system

Abstract

We provide the system. [Solution] Means of collecting audio data, A means for analyzing the audio data and extracting emotional states, A means for generating a self-care plan based on the said emotional state, Means for providing the self-care plan to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern workplace environment, workers are under excessive stress, and mental health problems are leading to a decline in corporate productivity and an increase in the turnover rate. In the prior art, it has been difficult to detect mental health problems early and provide individualized appropriate care. Therefore, there is a need to provide a system for effectively managing workers' mental health and preventing mental disorders.

Means for Solving the Problems

[0005] This invention provides a means for collecting voice data and analyzing that data to extract emotional states. Furthermore, it constructs a system that effectively provides mental health care by generating and providing individually tailored self-care plans to users based on those emotional states. This system continuously and individually supports the user's mental health through AI-powered voice analysis and optimized plan generation.

[0006] "Audio data" refers to information recorded in digital format from the words and sounds a user makes.

[0007] "Analysis" refers to the process of identifying features and patterns from collected audio data and extracting information such as emotional states.

[0008] "Emotional state" refers to information obtained through voice analysis that indicates the user's current mental or emotional state.

[0009] A "self-care plan" is a plan that includes specific action guidelines and suggestions for improving or maintaining a user's mental health, based on their analyzed emotional state.

[0010] A "user" is someone who uses this system to provide voice data and receive a self-care plan. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to a mental health management system that uses user voice data. When a user launches a dedicated application on their device and inputs voice data, the device sends that data to a server. The server uses a voice analysis engine to obtain the user's emotional state from the voice data. This emotional state is quantified based on information such as voice tone and speed. The obtained emotional state is recorded in a database and analyzed by an AI engine. Based on the recorded data, the AI ​​engine generates a self-care plan optimized for each user's individual mental state. This self-care plan includes AI counseling and training menus and is provided to the user via the device. By using this and carrying out the suggested activities and training, the user can improve or maintain their mental health.

[0033] As a concrete example, when a user feels mentally exhausted after work, they can use this system to understand their emotional state. For instance, if the analysis of voice data diagnoses a high stress level, the server generates a self-care plan that includes relaxation exercises and short meditation sessions. This plan is presented to the user via a terminal, and the user can perform these self-care activities according to the instructions. In this way, the present invention helps maintain and manage the user's mental health.

[0034] The following describes the processing flow.

[0035] Step 1:

[0036] The user launches a dedicated app on their device and begins the stress check. The device then presents the user with voice-based questions to assess their mental state.

[0037] Step 2:

[0038] When a user verbally answers a question presented to them, the device records the audio in real time. This recorded data is then sent directly to the server.

[0039] Step 3:

[0040] The server inputs the received audio data into the speech analysis engine and begins analyzing the data. The speech analysis engine identifies the user's emotional state from the audio data and quantifies the emotional state based on factors such as tone of voice, speed, and pauses.

[0041] Step 4:

[0042] The server saves the analysis results to a database and retains information about the emotional state.

[0043] Step 5:

[0044] The server passes stored emotional state data to an AI engine, which generates a self-care plan optimized for the user's current mental state. This plan is then compared with the user's past data to suggest specific and effective activities and training.

[0045] Step 6:

[0046] The server generates a self-care plan and sends it to the device. The device displays this plan to the user and provides details of the self-care activities to be undertaken.

[0047] Step 7:

[0048] The user reviews the presented self-care plan and selects the activities they wish to perform. Based on the user's selection, the device plays guided training or meditation exercises and provides specific instructions.

[0049] (Example 1)

[0050] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0051] In modern society, despite the importance of mental health management, there is a lack of adequate systems in place for users to understand their own mental state on a daily basis and take appropriate action. As a result, users unconsciously accumulate mental burdens, which can lead to more serious health problems. Therefore, there is a need for methods that allow users to easily and accurately understand their own emotional state and immediately take appropriate self-care measures.

[0052] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0053] In this invention, the server includes means for receiving voice information using a communication device, means for analyzing the voice information using a voice analysis mechanism to quantify the emotional state, and means for analyzing information stored in a recording device containing past emotional data using the analysis mechanism to generate self-care guidance based on the emotional state. This enables users to quickly understand their own emotional state and receive individually optimized self-care.

[0054] A "communication device" is a hardware or software system for sending and receiving data, and in this system, it plays the role of transmitting voice information to the server.

[0055] "Voice information" refers to voice data that a user inputs into the system, and is data in analog or digital format obtained based on the user's speech.

[0056] A "voice analysis mechanism" is a general term for algorithms and software that analyze voice information, extract features such as tone and speed of voice, and quantify emotional states.

[0057] "Emotional state" refers to data that indicates the user's emotional state and tendencies, derived from the characteristics of their voice by a voice analysis mechanism.

[0058] A "recording device" is a hardware or software system that stores collected data and analysis results for the long term and makes the data available for retrieval as needed.

[0059] An "analysis mechanism" is a general term for algorithms and software that analyze user trends using data stored in a recording device and generate appropriate advice and guidance based on the results obtained.

[0060] "Self-care guidance" refers to suggestions for maintaining or improving mental health, provided to users based on their analyzed emotional state.

[0061] A "user terminal" is a device used to provide self-care guidance to users, and includes smartphones, tablets, and other similar devices.

[0062] This mental health management system primarily consists of a terminal with a dedicated application installed for user voice input, and a server connected to it. Users launch the application on a device such as a smartphone or tablet and input voice information via the microphone. The terminal is equipped with a communication device to quickly transmit this voice information to the server.

[0063] The server analyzes the received audio information using an audio analysis mechanism. This analysis mechanism consists of software algorithms that convert the audio information into a digital format and extract features such as voice tone and speed. For example, the user's stress and tension levels can be quantified through audio frequency analysis. This quantified emotional state is stored in a recording device and further analyzed using a generative AI model.

[0064] By using generative AI models, such as natural language processing models, the server comprehensively evaluates user data, including past records, to generate optimal self-care guidance for the user. This guidance includes relaxation methods and activities tailored to the user's current situation. The guidance thus generated is presented via the user's terminal. The terminal can also provide guides for performing the suggested activities, as well as links to applications and websites as needed.

[0065] As a concrete example, suppose a user uses the application to voice-input "I'm tired today" after work. The system analyzes this utterance and determines that the user has a "high stress level." The server then generates self-care instructions, including relaxation exercises and short meditation sessions, and sends them to the user's terminal. The user can then follow the instructions displayed on the terminal to reduce stress. A suitable example of a prompt would be, "Please evaluate the emotional state of this voice and tell me what self-care is needed."

[0066] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0067] Step 1:

[0068] The user launches a dedicated application on their device and inputs voice information. The input voice is converted into a digital signal through the device's microphone. This digital voice data is then ready to be sent to the next step. Specifically, voice input begins when the user speaks on the topic of "today's meeting."

[0069] Step 2:

[0070] The terminal transmits digitized voice data to the server using a secure communication protocol. The input voice data is transferred from the terminal to the server, and the server receives the voice data. During this process, encryption is applied to maintain data integrity and security.

[0071] Step 3:

[0072] The server passes the received audio data to the speech analysis mechanism, which then begins the analysis. The speech analysis mechanism converts the audio data into text and further measures and analyzes the tone, speed, volume, and other aspects of the voice. For example, it analyzes the frequency characteristics of the most recent audio and outputs the user's emotional state as a numerical value. The output value might take the form of "Tension level: 60%, Fatigue level: 30%, Relaxation level: 10%."

[0073] Step 4:

[0074] The server stores quantified emotional data in a recording device and simultaneously analyzes this data using a generative AI model. During the analysis, comparisons are made with past data to understand the user's mental state and tendencies. As a result of this analysis, personalized self-care guidance is generated. This guidance includes practices such as meditation and breathing exercises.

[0075] Step 5:

[0076] The server sends the generated self-care instructions to the terminal and provides them to the user. The terminal displays the received self-care instructions on the application screen. The user can then perform the suggested self-care activities by following the instructions displayed on the terminal. Specifically, the user receives the instruction to "perform a 10-minute guided meditation" and begins the activity.

[0077] (Application Example 1)

[0078] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0079] In modern society, stress and fatigue tend to accumulate easily, and users are required to choose appropriate meals that suit their mental state. However, it is difficult for individuals to understand their own emotional state and make meal choices based on it in their daily lives. In this situation, there is a need for a system that can quickly analyze a user's emotional state and make effective meal recommendations based on the results.

[0080] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0081] In this invention, the server includes a device for collecting voice information, a device for analyzing the voice information to identify an emotional state, and a device for creating a meal recommendation plan based on the emotional state. This makes it possible for users to easily and quickly select a meal that is appropriate for their emotional state.

[0082] "Voice information" refers to data collected from the user's voice and converted into a format that can be processed within an electronic device.

[0083] "Emotional state" is a numerical representation of the user's psychological state and mood derived from analyzed audio information.

[0084] A "meal recommendation plan" is a plan or suggestion for presenting appropriate meal menus based on the user's emotional state.

[0085] A "collection device" is a device that acquires audio information and provides it to a system for processing and analysis.

[0086] An "analytical device" is a device that analyzes collected audio information and identifies the user's emotional state from it.

[0087] The "creation device" is a device that has the function of generating meal recommendation plans tailored to specific emotional states.

[0088] The "device provided" refers to a device that presents the generated meal recommendation plan to the user and supports them in making choices and implementing them.

[0089] The following system configuration and its operation will be described as embodiments for carrying out the invention.

[0090] This system implements a series of processes to collect voice information, analyze emotional states, and provide meal recommendation plans. First, the terminal collects the user's voice information using a voice input device. Next, the voice information is transmitted to the server via the internet. The server uses a voice analysis engine to identify the user's emotional state from the voice information.

[0091] For emotion analysis, natural language processing techniques and machine learning algorithms are often used. This allows the emotional state to be quantified based on characteristics such as the tone, speed, and intensity of the user's voice.

[0092] The server then uses a generative AI model to create an appropriate meal recommendation plan based on the analyzed emotional state. This plan includes menus optimized for that emotional state. For example, if the user is feeling stressed, a menu using ingredients with relaxation effects will be suggested.

[0093] The generated meal recommendation plan is sent to the device and presented to the user as specific options. The user can then order a meal based on this plan. This system allows users to quickly select the optimal meal that best suits their emotional state.

[0094] For example, if a user inputs the voice message "I'm very tired today," the system can suggest a relaxing meal such as "Recommended menu: Chicken soup, lavender tea." An example of such a prompt would be, "I'm very tired today. Please tell me what I can eat to relax."

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The terminal collects user voice information using a voice input device. The collected voice information is converted into digital data format. This data is transmitted to a server via the internet. The input is the user's voice, and the output is digitized voice data.

[0098] Step 2:

[0099] The server uses a speech analysis engine to analyze the received speech data. This analysis extracts features such as tone, speed, and intensity, and uses machine learning algorithms to identify the emotional state. The input is digitized speech data, and the output is numerical data representing the detected emotional state.

[0100] Step 3:

[0101] The server uses a generative AI model to create meal recommendation plans based on quantified emotional states. The generative AI model refers to a pre-trained database of ingredients and menus to suggest menus that are appropriate for the user's emotional state. The input is quantified data of emotional states, and the output is a specific meal recommendation plan.

[0102] Step 4:

[0103] The server sends the created meal recommendation plan to the terminal. The terminal displays this plan to the user, who can then select from the suggested menu items. The input is the meal recommendation plan, and the output is the user's selection.

[0104] Step 5:

[0105] The user places an order for food based on the displayed menu. Based on the user's selection, the terminal sends the order information to the relevant service. The input is the user's selection information, and the output is the confirmed order information.

[0106] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0107] This invention relates to a mental health management system that uses voice data collected from users to recognize their emotional state and provide a self-care plan. The user launches a dedicated app on their device and starts a stress check. The device presents questions to understand the user's emotional state through voice input. As the user responds, the device collects voice data and transmits it to a server. The server passes the voice data to an emotion engine for analysis. This emotion engine recognizes the user's emotions (e.g., stress, relief, excitement tendencies, etc.) and quantifies the results.

[0108] Based on the results of this emotion recognition, the server uses an AI engine to generate a customized self-care plan. This plan includes AI-led counseling and self-care activities optimized for the user's recognized emotional state. For example, if the user's emotions are determined to be stressful, a plan will be generated that includes relaxation exercises such as meditation, breathing exercises, and autogenic training.

[0109] The generated self-care plan is sent from the server to the terminal and provided to the user. The user accesses this plan through the terminal and implements the recommended self-care methods. In addition, the system sends alerts and additional notifications to the user regarding the persistence of certain emotional states and suggests further sessions, as needed. For example, if very high stress levels persist, this may include a suggestion for consultation with an additional professional.

[0110] In this way, this system utilizes voice data to accurately recognize the user's emotional state and provides personalized self-care plans based on that recognition, thereby enabling efficient mental health management.

[0111] The following describes the processing flow.

[0112] Step 1:

[0113] The user launches the dedicated app on their device and selects the "Start Stress Check" button. The device prompts the user to speak and presents questions.

[0114] Step 2:

[0115] While the user verbally answers the presented questions, the device records their voice. The recorded audio data is then sent directly to the server.

[0116] Step 3:

[0117] The server inputs the received audio data into the emotion engine. The emotion engine analyzes the audio data and extracts the user's emotional state. This process analyzes factors such as voice tone, speed, volume, and rhythm, and quantifies emotional indicators.

[0118] Step 4:

[0119] The server receives the analysis results from the emotion engine and saves the emotional state to a database. This information is later used to create a self-care plan.

[0120] Step 5:

[0121] The server uses an AI engine to generate a self-care plan tailored to the user based on their emotional state. This plan includes relaxation exercises, AI counseling, and daily stress management techniques.

[0122] Step 6:

[0123] The server generates a self-care plan and sends it to the device. The device displays the plan to the user and provides detailed instructions on the next steps and activities.

[0124] Step 7:

[0125] The user reviews the presented self-care plan and selects activities that suit them. The device provides audio or video guidance about the selected activities to support the user.

[0126] Step 8:

[0127] If necessary, the server generates an alert and notifies the user via their device if the user's emotional state exceeds a certain threshold. This can prompt the user to take additional self-care measures or seek professional help if needed.

[0128] (Example 2)

[0129] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0130] In modern society, stress management and emotional regulation are crucial issues. Traditional methods have made it difficult to accurately understand individual emotional states and provide optimal self-care plans based on them. In particular, there is a lack of individualized approaches, and general recommendations make it difficult to achieve effective mental health care.

[0131] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0132] In this invention, the server includes a device for collecting voice data, an analysis device for analyzing the voice data and extracting emotional states, a device for generating a self-care plan using a generation AI model based on the emotional states, a device for displaying the self-care plan to the user, and a device for sending notifications and additional suggestions to the user. This makes it possible to accurately grasp each individual's emotional state and provide a self-care plan that is individually optimized based on it.

[0133] "Voice data" refers to digital information obtained from voice input, and serves as fundamental data for analyzing the user's emotional state.

[0134] An "analysis device" refers to an electronic device or software that receives audio data and uses a specific algorithm to determine the emotional state of the user.

[0135] A "generative AI model" refers to an artificial intelligence algorithm that generates an optimal self-care plan based on the user's emotional data.

[0136] A "self-care plan" refers to an activity plan aimed at improving mental and physical health, tailored to the user's emotional state.

[0137] A "user" refers to an individual who attempts to manage their own emotional state using this system.

[0138] "Devices that send notifications and additional suggestions" refers to communication methods that encourage users to follow their self-care plans and provide new instructions or warnings as needed.

[0139] This mental health management system uses voice data to recognize the user's emotional state and provides a customized self-care plan based on the results. A specific implementation of the system is shown below.

[0140] First, the user launches a dedicated app on a device such as a smartphone or tablet. The app supports voice input and provides an interface that allows the user to begin the stress check. At this time, the device prepares to receive voice input using its built-in microphone.

[0141] Next, the device displays questions to the user in both audio and text, such as "How are you feeling today?". The user's responses are collected as audio data and transferred to a server in digital format. This process utilizes speech recognition software built into the device.

[0142] The received audio data is processed by an analysis device on the server. Specifically, an emotion engine analyzes the tone, tempo, and content of the audio to quantify and identify the user's emotional state. This analysis utilizes machine learning algorithms and natural language processing technologies.

[0143] The analysis results are input into a generating AI model, and the server generates an optimal self-care plan based on those results. This plan includes relaxation exercises and recommended activities for daily stress management, tailored to the user's individual condition.

[0144] Finally, the generated self-care plan is transferred from the server to the terminal and notified to the user. The user can access the self-care plan through the terminal and select and perform the suggested activities. The plan may also be provided to the user as voice guidance or step-by-step instructions.

[0145] For example, if a user is determined to be in a very high stress level, a plan is created that includes playing relaxation music and providing guidance on deep breathing exercises. Furthermore, the system continuously monitors the user's state and sends notifications as needed.

[0146] Example of a prompt:

[0147] "Design prompts that analyze the user's voice data to recognize their emotions and create a self-care plan for stress reduction. The plan should include relaxation exercises and suggestions for professional consultation."

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The user launches a dedicated app on their device. The user then clicks the "Start Stress Check" button on the app's interface. Input consists of the user launching the app and instructing the user to begin the stress check. This triggers the device to prepare a voice input mode and present the questions.

[0151] Step 2:

[0152] The device puts itself into voice input standby mode. The device displays a pre-set question (e.g., "How have you been lately?") in both text and audio, and records the user's voice input. The input consists of text information and the user's voice data, which is converted into digital audio data within the device as output. Speech recognition software makes this possible.

[0153] Step 3:

[0154] The terminal transfers the collected audio data to the server. The input here is digital audio data, and the output is the completion of the transfer of the audio data to the server. The terminal uses a communication module to send data to the server.

[0155] Step 4:

[0156] The server sends the received audio data to the analysis device. The server activates an emotion engine for audio analysis, using the received audio data as input. This engine uses a machine learning algorithm to analyze the audio data and identify the user's emotional state. The output is numerical data representing the emotional state.

[0157] Step 5:

[0158] The server generates a self-care plan using a generative AI model based on the results of the emotional state. The input is quantified emotional data, and the AI ​​uses this to suggest the most suitable self-care. The generated self-care plan is output. Specifically, it includes relaxation exercises and daily habit improvements that are appropriate for the user's condition.

[0159] Step 6:

[0160] The server sends the self-care plan it has created to the terminal. The input is the self-care plan, and the output is the transfer of the plan data to the terminal. The terminal receives this and sets up a notification display for the user.

[0161] Step 7:

[0162] Users review their self-care plan via their device and perform the suggested activities. The input consists of the content of the notified self-care plan, and users attempt to improve their daily routines based on it. User progress and feedback are used for future analysis.

[0163] (Application Example 2)

[0164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0165] In recent years, there has been a growing demand for public and retail facilities to alleviate the mental burdens users experience in their daily lives and to provide opportunities for refreshment. However, conventional programs and services have faced challenges in providing easily accessible, individualized self-care activities that are tailored to the emotional state of users. In particular, establishing a system to provide appropriate mental health care in a short amount of time while shopping in retail facilities has been difficult.

[0166] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0167] In this invention, the server includes means for collecting voice data, means for analyzing the voice data to extract emotional states, means for generating a self-care plan based on the emotional states, and means for making the self-care plan available within the retail facility via an interactive information device. This enables users within the retail facility to receive mental health care activities optimized for them in a short amount of time.

[0168] "Audio data" refers to data that captures the sounds emitted by a user as digital information.

[0169] "Analysis" is the process of extracting emotional states from acquired audio data.

[0170] "Emotional state" refers to information that indicates the user's psychological state, obtained through analysis.

[0171] A "self-care plan" is a set of activity proposals for relaxation and stress reduction, generated according to the user's emotional state.

[0172] "Provision" means making the generated self-care plan available for use by the user.

[0173] An "interactive information device" is a device equipped with an interface that can be directly operated by the user, and is a terminal used for inputting voice data and outputting self-care plans.

[0174] A "retail facility" is a commercial space for customers that provides goods and services, and includes, for example, shopping malls.

[0175] "Available" refers to a state where the user is able to execute their intended plan.

[0176] The system for implementing this invention collects voice data, analyzes emotional states, and generates a corresponding self-care plan. The hardware used is an interactive information device accessible to users, which is installed within a retail facility. The terminal is equipped with a suitable microphone and display, and allows data input by the user responding verbally.

[0177] The system's software primarily performs processing using speech recognition and emotion analysis algorithms. Voice data is collected through terminals and transferred to a server. The server converts the voice data into text using the Python library "speech_recognition". Then, based on that text, the emotional state is analyzed using specialized software called "emotion_recognition".

[0178] Based on the emotional state obtained through analysis, the server generates a self-care plan. The generated plan is provided to the user through an interactive information device. For example, if a stressful state is detected, instructions for a short, refreshing deep breathing exercise will be displayed on the screen and performed as an audio guide.

[0179] As a concrete example, this system could be used in a relaxation space within a shopping mall. An intuitive interface is employed to make it easy for users to access, allowing them to use the system immediately when feeling stressed.

[0180] Furthermore, the following are examples of prompts for a generative AI model:

[0181] "Please create a 5-minute meditation guide to help customers relax while shopping."

[0182] This prompt is used to guide users to appropriate mental health care activities.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The device collects voice data from the user. Voice input begins when the user answers questions into the microphone installed on the device. The input is in analog format and is recorded as digital voice data.

[0186] Step 2:

[0187] The terminal transfers the collected audio data to the server. The data is transmitted to the cloud server using high-speed digital communication technology. The input here is digital audio data, and the output is the same data stored on the server.

[0188] Step 3:

[0189] The server uses a speech recognition algorithm to convert audio data into text data. This is done using the Python library "speech_recognition". The input is audio data, and the output is text data after data processing.

[0190] Step 4:

[0191] The server analyzes text data to recognize the user's emotional state. Here, the "emotion_recognition" algorithm is used, and the input text data is analyzed for emotion and output as emotional state data.

[0192] Step 5:

[0193] The server generates a self-care plan based on emotional state data. Using a generative AI model, appropriate self-care activities (e.g., meditation guidance or exercise) are suggested. The input is emotional state data, and the generated self-care plan is output.

[0194] Step 6:

[0195] The terminal provides the user with a self-care plan transmitted from the server. The display device shows the plan details and provides audio guidance. The user can then perform self-care according to the displayed plan. The output is a self-care plan presented in a format that the user can understand.

[0196] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0197] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0198] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0202] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0203] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0204] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0205] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0206] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0207] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0208] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0209] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0210] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0211] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0212] This invention relates to a mental health management system that uses user voice data. When a user launches a dedicated application on their device and inputs voice data, the device sends that data to a server. The server uses a voice analysis engine to obtain the user's emotional state from the voice data. This emotional state is quantified based on information such as voice tone and speed. The obtained emotional state is recorded in a database and analyzed by an AI engine. Based on the recorded data, the AI ​​engine generates a self-care plan optimized for each user's individual mental state. This self-care plan includes AI counseling and training menus and is provided to the user via the device. By using this and carrying out the suggested activities and training, the user can improve or maintain their mental health.

[0213] As a concrete example, when a user feels mentally exhausted after work, they can use this system to understand their emotional state. For instance, if the analysis of voice data diagnoses a high stress level, the server generates a self-care plan that includes relaxation exercises and short meditation sessions. This plan is presented to the user via a terminal, and the user can perform these self-care activities according to the instructions. In this way, the present invention helps maintain and manage the user's mental health.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The user launches a dedicated app on their device and begins the stress check. The device then presents the user with voice-based questions to assess their mental state.

[0217] Step 2:

[0218] When a user verbally answers a question presented to them, the device records the audio in real time. This recorded data is then sent directly to the server.

[0219] Step 3:

[0220] The server inputs the received audio data into the speech analysis engine and begins analyzing the data. The speech analysis engine identifies the user's emotional state from the audio data and quantifies the emotional state based on factors such as tone of voice, speed, and pauses.

[0221] Step 4:

[0222] The server saves the analysis results to a database and retains information about the emotional state.

[0223] Step 5:

[0224] The server passes emotional state data it has stored to an AI engine, which generates a self-care plan optimized for the user's current mental state. This plan is then compared with the user's past data to suggest specific and effective activities and training.

[0225] Step 6:

[0226] The server generates a self-care plan and sends it to the device. The device displays this plan to the user and provides details of the self-care activities to be undertaken.

[0227] Step 7:

[0228] The user reviews the presented self-care plan and selects the activities they wish to perform. Based on the user's selection, the device plays guided training or meditation exercises and provides the user with specific instructions on how to carry them out.

[0229] (Example 1)

[0230] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0231] In modern society, despite the importance of mental health management, there is a lack of adequate systems in place for users to understand their own mental state on a daily basis and take appropriate action. As a result, users unconsciously accumulate mental burdens, which can lead to more serious health problems. Therefore, there is a need for methods that allow users to easily and accurately understand their own emotional state and immediately take appropriate self-care measures.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0233] In this invention, the server includes means for receiving voice information using a communication device, means for analyzing the voice information using a voice analysis mechanism to quantify the emotional state, and means for analyzing information stored in a recording device containing past emotional data using the analysis mechanism to generate self-care guidance based on the emotional state. This enables users to quickly understand their own emotional state and receive individually optimized self-care.

[0234] A "communication device" is a hardware or software system for sending and receiving data, and in this system, it plays the role of transmitting voice information to the server.

[0235] "Voice information" refers to voice data that a user inputs into the system, and is data in analog or digital format obtained based on the user's speech.

[0236] A "voice analysis mechanism" is a general term for algorithms and software that analyze voice information, extract features such as tone and speed of voice, and quantify emotional states.

[0237] "Emotional state" refers to data that indicates the user's emotional state and tendencies, derived from the characteristics of their voice by a voice analysis mechanism.

[0238] A "recording device" is a hardware or software system that stores collected data and analysis results for the long term and makes the data available for retrieval as needed.

[0239] An "analysis mechanism" is a general term for algorithms and software that analyze user trends using data stored in a recording device and generate appropriate advice and guidance based on the results obtained.

[0240] "Self-care guidance" refers to suggestions for maintaining or improving mental health, provided to users based on their analyzed emotional state.

[0241] A "user terminal" is a device used to provide self-care guidance to users, and includes smartphones, tablets, and other similar devices.

[0242] This mental health management system primarily consists of a terminal with a dedicated application installed for user voice input, and a server connected to it. Users launch the application on a device such as a smartphone or tablet and input voice information via the microphone. The terminal is equipped with a communication device to quickly transmit this voice information to the server.

[0243] The server analyzes the received audio information using an audio analysis mechanism. This analysis mechanism consists of software algorithms that convert the audio information into a digital format and extract features such as voice tone and speed. For example, the user's stress and tension levels can be quantified through audio frequency analysis. This quantified emotional state is stored in a recording device and further analyzed using a generative AI model.

[0244] By using generative AI models, such as natural language processing models, the server comprehensively evaluates user data, including past records, to generate optimal self-care guidance for the user. This guidance includes relaxation methods and activities tailored to the user's current situation. The guidance thus generated is presented via the user's terminal. The terminal can also provide guides for performing the suggested activities, as well as links to applications and websites as needed.

[0245] As a concrete example, suppose a user uses the application to voice-input "I'm tired today" after work. The system analyzes this utterance and determines that the user has a "high stress level." The server then generates self-care instructions, including relaxation exercises and short meditation sessions, and sends them to the user's terminal. The user can then follow the instructions displayed on the terminal to reduce stress. A suitable example of a prompt would be, "Please evaluate the emotional state of this voice and tell me what self-care is needed."

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The user launches a dedicated application on their device and inputs voice information. The input voice is converted into a digital signal through the device's microphone. This digital voice data is then ready to be sent to the next step. Specifically, voice input begins when the user speaks on the topic of "today's meeting."

[0249] Step 2:

[0250] The terminal transmits digitized voice data to the server using a secure communication protocol. The input voice data is transferred from the terminal to the server, and the server receives the voice data. During this process, encryption is applied to maintain data integrity and security.

[0251] Step 3:

[0252] The server passes the received audio data to the speech analysis mechanism, which then begins the analysis. The speech analysis mechanism converts the audio data into text and further measures and analyzes the tone, speed, volume, and other aspects of the voice. For example, it analyzes the frequency characteristics of the most recent audio and outputs the user's emotional state as a numerical value. The output value might take the form of "Tension level: 60%, Fatigue level: 30%, Relaxation level: 10%."

[0253] Step 4:

[0254] The server stores quantified emotional data in a recording device and simultaneously analyzes this data using a generative AI model. During the analysis, comparisons are made with past data to understand the user's mental state and tendencies. As a result of this analysis, personalized self-care guidance is generated. This guidance includes practices such as meditation and breathing exercises.

[0255] Step 5:

[0256] The server sends the generated self-care instructions to the terminal and provides them to the user. The terminal displays the received self-care instructions on the application screen. The user can then perform the suggested self-care activities by following the instructions displayed on the terminal. Specifically, the user receives the instruction to "perform a 10-minute guided meditation" and begins the activity.

[0257] (Application Example 1)

[0258] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] In modern society, stress and fatigue tend to accumulate easily, and users are required to choose appropriate meals that suit their mental state. However, it is difficult for individuals to understand their own emotional state and make meal choices based on it in their daily lives. In this situation, there is a need for a system that can quickly analyze a user's emotional state and make effective meal recommendations based on the results.

[0260] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0261] In this invention, the server includes a device for collecting voice information, a device for analyzing the voice information to identify an emotional state, and a device for creating a meal recommendation plan based on the emotional state. This makes it possible for users to easily and quickly select a meal that is appropriate for their emotional state.

[0262] "Voice information" refers to data collected from the user's voice and converted into a format that can be processed within an electronic device.

[0263] "Emotional state" is a numerical representation of the user's psychological state and mood derived from analyzed audio information.

[0264] A "meal recommendation plan" is a plan or suggestion for presenting appropriate meal menus based on the user's emotional state.

[0265] A "collection device" is a device that acquires audio information and provides it to a system for processing and analysis.

[0266] An "analytical device" is a device that analyzes collected audio information and identifies the user's emotional state from it.

[0267] The "creation device" is a device that has the function of generating meal recommendation plans tailored to specific emotional states.

[0268] The "device provided" refers to a device that presents the generated meal recommendation plan to the user and supports them in making choices and implementing them.

[0269] The following system configuration and its operation will be described as embodiments for carrying out the invention.

[0270] This system implements a series of processes to collect voice information, analyze emotional states, and provide meal recommendation plans. First, the terminal collects the user's voice information using a voice input device. Next, the voice information is transmitted to the server via the internet. The server uses a voice analysis engine to identify the user's emotional state from the voice information.

[0271] For emotion analysis, natural language processing techniques and machine learning algorithms are often used. This allows the emotional state to be quantified based on characteristics such as the tone, speed, and intensity of the user's voice.

[0272] The server then uses a generative AI model to create an appropriate meal recommendation plan based on the analyzed emotional state. This plan includes menus optimized for that emotional state. For example, if the user is feeling stressed, a menu using ingredients with relaxation effects will be suggested.

[0273] The generated meal recommendation plan is sent to the device and presented to the user as specific options. The user can then order a meal based on this plan. This system allows users to quickly select the optimal meal that best suits their emotional state.

[0274] For example, if a user inputs the voice message "I'm very tired today," the system can suggest a relaxing meal such as "Recommended menu: Chicken soup, lavender tea." An example of such a prompt would be, "I'm very tired today. Please suggest a relaxing meal."

[0275] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0276] Step 1:

[0277] The terminal collects the user's voice information using a voice input device. The collected voice information is converted into a digital data format. This data is transmitted to the server via the Internet. The input is the user's voice, and the output is digitized voice data.

[0278] Step 2:

[0279] The server uses a voice analysis engine to analyze the received voice data. In this analysis, feature quantities such as the tone, speed, and intensity of the voice are extracted, and the emotional state is specified using a machine learning algorithm. The input is digitized voice data, and the output is the quantified data of the detected emotional state. [[ID=I8]]

[0280] Step 3:

[0281] The server uses a generative AI model to create a meal recommendation plan based on the quantified emotional state. The generative AI model refers to a pre-learned database of ingredients and menus and proposes a menu suitable for the user's emotional state. The input is the quantified data of the emotional state, and the output is a specific meal recommendation plan.

[0282] [[ID=2%]] Step 4:

[0283] The server transmits the created meal recommendation plan to the terminal. The terminal displays this plan to the user, and the user can make a selection from the proposed menus. The input is the meal recommendation plan, and the output is the user's selection operation.

[0284] Step 5:

[0285] Based on the displayed menu, the user places a meal order. Based on the user's selection, the terminal sends the order information to the relevant service. The input is the user's selection information, and the output is the confirmed order information.

[0286] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0287] The present invention is a mental health management system that uses voice data collected from a user to recognize an emotional state and provide a self-care plan. The user launches a dedicated app on the terminal and starts a stress check. The terminal presents questions for grasping the user's emotional state through voice input. By the user's response, the terminal collects voice data and transfers it to the server. The server passes the voice data to the emotion engine for analysis. This emotion engine recognizes the user's emotions (e.g., stress, relaxation, excitement tendency, etc.) and quantifies the results.

[0288] Based on the result of this emotion recognition, the server generates a customized self-care plan using the AI engine. This plan includes AI-based counseling and self-care activities optimized for the recognized emotional state of the user. For example, when it is determined that the user's emotion is in a stress state, a plan including relaxation exercises such as meditation, breathing methods, and autogenic training methods is generated.

[0289] The generated self-care plan is sent from the server to the terminal and provided to the user. The user accesses this plan through the terminal and executes the recommended self-care methods. Furthermore, if necessary, the system sends alerts and additional notifications to the user regarding the continuation of a specific emotional state and proposes additional sessions. For example, if very high stress continues, a proposal for additional consultation by an expert may be included.

[0290] In this way, this system utilizes voice data to accurately recognize the user's emotional state and provides personalized self-care plans based on that recognition, thereby enabling efficient mental health management.

[0291] The following describes the processing flow.

[0292] Step 1:

[0293] The user launches the dedicated app on their device and selects the "Start Stress Check" button. The device prompts the user to speak and presents questions.

[0294] Step 2:

[0295] While the user verbally answers the presented questions, the device records their voice. The recorded audio data is then sent directly to the server.

[0296] Step 3:

[0297] The server inputs the received audio data into the emotion engine. The emotion engine analyzes the audio data and extracts the user's emotional state. This process analyzes factors such as voice tone, speed, volume, and rhythm, and quantifies emotional indicators.

[0298] Step 4:

[0299] The server receives the analysis results from the emotion engine and saves the emotional state to a database. This information is later used to create a self-care plan.

[0300] Step 5:

[0301] The server uses an AI engine to generate a self-care plan tailored to the user based on their emotional state. This plan includes relaxation exercises, AI counseling, and daily stress management techniques.

[0302] Step 6:

[0303] The server sends the self-care plan generated by it to the terminal. The terminal displays the plan to the user and details the steps and activities to be performed next.

[0304] Step 7:

[0305] The user checks the presented self-care plan and selects activities suitable for themselves. The terminal provides a guide for the selected activities in audio or video to support the user.

[0306] Step 8:

[0307] If necessary, when the user's emotional state exceeds a specific criterion, the server generates an alert and notifies the user through the terminal. This can prompt additional self-care or expert consultation if necessary.

[0308] (Example 2)

[0309] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In modern society, stress management and emotional regulation are important issues. With conventional methods, it has been difficult to accurately grasp individual emotional states and provide an optimal self-care plan based on them. In particular, there is a lack of an individualized approach, and there is a problem that effective mental care is difficult to achieve with general recommendations.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0312] In this invention, the server includes a device for collecting voice data, an analysis device for analyzing the voice data and extracting emotional states, a device for generating a self-care plan using a generation AI model based on the emotional states, a device for displaying the self-care plan to the user, and a device for sending notifications and additional suggestions to the user. This makes it possible to accurately grasp each individual's emotional state and provide a self-care plan that is individually optimized based on it.

[0313] "Voice data" refers to digital information obtained from voice input, and serves as fundamental data for analyzing the user's emotional state.

[0314] An "analysis device" refers to an electronic device or software that receives audio data and uses a specific algorithm to determine the emotional state of the user.

[0315] A "generative AI model" refers to an artificial intelligence algorithm that generates an optimal self-care plan based on the user's emotional data.

[0316] A "self-care plan" refers to an activity plan aimed at improving mental and physical health, tailored to the user's emotional state.

[0317] A "user" refers to an individual who attempts to manage their own emotional state using this system.

[0318] "Devices that send notifications and additional suggestions" refer to communication methods that encourage users to follow their self-care plans and provide new instructions or warnings as needed.

[0319] This mental health management system uses voice data to recognize the user's emotional state and provides a customized self-care plan based on the results. A specific implementation of the system is shown below.

[0320] First, the user launches a dedicated app on a device such as a smartphone or tablet. The app supports voice input and provides an interface that allows the user to begin the stress check. At this time, the device prepares to receive voice input using its built-in microphone.

[0321] Next, the device displays questions to the user in both audio and text, such as "How are you feeling today?". The user's responses are collected as audio data and transferred to a server in digital format. This process utilizes speech recognition software built into the device.

[0322] The received audio data is processed by an analysis device on the server. Specifically, an emotion engine analyzes the tone, tempo, and content of the audio to quantify and identify the user's emotional state. This analysis utilizes machine learning algorithms and natural language processing technologies.

[0323] The analysis results are input into a generating AI model, and the server generates an optimal self-care plan based on those results. This plan includes relaxation exercises and recommended activities for daily stress management, tailored to the user's individual condition.

[0324] Finally, the generated self-care plan is transferred from the server to the terminal and notified to the user. The user can access the self-care plan through the terminal and select and perform the suggested activities. The plan may also be provided to the user as voice guidance or step-by-step instructions.

[0325] For example, if a user is determined to be in a very high stress level, a plan is created that includes playing relaxation music and providing guidance on deep breathing exercises. Furthermore, the system continuously monitors the user's state and sends notifications as needed.

[0326] Example of a prompt:

[0327] "Design prompts that analyze the user's voice data to recognize their emotions and create a self-care plan for stress reduction. The plan should include relaxation exercises and suggestions for professional consultation."

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] The user launches a dedicated app on their device. The user then clicks the "Start Stress Check" button on the app's interface. Input consists of the user launching the app and instructing the user to begin the stress check. This triggers the device to prepare a voice input mode and present the questions.

[0331] Step 2:

[0332] The device puts itself into voice input standby mode. The device displays a pre-set question (e.g., "How have you been lately?") in both text and audio, and records the user's voice input. The input consists of text information and the user's voice data, which is converted into digital audio data within the device as output. Speech recognition software makes this possible.

[0333] Step 3:

[0334] The terminal transfers the collected audio data to the server. The input here is digital audio data, and the output is the completion of the audio data transfer to the server. The terminal uses a communication module to send data to the server.

[0335] Step 4:

[0336] The server sends the received audio data to the analysis device. The server activates an emotion engine for audio analysis, using the received audio data as input. This engine uses a machine learning algorithm to analyze the audio data and identify the user's emotional state. The output is numerical data representing the emotional state.

[0337] Step 5:

[0338] The server generates a self-care plan using a generative AI model based on the results of the emotional state. The input is quantified emotional data, and the AI ​​uses this to suggest the most suitable self-care. The generated self-care plan is output. Specifically, it includes relaxation exercises and daily habit improvements that are appropriate for the user's condition.

[0339] Step 6:

[0340] The server sends the self-care plan it has created to the terminal. The input is the self-care plan, and the output is the transfer of the plan data to the terminal. The terminal receives this and sets up a notification display for the user.

[0341] Step 7:

[0342] Users review their self-care plan via their device and perform the suggested activities. The input consists of the content of the notified self-care plan, and users attempt to improve their daily routines based on it. User progress and feedback are used for future analysis.

[0343] (Application Example 2)

[0344] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0345] In recent years, there has been a growing demand for public and retail facilities to alleviate the mental burdens users experience in their daily lives and to provide opportunities for refreshment. However, conventional programs and services have faced challenges in providing easily accessible, individualized self-care activities that are tailored to the emotional state of users. In particular, establishing a system to provide appropriate mental health care in a short amount of time while shopping in retail facilities has been difficult.

[0346] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0347] In this invention, the server includes means for collecting voice data, means for analyzing the voice data to extract emotional states, means for generating a self-care plan based on the emotional states, and means for making the self-care plan available within the retail facility via an interactive information device. This enables users within the retail facility to receive mental health care activities optimized for them in a short amount of time.

[0348] "Audio data" refers to data that captures the sounds emitted by a user as digital information.

[0349] "Analysis" is the process of extracting emotional states from acquired audio data.

[0350] "Emotional state" refers to information that indicates the user's psychological state, obtained through analysis.

[0351] A "self-care plan" is a set of activity proposals for relaxation and stress reduction, generated according to the user's emotional state.

[0352] "Provision" means making the generated self-care plan available for use by the user.

[0353] An "interactive information device" is a device equipped with an interface that can be directly operated by the user, and is a terminal used for inputting voice data and outputting self-care plans.

[0354] A "retail facility" is a commercial space for customers that provides goods and services, and includes, for example, shopping malls.

[0355] "Available" refers to a state where the user is able to execute their intended plan.

[0356] The system for implementing this invention collects voice data, analyzes emotional states, and generates a corresponding self-care plan. The hardware used is an interactive information device accessible to users, which is installed within a retail facility. The terminal is equipped with a suitable microphone and display, and allows data input by the user responding verbally.

[0357] The system's software primarily performs processing using speech recognition and emotion analysis algorithms. Voice data is collected through terminals and transferred to a server. The server converts the voice data into text using the Python library "speech_recognition". Then, based on that text, the emotional state is analyzed using specialized software called "emotion_recognition".

[0358] Based on the emotional state obtained through analysis, the server generates a self-care plan. The generated plan is provided to the user through an interactive information device. For example, if a stressful state is detected, instructions for a short, refreshing deep breathing exercise will be displayed on the screen and performed as an audio guide.

[0359] As a concrete example, this system could be used in a relaxation space within a shopping mall. An intuitive interface is employed to make it easy for users to access, allowing them to use the system immediately when feeling stressed.

[0360] Furthermore, the following are examples of prompts for a generative AI model:

[0361] "Please create a 5-minute meditation guide to help customers relax while shopping."

[0362] This prompt is used to guide users to appropriate mental health care activities.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The device collects voice data from the user. Voice input begins when the user answers questions into the microphone installed on the device. The input is in analog format and is recorded as digital voice data.

[0366] Step 2:

[0367] The terminal transfers the collected audio data to the server. The data is transmitted to the cloud server using high-speed digital communication technology. The input here is digital audio data, and the output is the same data stored on the server.

[0368] Step 3:

[0369] The server uses a speech recognition algorithm to convert audio data into text data. This is done using the Python library "speech_recognition". The input is audio data, and the output is text data after data processing.

[0370] Step 4:

[0371] The server analyzes text data to recognize the user's emotional state. Here, the "emotion_recognition" algorithm is used, and the input text data is analyzed for emotion and output as emotional state data.

[0372] Step 5:

[0373] The server generates a self-care plan based on emotional state data. Using a generative AI model, appropriate self-care activities (e.g., meditation guidance or exercise) are suggested. The input is emotional state data, and the generated self-care plan is output.

[0374] Step 6:

[0375] The terminal provides the user with a self-care plan transmitted from the server. The display device shows the plan details and provides audio guidance. The user can then perform self-care according to the displayed plan. The output is a self-care plan presented in a format that the user can understand.

[0376] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0377] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0378] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0379] [Third Embodiment]

[0380] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0381] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0382] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0383] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0384] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0385] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0386] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0387] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0388] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0389] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0390] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0391] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0392] This invention relates to a mental health management system that uses user voice data. When a user launches a dedicated application on their device and inputs voice data, the device sends that data to a server. The server uses a voice analysis engine to obtain the user's emotional state from the voice data. This emotional state is quantified based on information such as voice tone and speed. The obtained emotional state is recorded in a database and analyzed by an AI engine. Based on the recorded data, the AI ​​engine generates a self-care plan optimized for each user's individual mental state. This self-care plan includes AI counseling and training menus and is provided to the user via the device. By using this and carrying out the suggested activities and training, the user can improve or maintain their mental health.

[0393] As a concrete example, when a user feels mentally exhausted after work, they can use this system to understand their emotional state. For instance, if the analysis of voice data diagnoses a high stress level, the server generates a self-care plan that includes relaxation exercises and short meditation sessions. This plan is presented to the user via a terminal, and the user can perform these self-care activities according to the instructions. In this way, the present invention helps maintain and manage the user's mental health.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The user launches a dedicated app on their device and begins the stress check. The device then presents the user with voice-based questions to assess their mental state.

[0397] Step 2:

[0398] When a user verbally answers a question presented to them, the device records the audio in real time. This recorded data is then sent directly to the server.

[0399] Step 3:

[0400] The server inputs the received audio data into the speech analysis engine and begins analyzing the data. The speech analysis engine identifies the user's emotional state from the audio data and quantifies the emotional state based on factors such as tone of voice, speed, and pauses.

[0401] Step 4:

[0402] The server saves the analysis results to a database and retains information about the emotional state.

[0403] Step 5:

[0404] The server passes emotional state data it has stored to an AI engine, which generates a self-care plan optimized for the user's current mental state. This plan is then compared with the user's past data to suggest specific and effective activities and training.

[0405] Step 6:

[0406] The server generates a self-care plan and sends it to the device. The device displays this plan to the user and provides details of the self-care activities to be undertaken.

[0407] Step 7:

[0408] The user reviews the presented self-care plan and selects the activities they wish to perform. Based on the user's selection, the device plays guided training or meditation exercises and provides the user with specific instructions on how to carry them out.

[0409] (Example 1)

[0410] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0411] In modern society, despite the importance of mental health management, there is a lack of adequate systems in place for users to understand their own mental state on a daily basis and take appropriate action. As a result, users unconsciously accumulate mental burdens, which can lead to more serious health problems. Therefore, there is a need for methods that allow users to easily and accurately understand their own emotional state and immediately take appropriate self-care measures.

[0412] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0413] In this invention, the server includes means for receiving voice information using a communication device, means for analyzing the voice information using a voice analysis mechanism to quantify the emotional state, and means for analyzing information stored in a recording device containing past emotional data using the analysis mechanism to generate self-care guidance based on the emotional state. This enables users to quickly understand their own emotional state and receive individually optimized self-care.

[0414] A "communication device" is a hardware or software system for sending and receiving data, and in this system, it plays the role of transmitting voice information to the server.

[0415] "Voice information" refers to voice data that a user inputs into the system, and is data in analog or digital format obtained based on the user's speech.

[0416] A "voice analysis mechanism" is a general term for algorithms and software that analyze voice information, extract features such as tone and speed of voice, and quantify emotional states.

[0417] "Emotional state" refers to data that indicates the user's emotional state and tendencies, derived from the characteristics of their voice by a voice analysis mechanism.

[0418] A "recording device" is a hardware or software system that stores collected data and analysis results for the long term and makes the data available for retrieval as needed.

[0419] An "analysis mechanism" is a general term for algorithms and software that analyze user trends using data stored in a recording device and generate appropriate advice and guidance based on the results obtained.

[0420] "Self-care guidance" refers to suggestions for maintaining or improving mental health, provided to users based on their analyzed emotional state.

[0421] A "user terminal" is a device used to provide self-care guidance to users, and includes smartphones, tablets, and other similar devices.

[0422] This mental health management system primarily consists of a terminal with a dedicated application installed for user voice input, and a server connected to it. Users launch the application on a device such as a smartphone or tablet and input voice information via the microphone. The terminal is equipped with a communication device to quickly transmit this voice information to the server.

[0423] The server analyzes the received audio information using an audio analysis mechanism. This analysis mechanism consists of software algorithms that convert the audio information into a digital format and extract features such as voice tone and speed. For example, the user's stress and tension levels can be quantified through audio frequency analysis. This quantified emotional state is stored in a recording device and further analyzed using a generative AI model.

[0424] By using generative AI models, such as natural language processing models, the server comprehensively evaluates user data, including past records, to generate optimal self-care guidance for the user. This guidance includes relaxation methods and activities tailored to the user's current situation. The guidance thus generated is presented via the user's terminal. The terminal can also provide guides for performing the suggested activities, as well as links to applications and websites as needed.

[0425] As a concrete example, suppose a user uses the application to voice-input "I'm tired today" after work. The system analyzes this utterance and determines that the user has a "high stress level." The server then generates self-care instructions, including relaxation exercises and short meditation sessions, and sends them to the user's terminal. The user can then follow the instructions displayed on the terminal to reduce stress. A suitable example of a prompt would be, "Please evaluate the emotional state of this voice and tell me what self-care is needed."

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The user launches a dedicated application on their device and inputs voice information. The input voice is converted into a digital signal through the device's microphone. This digital voice data is then ready to be sent to the next step. Specifically, voice input begins when the user speaks on the topic of "today's meeting."

[0429] Step 2:

[0430] The terminal transmits digitized voice data to the server using a secure communication protocol. The input voice data is transferred from the terminal to the server, and the server receives the voice data. During this process, encryption is applied to maintain data integrity and security.

[0431] Step 3:

[0432] The server passes the received audio data to the speech analysis mechanism, which then begins the analysis. The speech analysis mechanism converts the audio data into text and further measures and analyzes the tone, speed, volume, and other aspects of the voice. For example, it analyzes the frequency characteristics of the most recent audio and outputs the user's emotional state as a numerical value. The output value might take the form of "Tension level: 60%, Fatigue level: 30%, Relaxation level: 10%."

[0433] Step 4:

[0434] The server stores quantified emotional data in a recording device and simultaneously analyzes this data using a generative AI model. During the analysis, comparisons are made with past data to understand the user's mental state and tendencies. As a result of this analysis, personalized self-care guidance is generated. This guidance includes practices such as meditation and breathing exercises.

[0435] Step 5:

[0436] The server sends the generated self-care instructions to the terminal and provides them to the user. The terminal displays the received self-care instructions on the application screen. The user can then perform the suggested self-care activities by following the instructions displayed on the terminal. Specifically, the user receives the instruction to "perform a 10-minute guided meditation" and begins the activity.

[0437] (Application Example 1)

[0438] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0439] In modern society, stress and fatigue tend to accumulate easily, and users are required to choose appropriate meals that suit their mental state. However, it is difficult for individuals to understand their own emotional state and make meal choices based on it in their daily lives. In this situation, there is a need for a system that can quickly analyze a user's emotional state and make effective meal recommendations based on the results.

[0440] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0441] In this invention, the server includes a device for collecting voice information, a device for analyzing the voice information to identify an emotional state, and a device for creating a meal recommendation plan based on the emotional state. This makes it possible for users to easily and quickly select a meal that is appropriate for their emotional state.

[0442] "Voice information" refers to data collected from the user's voice and converted into a format that can be processed within an electronic device.

[0443] "Emotional state" is a numerical representation of the user's psychological state and mood derived from analyzed audio information.

[0444] A "meal recommendation plan" is a plan or suggestion for presenting appropriate meal menus based on the user's emotional state.

[0445] A "collection device" is a device that acquires audio information and provides it to a system for processing and analysis.

[0446] An "analytical device" is a device that analyzes collected audio information and identifies the user's emotional state from it.

[0447] The "creation device" is a device that has the function of generating meal recommendation plans tailored to specific emotional states.

[0448] The "device provided" refers to a device that presents the generated meal recommendation plan to the user and supports them in making choices and implementing them.

[0449] The following system configuration and its operation will be described as embodiments for carrying out the invention.

[0450] This system implements a series of processes to collect voice information, analyze emotional states, and provide meal recommendation plans. First, the terminal collects the user's voice information using a voice input device. Next, the voice information is transmitted to the server via the internet. The server uses a voice analysis engine to identify the user's emotional state from the voice information.

[0451] For emotion analysis, natural language processing techniques and machine learning algorithms are often used. This allows the emotional state to be quantified based on characteristics such as the tone, speed, and intensity of the user's voice.

[0452] The server then uses a generative AI model to create an appropriate meal recommendation plan based on the analyzed emotional state. This plan includes menus optimized for that emotional state. For example, if the user is feeling stressed, a menu using ingredients with relaxation effects will be suggested.

[0453] The generated meal recommendation plan is sent to the device and presented to the user as specific options. The user can then order a meal based on this plan. This system allows users to quickly select the optimal meal that best suits their emotional state.

[0454] For example, if a user inputs the voice message "I'm very tired today," the system can suggest a relaxing meal such as "Recommended menu: Chicken soup, lavender tea." An example of such a prompt would be, "I'm very tired today. Please suggest a relaxing meal."

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The terminal collects user voice information using a voice input device. The collected voice information is converted into digital data format. This data is transmitted to a server via the internet. The input is the user's voice, and the output is digitized voice data.

[0458] Step 2:

[0459] The server uses a speech analysis engine to analyze the received speech data. This analysis extracts features such as tone, speed, and intensity, and uses machine learning algorithms to identify the emotional state. The input is digitized speech data, and the output is numerical data representing the detected emotional state.

[0460] Step 3:

[0461] The server uses a generative AI model to create meal recommendation plans based on quantified emotional states. The generative AI model refers to a pre-trained database of ingredients and menus to suggest menus that are appropriate for the user's emotional state. The input is quantified data of emotional states, and the output is a specific meal recommendation plan.

[0462] Step 4:

[0463] The server sends the created meal recommendation plan to the terminal. The terminal displays this plan to the user, who can then select from the suggested menu items. The input is the meal recommendation plan, and the output is the user's selection.

[0464] Step 5:

[0465] The user places an order for food based on the displayed menu. Based on the user's selection, the terminal sends the order information to the relevant service. The input is the user's selection information, and the output is the confirmed order information.

[0466] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0467] This invention relates to a mental health management system that uses voice data collected from users to recognize their emotional state and provide a self-care plan. The user launches a dedicated app on their device and starts a stress check. The device presents questions to understand the user's emotional state through voice input. As the user responds, the device collects voice data and transmits it to a server. The server passes the voice data to an emotion engine for analysis. This emotion engine recognizes the user's emotions (e.g., stress, relief, excitement tendencies, etc.) and quantifies the results.

[0468] Based on the results of this emotion recognition, the server uses an AI engine to generate a customized self-care plan. This plan includes AI-led counseling and self-care activities optimized for the user's recognized emotional state. For example, if the user's emotions are determined to be stressful, a plan will be generated that includes relaxation exercises such as meditation, breathing exercises, and autogenic training.

[0469] The generated self-care plan is sent from the server to the terminal and provided to the user. The user accesses this plan through the terminal and implements the recommended self-care methods. In addition, the system sends alerts and additional notifications to the user regarding the persistence of certain emotional states and suggests further sessions, as needed. For example, if very high stress levels persist, this may include a suggestion for consultation with an additional professional.

[0470] In this way, this system utilizes voice data to accurately recognize the user's emotional state and provides personalized self-care plans based on that recognition, thereby enabling efficient mental health management.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The user launches the dedicated app on their device and selects the "Start Stress Check" button. The device prompts the user to speak and presents questions.

[0474] Step 2:

[0475] While the user verbally answers the presented questions, the device records their voice. The recorded audio data is then sent directly to the server.

[0476] Step 3:

[0477] The server inputs the received audio data into the emotion engine. The emotion engine analyzes the audio data and extracts the user's emotional state. This process analyzes factors such as voice tone, speed, volume, and rhythm, and quantifies emotional indicators.

[0478] Step 4:

[0479] The server receives the analysis results from the emotion engine and saves the emotional state to a database. This information is later used to create a self-care plan.

[0480] Step 5:

[0481] The server uses an AI engine to generate a self-care plan tailored to the user based on their emotional state. This plan includes relaxation exercises, AI counseling, and daily stress management techniques.

[0482] Step 6:

[0483] The server generates a self-care plan and sends it to the device. The device displays the plan to the user and provides detailed instructions on the next steps and activities.

[0484] Step 7:

[0485] The user reviews the presented self-care plan and selects activities that suit them. The device provides audio or video guidance to support the user regarding the selected activities.

[0486] Step 8:

[0487] If necessary, the server generates an alert and notifies the user via their device if the user's emotional state exceeds a certain threshold. This can prompt the user to take additional self-care measures or seek professional help if needed.

[0488] (Example 2)

[0489] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] In modern society, stress management and emotional regulation are crucial issues. Traditional methods have made it difficult to accurately understand individual emotional states and provide optimal self-care plans based on them. In particular, there is a lack of individualized approaches, and general recommendations make it difficult to achieve effective mental health care.

[0491] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0492] In this invention, the server includes a device for collecting voice data, an analysis device for analyzing the voice data and extracting emotional states, a device for generating a self-care plan using a generation AI model based on the emotional states, a device for displaying the self-care plan to the user, and a device for sending notifications and additional suggestions to the user. This makes it possible to accurately grasp each individual's emotional state and provide a self-care plan that is individually optimized based on it.

[0493] "Voice data" refers to digital information obtained from voice input, and serves as fundamental data for analyzing the user's emotional state.

[0494] An "analysis device" refers to an electronic device or software that receives audio data and uses a specific algorithm to determine the emotional state of the user.

[0495] A "generative AI model" refers to an artificial intelligence algorithm that generates an optimal self-care plan based on the user's emotional data.

[0496] A "self-care plan" refers to an activity plan aimed at improving mental and physical health, tailored to the user's emotional state.

[0497] A "user" refers to an individual who attempts to manage their own emotional state using this system.

[0498] "Devices that send notifications and additional suggestions" refer to communication methods that encourage users to follow their self-care plans and provide new instructions or warnings as needed.

[0499] This mental health management system uses voice data to recognize the user's emotional state and provides a customized self-care plan based on the results. A specific implementation of the system is shown below.

[0500] First, the user launches a dedicated app on a device such as a smartphone or tablet. The app supports voice input and provides an interface that allows the user to begin the stress check. At this time, the device prepares to receive voice input using its built-in microphone.

[0501] Next, the device displays questions to the user in both audio and text, such as "How are you feeling today?". The user's responses are collected as audio data and transferred to a server in digital format. This process utilizes speech recognition software built into the device.

[0502] The received audio data is processed by an analysis device on the server. Specifically, an emotion engine analyzes the tone, tempo, and content of the audio to quantify and identify the user's emotional state. This analysis utilizes machine learning algorithms and natural language processing technologies.

[0503] The analysis results are input into a generating AI model, and the server generates an optimal self-care plan based on those results. This plan includes relaxation exercises and recommended activities for daily stress management, tailored to the user's individual condition.

[0504] Finally, the generated self-care plan is transferred from the server to the terminal and notified to the user. The user can access the self-care plan through the terminal and select and perform the suggested activities. The plan may also be provided to the user as voice guidance or step-by-step instructions.

[0505] For example, if a user is determined to be in a very high stress level, a plan is created that includes playing relaxation music and providing guidance on deep breathing exercises. Furthermore, the system continuously monitors the user's state and sends notifications as needed.

[0506] Example of a prompt:

[0507] "Design prompts that analyze the user's voice data to recognize their emotions and create a self-care plan for stress reduction. The plan should include relaxation exercises and suggestions for professional consultation."

[0508] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0509] Step 1:

[0510] The user launches a dedicated app on their device. The user then clicks the "Start Stress Check" button on the app's interface. Input consists of the user launching the app and instructing the user to begin the stress check. This triggers the device to prepare a voice input mode and present the questions.

[0511] Step 2:

[0512] The device puts itself into voice input standby mode. The device displays a pre-set question (e.g., "How have you been lately?") in both text and audio, and records the user's voice input. The input consists of text information and the user's voice data, which is converted into digital audio data within the device as output. Speech recognition software makes this possible.

[0513] Step 3:

[0514] The terminal transfers the collected audio data to the server. The input here is digital audio data, and the output is the completion of the audio data transfer to the server. The terminal uses a communication module to send data to the server.

[0515] Step 4:

[0516] The server sends the received audio data to the analysis device. The server activates an emotion engine for audio analysis, using the received audio data as input. This engine uses a machine learning algorithm to analyze the audio data and identify the user's emotional state. The output is numerical data representing the emotional state.

[0517] Step 5:

[0518] The server generates a self-care plan using a generative AI model based on the results of the emotional state. The input is quantified emotional data, and the AI ​​uses this to suggest the most suitable self-care. The generated self-care plan is output. Specifically, it includes relaxation exercises and daily habit improvements that are appropriate for the user's condition.

[0519] Step 6:

[0520] The server sends the self-care plan it has created to the terminal. The input is the self-care plan, and the output is the transfer of the plan data to the terminal. The terminal receives this and sets up a notification display for the user.

[0521] Step 7:

[0522] Users review their self-care plan via their device and perform the suggested activities. The input consists of the content of the notified self-care plan, and users attempt to improve their daily routines based on it. User progress and feedback are used for future analysis.

[0523] (Application Example 2)

[0524] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] In recent years, there has been a growing demand for public and retail facilities to alleviate the mental burdens users experience in their daily lives and to provide opportunities for refreshment. However, conventional programs and services have faced challenges in providing easily accessible, individualized self-care activities that are tailored to the emotional state of users. In particular, establishing a system to provide appropriate mental health care in a short amount of time while shopping in retail facilities has been difficult.

[0526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0527] In this invention, the server includes means for collecting voice data, means for analyzing the voice data to extract emotional states, means for generating a self-care plan based on the emotional states, and means for making the self-care plan available within the retail facility via an interactive information device. This enables users within the retail facility to receive mental health care activities optimized for them in a short amount of time.

[0528] "Audio data" refers to data that captures the sounds emitted by a user as digital information.

[0529] "Analysis" is the process of extracting emotional states from acquired audio data.

[0530] "Emotional state" refers to information that indicates the user's psychological state, obtained through analysis.

[0531] A "self-care plan" is a set of activity proposals for relaxation and stress reduction, generated according to the user's emotional state.

[0532] "Provision" means making the generated self-care plan available for use by the user.

[0533] An "interactive information device" is a device equipped with an interface that can be directly operated by the user, and is a terminal used for inputting voice data and outputting self-care plans.

[0534] A "retail facility" is a commercial space for customers that provides goods and services, and includes, for example, shopping malls.

[0535] "Available" refers to a state where the user is able to execute their intended plan.

[0536] The system for implementing this invention collects voice data, analyzes emotional states, and generates a corresponding self-care plan. The hardware used is an interactive information device accessible to users, which is installed within a retail facility. The terminal is equipped with a suitable microphone and display, and allows data input by the user responding verbally.

[0537] The system's software primarily performs processing using speech recognition and emotion analysis algorithms. Voice data is collected through terminals and transferred to a server. The server converts the voice data into text using the Python library "speech_recognition". Then, based on that text, the emotional state is analyzed using specialized software called "emotion_recognition".

[0538] Based on the emotional state obtained through analysis, the server generates a self-care plan. The generated plan is provided to the user through an interactive information device. For example, if a stressful state is detected, instructions for a short, refreshing deep breathing exercise will be displayed on the screen and performed as an audio guide.

[0539] As a concrete example, this system could be used in a relaxation space within a shopping mall. An intuitive interface is employed to make it easy for users to access, allowing them to use the system immediately when feeling stressed.

[0540] Furthermore, the following are examples of prompts for a generative AI model:

[0541] "Please create a 5-minute meditation guide to help customers relax while shopping."

[0542] This prompt is used to guide users to appropriate mental health care activities.

[0543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0544] Step 1:

[0545] The device collects voice data from the user. Voice input begins when the user answers questions into the microphone installed on the device. The input is in analog format and is recorded as digital voice data.

[0546] Step 2:

[0547] The terminal transfers the collected audio data to the server. The data is transmitted to the cloud server using high-speed digital communication technology. The input here is digital audio data, and the output is the same data stored on the server.

[0548] Step 3:

[0549] The server uses a speech recognition algorithm to convert audio data into text data. This is done using the Python library "speech_recognition". The input is audio data, and the output is text data after data processing.

[0550] Step 4:

[0551] The server analyzes text data to recognize the user's emotional state. Here, the "emotion_recognition" algorithm is used, and the input text data is analyzed for emotion and output as emotional state data.

[0552] Step 5:

[0553] The server generates a self-care plan based on emotional state data. Using a generative AI model, appropriate self-care activities (e.g., meditation guidance or exercise) are suggested. The input is emotional state data, and the generated self-care plan is output.

[0554] Step 6:

[0555] The terminal provides the user with a self-care plan transmitted from the server. The display device shows the plan details and provides audio guidance. The user can then perform self-care according to the displayed plan. The output is a self-care plan presented in a format that the user can understand.

[0556] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0557] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0558] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0562] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0563] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0564] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0565] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0566] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0567] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0568] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0569] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0570] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0571] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0572] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0573] This invention relates to a mental health management system that uses user voice data. When a user launches a dedicated application on their device and inputs voice data, the device sends that data to a server. The server uses a voice analysis engine to obtain the user's emotional state from the voice data. This emotional state is quantified based on information such as voice tone and speed. The obtained emotional state is recorded in a database and analyzed by an AI engine. Based on the recorded data, the AI ​​engine generates a self-care plan optimized for each user's individual mental state. This self-care plan includes AI counseling and training menus and is provided to the user via the device. By using this and carrying out the suggested activities and training, the user can improve or maintain their mental health.

[0574] As a concrete example, when a user feels mentally exhausted after work, they can use this system to understand their emotional state. For instance, if the analysis of voice data diagnoses a high stress level, the server generates a self-care plan that includes relaxation exercises and short meditation sessions. This plan is presented to the user via a terminal, and the user can perform these self-care activities according to the instructions. In this way, the present invention helps maintain and manage the user's mental health.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The user launches a dedicated app on their device and begins the stress check. The device then presents the user with voice-based questions to assess their mental state.

[0578] Step 2:

[0579] When a user verbally answers a question presented to them, the device records the audio in real time. This recorded data is then sent directly to the server.

[0580] Step 3:

[0581] The server inputs the received audio data into the speech analysis engine and begins analyzing the data. The speech analysis engine identifies the user's emotional state from the audio data and quantifies the emotional state based on factors such as tone of voice, speed, and pauses.

[0582] Step 4:

[0583] The server saves the analysis results to a database and retains information about the emotional state.

[0584] Step 5:

[0585] The server passes emotional state data it has stored to an AI engine, which generates a self-care plan optimized for the user's current mental state. This plan is then compared with the user's past data to suggest specific and effective activities and training.

[0586] Step 6:

[0587] The server generates a self-care plan and sends it to the device. The device displays this plan to the user and provides details of the self-care activities to be undertaken.

[0588] Step 7:

[0589] The user reviews the presented self-care plan and selects the activities they wish to perform. Based on the user's selection, the device plays guided training or meditation exercises and provides the user with specific instructions on how to carry them out.

[0590] (Example 1)

[0591] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0592] In modern society, despite the importance of mental health management, there is a lack of adequate systems in place for users to understand their own mental state on a daily basis and take appropriate action. As a result, users unconsciously accumulate mental burdens, which can lead to more serious health problems. Therefore, there is a need for methods that allow users to easily and accurately understand their own emotional state and immediately take appropriate self-care measures.

[0593] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0594] In this invention, the server includes means for receiving voice information using a communication device, means for analyzing the voice information using a voice analysis mechanism to quantify the emotional state, and means for analyzing information stored in a recording device containing past emotional data using the analysis mechanism to generate self-care guidance based on the emotional state. This enables users to quickly understand their own emotional state and receive individually optimized self-care.

[0595] A "communication device" is a hardware or software system for sending and receiving data, and in this system, it plays the role of transmitting voice information to the server.

[0596] "Voice information" refers to voice data that a user inputs into the system, and is data in analog or digital format obtained based on the user's speech.

[0597] A "voice analysis mechanism" is a general term for algorithms and software that analyze voice information, extract features such as tone and speed of voice, and quantify emotional states.

[0598] "Emotional state" refers to data that indicates the user's emotional state and tendencies, derived from the characteristics of their voice by a voice analysis mechanism.

[0599] A "recording device" is a hardware or software system that stores collected data and analysis results for the long term and makes the data available for retrieval as needed.

[0600] An "analysis mechanism" is a general term for algorithms and software that analyze user trends using data stored in a recording device and generate appropriate advice and guidance based on the results obtained.

[0601] "Self-care guidance" refers to suggestions for maintaining or improving mental health, provided to users based on their analyzed emotional state.

[0602] A "user terminal" is a device used to provide self-care guidance to users, and includes smartphones, tablets, and other similar devices.

[0603] This mental health management system primarily consists of a terminal with a dedicated application installed for user voice input, and a server connected to it. Users launch the application on a device such as a smartphone or tablet and input voice information via the microphone. The terminal is equipped with a communication device to quickly transmit this voice information to the server.

[0604] The server analyzes the received audio information using an audio analysis mechanism. This analysis mechanism consists of software algorithms that convert the audio information into a digital format and extract features such as voice tone and speed. For example, the user's stress and tension levels can be quantified through audio frequency analysis. This quantified emotional state is stored in a recording device and further analyzed using a generative AI model.

[0605] By using generative AI models, such as natural language processing models, the server comprehensively evaluates user data, including past records, to generate optimal self-care guidance for the user. This guidance includes relaxation methods and activities tailored to the user's current situation. The guidance thus generated is presented via the user's terminal. The terminal can also provide guides for performing the suggested activities, as well as links to applications and websites as needed.

[0606] As a concrete example, suppose a user uses the application to voice-input "I'm tired today" after work. The system analyzes this utterance and determines that the user has a "high stress level." The server then generates self-care instructions, including relaxation exercises and short meditation sessions, and sends them to the user's terminal. The user can then follow the instructions displayed on the terminal to reduce stress. A suitable example of a prompt would be, "Please evaluate the emotional state of this voice and tell me what self-care is needed."

[0607] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0608] Step 1:

[0609] The user launches a dedicated application on their device and inputs voice information. The input voice is converted into a digital signal through the device's microphone. This digital voice data is then ready to be sent to the next step. Specifically, voice input begins when the user speaks on the topic of "today's meeting."

[0610] Step 2:

[0611] The terminal transmits digitized voice data to the server using a secure communication protocol. The input voice data is transferred from the terminal to the server, and the server receives the voice data. During this process, encryption is applied to maintain data integrity and security.

[0612] Step 3:

[0613] The server passes the received audio data to the speech analysis mechanism, which then begins the analysis. The speech analysis mechanism converts the audio data into text and further measures and analyzes the tone, speed, volume, and other aspects of the voice. For example, it analyzes the frequency characteristics of the most recent audio and outputs the user's emotional state as a numerical value. The output value might take the form of "Tension level: 60%, Fatigue level: 30%, Relaxation level: 10%."

[0614] Step 4:

[0615] The server stores quantified emotional data in a recording device and simultaneously analyzes this data using a generative AI model. During the analysis, comparisons are made with past data to understand the user's mental state and tendencies. As a result of this analysis, personalized self-care guidance is generated. This guidance includes practices such as meditation and breathing exercises.

[0616] Step 5:

[0617] The server sends the generated self-care instructions to the terminal and provides them to the user. The terminal displays the received self-care instructions on the application screen. The user can then perform the suggested self-care activities by following the instructions displayed on the terminal. Specifically, the user receives the instruction to "perform a 10-minute guided meditation" and begins the activity.

[0618] (Application Example 1)

[0619] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] In modern society, stress and fatigue tend to accumulate easily, and users are required to choose appropriate meals that suit their mental state. However, it is difficult for individuals to understand their own emotional state and make meal choices based on it in their daily lives. In this situation, there is a need for a system that can quickly analyze a user's emotional state and make effective meal recommendations based on the results.

[0621] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0622] In this invention, the server includes a device for collecting voice information, a device for analyzing the voice information to identify an emotional state, and a device for creating a meal recommendation plan based on the emotional state. This makes it possible for users to easily and quickly select a meal that is appropriate for their emotional state.

[0623] "Voice information" refers to data collected from the user's voice and converted into a format that can be processed within an electronic device.

[0624] "Emotional state" is a numerical representation of the user's psychological state and mood derived from analyzed audio information.

[0625] A "meal recommendation plan" is a plan or suggestion for presenting appropriate meal menus based on the user's emotional state.

[0626] A "collection device" is a device that acquires audio information and provides it to a system for processing and analysis.

[0627] An "analytical device" is a device that analyzes collected audio information and identifies the user's emotional state from it.

[0628] The "creation device" is a device that has the function of generating meal recommendation plans tailored to specific emotional states.

[0629] The "device provided" refers to a device that presents the generated meal recommendation plan to the user and supports them in making choices and implementing them.

[0630] The following system configuration and its operation will be described as embodiments for carrying out the invention.

[0631] This system implements a series of processes to collect voice information, analyze emotional states, and provide meal recommendation plans. First, the terminal collects the user's voice information using a voice input device. Next, the voice information is transmitted to the server via the internet. The server uses a voice analysis engine to identify the user's emotional state from the voice information.

[0632] For emotion analysis, natural language processing techniques and machine learning algorithms are often used. This allows the emotional state to be quantified based on characteristics such as the tone, speed, and intensity of the user's voice.

[0633] The server then uses a generative AI model to create an appropriate meal recommendation plan based on the analyzed emotional state. This plan includes menus optimized for that emotional state. For example, if the user is feeling stressed, a menu using ingredients with relaxation effects will be suggested.

[0634] The generated meal recommendation plan is sent to the device and presented to the user as specific options. The user can then order a meal based on this plan. This system allows users to quickly select the optimal meal that best suits their emotional state.

[0635] For example, if a user inputs the voice message "I'm very tired today," the system can suggest a relaxing meal such as "Recommended menu: Chicken soup, lavender tea." An example of such a prompt would be, "I'm very tired today. Please suggest a relaxing meal."

[0636] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0637] Step 1:

[0638] The terminal collects user voice information using a voice input device. The collected voice information is converted into digital data format. This data is transmitted to a server via the internet. The input is the user's voice, and the output is digitized voice data.

[0639] Step 2:

[0640] The server uses a speech analysis engine to analyze the received speech data. This analysis extracts features such as tone, speed, and intensity, and uses machine learning algorithms to identify the emotional state. The input is digitized speech data, and the output is numerical data representing the detected emotional state.

[0641] Step 3:

[0642] The server uses a generative AI model to create meal recommendation plans based on quantified emotional states. The generative AI model refers to a pre-trained database of ingredients and menus to suggest menus that are appropriate for the user's emotional state. The input is quantified data of emotional states, and the output is a specific meal recommendation plan.

[0643] Step 4:

[0644] The server sends the created meal recommendation plan to the terminal. The terminal displays this plan to the user, who can then select from the suggested menu items. The input is the meal recommendation plan, and the output is the user's selection.

[0645] Step 5:

[0646] The user places an order for food based on the displayed menu. Based on the user's selection, the terminal sends the order information to the relevant service. The input is the user's selection information, and the output is the confirmed order information.

[0647] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0648] This invention relates to a mental health management system that uses voice data collected from users to recognize their emotional state and provide a self-care plan. The user launches a dedicated app on their device and starts a stress check. The device presents questions to understand the user's emotional state through voice input. As the user responds, the device collects voice data and transmits it to a server. The server passes the voice data to an emotion engine for analysis. This emotion engine recognizes the user's emotions (e.g., stress, relief, excitement tendencies, etc.) and quantifies the results.

[0649] Based on the results of this emotion recognition, the server uses an AI engine to generate a customized self-care plan. This plan includes AI-led counseling and self-care activities optimized for the user's recognized emotional state. For example, if the user's emotions are determined to be stressful, a plan will be generated that includes relaxation exercises such as meditation, breathing exercises, and autogenic training.

[0650] The generated self-care plan is sent from the server to the terminal and provided to the user. The user accesses this plan through the terminal and implements the recommended self-care methods. In addition, the system sends alerts and additional notifications to the user regarding the persistence of certain emotional states and suggests further sessions, as needed. For example, if very high stress levels persist, this may include a suggestion for consultation with an additional professional.

[0651] In this way, this system utilizes voice data to accurately recognize the user's emotional state and provides personalized self-care plans based on that recognition, thereby enabling efficient mental health management.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The user launches the dedicated app on their device and selects the "Start Stress Check" button. The device prompts the user to speak and presents questions.

[0655] Step 2:

[0656] While the user verbally answers the presented questions, the device records their voice. The recorded audio data is then sent directly to the server.

[0657] Step 3:

[0658] The server inputs the received audio data into the emotion engine. The emotion engine analyzes the audio data and extracts the user's emotional state. This process analyzes factors such as voice tone, speed, volume, and rhythm, and quantifies emotional indicators.

[0659] Step 4:

[0660] The server receives the analysis results from the emotion engine and saves the emotional state to a database. This information is later used to create a self-care plan.

[0661] Step 5:

[0662] The server uses an AI engine to generate a self-care plan tailored to the user based on their emotional state. This plan includes relaxation exercises, AI counseling, and daily stress management techniques.

[0663] Step 6:

[0664] The server generates a self-care plan and sends it to the device. The device displays the plan to the user and provides detailed instructions on the next steps and activities.

[0665] Step 7:

[0666] The user reviews the presented self-care plan and selects activities that suit them. The device provides audio or video guidance to support the user regarding the selected activities.

[0667] Step 8:

[0668] If necessary, the server generates an alert and notifies the user via their device if the user's emotional state exceeds a certain threshold. This can prompt the user to take additional self-care measures or seek professional help if needed.

[0669] (Example 2)

[0670] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] In modern society, stress management and emotional regulation are crucial issues. Traditional methods have made it difficult to accurately understand individual emotional states and provide optimal self-care plans based on them. In particular, there is a lack of individualized approaches, and general recommendations make it difficult to achieve effective mental health care.

[0672] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0673] In this invention, the server includes a device for collecting voice data, an analysis device for analyzing the voice data and extracting emotional states, a device for generating a self-care plan using a generation AI model based on the emotional states, a device for displaying the self-care plan to the user, and a device for sending notifications and additional suggestions to the user. This makes it possible to accurately grasp each individual's emotional state and provide a self-care plan that is individually optimized based on it.

[0674] "Voice data" refers to digital information obtained from voice input, and serves as fundamental data for analyzing the user's emotional state.

[0675] An "analysis device" refers to an electronic device or software that receives audio data and uses a specific algorithm to determine the emotional state of the user.

[0676] A "generative AI model" refers to an artificial intelligence algorithm that generates an optimal self-care plan based on the user's emotional data.

[0677] A "self-care plan" refers to an activity plan aimed at improving mental and physical health, tailored to the user's emotional state.

[0678] A "user" refers to an individual who attempts to manage their own emotional state using this system.

[0679] "Devices that send notifications and additional suggestions" refer to communication methods that encourage users to follow their self-care plans and provide new instructions or warnings as needed.

[0680] This mental health management system uses voice data to recognize the user's emotional state and provides a customized self-care plan based on the results. A specific implementation of the system is shown below.

[0681] First, the user launches a dedicated app on a device such as a smartphone or tablet. The app supports voice input and provides an interface that allows the user to begin the stress check. At this time, the device prepares to receive voice input using its built-in microphone.

[0682] Next, the device displays questions to the user in both audio and text, such as "How are you feeling today?". The user's responses are collected as audio data and transferred to a server in digital format. This process utilizes speech recognition software built into the device.

[0683] The received audio data is processed by an analysis device on the server. Specifically, an emotion engine analyzes the tone, tempo, and content of the audio to quantify and identify the user's emotional state. This analysis utilizes machine learning algorithms and natural language processing technologies.

[0684] The analysis results are input into a generating AI model, and the server generates an optimal self-care plan based on those results. This plan includes relaxation exercises and recommended activities for daily stress management, tailored to the user's individual condition.

[0685] Finally, the generated self-care plan is transferred from the server to the terminal and notified to the user. The user can access the self-care plan through the terminal and select and perform the suggested activities. The plan may also be provided to the user as voice guidance or step-by-step instructions.

[0686] For example, if a user is determined to be in a very high stress level, a plan is created that includes playing relaxation music and providing guidance on deep breathing exercises. Furthermore, the system continuously monitors the user's state and sends notifications as needed.

[0687] Example of a prompt:

[0688] "Design prompts that analyze the user's voice data to recognize their emotions and create a self-care plan for stress reduction. The plan should include relaxation exercises and suggestions for professional consultation."

[0689] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0690] Step 1:

[0691] The user launches a dedicated app on their device. The user then clicks the "Start Stress Check" button on the app's interface. Input consists of the user launching the app and instructing the user to begin the stress check. This triggers the device to prepare a voice input mode and present the questions.

[0692] Step 2:

[0693] The device puts itself into voice input standby mode. The device displays a pre-set question (e.g., "How have you been lately?") in both text and audio, and records the user's voice input. The input consists of text information and the user's voice data, which is converted into digital audio data within the device as output. Speech recognition software makes this possible.

[0694] Step 3:

[0695] The terminal transfers the collected audio data to the server. The input here is digital audio data, and the output is the completion of the audio data transfer to the server. The terminal uses a communication module to send data to the server.

[0696] Step 4:

[0697] The server sends the received audio data to the analysis device. The server activates an emotion engine for audio analysis, using the received audio data as input. This engine uses a machine learning algorithm to analyze the audio data and identify the user's emotional state. The output is numerical data representing the emotional state.

[0698] Step 5:

[0699] The server generates a self-care plan using a generative AI model based on the results of the emotional state. The input is quantified emotional data, and the AI ​​uses this to suggest the most suitable self-care. The generated self-care plan is output. Specifically, it includes relaxation exercises and daily habit improvements that are appropriate for the user's condition.

[0700] Step 6:

[0701] The server sends the self-care plan it has created to the terminal. The input is the self-care plan, and the output is the transfer of the plan data to the terminal. The terminal receives this and sets up a notification display for the user.

[0702] Step 7:

[0703] Users review their self-care plan via their device and perform the suggested activities. The input consists of the content of the notified self-care plan, and users attempt to improve their daily routines based on it. User progress and feedback are used for future analysis.

[0704] (Application Example 2)

[0705] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] In recent years, there has been a growing demand for public and retail facilities to alleviate the mental burdens users experience in their daily lives and to provide opportunities for refreshment. However, conventional programs and services have faced challenges in providing easily accessible, individualized self-care activities that are tailored to the emotional state of users. In particular, establishing a system to provide appropriate mental health care in a short amount of time while shopping in retail facilities has been difficult.

[0707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0708] In this invention, the server includes means for collecting voice data, means for analyzing the voice data to extract emotional states, means for generating a self-care plan based on the emotional states, and means for making the self-care plan available within the retail facility via an interactive information device. This enables users within the retail facility to receive mental health care activities optimized for them in a short amount of time.

[0709] "Audio data" refers to data that captures the sounds emitted by a user as digital information.

[0710] "Analysis" is the process of extracting emotional states from acquired audio data.

[0711] "Emotional state" refers to information that indicates the user's psychological state, obtained through analysis.

[0712] A "self-care plan" is a set of activity proposals for relaxation and stress reduction, generated according to the user's emotional state.

[0713] "Provision" means making the generated self-care plan available for use by the user.

[0714] An "interactive information device" is a device equipped with an interface that can be directly operated by the user, and is a terminal used for inputting voice data and outputting self-care plans.

[0715] A "retail facility" is a commercial space for customers that provides goods and services, and includes, for example, shopping malls.

[0716] "Available" refers to a state where the user is able to execute their intended plan.

[0717] The system for implementing this invention collects voice data, analyzes emotional states, and generates a corresponding self-care plan. The hardware used is an interactive information device accessible to users, which is installed within a retail facility. The terminal is equipped with a suitable microphone and display, and allows data input by the user responding verbally.

[0718] The system's software primarily performs processing using speech recognition and emotion analysis algorithms. Voice data is collected through terminals and transferred to a server. The server converts the voice data into text using the Python library "speech_recognition". Then, based on that text, the emotional state is analyzed using specialized software called "emotion_recognition".

[0719] Based on the emotional state obtained through analysis, the server generates a self-care plan. The generated plan is provided to the user through an interactive information device. For example, if a stressful state is detected, instructions for a short, refreshing deep breathing exercise will be displayed on the screen and performed as an audio guide.

[0720] As a concrete example, this system could be used in a relaxation space within a shopping mall. An intuitive interface is employed to make it easy for users to access, allowing them to use the system immediately when feeling stressed.

[0721] Furthermore, the following are examples of prompts for a generative AI model:

[0722] "Please create a 5-minute meditation guide to help customers relax while shopping."

[0723] This prompt is used to guide users to appropriate mental health care activities.

[0724] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0725] Step 1:

[0726] The device collects voice data from the user. Voice input begins when the user answers questions into the microphone installed on the device. The input is in analog format and is recorded as digital voice data.

[0727] Step 2:

[0728] The terminal transfers the collected audio data to the server. The data is transmitted to the cloud server using high-speed digital communication technology. The input here is digital audio data, and the output is the same data stored on the server.

[0729] Step 3:

[0730] The server uses a speech recognition algorithm to convert audio data into text data. This is done using the Python library "speech_recognition". The input is audio data, and the output is text data after data processing.

[0731] Step 4:

[0732] The server analyzes text data to recognize the user's emotional state. Here, the "emotion_recognition" algorithm is used, and the input text data is analyzed for emotion and output as emotional state data.

[0733] Step 5:

[0734] The server generates a self-care plan based on emotional state data. Using a generative AI model, appropriate self-care activities (e.g., meditation guidance or exercise) are suggested. The input is emotional state data, and the generated self-care plan is output.

[0735] Step 6:

[0736] The terminal provides the user with a self-care plan transmitted from the server. The display device shows the plan details and provides audio guidance. The user can then perform self-care according to the displayed plan. The output is a self-care plan presented in a format that the user can understand.

[0737] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0738] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0739] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0740] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0741] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0742] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0743] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0744] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0745] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0746] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0747] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0748] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0749] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0750] 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.

[0751] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0752] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0753] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0754] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0755] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0756] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0757] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0758] The following is further disclosed regarding the embodiments described above.

[0759] (Claim 1)

[0760] Means of collecting audio data,

[0761] A means for analyzing the audio data and extracting emotional states,

[0762] A means for generating a self-care plan based on the said emotional state,

[0763] Means for providing the self-care plan to the user,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, comprising means for quantifying and storing the results of analysis of collected audio data.

[0767] (Claim 3)

[0768] The system according to claim 1, comprising means for including AI-based counseling in a self-care plan.

[0769] "Example 1"

[0770] (Claim 1)

[0771] A means for receiving voice information using a communication device,

[0772] A means for analyzing the audio information using an audio analysis mechanism to quantify the emotional state,

[0773] A means for analyzing information stored in a recording device containing past emotional data using an analysis mechanism, and for generating self-care guidance based on the said emotional state,

[0774] Means for providing the self-care guidance to the user terminal,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising means for storing numerical data of emotional states in a recording device and analyzing it using an analysis mechanism.

[0778] (Claim 3)

[0779] The system according to claim 1, comprising means for incorporating artificial intelligence-based counseling into self-care guidance.

[0780] "Application Example 1"

[0781] (Claim 1)

[0782] A device for collecting audio information,

[0783] A device that analyzes the audio information to identify the emotional state,

[0784] A device that creates a meal recommendation plan based on emotional state,

[0785] A device that provides the recommended plan to the user,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, comprising a device for converting the results of the analyzed emotional state into numerical values ​​and recording them.

[0789] (Claim 3)

[0790] The system according to claim 1, comprising a device that selects menu items according to the emotional state of the user in a meal recommendation plan.

[0791] "Example 2 of combining an emotion engine"

[0792] (Claim 1)

[0793] A device for collecting audio data,

[0794] An analysis device that analyzes the audio data and extracts the emotional state,

[0795] A device that generates a self-care plan using an AI model based on the emotional state,

[0796] A device for displaying the self-care plan to the user,

[0797] A device that sends notifications and additional suggestions to the user,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, comprising a computing device for quantifying and storing the results of the analyzed audio data.

[0801] (Claim 3)

[0802] The system according to claim 1, comprising a device that incorporates an AI-based counseling function into a self-care plan.

[0803] "Application example 2 when combining with an emotional engine"

[0804] (Claim 1)

[0805] Means of collecting audio data,

[0806] A means for analyzing the audio data and extracting emotional states,

[0807] A means for generating a self-care plan based on the said emotional state,

[0808] Means for providing the self-care plan to the user,

[0809] Means for making the self-care plan available within a retail facility using interactive information devices,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising means for quantifying and storing the results of analysis of collected voice data, and displaying self-care activities provided by an interactive information device as options.

[0813] (Claim 3)

[0814] The system according to claim 1, wherein the self-care plan includes consultation by artificial intelligence, and the interactive information device provides means for making the consultation available. [Explanation of Symbols]

[0815] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting audio data, A means for analyzing the audio data and extracting emotional states, A means for generating a self-care plan based on the said emotional state, Means for providing the self-care plan to the user, A system that includes this.

2. The system according to claim 1, comprising means for quantifying and storing the results of analysis of collected audio data.

3. The system according to claim 1, comprising means for including AI-based counseling in the self-care plan.

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