system

Abstract

Provide a system. [Solution] a conversation means for allowing a user to have a conversation with a virtual character; data generation means for generating conversation data based on the conversation means; a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user; a risk assessment means for detecting a risk of mental illness in the user based on the analysis results; a notification means for notifying a user of a risk detection result; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, the number of people suffering from mental health problems is increasing. However, many people are unaware of their own mental illness, and those around them are also unaware of the changes. This can lead to the severity of mental illness, significantly impairing social and professional functioning. In particular, there is no established system for early detection of mental disorders and providing appropriate support through everyday chat. Therefore, the present invention aims to provide a system that can detect signs of mental illness early through conversations with virtual characters, thereby maintaining and improving mental health. [Means for solving the problem]

[0005] The present invention provides a system including a conversation means for a user to have a conversation with a virtual character, a data generation means for generating conversation data based on the conversation means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user of the risk detection results.The system also includes a stress analysis means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends, and a linking means for sending a notification to experts and close relatives if the detected risk is high.This allows the user to recognize mental abnormalities early and receive appropriate support, preventing them from becoming severe and maintaining mental health.

[0006] "User" refers to an individual who uses the system to converse with a virtual person.

[0007] "Virtual person" refers to a fictional character created by generative AI that converses with the user.

[0008] "Conversational means" refers to an interface that allows text or voice communication between a user and a virtual character.

[0009] "Data generation means" refers to a function that records the content of conversations sent and received through the conversation means and generates it as data in an analyzable format.

[0010] "Data analysis means" refers to the function of analyzing the generated conversation data and evaluating the user's mental state using natural language processing and emotion recognition technology.

[0011] The "risk assessment means" refers to a function that detects changes or abnormalities in the user's mental state based on the results obtained from the data analysis means and assesses the risk of mental illness.

[0012] "Notification Means" refers to the interface and process for notifying a user of the risk of psychosis detected by the Risk Assessment Means.

[0013] The "stress analysis means" refers to a function that analyzes the user's daily conversation patterns and identifies the type and degree of stress the user is experiencing based on the trends.

[0014] "Collaboration measures" refers to a function that sends notifications to experts and relatives if a user is deemed to be at high risk of mental illness, and promotes appropriate support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

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

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

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

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0036] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[0037] Program processing

[0038] User registration and initial settings

[0039] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[0040] Starting everyday conversations

[0041] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[0042] Recording and analyzing conversation content

[0043] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[0044] Risk Assessment and Notification

[0045] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. A collaborative method is also provided to notify experts or close relatives as needed.

[0046] Specific examples

[0047] Example of user registration and initial settings

[0048] 1. User: "I'll try downloading a new app."

[0049] 2. Terminal: "Please enter your name, date of birth, and gender."

[0050] 3. User: "Taro Yamada, January 1, 1990, male"

[0051] 4. Terminal: Sends input information to the server.

[0052] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0053] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0054] Examples of everyday conversation

[0055] 1. User: "I'm so tired today."

[0056] 2. Terminal: Sends user messages to the server.

[0057] 3. Server: Parses the message and generates an appropriate reply.

[0058] 4. Server: "That must have been tough. Did you do anything special to relax?" is sent to the device.

[0059] 5. Terminal: The virtual person's reply is displayed to the user.

[0060] Examples of risk assessment and notification

[0061] 1. Server: Updates the stress score based on the analysis results. The accumulated data indicates that the user's stress level is high.

[0062] 2. Server: Sets a warning flag and generates an alert message.

[0063] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[0064] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[0065] The processing flow will be explained below.

[0066] User registration and initial settings

[0067] Step 1:

[0068] A user downloads the app and opens the account creation screen.

[0069] Step 2:

[0070] The device will display an input form for name, date of birth, gender, etc.

[0071] Step 3:

[0072] The user enters the required information and clicks the send button.

[0073] Step 4:

[0074] The terminal transmits the input information to the server.

[0075] Step 5:

[0076] The server stores the received information in a database.

[0077] Step 6:

[0078] The user logs in for the first time.

[0079] Step 7:

[0080] The server checks the initial setup status of the user and finds that the initial setup has not been completed.

[0081] Step 8:

[0082] The server generates a virtual person profile for the AI ​​and sends it to the device.

[0083] Step 9:

[0084] The terminal displays the virtual person profile to the user.

[0085] Starting everyday conversations

[0086] Step 1:

[0087] To start a conversation with the virtual character, the user inputs a message and presses the send button.

[0088] Step 2:

[0089] The terminal sends the user's input to the server.

[0090] Recording and analyzing conversation content

[0091] Step 1:

[0092] The server records the received user messages in a database.

[0093] Step 2:

[0094] The server uses a generation AI to generate an appropriate reply.

[0095] Step 3:

[0096] The server sends the generated reply to the terminal.

[0097] Step 4:

[0098] The terminal displays the virtual person's reply to the user.

[0099] Risk Assessment and Notification

[0100] Step 1:

[0101] The server periodically analyzes the accumulated conversation data.

[0102] Step 2:

[0103] The server uses natural language processing algorithms to detect specific keywords and sentiment patterns.

[0104] Step 3:

[0105] The server calculates a stress score based on the detected keywords and patterns.

[0106] Step 4:

[0107] The server evaluates the user's stress score and sets a warning flag if a threshold is exceeded.

[0108] Step 5:

[0109] The server generates an alert message based on the warning flag and sends it to the terminal.

[0110] Step 6:

[0111] The terminal displays an alert message to the user.

[0112] Collaboration methods

[0113] Step 1:

[0114] If the server determines that the risk is high, it will notify experts and next of kin.

[0115] Step 2:

[0116] The server sends expert feedback and resources to the device.

[0117] Step 3:

[0118] The terminal displays contact information from the expert to the user.

[0119] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[0120] Example 1

[0121] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] In modern society, early detection and prevention of mental illness have become important issues. However, many people find it difficult to accurately grasp their own mental state, and psychological burdens often increase without them realizing it. In this situation, there is a need for a system that allows users to easily monitor their own mental state and take appropriate action when risks increase.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0124] In this invention, the server includes a communication means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user of the risk detection results, thereby enabling the user to objectively evaluate their own mental state and take appropriate measures early on.

[0125] "Communication means" is a function that provides channels and protocols for conversation between the user and the virtual character.

[0126] The "data generation means" is a process for generating conversation data between a user and a virtual character based on a communication means.

[0127] "Data analysis means" refers to the technology or algorithms used to analyze the generated conversation data and evaluate the user's mental state.

[0128] The "risk assessment means" is a function for detecting the user's risk of mental illness based on the results of the data analysis means.

[0129] The "notification means" is a method or device for notifying the user of the risk results detected by the risk assessment means.

[0130] The "stress analysis means" is a function for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[0131] "Collaboration methods" is a function for sending notifications to experts and next of kin when a high risk is detected.

[0132] The "storage means" is a process for storing the basic information entered by the user in a database.

[0133] The "profile generation means" is a function for generating a profile of a virtual person based on basic information about the user.

[0134] The "display means" is a function that displays the profile and reply message of the generated virtual person to the user.

[0135] "Reply generation means" is a function that uses generation AI to analyze conversation data and generate appropriate replies.

[0136] The "alert generation means" is a function that uses a generation AI to calculate a stress score and generate an alert message if the threshold is exceeded.

[0137] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[0138] 1. User registration and initial settings

[0139] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[0140] 2. Starting everyday conversations

[0141] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[0142] 3. Recording and analyzing conversation content

[0143] The server records the conversations between the user and the virtual persona in a database and analyzes them using automated natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[0144] 4. Risk Assessment and Notification

[0145] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. The server also provides the ability to notify experts or close relatives if necessary.

[0146] Example operation

[0147] Example of user registration and initial settings

[0148] 1. User: "I'll try downloading a new app."

[0149] 2. Terminal: "Please enter your name, date of birth, and gender."

[0150] 3. User: "Taro Tanaka, January 1, 1990, male"

[0151] 4. Terminal: Sends input information to the server.

[0152] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0153] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0154] Examples of everyday conversation

[0155] 1. User: "I'm so tired today."

[0156] 2. Terminal: Sends user messages to the server.

[0157] 3. Server: Parses the message and generates an appropriate reply.

[0158] 4. Server: Sends "That must have been tough. Did you do anything special to relax?" to the device.

[0159] 5. Terminal: Displays the virtual person's reply to the user.

[0160] Examples of risk assessment and notification

[0161] 1. Server: Updates the stress score based on the analysis results. Determines that the user's stress level is high based on the accumulated data.

[0162] 2. Server: Sets a warning flag and generates an alert message.

[0163] 3. On the device: Display the message to the user: "Your recent conversations seem to be causing you stress. Consider seeking professional help."

[0164] Examples of prompt statements

[0165] "Calculate a stress score based on the conversation between the user and a virtual person. Generate a Python program that will alert the user if their stress level is high."

[0166] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1: User registration and initial setup

[0169] 1. Input: The user enters basic information such as name, date of birth, and gender.

[0170] 2. Operation: The terminal receives the basic information entered by the user and sends it to the server.

[0171] 3. Output: The server stores the received basic information in a database.

[0172] 4. Operation: The server uses a generation AI to generate a virtual person's profile (name, age, hobbies, etc.).

[0173] 5. Output: The server sends the generated virtual person's profile to the terminal.

[0174] 6. Operation: The terminal displays the virtual person's profile to the user.

[0175] Step 2: Starting everyday conversations

[0176] 1. Input: The user types a message for the virtual person and presses the send button.

[0177] 2. Operation: The terminal sends the entered message to the server.

[0178] 3. Output: The server receives the message.

[0179] 4. Operation: The server analyzes the message using natural language processing techniques, such as morphological analysis and sentiment analysis.

[0180] 5. Output: The server uses AI to generate an appropriate reply based on the analysis results.

[0181] 6. Action: The server generates a reply and sends it to the device.

[0182] 7. Output: The device displays the virtual person's reply to the user.

[0183] Step 3: Record and analyze the conversation

[0184] 1. Input: Conversation data between the user and the virtual person.

[0185] 2. Operation: The server records the conversation between the user and the virtual character in a database.

[0186] 3. Output: Recorded conversation data.

[0187] 4. Operation: The server analyzes the recorded conversation data using natural language processing techniques, including morphological analysis, sentiment analysis, and keyword extraction.

[0188] 5. Output: The analysis results provide the frequency of occurrence of specific keywords and sentiment patterns.

[0189] Step 4: Risk assessment and communication

[0190] 1. Input: Frequency of keywords and sentiment patterns as analysis results.

[0191] 2. How it works: The server periodically evaluates the analysis results and calculates the user's stress score, which includes the frequency of certain keywords and negative emotions.

[0192] 3. Output: User's stress score.

[0193] 4. How it works: If the stress score exceeds a certain threshold, the server sets a warning flag and uses a generative AI to generate an appropriate alert message.

[0194] 5. Output: The alert message.

[0195] 6. Operation: The server sends the generated alert message to the terminal.

[0196] 7. Output: The device displays an alert message to the user: "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[0197] In this way, the overall processing of the system consists of processing and analyzing data based on user input information, and providing appropriate feedback and notifications based on the results.

[0198] (Application example 1)

[0199] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0200] Real-time monitoring of workers' mental health status and early detection of abnormalities are extremely important for work efficiency and safety. However, current systems lack effective means for managing workers' mental health, making it difficult to detect mental illness early. Furthermore, workers with deteriorating mental conditions may not receive appropriate support or warnings, potentially resulting in serious risks. This calls for a more effective mental health management system.

[0201] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0202] In this invention, the server includes a conversation means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the conversation means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user and a manager of the risk detection results. This allows on-site workers to interact with the virtual character on a daily basis and monitor their mental health status. Furthermore, if high stress is detected, a warning is generated and notified to the manager, allowing effective support. This system makes it possible to detect mental risks in workers early and take appropriate measures.

[0203] "Conversation means" refers to a communication means for a user and a virtual character to converse, through which they can exchange messages with each other.

[0204] "Data generation means" refers to means for processing and storing conversation data generated by the conversation means, and records the content of the conversation.

[0205] The "data analysis means" is a means for analyzing the generated conversation data and evaluating the mental state of the user.

[0206] The "risk assessment means" is a means for assessing and detecting the user's risk of mental illness based on the analysis results obtained by the data analysis means.

[0207] "Notification means" refers to a means for informing users and administrators of risk detection results, and involves sending appropriate alerts and notifications.

[0208] The "stress analysis means" is a means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[0209] The "warning means" is a means for generating a warning when high stress is detected and notifying the user and the administrator.

[0210] This invention describes a specific implementation of a "Mental Health Security Assistant" system, in which users interact with a virtual person via a smartphone or smart glasses, and their mental health is monitored.

[0211] Hardware

[0212] 1. Device: The smartphone or smart glasses used by the user.

[0213] 2. Server: A server for analyzing conversation data and assessing mental health status.

[0214] software

[0215] 1. Conversation medium: An application installed on a smartphone or smart glasses that provides an interface for the user and the virtual person to exchange messages.

[0216] 2. Data Generation Means: Software functionality for recording conversational data generated through conversational means.

[0217] 3. Data analysis tools: Software using natural language processing techniques (e.g., NLTK or spaCy) to analyze the generated conversation data and evaluate the user's mental state.

[0218] 4. Risk Assessment Instrument: Software functionality that assesses psychosis risk based on the results of data analysis instruments.

[0219] 5. Notification methods: Smartphone push notifications and email notifications are used to notify users and administrators of risk detection results.

[0220] 6. Stress analysis tool: Software that analyzes the user's daily conversation patterns to identify the type and degree of stress.

[0221] 7. Warning: A function that generates a warning and notifies you when high stress is detected.

[0222] System Operation

[0223] 1. User registration and initial setup:

[0224] Users download the application and enter their personal information (name, date of birth, gender).

[0225] The terminal sends the input information to the server, and the server stores the user information in a database.

[0226] The server uses the generative AI model to generate a profile of the virtual person and sends it to the terminal.

[0227] The terminal displays the virtual person's profile to the user.

[0228] 2. Starting everyday conversations:

[0229] The user inputs and sends messages to have everyday conversations with the virtual person.

[0230] The terminal transmits the input message to the server.

[0231] The server parses the message and generates an appropriate reply.

[0232] The generated reply is sent to the terminal and displayed to the user.

[0233] 3. Recording and Analysis of Conversation Content:

[0234] The conversation data is recorded in a database by the server.

[0235] The server uses natural language processing technology to analyze the conversation and detect specific keywords and emotional patterns.

[0236] If a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[0237] 4. Risk Assessment and Communication:

[0238] The server calculates a stress score based on the analysis results.

[0239] If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message.

[0240] The alert is sent to the terminal and notified to the user.

[0241] If necessary, notifications will be sent to experts and administrators.

[0242] Specific examples

[0243] 1. Example of user registration and initial setup:

[0244] A user enters information to register an account.

[0245] The terminal inputs "Yamada Taro, March 25, 1985, male."

[0246] The server stores the information in a database and generates a profile of the virtual person.

[0247] The device displays, "Hello, I'm a virtual person who will assist you."

[0248] 2. Examples from everyday conversation:

[0249] A user says, "I'm tired today."

[0250] The device sends a message to the server.

[0251] The server generates a reply: "That must have been tough. Did you do anything special to relax?"

[0252] The device displays the reply to the user.

[0253] 3. Examples of risk assessment and communication:

[0254] The server updates the stress score based on the analysis results.

[0255] Accumulative data indicates that the user's stress level is high.

[0256] The server generates a warning saying, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[0257] The device will notify the user and administrator of the warning.

[0258] In this way, we provide a system that allows users to interact with virtual characters on a daily basis and effectively monitor their mental health, which can improve work efficiency and safety on site.

[0259] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0260] Step 1:

[0261] User registration and initial settings

[0262] Input: The user enters personal information (name, date of birth, gender) into an app on their smartphone or smart glasses.

[0263] What happens: A user fills in the required information on a registration form and clicks the submit button.

[0264] Data processing: The terminal converts the input information into JSON format and sends it to the server.

[0265] Output: The server stores the received data in a database, generates a virtual person's profile using a generative AI model, and sends the generated profile to the device.

[0266] Step 2:

[0267] Viewing a virtual person's profile

[0268] Input: Virtual person profile data sent from the server.

[0269] Operation: Receives profile data generated by the server and transfers it to the device.

[0270] Data Processing: The generated profile is formatted to be displayed in a user-friendly interface.

[0271] Output: The device displays a message on the screen, such as "Hello, I'm your virtual assistant."

[0272] Step 3:

[0273] Starting everyday conversations

[0274] Input: The message the user sends to the virtual person (e.g., "I'm tired today").

[0275] Action: The user types a message and presses the send button.

[0276] Data processing: The terminal sends the entered message to the server as text data.

[0277] Output: The server parses the message and generates an appropriate reply to send to the device.

[0278] Step 4:

[0279] Recording and analyzing conversation content

[0280] Input: Conversation data and reply data sent from the terminal.

[0281] What it does: The server logs the received message to a database.

[0282] Data processing: The server analyzes the message using natural language processing technology, detects specific keywords and emotional patterns, and calculates a stress score.

[0283] Output: If the stress score exceeds a certain threshold, send the information to the risk assessment module.

[0284] Step 5:

[0285] Risk Assessment

[0286] Input: Analysis results of conversation data and stress score.

[0287] How it works: The server evaluates stress scores and monitors changes in mental state.

[0288] Data processing: Determine whether the stress score is high and perform a risk assessment.

[0289] Output: If high stress is detected, set a warning flag and generate an alert message.

[0290] Step 6:

[0291] Generate notifications and alerts

[0292] Input: Risk assessment results and alert message.

[0293] Action: The server sends an alert message to the device.

[0294] Data Processing: Formatting data to send notifications to experts or administrators as needed.

[0295] Output: A warning such as "Recent conversations seem to be causing stress. Please consider consulting a specialist" is displayed on the device. A notification is also sent to the specialist and administrator via email.

[0296] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0297] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[0298] Program processing

[0299] User registration and initial settings

[0300] The user downloads the application and enters the necessary information on the account creation screen. The input information includes name, date of birth, gender, etc., and is sent to the server via the device. The server stores the received information in a database, and when the user logs in for the first time, the generation AI generates a virtual person profile and sends it to the device. The device then displays the virtual person profile to the user.

[0301] Starting everyday conversations

[0302] The user types and sends a message to start a conversation with a virtual person. The message is then sent from the device to the server. The server records the received message in a database and analyzes it, after which the AI ​​generates an appropriate reply. This reply is then sent to the device and displayed to the user.

[0303] Recording and analyzing conversation content

[0304] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[0305] Risk Assessment and Notification

[0306] The server calculates the user's stress score based on the analysis results. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the device and notifies the user. It also provides a means of collaboration to send notifications to experts and relatives as needed.

[0307] Specific examples

[0308] Example of user registration and initial settings

[0309] 1. User: "I'm going to download and try out the new app."

[0310] 2. Terminal: "Please enter your name, date of birth, and gender."

[0311] 3. User: "Taro Yamada, January 1, 1990, male"

[0312] 4. Terminal: Sends input information to the server.

[0313] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0314] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0315] Examples of everyday conversation

[0316] 1. User: "I feel very tired and depressed today."

[0317] 2. Terminal: Sends user messages to the server.

[0318] 3. Server: Records messages in a database and performs analysis.

[0319] 4. Emotion engine: Classifies messages such as "tired" and "depressed" as negative emotions.

[0320] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[0321] 6. Server: Generates and sends the reply to the device.

[0322] 7. Terminal: The virtual person's reply is displayed to the user.

[0323] Examples of risk assessment and notification

[0324] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[0325] 2. Server: Sets a warning flag and generates an alert message.

[0326] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[0327] 4. Server: Sends notifications to professionals and next of kin as needed.

[0328] This allows the system to detect mental disorders in users early and provide appropriate support, preventing mental illness from worsening and helping users maintain their mental health.

[0329] The processing flow will be explained below.

[0330] User registration and initial settings

[0331] Step 1:

[0332] A user downloads the app and opens the account creation screen.

[0333] Step 2:

[0334] The device will display an input form for name, date of birth, gender, etc.

[0335] Step 3:

[0336] The user enters the required information and clicks the send button.

[0337] Step 4:

[0338] The terminal transmits the input information to the server.

[0339] Step 5:

[0340] The server stores the received information in a database.

[0341] Step 6:

[0342] The user logs in for the first time.

[0343] Step 7:

[0344] The server checks the initial setting status of the user and finds that the initial setting has not been completed.

[0345] Step 8:

[0346] The server generates a virtual person profile for the AI ​​and sends it to the device.

[0347] Step 9:

[0348] The terminal displays the generated virtual person profile to the user.

[0349] Starting everyday conversations

[0350] Step 1:

[0351] To start a conversation with the virtual person, the user inputs and sends a conversation message.

[0352] Step 2:

[0353] The terminal sends the user's input to the server.

[0354] Recording and analyzing conversation content

[0355] Step 1:

[0356] The server records the received user messages in a database.

[0357] Step 2:

[0358] The server uses a generation AI to generate an appropriate reply.

[0359] Step 3:

[0360] The server sends the generated reply to the terminal.

[0361] Step 4:

[0362] The terminal displays the virtual person's reply to the user.

[0363] Step 5:

[0364] The server analyzes the received conversation data using natural language processing technology.

[0365] Step 6:

[0366] The server detects specific keywords and emotional patterns.

[0367] Step 7:

[0368] The server uses an emotion engine to recognize the user's emotional state.

[0369] Risk Assessment and Notification

[0370] Step 1:

[0371] The server processes the emotional state detected by the emotion engine with data analysis means.

[0372] Step 2:

[0373] The server calculates the user's stress score based on the analysis results.

[0374] Step 3:

[0375] The server sets a warning flag if the stress score exceeds a threshold.

[0376] Step 4:

[0377] The server generates an alert message based on the warning flag.

[0378] Step 5:

[0379] The server sends the generated alert message to the terminal.

[0380] Step 6:

[0381] The terminal displays an alert message to the user.

[0382] Collaboration methods

[0383] Step 1:

[0384] If the server determines that the risk is high, it will notify experts and next of kin.

[0385] Step 2:

[0386] The server sends expert feedback and resources to the device.

[0387] Step 3:

[0388] The terminal displays contact information from the expert to the user.

[0389] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[0390] Example 2

[0391] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] In modern society, it is extremely important to detect signs of mental stress and mental illness early and provide support at the appropriate time. However, current systems and methods lack the means to effectively analyze and appropriately evaluate a user's mental state from their everyday conversations. Furthermore, there are insufficient consistent systems and collaborative methods for detecting the risk of mental illness and providing the necessary support. This often leads to the worsening of mental illness and delays in providing appropriate support, which presents a challenge.

[0393] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: conversation means for a conversation between a user and a virtual character; data generation means for generating conversation data based on the conversation means; data analysis means for analyzing the generated conversation data and evaluating the user's mental state; analysis means for analyzing the generated conversation data using natural language processing technology and detecting specific keywords and emotional patterns; risk assessment means for detecting the user's risk of mental illness based on the analysis results; notification means for notifying the user of the risk detection results; generation AI means for generating a profile of the virtual character; emotion engine means for recognizing the emotional state and classifying it into positive and negative emotions; and warning means for setting a warning flag and generating an alert message when a certain threshold is exceeded. This makes it possible to effectively analyze the user's mental state from everyday conversations, assess the risk early, and provide necessary support at an appropriate time.

[0394] A "user" is an individual who uses the system to converse with a virtual character.

[0395] A "virtual person" is a virtual character that is generated by a generation AI means and that interacts with the user.

[0396] The "conversation means" is an interface for the user and the virtual character to exchange messages via communication.

[0397] The "data generation means" is a device or software that generates conversation data between a user and a virtual character based on the conversation means.

[0398] The "data analysis means" is a device or software that analyzes the generated conversation data and evaluates the mental state of the user.

[0399] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0400] The "analysis means" is a device or software that analyzes the conversation data generated using natural language processing technology and detects specific keywords and emotional patterns.

[0401] A "risk assessment means" is a device or software that detects a user's risk of mental illness based on the analysis results.

[0402] The "notification means" is a device or software that notifies the user of the risk detection results.

[0403] "Generative AI means" refers to a device or software that utilizes artificial intelligence technology to generate a virtual person's profile based on user information.

[0404] The "emotion engine means" is a device or software that recognizes emotional states from conversation data and classifies positive and negative emotions.

[0405] The "warning means" is a device or software that sets a warning flag and generates an alert message when the user's stress score exceeds a certain threshold.

[0406] A "profile" is data that represents the attributes and characteristics of a virtual person generated by a generation AI means.

[0407] This invention is a system that allows users to detect early signs of mental illness through conversations with virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual characters, and an emotion engine that recognizes emotional states.

[0408] User registration and initial settings

[0409] First, the user downloads the application and enters the necessary information such as name, date of birth, and gender on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL (registered trademark)), and when the user logs in for the first time, a profile of the virtual person is generated using a generation AI (e.g., GPT-3 (registered trademark)). The generated profile is sent to the device and displayed to the user, allowing the user to start a conversation with the virtual person.

[0410] Examples:

[0411] 1. User: "I'm going to download and try out the new app."

[0412] 2. Terminal: "Please enter your name, date of birth, and gender."

[0413] 3. User: "Taro Yamada, January 1, 1990, male"

[0414] 4. Terminal: Sends input information to the server.

[0415] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0416] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0417] Starting everyday conversations

[0418] The user types a message and starts a conversation with a virtual person. The message is sent from the device to the server, which records the message in a database and analyzes it. The AI ​​then generates an appropriate reply, which is sent to the device. The device then displays the generated reply to the user.

[0419] Examples:

[0420] 1. User: "I feel very tired and depressed today."

[0421] 2. Terminal: Sends user messages to the server.

[0422] 3. Server: Records messages in a database and performs analysis.

[0423] 4. Emotion engine (e.g., IBM Watson® Tone Analyzer): Classifies "tired" and "depressed" from messages as negative emotions.

[0424] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[0425] 6. Server: Generates and sends the reply to the device.

[0426] 7. Terminal: The virtual person's reply is displayed to the user.

[0427] Recording and analyzing conversation content

[0428] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology (e.g., spaCy). When specific keywords or emotional patterns are detected, the data is further analyzed by an emotion engine, which classifies the message content as positive or negative and detects the user's emotional state.

[0429] Risk Assessment and Notification

[0430] The server calculates the user's stress score based on the analysis of the conversation content. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. This alert is sent to the user's device and notifies the user. There is also a function to notify experts or close relatives if necessary.

[0431] Examples:

[0432] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[0433] 2. Server: Sets a warning flag and generates an alert message.

[0434] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[0435] 4. Server: Sends notifications to professionals and next of kin as needed.

[0436] With the above configuration, this system can effectively analyze the user's mental state from everyday conversations, assess risk early, and provide necessary support at the appropriate time.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] System processing flow

[0439] Step 1:

[0440] The user downloads and installs the application.

[0441] Input: The action of downloading an application from the app store.

[0442] Output: The application is installed on the device and launched for the first time.

[0443] Step 2:

[0444] The user enters the necessary information on the account registration screen.

[0445] Specifically, the user enters data into fields for entering information such as name, date of birth, and gender.

[0446] Input: Information entered by the user, such as name, date of birth, and gender.

[0447] Output: The input information is collected by the terminal.

[0448] Step 3:

[0449] The terminal transmits the input information to the server.

[0450] Input: The registration information entered by the user.

[0451] Output: The registration information received by the server.

[0452] Step 4:

[0453] The server stores the received information in a database.

[0454] Input: Registration information sent from the device.

[0455] Output: User information stored in the database.

[0456] Step 5:

[0457] The server uses generative AI to generate a profile of the virtual person.

[0458] Specifically, the AI ​​sends a prompt message based on the registration information to generate profile data such as the virtual person's name, personality, and hobbies.

[0459] Input: User information stored in the database.

[0460] Output: A virtual person profile returned by the generation AI.

[0461] Step 6:

[0462] The terminal receives the virtual person's profile and displays it to the user.

[0463] Input: Virtual person profile data received from the server.

[0464] Output: A profile of the virtual person displayed on the device screen.

[0465] Step 7:

[0466] The user types and sends messages to initiate a conversation with the virtual person.

[0467] Input: A message typed by the user.

[0468] Output: Messages typed are displayed on the terminal.

[0469] Step 8:

[0470] The terminal sends a user message to the server.

[0471] Input: The message entered by the user.

[0472] Output: The message received by the server.

[0473] Step 9:

[0474] The server logs the message in a database.

[0475] Input: User message sent from the terminal.

[0476] Output: Messages logged to the database.

[0477] Step 10:

[0478] The server parses the message, sends a prompt to the generation AI, and generates an appropriate reply.

[0479] Specifically, it uses natural language processing technology to analyze the sentiment and meaning of the message, generates a prompt from the analysis results, and sends it to a generation AI to generate a reply.

[0480] Input: The message logged in the database.

[0481] Output: The virtual person's reply returned by the generation AI.

[0482] Step 11:

[0483] The terminal displays the generated reply to the user.

[0484] Input: The virtual person's reply data received from the server.

[0485] Output: The reply that appears on the device screen.

[0486] Step 12:

[0487] The server analyzes the conversation content and assesses the emotional state using an emotion engine.

[0488] Specifically, the server analyzes the conversation data to detect specific keywords and emotional patterns, which are then further classified by the emotion engine to evaluate the emotional state.

[0489] Input: Conversation data recorded in a database.

[0490] Output: Emotion state data classified by the emotion engine.

[0491] Step 13:

[0492] The server calculates the user's stress score based on the analysis results and performs a risk assessment.

[0493] Specifically, the system calculates a stress score based on the analysis results and emotional state data from the emotion engine. If the score exceeds a certain threshold, a risk assessment is performed.

[0494] Input: Emotional state data output by the emotion engine.

[0495] Output: Calculated stress score and risk assessment results.

[0496] Step 14:

[0497] The server sets warning flags based on the risk assessment results and generates alert messages.

[0498] Input: Risk assessment results.

[0499] Output: The generated alert message.

[0500] Step 15:

[0501] The terminal receives the alert message and displays it to the user.

[0502] Input: The alert message received from the server.

[0503] Output: The alert message that is displayed on the device screen.

[0504] Step 16:

[0505] The server will then send notifications to professionals and next of kin as appropriate.

[0506] Specifically, if the risk assessment results exceed a certain threshold, the system will initiate a process to send emails and notifications to experts and next of kin.

[0507] Input: Risk assessment results and configured notification conditions.

[0508] Output: Emails and notification messages sent.

[0509] Through these steps, the system can effectively analyze the user's mental state from their everyday conversations, assess risk early, and provide appropriate support.

[0510] (Application example 2)

[0511] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0512] In recent years, there has been a demand for early detection of mental illness and the provision of appropriate support, but many users find it difficult to notice changes in their own mental health. Furthermore, it is often difficult for them to voluntarily consult with experts or close relatives. This can lead to the progression and worsening of many mental illnesses. There is a need for a system that can monitor the user's mental state in real time through conversations, detect abnormalities early, and provide appropriate support.

[0513] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0514] In this invention, the server includes a communication means for a conversation between a user and a virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, an evaluation means for detecting the user's risk of mental abnormality based on the analysis result, a notification means for notifying the user of the risk detection result, a scoring means for calculating the user's stress score, and a reply generation means for generating a reply using a generative AI model. This makes it possible to monitor changes in the user's mental state in real time through everyday conversations with the user, and to immediately provide appropriate support or an alert if an abnormality is detected.

[0515] "Communication means" refers to an interface for conversation between the user and the virtual character, and refers to devices or software that enable the sending and receiving of messages.

[0516] "Data generation means" refers to a system that has the function of recording conversations exchanged through communication means and generating them as conversation data.

[0517] "Data analysis means" refers to software or algorithms that analyze the generated conversation data and evaluate the user's mental state.

[0518] "Evaluation means" refers to a system or algorithm that detects a user's risk of mental abnormality based on the analysis results.

[0519] "Notification means" refers to devices or software for notifying users of risk detection results.

[0520] A "scoring method" is a system or algorithm that calculates a user's stress score and evaluates their mental state numerically.

[0521] A "reply generation means" is a system that has the function of automatically generating an appropriate reply to a user using a generative AI model.

[0522] A "generative AI model" is a model that uses artificial intelligence, and is an algorithm or machine learning model that generates appropriate replies from conversations with users.

[0523] SUMMARY OF THE INVENTION

[0524] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generative AI model that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[0525] Hardware and software used

[0526] Hardware: Smartphones, tablets, personal computers

[0527] Software: Python, natural language processing libraries (e.g., TextBlob, NLTK), cloud server environment (e.g., AWS (registered trademark), Google (registered trademark) Cloud), machine learning model (generative AI model)

[0528] Program processing overview

[0529] User registration and initial settings

[0530] The user downloads the application onto their device and enters the required information (such as name, date of birth, and gender) on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The virtual person profile is then sent to the device and displayed to the user.

[0531] Starting everyday conversations

[0532] To start a conversation with a virtual character, a user inputs and sends a message. This message is sent from the device to the server. The server receives the message and records it in a database. The recorded message is analyzed using natural language processing technology, and the results are input into a generative AI model. The generated reply from the virtual character is sent to the device via the server and displayed to the user.

[0533] Recording and analyzing conversation content

[0534] The server records the conversations between the user and the virtual persona in a database and continuously analyzes them using natural language processing technology. When messages containing specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies these messages as positive or negative emotions and detects the user's emotional state.

[0535] Risk Assessment and Notification

[0536] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. This alert is sent to the device and notifies the user. It also provides a function to notify experts or close relatives if necessary.

[0537] Specific examples

[0538] Example of user registration and initial settings

[0539] 1. User: "I'm going to download and try out the new app."

[0540] 2. Terminal: "Please enter your name, date of birth, and gender."

[0541] 3. User: "Taro Tanaka, January 1, 1990, male"

[0542] 4. Terminal: Sends input information to the server.

[0543] 5. Server: Stores user information in a database and generates a virtual person profile.

[0544] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0545] Examples of everyday conversation

[0546] 1. User: "I feel very tired and depressed today."

[0547] 2. Terminal: Sends user messages to the server.

[0548] 3. Server: Records messages in a database and performs analysis.

[0549] 4. Emotion engine: Determines "tired" and "depressed" as negative emotions from messages.

[0550] 5. Generative AI model: Generates a response like, "That's tough. Have you done anything special to relax?"

[0551] 6. Server: Generates a reply and sends it to the device.

[0552] 7. Terminal: The virtual person's reply is displayed to the user.

[0553] Examples of risk assessment and notification

[0554] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on cumulative data.

[0555] 2. Server: Sets a warning flag and generates an alert message.

[0556] 3. Device: Display the message to the user, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[0557] 4. Server: Sends notifications to professionals and next of kin as needed.

[0558] Example prompt: "Your recent conversations seem to be causing you increased stress. Please consider seeking professional help."

[0559] As described above, this system helps users maintain their mental health by detecting mental abnormalities early and providing appropriate support.

[0560] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0561] Step 1: User registration and initial setup

[0562] The user downloads the application to their device and enters the required information (name, date of birth, gender) on the account creation screen. The device sends the entered information to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The generated virtual person profile is sent to the device and displayed to the user.

[0563] Input: User's name, date of birth, gender

[0564] Output: Virtual person profile

[0565] Specific operation: The server receives user information and stores it in a database. It then uses a generative AI model to generate a virtual person profile and sends it to the device.

[0566] Step 2: Starting everyday conversations

[0567] The user types a message into the virtual character and sends it. The device sends the message to the server. The server records the message in a database and analyzes it using natural language processing technology. The analysis results are input into a generative AI model, which generates an appropriate reply. The generated reply is sent to the device and displayed to the user.

[0568] Input: User's message

[0569] Output: Virtual person's reply

[0570] Specific operation: The device receives the user's message and sends it to the server. The server analyzes the message, generates an appropriate reply using a generative AI model, and sends the reply to the device.

[0571] Step 3: Record and analyze the conversation

[0572] The server records the conversation between the user and the virtual character in a database and continuously analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[0573] Input: Conversation between the user and the virtual person

[0574] Output: Parsed emotional state

[0575] How it works: The server receives messages and analyzes their emotional state using natural language processing techniques. If a specific emotional pattern is detected, the emotion engine analyzes the data in detail.

[0576] Step 4: Risk assessment and communication

[0577] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. The generated alert is sent to the device and notifies the user. It also notifies experts and relatives as needed.

[0578] Input: Analyzed emotional state and stress score

[0579] Output: Alert message

[0580] Specific operation: The server calculates the stress score and assesses the risk based on the analysis results and emotional state. If necessary, it sets a warning flag and generates an alert message that is sent to the device. It also notifies experts and close relatives.

[0581] Step 5: Review and respond to alerts

[0582] The user checks the alert message displayed on the device and takes appropriate action if necessary (such as consulting a specialist or practicing relaxation techniques).

[0583] Input: Alert message

[0584] Output: User's response action

[0585] Specific actions: The user checks the alert message sent to the device and takes appropriate action based on their health condition. If they contact a specialist or close relative, they will receive support.

[0586] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0587] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0589] [Second embodiment]

[0590] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0591] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0592] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0593] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0594] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0595] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0596] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0597] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0598] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0601] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0602] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[0603] Program processing

[0604] User registration and initial settings

[0605] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[0606] Starting everyday conversations

[0607] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[0608] Recording and analyzing conversation content

[0609] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[0610] Risk Assessment and Notification

[0611] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. A collaborative method is also provided to notify experts or close relatives as needed.

[0612] Specific examples

[0613] Example of user registration and initial settings

[0614] 1. User: "I'll try downloading a new app."

[0615] 2. Terminal: "Please enter your name, date of birth, and gender."

[0616] 3. User: "Taro Yamada, January 1, 1990, male"

[0617] 4. Terminal: Sends input information to the server.

[0618] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0619] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0620] Examples of everyday conversation

[0621] 1. User: "I'm so tired today."

[0622] 2. Terminal: Sends user messages to the server.

[0623] 3. Server: Parses the message and generates an appropriate reply.

[0624] 4. Server: "That must have been tough. Did you do anything special to relax?" is sent to the device.

[0625] 5. Terminal: The virtual person's reply is displayed to the user.

[0626] Examples of risk assessment and notification

[0627] 1. Server: Updates the stress score based on the analysis results. The accumulated data indicates that the user's stress level is high.

[0628] 2. Server: Sets a warning flag and generates an alert message.

[0629] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[0630] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[0631] The processing flow will be explained below.

[0632] User registration and initial settings

[0633] Step 1:

[0634] A user downloads the app and opens the account creation screen.

[0635] Step 2:

[0636] The device will display an input form for name, date of birth, gender, etc.

[0637] Step 3:

[0638] The user enters the required information and clicks the send button.

[0639] Step 4:

[0640] The terminal transmits the input information to the server.

[0641] Step 5:

[0642] The server stores the received information in a database.

[0643] Step 6:

[0644] The user logs in for the first time.

[0645] Step 7:

[0646] The server checks the initial setup status of the user and finds that the initial setup has not been completed.

[0647] Step 8:

[0648] The server generates a virtual person profile for the AI ​​and sends it to the device.

[0649] Step 9:

[0650] The terminal displays the virtual person profile to the user.

[0651] Starting everyday conversations

[0652] Step 1:

[0653] To start a conversation with the virtual character, the user inputs a message and presses the send button.

[0654] Step 2:

[0655] The terminal sends the user's input to the server.

[0656] Recording and analyzing conversation content

[0657] Step 1:

[0658] The server records the received user messages in a database.

[0659] Step 2:

[0660] The server uses a generation AI to generate an appropriate reply.

[0661] Step 3:

[0662] The server sends the generated reply to the terminal.

[0663] Step 4:

[0664] The terminal displays the virtual person's reply to the user.

[0665] Risk Assessment and Notification

[0666] Step 1:

[0667] The server periodically analyzes the accumulated conversation data.

[0668] Step 2:

[0669] The server uses natural language processing algorithms to detect specific keywords and sentiment patterns.

[0670] Step 3:

[0671] The server calculates a stress score based on the detected keywords and patterns.

[0672] Step 4:

[0673] The server evaluates the user's stress score and sets a warning flag if a threshold is exceeded.

[0674] Step 5:

[0675] The server generates an alert message based on the warning flag and sends it to the terminal.

[0676] Step 6:

[0677] The terminal displays an alert message to the user.

[0678] Collaboration methods

[0679] Step 1:

[0680] If the server determines that the risk is high, it will notify experts and next of kin.

[0681] Step 2:

[0682] The server sends expert feedback and resources to the device.

[0683] Step 3:

[0684] The terminal displays contact information from the expert to the user.

[0685] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[0686] Example 1

[0687] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0688] In modern society, early detection and prevention of mental illness have become important issues. However, many people find it difficult to accurately grasp their own mental state, and psychological burdens often increase without them realizing it. In this situation, there is a need for a system that allows users to easily monitor their own mental state and take appropriate action when risks increase.

[0689] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0690] In this invention, the server includes a communication means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user of the risk detection results, thereby enabling the user to objectively evaluate their own mental state and take appropriate measures early on.

[0691] "Communication means" is a function that provides channels and protocols for conversation between the user and the virtual character.

[0692] The "data generation means" is a process for generating conversation data between a user and a virtual character based on a communication means.

[0693] "Data analysis means" refers to the technology or algorithms used to analyze the generated conversation data and evaluate the user's mental state.

[0694] The "risk assessment means" is a function for detecting the user's risk of mental illness based on the results of the data analysis means.

[0695] The "notification means" is a method or device for notifying the user of the risk results detected by the risk assessment means.

[0696] The "stress analysis means" is a function for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[0697] "Collaboration methods" is a function for sending notifications to experts and next of kin when a high risk is detected.

[0698] The "storage means" is a process for storing the basic information entered by the user in a database.

[0699] The "profile generation means" is a function for generating a profile of a virtual person based on basic information about the user.

[0700] The "display means" is a function that displays the profile and reply message of the generated virtual person to the user.

[0701] "Reply generation means" is a function that uses generation AI to analyze conversation data and generate appropriate replies.

[0702] The "alert generation means" is a function that uses a generation AI to calculate a stress score and generate an alert message if the threshold is exceeded.

[0703] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[0704] 1. User registration and initial settings

[0705] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[0706] 2. Starting everyday conversations

[0707] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[0708] 3. Recording and analyzing conversation content

[0709] The server records the conversations between the user and the virtual persona in a database and analyzes them using automated natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[0710] 4. Risk Assessment and Notification

[0711] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. The server also provides the ability to notify experts or close relatives if necessary.

[0712] Example operation

[0713] Example of user registration and initial settings

[0714] 1. User: "I'll try downloading a new app."

[0715] 2. Terminal: "Please enter your name, date of birth, and gender."

[0716] 3. User: "Taro Tanaka, January 1, 1990, male"

[0717] 4. Terminal: Sends input information to the server.

[0718] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0719] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0720] Examples of everyday conversation

[0721] 1. User: "I'm so tired today."

[0722] 2. Terminal: Sends user messages to the server.

[0723] 3. Server: Parses the message and generates an appropriate reply.

[0724] 4. Server: Sends "That must have been tough. Did you do anything special to relax?" to the device.

[0725] 5. Terminal: Displays the virtual person's reply to the user.

[0726] Examples of risk assessment and notification

[0727] 1. Server: Updates the stress score based on the analysis results. Determines that the user's stress level is high based on the accumulated data.

[0728] 2. Server: Sets a warning flag and generates an alert message.

[0729] 3. On the device: Display the message to the user: "Your recent conversations seem to be causing you stress. Consider seeking professional help."

[0730] Examples of prompt statements

[0731] "Calculate a stress score based on the conversation between the user and a virtual person. Generate a Python program that will alert the user if their stress level is high."

[0732] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[0733] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0734] Step 1: User registration and initial setup

[0735] 1. Input: The user enters basic information such as name, date of birth, and gender.

[0736] 2. Operation: The terminal receives the basic information entered by the user and sends it to the server.

[0737] 3. Output: The server stores the received basic information in a database.

[0738] 4. Operation: The server uses a generation AI to generate a virtual person's profile (name, age, hobbies, etc.).

[0739] 5. Output: The server sends the generated virtual person's profile to the terminal.

[0740] 6. Operation: The terminal displays the virtual person's profile to the user.

[0741] Step 2: Starting everyday conversations

[0742] 1. Input: The user types a message for the virtual person and presses the send button.

[0743] 2. Operation: The terminal sends the entered message to the server.

[0744] 3. Output: The server receives the message.

[0745] 4. Operation: The server analyzes the message using natural language processing techniques, such as morphological analysis and sentiment analysis.

[0746] 5. Output: The server uses AI to generate an appropriate reply based on the analysis results.

[0747] 6. Action: The server generates a reply and sends it to the device.

[0748] 7. Output: The device displays the virtual person's reply to the user.

[0749] Step 3: Record and analyze the conversation

[0750] 1. Input: Conversation data between the user and the virtual person.

[0751] 2. Operation: The server records the conversation between the user and the virtual character in a database.

[0752] 3. Output: Recorded conversation data.

[0753] 4. Operation: The server analyzes the recorded conversation data using natural language processing techniques, including morphological analysis, sentiment analysis, and keyword extraction.

[0754] 5. Output: The analysis results provide the frequency of occurrence of specific keywords and sentiment patterns.

[0755] Step 4: Risk assessment and communication

[0756] 1. Input: Frequency of keywords and sentiment patterns as analysis results.

[0757] 2. How it works: The server periodically evaluates the analysis results and calculates the user's stress score, which includes the frequency of certain keywords and negative emotions.

[0758] 3. Output: User's stress score.

[0759] 4. How it works: If the stress score exceeds a certain threshold, the server sets a warning flag and uses a generative AI to generate an appropriate alert message.

[0760] 5. Output: The alert message.

[0761] 6. Operation: The server sends the generated alert message to the terminal.

[0762] 7. Output: The device displays an alert message to the user: "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[0763] In this way, the overall processing of the system consists of processing and analyzing data based on user input information, and providing appropriate feedback and notifications based on the results.

[0764] (Application example 1)

[0765] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0766] Real-time monitoring of workers' mental health status and early detection of abnormalities are extremely important for work efficiency and safety. However, current systems lack effective means for managing workers' mental health, making it difficult to detect mental illness early. Furthermore, workers with deteriorating mental conditions may not receive appropriate support or warnings, potentially resulting in serious risks. This calls for a more effective mental health management system.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0768] In this invention, the server includes a conversation means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the conversation means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user and a manager of the risk detection results. This allows on-site workers to interact with the virtual character on a daily basis and monitor their mental health status. Furthermore, if high stress is detected, a warning is generated and notified to the manager, allowing effective support. This system makes it possible to detect mental risks in workers early and take appropriate measures.

[0769] "Conversation means" refers to a communication means for a user and a virtual character to converse, through which they can exchange messages with each other.

[0770] "Data generation means" refers to means for processing and storing conversation data generated by the conversation means, and records the content of the conversation.

[0771] The "data analysis means" is a means for analyzing the generated conversation data and evaluating the mental state of the user.

[0772] The "risk assessment means" is a means for assessing and detecting the user's risk of mental illness based on the analysis results obtained by the data analysis means.

[0773] "Notification means" refers to a means for informing users and administrators of risk detection results, and involves sending appropriate alerts and notifications.

[0774] The "stress analysis means" is a means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[0775] The "warning means" is a means for generating a warning when high stress is detected and notifying the user and the administrator.

[0776] This invention describes a specific implementation of a "Mental Health Security Assistant" system, in which users interact with a virtual person via a smartphone or smart glasses, and their mental health is monitored.

[0777] Hardware

[0778] 1. Device: The smartphone or smart glasses used by the user.

[0779] 2. Server: A server for analyzing conversation data and assessing mental health status.

[0780] software

[0781] 1. Conversation medium: An application installed on a smartphone or smart glasses that provides an interface for the user and the virtual person to exchange messages.

[0782] 2. Data Generation Means: Software functionality for recording conversational data generated through conversational means.

[0783] 3. Data analysis tools: Software using natural language processing techniques (e.g., NLTK or spaCy) to analyze the generated conversation data and evaluate the user's mental state.

[0784] 4. Risk Assessment Instrument: Software functionality that assesses psychosis risk based on the results of data analysis instruments.

[0785] 5. Notification methods: Smartphone push notifications and email notifications are used to notify users and administrators of risk detection results.

[0786] 6. Stress analysis tool: Software that analyzes the user's daily conversation patterns to identify the type and degree of stress.

[0787] 7. Warning: A function that generates a warning and notifies you when high stress is detected.

[0788] System Operation

[0789] 1. User registration and initial setup:

[0790] Users download the application and enter their personal information (name, date of birth, gender).

[0791] The terminal sends the input information to the server, and the server stores the user information in a database.

[0792] The server uses the generative AI model to generate a profile of the virtual person and sends it to the terminal.

[0793] The terminal displays the virtual person's profile to the user.

[0794] 2. Starting everyday conversations:

[0795] The user inputs and sends messages to have everyday conversations with the virtual person.

[0796] The terminal transmits the input message to the server.

[0797] The server parses the message and generates an appropriate reply.

[0798] The generated reply is sent to the terminal and displayed to the user.

[0799] 3. Recording and Analysis of Conversation Content:

[0800] The conversation data is recorded in a database by the server.

[0801] The server uses natural language processing technology to analyze the conversation and detect specific keywords and emotional patterns.

[0802] If a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[0803] 4. Risk Assessment and Communication:

[0804] The server calculates a stress score based on the analysis results.

[0805] If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message.

[0806] The alert is sent to the terminal and notified to the user.

[0807] If necessary, notifications will be sent to experts and administrators.

[0808] Specific examples

[0809] 1. Example of user registration and initial setup:

[0810] A user enters information to register an account.

[0811] The terminal inputs "Yamada Taro, March 25, 1985, male."

[0812] The server stores the information in a database and generates a profile of the virtual person.

[0813] The device displays, "Hello, I'm a virtual person who will assist you."

[0814] 2. Examples from everyday conversation:

[0815] A user says, "I'm tired today."

[0816] The device sends a message to the server.

[0817] The server generates a reply: "That must have been tough. Did you do anything special to relax?"

[0818] The device displays the reply to the user.

[0819] 3. Examples of risk assessment and communication:

[0820] The server updates the stress score based on the analysis results.

[0821] Accumulative data indicates that the user's stress level is high.

[0822] The server generates a warning saying, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[0823] The device will notify the user and administrator of the warning.

[0824] In this way, we provide a system that allows users to interact with virtual characters on a daily basis and effectively monitor their mental health, which can improve work efficiency and safety on site.

[0825] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0826] Step 1:

[0827] User registration and initial settings

[0828] Input: The user enters personal information (name, date of birth, gender) into an app on their smartphone or smart glasses.

[0829] What happens: A user fills in the required information on a registration form and clicks the submit button.

[0830] Data processing: The terminal converts the input information into JSON format and sends it to the server.

[0831] Output: The server stores the received data in a database, generates a virtual person's profile using a generative AI model, and sends the generated profile to the device.

[0832] Step 2:

[0833] Viewing a virtual person's profile

[0834] Input: Virtual person profile data sent from the server.

[0835] Operation: Receives profile data generated by the server and transfers it to the device.

[0836] Data Processing: The generated profile is formatted to be displayed in a user-friendly interface.

[0837] Output: The device displays a message on the screen, such as "Hello, I'm your virtual assistant."

[0838] Step 3:

[0839] Starting everyday conversations

[0840] Input: The message the user sends to the virtual person (e.g., "I'm tired today").

[0841] Action: The user types a message and presses the send button.

[0842] Data processing: The terminal sends the entered message to the server as text data.

[0843] Output: The server parses the message and generates an appropriate reply to send to the device.

[0844] Step 4:

[0845] Recording and analyzing conversation content

[0846] Input: Conversation data and reply data sent from the terminal.

[0847] What it does: The server logs the received message to a database.

[0848] Data processing: The server analyzes the message using natural language processing technology, detects specific keywords and emotional patterns, and calculates a stress score.

[0849] Output: If the stress score exceeds a certain threshold, send the information to the risk assessment module.

[0850] Step 5:

[0851] Risk Assessment

[0852] Input: Analysis results of conversation data and stress score.

[0853] How it works: The server evaluates stress scores and monitors changes in mental state.

[0854] Data processing: Determine whether the stress score is high and perform a risk assessment.

[0855] Output: If high stress is detected, set a warning flag and generate an alert message.

[0856] Step 6:

[0857] Generate notifications and alerts

[0858] Input: Risk assessment results and alert message.

[0859] Action: The server sends an alert message to the device.

[0860] Data Processing: Formatting data to send notifications to experts or administrators as needed.

[0861] Output: A warning such as "Recent conversations seem to be causing stress. Please consider consulting a specialist" is displayed on the device. A notification is also sent to the specialist and administrator via email.

[0862] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0863] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[0864] Program processing

[0865] User registration and initial settings

[0866] The user downloads the application and enters the necessary information on the account creation screen. The input information includes name, date of birth, gender, etc., and is sent to the server via the device. The server stores the received information in a database, and when the user logs in for the first time, the generation AI generates a virtual person profile and sends it to the device. The device then displays the virtual person profile to the user.

[0867] Starting everyday conversations

[0868] The user types and sends a message to start a conversation with a virtual person. The message is then sent from the device to the server. The server records the received message in a database and analyzes it, after which the AI ​​generates an appropriate reply. This reply is then sent to the device and displayed to the user.

[0869] Recording and analyzing conversation content

[0870] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[0871] Risk Assessment and Notification

[0872] The server calculates the user's stress score based on the analysis results. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the device and notifies the user. It also provides a means of collaboration to send notifications to experts and relatives as needed.

[0873] Specific examples

[0874] Example of user registration and initial settings

[0875] 1. User: "I'm going to download and try out the new app."

[0876] 2. Terminal: "Please enter your name, date of birth, and gender."

[0877] 3. User: "Taro Yamada, January 1, 1990, male"

[0878] 4. Terminal: Sends input information to the server.

[0879] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0880] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0881] Examples of everyday conversation

[0882] 1. User: "I feel very tired and depressed today."

[0883] 2. Terminal: Sends user messages to the server.

[0884] 3. Server: Records messages in a database and performs analysis.

[0885] 4. Emotion engine: Classifies messages such as "tired" and "depressed" as negative emotions.

[0886] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[0887] 6. Server: Generates and sends the reply to the device.

[0888] 7. Terminal: The virtual person's reply is displayed to the user.

[0889] Examples of risk assessment and notification

[0890] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[0891] 2. Server: Sets a warning flag and generates an alert message.

[0892] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[0893] 4. Server: Sends notifications to professionals and next of kin as needed.

[0894] This allows the system to detect mental disorders in users early and provide appropriate support, preventing mental illness from worsening and helping users maintain their mental health.

[0895] The processing flow will be explained below.

[0896] User registration and initial settings

[0897] Step 1:

[0898] A user downloads the app and opens the account creation screen.

[0899] Step 2:

[0900] The device will display an input form for name, date of birth, gender, etc.

[0901] Step 3:

[0902] The user enters the required information and clicks the send button.

[0903] Step 4:

[0904] The terminal transmits the input information to the server.

[0905] Step 5:

[0906] The server stores the received information in a database.

[0907] Step 6:

[0908] The user logs in for the first time.

[0909] Step 7:

[0910] The server checks the initial setting status of the user and finds that the initial setting has not been completed.

[0911] Step 8:

[0912] The server generates a virtual person profile for the AI ​​and sends it to the device.

[0913] Step 9:

[0914] The terminal displays the generated virtual person profile to the user.

[0915] Starting everyday conversations

[0916] Step 1:

[0917] To start a conversation with the virtual person, the user inputs and sends a conversation message.

[0918] Step 2:

[0919] The terminal sends the user's input to the server.

[0920] Recording and analyzing conversation content

[0921] Step 1:

[0922] The server records the received user messages in a database.

[0923] Step 2:

[0924] The server uses a generation AI to generate an appropriate reply.

[0925] Step 3:

[0926] The server sends the generated reply to the terminal.

[0927] Step 4:

[0928] The terminal displays the virtual person's reply to the user.

[0929] Step 5:

[0930] The server analyzes the received conversation data using natural language processing technology.

[0931] Step 6:

[0932] The server detects specific keywords and emotional patterns.

[0933] Step 7:

[0934] The server uses an emotion engine to recognize the user's emotional state.

[0935] Risk Assessment and Notification

[0936] Step 1:

[0937] The server processes the emotional state detected by the emotion engine with data analysis means.

[0938] Step 2:

[0939] The server calculates the user's stress score based on the analysis results.

[0940] Step 3:

[0941] The server sets a warning flag if the stress score exceeds a threshold.

[0942] Step 4:

[0943] The server generates an alert message based on the warning flag.

[0944] Step 5:

[0945] The server sends the generated alert message to the terminal.

[0946] Step 6:

[0947] The terminal displays an alert message to the user.

[0948] Collaboration methods

[0949] Step 1:

[0950] If the server determines that the risk is high, it will notify experts and next of kin.

[0951] Step 2:

[0952] The server sends expert feedback and resources to the device.

[0953] Step 3:

[0954] The terminal displays contact information from the expert to the user.

[0955] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[0956] Example 2

[0957] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0958] In modern society, it is extremely important to detect signs of mental stress and mental illness early and provide support at the appropriate time. However, current systems and methods lack the means to effectively analyze and appropriately evaluate a user's mental state from their everyday conversations. Furthermore, there are insufficient consistent systems and collaborative methods for detecting the risk of mental illness and providing the necessary support. This often leads to the worsening of mental illness and delays in providing appropriate support, which presents a challenge.

[0959] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: conversation means for a conversation between a user and a virtual character; data generation means for generating conversation data based on the conversation means; data analysis means for analyzing the generated conversation data and evaluating the user's mental state; analysis means for analyzing the generated conversation data using natural language processing technology and detecting specific keywords and emotional patterns; risk assessment means for detecting the user's risk of mental illness based on the analysis results; notification means for notifying the user of the risk detection results; generation AI means for generating a profile of the virtual character; emotion engine means for recognizing the emotional state and classifying it into positive and negative emotions; and warning means for setting a warning flag and generating an alert message when a certain threshold is exceeded. This makes it possible to effectively analyze the user's mental state from everyday conversations, assess the risk early, and provide necessary support at an appropriate time.

[0960] A "user" is an individual who uses the system to converse with a virtual character.

[0961] A "virtual person" is a virtual character that is generated by a generation AI means and that interacts with the user.

[0962] The "conversation means" is an interface for the user and the virtual character to exchange messages via communication.

[0963] The "data generation means" is a device or software that generates conversation data between a user and a virtual character based on the conversation means.

[0964] The "data analysis means" is a device or software that analyzes the generated conversation data and evaluates the mental state of the user.

[0965] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0966] The "analysis means" is a device or software that analyzes the conversation data generated using natural language processing technology and detects specific keywords and emotional patterns.

[0967] A "risk assessment means" is a device or software that detects a user's risk of mental illness based on the analysis results.

[0968] The "notification means" is a device or software that notifies the user of the risk detection results.

[0969] "Generative AI means" refers to a device or software that utilizes artificial intelligence technology to generate a virtual person's profile based on user information.

[0970] The "emotion engine means" is a device or software that recognizes emotional states from conversation data and classifies positive and negative emotions.

[0971] The "warning means" is a device or software that sets a warning flag and generates an alert message when the user's stress score exceeds a certain threshold.

[0972] A "profile" is data that represents the attributes and characteristics of a virtual person generated by a generation AI means.

[0973] This invention is a system that allows users to detect early signs of mental illness through conversations with virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual characters, and an emotion engine that recognizes emotional states.

[0974] User registration and initial settings

[0975] First, the user downloads the application and enters the necessary information such as name, date of birth, and gender on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL), and when the user logs in for the first time, a profile of the virtual person is generated using a generative AI (e.g., GPT-3). The generated profile is sent to the device and displayed to the user, allowing the user to start a conversation with the virtual person.

[0976] Examples:

[0977] 1. User: "I'm going to download and try out the new app."

[0978] 2. Terminal: "Please enter your name, date of birth, and gender."

[0979] 3. User: "Taro Yamada, January 1, 1990, male"

[0980] 4. Terminal: Sends input information to the server.

[0981] 5. Server: Stores user information in a database and generates a virtual persona profile.

[0982] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[0983] Starting everyday conversations

[0984] The user types a message and starts a conversation with a virtual person. The message is sent from the device to the server, which records the message in a database and analyzes it. The AI ​​then generates an appropriate reply, which is sent to the device. The device then displays the generated reply to the user.

[0985] Examples:

[0986] 1. User: "I feel very tired and depressed today."

[0987] 2. Terminal: Sends user messages to the server.

[0988] 3. Server: Records messages in a database and performs analysis.

[0989] 4. Emotion engine (e.g., IBM Watson Tone Analyzer): Classifies "tired" and "depressed" from messages as negative emotions.

[0990] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[0991] 6. Server: Generates and sends the reply to the device.

[0992] 7. Terminal: The virtual person's reply is displayed to the user.

[0993] Recording and analyzing conversation content

[0994] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology (e.g., spaCy). When specific keywords or emotional patterns are detected, the data is further analyzed by an emotion engine, which classifies the message content as positive or negative and detects the user's emotional state.

[0995] Risk Assessment and Notification

[0996] The server calculates the user's stress score based on the analysis of the conversation content. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. This alert is sent to the user's device and notifies the user. There is also a function to notify experts or close relatives if necessary.

[0997] Examples:

[0998] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[0999] 2. Server: Sets a warning flag and generates an alert message.

[1000] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[1001] 4. Server: Sends notifications to professionals and next of kin as needed.

[1002] With the above configuration, this system can effectively analyze the user's mental state from everyday conversations, assess risk early, and provide necessary support at the appropriate time.

[1003] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1004] System processing flow

[1005] Step 1:

[1006] The user downloads and installs the application.

[1007] Input: The action of downloading an application from the app store.

[1008] Output: The application is installed on the device and launched for the first time.

[1009] Step 2:

[1010] The user enters the necessary information on the account registration screen.

[1011] Specifically, the user enters data into fields for entering information such as name, date of birth, and gender.

[1012] Input: Information entered by the user, such as name, date of birth, and gender.

[1013] Output: The input information is collected by the terminal.

[1014] Step 3:

[1015] The terminal transmits the input information to the server.

[1016] Input: The registration information entered by the user.

[1017] Output: The registration information received by the server.

[1018] Step 4:

[1019] The server stores the received information in a database.

[1020] Input: Registration information sent from the device.

[1021] Output: User information stored in the database.

[1022] Step 5:

[1023] The server uses generative AI to generate a profile of the virtual person.

[1024] Specifically, the AI ​​sends a prompt message based on the registration information to generate profile data such as the virtual person's name, personality, and hobbies.

[1025] Input: User information stored in the database.

[1026] Output: A virtual person profile returned by the generation AI.

[1027] Step 6:

[1028] The terminal receives the virtual person's profile and displays it to the user.

[1029] Input: Virtual person profile data received from the server.

[1030] Output: A profile of the virtual person displayed on the device screen.

[1031] Step 7:

[1032] The user types and sends messages to initiate a conversation with the virtual person.

[1033] Input: A message typed by the user.

[1034] Output: Messages typed are displayed on the terminal.

[1035] Step 8:

[1036] The terminal sends a user message to the server.

[1037] Input: The message entered by the user.

[1038] Output: The message received by the server.

[1039] Step 9:

[1040] The server logs the message in a database.

[1041] Input: User message sent from the terminal.

[1042] Output: Messages logged to the database.

[1043] Step 10:

[1044] The server parses the message, sends a prompt to the generation AI, and generates an appropriate reply.

[1045] Specifically, it uses natural language processing technology to analyze the sentiment and meaning of the message, generates a prompt from the analysis results, and sends it to a generation AI to generate a reply.

[1046] Input: The message logged in the database.

[1047] Output: The virtual person's reply returned by the generation AI.

[1048] Step 11:

[1049] The terminal displays the generated reply to the user.

[1050] Input: The virtual person's reply data received from the server.

[1051] Output: The reply that appears on the device screen.

[1052] Step 12:

[1053] The server analyzes the conversation content and assesses the emotional state using an emotion engine.

[1054] Specifically, the server analyzes the conversation data to detect specific keywords and emotional patterns, which are then further classified by the emotion engine to evaluate the emotional state.

[1055] Input: Conversation data recorded in a database.

[1056] Output: Emotion state data classified by the emotion engine.

[1057] Step 13:

[1058] The server calculates the user's stress score based on the analysis results and performs a risk assessment.

[1059] Specifically, the system calculates a stress score based on the analysis results and emotional state data from the emotion engine. If the score exceeds a certain threshold, a risk assessment is performed.

[1060] Input: Emotional state data output by the emotion engine.

[1061] Output: Calculated stress score and risk assessment results.

[1062] Step 14:

[1063] The server sets warning flags based on the risk assessment results and generates alert messages.

[1064] Input: Risk assessment results.

[1065] Output: The generated alert message.

[1066] Step 15:

[1067] The terminal receives the alert message and displays it to the user.

[1068] Input: The alert message received from the server.

[1069] Output: The alert message that is displayed on the device screen.

[1070] Step 16:

[1071] The server will then send notifications to professionals and next of kin as appropriate.

[1072] Specifically, if the risk assessment results exceed a certain threshold, the system will initiate a process to send emails and notifications to experts and next of kin.

[1073] Input: Risk assessment results and configured notification conditions.

[1074] Output: Emails and notification messages sent.

[1075] Through these steps, the system can effectively analyze the user's mental state from their everyday conversations, assess risk early, and provide appropriate support.

[1076] (Application example 2)

[1077] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1078] In recent years, there has been a demand for early detection of mental illness and the provision of appropriate support, but many users find it difficult to notice changes in their own mental health. Furthermore, it is often difficult for them to voluntarily consult with experts or close relatives. This can lead to the progression and worsening of many mental illnesses. There is a need for a system that can monitor the user's mental state in real time through conversations, detect abnormalities early, and provide appropriate support.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1080] In this invention, the server includes a communication means for a conversation between a user and a virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, an evaluation means for detecting the user's risk of mental abnormality based on the analysis result, a notification means for notifying the user of the risk detection result, a scoring means for calculating the user's stress score, and a reply generation means for generating a reply using a generative AI model. This makes it possible to monitor changes in the user's mental state in real time through everyday conversations with the user, and to immediately provide appropriate support or an alert if an abnormality is detected.

[1081] "Communication means" refers to an interface for conversation between the user and the virtual character, and refers to devices or software that enable the sending and receiving of messages.

[1082] "Data generation means" refers to a system that has the function of recording conversations exchanged through communication means and generating them as conversation data.

[1083] "Data analysis means" refers to software or algorithms that analyze the generated conversation data and evaluate the user's mental state.

[1084] "Evaluation means" refers to a system or algorithm that detects a user's risk of mental abnormality based on the analysis results.

[1085] "Notification means" refers to devices or software for notifying users of risk detection results.

[1086] A "scoring method" is a system or algorithm that calculates a user's stress score and evaluates their mental state numerically.

[1087] A "reply generation means" is a system that has the function of automatically generating an appropriate reply to a user using a generative AI model.

[1088] A "generative AI model" is a model that uses artificial intelligence, and is an algorithm or machine learning model that generates appropriate replies from conversations with users.

[1089] SUMMARY OF THE INVENTION

[1090] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generative AI model that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[1091] Hardware and software used

[1092] Hardware: Smartphones, tablets, personal computers

[1093] Software: Python, natural language processing libraries (e.g., TextBlob, NLTK), cloud server environment (e.g., AWS, Google Cloud), machine learning models (generative AI models)

[1094] Program processing overview

[1095] User registration and initial settings

[1096] The user downloads the application onto their device and enters the required information (such as name, date of birth, and gender) on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The virtual person profile is then sent to the device and displayed to the user.

[1097] Starting everyday conversations

[1098] To start a conversation with a virtual character, a user inputs and sends a message. This message is sent from the device to the server. The server receives the message and records it in a database. The recorded message is analyzed using natural language processing technology, and the results are input into a generative AI model. The generated reply from the virtual character is sent to the device via the server and displayed to the user.

[1099] Recording and analyzing conversation content

[1100] The server records the conversations between the user and the virtual persona in a database and continuously analyzes them using natural language processing technology. When messages containing specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies these messages as positive or negative emotions and detects the user's emotional state.

[1101] Risk Assessment and Notification

[1102] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. This alert is sent to the device and notifies the user. It also provides a function to notify experts or close relatives if necessary.

[1103] Specific examples

[1104] Example of user registration and initial settings

[1105] 1. User: "I'm going to download and try out the new app."

[1106] 2. Terminal: "Please enter your name, date of birth, and gender."

[1107] 3. User: "Taro Tanaka, January 1, 1990, male"

[1108] 4. Terminal: Sends input information to the server.

[1109] 5. Server: Stores user information in a database and generates a virtual person profile.

[1110] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1111] Examples of everyday conversation

[1112] 1. User: "I feel very tired and depressed today."

[1113] 2. Terminal: Sends user messages to the server.

[1114] 3. Server: Records messages in a database and performs analysis.

[1115] 4. Emotion engine: Determines "tired" and "depressed" as negative emotions from messages.

[1116] 5. Generative AI model: Generates a response like, "That's tough. Have you done anything special to relax?"

[1117] 6. Server: Generates a reply and sends it to the device.

[1118] 7. Terminal: The virtual person's reply is displayed to the user.

[1119] Examples of risk assessment and notification

[1120] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on cumulative data.

[1121] 2. Server: Sets a warning flag and generates an alert message.

[1122] 3. Device: Display the message to the user, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[1123] 4. Server: Sends notifications to professionals and next of kin as needed.

[1124] Example prompt: "Your recent conversations seem to be causing you increased stress. Please consider seeking professional help."

[1125] As described above, this system helps users maintain their mental health by detecting mental abnormalities early and providing appropriate support.

[1126] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1127] Step 1: User registration and initial setup

[1128] The user downloads the application to their device and enters the required information (name, date of birth, gender) on the account creation screen. The device sends the entered information to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The generated virtual person profile is sent to the device and displayed to the user.

[1129] Input: User's name, date of birth, gender

[1130] Output: Virtual person profile

[1131] Specific operation: The server receives user information and stores it in a database. It then uses a generative AI model to generate a virtual person profile and sends it to the device.

[1132] Step 2: Starting everyday conversations

[1133] The user types a message into the virtual character and sends it. The device sends the message to the server. The server records the message in a database and analyzes it using natural language processing technology. The analysis results are input into a generative AI model, which generates an appropriate reply. The generated reply is sent to the device and displayed to the user.

[1134] Input: User's message

[1135] Output: Virtual person's reply

[1136] Specific operation: The device receives the user's message and sends it to the server. The server analyzes the message, generates an appropriate reply using a generative AI model, and sends the reply to the device.

[1137] Step 3: Record and analyze the conversation

[1138] The server records the conversation between the user and the virtual character in a database and continuously analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[1139] Input: Conversation between the user and the virtual person

[1140] Output: Parsed emotional state

[1141] How it works: The server receives messages and analyzes their emotional state using natural language processing techniques. If a specific emotional pattern is detected, the emotion engine analyzes the data in detail.

[1142] Step 4: Risk assessment and communication

[1143] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. The generated alert is sent to the device and notifies the user. It also notifies experts and relatives as needed.

[1144] Input: Analyzed emotional state and stress score

[1145] Output: Alert message

[1146] Specific operation: The server calculates the stress score and assesses the risk based on the analysis results and emotional state. If necessary, it sets a warning flag and generates an alert message that is sent to the device. It also notifies experts and close relatives.

[1147] Step 5: Review and respond to alerts

[1148] The user checks the alert message displayed on the device and takes appropriate action if necessary (such as consulting a specialist or practicing relaxation techniques).

[1149] Input: Alert message

[1150] Output: User's response action

[1151] Specific actions: The user checks the alert message sent to the device and takes appropriate action based on their health condition. If they contact a specialist or close relative, they will receive support.

[1152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1153] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1154] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1155] [Third embodiment]

[1156] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1157] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1159] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1160] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1164] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1166] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1167] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1168] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[1169] Program processing

[1170] User registration and initial settings

[1171] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[1172] Starting everyday conversations

[1173] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[1174] Recording and analyzing conversation content

[1175] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[1176] Risk Assessment and Notification

[1177] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. A collaborative method is also provided to notify experts or close relatives as needed.

[1178] Specific examples

[1179] Example of user registration and initial settings

[1180] 1. User: "I'll try downloading a new app."

[1181] 2. Terminal: "Please enter your name, date of birth, and gender."

[1182] 3. User: "Taro Yamada, January 1, 1990, male"

[1183] 4. Terminal: Sends input information to the server.

[1184] 5. Server: Stores user information in a database and generates a virtual persona profile.

[1185] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1186] Examples of everyday conversation

[1187] 1. User: "I'm so tired today."

[1188] 2. Terminal: Sends user messages to the server.

[1189] 3. Server: Parses the message and generates an appropriate reply.

[1190] 4. Server: "That must have been tough. Did you do anything special to relax?" is sent to the device.

[1191] 5. Terminal: The virtual person's reply is displayed to the user.

[1192] Examples of risk assessment and notification

[1193] 1. Server: Updates the stress score based on the analysis results. The accumulated data indicates that the user's stress level is high.

[1194] 2. Server: Sets a warning flag and generates an alert message.

[1195] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[1196] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[1197] The processing flow will be explained below.

[1198] User registration and initial settings

[1199] Step 1:

[1200] A user downloads the app and opens the account creation screen.

[1201] Step 2:

[1202] The device will display an input form for name, date of birth, gender, etc.

[1203] Step 3:

[1204] The user enters the required information and clicks the send button.

[1205] Step 4:

[1206] The terminal transmits the input information to the server.

[1207] Step 5:

[1208] The server stores the received information in a database.

[1209] Step 6:

[1210] The user logs in for the first time.

[1211] Step 7:

[1212] The server checks the initial setup status of the user and finds that the initial setup has not been completed.

[1213] Step 8:

[1214] The server generates a virtual person profile for the AI ​​and sends it to the device.

[1215] Step 9:

[1216] The terminal displays the virtual person profile to the user.

[1217] Starting everyday conversations

[1218] Step 1:

[1219] To start a conversation with the virtual character, the user inputs a message and presses the send button.

[1220] Step 2:

[1221] The terminal sends the user's input to the server.

[1222] Recording and analyzing conversation content

[1223] Step 1:

[1224] The server records the received user messages in a database.

[1225] Step 2:

[1226] The server uses a generation AI to generate an appropriate reply.

[1227] Step 3:

[1228] The server sends the generated reply to the terminal.

[1229] Step 4:

[1230] The terminal displays the virtual person's reply to the user.

[1231] Risk Assessment and Notification

[1232] Step 1:

[1233] The server periodically analyzes the accumulated conversation data.

[1234] Step 2:

[1235] The server uses natural language processing algorithms to detect specific keywords and sentiment patterns.

[1236] Step 3:

[1237] The server calculates a stress score based on the detected keywords and patterns.

[1238] Step 4:

[1239] The server evaluates the user's stress score and sets a warning flag if a threshold is exceeded.

[1240] Step 5:

[1241] The server generates an alert message based on the warning flag and sends it to the terminal.

[1242] Step 6:

[1243] The terminal displays an alert message to the user.

[1244] Collaboration methods

[1245] Step 1:

[1246] If the server determines that the risk is high, it will notify experts and next of kin.

[1247] Step 2:

[1248] The server sends expert feedback and resources to the device.

[1249] Step 3:

[1250] The terminal displays contact information from the expert to the user.

[1251] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[1252] Example 1

[1253] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1254] In modern society, early detection and prevention of mental illness have become important issues. However, many people find it difficult to accurately grasp their own mental state, and psychological burdens often increase without them realizing it. In this situation, there is a need for a system that allows users to easily monitor their own mental state and take appropriate action when risks increase.

[1255] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1256] In this invention, the server includes a communication means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user of the risk detection results, thereby enabling the user to objectively evaluate their own mental state and take appropriate measures early on.

[1257] "Communication means" is a function that provides channels and protocols for conversation between the user and the virtual character.

[1258] The "data generation means" is a process for generating conversation data between a user and a virtual character based on a communication means.

[1259] "Data analysis means" refers to the technology or algorithms used to analyze the generated conversation data and evaluate the user's mental state.

[1260] The "risk assessment means" is a function for detecting the user's risk of mental illness based on the results of the data analysis means.

[1261] The "notification means" is a method or device for notifying the user of the risk results detected by the risk assessment means.

[1262] The "stress analysis means" is a function for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[1263] "Collaboration methods" is a function for sending notifications to experts and next of kin when a high risk is detected.

[1264] The "storage means" is a process for storing the basic information entered by the user in a database.

[1265] The "profile generation means" is a function for generating a profile of a virtual person based on basic information about the user.

[1266] The "display means" is a function that displays the profile and reply message of the generated virtual person to the user.

[1267] "Reply generation means" is a function that uses generation AI to analyze conversation data and generate appropriate replies.

[1268] The "alert generation means" is a function that uses a generation AI to calculate a stress score and generate an alert message if the threshold is exceeded.

[1269] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[1270] 1. User registration and initial settings

[1271] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[1272] 2. Starting everyday conversations

[1273] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[1274] 3. Recording and analyzing conversation content

[1275] The server records the conversations between the user and the virtual persona in a database and analyzes them using automated natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[1276] 4. Risk Assessment and Notification

[1277] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. The server also provides the ability to notify experts or close relatives if necessary.

[1278] Example operation

[1279] Example of user registration and initial settings

[1280] 1. User: "I'll try downloading a new app."

[1281] 2. Terminal: "Please enter your name, date of birth, and gender."

[1282] 3. User: "Taro Tanaka, January 1, 1990, male"

[1283] 4. Terminal: Sends input information to the server.

[1284] 5. Server: Stores user information in a database and generates a virtual persona profile.

[1285] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1286] Examples of everyday conversation

[1287] 1. User: "I'm so tired today."

[1288] 2. Terminal: Sends user messages to the server.

[1289] 3. Server: Parses the message and generates an appropriate reply.

[1290] 4. Server: Sends "That must have been tough. Did you do anything special to relax?" to the device.

[1291] 5. Terminal: Displays the virtual person's reply to the user.

[1292] Examples of risk assessment and notification

[1293] 1. Server: Updates the stress score based on the analysis results. Determines that the user's stress level is high based on the accumulated data.

[1294] 2. Server: Sets a warning flag and generates an alert message.

[1295] 3. On the device: Display the message to the user: "Your recent conversations seem to be causing you stress. Consider seeking professional help."

[1296] Examples of prompt statements

[1297] "Calculate a stress score based on the conversation between the user and a virtual person. Generate a Python program that will alert the user if their stress level is high."

[1298] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[1299] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1300] Step 1: User registration and initial setup

[1301] 1. Input: The user enters basic information such as name, date of birth, and gender.

[1302] 2. Operation: The terminal receives the basic information entered by the user and sends it to the server.

[1303] 3. Output: The server stores the received basic information in a database.

[1304] 4. Operation: The server uses a generation AI to generate a virtual person's profile (name, age, hobbies, etc.).

[1305] 5. Output: The server sends the generated virtual person's profile to the terminal.

[1306] 6. Operation: The terminal displays the virtual person's profile to the user.

[1307] Step 2: Starting everyday conversations

[1308] 1. Input: The user types a message for the virtual person and presses the send button.

[1309] 2. Operation: The terminal sends the entered message to the server.

[1310] 3. Output: The server receives the message.

[1311] 4. Operation: The server analyzes the message using natural language processing techniques, such as morphological analysis and sentiment analysis.

[1312] 5. Output: The server uses AI to generate an appropriate reply based on the analysis results.

[1313] 6. Action: The server generates a reply and sends it to the device.

[1314] 7. Output: The device displays the virtual person's reply to the user.

[1315] Step 3: Record and analyze the conversation

[1316] 1. Input: Conversation data between the user and the virtual person.

[1317] 2. Operation: The server records the conversation between the user and the virtual character in a database.

[1318] 3. Output: Recorded conversation data.

[1319] 4. Operation: The server analyzes the recorded conversation data using natural language processing techniques, including morphological analysis, sentiment analysis, and keyword extraction.

[1320] 5. Output: The analysis results provide the frequency of occurrence of specific keywords and sentiment patterns.

[1321] Step 4: Risk assessment and communication

[1322] 1. Input: Frequency of keywords and sentiment patterns as analysis results.

[1323] 2. How it works: The server periodically evaluates the analysis results and calculates the user's stress score, which includes the frequency of certain keywords and negative emotions.

[1324] 3. Output: User's stress score.

[1325] 4. How it works: If the stress score exceeds a certain threshold, the server sets a warning flag and uses a generative AI to generate an appropriate alert message.

[1326] 5. Output: The alert message.

[1327] 6. Operation: The server sends the generated alert message to the terminal.

[1328] 7. Output: The device displays an alert message to the user: "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[1329] In this way, the overall processing of the system consists of processing and analyzing data based on user input information, and providing appropriate feedback and notifications based on the results.

[1330] (Application example 1)

[1331] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1332] Real-time monitoring of workers' mental health status and early detection of abnormalities are extremely important for work efficiency and safety. However, current systems lack effective means for managing workers' mental health, making it difficult to detect mental illness early. Furthermore, workers with deteriorating mental conditions may not receive appropriate support or warnings, potentially resulting in serious risks. This calls for a more effective mental health management system.

[1333] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1334] In this invention, the server includes a conversation means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the conversation means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user and a manager of the risk detection results. This allows on-site workers to interact with the virtual character on a daily basis and monitor their mental health status. Furthermore, if high stress is detected, a warning is generated and notified to the manager, allowing effective support. This system makes it possible to detect mental risks in workers early and take appropriate measures.

[1335] "Conversation means" refers to a communication means for a user and a virtual character to converse, through which they can exchange messages with each other.

[1336] "Data generation means" refers to means for processing and storing conversation data generated by the conversation means, and records the content of the conversation.

[1337] The "data analysis means" is a means for analyzing the generated conversation data and evaluating the mental state of the user.

[1338] The "risk assessment means" is a means for assessing and detecting the user's risk of mental illness based on the analysis results obtained by the data analysis means.

[1339] "Notification means" refers to a means for informing users and administrators of risk detection results, and involves sending appropriate alerts and notifications.

[1340] The "stress analysis means" is a means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[1341] The "warning means" is a means for generating a warning when high stress is detected and notifying the user and the administrator.

[1342] This invention describes a specific implementation of a "Mental Health Security Assistant" system, in which users interact with a virtual person via a smartphone or smart glasses, and their mental health is monitored.

[1343] Hardware

[1344] 1. Device: The smartphone or smart glasses used by the user.

[1345] 2. Server: A server for analyzing conversation data and assessing mental health status.

[1346] software

[1347] 1. Conversation medium: An application installed on a smartphone or smart glasses that provides an interface for the user and the virtual person to exchange messages.

[1348] 2. Data Generation Means: Software functionality for recording conversational data generated through conversational means.

[1349] 3. Data analysis tools: Software using natural language processing techniques (e.g., NLTK or spaCy) to analyze the generated conversation data and evaluate the user's mental state.

[1350] 4. Risk Assessment Instrument: Software functionality that assesses psychosis risk based on the results of data analysis instruments.

[1351] 5. Notification methods: Smartphone push notifications and email notifications are used to notify users and administrators of risk detection results.

[1352] 6. Stress analysis tool: Software that analyzes the user's daily conversation patterns to identify the type and degree of stress.

[1353] 7. Warning: A function that generates a warning and notifies you when high stress is detected.

[1354] System Operation

[1355] 1. User registration and initial setup:

[1356] Users download the application and enter their personal information (name, date of birth, gender).

[1357] The terminal sends the input information to the server, and the server stores the user information in a database.

[1358] The server uses the generative AI model to generate a profile of the virtual person and sends it to the terminal.

[1359] The terminal displays the virtual person's profile to the user.

[1360] 2. Starting everyday conversations:

[1361] The user inputs and sends messages to have everyday conversations with the virtual person.

[1362] The terminal transmits the input message to the server.

[1363] The server parses the message and generates an appropriate reply.

[1364] The generated reply is sent to the terminal and displayed to the user.

[1365] 3. Recording and Analysis of Conversation Content:

[1366] The conversation data is recorded in a database by the server.

[1367] The server uses natural language processing technology to analyze the conversation and detect specific keywords and emotional patterns.

[1368] If a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[1369] 4. Risk Assessment and Communication:

[1370] The server calculates a stress score based on the analysis results.

[1371] If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message.

[1372] The alert is sent to the terminal and notified to the user.

[1373] If necessary, notifications will be sent to experts and administrators.

[1374] Specific examples

[1375] 1. Example of user registration and initial setup:

[1376] A user enters information to register an account.

[1377] The terminal inputs "Yamada Taro, March 25, 1985, male."

[1378] The server stores the information in a database and generates a profile of the virtual person.

[1379] The device displays, "Hello, I'm a virtual person who will assist you."

[1380] 2. Examples from everyday conversation:

[1381] A user says, "I'm tired today."

[1382] The device sends a message to the server.

[1383] The server generates a reply: "That must have been tough. Did you do anything special to relax?"

[1384] The device displays the reply to the user.

[1385] 3. Examples of risk assessment and communication:

[1386] The server updates the stress score based on the analysis results.

[1387] Accumulative data indicates that the user's stress level is high.

[1388] The server generates a warning saying, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[1389] The device will notify the user and administrator of the warning.

[1390] In this way, we provide a system that allows users to interact with virtual characters on a daily basis and effectively monitor their mental health, which can improve work efficiency and safety on site.

[1391] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1392] Step 1:

[1393] User registration and initial settings

[1394] Input: The user enters personal information (name, date of birth, gender) into an app on their smartphone or smart glasses.

[1395] What happens: A user fills in the required information on a registration form and clicks the submit button.

[1396] Data processing: The terminal converts the input information into JSON format and sends it to the server.

[1397] Output: The server stores the received data in a database, generates a virtual person's profile using a generative AI model, and sends the generated profile to the device.

[1398] Step 2:

[1399] Viewing a virtual person's profile

[1400] Input: Virtual person profile data sent from the server.

[1401] Operation: Receives profile data generated by the server and transfers it to the device.

[1402] Data Processing: The generated profile is formatted to be displayed in a user-friendly interface.

[1403] Output: The device displays a message on the screen, such as "Hello, I'm your virtual assistant."

[1404] Step 3:

[1405] Starting everyday conversations

[1406] Input: The message the user sends to the virtual person (e.g., "I'm tired today").

[1407] Action: The user types a message and presses the send button.

[1408] Data processing: The terminal sends the entered message to the server as text data.

[1409] Output: The server parses the message and generates an appropriate reply to send to the device.

[1410] Step 4:

[1411] Recording and analyzing conversation content

[1412] Input: Conversation data and reply data sent from the terminal.

[1413] What it does: The server logs the received message to a database.

[1414] Data processing: The server analyzes the message using natural language processing technology, detects specific keywords and emotional patterns, and calculates a stress score.

[1415] Output: If the stress score exceeds a certain threshold, send the information to the risk assessment module.

[1416] Step 5:

[1417] Risk Assessment

[1418] Input: Analysis results of conversation data and stress score.

[1419] How it works: The server evaluates stress scores and monitors changes in mental state.

[1420] Data processing: Determine whether the stress score is high and perform a risk assessment.

[1421] Output: If high stress is detected, set a warning flag and generate an alert message.

[1422] Step 6:

[1423] Generate notifications and alerts

[1424] Input: Risk assessment results and alert message.

[1425] Action: The server sends an alert message to the device.

[1426] Data Processing: Formatting data to send notifications to experts or administrators as needed.

[1427] Output: A warning such as "Recent conversations seem to be causing stress. Please consider consulting a specialist" is displayed on the device. A notification is also sent to the specialist and administrator via email.

[1428] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1429] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[1430] Program processing

[1431] User registration and initial settings

[1432] The user downloads the application and enters the necessary information on the account creation screen. The input information includes name, date of birth, gender, etc., and is sent to the server via the device. The server stores the received information in a database, and when the user logs in for the first time, the generation AI generates a virtual person profile and sends it to the device. The device then displays the virtual person profile to the user.

[1433] Starting everyday conversations

[1434] The user types and sends a message to start a conversation with a virtual person. The message is then sent from the device to the server. The server records the received message in a database and analyzes it, after which the AI ​​generates an appropriate reply. This reply is then sent to the device and displayed to the user.

[1435] Recording and analyzing conversation content

[1436] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[1437] Risk Assessment and Notification

[1438] The server calculates the user's stress score based on the analysis results. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the device and notifies the user. It also provides a means of collaboration to send notifications to experts and relatives as needed.

[1439] Specific examples

[1440] Example of user registration and initial settings

[1441] 1. User: "I'm going to download and try out the new app."

[1442] 2. Terminal: "Please enter your name, date of birth, and gender."

[1443] 3. User: "Taro Yamada, January 1, 1990, male"

[1444] 4. Terminal: Sends input information to the server.

[1445] 5. Server: Stores user information in a database and generates a virtual persona profile.

[1446] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1447] Examples of everyday conversation

[1448] 1. User: "I feel very tired and depressed today."

[1449] 2. Terminal: Sends user messages to the server.

[1450] 3. Server: Records messages in a database and performs analysis.

[1451] 4. Emotion engine: Classifies messages such as "tired" and "depressed" as negative emotions.

[1452] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[1453] 6. Server: Generates and sends the reply to the device.

[1454] 7. Terminal: The virtual person's reply is displayed to the user.

[1455] Examples of risk assessment and notification

[1456] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[1457] 2. Server: Sets a warning flag and generates an alert message.

[1458] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[1459] 4. Server: Sends notifications to professionals and next of kin as needed.

[1460] This allows the system to detect mental disorders in users early and provide appropriate support, preventing mental illness from worsening and helping users maintain their mental health.

[1461] The processing flow will be explained below.

[1462] User registration and initial settings

[1463] Step 1:

[1464] A user downloads the app and opens the account creation screen.

[1465] Step 2:

[1466] The device will display an input form for name, date of birth, gender, etc.

[1467] Step 3:

[1468] The user enters the required information and clicks the send button.

[1469] Step 4:

[1470] The terminal transmits the input information to the server.

[1471] Step 5:

[1472] The server stores the received information in a database.

[1473] Step 6:

[1474] The user logs in for the first time.

[1475] Step 7:

[1476] The server checks the initial setting status of the user and finds that the initial setting has not been completed.

[1477] Step 8:

[1478] The server generates a virtual person profile for the AI ​​and sends it to the device.

[1479] Step 9:

[1480] The terminal displays the generated virtual person profile to the user.

[1481] Starting everyday conversations

[1482] Step 1:

[1483] To start a conversation with the virtual person, the user inputs and sends a conversation message.

[1484] Step 2:

[1485] The terminal sends the user's input to the server.

[1486] Recording and analyzing conversation content

[1487] Step 1:

[1488] The server records the received user messages in a database.

[1489] Step 2:

[1490] The server uses a generation AI to generate an appropriate reply.

[1491] Step 3:

[1492] The server sends the generated reply to the terminal.

[1493] Step 4:

[1494] The terminal displays the virtual person's reply to the user.

[1495] Step 5:

[1496] The server analyzes the received conversation data using natural language processing technology.

[1497] Step 6:

[1498] The server detects specific keywords and emotional patterns.

[1499] Step 7:

[1500] The server uses an emotion engine to recognize the user's emotional state.

[1501] Risk Assessment and Notification

[1502] Step 1:

[1503] The server processes the emotional state detected by the emotion engine with data analysis means.

[1504] Step 2:

[1505] The server calculates the user's stress score based on the analysis results.

[1506] Step 3:

[1507] The server sets a warning flag if the stress score exceeds a threshold.

[1508] Step 4:

[1509] The server generates an alert message based on the warning flag.

[1510] Step 5:

[1511] The server sends the generated alert message to the terminal.

[1512] Step 6:

[1513] The terminal displays an alert message to the user.

[1514] Collaboration methods

[1515] Step 1:

[1516] If the server determines that the risk is high, it will notify experts and next of kin.

[1517] Step 2:

[1518] The server sends expert feedback and resources to the device.

[1519] Step 3:

[1520] The terminal displays contact information from the expert to the user.

[1521] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[1522] Example 2

[1523] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1524] In modern society, it is extremely important to detect signs of mental stress and mental illness early and provide support at the appropriate time. However, current systems and methods lack the means to effectively analyze and appropriately evaluate a user's mental state from their everyday conversations. Furthermore, there are insufficient consistent systems and collaborative methods for detecting the risk of mental illness and providing the necessary support. This often leads to the worsening of mental illness and delays in providing appropriate support, which presents a challenge.

[1525] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: conversation means for a conversation between a user and a virtual character; data generation means for generating conversation data based on the conversation means; data analysis means for analyzing the generated conversation data and evaluating the user's mental state; analysis means for analyzing the generated conversation data using natural language processing technology and detecting specific keywords and emotional patterns; risk assessment means for detecting the user's risk of mental illness based on the analysis results; notification means for notifying the user of the risk detection results; generation AI means for generating a profile of the virtual character; emotion engine means for recognizing the emotional state and classifying it into positive and negative emotions; and warning means for setting a warning flag and generating an alert message when a certain threshold is exceeded. This makes it possible to effectively analyze the user's mental state from everyday conversations, assess the risk early, and provide necessary support at an appropriate time.

[1526] A "user" is an individual who uses the system to converse with a virtual character.

[1527] A "virtual person" is a virtual character that is generated by a generation AI means and that interacts with the user.

[1528] The "conversation means" is an interface for the user and the virtual character to exchange messages via communication.

[1529] The "data generation means" is a device or software that generates conversation data between a user and a virtual character based on the conversation means.

[1530] The "data analysis means" is a device or software that analyzes the generated conversation data and evaluates the mental state of the user.

[1531] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1532] The "analysis means" is a device or software that analyzes the conversation data generated using natural language processing technology and detects specific keywords and emotional patterns.

[1533] A "risk assessment means" is a device or software that detects a user's risk of mental illness based on the analysis results.

[1534] The "notification means" is a device or software that notifies the user of the risk detection results.

[1535] "Generative AI means" refers to a device or software that utilizes artificial intelligence technology to generate a virtual person's profile based on user information.

[1536] The "emotion engine means" is a device or software that recognizes emotional states from conversation data and classifies positive and negative emotions.

[1537] The "warning means" is a device or software that sets a warning flag and generates an alert message when the user's stress score exceeds a certain threshold.

[1538] A "profile" is data that represents the attributes and characteristics of a virtual person generated by a generation AI means.

[1539] This invention is a system that allows users to detect early signs of mental illness through conversations with virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual characters, and an emotion engine that recognizes emotional states.

[1540] User registration and initial settings

[1541] First, the user downloads the application and enters the necessary information such as name, date of birth, and gender on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL), and when the user logs in for the first time, a profile of the virtual person is generated using a generative AI (e.g., GPT-3). The generated profile is sent to the device and displayed to the user, allowing the user to start a conversation with the virtual person.

[1542] Examples:

[1543] 1. User: "I'm going to download and try out the new app."

[1544] 2. Terminal: "Please enter your name, date of birth, and gender."

[1545] 3. User: "Taro Yamada, January 1, 1990, male"

[1546] 4. Terminal: Sends input information to the server.

[1547] 5. Server: Stores user information in a database and generates a virtual persona profile.

[1548] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1549] Starting everyday conversations

[1550] The user types a message and starts a conversation with a virtual person. The message is sent from the device to the server, which records the message in a database and analyzes it. The AI ​​then generates an appropriate reply, which is sent to the device. The device then displays the generated reply to the user.

[1551] Examples:

[1552] 1. User: "I feel very tired and depressed today."

[1553] 2. Terminal: Sends user messages to the server.

[1554] 3. Server: Records messages in a database and performs analysis.

[1555] 4. Emotion engine (e.g., IBM Watson Tone Analyzer): Classifies "tired" and "depressed" from messages as negative emotions.

[1556] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[1557] 6. Server: Generates and sends the reply to the device.

[1558] 7. Terminal: The virtual person's reply is displayed to the user.

[1559] Recording and analyzing conversation content

[1560] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology (e.g., spaCy). When specific keywords or emotional patterns are detected, the data is further analyzed by an emotion engine, which classifies the message content as positive or negative and detects the user's emotional state.

[1561] Risk Assessment and Notification

[1562] The server calculates the user's stress score based on the analysis of the conversation content. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. This alert is sent to the user's device and notifies the user. There is also a function to notify experts or close relatives if necessary.

[1563] Examples:

[1564] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[1565] 2. Server: Sets a warning flag and generates an alert message.

[1566] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[1567] 4. Server: Sends notifications to professionals and next of kin as needed.

[1568] With the above configuration, this system can effectively analyze the user's mental state from everyday conversations, assess risk early, and provide necessary support at the appropriate time.

[1569] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1570] System processing flow

[1571] Step 1:

[1572] The user downloads and installs the application.

[1573] Input: The action of downloading an application from the app store.

[1574] Output: The application is installed on the device and launched for the first time.

[1575] Step 2:

[1576] The user enters the necessary information on the account registration screen.

[1577] Specifically, the user enters data into fields for entering information such as name, date of birth, and gender.

[1578] Input: Information entered by the user, such as name, date of birth, and gender.

[1579] Output: The input information is collected by the terminal.

[1580] Step 3:

[1581] The terminal transmits the input information to the server.

[1582] Input: The registration information entered by the user.

[1583] Output: The registration information received by the server.

[1584] Step 4:

[1585] The server stores the received information in a database.

[1586] Input: Registration information sent from the device.

[1587] Output: User information stored in the database.

[1588] Step 5:

[1589] The server uses generative AI to generate a profile of the virtual person.

[1590] Specifically, the AI ​​sends a prompt message based on the registration information to generate profile data such as the virtual person's name, personality, and hobbies.

[1591] Input: User information stored in the database.

[1592] Output: A virtual person profile returned by the generation AI.

[1593] Step 6:

[1594] The terminal receives the virtual person's profile and displays it to the user.

[1595] Input: Virtual person profile data received from the server.

[1596] Output: A profile of the virtual person displayed on the device screen.

[1597] Step 7:

[1598] The user types and sends messages to initiate a conversation with the virtual person.

[1599] Input: A message typed by the user.

[1600] Output: Messages typed are displayed on the terminal.

[1601] Step 8:

[1602] The terminal sends a user message to the server.

[1603] Input: The message entered by the user.

[1604] Output: The message received by the server.

[1605] Step 9:

[1606] The server logs the message in a database.

[1607] Input: User message sent from the terminal.

[1608] Output: Messages logged to the database.

[1609] Step 10:

[1610] The server parses the message, sends a prompt to the generation AI, and generates an appropriate reply.

[1611] Specifically, it uses natural language processing technology to analyze the sentiment and meaning of the message, generates a prompt from the analysis results, and sends it to a generation AI to generate a reply.

[1612] Input: The message logged in the database.

[1613] Output: The virtual person's reply returned by the generation AI.

[1614] Step 11:

[1615] The terminal displays the generated reply to the user.

[1616] Input: The virtual person's reply data received from the server.

[1617] Output: The reply that appears on the device screen.

[1618] Step 12:

[1619] The server analyzes the conversation content and assesses the emotional state using an emotion engine.

[1620] Specifically, the server analyzes the conversation data to detect specific keywords and emotional patterns, which are then further classified by the emotion engine to evaluate the emotional state.

[1621] Input: Conversation data recorded in a database.

[1622] Output: Emotion state data classified by the emotion engine.

[1623] Step 13:

[1624] The server calculates the user's stress score based on the analysis results and performs a risk assessment.

[1625] Specifically, the system calculates a stress score based on the analysis results and emotional state data from the emotion engine. If the score exceeds a certain threshold, a risk assessment is performed.

[1626] Input: Emotional state data output by the emotion engine.

[1627] Output: Calculated stress score and risk assessment results.

[1628] Step 14:

[1629] The server sets warning flags based on the risk assessment results and generates alert messages.

[1630] Input: Risk assessment results.

[1631] Output: The generated alert message.

[1632] Step 15:

[1633] The terminal receives the alert message and displays it to the user.

[1634] Input: The alert message received from the server.

[1635] Output: The alert message that is displayed on the device screen.

[1636] Step 16:

[1637] The server will then send notifications to professionals and next of kin as appropriate.

[1638] Specifically, if the risk assessment results exceed a certain threshold, the system will initiate a process to send emails and notifications to experts and next of kin.

[1639] Input: Risk assessment results and configured notification conditions.

[1640] Output: Emails and notification messages sent.

[1641] Through these steps, the system can effectively analyze the user's mental state from their everyday conversations, assess risk early, and provide appropriate support.

[1642] (Application example 2)

[1643] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1644] In recent years, there has been a demand for early detection of mental illness and the provision of appropriate support, but many users find it difficult to notice changes in their own mental health. Furthermore, it is often difficult for them to voluntarily consult with experts or close relatives. This can lead to the progression and worsening of many mental illnesses. There is a need for a system that can monitor the user's mental state in real time through conversations, detect abnormalities early, and provide appropriate support.

[1645] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1646] In this invention, the server includes a communication means for a conversation between a user and a virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, an evaluation means for detecting the user's risk of mental abnormality based on the analysis result, a notification means for notifying the user of the risk detection result, a scoring means for calculating the user's stress score, and a reply generation means for generating a reply using a generative AI model. This makes it possible to monitor changes in the user's mental state in real time through everyday conversations with the user, and to immediately provide appropriate support or an alert if an abnormality is detected.

[1647] "Communication means" refers to an interface for conversation between the user and the virtual character, and refers to devices or software that enable the sending and receiving of messages.

[1648] "Data generation means" refers to a system that has the function of recording conversations exchanged through communication means and generating them as conversation data.

[1649] "Data analysis means" refers to software or algorithms that analyze the generated conversation data and evaluate the user's mental state.

[1650] "Evaluation means" refers to a system or algorithm that detects a user's risk of mental abnormality based on the analysis results.

[1651] "Notification means" refers to devices or software for notifying users of risk detection results.

[1652] A "scoring method" is a system or algorithm that calculates a user's stress score and evaluates their mental state numerically.

[1653] A "reply generation means" is a system that has the function of automatically generating an appropriate reply to a user using a generative AI model.

[1654] A "generative AI model" is a model that uses artificial intelligence, and is an algorithm or machine learning model that generates appropriate replies from conversations with users.

[1655] SUMMARY OF THE INVENTION

[1656] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generative AI model that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[1657] Hardware and software used

[1658] Hardware: Smartphones, tablets, personal computers

[1659] Software: Python, natural language processing libraries (e.g., TextBlob, NLTK), cloud server environment (e.g., AWS, Google Cloud), machine learning models (generative AI models)

[1660] Program processing overview

[1661] User registration and initial settings

[1662] The user downloads the application onto their device and enters the required information (such as name, date of birth, and gender) on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The virtual person profile is then sent to the device and displayed to the user.

[1663] Starting everyday conversations

[1664] To start a conversation with a virtual character, a user inputs and sends a message. This message is sent from the device to the server. The server receives the message and records it in a database. The recorded message is analyzed using natural language processing technology, and the results are input into a generative AI model. The generated reply from the virtual character is sent to the device via the server and displayed to the user.

[1665] Recording and analyzing conversation content

[1666] The server records the conversations between the user and the virtual persona in a database and continuously analyzes them using natural language processing technology. When messages containing specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies these messages as positive or negative emotions and detects the user's emotional state.

[1667] Risk Assessment and Notification

[1668] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. This alert is sent to the device and notifies the user. It also provides a function to notify experts or close relatives if necessary.

[1669] Specific examples

[1670] Example of user registration and initial settings

[1671] 1. User: "I'm going to download and try out the new app."

[1672] 2. Terminal: "Please enter your name, date of birth, and gender."

[1673] 3. User: "Taro Tanaka, January 1, 1990, male"

[1674] 4. Terminal: Sends input information to the server.

[1675] 5. Server: Stores user information in a database and generates a virtual person profile.

[1676] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1677] Examples of everyday conversation

[1678] 1. User: "I feel very tired and depressed today."

[1679] 2. Terminal: Sends user messages to the server.

[1680] 3. Server: Records messages in a database and performs analysis.

[1681] 4. Emotion engine: Determines "tired" and "depressed" as negative emotions from messages.

[1682] 5. Generative AI model: Generates a response like, "That's tough. Have you done anything special to relax?"

[1683] 6. Server: Generates a reply and sends it to the device.

[1684] 7. Terminal: The virtual person's reply is displayed to the user.

[1685] Examples of risk assessment and notification

[1686] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on cumulative data.

[1687] 2. Server: Sets a warning flag and generates an alert message.

[1688] 3. Device: Display the message to the user, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[1689] 4. Server: Sends notifications to professionals and next of kin as needed.

[1690] Example prompt: "Your recent conversations seem to be causing you increased stress. Please consider seeking professional help."

[1691] As described above, this system helps users maintain their mental health by detecting mental abnormalities early and providing appropriate support.

[1692] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1693] Step 1: User registration and initial setup

[1694] The user downloads the application to their device and enters the required information (name, date of birth, gender) on the account creation screen. The device sends the entered information to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The generated virtual person profile is sent to the device and displayed to the user.

[1695] Input: User's name, date of birth, gender

[1696] Output: Virtual person profile

[1697] Specific operation: The server receives user information and stores it in a database. It then uses a generative AI model to generate a virtual person profile and sends it to the device.

[1698] Step 2: Starting everyday conversations

[1699] The user types a message into the virtual character and sends it. The device sends the message to the server. The server records the message in a database and analyzes it using natural language processing technology. The analysis results are input into a generative AI model, which generates an appropriate reply. The generated reply is sent to the device and displayed to the user.

[1700] Input: User's message

[1701] Output: Virtual person's reply

[1702] Specific operation: The device receives the user's message and sends it to the server. The server analyzes the message, generates an appropriate reply using a generative AI model, and sends the reply to the device.

[1703] Step 3: Record and analyze the conversation

[1704] The server records the conversation between the user and the virtual character in a database and continuously analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[1705] Input: Conversation between the user and the virtual person

[1706] Output: Parsed emotional state

[1707] How it works: The server receives messages and analyzes their emotional state using natural language processing techniques. If a specific emotional pattern is detected, the emotion engine analyzes the data in detail.

[1708] Step 4: Risk assessment and communication

[1709] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. The generated alert is sent to the device and notifies the user. It also notifies experts and relatives as needed.

[1710] Input: Analyzed emotional state and stress score

[1711] Output: Alert message

[1712] Specific operation: The server calculates the stress score and assesses the risk based on the analysis results and emotional state. If necessary, it sets a warning flag and generates an alert message that is sent to the device. It also notifies experts and close relatives.

[1713] Step 5: Review and respond to alerts

[1714] The user checks the alert message displayed on the device and takes appropriate action if necessary (such as consulting a specialist or practicing relaxation techniques).

[1715] Input: Alert message

[1716] Output: User's response action

[1717] Specific actions: The user checks the alert message sent to the device and takes appropriate action based on their health condition. If they contact a specialist or close relative, they will receive support.

[1718] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1719] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1720] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1721] [Fourth embodiment]

[1722] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1723] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1724] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1725] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1726] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1727] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1728] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1729] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1730] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1731] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1732] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1733] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1734] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1735] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[1736] Program processing

[1737] User registration and initial settings

[1738] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[1739] Starting everyday conversations

[1740] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[1741] Recording and analyzing conversation content

[1742] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[1743] Risk Assessment and Notification

[1744] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. A collaborative method is also provided to notify experts or close relatives as needed.

[1745] Specific examples

[1746] Example of user registration and initial settings

[1747] 1. User: "I'll try downloading a new app."

[1748] 2. Terminal: "Please enter your name, date of birth, and gender."

[1749] 3. User: "Taro Yamada, January 1, 1990, male"

[1750] 4. Terminal: Sends input information to the server.

[1751] 5. Server: Stores user information in a database and generates a virtual persona profile.

[1752] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1753] Examples of everyday conversation

[1754] 1. User: "I'm so tired today."

[1755] 2. Terminal: Sends user messages to the server.

[1756] 3. Server: Parses the message and generates an appropriate reply.

[1757] 4. Server: "That must have been tough. Did you do anything special to relax?" is sent to the device.

[1758] 5. Terminal: The virtual person's reply is displayed to the user.

[1759] Examples of risk assessment and notification

[1760] 1. Server: Updates the stress score based on the analysis results. The accumulated data indicates that the user's stress level is high.

[1761] 2. Server: Sets a warning flag and generates an alert message.

[1762] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[1763] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[1764] The processing flow will be explained below.

[1765] User registration and initial settings

[1766] Step 1:

[1767] A user downloads the app and opens the account creation screen.

[1768] Step 2:

[1769] The device will display an input form for name, date of birth, gender, etc.

[1770] Step 3:

[1771] The user enters the required information and clicks the send button.

[1772] Step 4:

[1773] The terminal transmits the input information to the server.

[1774] Step 5:

[1775] The server stores the received information in a database.

[1776] Step 6:

[1777] The user logs in for the first time.

[1778] Step 7:

[1779] The server checks the initial setup status of the user and finds that the initial setup has not been completed.

[1780] Step 8:

[1781] The server generates a virtual person profile for the AI ​​and sends it to the device.

[1782] Step 9:

[1783] The terminal displays the virtual person profile to the user.

[1784] Starting everyday conversations

[1785] Step 1:

[1786] To start a conversation with the virtual character, the user inputs a message and presses the send button.

[1787] Step 2:

[1788] The terminal sends the user's input to the server.

[1789] Recording and analyzing conversation content

[1790] Step 1:

[1791] The server records the received user messages in a database.

[1792] Step 2:

[1793] The server uses a generation AI to generate an appropriate reply.

[1794] Step 3:

[1795] The server sends the generated reply to the terminal.

[1796] Step 4:

[1797] The terminal displays the virtual person's reply to the user.

[1798] Risk Assessment and Notification

[1799] Step 1:

[1800] The server periodically analyzes the accumulated conversation data.

[1801] Step 2:

[1802] The server uses natural language processing algorithms to detect specific keywords and sentiment patterns.

[1803] Step 3:

[1804] The server calculates a stress score based on the detected keywords and patterns.

[1805] Step 4:

[1806] The server evaluates the user's stress score and sets a warning flag if a threshold is exceeded.

[1807] Step 5:

[1808] The server generates an alert message based on the warning flag and sends it to the terminal.

[1809] Step 6:

[1810] The terminal displays an alert message to the user.

[1811] Collaboration methods

[1812] Step 1:

[1813] If the server determines that the risk is high, it will notify experts and next of kin.

[1814] Step 2:

[1815] The server sends expert feedback and resources to the device.

[1816] Step 3:

[1817] The terminal displays contact information from the expert to the user.

[1818] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[1819] Example 1

[1820] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1821] In modern society, early detection and prevention of mental illness have become important issues. However, many people find it difficult to accurately grasp their own mental state, and psychological burdens often increase without them realizing it. In this situation, there is a need for a system that allows users to easily monitor their own mental state and take appropriate action when risks increase.

[1822] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1823] In this invention, the server includes a communication means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user of the risk detection results, thereby enabling the user to objectively evaluate their own mental state and take appropriate measures early on.

[1824] "Communication means" is a function that provides channels and protocols for conversation between the user and the virtual character.

[1825] The "data generation means" is a process for generating conversation data between a user and a virtual character based on a communication means.

[1826] "Data analysis means" refers to the technology or algorithms used to analyze the generated conversation data and evaluate the user's mental state.

[1827] The "risk assessment means" is a function for detecting the user's risk of mental illness based on the results of the data analysis means.

[1828] The "notification means" is a method or device for notifying the user of the risk results detected by the risk assessment means.

[1829] The "stress analysis means" is a function for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[1830] "Collaboration methods" is a function for sending notifications to experts and next of kin when a high risk is detected.

[1831] The "storage means" is a process for storing the basic information entered by the user in a database.

[1832] The "profile generation means" is a function for generating a profile of a virtual person based on basic information about the user.

[1833] The "display means" is a function that displays the profile and reply message of the generated virtual person to the user.

[1834] "Reply generation means" is a function that uses generation AI to analyze conversation data and generate appropriate replies.

[1835] The "alert generation means" is a function that uses a generation AI to calculate a stress score and generate an alert message if the threshold is exceeded.

[1836] This invention is a system for early detection of signs of mental illness through conversations between users and virtual characters. The system mainly consists of a terminal used by the user, a server that processes conversation data, and a generation AI that generates replies from the virtual characters.

[1837] 1. User registration and initial settings

[1838] Users first download the application and create an account. When creating an account, they enter basic information such as their name, date of birth, and gender. The device sends this information to the server, which stores it in a database. When the user logs in for the first time, the AI ​​generates a virtual person's profile, which is displayed to the user on the device.

[1839] 2. Starting everyday conversations

[1840] When a user starts a conversation with a virtual character, they input and send a message. The input data is sent from the device to the server. The server analyzes the received message and generates an appropriate reply based on its content. The generated reply is sent to the device and displayed to the user.

[1841] 3. Recording and analyzing conversation content

[1842] The server records the conversations between the user and the virtual persona in a database and analyzes them using automated natural language processing technology. This analysis detects specific keywords and emotional patterns. For example, if a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[1843] 4. Risk Assessment and Notification

[1844] The server periodically evaluates the analysis results and calculates the user's stress score. If this stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the user's device and notifies the user. The server also provides the ability to notify experts or close relatives if necessary.

[1845] Example operation

[1846] Example of user registration and initial settings

[1847] 1. User: "I'll try downloading a new app."

[1848] 2. Terminal: "Please enter your name, date of birth, and gender."

[1849] 3. User: "Taro Tanaka, January 1, 1990, male"

[1850] 4. Terminal: Sends input information to the server.

[1851] 5. Server: Stores user information in a database and generates a virtual persona profile.

[1852] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[1853] Examples of everyday conversation

[1854] 1. User: "I'm so tired today."

[1855] 2. Terminal: Sends user messages to the server.

[1856] 3. Server: Parses the message and generates an appropriate reply.

[1857] 4. Server: Sends "That must have been tough. Did you do anything special to relax?" to the device.

[1858] 5. Terminal: Displays the virtual person's reply to the user.

[1859] Examples of risk assessment and notification

[1860] 1. Server: Updates the stress score based on the analysis results. Determines that the user's stress level is high based on the accumulated data.

[1861] 2. Server: Sets a warning flag and generates an alert message.

[1862] 3. On the device: Display the message to the user: "Your recent conversations seem to be causing you stress. Consider seeking professional help."

[1863] Examples of prompt statements

[1864] "Calculate a stress score based on the conversation between the user and a virtual person. Generate a Python program that will alert the user if their stress level is high."

[1865] In this way, the system can detect mental disorders in users early and provide appropriate support, helping to prevent the worsening of mental illness.

[1866] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1867] Step 1: User registration and initial setup

[1868] 1. Input: The user enters basic information such as name, date of birth, and gender.

[1869] 2. Operation: The terminal receives the basic information entered by the user and sends it to the server.

[1870] 3. Output: The server stores the received basic information in a database.

[1871] 4. Operation: The server uses a generation AI to generate a virtual person's profile (name, age, hobbies, etc.).

[1872] 5. Output: The server sends the generated virtual person's profile to the terminal.

[1873] 6. Operation: The terminal displays the virtual person's profile to the user.

[1874] Step 2: Starting everyday conversations

[1875] 1. Input: The user types a message for the virtual person and presses the send button.

[1876] 2. Operation: The terminal sends the entered message to the server.

[1877] 3. Output: The server receives the message.

[1878] 4. Operation: The server analyzes the message using natural language processing techniques, such as morphological analysis and sentiment analysis.

[1879] 5. Output: The server uses AI to generate an appropriate reply based on the analysis results.

[1880] 6. Action: The server generates a reply and sends it to the device.

[1881] 7. Output: The device displays the virtual person's reply to the user.

[1882] Step 3: Record and analyze the conversation

[1883] 1. Input: Conversation data between the user and the virtual person.

[1884] 2. Operation: The server records the conversation between the user and the virtual character in a database.

[1885] 3. Output: Recorded conversation data.

[1886] 4. Operation: The server analyzes the recorded conversation data using natural language processing techniques, including morphological analysis, sentiment analysis, and keyword extraction.

[1887] 5. Output: The analysis results provide the frequency of occurrence of specific keywords and sentiment patterns.

[1888] Step 4: Risk assessment and communication

[1889] 1. Input: Frequency of keywords and sentiment patterns as analysis results.

[1890] 2. How it works: The server periodically evaluates the analysis results and calculates the user's stress score, which includes the frequency of certain keywords and negative emotions.

[1891] 3. Output: User's stress score.

[1892] 4. How it works: If the stress score exceeds a certain threshold, the server sets a warning flag and uses a generative AI to generate an appropriate alert message.

[1893] 5. Output: The alert message.

[1894] 6. Operation: The server sends the generated alert message to the terminal.

[1895] 7. Output: The device displays an alert message to the user: "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[1896] In this way, the overall processing of the system consists of processing and analyzing data based on user input information, and providing appropriate feedback and notifications based on the results.

[1897] (Application example 1)

[1898] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1899] Real-time monitoring of workers' mental health status and early detection of abnormalities are extremely important for work efficiency and safety. However, current systems lack effective means for managing workers' mental health, making it difficult to detect mental illness early. Furthermore, workers with deteriorating mental conditions may not receive appropriate support or warnings, potentially resulting in serious risks. This calls for a more effective mental health management system.

[1900] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1901] In this invention, the server includes a conversation means for a conversation between the user and the virtual character, a data generation means for generating conversation data based on the conversation means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, a risk assessment means for detecting the user's risk of mental illness based on the analysis results, and a notification means for notifying the user and a manager of the risk detection results. This allows on-site workers to interact with the virtual character on a daily basis and monitor their mental health status. Furthermore, if high stress is detected, a warning is generated and notified to the manager, allowing effective support. This system makes it possible to detect mental risks in workers early and take appropriate measures.

[1902] "Conversation means" refers to a communication means for a user and a virtual character to converse, through which they can exchange messages with each other.

[1903] "Data generation means" refers to means for processing and storing conversation data generated by the conversation means, and records the content of the conversation.

[1904] The "data analysis means" is a means for analyzing the generated conversation data and evaluating the mental state of the user.

[1905] The "risk assessment means" is a means for assessing and detecting the user's risk of mental illness based on the analysis results obtained by the data analysis means.

[1906] "Notification means" refers to a means for informing users and administrators of risk detection results, and involves sending appropriate alerts and notifications.

[1907] The "stress analysis means" is a means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the trends.

[1908] The "warning means" is a means for generating a warning when high stress is detected and notifying the user and the administrator.

[1909] This invention describes a specific implementation of a "Mental Health Security Assistant" system, in which users interact with a virtual person via a smartphone or smart glasses, and their mental health is monitored.

[1910] Hardware

[1911] 1. Device: The smartphone or smart glasses used by the user.

[1912] 2. Server: A server for analyzing conversation data and assessing mental health status.

[1913] software

[1914] 1. Conversation medium: An application installed on a smartphone or smart glasses that provides an interface for the user and the virtual person to exchange messages.

[1915] 2. Data Generation Means: Software functionality for recording conversational data generated through conversational means.

[1916] 3. Data analysis tools: Software using natural language processing techniques (e.g., NLTK or spaCy) to analyze the generated conversation data and evaluate the user's mental state.

[1917] 4. Risk Assessment Instrument: Software functionality that assesses psychosis risk based on the results of data analysis instruments.

[1918] 5. Notification methods: Smartphone push notifications and email notifications are used to notify users and administrators of risk detection results.

[1919] 6. Stress analysis tool: Software that analyzes the user's daily conversation patterns to identify the type and degree of stress.

[1920] 7. Warning: A function that generates a warning and notifies you when high stress is detected.

[1921] System Operation

[1922] 1. User registration and initial setup:

[1923] Users download the application and enter their personal information (name, date of birth, gender).

[1924] The terminal sends the input information to the server, and the server stores the user information in a database.

[1925] The server uses the generative AI model to generate a profile of the virtual person and sends it to the terminal.

[1926] The terminal displays the virtual person's profile to the user.

[1927] 2. Starting everyday conversations:

[1928] The user inputs and sends messages to have everyday conversations with the virtual person.

[1929] The terminal transmits the input message to the server.

[1930] The server parses the message and generates an appropriate reply.

[1931] The generated reply is sent to the terminal and displayed to the user.

[1932] 3. Recording and Analysis of Conversation Content:

[1933] The conversation data is recorded in a database by the server.

[1934] The server uses natural language processing technology to analyze the conversation and detect specific keywords and emotional patterns.

[1935] If a user frequently uses keywords such as "tired" or "lonely," that data is used to calculate a stress score.

[1936] 4. Risk Assessment and Communication:

[1937] The server calculates a stress score based on the analysis results.

[1938] If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message.

[1939] The alert is sent to the terminal and notified to the user.

[1940] If necessary, notifications will be sent to experts and administrators.

[1941] Specific examples

[1942] 1. Example of user registration and initial setup:

[1943] A user enters information to register an account.

[1944] The terminal inputs "Yamada Taro, March 25, 1985, male."

[1945] The server stores the information in a database and generates a profile of the virtual person.

[1946] The device displays, "Hello, I'm a virtual person who will assist you."

[1947] 2. Examples from everyday conversation:

[1948] A user says, "I'm tired today."

[1949] The device sends a message to the server.

[1950] The server generates a reply: "That must have been tough. Did you do anything special to relax?"

[1951] The device displays the reply to the user.

[1952] 3. Examples of risk assessment and communication:

[1953] The server updates the stress score based on the analysis results.

[1954] Accumulative data indicates that the user's stress level is high.

[1955] The server generates a warning saying, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[1956] The device will notify the user and administrator of the warning.

[1957] In this way, we provide a system that allows users to interact with virtual characters on a daily basis and effectively monitor their mental health, which can improve work efficiency and safety on site.

[1958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1959] Step 1:

[1960] User registration and initial settings

[1961] Input: The user enters personal information (name, date of birth, gender) into an app on their smartphone or smart glasses.

[1962] What happens: A user fills in the required information on a registration form and clicks the submit button.

[1963] Data processing: The terminal converts the input information into JSON format and sends it to the server.

[1964] Output: The server stores the received data in a database, generates a virtual person's profile using a generative AI model, and sends the generated profile to the device.

[1965] Step 2:

[1966] Viewing a virtual person's profile

[1967] Input: Virtual person profile data sent from the server.

[1968] Operation: Receives profile data generated by the server and transfers it to the device.

[1969] Data Processing: The generated profile is formatted to be displayed in a user-friendly interface.

[1970] Output: The device displays a message on the screen, such as "Hello, I'm your virtual assistant."

[1971] Step 3:

[1972] Starting everyday conversations

[1973] Input: The message the user sends to the virtual person (e.g., "I'm tired today").

[1974] Action: The user types a message and presses the send button.

[1975] Data processing: The terminal sends the entered message to the server as text data.

[1976] Output: The server parses the message and generates an appropriate reply to send to the device.

[1977] Step 4:

[1978] Recording and analyzing conversation content

[1979] Input: Conversation data and reply data sent from the terminal.

[1980] What it does: The server logs the received message to a database.

[1981] Data processing: The server analyzes the message using natural language processing technology, detects specific keywords and emotional patterns, and calculates a stress score.

[1982] Output: If the stress score exceeds a certain threshold, send the information to the risk assessment module.

[1983] Step 5:

[1984] Risk Assessment

[1985] Input: Analysis results of conversation data and stress score.

[1986] How it works: The server evaluates stress scores and monitors changes in mental state.

[1987] Data processing: Determine whether the stress score is high and perform a risk assessment.

[1988] Output: If high stress is detected, set a warning flag and generate an alert message.

[1989] Step 6:

[1990] Generate notifications and alerts

[1991] Input: Risk assessment results and alert message.

[1992] Action: The server sends an alert message to the device.

[1993] Data Processing: Formatting data to send notifications to experts or administrators as needed.

[1994] Output: A warning such as "Recent conversations seem to be causing stress. Please consider consulting a specialist" is displayed on the device. A notification is also sent to the specialist and administrator via email.

[1995] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1996] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[1997] Program processing

[1998] User registration and initial settings

[1999] The user downloads the application and enters the necessary information on the account creation screen. The input information includes name, date of birth, gender, etc., and is sent to the server via the device. The server stores the received information in a database, and when the user logs in for the first time, the generation AI generates a virtual person profile and sends it to the device. The device then displays the virtual person profile to the user.

[2000] Starting everyday conversations

[2001] The user types and sends a message to start a conversation with a virtual person. The message is then sent from the device to the server. The server records the received message in a database and analyzes it, after which the AI ​​generates an appropriate reply. This reply is then sent to the device and displayed to the user.

[2002] Recording and analyzing conversation content

[2003] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[2004] Risk Assessment and Notification

[2005] The server calculates the user's stress score based on the analysis results. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. The generated alert is sent to the device and notifies the user. It also provides a means of collaboration to send notifications to experts and relatives as needed.

[2006] Specific examples

[2007] Example of user registration and initial settings

[2008] 1. User: "I'm going to download and try out the new app."

[2009] 2. Terminal: "Please enter your name, date of birth, and gender."

[2010] 3. User: "Taro Yamada, January 1, 1990, male"

[2011] 4. Terminal: Sends input information to the server.

[2012] 5. Server: Stores user information in a database and generates a virtual persona profile.

[2013] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[2014] Examples of everyday conversation

[2015] 1. User: "I feel very tired and depressed today."

[2016] 2. Terminal: Sends user messages to the server.

[2017] 3. Server: Records messages in a database and performs analysis.

[2018] 4. Emotion engine: Classifies messages such as "tired" and "depressed" as negative emotions.

[2019] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[2020] 6. Server: Generates and sends the reply to the device.

[2021] 7. Terminal: The virtual person's reply is displayed to the user.

[2022] Examples of risk assessment and notification

[2023] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[2024] 2. Server: Sets a warning flag and generates an alert message.

[2025] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[2026] 4. Server: Sends notifications to professionals and next of kin as needed.

[2027] This allows the system to detect mental disorders in users early and provide appropriate support, preventing mental illness from worsening and helping users maintain their mental health.

[2028] The processing flow will be explained below.

[2029] User registration and initial settings

[2030] Step 1:

[2031] A user downloads the app and opens the account creation screen.

[2032] Step 2:

[2033] The device will display an input form for name, date of birth, gender, etc.

[2034] Step 3:

[2035] The user enters the required information and clicks the send button.

[2036] Step 4:

[2037] The terminal transmits the input information to the server.

[2038] Step 5:

[2039] The server stores the received information in a database.

[2040] Step 6:

[2041] The user logs in for the first time.

[2042] Step 7:

[2043] The server checks the initial setting status of the user and finds that the initial setting has not been completed.

[2044] Step 8:

[2045] The server generates a virtual person profile for the AI ​​and sends it to the device.

[2046] Step 9:

[2047] The terminal displays the generated virtual person profile to the user.

[2048] Starting everyday conversations

[2049] Step 1:

[2050] To start a conversation with the virtual person, the user inputs and sends a conversation message.

[2051] Step 2:

[2052] The terminal sends the user's input to the server.

[2053] Recording and analyzing conversation content

[2054] Step 1:

[2055] The server records the received user messages in a database.

[2056] Step 2:

[2057] The server uses a generation AI to generate an appropriate reply.

[2058] Step 3:

[2059] The server sends the generated reply to the terminal.

[2060] Step 4:

[2061] The terminal displays the virtual person's reply to the user.

[2062] Step 5:

[2063] The server analyzes the received conversation data using natural language processing technology.

[2064] Step 6:

[2065] The server detects specific keywords and emotional patterns.

[2066] Step 7:

[2067] The server uses an emotion engine to recognize the user's emotional state.

[2068] Risk Assessment and Notification

[2069] Step 1:

[2070] The server processes the emotional state detected by the emotion engine with data analysis means.

[2071] Step 2:

[2072] The server calculates the user's stress score based on the analysis results.

[2073] Step 3:

[2074] The server sets a warning flag if the stress score exceeds a threshold.

[2075] Step 4:

[2076] The server generates an alert message based on the warning flag.

[2077] Step 5:

[2078] The server sends the generated alert message to the terminal.

[2079] Step 6:

[2080] The terminal displays an alert message to the user.

[2081] Collaboration methods

[2082] Step 1:

[2083] If the server determines that the risk is high, it will notify experts and next of kin.

[2084] Step 2:

[2085] The server sends expert feedback and resources to the device.

[2086] Step 3:

[2087] The terminal displays contact information from the expert to the user.

[2088] In this way, the user, terminal, and server each play their respective roles at each step of the program, continuously monitoring the user's mental state and providing appropriate support.

[2089] Example 2

[2090] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2091] In modern society, it is extremely important to detect signs of mental stress and mental illness early and provide support at the appropriate time. However, current systems and methods lack the means to effectively analyze and appropriately evaluate a user's mental state from their everyday conversations. Furthermore, there are insufficient consistent systems and collaborative methods for detecting the risk of mental illness and providing the necessary support. This often leads to the worsening of mental illness and delays in providing appropriate support, which presents a challenge.

[2092] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: conversation means for a conversation between a user and a virtual character; data generation means for generating conversation data based on the conversation means; data analysis means for analyzing the generated conversation data and evaluating the user's mental state; analysis means for analyzing the generated conversation data using natural language processing technology and detecting specific keywords and emotional patterns; risk assessment means for detecting the user's risk of mental illness based on the analysis results; notification means for notifying the user of the risk detection results; generation AI means for generating a profile of the virtual character; emotion engine means for recognizing the emotional state and classifying it into positive and negative emotions; and warning means for setting a warning flag and generating an alert message when a certain threshold is exceeded. This makes it possible to effectively analyze the user's mental state from everyday conversations, assess the risk early, and provide necessary support at an appropriate time.

[2093] A "user" is an individual who uses the system to converse with a virtual character.

[2094] A "virtual person" is a virtual character that is generated by a generation AI means and that interacts with the user.

[2095] The "conversation means" is an interface for the user and the virtual character to exchange messages via communication.

[2096] The "data generation means" is a device or software that generates conversation data between a user and a virtual character based on the conversation means.

[2097] The "data analysis means" is a device or software that analyzes the generated conversation data and evaluates the mental state of the user.

[2098] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[2099] The "analysis means" is a device or software that analyzes the conversation data generated using natural language processing technology and detects specific keywords and emotional patterns.

[2100] A "risk assessment means" is a device or software that detects a user's risk of mental illness based on the analysis results.

[2101] The "notification means" is a device or software that notifies the user of the risk detection results.

[2102] "Generative AI means" refers to a device or software that utilizes artificial intelligence technology to generate a virtual person's profile based on user information.

[2103] The "emotion engine means" is a device or software that recognizes emotional states from conversation data and classifies positive and negative emotions.

[2104] The "warning means" is a device or software that sets a warning flag and generates an alert message when the user's stress score exceeds a certain threshold.

[2105] A "profile" is data that represents the attributes and characteristics of a virtual person generated by a generation AI means.

[2106] This invention is a system that allows users to detect early signs of mental illness through conversations with virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generation AI that generates replies from the virtual characters, and an emotion engine that recognizes emotional states.

[2107] User registration and initial settings

[2108] First, the user downloads the application and enters the necessary information such as name, date of birth, and gender on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL), and when the user logs in for the first time, a profile of the virtual person is generated using a generative AI (e.g., GPT-3). The generated profile is sent to the device and displayed to the user, allowing the user to start a conversation with the virtual person.

[2109] Examples:

[2110] 1. User: "I'm going to download and try out the new app."

[2111] 2. Terminal: "Please enter your name, date of birth, and gender."

[2112] 3. User: "Taro Yamada, January 1, 1990, male"

[2113] 4. Terminal: Sends input information to the server.

[2114] 5. Server: Stores user information in a database and generates a virtual persona profile.

[2115] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[2116] Starting everyday conversations

[2117] The user types a message and starts a conversation with a virtual person. The message is sent from the device to the server, which records the message in a database and analyzes it. The AI ​​then generates an appropriate reply, which is sent to the device. The device then displays the generated reply to the user.

[2118] Examples:

[2119] 1. User: "I feel very tired and depressed today."

[2120] 2. Terminal: Sends user messages to the server.

[2121] 3. Server: Records messages in a database and performs analysis.

[2122] 4. Emotion engine (e.g., IBM Watson Tone Analyzer): Classifies "tired" and "depressed" from messages as negative emotions.

[2123] 5. Generative AI: Generates a reply like, "That's tough. Have you done anything special to relax?"

[2124] 6. Server: Generates and sends the reply to the device.

[2125] 7. Terminal: The virtual person's reply is displayed to the user.

[2126] Recording and analyzing conversation content

[2127] The server records the conversation between the user and the virtual character in a database and analyzes it using natural language processing technology (e.g., spaCy). When specific keywords or emotional patterns are detected, the data is further analyzed by an emotion engine, which classifies the message content as positive or negative and detects the user's emotional state.

[2128] Risk Assessment and Notification

[2129] The server calculates the user's stress score based on the analysis of the conversation content. The emotional state detected by the emotion engine is also included in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag for the user and generates an alert message. This alert is sent to the user's device and notifies the user. There is also a function to notify experts or close relatives if necessary.

[2130] Examples:

[2131] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on the accumulated data.

[2132] 2. Server: Sets a warning flag and generates an alert message.

[2133] 3. On the device: The user is prompted with the message, "Recent conversations seem to be causing you stress. Please consider seeking professional help."

[2134] 4. Server: Sends notifications to professionals and next of kin as needed.

[2135] With the above configuration, this system can effectively analyze the user's mental state from everyday conversations, assess risk early, and provide necessary support at the appropriate time.

[2136] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2137] System processing flow

[2138] Step 1:

[2139] The user downloads and installs the application.

[2140] Input: The action of downloading an application from the app store.

[2141] Output: The application is installed on the device and launched for the first time.

[2142] Step 2:

[2143] The user enters the necessary information on the account registration screen.

[2144] Specifically, the user enters data into fields for entering information such as name, date of birth, and gender.

[2145] Input: Information entered by the user, such as name, date of birth, and gender.

[2146] Output: The input information is collected by the terminal.

[2147] Step 3:

[2148] The terminal transmits the input information to the server.

[2149] Input: The registration information entered by the user.

[2150] Output: The registration information received by the server.

[2151] Step 4:

[2152] The server stores the received information in a database.

[2153] Input: Registration information sent from the device.

[2154] Output: User information stored in the database.

[2155] Step 5:

[2156] The server uses generative AI to generate a profile of the virtual person.

[2157] Specifically, the AI ​​sends a prompt message based on the registration information to generate profile data such as the virtual person's name, personality, and hobbies.

[2158] Input: User information stored in the database.

[2159] Output: A virtual person profile returned by the generation AI.

[2160] Step 6:

[2161] The terminal receives the virtual person's profile and displays it to the user.

[2162] Input: Virtual person profile data received from the server.

[2163] Output: A profile of the virtual person displayed on the device screen.

[2164] Step 7:

[2165] The user types and sends messages to initiate a conversation with the virtual person.

[2166] Input: A message typed by the user.

[2167] Output: Messages typed are displayed on the terminal.

[2168] Step 8:

[2169] The terminal sends a user message to the server.

[2170] Input: The message entered by the user.

[2171] Output: The message received by the server.

[2172] Step 9:

[2173] The server logs the message in a database.

[2174] Input: User message sent from the terminal.

[2175] Output: Messages logged to the database.

[2176] Step 10:

[2177] The server parses the message, sends a prompt to the generation AI, and generates an appropriate reply.

[2178] Specifically, it uses natural language processing technology to analyze the sentiment and meaning of the message, generates a prompt from the analysis results, and sends it to a generation AI to generate a reply.

[2179] Input: The message logged in the database.

[2180] Output: The virtual person's reply returned by the generation AI.

[2181] Step 11:

[2182] The terminal displays the generated reply to the user.

[2183] Input: The virtual person's reply data received from the server.

[2184] Output: The reply that appears on the device screen.

[2185] Step 12:

[2186] The server analyzes the conversation content and assesses the emotional state using an emotion engine.

[2187] Specifically, the server analyzes the conversation data to detect specific keywords and emotional patterns, which are then further classified by the emotion engine to evaluate the emotional state.

[2188] Input: Conversation data recorded in a database.

[2189] Output: Emotion state data classified by the emotion engine.

[2190] Step 13:

[2191] The server calculates the user's stress score based on the analysis results and performs a risk assessment.

[2192] Specifically, the system calculates a stress score based on the analysis results and emotional state data from the emotion engine. If the score exceeds a certain threshold, a risk assessment is performed.

[2193] Input: Emotional state data output by the emotion engine.

[2194] Output: Calculated stress score and risk assessment results.

[2195] Step 14:

[2196] The server sets warning flags based on the risk assessment results and generates alert messages.

[2197] Input: Risk assessment results.

[2198] Output: The generated alert message.

[2199] Step 15:

[2200] The terminal receives the alert message and displays it to the user.

[2201] Input: The alert message received from the server.

[2202] Output: The alert message that is displayed on the device screen.

[2203] Step 16:

[2204] The server will then send notifications to professionals and next of kin as appropriate.

[2205] Specifically, if the risk assessment results exceed a certain threshold, the system will initiate a process to send emails and notifications to experts and next of kin.

[2206] Input: Risk assessment results and configured notification conditions.

[2207] Output: Emails and notification messages sent.

[2208] Through these steps, the system can effectively analyze the user's mental state from their everyday conversations, assess risk early, and provide appropriate support.

[2209] (Application example 2)

[2210] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2211] In recent years, there has been a demand for early detection of mental illness and the provision of appropriate support, but many users find it difficult to notice changes in their own mental health. Furthermore, it is often difficult for them to voluntarily consult with experts or close relatives. This can lead to the progression and worsening of many mental illnesses. There is a need for a system that can monitor the user's mental state in real time through conversations, detect abnormalities early, and provide appropriate support.

[2212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2213] In this invention, the server includes a communication means for a conversation between a user and a virtual character, a data generation means for generating conversation data based on the communication means, a data analysis means for analyzing the generated conversation data and evaluating the user's mental state, an evaluation means for detecting the user's risk of mental abnormality based on the analysis result, a notification means for notifying the user of the risk detection result, a scoring means for calculating the user's stress score, and a reply generation means for generating a reply using a generative AI model. This makes it possible to monitor changes in the user's mental state in real time through everyday conversations with the user, and to immediately provide appropriate support or an alert if an abnormality is detected.

[2214] "Communication means" refers to an interface for conversation between the user and the virtual character, and refers to devices or software that enable the sending and receiving of messages.

[2215] "Data generation means" refers to a system that has the function of recording conversations exchanged through communication means and generating them as conversation data.

[2216] "Data analysis means" refers to software or algorithms that analyze the generated conversation data and evaluate the user's mental state.

[2217] "Evaluation means" refers to a system or algorithm that detects a user's risk of mental abnormality based on the analysis results.

[2218] "Notification means" refers to devices or software for notifying users of risk detection results.

[2219] A "scoring method" is a system or algorithm that calculates a user's stress score and evaluates their mental state numerically.

[2220] A "reply generation means" is a system that has the function of automatically generating an appropriate reply to a user using a generative AI model.

[2221] A "generative AI model" is a model that uses artificial intelligence, and is an algorithm or machine learning model that generates appropriate replies from conversations with users.

[2222] SUMMARY OF THE INVENTION

[2223] This invention is a system that detects early signs of mental illness through conversations between users and virtual characters and provides appropriate support. The system consists of a device used by the user, a server that processes conversation data, a generative AI model that generates replies from the virtual character, and an emotion engine that recognizes the user's emotional state.

[2224] Hardware and software used

[2225] Hardware: Smartphones, tablets, personal computers

[2226] Software: Python, natural language processing libraries (e.g., TextBlob, NLTK), cloud server environment (e.g., AWS, Google Cloud), machine learning models (generative AI models)

[2227] Program processing overview

[2228] User registration and initial settings

[2229] The user downloads the application onto their device and enters the required information (such as name, date of birth, and gender) on the account creation screen. The entered information is sent from the device to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The virtual person profile is then sent to the device and displayed to the user.

[2230] Starting everyday conversations

[2231] To start a conversation with a virtual character, a user inputs and sends a message. This message is sent from the device to the server. The server receives the message and records it in a database. The recorded message is analyzed using natural language processing technology, and the results are input into a generative AI model. The generated reply from the virtual character is sent to the device via the server and displayed to the user.

[2232] Recording and analyzing conversation content

[2233] The server records the conversations between the user and the virtual persona in a database and continuously analyzes them using natural language processing technology. When messages containing specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies these messages as positive or negative emotions and detects the user's emotional state.

[2234] Risk Assessment and Notification

[2235] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. This alert is sent to the device and notifies the user. It also provides a function to notify experts or close relatives if necessary.

[2236] Specific examples

[2237] Example of user registration and initial settings

[2238] 1. User: "I'm going to download and try out the new app."

[2239] 2. Terminal: "Please enter your name, date of birth, and gender."

[2240] 3. User: "Taro Tanaka, January 1, 1990, male"

[2241] 4. Terminal: Sends input information to the server.

[2242] 5. Server: Stores user information in a database and generates a virtual person profile.

[2243] 6. Terminal: Display the virtual person profile to the user. "Hello, I'm your virtual person."

[2244] Examples of everyday conversation

[2245] 1. User: "I feel very tired and depressed today."

[2246] 2. Terminal: Sends user messages to the server.

[2247] 3. Server: Records messages in a database and performs analysis.

[2248] 4. Emotion engine: Determines "tired" and "depressed" as negative emotions from messages.

[2249] 5. Generative AI model: Generates a response like, "That's tough. Have you done anything special to relax?"

[2250] 6. Server: Generates a reply and sends it to the device.

[2251] 7. Terminal: The virtual person's reply is displayed to the user.

[2252] Examples of risk assessment and notification

[2253] 1. Server: Updates the stress score based on the analysis results and the emotional state of the emotion engine. Determines that the user's stress level is high based on cumulative data.

[2254] 2. Server: Sets a warning flag and generates an alert message.

[2255] 3. Device: Display the message to the user, "Your recent conversations seem to be causing you stress. Please consider seeking professional help."

[2256] 4. Server: Sends notifications to professionals and next of kin as needed.

[2257] Example prompt: "Your recent conversations seem to be causing you increased stress. Please consider seeking professional help."

[2258] As described above, this system helps users maintain their mental health by detecting mental abnormalities early and providing appropriate support.

[2259] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2260] Step 1: User registration and initial setup

[2261] The user downloads the application to their device and enters the required information (name, date of birth, gender) on the account creation screen. The device sends the entered information to the server. The server stores the received information in a database and generates a virtual person profile using a generative AI model. The generated virtual person profile is sent to the device and displayed to the user.

[2262] Input: User's name, date of birth, gender

[2263] Output: Virtual person profile

[2264] Specific operation: The server receives user information and stores it in a database. It then uses a generative AI model to generate a virtual person profile and sends it to the device.

[2265] Step 2: Starting everyday conversations

[2266] The user types a message into the virtual character and sends it. The device sends the message to the server. The server records the message in a database and analyzes it using natural language processing technology. The analysis results are input into a generative AI model, which generates an appropriate reply. The generated reply is sent to the device and displayed to the user.

[2267] Input: User's message

[2268] Output: Virtual person's reply

[2269] Specific operation: The device receives the user's message and sends it to the server. The server analyzes the message, generates an appropriate reply using a generative AI model, and sends the reply to the device.

[2270] Step 3: Record and analyze the conversation

[2271] The server records the conversation between the user and the virtual character in a database and continuously analyzes it using natural language processing technology. When specific keywords or emotional patterns are detected, the data is further analyzed by the emotion engine, which classifies positive and negative emotions and detects the emotional state.

[2272] Input: Conversation between the user and the virtual person

[2273] Output: Parsed emotional state

[2274] How it works: The server receives messages and analyzes their emotional state using natural language processing techniques. If a specific emotional pattern is detected, the emotion engine analyzes the data in detail.

[2275] Step 4: Risk assessment and communication

[2276] The server calculates the user's stress score based on the analysis results, and also includes the emotional state detected by the emotion engine in this evaluation. If the stress score exceeds a certain threshold, the server sets a warning flag and generates an alert message. The generated alert is sent to the device and notifies the user. It also notifies experts and relatives as needed.

[2277] Input: Analyzed emotional state and stress score

[2278] Output: Alert message

[2279] Specific operation: The server calculates the stress score and assesses the risk based on the analysis results and emotional state. If necessary, it sets a warning flag and generates an alert message that is sent to the device. It also notifies experts and close relatives.

[2280] Step 5: Review and respond to alerts

[2281] The user checks the alert message displayed on the device and takes appropriate action if necessary (such as consulting a specialist or practicing relaxation techniques).

[2282] Input: Alert message

[2283] Output: User's response action

[2284] Specific actions: The user checks the alert message sent to the device and takes appropriate action based on their health condition. If they contact a specialist or close relative, they will receive support.

[2285] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2286] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2287] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2288] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2289] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2290] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2291] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2292] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2293] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2294] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2295] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2296] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2297] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2298] 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.

[2299] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2300] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2301] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2302] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2303] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2304] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2305] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2306] The following is further disclosed regarding the above embodiment.

[2307] (Claim 1)

[2308] a conversation means for allowing a user to have a conversation with a virtual character;

[2309] data generation means for generating conversation data based on the conversation means;

[2310] a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user;

[2311] a risk assessment means for detecting a risk of mental illness in the user based on the analysis results;

[2312] a notification means for notifying a user of a risk detection result;

[2313] A system including:

[2314] (Claim 2)

[2315] 2. The system according to claim 1, further comprising stress analysis means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the patterns.

[2316] (Claim 3)

[2317] 10. The system of claim 1, further comprising a coordination means for sending notifications to professionals and next of kin if a high risk is detected.

[2318] "Example 1"

[2319] (Claim 1)

[2320] a communication means for a conversation between the user and the virtual character;

[2321] data generation means for generating conversation data based on the communication means;

[2322] a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user;

[2323] a risk assessment means for detecting a risk of mental illness in the user based on the analysis results;

[2324] a notification means for notifying a user of a risk detection result;

[2325] A system including:

[2326] (Claim 2)

[2327] 2. The system according to claim 1, further comprising stress analysis means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the patterns.

[2328] (Claim 3)

[2329] 10. The system of claim 1, further comprising a coordination means for sending notifications to professionals and next of kin if a high risk is detected.

[2330] (Claim 4)

[2331] a storage means for storing the basic information entered by the user in a database;

[2332] a profile generation means for generating a profile of the virtual person based on the basic information;

[2333] a display means for displaying the generated profile to the user;

[2334] 10. The system of claim 1, further comprising:

[2335] (Claim 5)

[2336] The system of claim 1, further comprising a reply generation means for analyzing the conversation data and generating an appropriate reply using a generation AI.

[2337] (Claim 6)

[2338] 10. The system of claim 1, further comprising an alert generation means for using a generation AI to calculate a stress score and generate an alert message when a threshold is exceeded.

[2339] "Application Example 1"

[2340] (Claim 1)

[2341] a conversation means for allowing a user to have a conversation with a virtual character;

[2342] data generation means for generating conversation data based on the conversation means;

[2343] a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user;

[2344] a risk assessment means for detecting a risk of mental illness in the user based on the analysis results;

[2345] a notification means for notifying users and administrators of risk detection results;

[2346] A system including:

[2347] (Claim 2)

[2348] 2. The system according to claim 1, further comprising: stress analysis means for analyzing a user's daily conversation patterns and identifying the type and degree of stress from the trends; and warning means for generating a warning when high stress is detected.

[2349] (Claim 3)

[2350] 10. The system of claim 1, further comprising a coordination means for sending notifications to professionals and next of kin if a high risk is detected.

[2351] "Example 2: Combining Emotion Engines"

[2352] (Claim 1)

[2353] a conversation means for allowing a user to have a conversation with a virtual character;

[2354] data generation means for generating conversation data based on the conversation means;

[2355] a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user;

[2356] An analysis means for analyzing the conversation data generated using natural language processing technology and detecting specific keywords and emotion patterns;

[2357] a risk assessment means for detecting a risk of mental illness in the user based on the analysis results;

[2358] a notification means for notifying a user of a risk detection result;

[2359] a generating AI means for generating a profile of the virtual person;

[2360] an emotion engine means for recognizing emotional states and classifying positive and negative emotions;

[2361] a warning means for setting a warning flag and generating an alert message when a certain threshold is exceeded;

[2362] A system including:

[2363] (Claim 2)

[2364] 2. The system according to claim 1, further comprising stress analysis means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the patterns.

[2365] (Claim 3)

[2366] 10. The system of claim 1, further comprising a coordination means for sending notifications to professionals and next of kin if a high risk is detected.

[2367] "Application example 2 when combining emotion engines"

[2368] (Claim 1)

[2369] a communication means for a conversation between the user and the virtual character;

[2370] data generation means for generating conversation data based on the communication means;

[2371] a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user;

[2372] an evaluation means for detecting a risk of mental abnormality in the user based on the analysis result;

[2373] a notification means for notifying a user of a risk detection result;

[2374] a scoring means for calculating a stress score for the user;

[2375] A reply generation means for generating a reply using a generative AI model;

[2376] A system including:

[2377] (Claim 2)

[2378] 2. The system according to claim 1, further comprising an analysis means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the patterns.

[2379] (Claim 3)

[2380] 10. The system of claim 1, further comprising a coordination means for sending notifications to professionals and next of kin if a high risk is detected. [Explanation of symbols]

[2381] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a conversation means for allowing a user to have a conversation with a virtual character; data generation means for generating conversation data based on the conversation means; a data analysis means for analyzing the generated conversation data and evaluating the mental state of the user; a risk assessment means for detecting a risk of mental illness in the user based on the analysis results; a notification means for notifying a user of a risk detection result; A system including:

2. 2. The system according to claim 1, further comprising stress analysis means for analyzing the user's daily conversation patterns and identifying the type and degree of stress from the patterns.

3. The system of claim 1 , further comprising a coordination means for sending notifications to professionals and next of kin if a high risk is detected.

Images