a more serious emotional state, it will use a communication channel to send a notification to a pre-registered professional. This notification will include the user's emotional score and a summary of the situation, which will be used by the professional to respond quickly. In this step, the analysis results and emotional state are provided as prompt messages, generating information to encourage the professional to take action.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention adopts the following configuration to implement a system aimed at supporting users' mental health. Users input their consultation content and concerns into the system through a dedicated application. This input is transmitted to a server via a terminal. The server analyzes the received content using natural language processing technology and generates an appropriate response based on the results.

[0422] This generated response is sent from the server to the terminal and notified to the user. If the server determines that the consultation contains specific keywords, it prepares to provide additional support. For example, if the consultation contains expressions of stress or anxiety, it may provide information on stress management methods or access to relaxation resources.

[0423] Furthermore, if a user's inquiry requires expert judgment or intervention, the server will send a notification to a pre-configured expert. This notification will include part of the user's inquiry and analysis results, enabling the expert to respond quickly.

[0424] For example, if a user enters "I'm having trouble with relationships at school," the server analyzes this content, generates "tips for improving relationships" as a response, and notifies the user via their device. Furthermore, if the content contains certain keywords indicating the potential for the situation to escalate, it sends a notification to an expert along with detailed analysis results.

[0425] Thus, this system aims to provide more effective and timely mental health support by interacting with users in real time.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The user launches the application and enters their inquiry details. After entering the details, they press the send button to prepare the data for processing on their device.

[0429] Step 2:

[0430] The terminal performs the procedure of sending the consultation details entered by the user to the server. Once the transmission is complete, a notification of completion is displayed to the user on the terminal.

[0431] Step 3:

[0432] The server stores the data received from the terminal in a database for analysis and passes the contents to a natural language processing (NLP) analysis module. The analysis module processes the text data and performs sentiment analysis and keyword extraction.

[0433] Step 4:

[0434] The server's analysis module generates an appropriate response based on the extracted information. This response may include specific advice or suggestions for psychological support regarding the user's concerns.

[0435] Step 5:

[0436] The server performs communication processing to send the generated response to the terminal. As a result of the communication, the response message arrives at the user's terminal.

[0437] Step 6:

[0438] The terminal notifies the user of the response message sent from the server and indicates that a reply to the inquiry is available.

[0439] Step 7:

[0440] When the server detects specific keywords during analysis, it determines whether additional assistance is needed. If it determines that assistance is needed, it prepares to push notifications to the user with additional information or resources.

[0441] Step 8:

[0442] If the server determines that the consultation is urgent, it will send a notification to registered experts. This notification will include a summary of the user's consultation and the results of the analysis.

[0443] Step 9:

[0444] If a specialist receives notification, they will investigate the details and contact the user to provide direct assistance if necessary.

[0445] (Example 1)

[0446] 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."

[0447] In modern society, mental health problems are steadily increasing, yet there is still insufficient access to resources where individuals can easily seek advice. Furthermore, mechanisms for appropriately connecting individuals with professionals when support is needed are underdeveloped. In this situation, there is a need for a system that allows users to consult with professionals in real time with peace of mind, and to receive prompt intervention from experts when necessary.

[0448] 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.

[0449] In this invention, the server includes a device for acquiring consultation content, a device for analyzing the consultation content using language processing technology, and a device for generating a response based on the analyzed information. This enables users to quickly and effectively seek advice on mental health issues and receive support from experts as needed.

[0450] A "device for acquiring consultation content" is a device that collects the mental health consultation content entered by the user and sends it to the next processing stage.

[0451] "Language processing technology" refers to techniques for analyzing natural language and extracting meaning and emotion, and includes means of analyzing information based on text data.

[0452] A "device for analysis" is a device that uses language processing technology to analyze the acquired consultation content and extract emotions and important elements.

[0453] A "response generation device" is a device that automatically creates an appropriate response for the user based on analyzed information and prepares feedback for the user.

[0454] A "device that provides assistance when specific words are identified" is a device that provides additional information and support when specific keywords are included in the content of a consultation.

[0455] A "device that sends a warning when professional intervention is required" is a device that immediately sends a notification to a professional when it is determined that the user's consultation requires the attention of a professional.

[0456] Embodiments for carrying out this invention are described below.

[0457] Users input their mental health consultation details using a dedicated application. The terminal receives this input and sends it to the server using the secure communication protocol HTTPS. The server uses natural language processing (NLP) techniques to analyze the received consultation details. This technique utilizes open-source libraries such as "spaCy".

[0458] The server processes the consultation content using language processing technology, extracting emotions and important keywords. This allows for an analysis of the sentiment behind the consultation. Based on the analysis results, the server generates an appropriate response using a generative AI model. The GPT model, one of the generative AI models, is used for this response generation.

[0459] The generated response is sent back to the terminal via a secure communication protocol, and the terminal notifies the user of the response. If the consultation content contains specific keywords, the server will provide additional support information, such as materials on stress management or information on support services.

[0460] Furthermore, if the consultation requires professional intervention, the server will send a warning to a designated specialist. This warning includes a summary of the consultation and analysis results, allowing the specialist to respond quickly.

[0461] For example, if a user inputs "I have concerns about relationships at school," the server could analyze this inquiry, generate a response offering "hints and advice for improving relationships," and notify the user. An example of a prompt for the generating AI model would be, "When a user asks for advice about relationships at school, output the corresponding advice."

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] Users input their mental health consultation details using a dedicated application. The entered consultation details are saved on the device in text format. The user's input data is prepared for the next processing step using a secure communication protocol.

[0465] Step 2:

[0466] The device sends the saved consultation details to the server. This transmission uses the HTTPS protocol, and the data is transmitted to the server in an encrypted state. This protects user privacy and ensures secure data transfer.

[0467] Step 3:

[0468] The server receives the consultation content and analyzes the data using a natural language processing engine. This analysis includes sentiment analysis and keyword extraction of the input text. Specifically, a natural language processing library is used to analyze the sentence structure and classify the sentiment. The output consists of word-for-word analysis results and sentiment information.

[0469] Step 4:

[0470] The server sends a prompt message to the generating AI model based on the analysis results. The prompt message reflects solutions and advice for the user's problem derived from the analysis results. The generating AI model, such as a GPT model, receives this prompt and automatically generates an appropriate response. The response text is output, and the process proceeds to the next step.

[0471] Step 5:

[0472] The server receives the generated response text and prepares to send it to the terminal. This outputted response is then securely sent to the terminal again using the HTTPS protocol.

[0473] Step 6:

[0474] The terminal notifies the user of the response text received from the server. The user can then review the response through the application and receive any necessary support information. The notified response allows the user to consider further actions.

[0475] Step 7:

[0476] If the server determines that the consultation content contains specific keywords, it will prepare additional support information. For example, if keywords such as "stress" or "anxiety" are included, information on stress management methods and support organizations will be generated, and alerts will be sent to professionals as needed. This allows users to receive appropriate support tailored to their situation.

[0477] (Application Example 1)

[0478] 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."

[0479] In modern society, it is crucial to appropriately manage users' psychological states and provide necessary support promptly. However, many mental health support systems tend to rely heavily on user input, lacking continuous monitoring and coordination with professional support. Furthermore, there is a need for a system that can monitor users' emotional states in real time and respond quickly when abnormalities are detected.

[0480] 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.

[0481] In this invention, the server includes data acquisition means for receiving content from a user, information analysis means for analyzing the content, and data generation means for generating appropriate feedback based on the analysis results. This enables continuous monitoring of the user's emotional state and allows for the rapid generation and notification of warnings if an anomaly is detected.

[0482] "Data acquisition means" refers to a device or function for receiving content from a user and converting it into a format that can be processed within the system.

[0483] "Information analysis means" refers to a device or function that uses natural language processing technology to analyze acquired content and understand its content and meaning.

[0484] "Data generation means" refers to a device or function that generates appropriate feedback for the user based on the analysis results obtained by the information analysis means.

[0485] "Notification means" refers to a device or function for communicating generated feedback or warnings to the user.

[0486] "Support provision means" refers to a device or function that provides assistance or related information to the user when a specific discriminant word is detected.

[0487] "Communication transmission means" refers to a device or function for sending a notification to an expert when content from a user requires expert advice.

[0488] "State monitoring means" refers to a device or function for continuously monitoring a user's emotional state and detecting any abnormal state.

[0489] An "anomaly detection means" is a device or function for recognizing an anomaly in the user's emotional state detected by a state monitoring means and generating a warning.

[0490] In this invention, a server and a user terminal are the main components of the system. The server receives user content using data acquisition means, and then analyzes the content using information analysis means, which utilizes natural language processing technology.

[0491] Based on the analysis results, the server generates appropriate feedback using data generation means and notifies the user via notification means. This feedback takes into account the user's mental health status and, in some cases, includes alerts to professionals.

[0492] Furthermore, the server utilizes state monitoring mechanisms to continuously monitor the user's emotional state. This allows anomaly detection mechanisms to detect unusual emotional changes early and generate warnings for the user.

[0493] The server is expected to use "Transformers," a natural language processing library developed using the Python language. A concrete example of a prompt would be, "When a user enters 'I'm feeling stressed because I'm having trouble with relationships at work,' determine whether that emotion is negative." This is expected to fully utilize the characteristics of mental health support and provide better support to users.

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] The server receives user input from the terminal. This input is text data in which the user freely describes their feelings and worries. This input is then passed directly to the next processing step.

[0497] Step 2:

[0498] The server uses information analysis tools to analyze the received user input using the natural language processing library "Transformers." Specifically, it analyzes the sentiment of the input text and generates labels such as positive, negative, and neutral. This provides data to understand the emotional tendencies of the user's input.

[0499] Step 3:

[0500] The server generates feedback for the user through data generation means based on the analysis results. Using a generative AI model, it creates appropriate messages corresponding to the analyzed emotions. The messages generated by this prompt must contain helpful and supportive content for the user.

[0501] Step 4:

[0502] The server uses a notification mechanism to inform the user of the generated feedback. The feedback message is sent to the user's device as output, and the user checks it on their device.

[0503] Step 5:

[0504] The server uses state monitoring measures to continuously monitor the user's emotional state. If the anomaly detection measures detect an anomaly based on the trends in the analysis results, they generate a warning and determine whether it is necessary to contact an expert.

[0505] Step 6:

[0506] The server will use communication methods as needed to send alerts to experts. The transmitted data will include user input and the results of its analysis, enabling experts to respond quickly. This output is the alert notification received by the experts.

[0507] 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.

[0508] This invention is a system that analyzes user inquiries and provides appropriate support, and in particular, it implements a form that incorporates an emotion engine that recognizes the user's emotions. First, the user inputs their inquiry through a dedicated application. This input is sent from the terminal to the server.

[0509] When the server receives input, it analyzes its content using natural language processing technology. The analysis includes an emotion engine that extracts emotions from the text data of the consultation and classifies them into categories such as positive, negative, and neutral. This emotion identification result influences the response generation mechanism, adjusting the tone and content of the generated response.

[0510] For example, if a user enters "I've been feeling really down lately," the emotion engine recognizes this expression as a negative emotion. The response generation system then prepares a message of comfort or encouragement, which is then sent to the user via the device.

[0511] Furthermore, the emotion engine has a learning function that references the user's emotional history, improving the accuracy of emotion analysis by learning from previous consultation data. This function enables more personalized support through ongoing consultations.

[0512] Furthermore, if the server determines that the content of the consultation and the perceived emotions are serious enough to require professional intervention, it will send a notification to registered professionals. This notification will include a summary of the user's consultation and the results of the sentiment analysis, enabling professionals to respond promptly.

[0513] Notifications to users are delivered in real time via their devices, providing a fast interface, and users receive additional support and hotline information when needed. In this way, this system, which incorporates an emotion engine, enables users to receive rapid and accurate mental health support.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The user launches a dedicated application and enters their inquiry details. Once the input is complete, pressing the send button causes the terminal to process the data and begin sending it to the server.

[0517] Step 2:

[0518] The terminal uses a communication protocol to send data entered by the user to the server, ensuring the secure transfer of data.

[0519] Step 3:

[0520] The server saves the consultation details received from the terminal to a database. Afterward, it prepares to pass the data to an analysis module that uses natural language processing technology.

[0521] Step 4:

[0522] The server's analysis module analyzes the received consultation content and uses an emotion engine to recognize emotions along with extracting keywords. This includes the process of identifying emotions such as positive, negative, and neutral from the consultation content.

[0523] Step 5:

[0524] The emotion engine, based on the analysis results, further refines the analysis by referring to the user's emotional history. Through this process, it understands the user's unique emotional patterns and utilizes them to generate responses.

[0525] Step 6:

[0526] The server operates a response generation module based on the analysis results to create the optimal response message. This message is then adjusted to match the user's emotional state.

[0527] Step 7:

[0528] The generated response message is ready to be sent to the terminal via a notification mechanism, and the message is sent from the server to the terminal.

[0529] Step 8:

[0530] The terminal displays a response message sent from the server to the user. The user receives this message and receives feedback regarding the consultation.

[0531] Step 9:

[0532] The server also automatically sends notifications to registered professionals if the consultation content and sentiment analysis results indicate that urgent or professional intervention is required. This notification includes a summary of the user's consultation content and the results of the sentiment analysis.

[0533] Step 10:

[0534] Experts will receive notifications and, if necessary, take steps to communicate with users to provide additional support or intervention.

[0535] (Example 2)

[0536] 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."

[0537] Conventional systems had problems such as insufficient individualization of sentiment analysis for user inquiries, and difficulty in providing prompt expert intervention or additional support as needed. Furthermore, personalization of responses that took sentiment history into account was not sufficiently achieved, and user satisfaction was not sufficiently improved.

[0538] 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.

[0539] In this invention, the server includes communication means for receiving text data from a user, analysis means including an emotion engine for analyzing the text data and extracting emotions, and response generation means for generating a response using a generation AI model based on the analysis results. This makes it possible to generate personalized responses tailored to the content of the consultation and quickly notify the user, thereby providing appropriate support or expert intervention as needed.

[0540] "Communication means" refers to the technology and methods for receiving text data from a user and transmitting it to a server.

[0541] The "analysis method" is a mechanism that analyzes received text data using natural language processing technology, extracts emotions through an emotion engine, and classifies them into categories.

[0542] An "emotion engine" is a specialized technology for detecting emotions from text data and classifying them into categories such as positive, negative, and neutral.

[0543] A "response generation means" is a mechanism that uses a generative AI model to generate a response suitable for the user based on emotional information obtained by the analysis means.

[0544] A "notification method" is a method for transmitting the generated response to the user's terminal in real time.

[0545] A "means of providing support" refers to a method for providing additional support or assistance to a user when specific information is detected.

[0546] A "notification system" is a mechanism for sending notifications to experts when text data is deemed to require professional intervention.

[0547] "Learning methods" are techniques for analyzing past data to improve the accuracy of analysis and response generation.

[0548] "Personalization means" refers to methods for managing a user's emotional history and providing personalized responses tailored to each individual user.

[0549] This invention is a system aimed at supporting users' mental health, utilizing emotion analysis technology to provide appropriate support to users. Specifically, the server analyzes the content of the consultation received from the user and generates a response using a generative AI model.

[0550] The server receives text data from the user's terminal via a communication method. This text data is entered by the user using a dedicated application and sent from the terminal. The application used runs on iOS and Android platforms and has a user-friendly interface.

[0551] The received text data is analyzed using natural language processing techniques by an analysis system on the server. The analysis utilizes commercially available natural language processing libraries, and emotions are extracted from the text data through an emotion engine. This emotion engine has the ability to classify emotions into positive, negative, and neutral categories.

[0552] Based on the analyzed sentiment data, the server utilizes a generative AI model to generate appropriate responses. For example, the generative AI model automatically generates an appropriate response based on a prompt such as, "The user is feeling tired because they have been having trouble sleeping. Please provide a message of encouragement."

[0553] The generated response is delivered to the user's device in real time via a notification system. This allows the user to receive support quickly, and if necessary, hotline information and access information to resources from additional support channels are also provided.

[0554] Furthermore, the emotion engine learns from past consultations and improves its analytical accuracy, enabling more personalized support through ongoing consultations. In addition, if a serious consultation is detected, a notification system transmits the information to a specialist, allowing for prompt expert intervention. This overall system mechanism provides comprehensive and personalized support to the user.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] Users input their consultation details using a dedicated application. The information entered by the user is saved as text data on the device and sent to the server when the send button is pressed. The input data includes the user's consultation details and a timestamp. The server receives this data using a secure communication protocol.

[0558] Step 2:

[0559] The server analyzes the received text data. Using natural language processing technology as the analysis method, it activates an emotion engine to extract emotions from the text content. The received text data is output as emotion data categorized into positive, negative, and neutral. This emotion data is evaluated by the emotion engine, and a log of the analysis results is recorded on the server.

[0560] Step 3:

[0561] The server generates a response based on the analysis results. Using a generative AI model, it generates a response based on the prompt "Consider the user's emotion to be XX and generate an appropriate response." This prompt prompt causes the model to construct a response based on the input emotion data. The generated response has a tone and content appropriate for the user. The generated response message is saved on the server as output.

[0562] Step 4:

[0563] The server notifies the user of the generated response. Using a notification method, the generated response is sent to the user's device in real time. Here, the server uses push notification functionality to display the message on the device. The user can check the received message within the application and view the details.

[0564] Step 5:

[0565] If necessary, the server will provide additional support. This support system detects specific information from the emotion engine and analysis results, providing users with registered hotline information and access to resources. This information is personalized according to the user's situation, enabling them to be guided to the appropriate resources.

[0566] Step 6:

[0567] If the server determines that professional intervention is necessary, it will notify a specialist using a notification system. This notification will include analyzed sentiment data and a summary of the consultation. Based on this information, the specialist can respond quickly. The server will securely send notifications while maintaining the accuracy of the information.

[0568] (Application Example 2)

[0569] 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."

[0570] Current information and communication systems struggle to accurately understand users' emotional states and respond promptly based on that understanding. This makes it difficult to efficiently manage users' mental health and safety, and there is a problem of insufficient support, especially when users are experiencing high levels of stress and anxiety.

[0571] 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.

[0572] In this invention, the server includes communication means for receiving information from the user, analysis means for analyzing the information, and monitoring means for monitoring the user's emotional state in real time. This makes it possible to grasp the user's emotional state in real time and provide appropriate responses and support.

[0573] A "means of communication for receiving information" is a mechanism for receiving information entered by a user as data and transferring it to a system.

[0574] "Information analysis tools" are technologies for processing received information and evaluating its content and characteristics.

[0575] A "response-providing means" is a means of returning appropriate information or instructions to the user based on the analysis results.

[0576] "Transmission means" refers to the means by which generated responses or information are delivered to the user.

[0577] A "monitoring method that monitors emotional states in real time" is a technology that continuously observes and analyzes the user's psychological state.

[0578] A "warning mechanism that issues a warning" is a system that alerts the user when observed data indicates a dangerous or abnormal condition.

[0579] "Means of communication for sending out information" refers to means of reporting the situation to relevant parties and experts as needed and prompting them to take action.

[0580] A system that implements an application example of this invention has the function of analyzing the user's emotional state in real time and issuing warnings or contacting experts as needed.

[0581] First, the terminal acquires data through a communication method that receives information from the user. This data mainly consists of entered text information, but may also include detailed feedback indicating emotions. The received information is transferred to a server and processed by analytical means that analyze the information.

[0582] The server extracts the user's emotional state from the information using analysis methods that employ natural language processing techniques. Natural language processing typically utilizes machine learning models as sentiment analysis engines. These models evaluate the input text and classify it into emotional categories such as positive, negative, and neutral.

[0583] The server quantitatively evaluates stress levels and anxiety through a monitoring system that monitors the user's emotional state in real time. The evaluation results are compared to pre-set criteria, and if the criteria are exceeded, a warning system is activated to issue a warning. This system sends the user a message encouraging relaxation.

[0584] Furthermore, if a serious situation is identified, the server can send notifications to registered specialists through its designated communication channels. This allows for a swift response, protecting the user's safety and health.

[0585] For example, if a user enters "I've been suffering from excessive stress lately" into the device, the system immediately assesses that emotion as negative, and a warning system sends a message encouraging relaxation. If necessary, it also notifies registered professionals for further action.

[0586] Examples of prompt statements for a generative AI model are as follows:

[0587] "User input: 'I've been suffering from excessive stress lately.' Analyze this and generate appropriate emotion labels and responses."

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The terminal receives information from the user. This information is text data indicating the content of the consultation and emotions entered by the user. After receiving this data, the terminal uses a communication method to send it to the server.

[0591] Step 2:

[0592] The server analyzes data through natural language processing techniques using analytical means to parse information sent from the terminal. Using the text data received as input, the server extracts emotions using a machine learning model and classifies them into positive, negative, and neutral emotional categories. These analysis results are then used in the next step.

[0593] Step 3:

[0594] The server monitors the user's emotional state in real time based on the analyzed emotional data. Using monitoring tools, it evaluates whether the emotional score exceeds a set threshold. Based on the evaluation results, if the emotional score reaches an abnormal level, preparations are made to activate warning mechanisms.

[0595] Step 4:

[0596] If the emotion score exceeds a certain threshold, the server will send a message to the user via a warning system, encouraging relaxation. Specifically, it utilizes a generative AI model to create appropriate wording tailored to the situation and delivers it to the user via the transmission system.

[0597] Step 5:

[0598] If the server detects a more serious emotional state, it will use a communication channel to send a notification to a pre-registered professional. This notification will include the user's emotional score and a summary of the situation, which will be used by the professional to respond quickly. In this step, the analysis results and emotional state are provided as prompt messages, generating information to encourage the professional to take action.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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".

[0616] This invention adopts the following configuration to implement a system aimed at supporting users' mental health. Users input their consultation content and concerns into the system through a dedicated application. This input is transmitted to a server via a terminal. The server analyzes the received content using natural language processing technology and generates an appropriate response based on the results.

[0617] This generated response is sent from the server to the terminal and notified to the user. If the server determines that the consultation contains specific keywords, it prepares to provide additional support. For example, if the consultation contains expressions of stress or anxiety, it may provide information on stress management methods or access to relaxation resources.

[0618] Furthermore, if a user's inquiry requires expert judgment or intervention, the server will send a notification to a pre-configured expert. This notification will include part of the user's inquiry and analysis results, enabling the expert to respond quickly.

[0619] For example, if a user enters "I'm having trouble with relationships at school," the server analyzes this content, generates "tips for improving relationships" as a response, and notifies the user via their device. Furthermore, if the content contains certain keywords indicating the potential for the situation to escalate, it sends a notification to an expert along with detailed analysis results.

[0620] Thus, this system aims to provide more effective and timely mental health support by interacting with users in real time.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The user launches the application and enters their inquiry details. After entering the details, they press the send button to prepare the data for processing on their device.

[0624] Step 2:

[0625] The terminal performs the procedure of sending the consultation details entered by the user to the server. Once the transmission is complete, a notification of completion is displayed to the user on the terminal.

[0626] Step 3:

[0627] The server stores the data received from the terminal in a database for analysis and passes the contents to a natural language processing (NLP) analysis module. The analysis module processes the text data and performs sentiment analysis and keyword extraction.

[0628] Step 4:

[0629] The server's analysis module generates an appropriate response based on the extracted information. This response may include specific advice or suggestions for psychological support regarding the user's concerns.

[0630] Step 5:

[0631] The server performs communication processing to send the generated response to the terminal. As a result of the communication, the response message arrives at the user's terminal.

[0632] Step 6:

[0633] The terminal notifies the user of the response message sent from the server and indicates that a reply to the inquiry is available.

[0634] Step 7:

[0635] When the server detects specific keywords during analysis, it determines whether additional assistance is needed. If it determines that assistance is needed, it prepares to push notifications to the user with additional information or resources.

[0636] Step 8:

[0637] If the server determines that the consultation is urgent, it will send a notification to registered experts. This notification will include a summary of the user's consultation and the results of the analysis.

[0638] Step 9:

[0639] If a specialist receives notification, they will investigate the details and contact the user to provide direct assistance if necessary.

[0640] (Example 1)

[0641] 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".

[0642] In modern society, mental health problems are steadily increasing, yet there is still insufficient access to resources where individuals can easily seek advice. Furthermore, mechanisms for appropriately connecting individuals with professionals when support is needed are underdeveloped. In this situation, there is a need for a system that allows users to consult with professionals in real time with peace of mind, and to receive prompt intervention from experts when necessary.

[0643] 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.

[0644] In this invention, the server includes a device for acquiring consultation content, a device for analyzing the consultation content using language processing technology, and a device for generating a response based on the analyzed information. This enables users to quickly and effectively seek advice on mental health issues and receive support from experts as needed.

[0645] A "device for acquiring consultation content" is a device that collects the mental health consultation content entered by the user and sends it to the next processing stage.

[0646] "Language processing technology" refers to techniques for analyzing natural language and extracting meaning and emotion, and includes means of analyzing information based on text data.

[0647] A "device for analysis" is a device that uses language processing technology to analyze the acquired consultation content and extract emotions and important elements.

[0648] A "response generation device" is a device that automatically creates an appropriate response for the user based on analyzed information and prepares feedback for the user.

[0649] A "device that provides assistance when specific words are identified" is a device that provides additional information and support when specific keywords are included in the content of a consultation.

[0650] A "device that sends a warning when professional intervention is required" is a device that immediately sends a notification to a professional when it is determined that the user's consultation requires the attention of a professional.

[0651] Embodiments for carrying out this invention are described below.

[0652] Users input their mental health consultation details using a dedicated application. The terminal receives this input and sends it to the server using the secure communication protocol HTTPS. The server uses natural language processing (NLP) techniques to analyze the received consultation details. This technique utilizes open-source libraries such as "spaCy".

[0653] The server processes the consultation content using language processing technology, extracting emotions and important keywords. This allows for an analysis of the sentiment behind the consultation. Based on the analysis results, the server generates an appropriate response using a generative AI model. The GPT model, one of the generative AI models, is used for this response generation.

[0654] The generated response is sent back to the terminal via a secure communication protocol, and the terminal notifies the user of the response. If the consultation content contains specific keywords, the server will provide additional support information, such as materials on stress management or information on support services.

[0655] Furthermore, if the consultation requires professional intervention, the server will send a warning to a designated specialist. This warning includes a summary of the consultation and analysis results, allowing the specialist to respond quickly.

[0656] For example, if a user inputs "I have concerns about relationships at school," the server could analyze this inquiry, generate a response offering "hints and advice for improving relationships," and notify the user. An example of a prompt for the generating AI model would be, "When a user asks for advice about relationships at school, output the corresponding advice."

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] Users input their mental health consultation details using a dedicated application. The entered consultation details are saved on the device in text format. The user's input data is prepared for the next processing step using a secure communication protocol.

[0660] Step 2:

[0661] The device sends the saved consultation details to the server. This transmission uses the HTTPS protocol, and the data is transmitted to the server in an encrypted state. This protects user privacy and ensures secure data transfer.

[0662] Step 3:

[0663] The server receives the consultation content and analyzes the data using a natural language processing engine. This analysis includes sentiment analysis and keyword extraction of the input text. Specifically, a natural language processing library is used to analyze the sentence structure and classify the sentiment. The output consists of word-for-word analysis results and sentiment information.

[0664] Step 4:

[0665] The server sends a prompt message to the generating AI model based on the analysis results. The prompt message reflects solutions and advice for the user's problem derived from the analysis results. The generating AI model, such as a GPT model, receives this prompt and automatically generates an appropriate response. The response text is output, and the process proceeds to the next step.

[0666] Step 5:

[0667] The server receives the generated response text and prepares to send it to the terminal. This outputted response is then securely sent to the terminal again using the HTTPS protocol.

[0668] Step 6:

[0669] The terminal notifies the user of the response text received from the server. The user can then review the response through the application and receive any necessary support information. The notified response allows the user to consider further actions.

[0670] Step 7:

[0671] If the server determines that the consultation content contains specific keywords, it will prepare additional support information. For example, if keywords such as "stress" or "anxiety" are included, information on stress management methods and support organizations will be generated, and alerts will be sent to professionals as needed. This allows users to receive appropriate support tailored to their situation.

[0672] (Application Example 1)

[0673] 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".

[0674] In modern society, it is crucial to appropriately manage users' psychological states and provide necessary support promptly. However, many mental health support systems tend to rely heavily on user input, lacking continuous monitoring and coordination with professional support. Furthermore, there is a need for a system that can monitor users' emotional states in real time and respond quickly when abnormalities are detected.

[0675] 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.

[0676] In this invention, the server includes data acquisition means for receiving content from a user, information analysis means for analyzing the content, and data generation means for generating appropriate feedback based on the analysis results. This enables continuous monitoring of the user's emotional state and allows for the rapid generation and notification of warnings if an anomaly is detected.

[0677] "Data acquisition means" refers to a device or function for receiving content from a user and converting it into a format that can be processed within the system.

[0678] "Information analysis means" refers to a device or function that uses natural language processing technology to analyze acquired content and understand its content and meaning.

[0679] "Data generation means" refers to a device or function that generates appropriate feedback for the user based on the analysis results obtained by the information analysis means.

[0680] "Notification means" refers to a device or function for communicating generated feedback or warnings to the user.

[0681] "Support provision means" refers to a device or function that provides assistance or related information to the user when a specific discriminant word is detected.

[0682] "Communication transmission means" refers to a device or function for sending a notification to an expert when content from a user requires expert advice.

[0683] "State monitoring means" refers to a device or function for continuously monitoring a user's emotional state and detecting any abnormal state.

[0684] An "anomaly detection means" is a device or function for recognizing an anomaly in the user's emotional state detected by a state monitoring means and generating a warning.

[0685] In this invention, a server and a user terminal are the main components of the system. The server receives user content using data acquisition means, and then analyzes the content using information analysis means, which utilizes natural language processing technology.

[0686] Based on the analysis results, the server generates appropriate feedback using data generation means and notifies the user via notification means. This feedback takes into account the user's mental health status and, in some cases, includes alerts to professionals.

[0687] Furthermore, the server utilizes state monitoring mechanisms to continuously monitor the user's emotional state. This allows anomaly detection mechanisms to detect unusual emotional changes early and generate warnings for the user.

[0688] The server is expected to use "Transformers," a natural language processing library developed using the Python language. A concrete example of a prompt would be, "When a user enters 'I'm feeling stressed because I'm having trouble with relationships at work,' determine whether that emotion is negative." This is expected to fully utilize the characteristics of mental health support and provide better support to users.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The server receives user input from the terminal. This input is text data in which the user freely describes their feelings and worries. This input is then passed directly to the next processing step.

[0692] Step 2:

[0693] The server uses information analysis tools to analyze the received user input using the natural language processing library "Transformers." Specifically, it analyzes the sentiment of the input text and generates labels such as positive, negative, and neutral. This provides data to understand the emotional tendencies of the user's input.

[0694] Step 3:

[0695] The server generates feedback for the user through data generation means based on the analysis results. Using a generative AI model, it creates appropriate messages corresponding to the analyzed emotions. The messages generated by this prompt must contain helpful and supportive content for the user.

[0696] Step 4:

[0697] The server uses a notification mechanism to inform the user of the generated feedback. The feedback message is sent to the user's device as output, and the user checks it on their device.

[0698] Step 5:

[0699] The server uses state monitoring measures to continuously monitor the user's emotional state. If the anomaly detection measures detect an anomaly based on the trends in the analysis results, they generate a warning and determine whether it is necessary to contact an expert.

[0700] Step 6:

[0701] The server will use communication methods as needed to send alerts to experts. The transmitted data will include user input and the results of its analysis, enabling experts to respond quickly. This output is the alert notification received by the experts.

[0702] 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.

[0703] This invention is a system that analyzes user inquiries and provides appropriate support, and in particular, it implements a form that incorporates an emotion engine that recognizes the user's emotions. First, the user inputs their inquiry through a dedicated application. This input is sent from the terminal to the server.

[0704] When the server receives input, it analyzes its content using natural language processing technology. The analysis includes an emotion engine that extracts emotions from the text data of the consultation and classifies them into categories such as positive, negative, and neutral. This emotion identification result influences the response generation mechanism, adjusting the tone and content of the generated response.

[0705] For example, if a user enters "I've been feeling really down lately," the emotion engine recognizes this expression as a negative emotion. The response generation system then prepares a message of comfort or encouragement, which is then sent to the user via the device.

[0706] Furthermore, the emotion engine has a learning function that references the user's emotional history, improving the accuracy of emotion analysis by learning from previous consultation data. This function enables more personalized support through ongoing consultations.

[0707] Furthermore, if the server determines that the content of the consultation and the perceived emotions are serious enough to require professional intervention, it will send a notification to registered professionals. This notification will include a summary of the user's consultation and the results of the sentiment analysis, enabling professionals to respond promptly.

[0708] Notifications to users are delivered in real time via their devices, providing a fast interface, and users receive additional support and hotline information when needed. In this way, this system, which incorporates an emotion engine, enables users to receive rapid and accurate mental health support.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] The user launches a dedicated application and enters their inquiry details. Once the input is complete, pressing the send button causes the terminal to process the data and begin sending it to the server.

[0712] Step 2:

[0713] The terminal uses a communication protocol to send data entered by the user to the server, ensuring the secure transfer of data.

[0714] Step 3:

[0715] The server saves the consultation details received from the terminal to a database. Afterward, it prepares to pass the data to an analysis module that uses natural language processing technology.

[0716] Step 4:

[0717] The server's analysis module analyzes the received consultation content and uses an emotion engine to recognize emotions along with extracting keywords. This includes the process of identifying emotions such as positive, negative, and neutral from the consultation content.

[0718] Step 5:

[0719] The emotion engine, based on the analysis results, further refines the analysis by referring to the user's emotional history. Through this process, it understands the user's unique emotional patterns and utilizes them to generate responses.

[0720] Step 6:

[0721] The server operates a response generation module based on the analysis results to create the optimal response message. This message is then adjusted to match the user's emotional state.

[0722] Step 7:

[0723] The generated response message is ready to be sent to the terminal via a notification mechanism, and the message is sent from the server to the terminal.

[0724] Step 8:

[0725] The terminal displays a response message sent from the server to the user. The user receives this message and receives feedback regarding the consultation.

[0726] Step 9:

[0727] The server also automatically sends notifications to registered professionals if the consultation content and sentiment analysis results indicate that urgent or professional intervention is required. This notification includes a summary of the user's consultation content and the results of the sentiment analysis.

[0728] Step 10:

[0729] Experts will receive notifications and, if necessary, take steps to communicate with users to provide additional support or intervention.

[0730] (Example 2)

[0731] 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".

[0732] Conventional systems had problems such as insufficient individualization of sentiment analysis for user inquiries, and difficulty in providing prompt expert intervention or additional support as needed. Furthermore, personalization of responses that took sentiment history into account was not sufficiently achieved, and user satisfaction was not sufficiently improved.

[0733] 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.

[0734] In this invention, the server includes communication means for receiving text data from a user, analysis means including an emotion engine for analyzing the text data and extracting emotions, and response generation means for generating a response using a generation AI model based on the analysis results. This makes it possible to generate personalized responses tailored to the content of the consultation and quickly notify the user, thereby providing appropriate support or expert intervention as needed.

[0735] "Communication means" refers to the technology and methods for receiving text data from a user and transmitting it to a server.

[0736] The "analysis method" is a mechanism that analyzes received text data using natural language processing technology, extracts emotions through an emotion engine, and classifies them into categories.

[0737] An "emotion engine" is a specialized technology for detecting emotions from text data and classifying them into categories such as positive, negative, and neutral.

[0738] A "response generation means" is a mechanism that uses a generative AI model to generate a response suitable for the user based on emotional information obtained by the analysis means.

[0739] A "notification method" is a method for transmitting the generated response to the user's terminal in real time.

[0740] A "means of providing support" refers to a method for providing additional support or assistance to a user when specific information is detected.

[0741] A "notification system" is a mechanism for sending notifications to experts when text data is deemed to require professional intervention.

[0742] "Learning methods" are techniques for analyzing past data to improve the accuracy of analysis and response generation.

[0743] "Personalization means" refers to methods for managing a user's emotional history and providing personalized responses tailored to each individual user.

[0744] This invention is a system aimed at supporting users' mental health, utilizing emotion analysis technology to provide appropriate support to users. Specifically, the server analyzes the content of the consultation received from the user and generates a response using a generative AI model.

[0745] The server receives text data from the user's terminal via a communication method. This text data is entered by the user using a dedicated application and sent from the terminal. The application used runs on iOS and Android platforms and has a user-friendly interface.

[0746] The received text data is analyzed using natural language processing techniques by an analysis system on the server. The analysis utilizes commercially available natural language processing libraries, and emotions are extracted from the text data through an emotion engine. This emotion engine has the ability to classify emotions into positive, negative, and neutral categories.

[0747] Based on the analyzed sentiment data, the server utilizes a generative AI model to generate appropriate responses. For example, the generative AI model automatically generates an appropriate response based on a prompt such as, "The user is feeling tired because they have been having trouble sleeping. Please provide a message of encouragement."

[0748] The generated response is delivered to the user's device in real time via a notification system. This allows the user to receive support quickly, and if necessary, hotline information and access information to resources from additional support channels are also provided.

[0749] Furthermore, the emotion engine learns from past consultations and improves its analytical accuracy, enabling more personalized support through ongoing consultations. In addition, if a serious consultation is detected, a notification system transmits the information to a specialist, allowing for prompt expert intervention. This overall system mechanism provides comprehensive and personalized support to the user.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] Users input their consultation details using a dedicated application. The information entered by the user is saved as text data on the device and sent to the server when the send button is pressed. The input data includes the user's consultation details and a timestamp. The server receives this data using a secure communication protocol.

[0753] Step 2:

[0754] The server analyzes the received text data. Using natural language processing technology as the analysis method, it activates an emotion engine to extract emotions from the text content. The received text data is output as emotion data categorized into positive, negative, and neutral. This emotion data is evaluated by the emotion engine, and a log of the analysis results is recorded on the server.

[0755] Step 3:

[0756] The server generates a response based on the analysis results. Using a generative AI model, it generates a response based on the prompt "Consider the user's emotion to be XX and generate an appropriate response." This prompt prompt causes the model to construct a response based on the input emotion data. The generated response has a tone and content appropriate for the user. The generated response message is saved on the server as output.

[0757] Step 4:

[0758] The server notifies the user of the generated response. Using a notification method, the generated response is sent to the user's device in real time. Here, the server uses push notification functionality to display the message on the device. The user can check the received message within the application and view the details.

[0759] Step 5:

[0760] If necessary, the server will provide additional support. This support system detects specific information from the emotion engine and analysis results, providing users with registered hotline information and access to resources. This information is personalized according to the user's situation, enabling them to be guided to the appropriate resources.

[0761] Step 6:

[0762] If the server determines that professional intervention is necessary, it will notify a specialist using a notification system. This notification will include analyzed sentiment data and a summary of the consultation. Based on this information, the specialist can respond quickly. The server will securely send notifications while maintaining the accuracy of the information.

[0763] (Application Example 2)

[0764] 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".

[0765] Current information and communication systems struggle to accurately understand users' emotional states and respond promptly based on that understanding. This makes it difficult to efficiently manage users' mental health and safety, and there is a problem of insufficient support, especially when users are experiencing high levels of stress and anxiety.

[0766] 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.

[0767] In this invention, the server includes communication means for receiving information from the user, analysis means for analyzing the information, and monitoring means for monitoring the user's emotional state in real time. This makes it possible to grasp the user's emotional state in real time and provide appropriate responses and support.

[0768] A "means of communication for receiving information" is a mechanism for receiving information entered by a user as data and transferring it to a system.

[0769] "Information analysis tools" are technologies for processing received information and evaluating its content and characteristics.

[0770] A "response-providing means" is a means of returning appropriate information or instructions to the user based on the analysis results.

[0771] "Transmission means" refers to the means by which generated responses or information are delivered to the user.

[0772] A "monitoring method that monitors emotional states in real time" is a technology that continuously observes and analyzes the user's psychological state.

[0773] A "warning mechanism that issues a warning" is a system that alerts the user when observed data indicates a dangerous or abnormal condition.

[0774] "Means of communication for sending out information" refers to means of reporting the situation to relevant parties and experts as needed and prompting them to take action.

[0775] A system that implements an application example of this invention has the function of analyzing the user's emotional state in real time and issuing warnings or contacting experts as needed.

[0776] First, the terminal acquires data through a communication method that receives information from the user. This data mainly consists of entered text information, but may also include detailed feedback indicating emotions. The received information is transferred to a server and processed by analytical means that analyze the information.

[0777] The server extracts the user's emotional state from the information using analysis methods that employ natural language processing techniques. Natural language processing typically utilizes machine learning models as sentiment analysis engines. These models evaluate the input text and classify it into emotional categories such as positive, negative, and neutral.

[0778] The server quantitatively evaluates stress levels and anxiety through a monitoring system that monitors the user's emotional state in real time. The evaluation results are compared to pre-set criteria, and if the criteria are exceeded, a warning system is activated to issue a warning. This system sends the user a message encouraging relaxation.

[0779] Furthermore, if a serious situation is identified, the server can send notifications to registered specialists through its designated communication channels. This allows for a swift response, protecting the user's safety and health.

[0780] For example, if a user enters "I've been suffering from excessive stress lately" into the device, the system immediately assesses that emotion as negative, and a warning system sends a message encouraging relaxation. If necessary, it also notifies registered professionals for further action.

[0781] Examples of prompt statements for a generative AI model are as follows:

[0782] "User input: 'I've been suffering from excessive stress lately.' Analyze this and generate appropriate emotion labels and responses."

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The terminal receives information from the user. This information is text data indicating the content of the consultation and emotions entered by the user. After receiving this data, the terminal uses a communication method to send it to the server.

[0786] Step 2:

[0787] The server analyzes data through natural language processing techniques using analytical means to parse information sent from the terminal. Using the text data received as input, the server extracts emotions using a machine learning model and classifies them into positive, negative, and neutral emotional categories. These analysis results are then used in the next step.

[0788] Step 3:

[0789] The server monitors the user's emotional state in real time based on the analyzed emotional data. Using monitoring tools, it evaluates whether the emotional score exceeds a set threshold. Based on the evaluation results, if the emotional score reaches an abnormal level, preparations are made to activate warning mechanisms.

[0790] Step 4:

[0791] If the emotion score exceeds a certain threshold, the server will send a message to the user via a warning system, encouraging relaxation. Specifically, it utilizes a generative AI model to create appropriate wording tailored to the situation and delivers it to the user via the transmission system.

[0792] Step 5:

[0793] If the server detects a more serious emotional state, it will use a communication channel to send a notification to a pre-registered professional. This notification will include the user's emotional score and a summary of the situation, which will be used by the professional to respond quickly. In this step, the analysis results and emotional state are provided as prompt messages, generating information to encourage the professional to take action.

[0794] 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.

[0795] 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.

[0796] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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 as being incorporated by reference.

[0815] The following is further disclosed regarding the embodiments described above.

[0816] (Claim 1)

[0817] A means of receiving inquiries from users,

[0818] An analysis means for analyzing the aforementioned consultation content,

[0819] A response generation means that generates an appropriate response based on the analysis results,

[0820] A notification means for notifying the user of the aforementioned response,

[0821] A support provision method that provides additional support when a specific keyword is detected,

[0822] A notification means for issuing a notification when the aforementioned consultation content requires the intervention of a professional,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the analysis means has a function to extract emotions from the content of the consultation using natural language processing technology.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the support provision means provides the user with hotline information and access information to related support resources.

[0828] "Example 1"

[0829] (Claim 1)

[0830] A device for acquiring the content of consultations,

[0831] A device for analyzing the aforementioned consultation content using language processing technology,

[0832] A device that generates a response based on the analyzed information,

[0833] A device that transmits the generated response to the user,

[0834] A device that provides assistance when a specific word is identified,

[0835] A device that sends a warning when the content of the consultation requires professional intervention,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the analysis device has a function to analyze emotions from the content of the consultation using language processing technology.

[0839] (Claim 3)

[0840] The system according to claim 1, wherein the auxiliary device provides the user with emergency contact information and information on access to relevant support resources.

[0841] "Application Example 1"

[0842] (Claim 1)

[0843] A data acquisition means for receiving content from users,

[0844] Information analysis means for analyzing the aforementioned content,

[0845] A data generation means that generates appropriate feedback based on the analysis results,

[0846] A notification means for notifying the user of the aforementioned feedback,

[0847] A support provision means that provides assistance when a specific discriminant word is detected,

[0848] A communication means for sending a notification when the aforementioned content requires expert advice,

[0849] A state monitoring system that continuously monitors the user's emotional state,

[0850] An anomaly detection means that generates a warning when it detects an abnormality in the aforementioned emotional state,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the information analysis means has a function to extract psychological states from content using natural language processing technology.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the auxiliary provision means provides the user with contact service information and access information to related support resources.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] A means of receiving text data from a user,

[0859] An analysis means for analyzing the aforementioned text data,

[0860] The analysis means includes an emotion engine that has the function of extracting emotions from text data and classifying them into categories,

[0861] A response generation means that uses a generative AI model that generates a response based on the analysis results,

[0862] A notification means that notifies the user of the generated response in real time,

[0863] A support provision means that provides additional support when specific information is detected,

[0864] A notification means for sending a notification to a specialist when the aforementioned text data requires professional intervention,

[0865] A learning method equipped with a learning function and capable of analyzing past data to improve accuracy,

[0866] Personalization means for managing the user's emotional history and providing individualized responses,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, wherein the analysis means utilizes natural language processing technology to generate a response based on a prompt sentence generated by a generative AI model.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the support provision means provides the user with means to access hotline information and related resources.

[0872] "Application example 2 when combining with an emotional engine"

[0873] (Claim 1)

[0874] A means of communication for receiving information from users,

[0875] An analytical means for analyzing the aforementioned information,

[0876] A response-providing means that provides an appropriate response based on the analysis results,

[0877] A transmission means for sending the aforementioned response to the user,

[0878] A monitoring method for monitoring the user's emotional state in real time,

[0879] A warning means that issues a warning when the aforementioned emotional state exceeds a predetermined standard,

[0880] A means of sending a message when it is determined that the aforementioned emotional state requires professional intervention,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the analysis means has a function to extract emotions from information using natural language processing technology.

[0884] (Claim 3)

[0885] The system according to claim 1, wherein the warning means provides a message to the user that encourages relaxation. [Explanation of symbols]

[0886] 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 receiving inquiries from users, An analysis means for analyzing the aforementioned consultation content, A response generation means that generates an appropriate response based on the analysis results, A notification means for notifying the user of the aforementioned response, A support provision method that provides additional support when a specific keyword is detected, A notification means for issuing a notification when the aforementioned consultation content requires the intervention of a professional, A system that includes this.

2. The system according to claim 1, wherein the analysis means has a function to extract emotions from the content of the consultation using natural language processing technology.

3. The system according to claim 1, wherein the support provision means provides the user with hotline information and access information to related support resources.

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