system

Abstract

We provide the system. [Solution] A natural language processing method for analyzing the emotional state of users, A response generation means that generates a counseling response based on the analysis results, A means of suggesting relaxation techniques to present to the user, A system including control means for coordinating the above means.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the stress and anxiety experienced by individuals in their daily lives are increasing, which has exacerbated mental health problems. However, due to the high hurdles and costs for receiving professional mental health care, there is a problem that many people cannot receive appropriate support. For this reason, there is a need for a new method that allows individuals to easily and effectively manage their mental health.

Means for Solving the Problems

[0005] This invention includes a response generation means that uses natural language processing means to analyze the user's natural language input, identifies the user's emotional state, and generates a counseling response based on that emotional state. It also includes a relaxation suggestion means that proposes relaxation techniques necessary for caring for the user's mental health, and has a control means that operates these functions in coordination. Furthermore, by including a data management means that records the user's progress and visually indicates the status of improvement, it is possible to provide specific support tailored to individual circumstances. In addition, by including an emergency response means that provides contact information to a specialist when a serious psychological state is detected, rapid intervention can be achieved, and serious situations can be prevented.

[0006] "Natural language processing tools" are functions that analyze text data from users and understand its context and sentiment.

[0007] The "response generation means" is a function that generates feedback and counseling messages to be provided to the user based on the analysis results obtained by the natural language processing means.

[0008] A "relaxation suggestion method" is a function that selects and suggests the most suitable relaxation technique according to the user's emotional state and mental health condition.

[0009] "Control means" refers to functions that coordinate the operation of various functions such as natural language processing means, response generation means, and relaxation suggestion means.

[0010] "Data management means" refers to a function that records and visualizes the progress of a user's mental health, allowing users to understand their progress and track their improvement.

[0011] The "emergency response measures" are a function that provides contact information for experts and connects users to appropriate support when their psychological state is deemed critical. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] 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]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0015] In the following embodiments, a 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.

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

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system for supporting a user's mental health. The system comprises natural language processing means, response generation means, relaxation suggestion means, control means, data management means, and emergency response means. When a user inputs their emotions and thoughts as text, the terminal sends this data to the server. The server analyzes the text using the natural language processing means to identify the emotional state. Based on the analysis results, the response generation means generates feedback and questions for the user and sends them to the terminal. The user then sees these responses on the terminal's screen.

[0034] The relaxation suggestion system selects relaxation techniques such as breathing exercises and meditation based on the analyzed emotional state. The selected techniques are provided to the user as audio or video, and can be experienced through the device. Information on the user's practice of relaxation techniques is recorded and visualized by the data management system, allowing the user to track their mental health progress. Furthermore, if the user's psychological state is deemed critical, an emergency response system is activated, and information on contacting a specialist is displayed on the device. For example, if a user inputs "I've been feeling very anxious lately," the server analyzes the level of anxiety and suggests specific relaxation methods to alleviate it. In this way, the system provides customized support tailored to the user's individual condition.

[0035] The following describes the processing flow.

[0036] Step 1:

[0037] The user starts up their device and accesses the mental health care application. The user enters their current emotions and thoughts in text format into the input field.

[0038] Step 2:

[0039] The terminal sends text input from the user to the server. The transmitted data is passed to the server in real time.

[0040] Step 3:

[0041] The server uses natural language processing to analyze the user's text data, thereby identifying the user's emotional state and stress level. The analysis is based on the context and emotional expressions of the text.

[0042] Step 4:

[0043] Based on the analysis results, the server uses a response generation mechanism to generate feedback and counseling messages to be sent back to the user. The generated responses are empathetic and supportive, taking into account the user's situation.

[0044] Step 5:

[0045] The server sends the generated response to the terminal and presents it to the user. The response message from the server is displayed on the terminal screen.

[0046] Step 6:

[0047] The server selects the most suitable relaxation technique based on the user's emotional state. Specific methods such as breathing exercises or meditation are chosen based on the relaxation suggestion method.

[0048] Step 7:

[0049] The device provides the user with information on relaxation techniques sent from the server in text, audio, or video format. The user then performs the relaxation techniques according to the instructions.

[0050] Step 8:

[0051] The server records the results and progress of the user's relaxation techniques in a data management system. This allows the user to visually understand changes and improvements in their mental health.

[0052] Step 9:

[0053] If the user's mental state is deemed critical, the server will use emergency response measures to provide the terminal with information on how to contact a specialist. The terminal will then display a warning message along with the specialist's contact information.

[0054] (Example 1)

[0055] 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."

[0056] In modern society, maintaining and improving mental health is extremely important, but it is difficult to receive appropriate support tailored to individual needs. In particular, there is a lack of systems that analyze emotions using individualized approaches and provide appropriate feedback and support.

[0057] 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.

[0058] In this invention, the server includes a natural language processing means for analyzing input information via a communication device, a response generation means for creating information using a generation device based on the analysis results, and a suggestion means for selecting countermeasures to provide to the user. This makes it possible to accurately grasp each user's emotional and mental health state and provide individually optimized feedback and relaxation techniques.

[0059] "Inputted information" refers to text data provided by the user to the system and information related to its content.

[0060] A "communication device" refers to a device that has a network interface for sending and receiving data between a terminal and a server.

[0061] "Natural language processing means for analysis" refers to technology that processes text data provided by users and identifies their emotional state and meaning.

[0062] "Response generation means created by a generation device" refers to a mechanism that generates appropriate feedback or questions based on analyzed data.

[0063] "A means of proposing countermeasures to be provided to users" refers to a system that selects and provides relaxation techniques and support measures based on analysis results.

[0064] A "generative model" refers to an AI model used to process user data and create individually optimized information.

[0065] "Control means" refers to a function that oversees and manages the coordination of various means within a system.

[0066] "Management tools for recording progress and indicating mental health status" refers to functions that store user history data and visualize that data to understand progress and changes.

[0067] "A means of responding to situations requiring urgent attention and providing information for contacting specialists" refers to a system that assesses the user's condition and, if necessary, provides information on appropriate specialists and services.

[0068] This invention is a system for supporting the mental health of users and is constructed by combining different technologies. The system mainly uses natural language processing technology, information generation technology, proposal technology, and data management technology. A specific embodiment of this system is described below.

[0069] The user inputs information about their emotions and thoughts into a terminal, and this information is sent to a server via a communication device. The server analyzes the received text using natural language processing (NLTK) and SpaCy, which are language analysis software libraries for Python, and specifically uses algorithms that can detect the user's emotional state.

[0070] Next, the server uses a response generation device to create feedback and questions for the user based on the analysis results. In this step, a generation AI model is utilized to provide responses tailored to the user. An example of a prompt is, "How would you describe your recent feelings in one word?" Such prompts make it possible to gather further information from the user.

[0071] Next, through a suggestion mechanism for relaxation, breathing techniques and meditation methods are selected according to the analysis results and provided to the user via audio and video through the terminal. As a specific example, relaxation music videos can be played using the YouTube® API.

[0072] The data management system records system usage history and visualizes the progress of users' mental health. Visualization software such as Matplotlib and Plotly are used for this process. This allows users to see changes in their own condition through graphs and other visualizations.

[0073] Furthermore, if the server determines that the user's mental state is extremely serious, it will activate emergency response measures and provide the terminal with contact information for relevant professionals and counseling services. This function allows the user to receive professional support quickly.

[0074] The aim of this system is to facilitate personalized mental care for users by providing customized feedback and support according to specific conditions and circumstances.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user enters text about their feelings and thoughts into the device. The entered text data is temporarily stored on the device. For example, if the user enters "I've been feeling very anxious lately," this text will become the basis for subsequent processing.

[0078] Step 2:

[0079] The terminal sends the entered text data to the server using the HTTPS protocol. The input is in text format, and the server returns confirmation information sent to the server as output. This ensures data security while enabling analysis on the server.

[0080] Step 3:

[0081] The server analyzes the received text data using natural language processing techniques. It analyzes the input text and extracts features to identify emotional states. Specifically, it uses Python's NLTK or SpaCy to calculate an emotional score. The output is the identified emotional state.

[0082] Step 4:

[0083] Based on the analysis results, the server uses a response generation mechanism to generate feedback and questions for the user. The input for this step is the previously identified emotional state, and the output is a generated response sentence. The generating AI model provides an appropriate answer based on the prompt sentence, such as "Have you tried anything to alleviate your anxiety?"

[0084] Step 5:

[0085] The server sends the generated response to the terminal, and the terminal displays that response on the user's screen. The input is the generated response data, and the output includes the specific actions that are visually displayed on the terminal.

[0086] Step 6:

[0087] The relaxation suggestion system selects appropriate relaxation techniques based on the analysis results. Using the emotional analysis results as input, it suggests techniques such as "deep breathing" and "meditation." The output is a specific method suggested to the user, which is played back as a guide on the device.

[0088] Step 7:

[0089] The user performs a relaxation technique, and the terminal records the result. The input is the user's action, and the output is log information recorded in the data management system.

[0090] Step 8:

[0091] The server uses recorded data and data management tools to visualize user progress. Using past log information and analysis results as input, the output is visualized graphs and metrics. This allows users to track their mental health progress.

[0092] Step 9:

[0093] If the server determines that the user's psychological state is serious, it will use emergency response measures to provide contact information for a specialist. The input is the analyzed psychological state, and the output is appropriate contact information based on that. By displaying this information on the terminal, the user can receive support quickly.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern society, many people suffer from mental health problems, but there is a lack of readily available means to easily check their condition and address it on a daily basis. In particular, there is a lack of systems that allow for intuitive interaction using voice or improvement based on visualized data. In this situation, there is a need for a system that allows users to understand their own mental health and receive appropriate support quickly.

[0097] 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.

[0098] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, voice analysis means for analyzing voice input and converting it into text data, and data management means for managing emotional records and relaxation history. This enables the user to analyze their emotions in real time and be provided with appropriate relaxation methods, thereby supporting the improvement of their mental health.

[0099] "Natural language processing" refers to technologies that analyze text data entered by users to understand their emotions and intentions.

[0100] A "response generation method" is a technology that automatically generates and provides appropriate counseling and feedback to the user based on the analyzed emotional state.

[0101] A "relaxation suggestion method" is a technology that selects and proposes the most suitable relaxation technique according to the user's emotional state.

[0102] "Voice analysis means" refers to technology that converts voice input into text data and uses that data to analyze the user's emotions and intentions.

[0103] "Data management means" refers to technology for recording, managing, and visualizing the history of users' emotions and the status of their relaxation techniques.

[0104] The implementation of this invention requires a user-operated terminal and a server. The terminal is typically a smart device, such as a smartphone or tablet, which processes information through communication with the server. The server plays a major role in processing large amounts of data and utilizes natural language processing and speech analysis technologies.

[0105] The device first receives the user's voice input and sends that data to the server. The server converts the speech to text using SpeechRecognition, a Python speech recognition library. The text data is then analyzed using natural language processing libraries such as spaCy or transformers to identify the user's emotional state.

[0106] Based on the analysis results, the server automatically generates counseling responses using a generative AI model, and these responses are sent to the terminal. The terminal then presents them to the user visually or audibly. Furthermore, relaxation techniques are suggested according to the identified emotions. For example, content related to meditation or breathing exercises may be provided to the terminal, allowing the user to experience them.

[0107] Furthermore, as a data management tool, the relaxation techniques performed by users and their effects are recorded on a server using database technologies such as SQLite. This allows users to track their progress and visualize improvements in their mental health. In addition, as an emergency response measure, if a serious emotional state is detected, information on how to contact a specialist is displayed on the user's device.

[0108] As a concrete example of its use, if an elderly person inputs "I'm feeling a little restless today" via voice, the input is analyzed and the server suggests calming music or relaxing activities. An example of a prompt message would be, "I'm feeling anxious. Please give me some suggestions to calm down."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user inputs voice into the device. The device captures this voice data and prepares to send it to the server. The input is voice data, which is sent to the server in digital format.

[0112] Step 2:

[0113] The server receives audio data and converts it to text using the SpeechRecognition library. Here, the input is audio data and the output is text data. It performs the specific operation of analyzing the audio signal and converting it into a string.

[0114] Step 3:

[0115] The server performs natural language processing on the converted text data using spaCy and transformers. In this step, an emotion analysis model is used with the text data as input to output the user's emotional state. The specific operation is the identification of emotions.

[0116] Step 4:

[0117] The server uses a generative AI model to create the optimal counseling response based on the analysis results. The input is emotional state data, and the output is the response text. The specific operation is to execute the response generation algorithm.

[0118] Step 5:

[0119] The server selects appropriate techniques through relaxation suggestion mechanisms and creates suggested content. The input is emotional state, and the output is data on relaxation suggestions. The selection of a relaxation technique is the specific action.

[0120] Step 6:

[0121] The server sends the generated counseling responses and relaxation suggestions to the terminal. The terminal presents these to the user visually or audibly. The input is the response and suggestion data, and the output is the user interface. The processing for display is the specific action.

[0122] Step 7:

[0123] The terminal receives user feedback based on the responses and relaxation suggestions it receives. This feedback is then sent back to the system. The input is user feedback, and the output is recording in the database. Receiving feedback is the specific action.

[0124] Step 8:

[0125] The server manages the user's emotional state and relaxation history in an SQLite database and visualizes it as needed. Input is historical data, and output is visualized information. The specific operations are data management and visualization.

[0126] 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.

[0127] This invention is a system for comprehensively supporting a user's mental health, and comprises natural language processing means, response generation means, relaxation suggestion means, a so-called "emotion engine," and control means that oversee these means. Furthermore, by combining data management means and emergency response means, it provides monitoring and support for the user's health status.

[0128] When a user accesses the system using a terminal, the emotion engine first collects the user's facial expressions and voice data, and analyzes their emotional state in real time. The server integrates the results of the emotion engine along with natural language input to recognize the user's overall psychological state with high accuracy. As a result, the response generation means generates sophisticated counseling messages that take emotional nuances into account, rather than just text-based feedback, providing the user with guidance on what actions or thoughts they should take next.

[0129] In addition, the relaxation suggestion mechanism utilizes the output of the emotion engine to select relaxation techniques optimized for the user's specific emotions at that time, in addition to standard stress reduction techniques. For example, if the user is showing strong anxiety, a combination of breathing exercises and meditation may be suggested, and a detailed guide will be provided to the user through the device.

[0130] Furthermore, user interactions and emotional responses are recorded by a data management system, allowing users to visually track changes in their own mental health. If the emotional engine detects a serious psychological state, an emergency response system is activated, and information to contact a specialist is provided to the device.

[0131] In this way, this system can provide users with personalized and effective mental healthcare by organically combining multiple means, including an emotion engine.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user accesses the mental health support system via their device. The emotion engine uses the camera and microphone to collect the user's facial expressions and voice data.

[0135] Step 2:

[0136] The device sends collected emotional data and user text input to the server. The server uses an emotion engine to analyze facial expressions and tone of voice. It also analyzes the text data using natural language processing techniques.

[0137] Step 3:

[0138] The server integrates sentiment analysis and text analysis to recognize the user's emotional state with high accuracy. Based on the analyzed data, it determines the user's psychological state.

[0139] Step 4:

[0140] The server uses a response generation mechanism to generate counseling messages tailored to the user's emotional state. These messages include empathy for the user's current situation and specific advice.

[0141] Step 5:

[0142] The server sends the generated counseling message to the terminal and presents it to the user. The message is displayed on the terminal screen, allowing the user to confirm it.

[0143] Step 6:

[0144] The server uses relaxation suggestion tools to select the most suitable relaxation technique for the user's emotional state. The selected technique is tailored to the user's situation.

[0145] Step 7:

[0146] The device provides users with selected relaxation techniques. Specifically, it presents guides for breathing exercises and meditation in audio and video formats.

[0147] Step 8:

[0148] The server records user interaction data and emotional response history using data management tools. This allows users to visually understand changes in their own mental health.

[0149] Step 9:

[0150] If the emotion engine detects a critical psychological state, the server will activate emergency response measures. It will provide the terminal with contact information for specialists and encourage the user to take prompt action.

[0151] (Example 2)

[0152] 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".

[0153] In modern society, users often struggle to access appropriate and effective support to address personal mental health issues. Conventional counseling services are not readily available, and providing advice and specific coping strategies tailored to a user's real-time emotional state is difficult. Therefore, a system is needed that accurately assesses a user's psychological state and provides timely, optimally tailored feedback and support.

[0154] 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.

[0155] In this invention, the server includes a data processing device for analyzing the user's emotional state, an information processing device for integrating the analysis results and natural language input to recognize the psychological state, and a generation means for creating a response that takes emotional nuances into consideration. This enables the timely provision of personalized counseling and relaxation techniques based on the user's psychological state.

[0156] A "data processing device" is a device that analyzes emotional data collected from users and identifies their state.

[0157] An "information processing device" is a device that combines analyzed emotional data with natural language input from the user to recognize the overall psychological state.

[0158] A "generation method" is a means of automatically creating responses and advice that incorporate the nuances of the user's emotions.

[0159] A "control device" is a device that enables smooth system operation by integrating and coordinating various means.

[0160] A "recording method" refers to a means of saving the user's emotional changes and interaction history to a database.

[0161] "Response measures" refer to means of detecting serious psychological situations and promptly providing information to contact specialists.

[0162] The server operates a system that supports users' mental health. This system comprises a data processing device, an information processing device, a generation means, a control device, a recording means, and a response means.

[0163] When a user accesses the system through a terminal, the terminal uses a high-resolution camera and microphone to collect the user's facial expressions and voice data. Specifically, image recognition software is used for image processing, and a voice processing library is used for voice analysis. This data is analyzed by a data processing unit on the server. In this analysis process, for example, the user's emotions are classified as "joy," "sadness," "anxiety," etc.

[0164] The analysis results are integrated with natural language input from the user (e.g., chat messages) by an information processing device to recognize the user's overall psychological state. This process utilizes a natural language processing engine (e.g., a natural language model).

[0165] Subsequently, the generation mechanism utilizes a generative AI model to create a response based on the user's emotions and state. This response takes emotional nuances into account, and for example, a message such as "Take a deep breath and relax" is generated and displayed on the device.

[0166] On the other hand, the recording means stores a history of the user's emotional state in data storage, thereby providing foundational data for visualizing long-term changes in mental health.

[0167] Furthermore, if a serious psychological state is detected, response measures are set up, and information to contact a specialist is promptly provided to the device.

[0168] For example, if a user enters a prompt such as "I've been feeling very anxious lately and can't sleep," the system can analyze the data from their voice and facial expressions, suggest appropriate relaxation techniques, and, if necessary, provide a way to contact a specialist.

[0169] The entire system is managed by a control device that coordinates these means, enabling it to provide users with comprehensive and personalized mental healthcare.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device collects the user's facial expressions and audio data using a high-resolution camera and microphone. At this time, the device prompts the user with the question, "How are you feeling right now?" The input data consists of the user's video and audio data, which is sent to the server as the basic input for emotion analysis.

[0173] Step 2:

[0174] The server converts the received audio data into text and simultaneously analyzes facial expression data using image recognition software. The input data consists of audio and images, and the data processing unit generates output classified into emotion categories (e.g., "joy," "sadness," "anxiety").

[0175] Step 3:

[0176] The server uses a natural language processing engine to analyze the text converted from speech and the prompt sentences entered by the user, and integrates them with sentiment analysis results. This process outputs an overall psychological state, which serves as the basis for response generation.

[0177] Step 4:

[0178] The server uses a generative AI model to generate responses and advice that are appropriate for the user's psychological state. Specifically, depending on the nuances of the emotions, messages such as "Try meditating for 5 minutes" will be output.

[0179] Step 5:

[0180] Based on the data from the emotion engine, the server selects relaxation techniques optimized for the user's state. This process suggests techniques such as breathing exercises for high anxiety levels and music therapy for low stress levels.

[0181] Step 6:

[0182] The terminal displays response messages from the server and suggested relaxation techniques to the user. Simultaneously, it provides visual and audio guidance regarding relaxation methods.

[0183] Step 7:

[0184] The server records the user's emotional state and interaction history in a database and generates analytical reports for visualization as needed.

[0185] Step 8:

[0186] If the server detects a serious psychological state based on the analysis results, it will display emergency contact information on the terminal and provide the user with instructions on how to connect with a specialist. A rapid notification system is utilized in this process.

[0187] (Application Example 2)

[0188] 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 device 14 will be referred to as the "terminal."

[0189] In modern society, mental health care is a crucial issue, but many people find self-management difficult and have limited opportunities to receive professional support. To address this problem, there is a need for a system that can regularly monitor an individual's emotional state and provide appropriate care immediately. Furthermore, solutions are needed to enable sustainable health management within the home environment.

[0190] 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.

[0191] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, a robotic device for supporting the user's health in a home environment, information output means for providing relaxation techniques and actions for improving health based on the analysis results, and a sensor unit for acquiring the user's voice and facial expression data. This enables daily and constant monitoring of mental health and immediate feedback.

[0192] "Natural language processing" refers to technologies that analyze a user's voice or text to understand their intentions and emotions.

[0193] A "response generation means" is a technology that generates appropriate feedback or counseling messages based on the analyzed emotional state of the user.

[0194] A "relaxation suggestion method" is a function that identifies and suggests relaxation techniques suitable for the user's emotional state.

[0195] A "robot device" is hardware installed in a home environment to support the user's health.

[0196] An "information output means" is a means of providing users with relaxation techniques and actions to improve their health.

[0197] A "sensor unit" is a device used to acquire data such as the user's voice and facial expressions.

[0198] To realize this invention, a robotic device installed in a home environment is utilized. This robotic device is equipped with a sensor unit for collecting user voice and facial expression data. The sensor unit converts the voice data spoken by the user into text using speech recognition software (e.g., Google's Speech-to-Text API), and the facial expression data is analyzed using image processing technology (e.g., the OpenCV library).

[0199] The server analyzes this data through natural language processing to understand the user's emotional state. Based on the results of the emotional analysis, the response generation system uses AI technology (e.g., OpenAI's ChatGPT model) to generate appropriate counseling responses and relaxation techniques, and sends instructions to the robot device through the information output system. The robot device provides the generated information to the user via voice or display.

[0200] For example, if a user is experiencing high levels of stress, the robot device might respond with something like, "Hello. I can sense a little tension in your voice. Let's take a deep breath. I'll count with you, so please keep pace with me."

[0201] Furthermore, the robotic device transmits the user's daily emotional data to a server, where it is stored by a data management system. This stored data is then provided to the user using visualization tools (e.g., Tableau) for the purpose of long-term improvement of mental health. In addition, if a serious psychological state is detected, the server generates information to contact a professional through an emergency response system.

[0202] An example of a prompt for the generative AI model would be text such as, "Please come up with relaxation suggestions for when the user expresses anxiety."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The user begins interacting with the robot device. The user's voice and facial expressions are captured by a sensor unit, and this data is collected as digital signals. The voice data is sent to a server.

[0206] Step 2:

[0207] The server uses speech recognition software to convert speech data into text. The converted text data is then input into a natural language processing system for emotional state analysis. The analysis results include the type and intensity of the emotion.

[0208] Step 3:

[0209] Based on the analysis results of natural language processing, the server uses a generative AI model to determine what kind of counseling response or relaxation technique is appropriate. This determination is made by querying the AI ​​model with prompt sentences. For example, a prompt sentence such as "Please consider relaxation suggestions for when the user expresses anxiety" is generated. The generated responses and suggestions are returned to the server.

[0210] Step 4:

[0211] The server transfers the generated responses and suggestions to an information output device and sends instructions to the robot device. The robot device provides the user with counseling messages and relaxation techniques through voice and display. Specific actions include providing voice guidance on deep breathing exercises.

[0212] Step 5:

[0213] The robotic device transmits data on the user's interactions and responses to a server. The server uses data management tools to store this data and record the user's mental health progress.

[0214] Step 6:

[0215] The server analyzes accumulated data and generates information to visualize long-term mental health trends. This information is provided to the user. Furthermore, if a serious psychological condition is detected, the server generates and provides emergency response information to contact a professional.

[0216] 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.

[0217] 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 (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention provides a system for supporting a user's mental health. The system comprises natural language processing means, response generation means, relaxation suggestion means, control means, data management means, and emergency response means. When a user inputs their emotions and thoughts as text, the terminal sends this data to the server. The server analyzes the text using the natural language processing means to identify the emotional state. Based on the analysis results, the response generation means generates feedback and questions for the user and sends them to the terminal. The user then sees these responses on the terminal's screen.

[0233] The relaxation suggestion system selects relaxation techniques such as breathing exercises and meditation based on the analyzed emotional state. The selected techniques are provided to the user as audio or video, and can be experienced through the device. Information on the user's practice of relaxation techniques is recorded and visualized by the data management system, allowing the user to track their mental health progress. Furthermore, if the user's psychological state is deemed critical, an emergency response system is activated, and information on contacting a specialist is displayed on the device. For example, if a user inputs "I've been feeling very anxious lately," the server analyzes the level of anxiety and suggests specific relaxation methods to alleviate it. In this way, the system provides customized support tailored to the user's individual condition.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user starts up their device and accesses the mental health care application. The user enters their current emotions and thoughts in text format into the input field.

[0237] Step 2:

[0238] The terminal sends text input from the user to the server. The transmitted data is passed to the server in real time.

[0239] Step 3:

[0240] The server uses natural language processing to analyze the user's text data, thereby identifying the user's emotional state and stress level. The analysis is based on the context and emotional expressions of the text.

[0241] Step 4:

[0242] Based on the analysis results, the server uses a response generation mechanism to generate feedback and counseling messages to be sent back to the user. The generated responses are empathetic and supportive, taking into account the user's situation.

[0243] Step 5:

[0244] The server sends the generated response to the terminal and presents it to the user. The response message from the server is displayed on the terminal screen.

[0245] Step 6:

[0246] The server selects the most suitable relaxation technique based on the user's emotional state. Specific methods such as breathing exercises or meditation are chosen based on the relaxation suggestion method.

[0247] Step 7:

[0248] The device provides the user with information on relaxation techniques sent from the server in text, audio, or video format. The user then performs the relaxation techniques according to the instructions.

[0249] Step 8:

[0250] The server records the results and progress of the user's relaxation techniques in a data management system. This allows the user to visually understand changes and improvements in their mental health.

[0251] Step 9:

[0252] If the user's mental state is deemed critical, the server will use emergency response measures to provide the terminal with information on how to contact a specialist. The terminal will then display a warning message along with the specialist's contact information.

[0253] (Example 1)

[0254] 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."

[0255] In modern society, maintaining and improving mental health is extremely important, but it is difficult to receive appropriate support tailored to individual needs. In particular, there is a lack of systems that analyze emotions using individualized approaches and provide appropriate feedback and support.

[0256] 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.

[0257] In this invention, the server includes a natural language processing means for analyzing input information via a communication device, a response generation means for creating information using a generation device based on the analysis results, and a suggestion means for selecting countermeasures to provide to the user. This makes it possible to accurately grasp each user's emotional and mental health state and provide individually optimized feedback and relaxation techniques.

[0258] "Inputted information" refers to text data provided by the user to the system and information related to its content.

[0259] A "communication device" refers to a device that has a network interface for sending and receiving data between a terminal and a server.

[0260] "Natural language processing means for analysis" refers to technology that processes text data provided by users and identifies their emotional state and meaning.

[0261] "Response generation means created by a generation device" refers to a mechanism that generates appropriate feedback or questions based on analyzed data.

[0262] "A means of proposing countermeasures to be provided to users" refers to a system that selects and provides relaxation techniques and support measures based on analysis results.

[0263] A "generative model" refers to an AI model used to process user data and create individually optimized information.

[0264] "Control means" refers to a function that oversees and manages the coordination of various means within a system.

[0265] "Management tools for recording progress and indicating mental health status" refers to functions that store user history data and visualize that data to understand progress and changes.

[0266] "A means of responding to situations requiring urgent attention and providing information for contacting specialists" refers to a system that assesses the user's condition and, if necessary, provides information on appropriate specialists and services.

[0267] This invention is a system for supporting the mental health of users and is constructed by combining different technologies. The system mainly uses natural language processing technology, information generation technology, proposal technology, and data management technology. A specific embodiment of this system is described below.

[0268] The user inputs information about their emotions and thoughts into a terminal, and this information is sent to a server via a communication device. The server analyzes the received text using natural language processing (NLTK) and SpaCy, which are language analysis software libraries for Python, and specifically uses algorithms that can detect the user's emotional state.

[0269] Next, the server uses a response generation device to create feedback and questions for the user based on the analysis results. In this step, a generation AI model is utilized to provide responses tailored to the user. An example of a prompt is, "How would you describe your recent feelings in one word?" Such prompts make it possible to gather further information from the user.

[0270] Next, through a suggestion mechanism for relaxation, breathing techniques and meditation methods are selected according to the analysis results and provided to the user via audio or video through the terminal. As a specific example, relaxation music videos can be played using the YouTube API.

[0271] The data management system records system usage history and visualizes the progress of users' mental health. Visualization software such as Matplotlib and Plotly are used for this process. This allows users to see changes in their own condition through graphs and other visualizations.

[0272] Furthermore, if the server determines that the user's mental state is extremely serious, it will activate emergency response measures and provide the terminal with contact information for relevant professionals and counseling services. This function allows the user to receive professional support quickly.

[0273] The aim of this system is to facilitate personalized mental care for users by providing customized feedback and support according to specific conditions and circumstances.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user inputs text related to their own emotions and thoughts into the terminal. The input text data is temporarily saved on the terminal as it is. For example, if the user inputs "Recently, I feel very anxious", this text becomes the basic data for subsequent processing.

[0277] Step 2:

[0278] The terminal sends the input text data to the server using the HTTPS protocol. The input is in text format, and confirmation information sent to the server is returned as output. This ensures the security of the data while enabling analysis on the server.

[0279] Step 3:

[0280] The server analyzes the received text data using natural language processing means. It analyzes the input text and extracts features for identifying the emotional state from it. Specifically, it calculates an emotion score using NLTK or SpaCy in Python. As output, the emotional state is identified.

[0281] Step 4:

[0282] Based on the analysis results, the server uses response generation means to generate feedback and questions for the user. The input for this step is the previously identified emotional state, and a generated response sentence is obtained as output. The generation AI model provides appropriate answers such as "Have you tried anything to relieve your anxiety?" based on the prompt sentence.

[0283] Step 5:

[0284] The server sends the generated response to the terminal, and the terminal displays the response on the user's screen. The input is the generated response data, and the output includes specific actions visually displayed on the terminal.

[0285] Step 6:

[0286] The relaxation proposal means selects an appropriate relaxation technique based on the analysis result. Using the analysis result of emotions as input, it proposes techniques such as "deep breathing" and "meditation". The output is the specific method proposed to the user and is reproduced as a guide on the terminal.

[0287] Step 7:

[0288] The user executes the relaxation technique, and the terminal records the result. The input is the operation result of the user, and the output is the log information recorded by the data management means.

[0289] Step 8:

[0290] The server uses the recorded data to visualize the user's progress with the data management means. Using the past log information and analysis result as input, the output becomes a visualized graph or indicator. This allows the user to confirm the progress of their mental health.

[0291] Step 9:

[0292] When it is determined that the user's mental state is serious, the server uses the emergency response means to provide the contact information of an expert. The input is the analyzed mental state, and the output is the appropriate contact information based on it. By displaying the information on the terminal, the user can receive support quickly.

[0293] (Application Example 1)

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

[0295] In modern society, many people suffer from mental health problems, but there is a lack of readily available means to easily check their condition and address it on a daily basis. In particular, there is a lack of systems that allow for intuitive interaction using voice or improvement based on visualized data. In this situation, there is a need for a system that allows users to understand their own mental health and receive appropriate support quickly.

[0296] 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.

[0297] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, voice analysis means for analyzing voice input and converting it into text data, and data management means for managing emotional records and relaxation history. This enables the user to analyze their emotions in real time and be provided with appropriate relaxation methods, thereby supporting the improvement of their mental health.

[0298] "Natural language processing" refers to technologies that analyze text data entered by users to understand their emotions and intentions.

[0299] A "response generation method" is a technology that automatically generates and provides appropriate counseling and feedback to the user based on an analyzed emotional state.

[0300] A "relaxation suggestion method" is a technology that selects and proposes the most suitable relaxation technique according to the user's emotional state.

[0301] "Voice analysis means" refers to technology that converts voice input into text data and uses that data to analyze the user's emotions and intentions.

[0302] "Data management means" refers to technology for recording, managing, and visualizing the history of users' emotions and the status of their relaxation techniques.

[0303] The implementation of this invention requires a user-operated terminal and a server. The terminal is typically a smart device, such as a smartphone or tablet, which processes information through communication with the server. The server plays a major role in processing large amounts of data and utilizes natural language processing and speech analysis technologies.

[0304] The device first receives the user's voice input and sends that data to the server. The server converts the speech to text using SpeechRecognition, a Python speech recognition library. The text data is then analyzed using natural language processing libraries such as spaCy or transformers to identify the user's emotional state.

[0305] Based on the analysis results, the server automatically generates counseling responses using a generative AI model, and these responses are sent to the terminal. The terminal then presents them to the user visually or audibly. Furthermore, relaxation techniques are suggested according to the identified emotions. For example, content related to meditation or breathing exercises may be provided to the terminal, allowing the user to experience them.

[0306] Furthermore, as a data management tool, the relaxation techniques performed by users and their effects are recorded on a server using database technologies such as SQLite. This allows users to track their progress and visualize improvements in their mental health. In addition, as an emergency response measure, if a serious emotional state is detected, information on how to contact a specialist is displayed on the user's device.

[0307] As a specific example of use, when an elderly person inputs "I'm a bit restless today" by voice, the input is analyzed, and the server proposes calming music or relaxing activities. Examples of prompt sentences include "I'm feeling anxious. Please give me suggestions to calm down."

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The user inputs voice towards the terminal. The terminal captures the voice data and prepares to send it to the server. The input is voice data, which is sent to the server in digital format.

[0311] Step 2:

[0312] The server receives the voice data and uses the SpeechRecognition library to convert the voice into text. Here, the input is voice data and the output is text data. Specific operations are performed to analyze the voice signal and convert it into a character string.

[0313] Step 3:

[0314] The server performs natural language processing on the converted text data using spaCy or transformers. In this step, using the sentiment analysis model with the text data as input, the server outputs the user's sentiment state. Identifying the sentiment is a specific operation.

[0315] Step 4:

[0316] Based on the analysis result, the server uses the generative AI model to create an optimal counseling response. The input is the data of the sentiment state, and the output is the response text. The specific operation is to execute the response generation algorithm.

[0317] Step 5:

[0318] The server selects appropriate techniques through relaxation suggestion mechanisms and creates suggested content. The input is emotional state, and the output is data on relaxation suggestions. The selection of a relaxation technique is the specific action.

[0319] Step 6:

[0320] The server sends the generated counseling responses and relaxation suggestions to the terminal. The terminal presents these to the user visually or audibly. The input is the response and suggestion data, and the output is the user interface. The processing for display is the specific action.

[0321] Step 7:

[0322] The terminal receives user feedback based on the responses and relaxation suggestions it receives. This feedback is then sent back to the system. The input is user feedback, and the output is recording in the database. Receiving feedback is the specific action.

[0323] Step 8:

[0324] The server manages the user's emotional state and relaxation history in an SQLite database and visualizes it as needed. Input is historical data, and output is visualized information. The specific operations are data management and visualization.

[0325] 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.

[0326] This invention is a system for comprehensively supporting a user's mental health, and comprises natural language processing means, response generation means, relaxation suggestion means, a so-called "emotion engine," and control means that oversee these means. Furthermore, by combining data management means and emergency response means, it provides monitoring and support for the user's health status.

[0327] When a user accesses the system using a terminal, the emotion engine first collects the user's facial expressions and voice data, and analyzes their emotional state in real time. The server integrates the results of the emotion engine along with natural language input to recognize the user's overall psychological state with high accuracy. As a result, the response generation means generates sophisticated counseling messages that take emotional nuances into account, rather than just text-based feedback, providing the user with guidance on what actions or thoughts they should take next.

[0328] In addition, the relaxation suggestion mechanism utilizes the output of the emotion engine to select relaxation techniques optimized for the user's specific emotions at that time, in addition to standard stress reduction techniques. For example, if the user is showing strong anxiety, a combination of breathing exercises and meditation may be suggested, and a detailed guide will be provided to the user through the device.

[0329] Furthermore, user interactions and emotional responses are recorded by a data management system, allowing users to visually track changes in their own mental health. If the emotional engine detects a serious psychological state, an emergency response system is activated, and information to contact a specialist is provided to the device.

[0330] In this way, this system can provide users with personalized and effective mental healthcare by organically combining multiple means, including an emotion engine.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user accesses the mental health support system via their device. The emotion engine uses the camera and microphone to collect the user's facial expressions and voice data.

[0334] Step 2:

[0335] The device sends collected emotional data and user text input to the server. The server uses an emotion engine to analyze facial expressions and tone of voice. It also analyzes the text data using natural language processing techniques.

[0336] Step 3:

[0337] The server integrates sentiment analysis and text analysis to recognize the user's emotional state with high accuracy. Based on the analyzed data, it determines the user's psychological state.

[0338] Step 4:

[0339] The server uses a response generation mechanism to generate counseling messages tailored to the user's emotional state. These messages include empathy for the user's current situation and specific advice.

[0340] Step 5:

[0341] The server sends the generated counseling message to the terminal and presents it to the user. The message is displayed on the terminal screen, allowing the user to confirm it.

[0342] Step 6:

[0343] The server uses a relaxation suggestion mechanism to select the most suitable relaxation technique for the user's emotional state. The selected technique is tailored to the user's situation.

[0344] Step 7:

[0345] The device provides users with selected relaxation techniques. Specifically, it presents guides for breathing exercises and meditation in audio and video formats.

[0346] Step 8:

[0347] The server records user interaction data and emotional response history using data management tools. This allows users to visually understand changes in their own mental health.

[0348] Step 9:

[0349] If the emotion engine detects a critical psychological state, the server will activate emergency response measures. It will provide the terminal with contact information for specialists and encourage the user to take prompt action.

[0350] (Example 2)

[0351] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0352] In modern society, users often struggle to access appropriate and effective support to address personal mental health issues. Conventional counseling services are not readily available, and providing advice and specific coping strategies tailored to a user's real-time emotional state is difficult. Therefore, a system is needed that accurately assesses a user's psychological state and provides timely, optimally tailored feedback and support.

[0353] 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.

[0354] In this invention, the server includes a data processing device for analyzing the user's emotional state, an information processing device for integrating the analysis results and natural language input to recognize the psychological state, and a generation means for creating a response that takes emotional nuances into consideration. This enables the timely provision of personalized counseling and relaxation techniques based on the user's psychological state.

[0355] A "data processing device" is a device that analyzes emotional data collected from users and identifies their state.

[0356] An "information processing device" is a device that combines analyzed emotional data with natural language input from the user to recognize the overall psychological state.

[0357] A "generation method" is a means of automatically creating responses and advice that incorporate the nuances of the user's emotions.

[0358] A "control device" is a device that enables smooth system operation by integrating and coordinating various means.

[0359] A "recording method" refers to a means of saving the user's emotional changes and interaction history to a database.

[0360] "Response measures" refer to means of detecting serious psychological situations and promptly providing information to contact specialists.

[0361] The server operates a system that supports users' mental health. This system comprises a data processing device, an information processing device, a generation means, a control device, a recording means, and a response means.

[0362] When a user accesses the system through a terminal, the terminal uses a high-resolution camera and microphone to collect the user's facial expressions and voice data. Specifically, image recognition software is used for image processing, and a voice processing library is used for voice analysis. This data is analyzed by a data processing unit on the server. In this analysis process, for example, the user's emotions are classified as "joy," "sadness," "anxiety," etc.

[0363] The analysis results are integrated with natural language input from the user (e.g., chat messages) by an information processing device to recognize the user's overall psychological state. This process utilizes a natural language processing engine (e.g., a natural language model).

[0364] Subsequently, the generation mechanism utilizes a generative AI model to create a response based on the user's emotions and state. This response takes emotional nuances into account, and for example, a message such as "Take a deep breath and relax" is generated and displayed on the device.

[0365] On the other hand, the recording means stores a history of the user's emotional state in data storage, thereby providing foundational data for visualizing long-term changes in mental health.

[0366] Furthermore, if a serious psychological state is detected, response measures are set up, and information to contact a specialist is promptly provided to the device.

[0367] For example, if a user enters a prompt such as "I've been feeling very anxious lately and can't sleep," the system can analyze the data from their voice and facial expressions, suggest appropriate relaxation techniques, and, if necessary, provide a way to contact a specialist.

[0368] The entire system is managed by a control device that coordinates these means, enabling it to provide users with comprehensive and personalized mental healthcare.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The device collects the user's facial expressions and audio data using a high-resolution camera and microphone. At this time, the device prompts the user with the question, "How are you feeling right now?" The input data consists of the user's video and audio data, which is sent to the server as the basic input for emotion analysis.

[0372] Step 2:

[0373] The server converts the received audio data into text and simultaneously analyzes facial expression data using image recognition software. The input data consists of audio and images, and the data processing unit generates output classified into emotion categories (e.g., "joy," "sadness," "anxiety").

[0374] Step 3:

[0375] The server uses a natural language processing engine to analyze the text converted from speech and the prompt sentences entered by the user, and integrates them with the sentiment analysis results. This process outputs an overall psychological state, which serves as the basis for response generation.

[0376] Step 4:

[0377] The server uses a generative AI model to generate responses and advice that are appropriate for the user's psychological state. Specifically, depending on the nuances of the emotions, messages such as "Try meditating for 5 minutes" will be output.

[0378] Step 5:

[0379] Based on the data from the emotion engine, the server selects relaxation techniques optimized for the user's state. This process suggests techniques such as breathing exercises for high anxiety levels and music therapy for low stress levels.

[0380] Step 6:

[0381] The terminal displays response messages from the server and suggested relaxation techniques to the user. Simultaneously, it provides visual and audio guidance regarding relaxation methods.

[0382] Step 7:

[0383] The server records the user's emotional state and interaction history in a database and generates analytical reports for visualization as needed.

[0384] Step 8:

[0385] If the server detects a serious psychological state based on the analysis results, it will display emergency contact information on the terminal and provide the user with instructions on how to connect with a specialist. A rapid notification system is utilized in this process.

[0386] (Application Example 2)

[0387] 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."

[0388] In modern society, mental health care is a crucial issue, but many people find self-management difficult and have limited opportunities to receive professional support. To address this problem, there is a need for a system that can regularly monitor an individual's emotional state and provide appropriate care immediately. Furthermore, solutions are needed to enable sustainable health management within the home environment.

[0389] 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.

[0390] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, a robotic device for supporting the user's health in a home environment, information output means for providing relaxation techniques and actions for improving health based on the analysis results, and a sensor unit for acquiring the user's voice and facial expression data. This enables daily and constant monitoring of mental health and immediate feedback.

[0391] "Natural language processing" refers to technologies that analyze a user's voice or text to understand their intentions and emotions.

[0392] A "response generation means" is a technology that generates appropriate feedback or counseling messages based on the analyzed emotional state of the user.

[0393] A "relaxation suggestion method" is a function that identifies and suggests relaxation techniques suitable for the user's emotional state.

[0394] A "robot device" is hardware installed in a home environment to support the user's health.

[0395] An "information output means" is a means of providing users with relaxation techniques and actions to improve their health.

[0396] A "sensor unit" is a device used to acquire data such as the user's voice and facial expressions.

[0397] To realize this invention, a robotic device installed in a home environment is utilized. This robotic device is equipped with a sensor unit for collecting user voice and facial expression data. The sensor unit converts the voice data spoken by the user into text using speech recognition software (e.g., Google's Speech-to-Text API), and the facial expression data is analyzed using image processing technology (e.g., OpenCV library).

[0398] The server analyzes this data through natural language processing to understand the user's emotional state. Based on the results of the emotional analysis, the response generation system uses AI technology (e.g., OpenAI's ChatGPT model) to generate appropriate counseling responses and relaxation techniques, and sends instructions to the robot device through the information output system. The robot device provides the generated information to the user via voice or display.

[0399] For example, if a user is experiencing high levels of stress, the robot device might respond with something like, "Hello. I can sense a little tension in your voice. Let's take a deep breath. I'll count with you, so please keep pace with me."

[0400] Furthermore, the robotic device transmits the user's daily emotional data to a server, where it is stored by a data management system. This stored data is then provided to the user using visualization tools (e.g., Tableau) for the purpose of long-term improvement of mental health. In addition, if a serious psychological state is detected, the server generates information to contact a professional through an emergency response system.

[0401] An example of a prompt for the generative AI model would be text such as, "Please come up with relaxation suggestions for when the user expresses anxiety."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user begins interacting with the robot device. The user's voice and facial expressions are captured by a sensor unit, and this data is collected as digital signals. The voice data is sent to a server.

[0405] Step 2:

[0406] The server uses speech recognition software to convert speech data into text. The converted text data is then input into a natural language processing system for emotional state analysis. The analysis results include the type and intensity of the emotion.

[0407] Step 3:

[0408] Based on the analysis results of natural language processing, the server uses a generative AI model to determine what kind of counseling response or relaxation technique is appropriate. This determination is made by querying the AI ​​model with prompt sentences. For example, a prompt sentence such as "Please consider relaxation suggestions for when the user expresses anxiety" is generated. The generated responses and suggestions are returned to the server.

[0409] Step 4:

[0410] The server transfers the generated responses and suggestions to an information output device and sends instructions to the robot device. The robot device provides the user with counseling messages and relaxation techniques through voice and display. Specific actions include providing voice guidance on deep breathing exercises.

[0411] Step 5:

[0412] The robotic device transmits data on the user's interactions and responses to a server. The server uses data management tools to store this data and record the user's mental health progress.

[0413] Step 6:

[0414] The server analyzes accumulated data and generates information to visualize long-term mental health trends. This information is provided to the user. Furthermore, if a serious psychological condition is detected, the server generates and provides emergency response information to contact a professional.

[0415] 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.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention provides a system for supporting a user's mental health. The system comprises natural language processing means, response generation means, relaxation suggestion means, control means, data management means, and emergency response means. When a user inputs their emotions and thoughts as text, the terminal sends this data to the server. The server analyzes the text using the natural language processing means to identify the emotional state. Based on the analysis results, the response generation means generates feedback and questions for the user and sends them to the terminal. The user then sees these responses on the terminal's screen.

[0432] The relaxation suggestion system selects relaxation techniques such as breathing exercises and meditation based on the analyzed emotional state. The selected techniques are provided to the user as audio or video, and can be experienced through the device. Information on the user's practice of relaxation techniques is recorded and visualized by the data management system, allowing the user to track their mental health progress. Furthermore, if the user's psychological state is deemed critical, an emergency response system is activated, and information on contacting a specialist is displayed on the device. For example, if a user inputs "I've been feeling very anxious lately," the server analyzes the level of anxiety and suggests specific relaxation methods to alleviate it. In this way, the system provides customized support tailored to the user's individual condition.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The user starts up their device and accesses the mental health care application. The user enters their current emotions and thoughts in text format into the input field.

[0436] Step 2:

[0437] The terminal sends text input from the user to the server. The transmitted data is passed to the server in real time.

[0438] Step 3:

[0439] The server uses natural language processing to analyze the user's text data, thereby identifying the user's emotional state and stress level. The analysis is based on the context and emotional expressions of the text.

[0440] Step 4:

[0441] Based on the analysis results, the server uses a response generation mechanism to generate feedback and counseling messages to be sent back to the user. The generated responses are empathetic and supportive, taking into account the user's situation.

[0442] Step 5:

[0443] The server sends the generated response to the terminal and presents it to the user. The response message from the server is displayed on the terminal screen.

[0444] Step 6:

[0445] The server selects the most suitable relaxation technique based on the user's emotional state. Specific methods such as breathing exercises or meditation are chosen based on the relaxation suggestion method.

[0446] Step 7:

[0447] The device provides the user with information on relaxation techniques sent from the server in text, audio, or video format. The user then performs the relaxation techniques according to the instructions.

[0448] Step 8:

[0449] The server records the results and progress of the user's relaxation techniques in a data management system. This allows the user to visually understand changes and improvements in their mental health.

[0450] Step 9:

[0451] If the user's mental state is deemed critical, the server will use emergency response measures to provide the terminal with information on how to contact a specialist. The terminal will then display a warning message along with the specialist's contact information.

[0452] (Example 1)

[0453] 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."

[0454] In modern society, maintaining and improving mental health is extremely important, but it is difficult to receive appropriate support tailored to individual needs. In particular, there is a lack of systems that analyze emotions using individualized approaches and provide appropriate feedback and support.

[0455] 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.

[0456] In this invention, the server includes a natural language processing means for analyzing input information via a communication device, a response generation means for creating information using a generation device based on the analysis results, and a suggestion means for selecting countermeasures to provide to the user. This makes it possible to accurately grasp each user's emotional and mental health state and provide individually optimized feedback and relaxation techniques.

[0457] "Inputted information" refers to text data provided by the user to the system and information related to its content.

[0458] A "communication device" refers to a device that has a network interface for sending and receiving data between a terminal and a server.

[0459] "Natural language processing means for analysis" refers to technology that processes text data provided by users and identifies their emotional state and meaning.

[0460] "Response generation means created by a generation device" refers to a mechanism that generates appropriate feedback or questions based on analyzed data.

[0461] "A means of proposing countermeasures to be provided to users" refers to a system that selects and provides relaxation techniques and support measures based on analysis results.

[0462] A "generative model" refers to an AI model used to process user data and create individually optimized information.

[0463] "Control means" refers to a function that oversees and manages the coordination of various means within a system.

[0464] "Management tools for recording progress and indicating mental health status" refers to functions that store user history data and visualize that data to understand progress and changes.

[0465] "A means of responding to situations requiring urgent attention and providing information for contacting specialists" refers to a system that assesses the user's condition and, if necessary, provides information on appropriate specialists and services.

[0466] This invention is a system for supporting the mental health of users and is constructed by combining different technologies. The system mainly uses natural language processing technology, information generation technology, proposal technology, and data management technology. A specific embodiment of this system is described below.

[0467] The user inputs information about their emotions and thoughts into a terminal, and this information is sent to a server via a communication device. The server analyzes the received text using natural language processing (NLTK) and SpaCy, which are language analysis software libraries for Python, and specifically uses algorithms that can detect the user's emotional state.

[0468] Next, the server uses a response generation device to create feedback and questions for the user based on the analysis results. In this step, a generation AI model is utilized to provide responses tailored to the user. An example of a prompt is, "How would you describe your recent feelings in one word?" Such prompts make it possible to gather further information from the user.

[0469] Next, through a suggestion mechanism for relaxation, breathing techniques and meditation methods are selected according to the analysis results and provided to the user via audio or video through the terminal. As a specific example, relaxation music videos can be played using the YouTube API.

[0470] The data management system records system usage history and visualizes the progress of users' mental health. Visualization software such as Matplotlib and Plotly are used for this process. This allows users to see changes in their own condition through graphs and other visualizations.

[0471] Furthermore, if the server determines that the user's mental state is extremely serious, it will activate emergency response measures and provide the terminal with contact information for relevant professionals and counseling services. This function allows the user to receive professional support quickly.

[0472] The aim of this system is to facilitate personalized mental care for users by providing customized feedback and support according to specific conditions and circumstances.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The user enters text about their feelings and thoughts into the device. The entered text data is temporarily stored on the device. For example, if the user enters "I've been feeling very anxious lately," this text will become the basis for subsequent processing.

[0476] Step 2:

[0477] The terminal sends the entered text data to the server using the HTTPS protocol. The input is in text format, and the server returns confirmation information sent to the server as output. This ensures data security while enabling analysis on the server.

[0478] Step 3:

[0479] The server analyzes the received text data using natural language processing techniques. It analyzes the input text and extracts features to identify emotional states. Specifically, it uses Python's NLTK or SpaCy to calculate an emotional score. The output is the identified emotional state.

[0480] Step 4:

[0481] Based on the analysis results, the server uses a response generation mechanism to generate feedback and questions for the user. The input for this step is the previously identified emotional state, and the output is a generated response sentence. The generating AI model provides an appropriate answer based on the prompt sentence, such as "Have you tried anything to alleviate your anxiety?"

[0482] Step 5:

[0483] The server sends the generated response to the terminal, and the terminal displays that response on the user's screen. The input is the generated response data, and the output includes the specific actions that are visually displayed on the terminal.

[0484] Step 6:

[0485] The relaxation suggestion system selects appropriate relaxation techniques based on the analysis results. Using the emotional analysis results as input, it suggests techniques such as "deep breathing" and "meditation." The output is a specific method suggested to the user, which is played back as a guide on the device.

[0486] Step 7:

[0487] The user performs a relaxation technique, and the terminal records the result. The input is the user's action, and the output is log information recorded in the data management system.

[0488] Step 8:

[0489] The server uses recorded data and data management tools to visualize user progress. Using past log information and analysis results as input, the output is visualized graphs and metrics. This allows users to track their mental health progress.

[0490] Step 9:

[0491] If the server determines that the user's psychological state is serious, it will use emergency response measures to provide contact information for a specialist. The input is the analyzed psychological state, and the output is appropriate contact information based on that. By displaying this information on the terminal, the user can receive support quickly.

[0492] (Application Example 1)

[0493] 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."

[0494] In modern society, many people suffer from mental health problems, but there is a lack of readily available means to easily check their condition and address it on a daily basis. In particular, there is a lack of systems that allow for intuitive interaction using voice or improvement based on visualized data. In this situation, there is a need for a system that allows users to understand their own mental health and receive appropriate support quickly.

[0495] 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.

[0496] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, voice analysis means for analyzing voice input and converting it into text data, and data management means for managing emotional records and relaxation history. This enables the user to analyze their emotions in real time and be provided with appropriate relaxation methods, thereby supporting the improvement of their mental health.

[0497] "Natural language processing" refers to technologies that analyze text data entered by users to understand their emotions and intentions.

[0498] A "response generation method" is a technology that automatically generates and provides appropriate counseling and feedback to the user based on the analyzed emotional state.

[0499] A "relaxation suggestion method" is a technology that selects and proposes the most suitable relaxation technique according to the user's emotional state.

[0500] "Voice analysis means" refers to technology that converts voice input into text data and uses that data to analyze the user's emotions and intentions.

[0501] "Data management means" refers to technology for recording, managing, and visualizing the history of users' emotions and the status of their relaxation techniques.

[0502] The implementation of this invention requires a user-operated terminal and a server. The terminal is typically a smart device, such as a smartphone or tablet, which processes information through communication with the server. The server plays a major role in processing large amounts of data and utilizes natural language processing and speech analysis technologies.

[0503] The device first receives the user's voice input and sends that data to the server. The server converts the speech to text using SpeechRecognition, a Python speech recognition library. The text data is then analyzed using natural language processing libraries such as spaCy or transformers to identify the user's emotional state.

[0504] Based on the analysis results, the server automatically generates counseling responses using a generative AI model, and these responses are sent to the terminal. The terminal then presents them to the user visually or audibly. Furthermore, relaxation techniques are suggested according to the identified emotions. For example, content related to meditation or breathing exercises may be provided to the terminal, allowing the user to experience them.

[0505] Furthermore, as a data management tool, the relaxation techniques performed by users and their effects are recorded on a server using database technologies such as SQLite. This allows users to track their progress and visualize improvements in their mental health. In addition, as an emergency response measure, if a serious emotional state is detected, information on how to contact a specialist is displayed on the user's device.

[0506] As a concrete example of its use, if an elderly person inputs "I'm feeling a little restless today" via voice, the input is analyzed and the server suggests calming music or relaxing activities. An example of a prompt message would be, "I'm feeling anxious. Please give me some suggestions to calm down."

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user inputs voice into the device. The device captures this voice data and prepares to send it to the server. The input is voice data, which is sent to the server in digital format.

[0510] Step 2:

[0511] The server receives audio data and converts it to text using the SpeechRecognition library. Here, the input is audio data and the output is text data. It performs the specific operation of analyzing the audio signal and converting it into a string.

[0512] Step 3:

[0513] The server performs natural language processing on the converted text data using spaCy and transformers. In this step, an emotion analysis model is used with the text data as input to output the user's emotional state. The specific operation is the identification of emotions.

[0514] Step 4:

[0515] The server uses a generative AI model to create the optimal counseling response based on the analysis results. The input is emotional state data, and the output is the response text. The specific operation is to execute the response generation algorithm.

[0516] Step 5:

[0517] The server selects appropriate techniques through relaxation suggestion mechanisms and creates suggested content. The input is emotional state, and the output is data on relaxation suggestions. The selection of a relaxation technique is the specific action.

[0518] Step 6:

[0519] The server sends the generated counseling responses and relaxation suggestions to the terminal. The terminal presents these to the user visually or audibly. The input is the response and suggestion data, and the output is the user interface. The processing for display is the specific action.

[0520] Step 7:

[0521] The terminal receives user feedback based on the responses and relaxation suggestions it receives. This feedback is then sent back to the system. The input is user feedback, and the output is recording in the database. Receiving feedback is the specific action.

[0522] Step 8:

[0523] The server manages the user's emotional state and relaxation history in an SQLite database and visualizes it as needed. Input is historical data, and output is visualized information. The specific operations are data management and visualization.

[0524] 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.

[0525] This invention is a system for comprehensively supporting a user's mental health, and comprises natural language processing means, response generation means, relaxation suggestion means, a so-called "emotion engine," and control means that oversee these means. Furthermore, by combining data management means and emergency response means, it provides monitoring and support for the user's health status.

[0526] When a user accesses the system using a terminal, the emotion engine first collects the user's facial expressions and voice data, and analyzes their emotional state in real time. The server integrates the results of the emotion engine along with natural language input to recognize the user's overall psychological state with high accuracy. As a result, the response generation means generates sophisticated counseling messages that take emotional nuances into account, rather than just text-based feedback, providing the user with guidance on what actions or thoughts they should take next.

[0527] In addition, the relaxation suggestion mechanism utilizes the output of the emotion engine to select relaxation techniques optimized for the user's specific emotions at that time, in addition to standard stress reduction techniques. For example, if the user is showing strong anxiety, a combination of breathing exercises and meditation may be suggested, and a detailed guide will be provided to the user through the device.

[0528] Furthermore, user interactions and emotional responses are recorded by a data management system, allowing users to visually track changes in their own mental health. If the emotional engine detects a serious psychological state, an emergency response system is activated, and information to contact a specialist is provided to the device.

[0529] In this way, this system can provide users with personalized and effective mental healthcare by organically combining multiple means, including an emotion engine.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The user accesses the mental health support system via their device. The emotion engine uses the camera and microphone to collect the user's facial expressions and voice data.

[0533] Step 2:

[0534] The device sends collected emotional data and user text input to the server. The server uses an emotion engine to analyze facial expressions and tone of voice. It also analyzes the text data using natural language processing techniques.

[0535] Step 3:

[0536] The server integrates sentiment analysis and text analysis to recognize the user's emotional state with high accuracy. Based on the analyzed data, it determines the user's psychological state.

[0537] Step 4:

[0538] The server uses a response generation mechanism to generate counseling messages tailored to the user's emotional state. These messages include empathy for the user's current situation and specific advice.

[0539] Step 5:

[0540] The server sends the generated counseling message to the terminal and presents it to the user. The message is displayed on the terminal screen, allowing the user to confirm it.

[0541] Step 6:

[0542] The server uses relaxation suggestion tools to select the most suitable relaxation technique for the user's emotional state. The selected technique is tailored to the user's situation.

[0543] Step 7:

[0544] The device provides users with selected relaxation techniques. Specifically, it presents guides for breathing exercises and meditation in audio and video formats.

[0545] Step 8:

[0546] The server records user interaction data and emotional response history using data management tools. This allows users to visually understand changes in their own mental health.

[0547] Step 9:

[0548] If the emotion engine detects a critical psychological state, the server will activate emergency response measures. It will provide the terminal with contact information for specialists and encourage the user to take prompt action.

[0549] (Example 2)

[0550] 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."

[0551] In modern society, users often struggle to access appropriate and effective support to address personal mental health issues. Conventional counseling services are not readily available, and providing advice and specific coping strategies tailored to a user's real-time emotional state is difficult. Therefore, a system is needed that accurately assesses a user's psychological state and provides timely, optimally tailored feedback and support.

[0552] 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.

[0553] In this invention, the server includes a data processing device for analyzing the user's emotional state, an information processing device for integrating the analysis results and natural language input to recognize the psychological state, and a generation means for creating a response that takes emotional nuances into consideration. This enables the timely provision of personalized counseling and relaxation techniques based on the user's psychological state.

[0554] A "data processing device" is a device that analyzes emotional data collected from users and identifies their state.

[0555] An "information processing device" is a device that combines analyzed emotional data with natural language input from the user to recognize the overall psychological state.

[0556] A "generation method" is a means of automatically creating responses and advice that incorporate the nuances of the user's emotions.

[0557] A "control device" is a device that enables smooth system operation by integrating and coordinating various means.

[0558] A "recording method" refers to a means of saving the user's emotional changes and interaction history to a database.

[0559] "Response measures" refer to means of detecting serious psychological situations and promptly providing information to contact specialists.

[0560] The server operates a system that supports users' mental health. This system comprises a data processing device, an information processing device, a generation means, a control device, a recording means, and a response means.

[0561] When a user accesses the system through a terminal, the terminal uses a high-resolution camera and microphone to collect the user's facial expressions and voice data. Specifically, image recognition software is used for image processing, and a voice processing library is used for voice analysis. This data is analyzed by a data processing unit on the server. In this analysis process, for example, the user's emotions are classified as "joy," "sadness," "anxiety," etc.

[0562] The analysis results are integrated with natural language input from the user (e.g., chat messages) by an information processing device to recognize the user's overall psychological state. This process utilizes a natural language processing engine (e.g., a natural language model).

[0563] Subsequently, the generation mechanism utilizes a generative AI model to create a response based on the user's emotions and state. This response takes emotional nuances into account, and for example, a message such as "Take a deep breath and relax" is generated and displayed on the device.

[0564] On the other hand, the recording means stores a history of the user's emotional state in data storage, thereby providing foundational data for visualizing long-term changes in mental health.

[0565] Furthermore, if a serious psychological state is detected, response measures are set up, and information to contact a specialist is promptly provided to the device.

[0566] For example, if a user enters a prompt such as "I've been feeling very anxious lately and can't sleep," the system can analyze the data from their voice and facial expressions, suggest appropriate relaxation techniques, and, if necessary, provide a way to contact a specialist.

[0567] The entire system is managed by a control device that coordinates these means, enabling it to provide users with comprehensive and personalized mental healthcare.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The device collects the user's facial expressions and audio data using a high-resolution camera and microphone. At this time, the device prompts the user with the question, "How are you feeling right now?" The input data consists of the user's video and audio data, which is sent to the server as the basic input for emotion analysis.

[0571] Step 2:

[0572] The server converts the received audio data into text and simultaneously analyzes facial expression data using image recognition software. The input data consists of audio and images, and the data processing unit generates output classified into emotion categories (e.g., "joy," "sadness," "anxiety").

[0573] Step 3:

[0574] The server uses a natural language processing engine to analyze the text converted from speech and the prompt sentences entered by the user, and integrates them with sentiment analysis results. This process outputs an overall psychological state, which serves as the basis for response generation.

[0575] Step 4:

[0576] The server uses a generative AI model to generate responses and advice that are appropriate for the user's psychological state. Specifically, depending on the nuances of the emotions, messages such as "Try meditating for 5 minutes" will be output.

[0577] Step 5:

[0578] Based on the data from the emotion engine, the server selects relaxation techniques optimized for the user's state. This process suggests techniques such as breathing exercises for high anxiety levels and music therapy for low stress levels.

[0579] Step 6:

[0580] The terminal displays response messages from the server and suggested relaxation techniques to the user. Simultaneously, it provides visual and audio guidance regarding relaxation methods.

[0581] Step 7:

[0582] The server records the user's emotional state and interaction history in a database and generates analytical reports for visualization as needed.

[0583] Step 8:

[0584] If the server detects a serious psychological state based on the analysis results, it will display emergency contact information on the terminal and provide the user with instructions on how to connect with a specialist. A rapid notification system is utilized in this process.

[0585] (Application Example 2)

[0586] 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."

[0587] In modern society, mental health care is a crucial issue, but many people find self-management difficult and have limited opportunities to receive professional support. To address this problem, there is a need for a system that can regularly monitor an individual's emotional state and provide appropriate care immediately. Furthermore, solutions are needed to enable sustainable health management within the home environment.

[0588] 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.

[0589] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, a robotic device for supporting the user's health in a home environment, information output means for providing relaxation techniques and actions for improving health based on the analysis results, and a sensor unit for acquiring the user's voice and facial expression data. This enables daily and constant monitoring of mental health and immediate feedback.

[0590] "Natural language processing" refers to technologies that analyze a user's voice or text to understand their intentions and emotions.

[0591] A "response generation means" is a technology that generates appropriate feedback or counseling messages based on the analyzed emotional state of the user.

[0592] A "relaxation suggestion method" is a function that identifies and suggests relaxation techniques suitable for the user's emotional state.

[0593] A "robot device" is hardware installed in a home environment to support the user's health.

[0594] An "information output means" is a means of providing users with relaxation techniques and actions to improve their health.

[0595] A "sensor unit" is a device used to acquire data such as the user's voice and facial expressions.

[0596] To realize this invention, a robotic device installed in a home environment is utilized. This robotic device is equipped with a sensor unit for collecting user voice and facial expression data. The sensor unit converts the voice data spoken by the user into text using speech recognition software (e.g., Google's Speech-to-Text API), and the facial expression data is analyzed using image processing technology (e.g., OpenCV library).

[0597] The server analyzes this data through natural language processing to understand the user's emotional state. Based on the results of the emotional analysis, the response generation system uses AI technology (e.g., OpenAI's ChatGPT model) to generate appropriate counseling responses and relaxation techniques, and sends instructions to the robot device through the information output system. The robot device provides the generated information to the user via voice or display.

[0598] For example, if a user is experiencing high levels of stress, the robot device might respond with something like, "Hello. I can sense a little tension in your voice. Let's take a deep breath. I'll count with you, so please keep pace with me."

[0599] Furthermore, the robotic device transmits the user's daily emotional data to a server, where it is stored by a data management system. This stored data is then provided to the user using visualization tools (e.g., Tableau) for the purpose of long-term improvement of mental health. In addition, if a serious psychological state is detected, the server generates information to contact a professional through an emergency response system.

[0600] An example of a prompt for the generative AI model would be text such as, "Please come up with relaxation suggestions for when the user expresses anxiety."

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The user begins interacting with the robot device. The user's voice and facial expressions are captured by a sensor unit, and this data is collected as digital signals. The voice data is sent to a server.

[0604] Step 2:

[0605] The server uses speech recognition software to convert speech data into text. The converted text data is then input into a natural language processing system for emotional state analysis. The analysis results include the type and intensity of the emotion.

[0606] Step 3:

[0607] Based on the analysis results of natural language processing, the server uses a generative AI model to determine what kind of counseling response or relaxation technique is appropriate. This determination is made by querying the AI ​​model with prompt sentences. For example, a prompt sentence such as "Please consider relaxation suggestions for when the user expresses anxiety" is generated. The generated responses and suggestions are returned to the server.

[0608] Step 4:

[0609] The server transfers the generated responses and suggestions to an information output device and sends instructions to the robot device. The robot device provides the user with counseling messages and relaxation techniques through voice and display. Specific actions include providing voice guidance on deep breathing exercises.

[0610] Step 5:

[0611] The robotic device transmits data on the user's interactions and responses to a server. The server uses data management tools to store this data and record the user's mental health progress.

[0612] Step 6:

[0613] The server analyzes accumulated data and generates information to visualize long-term mental health trends. This information is provided to the user. Furthermore, if a serious psychological condition is detected, the server generates and provides emergency response information to contact a professional.

[0614] 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.

[0615] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0616] 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.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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".

[0631] This invention provides a system for supporting a user's mental health. The system comprises natural language processing means, response generation means, relaxation suggestion means, control means, data management means, and emergency response means. When a user inputs their emotions and thoughts as text, the terminal sends this data to the server. The server analyzes the text using the natural language processing means to identify the emotional state. Based on the analysis results, the response generation means generates feedback and questions for the user and sends them to the terminal. The user then sees these responses on the terminal's screen.

[0632] The relaxation suggestion system selects relaxation techniques such as breathing exercises and meditation based on the analyzed emotional state. The selected techniques are provided to the user as audio or video, and can be experienced through the device. Information on the user's practice of relaxation techniques is recorded and visualized by the data management system, allowing the user to track their mental health progress. Furthermore, if the user's psychological state is deemed critical, an emergency response system is activated, and information on contacting a specialist is displayed on the device. For example, if a user inputs "I've been feeling very anxious lately," the server analyzes the level of anxiety and suggests specific relaxation methods to alleviate it. In this way, the system provides customized support tailored to the user's individual condition.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The user starts up their device and accesses the mental health care application. The user enters their current emotions and thoughts in text format into the input field.

[0636] Step 2:

[0637] The terminal sends text input from the user to the server. The transmitted data is passed to the server in real time.

[0638] Step 3:

[0639] The server uses natural language processing to analyze the user's text data, thereby identifying the user's emotional state and stress level. The analysis is based on the context and emotional expressions of the text.

[0640] Step 4:

[0641] Based on the analysis results, the server uses a response generation mechanism to generate feedback and counseling messages to be sent back to the user. The generated responses are empathetic and supportive, taking into account the user's situation.

[0642] Step 5:

[0643] The server sends the generated response to the terminal and presents it to the user. The response message from the server is displayed on the terminal screen.

[0644] Step 6:

[0645] The server selects the most suitable relaxation technique based on the user's emotional state. Specific methods such as breathing exercises or meditation are chosen based on the relaxation suggestion method.

[0646] Step 7:

[0647] The device provides the user with information on relaxation techniques sent from the server in text, audio, or video format. The user then performs the relaxation techniques according to the instructions.

[0648] Step 8:

[0649] The server records the results and progress of the user's relaxation techniques in a data management system. This allows the user to visually understand changes and improvements in their mental health.

[0650] Step 9:

[0651] If the user's mental state is deemed critical, the server will use emergency response measures to provide the terminal with information on how to contact a specialist. The terminal will then display a warning message along with the specialist's contact information.

[0652] (Example 1)

[0653] 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".

[0654] In modern society, maintaining and improving mental health is extremely important, but it is difficult to receive appropriate support tailored to individual needs. In particular, there is a lack of systems that analyze emotions using individualized approaches and provide appropriate feedback and support.

[0655] 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.

[0656] In this invention, the server includes a natural language processing means for analyzing input information via a communication device, a response generation means for creating information using a generation device based on the analysis results, and a suggestion means for selecting countermeasures to provide to the user. This makes it possible to accurately grasp each user's emotional and mental health state and provide individually optimized feedback and relaxation techniques.

[0657] "Inputted information" refers to text data provided by the user to the system and information related to its content.

[0658] A "communication device" refers to a device that has a network interface for sending and receiving data between a terminal and a server.

[0659] "Natural language processing means for analysis" refers to technology that processes text data provided by users and identifies their emotional state and meaning.

[0660] "Response generation means created by a generation device" refers to a mechanism that generates appropriate feedback or questions based on analyzed data.

[0661] "A means of proposing countermeasures to be provided to users" refers to a system that selects and provides relaxation techniques and support measures based on analysis results.

[0662] A "generative model" refers to an AI model used to process user data and create individually optimized information.

[0663] "Control means" refers to a function that oversees and manages the coordination of various means within a system.

[0664] "Management tools for recording progress and indicating mental health status" refers to functions that store user history data and visualize that data to understand progress and changes.

[0665] "A means of responding to situations requiring urgent attention and providing information for contacting specialists" refers to a system that assesses the user's condition and, if necessary, provides information on appropriate specialists and services.

[0666] This invention is a system for supporting the mental health of users and is constructed by combining different technologies. The system mainly uses natural language processing technology, information generation technology, proposal technology, and data management technology. A specific embodiment of this system is described below.

[0667] The user inputs information about their emotions and thoughts into a terminal, and this information is sent to a server via a communication device. The server analyzes the received text using natural language processing (NLTK) and SpaCy, which are language analysis software libraries for Python, and specifically uses algorithms that can detect the user's emotional state.

[0668] Next, the server uses a response generation device to create feedback and questions for the user based on the analysis results. In this step, a generation AI model is utilized to provide responses tailored to the user. An example of a prompt is, "How would you describe your recent feelings in one word?" Such prompts make it possible to gather further information from the user.

[0669] Next, through a suggestion mechanism for relaxation, breathing techniques and meditation methods are selected according to the analysis results and provided to the user via audio or video through the terminal. As a specific example, relaxation music videos can be played using the YouTube API.

[0670] The data management system records system usage history and visualizes the progress of users' mental health. Visualization software such as Matplotlib and Plotly are used for this process. This allows users to see changes in their own condition through graphs and other visualizations.

[0671] Furthermore, if the server determines that the user's mental state is extremely serious, it will activate emergency response measures and provide the terminal with contact information for relevant professionals and counseling services. This function allows the user to receive professional support quickly.

[0672] The aim of this system is to facilitate personalized mental care for users by providing customized feedback and support according to specific conditions and circumstances.

[0673] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0674] Step 1:

[0675] The user enters text about their feelings and thoughts into the device. The entered text data is temporarily stored on the device. For example, if the user enters "I've been feeling very anxious lately," this text will become the basis for subsequent processing.

[0676] Step 2:

[0677] The terminal sends the entered text data to the server using the HTTPS protocol. The input is in text format, and the server returns confirmation information sent to the server as output. This ensures data security while enabling analysis on the server.

[0678] Step 3:

[0679] The server analyzes the received text data using natural language processing techniques. It analyzes the input text and extracts features to identify emotional states. Specifically, it uses Python's NLTK or SpaCy to calculate an emotional score. The output is the identified emotional state.

[0680] Step 4:

[0681] Based on the analysis results, the server uses a response generation mechanism to generate feedback and questions for the user. The input for this step is the previously identified emotional state, and the output is a generated response sentence. The generating AI model provides an appropriate answer based on the prompt sentence, such as "Have you tried anything to alleviate your anxiety?"

[0682] Step 5:

[0683] The server sends the generated response to the terminal, and the terminal displays that response on the user's screen. The input is the generated response data, and the output includes the specific actions that are visually displayed on the terminal.

[0684] Step 6:

[0685] The relaxation suggestion system selects appropriate relaxation techniques based on the analysis results. Using the emotional analysis results as input, it suggests techniques such as "deep breathing" and "meditation." The output is a specific method suggested to the user, which is played back as a guide on the device.

[0686] Step 7:

[0687] The user performs a relaxation technique, and the terminal records the result. The input is the user's action, and the output is log information recorded in the data management system.

[0688] Step 8:

[0689] The server uses recorded data and data management tools to visualize user progress. Using past log information and analysis results as input, the output is visualized graphs and metrics. This allows users to track their mental health progress.

[0690] Step 9:

[0691] If the server determines that the user's psychological state is serious, it will use emergency response measures to provide contact information for a specialist. The input is the analyzed psychological state, and the output is appropriate contact information based on that. By displaying this information on the terminal, the user can receive support quickly.

[0692] (Application Example 1)

[0693] 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".

[0694] In modern society, many people suffer from mental health problems, but there is a lack of readily available means to easily check their condition and address it on a daily basis. In particular, there is a lack of systems that allow for intuitive interaction using voice or improvement based on visualized data. In this situation, there is a need for a system that allows users to understand their own mental health and receive appropriate support quickly.

[0695] 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.

[0696] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, voice analysis means for analyzing voice input and converting it into text data, and data management means for managing emotional records and relaxation history. This enables the user to analyze their emotions in real time and be provided with appropriate relaxation methods, thereby supporting the improvement of their mental health.

[0697] "Natural language processing" refers to technologies that analyze text data entered by users to understand their emotions and intentions.

[0698] A "response generation method" is a technology that automatically generates and provides appropriate counseling and feedback to the user based on the analyzed emotional state.

[0699] A "relaxation suggestion method" is a technology that selects and proposes the most suitable relaxation technique according to the user's emotional state.

[0700] "Voice analysis means" refers to technology that converts voice input into text data and uses that data to analyze the user's emotions and intentions.

[0701] "Data management means" refers to technology for recording, managing, and visualizing the history of users' emotions and the status of their relaxation techniques.

[0702] The implementation of this invention requires a user-operated terminal and a server. The terminal is typically a smart device, such as a smartphone or tablet, which processes information through communication with the server. The server plays a major role in processing large amounts of data and utilizes natural language processing and speech analysis technologies.

[0703] The device first receives the user's voice input and sends that data to the server. The server converts the speech to text using SpeechRecognition, a Python speech recognition library. The text data is then analyzed using natural language processing libraries such as spaCy or transformers to identify the user's emotional state.

[0704] Based on the analysis results, the server automatically generates counseling responses using a generative AI model, and these responses are sent to the terminal. The terminal then presents them to the user visually or audibly. Furthermore, relaxation techniques are suggested according to the identified emotions. For example, content related to meditation or breathing exercises may be provided to the terminal, allowing the user to experience them.

[0705] Furthermore, as a data management tool, the relaxation techniques performed by users and their effects are recorded on a server using database technologies such as SQLite. This allows users to track their progress and visualize improvements in their mental health. In addition, as an emergency response measure, if a serious emotional state is detected, information on how to contact a specialist is displayed on the user's device.

[0706] As a concrete example of its use, if an elderly person inputs "I'm feeling a little restless today" via voice, the input is analyzed and the server suggests calming music or relaxing activities. An example of a prompt message would be, "I'm feeling anxious. Please give me some suggestions to calm down."

[0707] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0708] Step 1:

[0709] The user inputs voice into the device. The device captures this voice data and prepares to send it to the server. The input is voice data, which is sent to the server in digital format.

[0710] Step 2:

[0711] The server receives audio data and converts it to text using the SpeechRecognition library. Here, the input is audio data and the output is text data. It performs the specific operation of analyzing the audio signal and converting it into a string.

[0712] Step 3:

[0713] The server performs natural language processing on the converted text data using spaCy and transformers. In this step, an emotion analysis model is used with the text data as input to output the user's emotional state. The specific operation is the identification of emotions.

[0714] Step 4:

[0715] The server uses a generative AI model to create the optimal counseling response based on the analysis results. The input is emotional state data, and the output is the response text. The specific operation is to execute the response generation algorithm.

[0716] Step 5:

[0717] The server selects appropriate techniques through relaxation suggestion mechanisms and creates suggested content. The input is emotional state, and the output is data on relaxation suggestions. The selection of a relaxation technique is the specific action.

[0718] Step 6:

[0719] The server sends the generated counseling responses and relaxation suggestions to the terminal. The terminal presents these to the user visually or audibly. The input is the response and suggestion data, and the output is the user interface. The processing for display is the specific action.

[0720] Step 7:

[0721] The terminal receives user feedback based on the responses and relaxation suggestions it receives. This feedback is then sent back to the system. The input is user feedback, and the output is recording in the database. Receiving feedback is the specific action.

[0722] Step 8:

[0723] The server manages the user's emotional state and relaxation history in an SQLite database and visualizes it as needed. Input is historical data, and output is visualized information. The specific operations are data management and visualization.

[0724] 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.

[0725] This invention is a system for comprehensively supporting a user's mental health, and comprises natural language processing means, response generation means, relaxation suggestion means, a so-called "emotion engine," and control means that oversee these means. Furthermore, by combining data management means and emergency response means, it provides monitoring and support for the user's health status.

[0726] When a user accesses the system using a terminal, the emotion engine first collects the user's facial expressions and voice data, and analyzes their emotional state in real time. The server integrates the results of the emotion engine along with natural language input to recognize the user's overall psychological state with high accuracy. As a result, the response generation means generates sophisticated counseling messages that take emotional nuances into account, rather than just text-based feedback, providing the user with guidance on what actions or thoughts they should take next.

[0727] In addition, the relaxation suggestion mechanism utilizes the output of the emotion engine to select relaxation techniques optimized for the user's specific emotions at that time, in addition to standard stress reduction techniques. For example, if the user is showing strong anxiety, a combination of breathing exercises and meditation may be suggested, and a detailed guide will be provided to the user through the device.

[0728] Furthermore, user interactions and emotional responses are recorded by a data management system, allowing users to visually track changes in their own mental health. If the emotional engine detects a serious psychological state, an emergency response system is activated, and information to contact a specialist is provided to the device.

[0729] In this way, this system can provide users with personalized and effective mental healthcare by organically combining multiple means, including an emotion engine.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The user accesses the mental health support system via their device. The emotion engine uses the camera and microphone to collect the user's facial expressions and voice data.

[0733] Step 2:

[0734] The device sends collected emotional data and user text input to the server. The server uses an emotion engine to analyze facial expressions and tone of voice. It also analyzes the text data using natural language processing techniques.

[0735] Step 3:

[0736] The server integrates sentiment analysis and text analysis to recognize the user's emotional state with high accuracy. Based on the analyzed data, it determines the user's psychological state.

[0737] Step 4:

[0738] The server uses a response generation mechanism to generate counseling messages tailored to the user's emotional state. These messages include empathy for the user's current situation and specific advice.

[0739] Step 5:

[0740] The server sends the generated counseling message to the terminal and presents it to the user. The message is displayed on the terminal screen, allowing the user to confirm it.

[0741] Step 6:

[0742] The server uses relaxation suggestion tools to select the most suitable relaxation technique for the user's emotional state. The selected technique is tailored to the user's situation.

[0743] Step 7:

[0744] The device provides users with selected relaxation techniques. Specifically, it presents guides for breathing exercises and meditation in audio and video formats.

[0745] Step 8:

[0746] The server records user interaction data and emotional response history using data management tools. This allows users to visually understand changes in their own mental health.

[0747] Step 9:

[0748] If the emotion engine detects a critical psychological state, the server will activate emergency response measures. It will provide the terminal with contact information for specialists and encourage the user to take prompt action.

[0749] (Example 2)

[0750] 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".

[0751] In modern society, users often struggle to access appropriate and effective support to address personal mental health issues. Conventional counseling services are not readily available, and providing advice and specific coping strategies tailored to a user's real-time emotional state is difficult. Therefore, a system is needed that accurately assesses a user's psychological state and provides timely, optimally tailored feedback and support.

[0752] 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.

[0753] In this invention, the server includes a data processing device for analyzing the user's emotional state, an information processing device for integrating the analysis results and natural language input to recognize the psychological state, and a generation means for creating a response that takes emotional nuances into consideration. This enables the timely provision of personalized counseling and relaxation techniques based on the user's psychological state.

[0754] A "data processing device" is a device that analyzes emotional data collected from users and identifies their state.

[0755] An "information processing device" is a device that combines analyzed emotional data with natural language input from the user to recognize the overall psychological state.

[0756] A "generation method" is a means of automatically creating responses and advice that incorporate the nuances of the user's emotions.

[0757] A "control device" is a device that enables smooth system operation by integrating and coordinating various means.

[0758] A "recording method" refers to a means of saving the user's emotional changes and interaction history to a database.

[0759] "Response measures" refer to means of detecting serious psychological situations and promptly providing information to contact specialists.

[0760] The server operates a system that supports users' mental health. This system comprises a data processing device, an information processing device, a generation means, a control device, a recording means, and a response means.

[0761] When a user accesses the system through a terminal, the terminal uses a high-resolution camera and microphone to collect the user's facial expressions and voice data. Specifically, image recognition software is used for image processing, and a voice processing library is used for voice analysis. This data is analyzed by a data processing unit on the server. In this analysis process, for example, the user's emotions are classified as "joy," "sadness," "anxiety," etc.

[0762] The analysis results are integrated with natural language input from the user (e.g., chat messages) by an information processing device to recognize the user's overall psychological state. This process utilizes a natural language processing engine (e.g., a natural language model).

[0763] Subsequently, the generation mechanism utilizes a generative AI model to create a response based on the user's emotions and state. This response takes emotional nuances into account, and for example, a message such as "Take a deep breath and relax" is generated and displayed on the device.

[0764] On the other hand, the recording means stores a history of the user's emotional state in data storage, thereby providing foundational data for visualizing long-term changes in mental health.

[0765] Furthermore, if a serious psychological state is detected, response measures are set up, and information to contact a specialist is promptly provided to the device.

[0766] For example, if a user enters a prompt such as "I've been feeling very anxious lately and can't sleep," the system can analyze the data from their voice and facial expressions, suggest appropriate relaxation techniques, and, if necessary, provide a way to contact a specialist.

[0767] The entire system is managed by a control device that coordinates these means, enabling it to provide users with comprehensive and personalized mental healthcare.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The device collects the user's facial expressions and audio data using a high-resolution camera and microphone. At this time, the device prompts the user with the question, "How are you feeling right now?" The input data consists of the user's video and audio data, which is sent to the server as the basic input for emotion analysis.

[0771] Step 2:

[0772] The server converts the received audio data into text and simultaneously analyzes facial expression data using image recognition software. The input data consists of audio and images, and the data processing unit generates output classified into emotion categories (e.g., "joy," "sadness," "anxiety").

[0773] Step 3:

[0774] The server uses a natural language processing engine to analyze the text converted from speech and the prompt sentences entered by the user, and integrates them with sentiment analysis results. This process outputs an overall psychological state, which serves as the basis for response generation.

[0775] Step 4:

[0776] The server uses a generative AI model to generate responses and advice that are appropriate for the user's psychological state. Specifically, depending on the nuances of the emotions, messages such as "Try meditating for 5 minutes" will be output.

[0777] Step 5:

[0778] Based on the data from the emotion engine, the server selects relaxation techniques optimized for the user's state. This process suggests techniques such as breathing exercises for high anxiety levels and music therapy for low stress levels.

[0779] Step 6:

[0780] The terminal displays response messages from the server and suggested relaxation techniques to the user. Simultaneously, it provides visual and audio guidance regarding relaxation methods.

[0781] Step 7:

[0782] The server records the user's emotional state and interaction history in a database and generates analytical reports for visualization as needed.

[0783] Step 8:

[0784] If the server detects a serious psychological state based on the analysis results, it will display emergency contact information on the terminal and provide the user with instructions on how to connect with a specialist. A rapid notification system is utilized in this process.

[0785] (Application Example 2)

[0786] 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".

[0787] In modern society, mental health care is a crucial issue, but many people find self-management difficult and have limited opportunities to receive professional support. To address this problem, there is a need for a system that can regularly monitor an individual's emotional state and provide appropriate care immediately. Furthermore, solutions are needed to enable sustainable health management within the home environment.

[0788] 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.

[0789] In this invention, the server includes natural language processing means for analyzing the user's emotional state, response generation means for generating counseling responses based on the analysis results, relaxation suggestion means for identifying relaxation techniques to present to the user, a robotic device for supporting the user's health in a home environment, information output means for providing relaxation techniques and actions for improving health based on the analysis results, and a sensor unit for acquiring the user's voice and facial expression data. This enables daily and constant monitoring of mental health and immediate feedback.

[0790] "Natural language processing" refers to technologies that analyze a user's voice or text to understand their intentions and emotions.

[0791] A "response generation means" is a technology that generates appropriate feedback or counseling messages based on the analyzed emotional state of the user.

[0792] A "relaxation suggestion method" is a function that identifies and suggests relaxation techniques suitable for the user's emotional state.

[0793] A "robot device" is hardware installed in a home environment to support the user's health.

[0794] An "information output means" is a means of providing users with relaxation techniques and actions to improve their health.

[0795] A "sensor unit" is a device used to acquire data such as the user's voice and facial expressions.

[0796] To realize this invention, a robotic device installed in a home environment is utilized. This robotic device is equipped with a sensor unit for collecting user voice and facial expression data. The sensor unit converts the voice data spoken by the user into text using speech recognition software (e.g., Google's Speech-to-Text API), and the facial expression data is analyzed using image processing technology (e.g., OpenCV library).

[0797] The server analyzes this data through natural language processing to understand the user's emotional state. Based on the results of the emotional analysis, the response generation system uses AI technology (e.g., OpenAI's ChatGPT model) to generate appropriate counseling responses and relaxation techniques, and sends instructions to the robot device through the information output system. The robot device provides the generated information to the user via voice or display.

[0798] For example, if a user is experiencing high levels of stress, the robot device might respond with something like, "Hello. I can sense a little tension in your voice. Let's take a deep breath. I'll count with you, so please keep pace with me."

[0799] Furthermore, the robotic device transmits the user's daily emotional data to a server, where it is stored by a data management system. This stored data is then provided to the user using visualization tools (e.g., Tableau) for the purpose of long-term improvement of mental health. In addition, if a serious psychological state is detected, the server generates information to contact a professional through an emergency response system.

[0800] An example of a prompt for the generative AI model would be text such as, "Please come up with relaxation suggestions for when the user expresses anxiety."

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The user begins interacting with the robot device. The user's voice and facial expressions are captured by a sensor unit, and this data is collected as digital signals. The voice data is sent to a server.

[0804] Step 2:

[0805] The server uses speech recognition software to convert speech data into text. The converted text data is then input into a natural language processing system for emotional state analysis. The analysis results include the type and intensity of the emotion.

[0806] Step 3:

[0807] Based on the analysis results of natural language processing, the server uses a generative AI model to determine what kind of counseling response or relaxation technique is appropriate. This determination is made by querying the AI ​​model with prompt sentences. For example, a prompt sentence such as "Please consider relaxation suggestions for when the user expresses anxiety" is generated. The generated responses and suggestions are returned to the server.

[0808] Step 4:

[0809] The server transfers the generated responses and suggestions to an information output device and sends instructions to the robot device. The robot device provides the user with counseling messages and relaxation techniques through voice and display. Specific actions include providing voice guidance on deep breathing exercises.

[0810] Step 5:

[0811] The robotic device transmits data on the user's interactions and responses to a server. The server uses data management tools to store this data and record the user's mental health progress.

[0812] Step 6:

[0813] The server analyzes accumulated data and generates information to visualize long-term mental health trends. This information is provided to the user. Furthermore, if a serious psychological condition is detected, the server generates and provides emergency response information to contact a professional.

[0814] 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.

[0815] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A natural language processing method for analyzing the emotional state of users,

[0838] A response generation means that generates a counseling response based on the analysis results,

[0839] A means of suggesting relaxation techniques to present to the user,

[0840] A system including control means for coordinating the above means.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising data management means for recording user progress and visualizing improvements in mental health.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising emergency response means for providing contact information to a specialist when a serious psychological state is detected.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A natural language processing means for analyzing input information via a communication device,

[0848] A response generation means that generates information using a generation device based on the analysis results,

[0849] A means of proposing countermeasures to be provided to users,

[0850] A generative model that creates information optimized for individuals,

[0851] A system including control means for coordinating a series of means.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising means for recording progress and indicating mental health status.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for determining a highly urgent situation and providing information for contacting experts.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] A natural language processing method for analyzing the emotional state of users,

[0859] A response generation means that generates a counseling response based on the analysis results,

[0860] A means of suggesting relaxation techniques to present to the user,

[0861] A voice analysis means that analyzes voice input and converts it into text data,

[0862] A data management system for managing emotional records and relaxation history,

[0863] A system including control means for coordinating the above means.

[0864] (Claim 2)

[0865] The system according to claim 1, further comprising data management means for recording user progress and visualizing improvements in mental health.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising emergency response means for providing contact information to a specialist when a serious psychological state is detected.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] A data processing device for analyzing the emotional state of a user,

[0871] An information processing device for recognizing psychological states by integrating analysis results and natural language input,

[0872] A generation method for creating answers that take into account emotional nuances,

[0873] A selection method for proposing relaxation techniques based on the results of emotional analysis,

[0874] A system including a control device for coordinating the above means.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising recording means for storing a history of data relating to the user's state and for visualizing changes in the state.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for identifying a serious psychological condition and providing the user with information to contact a specialist.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A natural language processing method for analyzing the emotional state of users,

[0882] A response generation means that generates a counseling response based on the analysis results,

[0883] A means of suggesting relaxation techniques to present to the user,

[0884] Robotic devices to support health conditions in the home environment,

[0885] An information output method that provides relaxation techniques and actions for improving health based on the analysis results,

[0886] A sensor unit for acquiring user voice and facial expression data,

[0887] A system including control means for coordinating the above means.

[0888] (Claim 2)

[0889] The system according to claim 1, further comprising data management means for recording user progress and visualizing improvements in mental health, for conducting long-term health assessments within the home.

[0890] (Claim 3)

[0891] The system according to claim 1, further comprising emergency response means for providing contact information to a specialist when a serious psychological state is detected, enabling monitoring and support in a home environment. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A natural language processing method for analyzing the emotional state of users, A response generation means that generates a counseling response based on the analysis results, A means of suggesting relaxation techniques to present to the user, A system including control means for coordinating the above means.

2. The system according to claim 1, further comprising data management means for recording user progress and visualizing improvements in mental health.

3. The system according to claim 1, further comprising emergency response means for providing contact information to a specialist when a serious psychological state is detected.

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