System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting self-assessment data from a user regarding mental health; means for analyzing the collected self-assessment data to assess a mental health status of the user; means for generating a personalized mental health care recommendation based on the assessment; means for notifying the user of the generated recommendation; means for collecting and analyzing behavior data of the user to assess progress of the mental health status; and means for providing feedback to the user based on the progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, mental health problems in the workplace have been increasing, with many workers taking long-term leave or quitting their jobs, especially in large-scale workplaces. This situation has a significant impact not only on the workers themselves, but also on those around them and the company. Mental health care requires individualized attention, but this requires a great deal of time and effort, and there are limitations to how much can be done manually. Therefore, a system is needed to provide efficient, individualized mental health care. [Means for solving the problem]

[0005] The present invention provides a system for assessing a user's mental health status by collecting and analyzing self-assessment data from the user. The system includes a means for generating personalized care suggestions based on the assessment and notifying the user. The system also includes a means for collecting and analyzing the user's behavioral data, evaluating progress in the user's mental health status, and providing feedback based on the progress. Specifically, the system includes a means for collecting data from the user, a means for analyzing data using AI, a means for generating personalized care suggestions, a notification means, a means for collecting behavioral data, a means for evaluating progress, and a means for providing feedback. This makes it possible to provide efficient and individualized mental health care, making the individual, those around them, and the company happy.

[0006] "User" refers to an individual who uses the system, specifically an employee or staff member who is eligible to receive mental health care.

[0007] "Self-assessment data" is information about the mental health state entered by the user himself / herself, and includes stress level, mood evaluation, and the like.

[0008] "Collection means" refers to the functions and devices for acquiring data from users and transmitting it to the system.

[0009] "Analysis Tools" refers to the functions and algorithms used to process and analyze the collected data and assess the user's mental health status.

[0010] "Personalized care suggestions" are suggestions for specific actions and services to provide optimal mental health care based on the mental health status of each individual user.

[0011] The "proposal generation means" refers to a function or algorithm that generates care proposals suited to the user based on the analysis results.

[0012] "Notification means" refers to a function or device for notifying the user of the generated care suggestions.

[0013] "Behavioral data" is information about mental health care actions that a user actually takes.

[0014] "Progress assessment means" refers to functions and algorithms for analyzing a user's behavioral data and assessing changes in their mental health status.

[0015] The "feedback providing means" refers to a function or device for informing the user of the results of progress assessment, and is intended to support the user's ongoing care. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[0038] Data collection

[0039] Users enter self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10.

[0040] The terminal collects the self-assessment data entered by the user. The terminal may be an electronic device such as a smartphone, tablet, or PC.

[0041] Data transmission and storage

[0042] The device sends the collected self-evaluation data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, and feedback information.

[0043] Analyzing the data

[0044] The server analyzes the stored self-assessment data using AI algorithms, which evaluates the user's current mental health status and displays it in numerical and graphical form.

[0045] As a specific example, if a user's stress level is high, abnormal values ​​can be detected by comparing them with past data and an abnormality alert can be generated in real time.

[0046] Generate personalized suggestions

[0047] The server generates personalized mental health care recommendations based on the analysis results. For example, if a user's stress level is high, it might suggest taking a 30-minute stretching break or receiving online counseling once a week.

[0048] Proposal Notification

[0049] The server sends the generated proposal to the device, and the device notifies the user of the proposal, for example, by a pop-up message, email, or in-app notification.

[0050] Behavioral data collection

[0051] The user acts on the mental health care suggestions provided, for example, by taking a 30-minute stretching break and participating in weekly online counseling sessions.

[0052] The device collects user behavior data and sends it to the server, which also stores the data in a database.

[0053] Progress evaluation and feedback

[0054] The server analyzes the collected behavioral data and evaluates the user's progress in mental health. Based on the evaluation results, it generates feedback and notifies the user, for example, "Your stress level has decreased as a result of your recent behavior."

[0055] The device displays progress assessments and feedback to the user, allowing them to know the extent to which their mental health is improving.

[0056] As described above, the system of the present invention can quickly and effectively respond to the individual needs of the user, making the user, those around him / her, and the company happy.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. For example, they answer 7 to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[0060] Step 2:

[0061] The device collects the self-assessment data entered by the user (e.g., stress level 7). The collected data also includes metadata such as the date and time of the response.

[0062] Step 3:

[0063] The device sends the collected self-assessment data to the server using methods such as API calls and HTTP requests.

[0064] Step 4:

[0065] The server stores the received self-assessment data in a database that also stores past survey results and other related data.

[0066] Step 5:

[0067] The server analyzes the stored self-assessment data using an AI algorithm. As a result of the analysis, the user's mental health status is scored. For example, a stress level of 7 may be judged as "high stress."

[0068] Step 6:

[0069] The server then generates personalized mental health care recommendations based on the analysis results, such as a 30-minute stretching break or weekly online counseling sessions.

[0070] Step 7:

[0071] The server then transmits the generated proposal to the terminal. This transmission requires real-time communication and uses a low-latency communication method.

[0072] Step 8:

[0073] The device notifies the user of the suggestions received from the server via a pop-up message, email, in-app notification, etc.

[0074] Step 9:

[0075] The user receives a notification from the device and takes a suggested action, such as taking a 30-minute stretching break or attending a counseling session.

[0076] Step 10:

[0077] The terminal collects user behavioral data, including break times and counseling participation history.

[0078] Step 11:

[0079] The device sends the collected behavioral data to a server, which collects and transmits the behavioral data in real time.

[0080] Step 12:

[0081] The server stores the behavioral data in a database and accumulates it together with past data.

[0082] Step 13:

[0083] The server analyzes the collected behavioral data to assess the user's mental health progress, for example, assessing fluctuations in stress levels after stretching breaks.

[0084] Step 14:

[0085] The server generates feedback based on the analysis results, for example, "As a result of taking a break, your stress level has decreased."

[0086] Step 15:

[0087] The server transmits the generated feedback to the terminal, and the feedback is transmitted periodically as appropriate.

[0088] Step 16:

[0089] The device provides feedback to the user, allowing them to monitor their own mental health progress.

[0090] Through the above processing steps, this system efficiently supports the user's mental health care.

[0091] Example 1

[0092] 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."

[0093] In modern society, many people suffer from stress and fatigue, making mental health care important. However, previous mental health care systems have struggled to provide personalized suggestions to individual users or evaluate progress and provide feedback based on actual behavioral data. There are also limited methods for users to accurately assess their own mental health status. Furthermore, there is a lack of systems that can effectively analyze collected data to generate appropriate suggestions and notifications.

[0094] 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.

[0095] In this invention, the server includes: means for collecting self-assessment data on mental health from a user; means for transmitting the collected self-assessment data to the server via a terminal; means for storing the collected self-assessment data in a database; means for analyzing the stored self-assessment data using an AI algorithm to evaluate the user's mental health status; means for generating personalized mental health care suggestions based on the evaluation; means for notifying the user of the generated suggestions; means for collecting user behavioral data based on the mental health care suggestions; means for transmitting the collected behavioral data to the server and storing it in the database; means for analyzing the stored behavioral data to evaluate progress in the user's mental health status; means for providing feedback to the user based on the progress; means for the user to actually take action in accordance with the suggestions notified to the user; and means for transmitting the behavioral data based on the suggestions to the server again. This enables efficient and personalized mental health care for the user. Specifically, the system can accurately evaluate the user's mental health status and provide appropriate care suggestions based on the evaluation. Furthermore, by evaluating progress based on the user's behavioral data and providing appropriate feedback, it is possible to support long-term mental health improvement.

[0096] A "database" is a computer-based system for organizing and storing collected data, and for retrieving and managing it as needed.

[0097] "Mental health care" refers to activities and services aimed at maintaining and improving the mental health of users.

[0098] "Self-assessment data" is psychological assessment information such as stress level, mood, and fatigue level input by the user himself / herself.

[0099] A "terminal" is an electronic device such as a smartphone, tablet, or PC that a user uses to input and transmit self-assessment data.

[0100] The "server" is a central management system that stores collected data, analyzes it using AI algorithms, and provides feedback to users.

[0101] An "AI algorithm" is a method or program that uses artificial intelligence technology to analyze data.

[0102] "Personalized suggestions" are mental health care suggestions customized based on a user's individual data and needs.

[0103] "Behavioral data" refers to data on mental health care activities actually undertaken by the user and actions suggested by the system.

[0104] "Feedback" refers to an assessment of mental health status and instructions for improvement that the system provides to the user based on the analysis results.

[0105] "Notifications" are messages or alerts sent from the system to the user's device to provide suggestions or feedback.

[0106] "Progress assessment" is the process of evaluating changes and improvements in a user's mental health status based on collected behavioral data.

[0107] That's it. These definitions clarify the meaning of key terms contained in the claims.

[0108] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[0109] Data collection

[0110] Users input self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10. This data is entered using devices such as smartphones, tablets, and PCs.

[0111] Data transmission and storage

[0112] The device collects the self-evaluation data entered by the user and transmits it in real time to a server, which stores the received data in a database that also stores past self-evaluation data, behavioral data, and feedback information.

[0113] Analyzing the data

[0114] The server analyzes the stored data using AI algorithms such as Tensorflow (registered trademark) or Scikit-learn implemented in Python. This analysis evaluates the user's current mental health status and displays the results in numerical and graphical format. For example, if the user's stress level is high, the server can compare it with past data to detect anomalies and generate an anomaly alert in real time.

[0115] Generate personalized suggestions

[0116] The server generates personalized mental health care suggestions based on the analysis results, such as suggesting a 30-minute stretching break or weekly online counseling if the user's stress level is high.

[0117] Proposal Notification

[0118] The server sends the generated proposal to the device, which notifies the user of the proposal via a pop-up message, email, or in-app notification.

[0119] Behavioral data collection

[0120] The user acts in accordance with the mental health care suggestions provided. For example, they may choose to take a 30-minute stretching break and participate in weekly online counseling sessions. The device collects the user's behavioral data and sends it to the server. This behavioral data is also stored in a database.

[0121] Progress evaluation and feedback

[0122] The server analyzes the collected behavioral data and evaluates the progress of the user's mental health condition. Based on the evaluation results, it generates feedback and notifies the user. For example, the feedback may say, "As a result of your recent actions, your stress level has decreased." The device then displays the progress evaluation and the feedback content to the user. This allows the user to know the degree of improvement in their mental health condition.

[0123] Specific examples

[0124] Specific examples are shown below.

[0125] User input and collection

[0126] Using the device, users rate their stress level as 8 / 10, their mood as 4 / 10, and their fatigue as 7 / 10.

[0127] The terminal collects the data entered by the user.

[0128] Sending data

[0129] The device sends the collected data to a server, which stores the data in a database.

[0130] Analyzing the data

[0131] The server analyzes the stored data using AI algorithms to detect when a user's stress level is high.

[0132] Proposal generation and notification

[0133] Based on the analysis results, the server will suggest a 30-minute stretching break, and the device will notify the user of the suggestion in a pop-up message.

[0134] Action implementation and data collection

[0135] The user follows the suggestion and takes a 30-minute stretching break. The device collects data during the break (time, amount of exercise, etc.).

[0136] Progress evaluation and feedback

[0137] The server analyzes the collected behavioral data and evaluates that the stress level is decreasing. The server generates feedback that says "your stress level has improved" and notifies the user. The device displays the feedback to the user.

[0138] Example prompts for generative AI models

[0139] "Suggest a response when a user rates their stress level as 8 / 10."

[0140] That's it. This system is designed to provide efficient and personalized mental health care for users. By utilizing generative AI models and data analysis, it is able to respond quickly and effectively to user needs.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Processing Steps

[0143] Step 1: User enters self-assessment data

[0144] Input: The user uses a device such as a smartphone or PC to input self-assessment data such as stress level (e.g., 8 / 10), mood state (e.g., 4 / 10), and fatigue (e.g., 7 / 10).

[0145] Output: The self-assessment data is saved as input to the terminal.

[0146] How it works: Users enter their evaluation data using a dedicated app or web interface, which is then stored in the device's memory in real time.

[0147] Step 2: Submit self-assessment data

[0148] Input: Self-assessment data collected in Step 1.

[0149] Output: The collected self-assessment data is sent to the server.

[0150] Specific operation: When the user presses the "Submit" button, the device sends the evaluation data to the server via the Internet. The data is sent in the form of an HTTP request.

[0151] Step 3: Save your data

[0152] Input: Self-assessment data submitted in step 2.

[0153] Output: Self-assessment data stored in a database on the server.

[0154] Specific operation: The data received on the server side is stored in a MySQL (registered trademark) or PostgreSQL database via a PHP or Python script. This process is performed asynchronously.

[0155] Step 4: Analyze the data

[0156] Input: Self-assessment data stored in a database.

[0157] Output: User's mental health status assessment results (in numerical and graphical formats).

[0158] How it works: The server periodically retrieves data from the database and analyzes it using AI algorithms implemented in Python, such as TensorFlow and Scikit-learn, to calculate trends in stress levels and detect outliers.

[0159] Step 5: Generate personalized suggestions

[0160] Input: Analysis results from step 4.

[0161] Output: Personalized mental health care suggestions for the user.

[0162] How it works: Based on the analysis results, the server selects the most appropriate suggestion from a pre-defined list of suggestions (e.g., take a break, get online counseling, etc.) and customizes it based on the user profile.

[0163] Step 6: Proposal Notification

[0164] Input: Mental healthcare suggestions generated in step 5.

[0165] Output: The suggestion that will be displayed on the user's device as a notification.

[0166] Specific operation: The server sends the generated proposal to the user's device, and the device notifies the user of the received proposal via a pop-up message, email, or in-app notification.

[0167] Step 7: User Actions

[0168] Input: The proposal communicated in step 6.

[0169] Output: User behavior data based on suggestions.

[0170] Specific actions: The user acts according to the notified suggestion, for example, taking a 30-minute stretching break. The device collects behavioral data (time, amount of exercise, etc.) in real time.

[0171] Step 8: Sending behavioral data

[0172] Input: Behavioral data collected in step 7.

[0173] Output: The behavioral data sent to the server.

[0174] Specific operation: After accumulating a certain amount of behavioral data, the device sends it to a server via the Internet. The data is sent in the form of an HTTP request.

[0175] Step 9: Progress assessment and feedback

[0176] Input: Behavioral data submitted in step 8.

[0177] Output: Mental health status progress assessment results and feedback.

[0178] Specific operation: The server compares the collected behavioral data with past self-assessment data to evaluate progress such as reducing stress levels. Based on the evaluation results, it generates feedback such as "Your stress level has decreased as a result of your recent actions" and notifies the user. The device displays the feedback to the user.

[0179] By following the above steps, the system provides the user with efficient and personalized mental health care.

[0180] (Application example 1)

[0181] 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."

[0182] While the use of self-driving vehicles has expanded in recent years, there are concerns that stress and fatigue during long driving and travel times may affect the health of drivers and passengers. Maintaining good mental health contributes to the user's driving performance and safety, so mental health management is becoming increasingly important in self-driving vehicles as well. However, currently, there is no fully established system that monitors the mental health of drivers and passengers in self-driving vehicles in real time and provides appropriate care. In response to this, the present invention aims to provide a system that continuously monitors the mental health status of drivers and passengers in self-driving vehicles and makes personalized suggestions.

[0183] 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.

[0184] In this invention, the server includes means for collecting self-assessment data regarding mental health from the user, means for collecting physiological data and behavioral data of the user from on-board sensors, means for analyzing the collected self-assessment data and physiological data to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation, means for notifying the user of the suggestions generated using another terminal, means for collecting and analyzing the user's behavioral data to evaluate progress in the mental health status, and means for providing feedback to the user based on the progress. This makes it possible to analyze the user's mental health status in real time while in an autonomous vehicle and provide appropriate care.

[0185] The "means for collecting self-assessment data on mental health from users" refers to a function that provides an interface that allows users to input their mental health status, such as their stress level and fatigue level, as numerical values ​​and comments via a device such as a smartphone or an in-car display.

[0186] "Means of collecting user physiological data and behavioral data from in-vehicle sensors" refers to a function that uses in-vehicle sensors (e.g., heart rate monitors, driving behavior analysis cameras) to obtain data on the user's physiological indicators and behavior in real time.

[0187] "Means for analyzing collected self-assessment data and physiological data to assess the user's mental health status" refers to a function that analyzes collected data using an AI algorithm (for example, using TensorFlow or PyTorch) and evaluates the user's mental health status in the form of numbers, graphs, etc.

[0188] The "means for generating personalized mental health care suggestions based on the evaluation" is a function for generating relaxation methods and rest suggestions appropriate to the user's current mental health state based on the analysis results.

[0189] "Means for notifying the user of suggestions generated using another device" refers to a function for notifying the user of the generated mental health care suggestions in the form of a pop-up message, voice guidance, etc., via an in-vehicle display or smartphone.

[0190] "Means for collecting and analyzing user behavioral data to evaluate progress in mental health status" refers to a function that collects data on actions taken by users based on suggestions (e.g., taking a break or stretching), analyzes that data, and evaluates the degree of improvement in their mental health status.

[0191] "Means for providing feedback to the user based on progress" refers to a function for providing feedback generated based on the results of the user's actions (e.g., "Your stress level has decreased") to the user via the terminal.

[0192] The present invention provides a system for analyzing the user's mental health in real time within an autonomous vehicle and providing personalized care based on the results. A specific method for implementing this system is described below.

[0193] Data collection

[0194] First, the user inputs self-evaluation data (stress level, fatigue level, etc.) using a smartphone or in-car display. This data is recorded as numbers and comments. In addition, in-car sensors (heart rate monitor, driving behavior analysis camera, etc.) collect the user's physiological and behavioral data in real time.

[0195] Data transmission and storage

[0196] The device sends the collected data to an in-vehicle server or a cloud server, which stores the data in a database. The database also stores the user's past self-evaluation data and behavioral data, and uses it for future analysis.

[0197] Analyzing the data

[0198] The server then analyzes the stored data using AI algorithms (for example, TensorFlow or PyTorch). This analysis evaluates the user's mental health status in numerical and graphical formats. If the user's stress level indicates an abnormal value, an anomaly alert is generated in real time.

[0199] Generate personalized suggestions

[0200] The server generates personalized mental health care suggestions based on the analysis results, such as "take a break at the next service area" or "turn on the seat heater."

[0201] Proposal Notification

[0202] The generated suggestions are notified to the user via the device, using methods such as a pop-up message on the in-car display, voice guidance, or a notification on a smartphone.

[0203] Behavioral data collection

[0204] If the user performs the suggested action, the results of that action are recorded. Specifically, data such as the time taken for a break and the method of relaxation is saved.

[0205] Progress evaluation and feedback

[0206] Finally, the server analyzes the collected behavioral data and evaluates the progress of the user's mental health. Based on this, feedback is provided to the user. For example, the feedback could be, "As a result of your recent break, your stress level has decreased." This allows the user to understand the progress of their mental health.

[0207] Examples of concrete examples and prompts

[0208] Specific examples include "take a break at the next service area," "turn on the seat heater," and "listen to relaxing music." An example of a prompt for the generative AI model is "generate appropriate suggestions when the user inputs a stress level of 8 and a fatigue level of 7. For example, take a break at the next service area or listen to relaxing music."

[0209] This will enable effective management of the user's mental health in an autonomous vehicle, supporting a comfortable and safe journey.

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

[0211] Step 1:

[0212] The user inputs self-assessment data about their mental health (stress level, fatigue level, etc.) via a smartphone or in-car display. The input data is then sent to the terminal via the interface. The input data includes the user's subjective assessment (e.g., "stress level is 8 / 10").

[0213] Step 2:

[0214] The device continuously collects the user's physiological and behavioral data from in-vehicle sensors (heart rate monitor, driving behavior analysis camera, etc.). This data includes heart rate, breathing rate, facial expression changes, driving behavior patterns, etc. The collected data is temporarily stored on the device.

[0215] Step 3:

[0216] The device transmits the collected self-assessment data and physiological data to an in-vehicle server or a cloud server, where the data is stored in the server's database. The stored data includes various physiological data and self-assessment data.

[0217] Step 4:

[0218] The server analyzes the stored data using AI algorithms (using TensorFlow and PyTorch) to evaluate the user's mental health status. Collected physiological data and self-assessment data are used as input, and these are standardized and normalized before being fed into the AI ​​model. The output is the user's current mental health status in numerical and graphical form, and an alert is generated if an abnormal value is detected.

[0219] Step 5:

[0220] The server generates personalized mental health care suggestions based on the analysis results. For example, if the stress level is high, it generates suggestions such as "take a break at the next service area" or "turn on the seat heater." The prompt text used is "If the user inputs a stress level of 8 and a fatigue level of 7, generate appropriate suggestions. For example, take a break at the next service area or listen to relaxing music."

[0221] Step 6:

[0222] The device notifies the user of the suggestions sent from the server. Notification methods include pop-up messages on the in-car display, voice guidance, and notifications on a smartphone. This allows the user to receive mental health care suggestions from the system at the appropriate time.

[0223] Step 7:

[0224] The user acts in accordance with the notified suggestion (for example, taking a break). At this time, the user's behavioral data (such as the time and method of taking the break) is again input into the device.

[0225] Step 8:

[0226] The device sends the user's behavioral data to a server, which then analyzes the data and evaluates the user's improvement in mental health. This allows the server to generate feedback based on the user's progress (e.g., "Your stress level has decreased").

[0227] Step 9:

[0228] Feedback is sent to the user via the device, allowing them to see how their mental health has improved.

[0229] 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.

[0230] The system of the present invention is designed to efficiently and individually support the mental health care of users, and by combining an emotion engine, it provides greater precision and flexibility. The system has a user, a terminal, a server, and an emotion engine as its main components, and operates as follows.

[0231] Data collection and emotion recognition

[0232] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will recognize "fatigue" from the user's facial expression.

[0233] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[0234] Data transmission and storage

[0235] The device sends the collected self-evaluation data and emotional data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[0236] Analyzing the data

[0237] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user, which is displayed in numerical and graphical format.

[0238] Generate personalized suggestions

[0239] The server generates personalized mental health care suggestions based on the analysis results. By taking the user's emotional state into account, the realism and appropriateness of the suggestions are improved. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," the server recommends a "light walk."

[0240] Proposal Notification

[0241] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be sent in a gentle tone.

[0242] Behavioral data collection

[0243] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[0244] The device collects and transmits the user's behavioral and emotional data to the server. This behavioral data includes information such as break times, counseling participation history, and emotional state during the activity.

[0245] Progress evaluation and feedback

[0246] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health status. For example, it evaluates the fluctuation of stress levels after a stretching break and the "feeling of relaxation." Based on the evaluation results, it generates feedback and notifies the user.

[0247] The device will provide feedback to the user, allowing them to see their mental health progress, for example, "As a result of your break, your stress levels have decreased and you feel more relaxed."

[0248] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users, making them, their surroundings, and their company happy. By combining it with an emotion engine, it is possible to provide even more accurate mental health care.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if a user answers that their stress level is "7," the emotion engine will recognize "fatigue" from their facial expression.

[0252] Step 2:

[0253] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[0254] Step 3:

[0255] The device sends the collected self-evaluation data and emotion data to the server. The sent data is transmitted using a communication method such as an HTTP request.

[0256] Step 4:

[0257] The server stores the received self-evaluation and emotion data in a database that also stores past survey results and other related data.

[0258] Step 5:

[0259] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's mental health status. For example, a mental health score is generated based on a stress level of 7 and a "feeling of fatigue."

[0260] Step 6:

[0261] The server generates personalized mental health care recommendations based on the analysis results, taking into account the user's emotional state. For example, if the user is feeling fatigued due to high stress levels, the server will suggest a 30-minute stretching break, as well as recommend a light walk.

[0262] Step 7:

[0263] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is in a high-stress state, the notification will be sent in a gentle tone.

[0264] Step 8:

[0265] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[0266] Step 9:

[0267] The device collects user behavioral and emotional data, including break times, counseling participation history, and emotional states during activities.

[0268] Step 10:

[0269] The device sends the collected behavioral and emotional data to the server, also using a communication method such as an HTTP request.

[0270] Step 11:

[0271] The server stores the behavioral data and emotion data in a database, accumulating them together with past data.

[0272] Step 12:

[0273] The server analyzes the collected behavioral and emotional data to assess the user's mental health progress, for example, assessing stress level fluctuations and "feeling relaxed" after a stretching break.

[0274] Step 13:

[0275] The server generates feedback based on the analysis results, for example, "As a result of the break, your stress level has decreased and you feel more relaxed."

[0276] Step 14:

[0277] The server transmits the generated feedback to the terminal, which provides the feedback as needed.

[0278] Step 15:

[0279] The device provides feedback to the user, allowing them to see the progress of their mental health status, so they can understand how their own behavior has affected their mental health.

[0280] Through the above processing steps, this system will more effectively support the user's mental health care. By combining it with an emotion engine, more precise care and feedback can be provided, making the user, those around them, and the company happier.

[0281] Example 2

[0282] 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."

[0283] Conventional mental health care systems rely solely on users' self-assessment data and are unable to grasp changes in users' emotions and behaviors in real time. This results in low accuracy and effectiveness of the care provided, making it difficult to make personalized suggestions suited to individual needs. Collecting user behavioral data to evaluate the progress of mental health status and provide appropriate feedback has also been a challenge.

[0284] 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 means for analyzing the user's facial expressions, tone of voice, and text input in real time to generate emotional data, means for storing the collected self-evaluation data and emotional data in a database, and means for analyzing the stored data based on an AI algorithm and evaluating the user's mental health state. This makes it possible to grasp changes in the user's emotions and behavior in real time. In addition, the collected data can be used to personalize the user's mental health care and provide more accurate suggestions and feedback.

[0285] "User" refers to an individual who uses the system to receive mental health care services.

[0286] "Mental health" refers to mental and emotional well-being and is a concept that includes stress, fatigue, anxiety, etc.

[0287] "Self-assessment data" refers to information provided by users when they self-assess their mental health status. Specifically, this data is expressed as numerical values ​​or text, such as stress levels and emotional states.

[0288] "Emotion data" refers to information about emotions that the emotion engine analyzes and extracts from the user's facial expressions, tone of voice, text input, etc.

[0289] An "emotion engine" refers to software or hardware that analyzes input data such as a user's facial expressions, voice, and text, and recognizes their emotional state.

[0290] "Server" refers to the computer system within the system that is responsible for storing and analyzing data, generating and distributing mental health care suggestions, etc.

[0291] A "database" refers to a system that structures, stores, and manages self-evaluation data, emotional data, behavioral data, and other data collected from users.

[0292] "AI Algorithms" refers to the artificial intelligence technologies and computational methods used to analyze a user's self-assessment data and emotional data, assess their mental health status, and generate recommendations.

[0293] "Personalized mental health care suggestions" refer to mental health care suggestions generated based on individual user data and optimized for the user's specific needs and condition.

[0294] "Behavioral data" refers to data on the user's behavior when implementing the suggestions and the emotional state recognized during the behavior. Specifically, it includes the length of breaks, counseling participation history, and the emotional state during the behavior.

[0295] "Progress assessment" refers to the process of analyzing and evaluating changes and improvements in a user's mental health status based on behavioral and emotional data.

[0296] "Feedback" refers to the evaluation, areas for improvement, and information on next actions provided to the user based on the progress evaluation results.

[0297] The system of the present invention is designed to efficiently and individually support users' mental health care, and by combining it with an emotion engine, it provides greater precision and flexibility. The system operates with a user, a terminal, a server, and an emotion engine as its main components.

[0298] Data collection and emotion recognition

[0299] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will detect a "feeling of fatigue" from the user's facial expression.

[0300] The device collects the user's self-evaluation data and emotion data recognized by the emotion engine, including the question content, answer, analyzed emotion, and answer date and time.

[0301] Data transmission and storage

[0302] The device sends the collected self-evaluation data and emotion data to the server using HTTPS as the communication protocol.

[0303] The server stores the received data in a database, which also stores past self-evaluation data, behavioral data, emotional data, and feedback information. The database is typically a relational database such as MySQL or PostgreSQL.

[0304] Analyzing the data

[0305] The server analyzes the stored data using an AI algorithm, which evaluates the user's current mental health state. For example, if a user answers "stress level 7" and "feeling tired" is detected, the AI ​​algorithm calculates the user's mental health score and displays it in the user interface in numerical and graphical form.

[0306] Proposal Generation

[0307] The server generates personalized mental health care suggestions based on the analysis results. For example, it suggests a "30-minute stretching break" and recommends a "light walk" if the user feels "fatigue." This improves the realism and appropriateness of the suggestions.

[0308] Proposal Notification

[0309] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted based on the user's emotional state. For example, in-app notifications and push notifications are used, and gentle messages are sent to users in high stress states.

[0310] Behavioral data collection

[0311] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time.

[0312] The device collects the user's behavioral and emotional data and sends it back to the server. The collected data includes the length of breaks, counseling participation history, and emotional state during the activity.

[0313] Progress evaluation and feedback

[0314] The server analyzes the collected behavioral and emotional data to evaluate the user's mental health progress. For example, it evaluates stress levels and "feeling relaxed" after a stretching break. Based on the evaluation results, feedback is generated and notified to the user.

[0315] The device provides feedback to the user, allowing them to see the progress of their mental health. A specific feedback message provided is, "As a result of your break, your stress level has decreased and you feel more relaxed."

[0316] Specific examples and prompts for the generative AI model

[0317] Specific examples

[0318] 1. The user launches the application and answers "7" to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[0319] 2. The emotion engine detects "fatigue" from the user's facial expression.

[0320] 3. The device sends the collected data to the server.

[0321] 4. The server analyzes the data and generates the user's mental health score.

[0322] 5. The server suggests a "30-minute stretching break" and recommends a "light walk."

[0323] 6. The device notifies the user of this offer.

[0324] 7. The user performs the suggested action.

[0325] 8. The device collects behavioral and emotional data and sends it to the server.

[0326] 9. The server generates the evaluation results and feedback and notifies the user.

[0327] 10. Your device will notify you that "As a result of your break, your stress levels have decreased and you feel more relaxed."

[0328] Prompt Sentence Examples

[0329] Specific questions such as "How would you rate your stress level over the past week on a scale of 1 to 10?" can be fed into the generative AI model to generate optimal feedback and suggestions.

[0330] As described above, the system of the present invention can efficiently support the user's mental health care and provide personalized care according to individual needs.

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Step 1: Collecting data from users

[0333] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. The input includes the user's answers to each item in the questionnaire. At this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. A specific example is when the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10." The data output is the user's self-assessment data and emotion data recognized by the emotion engine (e.g., "fatigue").

[0334] Step 2: Send and store data

[0335] The device sends the collected user self-evaluation data and emotional data to the server. The input includes the user's answers and analyzed emotional data. The specific communication method used is the HTTPS protocol. The server stores the received data in a database. The data output is the self-evaluation data and emotional data stored in the database.

[0336] Step 3: Analyze the data

[0337] The server uses an AI algorithm to analyze the stored user self-evaluation data and emotional data. The input includes multiple user data stored in the database. Specific data processing and calculation involves the AI ​​algorithm evaluating the user's stress level and emotional state and calculating a mental health score. The analysis results are generated in numerical and graphical format and displayed on the user interface.

[0338] Step 4: Generate proposals

[0339] The server generates personalized mental health care suggestions based on the analysis results. The inputs include the analyzed mental health score and the user's emotional state. As specific data processing and calculation, the suggestion generation algorithm selects the optimal action for the user. For example, a user with a high stress level and feeling "fatigue" might be recommended a "30-minute stretching break" or a "light walk." The output is the generated suggestion.

[0340] Step 5: Proposal Notification

[0341] The server sends the generated suggestions to the device. The input includes the suggestion content. The device notifies the user of the suggestion. Specific actions include in-app notifications and push notifications. The tone and method of the notification are adjusted based on the user's emotional state. The output is the user receiving the suggestion.

[0342] Step 6: Collect behavioral data

[0343] The user receives a notification from the device and performs the suggested action, such as taking a 30-minute stretching break or participating in online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time. The input includes the user's behavior and emotion data. The output is the recorded behavioral data and emotion data.

[0344] Step 7: Progress assessment and feedback

[0345] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The input includes behavioral and emotional data. Specific data processing and calculations involve an AI algorithm evaluating the progress and generating feedback based on the results. For example, changes in stress levels and improvements in "relaxation" after a stretching break are evaluated. The output is a generated feedback message that is notified to the user.

[0346] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users and provide highly accurate mental health care.

[0347] (Application example 2)

[0348] 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."

[0349] There is a lack of systems for efficiently and individually managing and supporting employees' mental health status. Conventional systems have difficulty collecting and analyzing users' mental health data in real time, which has led to issues with the accuracy and effectiveness of providing personalized suggestions. There is also a need for systems that provide appropriate feedback based on employees' emotional state.

[0350] 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.

[0351] In this invention, the server includes means for collecting self-assessment data on mental health from users, means for analyzing the collected self-assessment data and emotional data including facial expressions and tone of voice analyzed in real time to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation and notifying the user of the suggestions, means for generating suggested messages according to individual needs using a generative AI model, means for collecting and analyzing the user's behavioral data and their emotional states during that time to evaluate the progress of the user's mental health status, and means for providing feedback to the user based on the progress. This makes it possible to grasp the mental health status of employees in real time and provide personalized suggestions.

[0352] "User" refers to a person who uses the system to manage their mental health.

[0353] "Mental health" refers to the state of mental health and evaluates emotions and psychological states, including stress, fatigue, and relaxation.

[0354] "Self-assessment data" refers to data entered by a user after assessing their own mental health status.

[0355] "Emotional Data" refers to emotional data, including a user's facial expressions, tone of voice, and text input, analyzed in real time.

[0356] "Personalized suggestions" refers to specific improvement suggestions generated based on each user's individual mental health status.

[0357] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze user data and generate optimal suggested messages.

[0358] "Behavioral data" refers to data regarding the actions taken by the user based on mental health care suggestions.

[0359] "Feedback" refers to information, including progress assessment, obtained as a result of actions taken by a user.

[0360] "Server" refers to a central computer system for storing data collected from users in a database, performing analysis, and generating suggestions and feedback.

[0361] "Notification means" refers to a mechanism for communicating generated suggestions to a user, including devices such as smartphones or smart glasses.

[0362] The present invention is to build a system for mental health care, which consists of a user, a terminal, a server, and an emotion engine as its main components.

[0363] Data collection and emotion recognition

[0364] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated that analyzes the user's facial expressions, tone of voice, and text input in real time. As a specific example, when answering the question, "Please rate your stress level today on a scale of 1 to 10," the engine recognizes "fatigue" from the user's facial expression.

[0365] Data transmission and storage

[0366] The device collects self-evaluation data and emotional data entered by the user. This data includes metadata such as the date and time of the response and the user's emotional state. The collected data is sent to a server and stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[0367] Analyzing the data

[0368] The server analyzes the stored data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user and displays it in numerical and graphical format.

[0369] Generate personalized suggestions

[0370] The server generates personalized mental health care suggestions based on the analysis results. Using a generative AI model, it generates specific suggestion messages that take the user's emotional state into account. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," it recommends a "light walk."

[0371] Proposal Notification

[0372] The server sends the generated suggestions to the device, which then notifies the user. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be delivered in a gentle tone.

[0373] Behavioral data collection

[0374] The user receives a notification from the device and performs the suggested action, such as "take a 30-minute stretching break." The emotion engine also recognizes and records the user's emotions during the action in real time. The device collects the user's behavioral and emotional data and sends it to the server. The behavioral data includes the break time and the emotional state during the action.

[0375] Progress evaluation and feedback

[0376] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health condition. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break. It generates feedback based on the evaluation results and notifies the user. The device notifies the user of the feedback, allowing the user to check the progress of their own mental health condition. As a specific example, the server may provide feedback such as "As a result of the break, your stress level has decreased and your sense of relaxation has improved."

[0377] Prompt Sentence Examples

[0378] "Your stress levels seem high today. Why not try some deep breathing or yoga? I suggest you take a 10-minute break."

[0379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0380] Step 1:

[0381] Users launch the application on their device and answer a self-assessment questionnaire about their mental health. The user enters their answers into the questionnaire using text, voice, and facial expressions. The output is collected self-assessment data and real-time analyzed emotion data. The emotion engine then analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[0382] Step 2:

[0383] The device sends the collected self-evaluation data and emotional data to the server. The input requires the data the user answers on the device. The output is the data sent to the server, including metadata such as the date and time of the answer and the user's emotional state.

[0384] Step 3:

[0385] The server stores the transmitted data in a database. The inputs required are the self-evaluation data and emotion data transmitted from the device. The output is user data stored in the database. The stored data includes past self-evaluation data, behavioral data, emotion data, feedback information, etc.

[0386] Step 4:

[0387] The server analyzes the stored data using an AI algorithm. The inputs required are the user data stored in the database and the AI ​​algorithm. The output is a mental health assessment and score as the analysis result. This evaluates the user's current mental health state.

[0388] Step 5:

[0389] The server generates personalized mental health care suggestions based on the analysis results. The inputs required are the analysis results and a generative AI model. The output is a specific generated suggestion message that takes into account the user's emotional state. For example, suggestions such as "take a 30-minute stretching break" or "take a light walk" are generated.

[0390] Step 6:

[0391] The server sends the generated suggestion to the terminal, and the terminal notifies the user. As input, the generated suggestion message is required. As output, the suggestion to be notified to the user is obtained. For example, if the user is "high stress", a message in a gentle tone is notified.

[0392] Step 7:

[0393] The user receives a notification from the device and takes the suggested action. The input required is the notified suggestion. The output is the user's behavioral data. For example, taking a "30-minute stretching break" or going for a "light walk."

[0394] Step 8:

[0395] The device also recognizes and records the user's emotions in real time during their actions. The inputs are the user's behavioral data and an emotion engine. The output is emotional data during the action. This emotional data is then used for later analysis.

[0396] Step 9:

[0397] The device transmits the user's behavioral data and emotional data to the server. Recorded behavioral data and emotional data are required as input. Data is output to the server. This behavioral data includes rest periods, emotional states during activities, etc.

[0398] Step 10:

[0399] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The inputs are the user's behavioral data and an AI algorithm. The output is a progress assessment result. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break.

[0400] Step 11:

[0401] The server generates feedback based on the evaluation results and notifies the user. As input, the progress evaluation results and the generated feedback message are required. As output, the server obtains feedback to be notified to the user. For example, the server may provide feedback such as "As a result of taking a break, your stress level has decreased and your sense of relaxation has improved."

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Second embodiment]

[0406] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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."

[0418] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[0419] Data collection

[0420] Users enter self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10.

[0421] The terminal collects the self-assessment data entered by the user. The terminal may be an electronic device such as a smartphone, tablet, or PC.

[0422] Data transmission and storage

[0423] The device sends the collected self-evaluation data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, and feedback information.

[0424] Analyzing the data

[0425] The server analyzes the stored self-assessment data using AI algorithms, which evaluates the user's current mental health status and displays it in numerical and graphical form.

[0426] As a specific example, if a user's stress level is high, abnormal values ​​can be detected by comparing them with past data and an abnormality alert can be generated in real time.

[0427] Generate personalized suggestions

[0428] The server generates personalized mental health care recommendations based on the analysis results. For example, if a user's stress level is high, it might suggest taking a 30-minute stretching break or receiving online counseling once a week.

[0429] Proposal Notification

[0430] The server sends the generated proposal to the device, and the device notifies the user of the proposal, for example, by a pop-up message, email, or in-app notification.

[0431] Behavioral data collection

[0432] The user acts on the mental health care suggestions provided, for example, by taking a 30-minute stretching break and participating in weekly online counseling sessions.

[0433] The device collects user behavior data and sends it to the server, which also stores the data in a database.

[0434] Progress evaluation and feedback

[0435] The server analyzes the collected behavioral data and evaluates the user's progress in mental health. Based on the evaluation results, it generates feedback and notifies the user, for example, "Your stress level has decreased as a result of your recent behavior."

[0436] The device displays progress assessments and feedback to the user, allowing them to know the extent to which their mental health is improving.

[0437] As described above, the system of the present invention can quickly and effectively respond to the individual needs of the user, making the user, those around him / her, and the company happy.

[0438] The processing flow will be explained below.

[0439] Step 1:

[0440] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. For example, they answer 7 to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[0441] Step 2:

[0442] The device collects the self-assessment data entered by the user (e.g., stress level 7). The collected data also includes metadata such as the date and time of the response.

[0443] Step 3:

[0444] The device sends the collected self-assessment data to the server using methods such as API calls and HTTP requests.

[0445] Step 4:

[0446] The server stores the received self-assessment data in a database that also stores past survey results and other related data.

[0447] Step 5:

[0448] The server analyzes the stored self-assessment data using an AI algorithm. As a result of the analysis, the user's mental health status is scored. For example, a stress level of 7 may be judged as "high stress."

[0449] Step 6:

[0450] The server then generates personalized mental health care recommendations based on the analysis results, such as a 30-minute stretching break or weekly online counseling sessions.

[0451] Step 7:

[0452] The server then transmits the generated proposal to the terminal. This transmission requires real-time communication and uses a low-latency communication method.

[0453] Step 8:

[0454] The device notifies the user of the suggestions received from the server via a pop-up message, email, in-app notification, etc.

[0455] Step 9:

[0456] The user receives a notification from the device and takes a suggested action, such as taking a 30-minute stretching break or attending a counseling session.

[0457] Step 10:

[0458] The terminal collects user behavioral data, including break times and counseling participation history.

[0459] Step 11:

[0460] The device sends the collected behavioral data to a server, which collects and transmits the behavioral data in real time.

[0461] Step 12:

[0462] The server stores the behavioral data in a database and accumulates it together with past data.

[0463] Step 13:

[0464] The server analyzes the collected behavioral data to assess the user's mental health progress, for example, assessing fluctuations in stress levels after stretching breaks.

[0465] Step 14:

[0466] The server generates feedback based on the analysis results, for example, "As a result of taking a break, your stress level has decreased."

[0467] Step 15:

[0468] The server transmits the generated feedback to the terminal, and the feedback is transmitted periodically as appropriate.

[0469] Step 16:

[0470] The device provides feedback to the user, allowing them to monitor their own mental health progress.

[0471] Through the above processing steps, this system efficiently supports the user's mental health care.

[0472] Example 1

[0473] 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."

[0474] In modern society, many people suffer from stress and fatigue, making mental health care important. However, previous mental health care systems have struggled to provide personalized suggestions to individual users or evaluate progress and provide feedback based on actual behavioral data. There are also limited methods for users to accurately assess their own mental health status. Furthermore, there is a lack of systems that can effectively analyze collected data to generate appropriate suggestions and notifications.

[0475] 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.

[0476] In this invention, the server includes: means for collecting self-assessment data on mental health from a user; means for transmitting the collected self-assessment data to the server via a terminal; means for storing the collected self-assessment data in a database; means for analyzing the stored self-assessment data using an AI algorithm to evaluate the user's mental health status; means for generating personalized mental health care suggestions based on the evaluation; means for notifying the user of the generated suggestions; means for collecting user behavioral data based on the mental health care suggestions; means for transmitting the collected behavioral data to the server and storing it in the database; means for analyzing the stored behavioral data to evaluate progress in the user's mental health status; means for providing feedback to the user based on the progress; means for the user to actually take action in accordance with the suggestions notified to the user; and means for transmitting the behavioral data based on the suggestions to the server again. This enables efficient and personalized mental health care for the user. Specifically, the system can accurately evaluate the user's mental health status and provide appropriate care suggestions based on the evaluation. Furthermore, by evaluating progress based on the user's behavioral data and providing appropriate feedback, it is possible to support long-term mental health improvement.

[0477] A "database" is a computer-based system for organizing and storing collected data, and for retrieving and managing it as needed.

[0478] "Mental health care" refers to activities and services aimed at maintaining and improving the mental health of users.

[0479] "Self-assessment data" is psychological assessment information such as stress level, mood, and fatigue level input by the user himself / herself.

[0480] A "terminal" is an electronic device such as a smartphone, tablet, or PC that a user uses to input and transmit self-assessment data.

[0481] The "server" is a central management system that stores collected data, analyzes it using AI algorithms, and provides feedback to users.

[0482] An "AI algorithm" is a method or program that uses artificial intelligence technology to analyze data.

[0483] "Personalized suggestions" are mental health care suggestions customized based on a user's individual data and needs.

[0484] "Behavioral data" refers to data on mental health care activities actually undertaken by the user and actions suggested by the system.

[0485] "Feedback" refers to an assessment of mental health status and instructions for improvement that the system provides to the user based on the analysis results.

[0486] "Notifications" are messages or alerts sent from the system to the user's device to provide suggestions or feedback.

[0487] "Progress assessment" is the process of evaluating changes and improvements in a user's mental health status based on collected behavioral data.

[0488] That's it. These definitions clarify the meaning of key terms contained in the claims.

[0489] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[0490] Data collection

[0491] Users input self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10. This data is entered using devices such as smartphones, tablets, and PCs.

[0492] Data transmission and storage

[0493] The device collects the self-evaluation data entered by the user and transmits it in real time to a server, which stores the received data in a database that also stores past self-evaluation data, behavioral data, and feedback information.

[0494] Analyzing the data

[0495] The server analyzes the stored data using AI algorithms such as Tensorflow or Scikit-learn implemented in Python. This analysis evaluates the user's current mental health status and displays the results in numerical and graphical formats. For example, if the user's stress level is high, the server can compare it with past data to detect anomalies and generate an anomaly alert in real time.

[0496] Generate personalized suggestions

[0497] The server generates personalized mental health care suggestions based on the analysis results, such as suggesting a 30-minute stretching break or weekly online counseling if the user's stress level is high.

[0498] Proposal Notification

[0499] The server sends the generated proposal to the device, which notifies the user of the proposal via a pop-up message, email, or in-app notification.

[0500] Behavioral data collection

[0501] The user acts in accordance with the mental health care suggestions provided. For example, they may choose to take a 30-minute stretching break and participate in weekly online counseling sessions. The device collects the user's behavioral data and sends it to the server. This behavioral data is also stored in a database.

[0502] Progress evaluation and feedback

[0503] The server analyzes the collected behavioral data and evaluates the progress of the user's mental health condition. Based on the evaluation results, it generates feedback and notifies the user. For example, the feedback may say, "As a result of your recent actions, your stress level has decreased." The device then displays the progress evaluation and the feedback content to the user. This allows the user to know the degree of improvement in their mental health condition.

[0504] Specific examples

[0505] Specific examples are shown below.

[0506] User input and collection

[0507] Using the device, users rate their stress level as 8 / 10, their mood as 4 / 10, and their fatigue as 7 / 10.

[0508] The terminal collects the data entered by the user.

[0509] Sending data

[0510] The device sends the collected data to a server, which stores the data in a database.

[0511] Analyzing the data

[0512] The server analyzes the stored data using AI algorithms to detect when a user's stress level is high.

[0513] Proposal generation and notification

[0514] Based on the analysis results, the server will suggest a 30-minute stretching break, and the device will notify the user of the suggestion in a pop-up message.

[0515] Action implementation and data collection

[0516] The user follows the suggestion and takes a 30-minute stretching break. The device collects data during the break (time, amount of exercise, etc.).

[0517] Progress evaluation and feedback

[0518] The server analyzes the collected behavioral data and evaluates that the stress level is decreasing. The server generates feedback that says "your stress level has improved" and notifies the user. The device displays the feedback to the user.

[0519] Example prompts for generative AI models

[0520] "Suggest a response when a user rates their stress level as 8 / 10."

[0521] That's it. This system is designed to provide efficient and personalized mental health care for users. By utilizing generative AI models and data analysis, it is able to respond quickly and effectively to user needs.

[0522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0523] Processing Steps

[0524] Step 1: User enters self-assessment data

[0525] Input: The user uses a device such as a smartphone or PC to input self-assessment data such as stress level (e.g., 8 / 10), mood state (e.g., 4 / 10), and fatigue (e.g., 7 / 10).

[0526] Output: The self-assessment data is saved as input to the terminal.

[0527] How it works: Users enter their evaluation data using a dedicated app or web interface, which is then stored in the device's memory in real time.

[0528] Step 2: Submit self-assessment data

[0529] Input: Self-assessment data collected in Step 1.

[0530] Output: The collected self-assessment data is sent to the server.

[0531] Specific operation: When the user presses the "Submit" button, the device sends the evaluation data to the server via the Internet. The data is sent in the form of an HTTP request.

[0532] Step 3: Save your data

[0533] Input: Self-assessment data submitted in step 2.

[0534] Output: Self-assessment data stored in a database on the server.

[0535] Specific operation: The data received on the server side is stored in a MySQL or PostgreSQL database via a PHP or Python script. This process is performed asynchronously.

[0536] Step 4: Analyze the data

[0537] Input: Self-assessment data stored in a database.

[0538] Output: User's mental health status assessment results (in numerical and graphical formats).

[0539] How it works: The server periodically retrieves data from the database and analyzes it using AI algorithms implemented in Python, such as TensorFlow and Scikit-learn, to calculate trends in stress levels and detect outliers.

[0540] Step 5: Generate personalized suggestions

[0541] Input: Analysis results from step 4.

[0542] Output: Personalized mental health care suggestions for the user.

[0543] How it works: Based on the analysis results, the server selects the most appropriate suggestion from a pre-defined list of suggestions (e.g., take a break, get online counseling, etc.) and customizes it based on the user profile.

[0544] Step 6: Proposal Notification

[0545] Input: Mental healthcare suggestions generated in step 5.

[0546] Output: The suggestion that will be displayed on the user's device as a notification.

[0547] Specific operation: The server sends the generated proposal to the user's device, and the device notifies the user of the received proposal via a pop-up message, email, or in-app notification.

[0548] Step 7: User Actions

[0549] Input: The proposal communicated in step 6.

[0550] Output: User behavior data based on suggestions.

[0551] Specific actions: The user acts according to the notified suggestion, for example, taking a 30-minute stretching break. The device collects behavioral data (time, amount of exercise, etc.) in real time.

[0552] Step 8: Sending behavioral data

[0553] Input: Behavioral data collected in step 7.

[0554] Output: The behavioral data sent to the server.

[0555] Specific operation: After accumulating a certain amount of behavioral data, the device sends it to a server via the Internet. The data is sent in the form of an HTTP request.

[0556] Step 9: Progress assessment and feedback

[0557] Input: Behavioral data submitted in step 8.

[0558] Output: Mental health status progress assessment results and feedback.

[0559] Specific operation: The server compares the collected behavioral data with past self-assessment data to evaluate progress such as reducing stress levels. Based on the evaluation results, it generates feedback such as "Your stress level has decreased as a result of your recent actions" and notifies the user. The device displays the feedback to the user.

[0560] By following the above steps, the system provides the user with efficient and personalized mental health care.

[0561] (Application example 1)

[0562] 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."

[0563] While the use of self-driving vehicles has expanded in recent years, there are concerns that stress and fatigue during long driving and travel times may affect the health of drivers and passengers. Maintaining good mental health contributes to the user's driving performance and safety, so mental health management is becoming increasingly important in self-driving vehicles as well. However, currently, there is no fully established system that monitors the mental health of drivers and passengers in self-driving vehicles in real time and provides appropriate care. In response to this, the present invention aims to provide a system that continuously monitors the mental health status of drivers and passengers in self-driving vehicles and makes personalized suggestions.

[0564] 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.

[0565] In this invention, the server includes means for collecting self-assessment data regarding mental health from the user, means for collecting physiological data and behavioral data of the user from on-board sensors, means for analyzing the collected self-assessment data and physiological data to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation, means for notifying the user of the suggestions generated using another terminal, means for collecting and analyzing the user's behavioral data to evaluate progress in the mental health status, and means for providing feedback to the user based on the progress. This makes it possible to analyze the user's mental health status in real time while in an autonomous vehicle and provide appropriate care.

[0566] The "means for collecting self-assessment data on mental health from users" refers to a function that provides an interface that allows users to input their mental health status, such as their stress level and fatigue level, as numerical values ​​and comments via a device such as a smartphone or an in-car display.

[0567] "Means of collecting user physiological data and behavioral data from in-vehicle sensors" refers to a function that uses in-vehicle sensors (e.g., heart rate monitors, driving behavior analysis cameras) to obtain data on the user's physiological indicators and behavior in real time.

[0568] "Means for analyzing collected self-assessment data and physiological data to assess the user's mental health status" refers to a function that analyzes collected data using an AI algorithm (for example, using TensorFlow or PyTorch) and evaluates the user's mental health status in the form of numbers, graphs, etc.

[0569] The "means for generating personalized mental health care suggestions based on the evaluation" is a function for generating relaxation methods and rest suggestions appropriate to the user's current mental health state based on the analysis results.

[0570] "Means for notifying the user of suggestions generated using another device" refers to a function for notifying the user of the generated mental health care suggestions in the form of a pop-up message, voice guidance, etc., via an in-vehicle display or smartphone.

[0571] "Means for collecting and analyzing user behavioral data to evaluate progress in mental health status" refers to a function that collects data on actions taken by users based on suggestions (e.g., taking a break or stretching), analyzes that data, and evaluates the degree of improvement in their mental health status.

[0572] "Means for providing feedback to the user based on progress" refers to a function for providing feedback generated based on the results of the user's actions (e.g., "Your stress level has decreased") to the user via the terminal.

[0573] The present invention provides a system for analyzing the user's mental health in real time within an autonomous vehicle and providing personalized care based on the results. A specific method for implementing this system is described below.

[0574] Data collection

[0575] First, the user inputs self-evaluation data (stress level, fatigue level, etc.) using a smartphone or in-car display. This data is recorded as numbers and comments. In addition, in-car sensors (heart rate monitor, driving behavior analysis camera, etc.) collect the user's physiological and behavioral data in real time.

[0576] Data transmission and storage

[0577] The device sends the collected data to an in-vehicle server or a cloud server, which stores the data in a database. The database also stores the user's past self-evaluation data and behavioral data, and uses it for future analysis.

[0578] Analyzing the data

[0579] The server then analyzes the stored data using AI algorithms (for example, TensorFlow or PyTorch). This analysis evaluates the user's mental health status in numerical and graphical formats. If the user's stress level indicates an abnormal value, an anomaly alert is generated in real time.

[0580] Generate personalized suggestions

[0581] The server generates personalized mental health care suggestions based on the analysis results, such as "take a break at the next service area" or "turn on the seat heater."

[0582] Proposal Notification

[0583] The generated suggestions are notified to the user via the device, using methods such as a pop-up message on the in-car display, voice guidance, or a notification on a smartphone.

[0584] Behavioral data collection

[0585] If the user performs the suggested action, the results of that action are recorded. Specifically, data such as the time taken for a break and the method of relaxation is saved.

[0586] Progress evaluation and feedback

[0587] Finally, the server analyzes the collected behavioral data and evaluates the progress of the user's mental health. Based on this, feedback is provided to the user. For example, the feedback could be, "As a result of your recent break, your stress level has decreased." This allows the user to understand the progress of their mental health.

[0588] Examples of concrete examples and prompts

[0589] Specific examples include "take a break at the next service area," "turn on the seat heater," and "listen to relaxing music." An example of a prompt for the generative AI model is "generate appropriate suggestions when the user inputs a stress level of 8 and a fatigue level of 7. For example, take a break at the next service area or listen to relaxing music."

[0590] This will enable effective management of the user's mental health in an autonomous vehicle, supporting a comfortable and safe journey.

[0591] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0592] Step 1:

[0593] The user inputs self-assessment data about their mental health (stress level, fatigue level, etc.) via a smartphone or in-car display. The input data is then sent to the terminal via the interface. The input data includes the user's subjective assessment (e.g., "stress level is 8 / 10").

[0594] Step 2:

[0595] The device continuously collects the user's physiological and behavioral data from in-vehicle sensors (heart rate monitor, driving behavior analysis camera, etc.). This data includes heart rate, breathing rate, facial expression changes, driving behavior patterns, etc. The collected data is temporarily stored on the device.

[0596] Step 3:

[0597] The device transmits the collected self-assessment data and physiological data to an in-vehicle server or a cloud server, where the data is stored in the server's database. The stored data includes various physiological data and self-assessment data.

[0598] Step 4:

[0599] The server analyzes the stored data using AI algorithms (using TensorFlow and PyTorch) to evaluate the user's mental health status. Collected physiological data and self-assessment data are used as input, and these are standardized and normalized before being fed into the AI ​​model. The output is the user's current mental health status in numerical and graphical form, and an alert is generated if an abnormal value is detected.

[0600] Step 5:

[0601] The server generates personalized mental health care suggestions based on the analysis results. For example, if the stress level is high, it generates suggestions such as "take a break at the next service area" or "turn on the seat heater." The prompt text used is "If the user inputs a stress level of 8 and a fatigue level of 7, generate appropriate suggestions. For example, take a break at the next service area or listen to relaxing music."

[0602] Step 6:

[0603] The device notifies the user of the suggestions sent from the server. Notification methods include pop-up messages on the in-car display, voice guidance, and notifications on a smartphone. This allows the user to receive mental health care suggestions from the system at the appropriate time.

[0604] Step 7:

[0605] The user acts in accordance with the notified suggestion (for example, taking a break). At this time, the user's behavioral data (such as the time and method of taking the break) is again input into the device.

[0606] Step 8:

[0607] The device sends the user's behavioral data to a server, which then analyzes the data and evaluates the user's improvement in mental health. This allows the server to generate feedback based on the user's progress (e.g., "Your stress level has decreased").

[0608] Step 9:

[0609] Feedback is sent to the user via the device, allowing them to see how their mental health has improved.

[0610] 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.

[0611] The system of the present invention is designed to efficiently and individually support the mental health care of users, and by combining an emotion engine, it provides greater precision and flexibility. The system has a user, a terminal, a server, and an emotion engine as its main components, and operates as follows.

[0612] Data collection and emotion recognition

[0613] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will recognize "fatigue" from the user's facial expression.

[0614] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[0615] Data transmission and storage

[0616] The device sends the collected self-evaluation data and emotional data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[0617] Analyzing the data

[0618] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user, which is displayed in numerical and graphical format.

[0619] Generate personalized suggestions

[0620] The server generates personalized mental health care suggestions based on the analysis results. By taking the user's emotional state into account, the realism and appropriateness of the suggestions are improved. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," the server recommends a "light walk."

[0621] Proposal Notification

[0622] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be sent in a gentle tone.

[0623] Behavioral data collection

[0624] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[0625] The device collects and transmits the user's behavioral and emotional data to the server. This behavioral data includes information such as break times, counseling participation history, and emotional state during the activity.

[0626] Progress evaluation and feedback

[0627] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health status. For example, it evaluates the fluctuation of stress levels after a stretching break and the "feeling of relaxation." Based on the evaluation results, it generates feedback and notifies the user.

[0628] The device will provide feedback to the user, allowing them to see their mental health progress, for example, "As a result of your break, your stress levels have decreased and you feel more relaxed."

[0629] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users, making them, their surroundings, and their company happy. By combining it with an emotion engine, it is possible to provide even more accurate mental health care.

[0630] The processing flow will be explained below.

[0631] Step 1:

[0632] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if a user answers that their stress level is "7," the emotion engine will recognize "fatigue" from their facial expression.

[0633] Step 2:

[0634] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[0635] Step 3:

[0636] The device sends the collected self-evaluation data and emotion data to the server. The sent data is transmitted using a communication method such as an HTTP request.

[0637] Step 4:

[0638] The server stores the received self-evaluation and emotion data in a database that also stores past survey results and other related data.

[0639] Step 5:

[0640] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's mental health status. For example, a mental health score is generated based on a stress level of 7 and a "feeling of fatigue."

[0641] Step 6:

[0642] The server generates personalized mental health care recommendations based on the analysis results, taking into account the user's emotional state. For example, if the user is feeling fatigued due to high stress levels, the server will suggest a 30-minute stretching break, as well as recommend a light walk.

[0643] Step 7:

[0644] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is in a high-stress state, the notification will be sent in a gentle tone.

[0645] Step 8:

[0646] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[0647] Step 9:

[0648] The device collects user behavioral and emotional data, including break times, counseling participation history, and emotional states during activities.

[0649] Step 10:

[0650] The device sends the collected behavioral and emotional data to the server, also using a communication method such as an HTTP request.

[0651] Step 11:

[0652] The server stores the behavioral data and emotion data in a database, accumulating them together with past data.

[0653] Step 12:

[0654] The server analyzes the collected behavioral and emotional data to assess the user's mental health progress, for example, assessing stress level fluctuations and "feeling relaxed" after a stretching break.

[0655] Step 13:

[0656] The server generates feedback based on the analysis results, for example, "As a result of the break, your stress level has decreased and you feel more relaxed."

[0657] Step 14:

[0658] The server transmits the generated feedback to the terminal, which provides the feedback as needed.

[0659] Step 15:

[0660] The device provides feedback to the user, allowing them to see the progress of their mental health status, so they can understand how their own behavior has affected their mental health.

[0661] Through the above processing steps, this system will more effectively support the user's mental health care. By combining it with an emotion engine, more precise care and feedback can be provided, making the user, those around them, and the company happier.

[0662] Example 2

[0663] 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."

[0664] Conventional mental health care systems rely solely on users' self-assessment data and are unable to grasp changes in users' emotions and behaviors in real time. This results in low accuracy and effectiveness of the care provided, making it difficult to make personalized suggestions suited to individual needs. Collecting user behavioral data to evaluate the progress of mental health status and provide appropriate feedback has also been a challenge.

[0665] 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 means for analyzing the user's facial expressions, tone of voice, and text input in real time to generate emotional data, means for storing the collected self-evaluation data and emotional data in a database, and means for analyzing the stored data based on an AI algorithm and evaluating the user's mental health state. This makes it possible to grasp changes in the user's emotions and behavior in real time. In addition, the collected data can be used to personalize the user's mental health care and provide more accurate suggestions and feedback.

[0666] "User" refers to an individual who uses the system to receive mental health care services.

[0667] "Mental health" refers to mental and emotional well-being and is a concept that includes stress, fatigue, anxiety, etc.

[0668] "Self-assessment data" refers to information provided by users when they self-assess their mental health status. Specifically, this data is expressed as numerical values ​​or text, such as stress levels and emotional states.

[0669] "Emotion data" refers to information about emotions that the emotion engine analyzes and extracts from the user's facial expressions, tone of voice, text input, etc.

[0670] An "emotion engine" refers to software or hardware that analyzes input data such as a user's facial expressions, voice, and text, and recognizes their emotional state.

[0671] "Server" refers to the computer system within the system that is responsible for storing and analyzing data, generating and distributing mental health care suggestions, etc.

[0672] A "database" refers to a system that structures, stores, and manages self-evaluation data, emotional data, behavioral data, and other data collected from users.

[0673] "AI Algorithms" refers to the artificial intelligence technologies and computational methods used to analyze a user's self-assessment data and emotional data, assess their mental health status, and generate recommendations.

[0674] "Personalized mental health care suggestions" refer to mental health care suggestions generated based on individual user data and optimized for the user's specific needs and condition.

[0675] "Behavioral data" refers to data on the user's behavior when implementing the suggestions and the emotional state recognized during the behavior. Specifically, it includes the length of breaks, counseling participation history, and the emotional state during the behavior.

[0676] "Progress assessment" refers to the process of analyzing and evaluating changes and improvements in a user's mental health status based on behavioral and emotional data.

[0677] "Feedback" refers to the evaluation, areas for improvement, and information on next actions provided to the user based on the progress evaluation results.

[0678] The system of the present invention is designed to efficiently and individually support users' mental health care, and by combining it with an emotion engine, it provides greater precision and flexibility. The system operates with a user, a terminal, a server, and an emotion engine as its main components.

[0679] Data collection and emotion recognition

[0680] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will detect a "feeling of fatigue" from the user's facial expression.

[0681] The device collects the user's self-evaluation data and emotion data recognized by the emotion engine, including the question content, answer, analyzed emotion, and answer date and time.

[0682] Data transmission and storage

[0683] The device sends the collected self-evaluation data and emotion data to the server using HTTPS as the communication protocol.

[0684] The server stores the received data in a database, which also stores past self-evaluation data, behavioral data, emotional data, and feedback information. The database is typically a relational database such as MySQL or PostgreSQL.

[0685] Analyzing the data

[0686] The server analyzes the stored data using an AI algorithm, which evaluates the user's current mental health state. For example, if a user answers "stress level 7" and "feeling tired" is detected, the AI ​​algorithm calculates the user's mental health score and displays it in the user interface in numerical and graphical form.

[0687] Proposal Generation

[0688] The server generates personalized mental health care suggestions based on the analysis results. For example, it suggests a "30-minute stretching break" and recommends a "light walk" if the user feels "fatigue." This improves the realism and appropriateness of the suggestions.

[0689] Proposal Notification

[0690] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted based on the user's emotional state. For example, in-app notifications and push notifications are used, and gentle messages are sent to users in high stress states.

[0691] Behavioral data collection

[0692] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time.

[0693] The device collects the user's behavioral and emotional data and sends it back to the server. The collected data includes the length of breaks, counseling participation history, and emotional state during the activity.

[0694] Progress evaluation and feedback

[0695] The server analyzes the collected behavioral and emotional data to evaluate the user's mental health progress. For example, it evaluates stress levels and "feeling relaxed" after a stretching break. Based on the evaluation results, feedback is generated and notified to the user.

[0696] The device provides feedback to the user, allowing them to see the progress of their mental health. A specific feedback message provided is, "As a result of your break, your stress level has decreased and you feel more relaxed."

[0697] Specific examples and prompts for the generative AI model

[0698] Specific examples

[0699] 1. The user launches the application and answers "7" to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[0700] 2. The emotion engine detects "fatigue" from the user's facial expression.

[0701] 3. The device sends the collected data to the server.

[0702] 4. The server analyzes the data and generates the user's mental health score.

[0703] 5. The server suggests a "30-minute stretching break" and recommends a "light walk."

[0704] 6. The device notifies the user of this offer.

[0705] 7. The user performs the suggested action.

[0706] 8. The device collects behavioral and emotional data and sends it to the server.

[0707] 9. The server generates the evaluation results and feedback and notifies the user.

[0708] 10. Your device will notify you that "As a result of your break, your stress levels have decreased and you feel more relaxed."

[0709] Prompt Sentence Examples

[0710] Specific questions such as "How would you rate your stress level over the past week on a scale of 1 to 10?" can be fed into the generative AI model to generate optimal feedback and suggestions.

[0711] As described above, the system of the present invention can efficiently support the user's mental health care and provide personalized care according to individual needs.

[0712] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0713] Step 1: Collecting data from users

[0714] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. The input includes the user's answers to each item in the questionnaire. At this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. A specific example is when the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10." The data output is the user's self-assessment data and emotion data recognized by the emotion engine (e.g., "fatigue").

[0715] Step 2: Send and store data

[0716] The device sends the collected user self-evaluation data and emotional data to the server. The input includes the user's answers and analyzed emotional data. The specific communication method used is the HTTPS protocol. The server stores the received data in a database. The data output is the self-evaluation data and emotional data stored in the database.

[0717] Step 3: Analyze the data

[0718] The server uses an AI algorithm to analyze the stored user self-evaluation data and emotional data. The input includes multiple user data stored in the database. Specific data processing and calculation involves the AI ​​algorithm evaluating the user's stress level and emotional state and calculating a mental health score. The analysis results are generated in numerical and graphical format and displayed on the user interface.

[0719] Step 4: Generate proposals

[0720] The server generates personalized mental health care suggestions based on the analysis results. The inputs include the analyzed mental health score and the user's emotional state. As specific data processing and calculation, the suggestion generation algorithm selects the optimal action for the user. For example, a user with a high stress level and feeling "fatigue" might be recommended a "30-minute stretching break" or a "light walk." The output is the generated suggestion.

[0721] Step 5: Proposal Notification

[0722] The server sends the generated suggestions to the device. The input includes the suggestion content. The device notifies the user of the suggestion. Specific actions include in-app notifications and push notifications. The tone and method of the notification are adjusted based on the user's emotional state. The output is the user receiving the suggestion.

[0723] Step 6: Collect behavioral data

[0724] The user receives a notification from the device and performs the suggested action, such as taking a 30-minute stretching break or participating in online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time. The input includes the user's behavior and emotion data. The output is the recorded behavioral data and emotion data.

[0725] Step 7: Progress assessment and feedback

[0726] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The input includes behavioral and emotional data. Specific data processing and calculations involve an AI algorithm evaluating the progress and generating feedback based on the results. For example, changes in stress levels and improvements in "relaxation" after a stretching break are evaluated. The output is a generated feedback message that is notified to the user.

[0727] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users and provide highly accurate mental health care.

[0728] (Application example 2)

[0729] 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."

[0730] There is a lack of systems for efficiently and individually managing and supporting employees' mental health status. Conventional systems have difficulty collecting and analyzing users' mental health data in real time, which has led to issues with the accuracy and effectiveness of providing personalized suggestions. There is also a need for systems that provide appropriate feedback based on employees' emotional state.

[0731] 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.

[0732] In this invention, the server includes means for collecting self-assessment data on mental health from users, means for analyzing the collected self-assessment data and emotional data including facial expressions and tone of voice analyzed in real time to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation and notifying the user of the suggestions, means for generating suggested messages according to individual needs using a generative AI model, means for collecting and analyzing the user's behavioral data and their emotional states during that time to evaluate the progress of the user's mental health status, and means for providing feedback to the user based on the progress. This makes it possible to grasp the mental health status of employees in real time and provide personalized suggestions.

[0733] "User" refers to a person who uses the system to manage their mental health.

[0734] "Mental health" refers to the state of mental health and evaluates emotions and psychological states, including stress, fatigue, and relaxation.

[0735] "Self-assessment data" refers to data entered by a user after assessing their own mental health status.

[0736] "Emotional Data" refers to emotional data, including a user's facial expressions, tone of voice, and text input, analyzed in real time.

[0737] "Personalized suggestions" refers to specific improvement suggestions generated based on each user's individual mental health status.

[0738] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze user data and generate optimal suggested messages.

[0739] "Behavioral data" refers to data regarding the actions taken by the user based on mental health care suggestions.

[0740] "Feedback" refers to information, including progress assessment, obtained as a result of actions taken by a user.

[0741] "Server" refers to a central computer system for storing data collected from users in a database, performing analysis, and generating suggestions and feedback.

[0742] "Notification means" refers to a mechanism for communicating generated suggestions to a user, including devices such as smartphones or smart glasses.

[0743] The present invention is to build a system for mental health care, which consists of a user, a terminal, a server, and an emotion engine as its main components.

[0744] Data collection and emotion recognition

[0745] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated that analyzes the user's facial expressions, tone of voice, and text input in real time. As a specific example, when answering the question, "Please rate your stress level today on a scale of 1 to 10," the engine recognizes "fatigue" from the user's facial expression.

[0746] Data transmission and storage

[0747] The device collects self-evaluation data and emotional data entered by the user. This data includes metadata such as the date and time of the response and the user's emotional state. The collected data is sent to a server and stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[0748] Analyzing the data

[0749] The server analyzes the stored data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user and displays it in numerical and graphical format.

[0750] Generate personalized suggestions

[0751] The server generates personalized mental health care suggestions based on the analysis results. Using a generative AI model, it generates specific suggestion messages that take the user's emotional state into account. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," it recommends a "light walk."

[0752] Proposal Notification

[0753] The server sends the generated suggestions to the device, which then notifies the user. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be delivered in a gentle tone.

[0754] Behavioral data collection

[0755] The user receives a notification from the device and performs the suggested action, such as "take a 30-minute stretching break." The emotion engine also recognizes and records the user's emotions during the action in real time. The device collects the user's behavioral and emotional data and sends it to the server. The behavioral data includes the break time and the emotional state during the action.

[0756] Progress evaluation and feedback

[0757] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health condition. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break. It generates feedback based on the evaluation results and notifies the user. The device notifies the user of the feedback, allowing the user to check the progress of their own mental health condition. As a specific example, the server may provide feedback such as "As a result of the break, your stress level has decreased and your sense of relaxation has improved."

[0758] Prompt Sentence Examples

[0759] "Your stress levels seem high today. Why not try some deep breathing or yoga? I suggest you take a 10-minute break."

[0760] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0761] Step 1:

[0762] Users launch the application on their device and answer a self-assessment questionnaire about their mental health. The user enters their answers into the questionnaire using text, voice, and facial expressions. The output is collected self-assessment data and real-time analyzed emotion data. The emotion engine then analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[0763] Step 2:

[0764] The device sends the collected self-evaluation data and emotional data to the server. The input requires the data the user answers on the device. The output is the data sent to the server, including metadata such as the date and time of the answer and the user's emotional state.

[0765] Step 3:

[0766] The server stores the transmitted data in a database. The inputs required are the self-evaluation data and emotion data transmitted from the device. The output is user data stored in the database. The stored data includes past self-evaluation data, behavioral data, emotion data, feedback information, etc.

[0767] Step 4:

[0768] The server analyzes the stored data using an AI algorithm. The inputs required are the user data stored in the database and the AI ​​algorithm. The output is a mental health assessment and score as the analysis result. This evaluates the user's current mental health state.

[0769] Step 5:

[0770] The server generates personalized mental health care suggestions based on the analysis results. The inputs required are the analysis results and a generative AI model. The output is a specific generated suggestion message that takes into account the user's emotional state. For example, suggestions such as "take a 30-minute stretching break" or "take a light walk" are generated.

[0771] Step 6:

[0772] The server sends the generated suggestion to the terminal, and the terminal notifies the user. As input, the generated suggestion message is required. As output, the suggestion to be notified to the user is obtained. For example, if the user is "high stress", a message in a gentle tone is notified.

[0773] Step 7:

[0774] The user receives a notification from the device and takes the suggested action. The input required is the notified suggestion. The output is the user's behavioral data. For example, taking a "30-minute stretching break" or going for a "light walk."

[0775] Step 8:

[0776] The device also recognizes and records the user's emotions in real time during their actions. The inputs are the user's behavioral data and an emotion engine. The output is emotional data during the action. This emotional data is then used for later analysis.

[0777] Step 9:

[0778] The device transmits the user's behavioral data and emotional data to the server. Recorded behavioral data and emotional data are required as input. Data is output to the server. This behavioral data includes rest periods, emotional states during activities, etc.

[0779] Step 10:

[0780] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The inputs are the user's behavioral data and an AI algorithm. The output is a progress assessment result. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break.

[0781] Step 11:

[0782] The server generates feedback based on the evaluation results and notifies the user. As input, the progress evaluation results and the generated feedback message are required. As output, the server obtains feedback to be notified to the user. For example, the server may provide feedback such as "As a result of taking a break, your stress level has decreased and your sense of relaxation has improved."

[0783] 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.

[0784] 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.

[0785] 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.

[0786] [Third embodiment]

[0787] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0788] 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.

[0789] 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).

[0790] 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.

[0791] 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.

[0792] 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).

[0793] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[0800] Data collection

[0801] Users enter self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10.

[0802] The terminal collects the self-assessment data entered by the user. The terminal may be an electronic device such as a smartphone, tablet, or PC.

[0803] Data transmission and storage

[0804] The device sends the collected self-evaluation data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, and feedback information.

[0805] Analyzing the data

[0806] The server analyzes the stored self-assessment data using AI algorithms, which evaluates the user's current mental health status and displays it in numerical and graphical form.

[0807] As a specific example, if a user's stress level is high, abnormal values ​​can be detected by comparing them with past data and an abnormality alert can be generated in real time.

[0808] Generate personalized suggestions

[0809] The server generates personalized mental health care recommendations based on the analysis results. For example, if a user's stress level is high, it might suggest taking a 30-minute stretching break or receiving online counseling once a week.

[0810] Proposal Notification

[0811] The server sends the generated proposal to the device, and the device notifies the user of the proposal, for example, by a pop-up message, email, or in-app notification.

[0812] Behavioral data collection

[0813] The user acts on the mental health care suggestions provided, for example, by taking a 30-minute stretching break and participating in weekly online counseling sessions.

[0814] The device collects user behavior data and sends it to the server, which also stores the data in a database.

[0815] Progress evaluation and feedback

[0816] The server analyzes the collected behavioral data and evaluates the user's progress in mental health. Based on the evaluation results, it generates feedback and notifies the user, for example, "Your stress level has decreased as a result of your recent behavior."

[0817] The device displays progress assessments and feedback to the user, allowing them to know the extent to which their mental health is improving.

[0818] As described above, the system of the present invention can quickly and effectively respond to the individual needs of the user, making the user, those around him / her, and the company happy.

[0819] The processing flow will be explained below.

[0820] Step 1:

[0821] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. For example, they answer 7 to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[0822] Step 2:

[0823] The device collects the self-assessment data entered by the user (e.g., stress level 7). The collected data also includes metadata such as the date and time of the response.

[0824] Step 3:

[0825] The device sends the collected self-assessment data to the server using methods such as API calls and HTTP requests.

[0826] Step 4:

[0827] The server stores the received self-assessment data in a database that also stores past survey results and other related data.

[0828] Step 5:

[0829] The server analyzes the stored self-assessment data using an AI algorithm. As a result of the analysis, the user's mental health status is scored. For example, a stress level of 7 may be judged as "high stress."

[0830] Step 6:

[0831] The server then generates personalized mental health care recommendations based on the analysis results, such as a 30-minute stretching break or weekly online counseling sessions.

[0832] Step 7:

[0833] The server then transmits the generated proposal to the terminal. This transmission requires real-time communication and uses a low-latency communication method.

[0834] Step 8:

[0835] The device notifies the user of the suggestions received from the server via a pop-up message, email, in-app notification, etc.

[0836] Step 9:

[0837] The user receives a notification from the device and takes a suggested action, such as taking a 30-minute stretching break or attending a counseling session.

[0838] Step 10:

[0839] The terminal collects user behavioral data, including break times and counseling participation history.

[0840] Step 11:

[0841] The device sends the collected behavioral data to a server, which collects and transmits the behavioral data in real time.

[0842] Step 12:

[0843] The server stores the behavioral data in a database and accumulates it together with past data.

[0844] Step 13:

[0845] The server analyzes the collected behavioral data to assess the user's mental health progress, for example, assessing fluctuations in stress levels after stretching breaks.

[0846] Step 14:

[0847] The server generates feedback based on the analysis results, for example, "As a result of taking a break, your stress level has decreased."

[0848] Step 15:

[0849] The server transmits the generated feedback to the terminal, and the feedback is transmitted periodically as appropriate.

[0850] Step 16:

[0851] The device provides feedback to the user, allowing them to monitor their own mental health progress.

[0852] Through the above processing steps, this system efficiently supports the user's mental health care.

[0853] Example 1

[0854] 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."

[0855] In modern society, many people suffer from stress and fatigue, making mental health care important. However, previous mental health care systems have struggled to provide personalized suggestions to individual users or evaluate progress and provide feedback based on actual behavioral data. There are also limited methods for users to accurately assess their own mental health status. Furthermore, there is a lack of systems that can effectively analyze collected data to generate appropriate suggestions and notifications.

[0856] 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.

[0857] In this invention, the server includes: means for collecting self-assessment data on mental health from a user; means for transmitting the collected self-assessment data to the server via a terminal; means for storing the collected self-assessment data in a database; means for analyzing the stored self-assessment data using an AI algorithm to evaluate the user's mental health status; means for generating personalized mental health care suggestions based on the evaluation; means for notifying the user of the generated suggestions; means for collecting user behavioral data based on the mental health care suggestions; means for transmitting the collected behavioral data to the server and storing it in the database; means for analyzing the stored behavioral data to evaluate progress in the user's mental health status; means for providing feedback to the user based on the progress; means for the user to actually take action in accordance with the suggestions notified to the user; and means for transmitting the behavioral data based on the suggestions to the server again. This enables efficient and personalized mental health care for the user. Specifically, the system can accurately evaluate the user's mental health status and provide appropriate care suggestions based on the evaluation. Furthermore, by evaluating progress based on the user's behavioral data and providing appropriate feedback, it is possible to support long-term mental health improvement.

[0858] A "database" is a computer-based system for organizing and storing collected data, and for retrieving and managing it as needed.

[0859] "Mental health care" refers to activities and services aimed at maintaining and improving the mental health of users.

[0860] "Self-assessment data" is psychological assessment information such as stress level, mood, and fatigue level input by the user himself / herself.

[0861] A "terminal" is an electronic device such as a smartphone, tablet, or PC that a user uses to input and transmit self-assessment data.

[0862] The "server" is a central management system that stores collected data, analyzes it using AI algorithms, and provides feedback to users.

[0863] An "AI algorithm" is a method or program that uses artificial intelligence technology to analyze data.

[0864] "Personalized suggestions" are mental health care suggestions customized based on a user's individual data and needs.

[0865] "Behavioral data" refers to data on mental health care activities actually undertaken by the user and actions suggested by the system.

[0866] "Feedback" refers to an assessment of mental health status and instructions for improvement that the system provides to the user based on the analysis results.

[0867] "Notifications" are messages or alerts sent from the system to the user's device to provide suggestions or feedback.

[0868] "Progress assessment" is the process of evaluating changes and improvements in a user's mental health status based on collected behavioral data.

[0869] That's it. These definitions clarify the meaning of key terms contained in the claims.

[0870] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[0871] Data collection

[0872] Users input self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10. This data is entered using devices such as smartphones, tablets, and PCs.

[0873] Data transmission and storage

[0874] The device collects the self-evaluation data entered by the user and transmits it in real time to a server, which stores the received data in a database that also stores past self-evaluation data, behavioral data, and feedback information.

[0875] Analyzing the data

[0876] The server analyzes the stored data using AI algorithms such as Tensorflow or Scikit-learn implemented in Python. This analysis evaluates the user's current mental health status and displays the results in numerical and graphical formats. For example, if the user's stress level is high, the server can compare it with past data to detect anomalies and generate an anomaly alert in real time.

[0877] Generate personalized suggestions

[0878] The server generates personalized mental health care suggestions based on the analysis results, such as suggesting a 30-minute stretching break or weekly online counseling if the user's stress level is high.

[0879] Proposal Notification

[0880] The server sends the generated proposal to the device, which notifies the user of the proposal via a pop-up message, email, or in-app notification.

[0881] Behavioral data collection

[0882] The user acts in accordance with the mental health care suggestions provided. For example, they may choose to take a 30-minute stretching break and participate in weekly online counseling sessions. The device collects the user's behavioral data and sends it to the server. This behavioral data is also stored in a database.

[0883] Progress evaluation and feedback

[0884] The server analyzes the collected behavioral data and evaluates the progress of the user's mental health condition. Based on the evaluation results, it generates feedback and notifies the user. For example, the feedback may say, "As a result of your recent actions, your stress level has decreased." The device then displays the progress evaluation and the feedback content to the user. This allows the user to know the degree of improvement in their mental health condition.

[0885] Specific examples

[0886] Specific examples are shown below.

[0887] User input and collection

[0888] Using the device, users rate their stress level as 8 / 10, their mood as 4 / 10, and their fatigue as 7 / 10.

[0889] The terminal collects the data entered by the user.

[0890] Sending data

[0891] The device sends the collected data to a server, which stores the data in a database.

[0892] Analyzing the data

[0893] The server analyzes the stored data using AI algorithms to detect when a user's stress level is high.

[0894] Proposal generation and notification

[0895] Based on the analysis results, the server will suggest a 30-minute stretching break, and the device will notify the user of the suggestion in a pop-up message.

[0896] Action implementation and data collection

[0897] The user follows the suggestion and takes a 30-minute stretching break. The device collects data during the break (time, amount of exercise, etc.).

[0898] Progress evaluation and feedback

[0899] The server analyzes the collected behavioral data and evaluates that the stress level is decreasing. The server generates feedback that says "your stress level has improved" and notifies the user. The device displays the feedback to the user.

[0900] Example prompts for generative AI models

[0901] "Suggest a response when a user rates their stress level as 8 / 10."

[0902] That's it. This system is designed to provide efficient and personalized mental health care for users. By utilizing generative AI models and data analysis, it is able to respond quickly and effectively to user needs.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Processing Steps

[0905] Step 1: User enters self-assessment data

[0906] Input: The user uses a device such as a smartphone or PC to input self-assessment data such as stress level (e.g., 8 / 10), mood state (e.g., 4 / 10), and fatigue (e.g., 7 / 10).

[0907] Output: The self-assessment data is saved as input to the terminal.

[0908] How it works: Users enter their evaluation data using a dedicated app or web interface, which is then stored in the device's memory in real time.

[0909] Step 2: Submit self-assessment data

[0910] Input: Self-assessment data collected in Step 1.

[0911] Output: The collected self-assessment data is sent to the server.

[0912] Specific operation: When the user presses the "Submit" button, the device sends the evaluation data to the server via the Internet. The data is sent in the form of an HTTP request.

[0913] Step 3: Save your data

[0914] Input: Self-assessment data submitted in step 2.

[0915] Output: Self-assessment data stored in a database on the server.

[0916] Specific operation: The data received on the server side is stored in a MySQL or PostgreSQL database via a PHP or Python script. This process is performed asynchronously.

[0917] Step 4: Analyze the data

[0918] Input: Self-assessment data stored in a database.

[0919] Output: User's mental health status assessment results (in numerical and graphical formats).

[0920] How it works: The server periodically retrieves data from the database and analyzes it using AI algorithms implemented in Python, such as TensorFlow and Scikit-learn, to calculate trends in stress levels and detect outliers.

[0921] Step 5: Generate personalized suggestions

[0922] Input: Analysis results from step 4.

[0923] Output: Personalized mental health care suggestions for the user.

[0924] How it works: Based on the analysis results, the server selects the most appropriate suggestion from a pre-defined list of suggestions (e.g., take a break, get online counseling, etc.) and customizes it based on the user profile.

[0925] Step 6: Proposal Notification

[0926] Input: Mental healthcare suggestions generated in step 5.

[0927] Output: The suggestion that will be displayed on the user's device as a notification.

[0928] Specific operation: The server sends the generated proposal to the user's device, and the device notifies the user of the received proposal via a pop-up message, email, or in-app notification.

[0929] Step 7: User Actions

[0930] Input: The proposal communicated in step 6.

[0931] Output: User behavior data based on suggestions.

[0932] Specific actions: The user acts according to the notified suggestion, for example, taking a 30-minute stretching break. The device collects behavioral data (time, amount of exercise, etc.) in real time.

[0933] Step 8: Sending behavioral data

[0934] Input: Behavioral data collected in step 7.

[0935] Output: The behavioral data sent to the server.

[0936] Specific operation: After accumulating a certain amount of behavioral data, the device sends it to a server via the Internet. The data is sent in the form of an HTTP request.

[0937] Step 9: Progress assessment and feedback

[0938] Input: Behavioral data submitted in step 8.

[0939] Output: Mental health status progress assessment results and feedback.

[0940] Specific operation: The server compares the collected behavioral data with past self-assessment data to evaluate progress such as reducing stress levels. Based on the evaluation results, it generates feedback such as "Your stress level has decreased as a result of your recent actions" and notifies the user. The device displays the feedback to the user.

[0941] By following the above steps, the system provides the user with efficient and personalized mental health care.

[0942] (Application example 1)

[0943] 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."

[0944] While the use of self-driving vehicles has expanded in recent years, there are concerns that stress and fatigue during long driving and travel times may affect the health of drivers and passengers. Maintaining good mental health contributes to the user's driving performance and safety, so mental health management is becoming increasingly important in self-driving vehicles as well. However, currently, there is no fully established system that monitors the mental health of drivers and passengers in self-driving vehicles in real time and provides appropriate care. In response to this, the present invention aims to provide a system that continuously monitors the mental health status of drivers and passengers in self-driving vehicles and makes personalized suggestions.

[0945] 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.

[0946] In this invention, the server includes means for collecting self-assessment data regarding mental health from the user, means for collecting physiological data and behavioral data of the user from on-board sensors, means for analyzing the collected self-assessment data and physiological data to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation, means for notifying the user of the suggestions generated using another terminal, means for collecting and analyzing the user's behavioral data to evaluate progress in the mental health status, and means for providing feedback to the user based on the progress. This makes it possible to analyze the user's mental health status in real time while in an autonomous vehicle and provide appropriate care.

[0947] The "means for collecting self-assessment data on mental health from users" refers to a function that provides an interface that allows users to input their mental health status, such as their stress level and fatigue level, as numerical values ​​and comments via a device such as a smartphone or an in-car display.

[0948] "Means of collecting user physiological data and behavioral data from in-vehicle sensors" refers to a function that uses in-vehicle sensors (e.g., heart rate monitors, driving behavior analysis cameras) to obtain data on the user's physiological indicators and behavior in real time.

[0949] "Means for analyzing collected self-assessment data and physiological data to assess the user's mental health status" refers to a function that analyzes collected data using an AI algorithm (for example, using TensorFlow or PyTorch) and evaluates the user's mental health status in the form of numbers, graphs, etc.

[0950] The "means for generating personalized mental health care suggestions based on the evaluation" is a function for generating relaxation methods and rest suggestions appropriate to the user's current mental health state based on the analysis results.

[0951] "Means for notifying the user of suggestions generated using another device" refers to a function for notifying the user of the generated mental health care suggestions in the form of a pop-up message, voice guidance, etc., via an in-vehicle display or smartphone.

[0952] "Means for collecting and analyzing user behavioral data to evaluate progress in mental health status" refers to a function that collects data on actions taken by users based on suggestions (e.g., taking a break or stretching), analyzes that data, and evaluates the degree of improvement in their mental health status.

[0953] "Means for providing feedback to the user based on progress" refers to a function for providing feedback generated based on the results of the user's actions (e.g., "Your stress level has decreased") to the user via the terminal.

[0954] The present invention provides a system for analyzing the user's mental health in real time within an autonomous vehicle and providing personalized care based on the results. A specific method for implementing this system is described below.

[0955] Data collection

[0956] First, the user inputs self-evaluation data (stress level, fatigue level, etc.) using a smartphone or in-car display. This data is recorded as numbers and comments. In addition, in-car sensors (heart rate monitor, driving behavior analysis camera, etc.) collect the user's physiological and behavioral data in real time.

[0957] Data transmission and storage

[0958] The device sends the collected data to an in-vehicle server or a cloud server, which stores the data in a database. The database also stores the user's past self-evaluation data and behavioral data, and uses it for future analysis.

[0959] Analyzing the data

[0960] The server then analyzes the stored data using AI algorithms (for example, TensorFlow or PyTorch). This analysis evaluates the user's mental health status in numerical and graphical formats. If the user's stress level indicates an abnormal value, an anomaly alert is generated in real time.

[0961] Generate personalized suggestions

[0962] The server generates personalized mental health care suggestions based on the analysis results, such as "take a break at the next service area" or "turn on the seat heater."

[0963] Proposal Notification

[0964] The generated suggestions are notified to the user via the device, using methods such as a pop-up message on the in-car display, voice guidance, or a notification on a smartphone.

[0965] Behavioral data collection

[0966] If the user performs the suggested action, the results of that action are recorded. Specifically, data such as the time taken for a break and the method of relaxation is saved.

[0967] Progress evaluation and feedback

[0968] Finally, the server analyzes the collected behavioral data and evaluates the progress of the user's mental health. Based on this, feedback is provided to the user. For example, the feedback could be, "As a result of your recent break, your stress level has decreased." This allows the user to understand the progress of their mental health.

[0969] Examples of concrete examples and prompts

[0970] Specific examples include "take a break at the next service area," "turn on the seat heater," and "listen to relaxing music." An example of a prompt for the generative AI model is "generate appropriate suggestions when the user inputs a stress level of 8 and a fatigue level of 7. For example, take a break at the next service area or listen to relaxing music."

[0971] This will enable effective management of the user's mental health in an autonomous vehicle, supporting a comfortable and safe journey.

[0972] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0973] Step 1:

[0974] The user inputs self-assessment data about their mental health (stress level, fatigue level, etc.) via a smartphone or in-car display. The input data is then sent to the terminal via the interface. The input data includes the user's subjective assessment (e.g., "stress level is 8 / 10").

[0975] Step 2:

[0976] The device continuously collects the user's physiological and behavioral data from in-vehicle sensors (heart rate monitor, driving behavior analysis camera, etc.). This data includes heart rate, breathing rate, facial expression changes, driving behavior patterns, etc. The collected data is temporarily stored on the device.

[0977] Step 3:

[0978] The device transmits the collected self-assessment data and physiological data to an in-vehicle server or a cloud server, where the data is stored in the server's database. The stored data includes various physiological data and self-assessment data.

[0979] Step 4:

[0980] The server analyzes the stored data using AI algorithms (using TensorFlow and PyTorch) to evaluate the user's mental health status. Collected physiological data and self-assessment data are used as input, and these are standardized and normalized before being fed into the AI ​​model. The output is the user's current mental health status in numerical and graphical form, and an alert is generated if an abnormal value is detected.

[0981] Step 5:

[0982] The server generates personalized mental health care suggestions based on the analysis results. For example, if the stress level is high, it generates suggestions such as "take a break at the next service area" or "turn on the seat heater." The prompt text used is "If the user inputs a stress level of 8 and a fatigue level of 7, generate appropriate suggestions. For example, take a break at the next service area or listen to relaxing music."

[0983] Step 6:

[0984] The device notifies the user of the suggestions sent from the server. Notification methods include pop-up messages on the in-car display, voice guidance, and notifications on a smartphone. This allows the user to receive mental health care suggestions from the system at the appropriate time.

[0985] Step 7:

[0986] The user acts in accordance with the notified suggestion (for example, taking a break). At this time, the user's behavioral data (such as the time and method of taking the break) is again input into the device.

[0987] Step 8:

[0988] The device sends the user's behavioral data to a server, which then analyzes the data and evaluates the user's improvement in mental health. This allows the server to generate feedback based on the user's progress (e.g., "Your stress level has decreased").

[0989] Step 9:

[0990] Feedback is sent to the user via the device, allowing them to see how their mental health has improved.

[0991] 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.

[0992] The system of the present invention is designed to efficiently and individually support the mental health care of users, and by combining an emotion engine, it provides greater precision and flexibility. The system has a user, a terminal, a server, and an emotion engine as its main components, and operates as follows.

[0993] Data collection and emotion recognition

[0994] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will recognize "fatigue" from the user's facial expression.

[0995] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[0996] Data transmission and storage

[0997] The device sends the collected self-evaluation data and emotional data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[0998] Analyzing the data

[0999] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user, which is displayed in numerical and graphical format.

[1000] Generate personalized suggestions

[1001] The server generates personalized mental health care suggestions based on the analysis results. By taking the user's emotional state into account, the realism and appropriateness of the suggestions are improved. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," the server recommends a "light walk."

[1002] Proposal Notification

[1003] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be sent in a gentle tone.

[1004] Behavioral data collection

[1005] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[1006] The device collects and transmits the user's behavioral and emotional data to the server. This behavioral data includes information such as break times, counseling participation history, and emotional state during the activity.

[1007] Progress evaluation and feedback

[1008] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health status. For example, it evaluates the fluctuation of stress levels after a stretching break and the "feeling of relaxation." Based on the evaluation results, it generates feedback and notifies the user.

[1009] The device will provide feedback to the user, allowing them to see their mental health progress, for example, "As a result of your break, your stress levels have decreased and you feel more relaxed."

[1010] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users, making them, their surroundings, and their company happy. By combining it with an emotion engine, it is possible to provide even more accurate mental health care.

[1011] The processing flow will be explained below.

[1012] Step 1:

[1013] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if a user answers that their stress level is "7," the emotion engine will recognize "fatigue" from their facial expression.

[1014] Step 2:

[1015] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[1016] Step 3:

[1017] The device sends the collected self-evaluation data and emotion data to the server. The sent data is transmitted using a communication method such as an HTTP request.

[1018] Step 4:

[1019] The server stores the received self-evaluation and emotion data in a database that also stores past survey results and other related data.

[1020] Step 5:

[1021] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's mental health status. For example, a mental health score is generated based on a stress level of 7 and a "feeling of fatigue."

[1022] Step 6:

[1023] The server generates personalized mental health care recommendations based on the analysis results, taking into account the user's emotional state. For example, if the user is feeling fatigued due to high stress levels, the server will suggest a 30-minute stretching break, as well as recommend a light walk.

[1024] Step 7:

[1025] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is in a high-stress state, the notification will be sent in a gentle tone.

[1026] Step 8:

[1027] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[1028] Step 9:

[1029] The device collects user behavioral and emotional data, including break times, counseling participation history, and emotional states during activities.

[1030] Step 10:

[1031] The device sends the collected behavioral and emotional data to the server, also using a communication method such as an HTTP request.

[1032] Step 11:

[1033] The server stores the behavioral data and emotion data in a database, accumulating them together with past data.

[1034] Step 12:

[1035] The server analyzes the collected behavioral and emotional data to assess the user's mental health progress, for example, assessing stress level fluctuations and "feeling relaxed" after a stretching break.

[1036] Step 13:

[1037] The server generates feedback based on the analysis results, for example, "As a result of the break, your stress level has decreased and you feel more relaxed."

[1038] Step 14:

[1039] The server transmits the generated feedback to the terminal, which provides the feedback as needed.

[1040] Step 15:

[1041] The device provides feedback to the user, allowing them to see the progress of their mental health status, so they can understand how their own behavior has affected their mental health.

[1042] Through the above processing steps, this system will more effectively support the user's mental health care. By combining it with an emotion engine, more precise care and feedback can be provided, making the user, those around them, and the company happier.

[1043] Example 2

[1044] 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."

[1045] Conventional mental health care systems rely solely on users' self-assessment data and are unable to grasp changes in users' emotions and behaviors in real time. This results in low accuracy and effectiveness of the care provided, making it difficult to make personalized suggestions suited to individual needs. Collecting user behavioral data to evaluate the progress of mental health status and provide appropriate feedback has also been a challenge.

[1046] 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 means for analyzing the user's facial expressions, tone of voice, and text input in real time to generate emotional data, means for storing the collected self-evaluation data and emotional data in a database, and means for analyzing the stored data based on an AI algorithm and evaluating the user's mental health state. This makes it possible to grasp changes in the user's emotions and behavior in real time. In addition, the collected data can be used to personalize the user's mental health care and provide more accurate suggestions and feedback.

[1047] "User" refers to an individual who uses the system to receive mental health care services.

[1048] "Mental health" refers to mental and emotional well-being and is a concept that includes stress, fatigue, anxiety, etc.

[1049] "Self-assessment data" refers to information provided by users when they self-assess their mental health status. Specifically, this data is expressed as numerical values ​​or text, such as stress levels and emotional states.

[1050] "Emotion data" refers to information about emotions that the emotion engine analyzes and extracts from the user's facial expressions, tone of voice, text input, etc.

[1051] An "emotion engine" refers to software or hardware that analyzes input data such as a user's facial expressions, voice, and text, and recognizes their emotional state.

[1052] "Server" refers to the computer system within the system that is responsible for storing and analyzing data, generating and distributing mental health care suggestions, etc.

[1053] A "database" refers to a system that structures, stores, and manages self-evaluation data, emotional data, behavioral data, and other data collected from users.

[1054] "AI Algorithms" refers to the artificial intelligence technologies and computational methods used to analyze a user's self-assessment data and emotional data, assess their mental health status, and generate recommendations.

[1055] "Personalized mental health care suggestions" refer to mental health care suggestions generated based on individual user data and optimized for the user's specific needs and condition.

[1056] "Behavioral data" refers to data on the user's behavior when implementing the suggestions and the emotional state recognized during the behavior. Specifically, it includes the length of breaks, counseling participation history, and the emotional state during the behavior.

[1057] "Progress assessment" refers to the process of analyzing and evaluating changes and improvements in a user's mental health status based on behavioral and emotional data.

[1058] "Feedback" refers to the evaluation, areas for improvement, and information on next actions provided to the user based on the progress evaluation results.

[1059] The system of the present invention is designed to efficiently and individually support users' mental health care, and by combining it with an emotion engine, it provides greater precision and flexibility. The system operates with a user, a terminal, a server, and an emotion engine as its main components.

[1060] Data collection and emotion recognition

[1061] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will detect a "feeling of fatigue" from the user's facial expression.

[1062] The device collects the user's self-evaluation data and emotion data recognized by the emotion engine, including the question content, answer, analyzed emotion, and answer date and time.

[1063] Data transmission and storage

[1064] The device sends the collected self-evaluation data and emotion data to the server using HTTPS as the communication protocol.

[1065] The server stores the received data in a database, which also stores past self-evaluation data, behavioral data, emotional data, and feedback information. The database is typically a relational database such as MySQL or PostgreSQL.

[1066] Analyzing the data

[1067] The server analyzes the stored data using an AI algorithm, which evaluates the user's current mental health state. For example, if a user answers "stress level 7" and "feeling tired" is detected, the AI ​​algorithm calculates the user's mental health score and displays it in the user interface in numerical and graphical form.

[1068] Proposal Generation

[1069] The server generates personalized mental health care suggestions based on the analysis results. For example, it suggests a "30-minute stretching break" and recommends a "light walk" if the user feels "fatigue." This improves the realism and appropriateness of the suggestions.

[1070] Proposal Notification

[1071] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted based on the user's emotional state. For example, in-app notifications and push notifications are used, and gentle messages are sent to users in high stress states.

[1072] Behavioral data collection

[1073] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time.

[1074] The device collects the user's behavioral and emotional data and sends it back to the server. The collected data includes the length of breaks, counseling participation history, and emotional state during the activity.

[1075] Progress evaluation and feedback

[1076] The server analyzes the collected behavioral and emotional data to evaluate the user's mental health progress. For example, it evaluates stress levels and "feeling relaxed" after a stretching break. Based on the evaluation results, feedback is generated and notified to the user.

[1077] The device provides feedback to the user, allowing them to see the progress of their mental health. A specific feedback message provided is, "As a result of your break, your stress level has decreased and you feel more relaxed."

[1078] Specific examples and prompts for the generative AI model

[1079] Specific examples

[1080] 1. The user launches the application and answers "7" to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[1081] 2. The emotion engine detects "fatigue" from the user's facial expression.

[1082] 3. The device sends the collected data to the server.

[1083] 4. The server analyzes the data and generates the user's mental health score.

[1084] 5. The server suggests a "30-minute stretching break" and recommends a "light walk."

[1085] 6. The device notifies the user of this offer.

[1086] 7. The user performs the suggested action.

[1087] 8. The device collects behavioral and emotional data and sends it to the server.

[1088] 9. The server generates the evaluation results and feedback and notifies the user.

[1089] 10. Your device will notify you that "As a result of your break, your stress levels have decreased and you feel more relaxed."

[1090] Prompt Sentence Examples

[1091] Specific questions such as "How would you rate your stress level over the past week on a scale of 1 to 10?" can be fed into the generative AI model to generate optimal feedback and suggestions.

[1092] As described above, the system of the present invention can efficiently support the user's mental health care and provide personalized care according to individual needs.

[1093] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1094] Step 1: Collecting data from users

[1095] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. The input includes the user's answers to each item in the questionnaire. At this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. A specific example is when the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10." The data output is the user's self-assessment data and emotion data recognized by the emotion engine (e.g., "fatigue").

[1096] Step 2: Send and store data

[1097] The device sends the collected user self-evaluation data and emotional data to the server. The input includes the user's answers and analyzed emotional data. The specific communication method used is the HTTPS protocol. The server stores the received data in a database. The data output is the self-evaluation data and emotional data stored in the database.

[1098] Step 3: Analyze the data

[1099] The server uses an AI algorithm to analyze the stored user self-evaluation data and emotional data. The input includes multiple user data stored in the database. Specific data processing and calculation involves the AI ​​algorithm evaluating the user's stress level and emotional state and calculating a mental health score. The analysis results are generated in numerical and graphical format and displayed on the user interface.

[1100] Step 4: Generate proposals

[1101] The server generates personalized mental health care suggestions based on the analysis results. The inputs include the analyzed mental health score and the user's emotional state. As specific data processing and calculation, the suggestion generation algorithm selects the optimal action for the user. For example, a user with a high stress level and feeling "fatigue" might be recommended a "30-minute stretching break" or a "light walk." The output is the generated suggestion.

[1102] Step 5: Proposal Notification

[1103] The server sends the generated suggestions to the device. The input includes the suggestion content. The device notifies the user of the suggestion. Specific actions include in-app notifications and push notifications. The tone and method of the notification are adjusted based on the user's emotional state. The output is the user receiving the suggestion.

[1104] Step 6: Collect behavioral data

[1105] The user receives a notification from the device and performs the suggested action, such as taking a 30-minute stretching break or participating in online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time. The input includes the user's behavior and emotion data. The output is the recorded behavioral data and emotion data.

[1106] Step 7: Progress assessment and feedback

[1107] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The input includes behavioral and emotional data. Specific data processing and calculations involve an AI algorithm evaluating the progress and generating feedback based on the results. For example, changes in stress levels and improvements in "relaxation" after a stretching break are evaluated. The output is a generated feedback message that is notified to the user.

[1108] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users and provide highly accurate mental health care.

[1109] (Application example 2)

[1110] 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."

[1111] There is a lack of systems for efficiently and individually managing and supporting employees' mental health status. Conventional systems have difficulty collecting and analyzing users' mental health data in real time, which has led to issues with the accuracy and effectiveness of providing personalized suggestions. There is also a need for systems that provide appropriate feedback based on employees' emotional state.

[1112] 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.

[1113] In this invention, the server includes means for collecting self-assessment data on mental health from users, means for analyzing the collected self-assessment data and emotional data including facial expressions and tone of voice analyzed in real time to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation and notifying the user of the suggestions, means for generating suggested messages according to individual needs using a generative AI model, means for collecting and analyzing the user's behavioral data and their emotional states during that time to evaluate the progress of the user's mental health status, and means for providing feedback to the user based on the progress. This makes it possible to grasp the mental health status of employees in real time and provide personalized suggestions.

[1114] "User" refers to a person who uses the system to manage their mental health.

[1115] "Mental health" refers to the state of mental health and evaluates emotions and psychological states, including stress, fatigue, and relaxation.

[1116] "Self-assessment data" refers to data entered by a user after assessing their own mental health status.

[1117] "Emotional Data" refers to emotional data, including a user's facial expressions, tone of voice, and text input, analyzed in real time.

[1118] "Personalized suggestions" refers to specific improvement suggestions generated based on each user's individual mental health status.

[1119] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze user data and generate optimal suggested messages.

[1120] "Behavioral data" refers to data regarding the actions taken by the user based on mental health care suggestions.

[1121] "Feedback" refers to information, including progress assessment, obtained as a result of actions taken by a user.

[1122] "Server" refers to a central computer system for storing data collected from users in a database, performing analysis, and generating suggestions and feedback.

[1123] "Notification means" refers to a mechanism for communicating generated suggestions to a user, including devices such as smartphones or smart glasses.

[1124] The present invention is to build a system for mental health care, which consists of a user, a terminal, a server, and an emotion engine as its main components.

[1125] Data collection and emotion recognition

[1126] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated that analyzes the user's facial expressions, tone of voice, and text input in real time. As a specific example, when answering the question, "Please rate your stress level today on a scale of 1 to 10," the engine recognizes "fatigue" from the user's facial expression.

[1127] Data transmission and storage

[1128] The device collects self-evaluation data and emotional data entered by the user. This data includes metadata such as the date and time of the response and the user's emotional state. The collected data is sent to a server and stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[1129] Analyzing the data

[1130] The server analyzes the stored data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user and displays it in numerical and graphical format.

[1131] Generate personalized suggestions

[1132] The server generates personalized mental health care suggestions based on the analysis results. Using a generative AI model, it generates specific suggestion messages that take the user's emotional state into account. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," it recommends a "light walk."

[1133] Proposal Notification

[1134] The server sends the generated suggestions to the device, which then notifies the user. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be delivered in a gentle tone.

[1135] Behavioral data collection

[1136] The user receives a notification from the device and performs the suggested action, such as "take a 30-minute stretching break." The emotion engine also recognizes and records the user's emotions during the action in real time. The device collects the user's behavioral and emotional data and sends it to the server. The behavioral data includes the break time and the emotional state during the action.

[1137] Progress evaluation and feedback

[1138] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health condition. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break. It generates feedback based on the evaluation results and notifies the user. The device notifies the user of the feedback, allowing the user to check the progress of their own mental health condition. As a specific example, the server may provide feedback such as "As a result of the break, your stress level has decreased and your sense of relaxation has improved."

[1139] Prompt Sentence Examples

[1140] "Your stress levels seem high today. Why not try some deep breathing or yoga? I suggest you take a 10-minute break."

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] Users launch the application on their device and answer a self-assessment questionnaire about their mental health. The user enters their answers into the questionnaire using text, voice, and facial expressions. The output is collected self-assessment data and real-time analyzed emotion data. The emotion engine then analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[1144] Step 2:

[1145] The device sends the collected self-evaluation data and emotional data to the server. The input requires the data the user answers on the device. The output is the data sent to the server, including metadata such as the date and time of the answer and the user's emotional state.

[1146] Step 3:

[1147] The server stores the transmitted data in a database. The inputs required are the self-evaluation data and emotion data transmitted from the device. The output is user data stored in the database. The stored data includes past self-evaluation data, behavioral data, emotion data, feedback information, etc.

[1148] Step 4:

[1149] The server analyzes the stored data using an AI algorithm. The inputs required are the user data stored in the database and the AI ​​algorithm. The output is a mental health assessment and score as the analysis result. This evaluates the user's current mental health state.

[1150] Step 5:

[1151] The server generates personalized mental health care suggestions based on the analysis results. The inputs required are the analysis results and a generative AI model. The output is a specific generated suggestion message that takes into account the user's emotional state. For example, suggestions such as "take a 30-minute stretching break" or "take a light walk" are generated.

[1152] Step 6:

[1153] The server sends the generated suggestion to the terminal, and the terminal notifies the user. As input, the generated suggestion message is required. As output, the suggestion to be notified to the user is obtained. For example, if the user is "high stress", a message in a gentle tone is notified.

[1154] Step 7:

[1155] The user receives a notification from the device and takes the suggested action. The input required is the notified suggestion. The output is the user's behavioral data. For example, taking a "30-minute stretching break" or going for a "light walk."

[1156] Step 8:

[1157] The device also recognizes and records the user's emotions in real time during their actions. The inputs are the user's behavioral data and an emotion engine. The output is emotional data during the action. This emotional data is then used for later analysis.

[1158] Step 9:

[1159] The device transmits the user's behavioral data and emotional data to the server. Recorded behavioral data and emotional data are required as input. Data is output to the server. This behavioral data includes rest periods, emotional states during activities, etc.

[1160] Step 10:

[1161] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The inputs are the user's behavioral data and an AI algorithm. The output is a progress assessment result. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break.

[1162] Step 11:

[1163] The server generates feedback based on the evaluation results and notifies the user. As input, the progress evaluation results and the generated feedback message are required. As output, the server obtains feedback to be notified to the user. For example, the server may provide feedback such as "As a result of taking a break, your stress level has decreased and your sense of relaxation has improved."

[1164] 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.

[1165] 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.

[1166] 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.

[1167] [Fourth embodiment]

[1168] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1169] 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.

[1170] 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).

[1171] 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.

[1172] 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.

[1173] 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).

[1174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1175] 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.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] 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.

[1180] 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."

[1181] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[1182] Data collection

[1183] Users enter self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10.

[1184] The terminal collects the self-assessment data entered by the user. The terminal may be an electronic device such as a smartphone, tablet, or PC.

[1185] Data transmission and storage

[1186] The device sends the collected self-evaluation data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, and feedback information.

[1187] Analyzing the data

[1188] The server analyzes the stored self-assessment data using AI algorithms, which evaluates the user's current mental health status and displays it in numerical and graphical form.

[1189] As a specific example, if a user's stress level is high, abnormal values ​​can be detected by comparing them with past data and an abnormality alert can be generated in real time.

[1190] Generate personalized suggestions

[1191] The server generates personalized mental health care recommendations based on the analysis results. For example, if a user's stress level is high, it might suggest taking a 30-minute stretching break or receiving online counseling once a week.

[1192] Proposal Notification

[1193] The server sends the generated proposal to the device, and the device notifies the user of the proposal, for example, by a pop-up message, email, or in-app notification.

[1194] Behavioral data collection

[1195] The user acts on the mental health care suggestions provided, for example, by taking a 30-minute stretching break and participating in weekly online counseling sessions.

[1196] The device collects user behavior data and sends it to the server, which also stores the data in a database.

[1197] Progress evaluation and feedback

[1198] The server analyzes the collected behavioral data and evaluates the user's progress in mental health. Based on the evaluation results, it generates feedback and notifies the user, for example, "Your stress level has decreased as a result of your recent behavior."

[1199] The device displays progress assessments and feedback to the user, allowing them to know the extent to which their mental health is improving.

[1200] As described above, the system of the present invention can quickly and effectively respond to the individual needs of the user, making the user, those around him / her, and the company happy.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. For example, they answer 7 to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[1204] Step 2:

[1205] The device collects the self-assessment data entered by the user (e.g., stress level 7). The collected data also includes metadata such as the date and time of the response.

[1206] Step 3:

[1207] The device sends the collected self-assessment data to the server using methods such as API calls and HTTP requests.

[1208] Step 4:

[1209] The server stores the received self-assessment data in a database that also stores past survey results and other related data.

[1210] Step 5:

[1211] The server analyzes the stored self-assessment data using an AI algorithm. As a result of the analysis, the user's mental health status is scored. For example, a stress level of 7 may be judged as "high stress."

[1212] Step 6:

[1213] The server then generates personalized mental health care recommendations based on the analysis results, such as a 30-minute stretching break or weekly online counseling sessions.

[1214] Step 7:

[1215] The server then transmits the generated proposal to the terminal. This transmission requires real-time communication and uses a low-latency communication method.

[1216] Step 8:

[1217] The device notifies the user of the suggestions received from the server via a pop-up message, email, in-app notification, etc.

[1218] Step 9:

[1219] The user receives a notification from the device and takes a suggested action, such as taking a 30-minute stretching break or attending a counseling session.

[1220] Step 10:

[1221] The terminal collects user behavioral data, including break times and counseling participation history.

[1222] Step 11:

[1223] The device sends the collected behavioral data to a server, which collects and transmits the behavioral data in real time.

[1224] Step 12:

[1225] The server stores the behavioral data in a database and accumulates it together with past data.

[1226] Step 13:

[1227] The server analyzes the collected behavioral data to assess the user's mental health progress, for example, assessing fluctuations in stress levels after stretching breaks.

[1228] Step 14:

[1229] The server generates feedback based on the analysis results, for example, "As a result of taking a break, your stress level has decreased."

[1230] Step 15:

[1231] The server transmits the generated feedback to the terminal, and the feedback is transmitted periodically as appropriate.

[1232] Step 16:

[1233] The device provides feedback to the user, allowing them to monitor their own mental health progress.

[1234] Through the above processing steps, this system efficiently supports the user's mental health care.

[1235] Example 1

[1236] 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."

[1237] In modern society, many people suffer from stress and fatigue, making mental health care important. However, previous mental health care systems have struggled to provide personalized suggestions to individual users or evaluate progress and provide feedback based on actual behavioral data. There are also limited methods for users to accurately assess their own mental health status. Furthermore, there is a lack of systems that can effectively analyze collected data to generate appropriate suggestions and notifications.

[1238] 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.

[1239] In this invention, the server includes: means for collecting self-assessment data on mental health from a user; means for transmitting the collected self-assessment data to the server via a terminal; means for storing the collected self-assessment data in a database; means for analyzing the stored self-assessment data using an AI algorithm to evaluate the user's mental health status; means for generating personalized mental health care suggestions based on the evaluation; means for notifying the user of the generated suggestions; means for collecting user behavioral data based on the mental health care suggestions; means for transmitting the collected behavioral data to the server and storing it in the database; means for analyzing the stored behavioral data to evaluate progress in the user's mental health status; means for providing feedback to the user based on the progress; means for the user to actually take action in accordance with the suggestions notified to the user; and means for transmitting the behavioral data based on the suggestions to the server again. This enables efficient and personalized mental health care for the user. Specifically, the system can accurately evaluate the user's mental health status and provide appropriate care suggestions based on the evaluation. Furthermore, by evaluating progress based on the user's behavioral data and providing appropriate feedback, it is possible to support long-term mental health improvement.

[1240] A "database" is a computer-based system for organizing and storing collected data, and for retrieving and managing it as needed.

[1241] "Mental health care" refers to activities and services aimed at maintaining and improving the mental health of users.

[1242] "Self-assessment data" is psychological assessment information such as stress level, mood, and fatigue level input by the user himself / herself.

[1243] A "terminal" is an electronic device such as a smartphone, tablet, or PC that a user uses to input and transmit self-assessment data.

[1244] The "server" is a central management system that stores collected data, analyzes it using AI algorithms, and provides feedback to users.

[1245] An "AI algorithm" is a method or program that uses artificial intelligence technology to analyze data.

[1246] "Personalized suggestions" are mental health care suggestions customized based on a user's individual data and needs.

[1247] "Behavioral data" refers to data on mental health care activities actually undertaken by the user and actions suggested by the system.

[1248] "Feedback" refers to an assessment of mental health status and instructions for improvement that the system provides to the user based on the analysis results.

[1249] "Notifications" are messages or alerts sent from the system to the user's device to provide suggestions or feedback.

[1250] "Progress assessment" is the process of evaluating changes and improvements in a user's mental health status based on collected behavioral data.

[1251] That's it. These definitions clarify the meaning of key terms contained in the claims.

[1252] The present invention is a system for efficiently providing mental health care to users. This system mainly consists of a user, a terminal, and a server, and operates as follows.

[1253] Data collection

[1254] Users input self-assessment data about their mental health, such as stress levels, mood, and fatigue, on a scale of 1 to 10. This data is entered using devices such as smartphones, tablets, and PCs.

[1255] Data transmission and storage

[1256] The device collects the self-evaluation data entered by the user and transmits it in real time to a server, which stores the received data in a database that also stores past self-evaluation data, behavioral data, and feedback information.

[1257] Analyzing the data

[1258] The server analyzes the stored data using AI algorithms such as Tensorflow or Scikit-learn implemented in Python. This analysis evaluates the user's current mental health status and displays the results in numerical and graphical formats. For example, if the user's stress level is high, the server can compare it with past data to detect anomalies and generate an anomaly alert in real time.

[1259] Generate personalized suggestions

[1260] The server generates personalized mental health care suggestions based on the analysis results, such as suggesting a 30-minute stretching break or weekly online counseling if the user's stress level is high.

[1261] Proposal Notification

[1262] The server sends the generated proposal to the device, which notifies the user of the proposal via a pop-up message, email, or in-app notification.

[1263] Behavioral data collection

[1264] The user acts in accordance with the mental health care suggestions provided. For example, they may choose to take a 30-minute stretching break and participate in weekly online counseling sessions. The device collects the user's behavioral data and sends it to the server. This behavioral data is also stored in a database.

[1265] Progress evaluation and feedback

[1266] The server analyzes the collected behavioral data and evaluates the progress of the user's mental health condition. Based on the evaluation results, it generates feedback and notifies the user. For example, the feedback may say, "As a result of your recent actions, your stress level has decreased." The device then displays the progress evaluation and the feedback content to the user. This allows the user to know the degree of improvement in their mental health condition.

[1267] Specific examples

[1268] Specific examples are shown below.

[1269] User input and collection

[1270] Using the device, users rate their stress level as 8 / 10, their mood as 4 / 10, and their fatigue as 7 / 10.

[1271] The terminal collects the data entered by the user.

[1272] Sending data

[1273] The device sends the collected data to a server, which stores the data in a database.

[1274] Analyzing the data

[1275] The server analyzes the stored data using AI algorithms to detect when a user's stress level is high.

[1276] Proposal generation and notification

[1277] Based on the analysis results, the server will suggest a 30-minute stretching break, and the device will notify the user of the suggestion in a pop-up message.

[1278] Action implementation and data collection

[1279] The user follows the suggestion and takes a 30-minute stretching break. The device collects data during the break (time, amount of exercise, etc.).

[1280] Progress evaluation and feedback

[1281] The server analyzes the collected behavioral data and evaluates that the stress level is decreasing. The server generates feedback that says "your stress level has improved" and notifies the user. The device displays the feedback to the user.

[1282] Example prompts for generative AI models

[1283] "Suggest a response when a user rates their stress level as 8 / 10."

[1284] That's it. This system is designed to provide efficient and personalized mental health care for users. By utilizing generative AI models and data analysis, it is able to respond quickly and effectively to user needs.

[1285] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1286] Processing Steps

[1287] Step 1: User enters self-assessment data

[1288] Input: The user uses a device such as a smartphone or PC to input self-assessment data such as stress level (e.g., 8 / 10), mood state (e.g., 4 / 10), and fatigue (e.g., 7 / 10).

[1289] Output: The self-assessment data is saved as input to the terminal.

[1290] How it works: Users enter their evaluation data using a dedicated app or web interface, which is then stored in the device's memory in real time.

[1291] Step 2: Submit self-assessment data

[1292] Input: Self-assessment data collected in Step 1.

[1293] Output: The collected self-assessment data is sent to the server.

[1294] Specific operation: When the user presses the "Submit" button, the device sends the evaluation data to the server via the Internet. The data is sent in the form of an HTTP request.

[1295] Step 3: Save your data

[1296] Input: Self-assessment data submitted in step 2.

[1297] Output: Self-assessment data stored in a database on the server.

[1298] Specific operation: The data received on the server side is stored in a MySQL or PostgreSQL database via a PHP or Python script. This process is performed asynchronously.

[1299] Step 4: Analyze the data

[1300] Input: Self-assessment data stored in a database.

[1301] Output: User's mental health status assessment results (in numerical and graphical formats).

[1302] How it works: The server periodically retrieves data from the database and analyzes it using AI algorithms implemented in Python, such as TensorFlow and Scikit-learn, to calculate trends in stress levels and detect outliers.

[1303] Step 5: Generate personalized suggestions

[1304] Input: Analysis results from step 4.

[1305] Output: Personalized mental health care suggestions for the user.

[1306] How it works: Based on the analysis results, the server selects the most appropriate suggestion from a pre-defined list of suggestions (e.g., take a break, get online counseling, etc.) and customizes it based on the user profile.

[1307] Step 6: Proposal Notification

[1308] Input: Mental healthcare suggestions generated in step 5.

[1309] Output: The suggestion that will be displayed on the user's device as a notification.

[1310] Specific operation: The server sends the generated proposal to the user's device, and the device notifies the user of the received proposal via a pop-up message, email, or in-app notification.

[1311] Step 7: User Actions

[1312] Input: The proposal communicated in step 6.

[1313] Output: User behavior data based on suggestions.

[1314] Specific actions: The user acts according to the notified suggestion, for example, taking a 30-minute stretching break. The device collects behavioral data (time, amount of exercise, etc.) in real time.

[1315] Step 8: Sending behavioral data

[1316] Input: Behavioral data collected in step 7.

[1317] Output: The behavioral data sent to the server.

[1318] Specific operation: After accumulating a certain amount of behavioral data, the device sends it to a server via the Internet. The data is sent in the form of an HTTP request.

[1319] Step 9: Progress assessment and feedback

[1320] Input: Behavioral data submitted in step 8.

[1321] Output: Mental health status progress assessment results and feedback.

[1322] Specific operation: The server compares the collected behavioral data with past self-assessment data to evaluate progress such as reducing stress levels. Based on the evaluation results, it generates feedback such as "Your stress level has decreased as a result of your recent actions" and notifies the user. The device displays the feedback to the user.

[1323] By following the above steps, the system provides the user with efficient and personalized mental health care.

[1324] (Application example 1)

[1325] 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."

[1326] While the use of self-driving vehicles has expanded in recent years, there are concerns that stress and fatigue during long driving and travel times may affect the health of drivers and passengers. Maintaining good mental health contributes to the user's driving performance and safety, so mental health management is becoming increasingly important in self-driving vehicles as well. However, currently, there is no fully established system that monitors the mental health of drivers and passengers in self-driving vehicles in real time and provides appropriate care. In response to this, the present invention aims to provide a system that continuously monitors the mental health status of drivers and passengers in self-driving vehicles and makes personalized suggestions.

[1327] 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.

[1328] In this invention, the server includes means for collecting self-assessment data regarding mental health from the user, means for collecting physiological data and behavioral data of the user from on-board sensors, means for analyzing the collected self-assessment data and physiological data to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation, means for notifying the user of the suggestions generated using another terminal, means for collecting and analyzing the user's behavioral data to evaluate progress in the mental health status, and means for providing feedback to the user based on the progress. This makes it possible to analyze the user's mental health status in real time while in an autonomous vehicle and provide appropriate care.

[1329] The "means for collecting self-assessment data on mental health from users" refers to a function that provides an interface that allows users to input their mental health status, such as their stress level and fatigue level, as numerical values ​​and comments via a device such as a smartphone or an in-car display.

[1330] "Means of collecting user physiological data and behavioral data from in-vehicle sensors" refers to a function that uses in-vehicle sensors (e.g., heart rate monitors, driving behavior analysis cameras) to obtain data on the user's physiological indicators and behavior in real time.

[1331] "Means for analyzing collected self-assessment data and physiological data to assess the user's mental health status" refers to a function that analyzes collected data using an AI algorithm (for example, using TensorFlow or PyTorch) and evaluates the user's mental health status in the form of numbers, graphs, etc.

[1332] The "means for generating personalized mental health care suggestions based on the evaluation" is a function for generating relaxation methods and rest suggestions appropriate to the user's current mental health state based on the analysis results.

[1333] "Means for notifying the user of suggestions generated using another device" refers to a function for notifying the user of the generated mental health care suggestions in the form of a pop-up message, voice guidance, etc., via an in-vehicle display or smartphone.

[1334] "Means for collecting and analyzing user behavioral data to evaluate progress in mental health status" refers to a function that collects data on actions taken by users based on suggestions (e.g., taking a break or stretching), analyzes that data, and evaluates the degree of improvement in their mental health status.

[1335] "Means for providing feedback to the user based on progress" refers to a function for providing feedback generated based on the results of the user's actions (e.g., "Your stress level has decreased") to the user via the terminal.

[1336] The present invention provides a system for analyzing the user's mental health in real time within an autonomous vehicle and providing personalized care based on the results. A specific method for implementing this system is described below.

[1337] Data collection

[1338] First, the user inputs self-evaluation data (stress level, fatigue level, etc.) using a smartphone or in-car display. This data is recorded as numbers and comments. In addition, in-car sensors (heart rate monitor, driving behavior analysis camera, etc.) collect the user's physiological and behavioral data in real time.

[1339] Data transmission and storage

[1340] The device sends the collected data to an in-vehicle server or a cloud server, which stores the data in a database. The database also stores the user's past self-evaluation data and behavioral data, and uses it for future analysis.

[1341] Analyzing the data

[1342] The server then analyzes the stored data using AI algorithms (for example, TensorFlow or PyTorch). This analysis evaluates the user's mental health status in numerical and graphical formats. If the user's stress level indicates an abnormal value, an anomaly alert is generated in real time.

[1343] Generate personalized suggestions

[1344] The server generates personalized mental health care suggestions based on the analysis results, such as "take a break at the next service area" or "turn on the seat heater."

[1345] Proposal Notification

[1346] The generated suggestions are notified to the user via the device, using methods such as a pop-up message on the in-car display, voice guidance, or a notification on a smartphone.

[1347] Behavioral data collection

[1348] If the user performs the suggested action, the results of that action are recorded. Specifically, data such as the time taken for a break and the method of relaxation is saved.

[1349] Progress evaluation and feedback

[1350] Finally, the server analyzes the collected behavioral data and evaluates the progress of the user's mental health. Based on this, feedback is provided to the user. For example, the feedback could be, "As a result of your recent break, your stress level has decreased." This allows the user to understand the progress of their mental health.

[1351] Examples of concrete examples and prompts

[1352] Specific examples include "take a break at the next service area," "turn on the seat heater," and "listen to relaxing music." An example of a prompt for the generative AI model is "generate appropriate suggestions when the user inputs a stress level of 8 and a fatigue level of 7. For example, take a break at the next service area or listen to relaxing music."

[1353] This will enable effective management of the user's mental health in an autonomous vehicle, supporting a comfortable and safe journey.

[1354] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1355] Step 1:

[1356] The user inputs self-assessment data about their mental health (stress level, fatigue level, etc.) via a smartphone or in-car display. The input data is then sent to the terminal via the interface. The input data includes the user's subjective assessment (e.g., "stress level is 8 / 10").

[1357] Step 2:

[1358] The device continuously collects the user's physiological and behavioral data from in-vehicle sensors (heart rate monitor, driving behavior analysis camera, etc.). This data includes heart rate, breathing rate, facial expression changes, driving behavior patterns, etc. The collected data is temporarily stored on the device.

[1359] Step 3:

[1360] The device transmits the collected self-assessment data and physiological data to an in-vehicle server or a cloud server, where the data is stored in the server's database. The stored data includes various physiological data and self-assessment data.

[1361] Step 4:

[1362] The server analyzes the stored data using AI algorithms (using TensorFlow and PyTorch) to evaluate the user's mental health status. Collected physiological data and self-assessment data are used as input, and these are standardized and normalized before being fed into the AI ​​model. The output is the user's current mental health status in numerical and graphical form, and an alert is generated if an abnormal value is detected.

[1363] Step 5:

[1364] The server generates personalized mental health care suggestions based on the analysis results. For example, if the stress level is high, it generates suggestions such as "take a break at the next service area" or "turn on the seat heater." The prompt text used is "If the user inputs a stress level of 8 and a fatigue level of 7, generate appropriate suggestions. For example, take a break at the next service area or listen to relaxing music."

[1365] Step 6:

[1366] The device notifies the user of the suggestions sent from the server. Notification methods include pop-up messages on the in-car display, voice guidance, and notifications on a smartphone. This allows the user to receive mental health care suggestions from the system at the appropriate time.

[1367] Step 7:

[1368] The user acts in accordance with the notified suggestion (for example, taking a break). At this time, the user's behavioral data (such as the time and method of taking the break) is again input into the device.

[1369] Step 8:

[1370] The device sends the user's behavioral data to a server, which then analyzes the data and evaluates the user's improvement in mental health. This allows the server to generate feedback based on the user's progress (e.g., "Your stress level has decreased").

[1371] Step 9:

[1372] Feedback is sent to the user via the device, allowing them to see how their mental health has improved.

[1373] 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.

[1374] The system of the present invention is designed to efficiently and individually support the mental health care of users, and by combining an emotion engine, it provides greater precision and flexibility. The system has a user, a terminal, a server, and an emotion engine as its main components, and operates as follows.

[1375] Data collection and emotion recognition

[1376] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will recognize "fatigue" from the user's facial expression.

[1377] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[1378] Data transmission and storage

[1379] The device sends the collected self-evaluation data and emotional data to a server, where it is stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[1380] Analyzing the data

[1381] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user, which is displayed in numerical and graphical format.

[1382] Generate personalized suggestions

[1383] The server generates personalized mental health care suggestions based on the analysis results. By taking the user's emotional state into account, the realism and appropriateness of the suggestions are improved. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," the server recommends a "light walk."

[1384] Proposal Notification

[1385] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be sent in a gentle tone.

[1386] Behavioral data collection

[1387] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[1388] The device collects and transmits the user's behavioral and emotional data to the server. This behavioral data includes information such as break times, counseling participation history, and emotional state during the activity.

[1389] Progress evaluation and feedback

[1390] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health status. For example, it evaluates the fluctuation of stress levels after a stretching break and the "feeling of relaxation." Based on the evaluation results, it generates feedback and notifies the user.

[1391] The device will provide feedback to the user, allowing them to see their mental health progress, for example, "As a result of your break, your stress levels have decreased and you feel more relaxed."

[1392] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users, making them, their surroundings, and their company happy. By combining it with an emotion engine, it is possible to provide even more accurate mental health care.

[1393] The processing flow will be explained below.

[1394] Step 1:

[1395] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated, analyzing the user's facial expressions, tone of voice, and text input in real time. For example, if a user answers that their stress level is "7," the emotion engine will recognize "fatigue" from their facial expression.

[1396] Step 2:

[1397] The device collects self-evaluation data entered by the user and emotional data recognized by the emotion engine, including metadata such as the date and time of the response and the user's emotional state.

[1398] Step 3:

[1399] The device sends the collected self-evaluation data and emotion data to the server. The sent data is transmitted using a communication method such as an HTTP request.

[1400] Step 4:

[1401] The server stores the received self-evaluation and emotion data in a database that also stores past survey results and other related data.

[1402] Step 5:

[1403] The server analyzes the stored self-evaluation data and emotional data using an AI algorithm. This analysis evaluates the user's mental health status. For example, a mental health score is generated based on a stress level of 7 and a "feeling of fatigue."

[1404] Step 6:

[1405] The server generates personalized mental health care recommendations based on the analysis results, taking into account the user's emotional state. For example, if the user is feeling fatigued due to high stress levels, the server will suggest a 30-minute stretching break, as well as recommend a light walk.

[1406] Step 7:

[1407] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted according to the user's emotional state. For example, if the user is in a high-stress state, the notification will be sent in a gentle tone.

[1408] Step 8:

[1409] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. The emotion engine also recognizes and records the user's emotions in real time during the action.

[1410] Step 9:

[1411] The device collects user behavioral and emotional data, including break times, counseling participation history, and emotional states during activities.

[1412] Step 10:

[1413] The device sends the collected behavioral and emotional data to the server, also using a communication method such as an HTTP request.

[1414] Step 11:

[1415] The server stores the behavioral data and emotion data in a database, accumulating them together with past data.

[1416] Step 12:

[1417] The server analyzes the collected behavioral and emotional data to assess the user's mental health progress, for example, assessing stress level fluctuations and "feeling relaxed" after a stretching break.

[1418] Step 13:

[1419] The server generates feedback based on the analysis results, for example, "As a result of the break, your stress level has decreased and you feel more relaxed."

[1420] Step 14:

[1421] The server transmits the generated feedback to the terminal, which provides the feedback as needed.

[1422] Step 15:

[1423] The device provides feedback to the user, allowing them to see the progress of their mental health status, so they can understand how their own behavior has affected their mental health.

[1424] Through the above processing steps, this system will more effectively support the user's mental health care. By combining it with an emotion engine, more precise care and feedback can be provided, making the user, those around them, and the company happier.

[1425] Example 2

[1426] 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."

[1427] Conventional mental health care systems rely solely on users' self-assessment data and are unable to grasp changes in users' emotions and behaviors in real time. This results in low accuracy and effectiveness of the care provided, making it difficult to make personalized suggestions suited to individual needs. Collecting user behavioral data to evaluate the progress of mental health status and provide appropriate feedback has also been a challenge.

[1428] 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 means for analyzing the user's facial expressions, tone of voice, and text input in real time to generate emotional data, means for storing the collected self-evaluation data and emotional data in a database, and means for analyzing the stored data based on an AI algorithm and evaluating the user's mental health state. This makes it possible to grasp changes in the user's emotions and behavior in real time. In addition, the collected data can be used to personalize the user's mental health care and provide more accurate suggestions and feedback.

[1429] "User" refers to an individual who uses the system to receive mental health care services.

[1430] "Mental health" refers to mental and emotional well-being and is a concept that includes stress, fatigue, anxiety, etc.

[1431] "Self-assessment data" refers to information provided by users when they self-assess their mental health status. Specifically, this data is expressed as numerical values ​​or text, such as stress levels and emotional states.

[1432] "Emotion data" refers to information about emotions that the emotion engine analyzes and extracts from the user's facial expressions, tone of voice, text input, etc.

[1433] An "emotion engine" refers to software or hardware that analyzes input data such as a user's facial expressions, voice, and text, and recognizes their emotional state.

[1434] "Server" refers to the computer system within the system that is responsible for storing and analyzing data, generating and distributing mental health care suggestions, etc.

[1435] A "database" refers to a system that structures, stores, and manages self-evaluation data, emotional data, behavioral data, and other data collected from users.

[1436] "AI Algorithms" refers to the artificial intelligence technologies and computational methods used to analyze a user's self-assessment data and emotional data, assess their mental health status, and generate recommendations.

[1437] "Personalized mental health care suggestions" refer to mental health care suggestions generated based on individual user data and optimized for the user's specific needs and condition.

[1438] "Behavioral data" refers to data on the user's behavior when implementing the suggestions and the emotional state recognized during the behavior. Specifically, it includes the length of breaks, counseling participation history, and the emotional state during the behavior.

[1439] "Progress assessment" refers to the process of analyzing and evaluating changes and improvements in a user's mental health status based on behavioral and emotional data.

[1440] "Feedback" refers to the evaluation, areas for improvement, and information on next actions provided to the user based on the progress evaluation results.

[1441] The system of the present invention is designed to efficiently and individually support users' mental health care, and by combining it with an emotion engine, it provides greater precision and flexibility. The system operates with a user, a terminal, a server, and an emotion engine as its main components.

[1442] Data collection and emotion recognition

[1443] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. For example, if the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10," the emotion engine will detect a "feeling of fatigue" from the user's facial expression.

[1444] The device collects the user's self-evaluation data and emotion data recognized by the emotion engine, including the question content, answer, analyzed emotion, and answer date and time.

[1445] Data transmission and storage

[1446] The device sends the collected self-evaluation data and emotion data to the server using HTTPS as the communication protocol.

[1447] The server stores the received data in a database, which also stores past self-evaluation data, behavioral data, emotional data, and feedback information. The database is typically a relational database such as MySQL or PostgreSQL.

[1448] Analyzing the data

[1449] The server analyzes the stored data using an AI algorithm, which evaluates the user's current mental health state. For example, if a user answers "stress level 7" and "feeling tired" is detected, the AI ​​algorithm calculates the user's mental health score and displays it in the user interface in numerical and graphical form.

[1450] Proposal Generation

[1451] The server generates personalized mental health care suggestions based on the analysis results. For example, it suggests a "30-minute stretching break" and recommends a "light walk" if the user feels "fatigue." This improves the realism and appropriateness of the suggestions.

[1452] Proposal Notification

[1453] The server sends the generated suggestions to the device, which then notifies the user of the suggestions. The notification method is adjusted based on the user's emotional state. For example, in-app notifications and push notifications are used, and gentle messages are sent to users in high stress states.

[1454] Behavioral data collection

[1455] The user receives a notification from the device and takes the suggested action, such as taking a 30-minute stretching break or participating in weekly online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time.

[1456] The device collects the user's behavioral and emotional data and sends it back to the server. The collected data includes the length of breaks, counseling participation history, and emotional state during the activity.

[1457] Progress evaluation and feedback

[1458] The server analyzes the collected behavioral and emotional data to evaluate the user's mental health progress. For example, it evaluates stress levels and "feeling relaxed" after a stretching break. Based on the evaluation results, feedback is generated and notified to the user.

[1459] The device provides feedback to the user, allowing them to see the progress of their mental health. A specific feedback message provided is, "As a result of your break, your stress level has decreased and you feel more relaxed."

[1460] Specific examples and prompts for the generative AI model

[1461] Specific examples

[1462] 1. The user launches the application and answers "7" to the question, "How would you rate your stress level over the past week on a scale of 1 to 10?"

[1463] 2. The emotion engine detects "fatigue" from the user's facial expression.

[1464] 3. The device sends the collected data to the server.

[1465] 4. The server analyzes the data and generates the user's mental health score.

[1466] 5. The server suggests a "30-minute stretching break" and recommends a "light walk."

[1467] 6. The device notifies the user of this offer.

[1468] 7. The user performs the suggested action.

[1469] 8. The device collects behavioral and emotional data and sends it to the server.

[1470] 9. The server generates the evaluation results and feedback and notifies the user.

[1471] 10. Your device will notify you that "As a result of your break, your stress levels have decreased and you feel more relaxed."

[1472] Prompt Sentence Examples

[1473] Specific questions such as "How would you rate your stress level over the past week on a scale of 1 to 10?" can be fed into the generative AI model to generate optimal feedback and suggestions.

[1474] As described above, the system of the present invention can efficiently support the user's mental health care and provide personalized care according to individual needs.

[1475] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1476] Step 1: Collecting data from users

[1477] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. The input includes the user's answers to each item in the questionnaire. At this time, the emotion engine analyzes the user's facial expressions, tone of voice, and text input in real time. A specific example is when the user answers "7" to the question, "Please rate your stress level over the past week on a scale of 1 to 10." The data output is the user's self-assessment data and emotion data recognized by the emotion engine (e.g., "fatigue").

[1478] Step 2: Send and store data

[1479] The device sends the collected user self-evaluation data and emotional data to the server. The input includes the user's answers and analyzed emotional data. The specific communication method used is the HTTPS protocol. The server stores the received data in a database. The data output is the self-evaluation data and emotional data stored in the database.

[1480] Step 3: Analyze the data

[1481] The server uses an AI algorithm to analyze the stored user self-evaluation data and emotional data. The input includes multiple user data stored in the database. Specific data processing and calculation involves the AI ​​algorithm evaluating the user's stress level and emotional state and calculating a mental health score. The analysis results are generated in numerical and graphical format and displayed on the user interface.

[1482] Step 4: Generate proposals

[1483] The server generates personalized mental health care suggestions based on the analysis results. The inputs include the analyzed mental health score and the user's emotional state. As specific data processing and calculation, the suggestion generation algorithm selects the optimal action for the user. For example, a user with a high stress level and feeling "fatigue" might be recommended a "30-minute stretching break" or a "light walk." The output is the generated suggestion.

[1484] Step 5: Proposal Notification

[1485] The server sends the generated suggestions to the device. The input includes the suggestion content. The device notifies the user of the suggestion. Specific actions include in-app notifications and push notifications. The tone and method of the notification are adjusted based on the user's emotional state. The output is the user receiving the suggestion.

[1486] Step 6: Collect behavioral data

[1487] The user receives a notification from the device and performs the suggested action, such as taking a 30-minute stretching break or participating in online counseling. During this time, the emotion engine recognizes and records the user's emotions in real time. The input includes the user's behavior and emotion data. The output is the recorded behavioral data and emotion data.

[1488] Step 7: Progress assessment and feedback

[1489] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The input includes behavioral and emotional data. Specific data processing and calculations involve an AI algorithm evaluating the progress and generating feedback based on the results. For example, changes in stress levels and improvements in "relaxation" after a stretching break are evaluated. The output is a generated feedback message that is notified to the user.

[1490] As a result, the system of the present invention can respond quickly and effectively to the individual needs of users and provide highly accurate mental health care.

[1491] (Application example 2)

[1492] 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."

[1493] There is a lack of systems for efficiently and individually managing and supporting employees' mental health status. Conventional systems have difficulty collecting and analyzing users' mental health data in real time, which has led to issues with the accuracy and effectiveness of providing personalized suggestions. There is also a need for systems that provide appropriate feedback based on employees' emotional state.

[1494] 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.

[1495] In this invention, the server includes means for collecting self-assessment data on mental health from users, means for analyzing the collected self-assessment data and emotional data including facial expressions and tone of voice analyzed in real time to evaluate the user's mental health status, means for generating personalized mental health care suggestions based on the evaluation and notifying the user of the suggestions, means for generating suggested messages according to individual needs using a generative AI model, means for collecting and analyzing the user's behavioral data and their emotional states during that time to evaluate the progress of the user's mental health status, and means for providing feedback to the user based on the progress. This makes it possible to grasp the mental health status of employees in real time and provide personalized suggestions.

[1496] "User" refers to a person who uses the system to manage their mental health.

[1497] "Mental health" refers to the state of mental health and evaluates emotions and psychological states, including stress, fatigue, and relaxation.

[1498] "Self-assessment data" refers to data entered by a user after assessing their own mental health status.

[1499] "Emotional Data" refers to emotional data, including a user's facial expressions, tone of voice, and text input, analyzed in real time.

[1500] "Personalized suggestions" refers to specific improvement suggestions generated based on each user's individual mental health status.

[1501] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze user data and generate optimal suggested messages.

[1502] "Behavioral data" refers to data regarding the actions taken by the user based on mental health care suggestions.

[1503] "Feedback" refers to information, including progress assessment, obtained as a result of actions taken by a user.

[1504] "Server" refers to a central computer system for storing data collected from users in a database, performing analysis, and generating suggestions and feedback.

[1505] "Notification means" refers to a mechanism for communicating generated suggestions to a user, including devices such as smartphones or smart glasses.

[1506] The present invention is to build a system for mental health care, which consists of a user, a terminal, a server, and an emotion engine as its main components.

[1507] Data collection and emotion recognition

[1508] The user launches the application on their device and answers a self-assessment questionnaire about their mental health. During this process, an emotion engine is activated that analyzes the user's facial expressions, tone of voice, and text input in real time. As a specific example, when answering the question, "Please rate your stress level today on a scale of 1 to 10," the engine recognizes "fatigue" from the user's facial expression.

[1509] Data transmission and storage

[1510] The device collects self-evaluation data and emotional data entered by the user. This data includes metadata such as the date and time of the response and the user's emotional state. The collected data is sent to a server and stored in a database. This database also stores past self-evaluation data, behavioral data, emotional data, and feedback information.

[1511] Analyzing the data

[1512] The server analyzes the stored data using an AI algorithm. This analysis evaluates the user's current mental health state. For example, based on a stress level of 7 and a "feeling of fatigue," the server generates a mental health score for the user and displays it in numerical and graphical format.

[1513] Generate personalized suggestions

[1514] The server generates personalized mental health care suggestions based on the analysis results. Using a generative AI model, it generates specific suggestion messages that take the user's emotional state into account. For example, in addition to suggesting a "30-minute stretching break," if the user feels "fatigue," it recommends a "light walk."

[1515] Proposal Notification

[1516] The server sends the generated suggestions to the device, which then notifies the user. The notification method is adjusted according to the user's emotional state. For example, if the user is "high stress," the notification will be delivered in a gentle tone.

[1517] Behavioral data collection

[1518] The user receives a notification from the device and performs the suggested action, such as "take a 30-minute stretching break." The emotion engine also recognizes and records the user's emotions during the action in real time. The device collects the user's behavioral and emotional data and sends it to the server. The behavioral data includes the break time and the emotional state during the action.

[1519] Progress evaluation and feedback

[1520] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health condition. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break. It generates feedback based on the evaluation results and notifies the user. The device notifies the user of the feedback, allowing the user to check the progress of their own mental health condition. As a specific example, the server may provide feedback such as "As a result of the break, your stress level has decreased and your sense of relaxation has improved."

[1521] Prompt Sentence Examples

[1522] "Your stress levels seem high today. Why not try some deep breathing or yoga? I suggest you take a 10-minute break."

[1523] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1524] Step 1:

[1525] Users launch the application on their device and answer a self-assessment questionnaire about their mental health. The user enters their answers into the questionnaire using text, voice, and facial expressions. The output is collected self-assessment data and real-time analyzed emotion data. The emotion engine then analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[1526] Step 2:

[1527] The device sends the collected self-evaluation data and emotional data to the server. The input requires the data the user answers on the device. The output is the data sent to the server, including metadata such as the date and time of the answer and the user's emotional state.

[1528] Step 3:

[1529] The server stores the transmitted data in a database. The inputs required are the self-evaluation data and emotion data transmitted from the device. The output is user data stored in the database. The stored data includes past self-evaluation data, behavioral data, emotion data, feedback information, etc.

[1530] Step 4:

[1531] The server analyzes the stored data using an AI algorithm. The inputs required are the user data stored in the database and the AI ​​algorithm. The output is a mental health assessment and score as the analysis result. This evaluates the user's current mental health state.

[1532] Step 5:

[1533] The server generates personalized mental health care suggestions based on the analysis results. The inputs required are the analysis results and a generative AI model. The output is a specific generated suggestion message that takes into account the user's emotional state. For example, suggestions such as "take a 30-minute stretching break" or "take a light walk" are generated.

[1534] Step 6:

[1535] The server sends the generated suggestion to the terminal, and the terminal notifies the user. As input, the generated suggestion message is required. As output, the suggestion to be notified to the user is obtained. For example, if the user is "high stress", a message in a gentle tone is notified.

[1536] Step 7:

[1537] The user receives a notification from the device and takes the suggested action. The input required is the notified suggestion. The output is the user's behavioral data. For example, taking a "30-minute stretching break" or going for a "light walk."

[1538] Step 8:

[1539] The device also recognizes and records the user's emotions in real time during their actions. The inputs are the user's behavioral data and an emotion engine. The output is emotional data during the action. This emotional data is then used for later analysis.

[1540] Step 9:

[1541] The device transmits the user's behavioral data and emotional data to the server. Recorded behavioral data and emotional data are required as input. Data is output to the server. This behavioral data includes rest periods, emotional states during activities, etc.

[1542] Step 10:

[1543] The server analyzes the collected behavioral and emotional data to evaluate the progress of the user's mental health. The inputs are the user's behavioral data and an AI algorithm. The output is a progress assessment result. For example, it evaluates the fluctuation of stress levels and the "sense of relaxation" after a stretching break.

[1544] Step 11:

[1545] The server generates feedback based on the evaluation results and notifies the user. As input, the progress evaluation results and the generated feedback message are required. As output, the server obtains feedback to be notified to the user. For example, the server may provide feedback such as "As a result of taking a break, your stress level has decreased and your sense of relaxation has improved."

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] FIG. 9 illustrates 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 behaviors 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.

[1551] 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.

[1552] 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).

[1553] 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.

[1554] 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."

[1555] 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.

[1556] 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).

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] The following is further disclosed regarding the above embodiment.

[1568] (Claim 1)

[1569] means for collecting self-assessed mental health data from a user;

[1570] a means for analyzing the collected self-assessment data to assess the user's mental health status;

[1571] means for generating personalized mental health care recommendations based on the assessment;

[1572] means for notifying the user of the generated suggestions;

[1573] a means for collecting and analyzing user behavioral data to assess progress in mental health status;

[1574] The system includes a means for providing feedback to the user based on progress.

[1575] (Claim 2)

[1576] 10. The system of claim 1, further comprising means for storing data collected from users in a database.

[1577] (Claim 3)

[1578] 10. The system of claim 1, further comprising means for analyzing the user's self-assessment data using an AI algorithm.

[1579] "Example 1"

[1580] (Claim 1)

[1581] means for collecting self-assessed mental health data from a user;

[1582] means for transmitting the collected self-assessment data to a server via the terminal;

[1583] a means for storing the collected self-assessment data in a database;

[1584] A means for analyzing the stored self-assessment data using an AI algorithm to assess the user's mental health status;

[1585] means for generating personalized mental health care recommendations based on the assessment;

[1586] means for notifying the user of the generated suggestions;

[1587] A means for collecting user behavior data based on mental health care suggestions;

[1588] means for transmitting the collected behavioral data to a server and storing it in a database;

[1589] a means for analyzing the stored behavioral data and assessing progress in mental health status;

[1590] means for providing feedback to the user based on progress;

[1591] A means for actually taking action according to the suggestions notified to the user;

[1592] The system includes means for transmitting behavioral data based on the suggestions back to the server.

[1593] (Claim 2)

[1594] 2. The system of claim 1, wherein the data collected from the user is stored in a database and an AI algorithm is used to generate the analysis results as personalized mental health care suggestions.

[1595] (Claim 3)

[1596] The system according to claim 1, wherein the system automatically provides feedback based on the results of collecting, analyzing, and evaluating user behavior data based on mental health care suggestions.

[1597] That's it. We have incorporated the new technology in accordance with the scope of the patent claims.

[1598] "Application Example 1"

[1599] Claims

[1600] (Claim 1)

[1601] means for collecting self-assessed mental health data from a user;

[1602] A means of collecting user physiological and behavioral data from in-vehicle sensors;

[1603] means for analyzing the collected self-assessment data and physiological data to assess the user's mental health status;

[1604] means for generating personalized mental health care recommendations based on the assessment;

[1605] means for notifying a user of a proposal generated using another terminal;

[1606] a means for collecting and analyzing user behavioral data to assess progress in mental health status;

[1607] A means of providing feedback to users based on their progress

[1608] A system including:

[1609] (Claim 2)

[1610] 10. The system of claim 1, further comprising means for storing data collected from users in a database.

[1611] (Claim 3)

[1612] 10. The system of claim 1, further comprising means for analyzing the user's self-assessment data and physiological data using an AI algorithm.

[1613] "Example 2: Combining Emotion Engines"

[1614] (Claim 1)

[1615] means for collecting self-assessed mental health data from a user;

[1616] A means of generating emotion data by analyzing the user's facial expressions, tone of voice, and text input in real time;

[1617] means for transmitting the collected self-evaluation data and emotion data to a server;

[1618] means for storing the collected self-evaluation data and emotion data in a database;

[1619] A means for analyzing the stored data based on an AI algorithm to assess the user's mental health status;

[1620] a means for generating personalized mental health care recommendations based on the assessment; and

[1621] means for notifying the user of the generated suggestions;

[1622] a means for collecting and analyzing user behavioral data to assess progress in mental health status;

[1623] The system includes a means for providing feedback to the user based on progress.

[1624] (Claim 2)

[1625] 10. The system of claim 1, further comprising means for recognizing and recording emotional data in real time when a user performs an action based on the suggestion.

[1626] (Claim 3)

[1627] 2. The system of claim 1, further comprising means for generating a mental health score for the user based on the analyzed data and displaying it in numerical or graphical form.

[1628] "Application example 2 when combining emotion engines"

[1629] (Claim 1)

[1630] means for collecting self-assessed mental health data from a user;

[1631] a means for analyzing the collected self-assessment data and emotion data including facial expressions and tone of voice analyzed in real time to assess the user's mental health state;

[1632] means for generating personalized mental health care recommendations based on the assessment and communicating the recommendations to the user;

[1633] A means for generating suggested messages according to individual needs using a generative AI model;

[1634] A means for collecting and analyzing the user's behavioral data and their emotional state during that time to assess the progress of their mental health condition;

[1635] The system includes a means for providing feedback to the user based on progress.

[1636] (Claim 2)

[1637] 10. The system of claim 1, further comprising means for storing data collected from users in a database.

[1638] (Claim 3)

[1639] 10. The system of claim 1, further comprising means for analyzing the user's self-evaluation data and emotion data using an AI algorithm. [Explanation of symbols]

[1640] 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. means for collecting self-assessed mental health data from a user; a means for analyzing the collected self-assessment data to assess the user's mental health status; means for generating personalized mental health care recommendations based on the assessment; means for notifying the user of the generated suggestions; a means for collecting and analyzing user behavioral data to assess progress in mental health status; The system includes a means for providing feedback to the user based on progress.

2. 10. The system of claim 1, further comprising means for storing data collected from users in a database.

3. The system of claim 1, further comprising means for analyzing the user's self-assessment data using an AI algorithm.

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