system

Abstract

We provide the system. [Solution] An input means for receiving input from the user, An emotion analysis means for analyzing data received from the aforementioned input means and evaluating the user's emotional state, A generation means for generating counseling content for the user based on the emotional state evaluated by the emotion analysis means, An output means that provides the user with the counseling content generated by the generation means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, many individuals have mental health problems and are required to receive appropriate psychological support immediately. However, at present, there is a lack of support by experts, and there is a problem that it is difficult to quickly respond to individual needs. In addition, since systems that can appropriately grasp changes in emotional states and provide effective counseling are limited, many users are in a situation where they cannot obtain appropriate support.

Means for Solving the Problems

[0005] This invention provides a system that receives input from a user, analyzes the data to evaluate the user's emotional state, and generates counseling content based on that evaluation. The system provides this counseling content to the user and can further improve the accuracy of the counseling content by receiving and analyzing feedback from the user. In addition, by continuously monitoring the user's mental health state and generating reports based on that data, the system can respond quickly to changes in the user's condition and provide more effective support.

[0006] "Input means" refers to a device or process for receiving data from a user.

[0007] "Emotion analysis means" refers to a device or process for processing received data and evaluating the user's emotional state.

[0008] "Generative means" refers to a device or process for creating appropriate counseling content based on an evaluated emotional state.

[0009] "Output means" refers to a device or process for communicating the generated counseling content to the user.

[0010] "Feedback processing means" refers to a device or process for processing feedback received from users and using it to improve the system.

[0011] "Monitoring means" refers to a device or process for continuously observing a user's mental health status and recording and managing that information. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0013] <00,00086>Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

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

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[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] The system according to this invention functions between a server, a terminal, and a user. The server analyzes input from the user, uses an emotion analysis model and natural language generation (NLG) to assess the user's mental health status, and generates personalized counseling content. This counseling content may include specific advice such as appropriate relaxation methods and stress management techniques for daily life.

[0034] The terminal provides an interface for the user to interact with the server. The user inputs information about their emotions and state into the terminal, and this data is sent to the server. In response from the server, generated counseling or resources are displayed on the terminal.

[0035] Users receive counseling content provided by the server and use it to improve themselves. They also input feedback on the counseling content and service into their terminals, which the server collects and analyzes. This feedback is used to improve future counseling services.

[0036] Furthermore, the server has the capability to continuously monitor the user's mental health status and generate reports. This makes it possible to respond quickly and effectively to changes in the user's emotional state.

[0037] For example, if a user inputs "I've been feeling down lately due to work pressure" into their device, the device sends this information to the server. The server, through emotion analysis, determines that the user is experiencing high stress levels and suggests appropriate stress reduction methods. These suggestions are displayed to the user through their device, allowing them to understand the steps to implement them. The server then uses this feedback to make improvements that will benefit other users as well.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users input their mental health status and concerns into the device. For example, they might enter specific concerns as text, such as "I've been having trouble sleeping at night lately."

[0041] Step 2:

[0042] The terminal sends the entered text data to the server. This communication takes place in real time over the internet.

[0043] Step 3:

[0044] The server analyzes the received text data using a natural language processing (NLP) module. This involves tokenization of the input sentence, morphological analysis, and contextual understanding.

[0045] Step 4:

[0046] The server uses an emotion analysis model to evaluate the user's emotional state based on the analyzed data. For example, it identifies emotions such as anxiety and stress.

[0047] Step 5:

[0048] The server uses a counseling generation module to generate counseling content tailored to the user's emotional state. For example, it might suggest relaxation techniques or recommended lifestyle habits.

[0049] Step 6:

[0050] The server sends the generated counseling content to the terminal. This is also done quickly via the internet.

[0051] Step 7:

[0052] The terminal displays the counseling content received from the server to the user. Typically, this is presented on the screen as text or other visual elements.

[0053] Step 8:

[0054] Users try the provided counseling and input feedback on its effectiveness into the device. This feedback can be entered in a free-form format.

[0055] Step 9:

[0056] The device sends user feedback to the server. This feedback is saved as data for generating the next counseling session.

[0057] Step 10:

[0058] The server uses machine learning algorithms to enhance the counseling generation process based on feedback. This will further improve the user experience in the future.

[0059] Step 11:

[0060] The server continuously monitors the user's mental health status and generates periodic reports. These reports are managed in a way that allows access to the user themselves or a healthcare professional.

[0061] (Example 1)

[0062] 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."

[0063] Traditional psychological support systems have struggled to effectively address the individual psychological states of each user. Furthermore, there have been insufficient methods for appropriately incorporating user feedback and improving service quality. As a result, it has been difficult to provide optimal advice to users, limiting the effectiveness of maintaining and managing their mental health.

[0064] 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.

[0065] In this invention, the server includes communication means for receiving data from users, analysis means for analyzing the information received from the communication means and evaluating the user's psychological state, and generation means for generating information for the user based on the psychological state evaluated by the analysis means. This makes it possible to provide optimal advice tailored to the user's psychological state and to continuously improve the quality of the service by utilizing feedback from users.

[0066] "Communication means" refers to the means of receiving data from users and processing it within the system.

[0067] "Analysis means" refers to a means of processing received information and evaluating the user's psychological state.

[0068] "Generation means" refers to the means of creating and providing optimal information to users based on analysis.

[0069] "Presentation means" refers to a means of effectively providing generated information to users visually or audibly.

[0070] A "response processing means" is a means for receiving responses from users, analyzing their content, and using that information to improve the generation means.

[0071] "Observational means" refers to methods for continuously monitoring the psychological health of users and creating reports based on the results.

[0072] This invention is a system aimed at supporting the psychological health of users and has a configuration that functions between a server, a terminal, and the user.

[0073] The server operates on a cloud computing environment and performs the following processes by combining multiple software modules. First, it receives text data about the user's emotional state sent from the terminal via a communication method. This system uses secure protocols for receiving and sending data, ensuring the security of communication, for example, by SSL / TLS.

[0074] The received data is analyzed using natural language processing algorithms installed on the server. For example, a common natural language processing tool, such as an NLP API, is used to evaluate whether the user's emotions fall into the positive, negative, or neutral categories.

[0075] Based on the analysis results, the server utilizes a generative AI model to generate personalized advice and counseling content. A general-purpose AI model, such as a generative AI, is used as the generation method. The following prompt statements are used in this generation process:

[0076] "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods."

[0077] The generated information is sent as output to the terminal and presented to the user visually. The terminal provides a flexible user interface to display this output information in an easy-to-understand manner.

[0078] Based on the advice displayed on the device, users can take actionable steps in their daily lives. Users also input feedback via the device regarding the effectiveness of these actions and the ease of use of the system, which the server receives. This feedback is analyzed by a response processing system and used to improve the system.

[0079] Furthermore, the server periodically monitors the user's mental health status through observation tools and generates reports. This makes it possible to understand the user's psychological tendencies and support the long-term maintenance and management of their mental health.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user inputs text data about their psychological state into the terminal. The terminal receives this input and formats it as digital data. This data includes information related to the user's emotions and mental health. For example, the input might include something like, "I've been feeling tired lately because I have a lot of tasks." The formatted data is securely transmitted to the server via the SSL / TLS protocol.

[0083] Step 2:

[0084] The server analyzes the digital data received from the terminal. This analysis is performed using an emotion analysis algorithm. First, the received data is passed through a natural language processing API to identify emotions in the text. The input is the received text data, and the output is a positive, negative, or neutral emotional state. Specific operations include keyword extraction and emotion score calculation.

[0085] Step 3:

[0086] The server uses a generative AI model based on the analysis results to generate optimal advice for the user. The generative AI model operates using the prompt "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods." The input is the analyzed emotional state and the prompt, and the output is the generated advice. Specifically, text related to stress relief methods and relaxation techniques is generated.

[0087] Step 4:

[0088] The server sends the generated advice to the terminal. The terminal utilizes a user interface to effectively display this information to the user. The input is the generated text advice, and the output is a visually formatted display. Specific actions include adjusting font size and color, and displaying in rich text format.

[0089] Step 5:

[0090] Users take action based on the advice displayed on their device. They then provide feedback via the device regarding the results of their actions and their satisfaction with the advice. This feedback is entered into the device as text and numerical data and then sent to the server. Specifically, it includes an evaluation of the effectiveness of the actions taken by the user.

[0091] Step 6:

[0092] The server analyzes the received feedback and adjusts the parameters of the generated AI model, thereby improving the overall system performance. The input is user feedback data, and the output is the updated model parameters. Specific operations include statistical analysis of the feedback and trend analysis.

[0093] Step 7:

[0094] The server continuously monitors the user's psychological health using observational tools and generates periodic reports. Inputs are historical emotional data and feedback data, and output is a visualized report. Specifically, this includes graph creation and time-series data analysis.

[0095] (Application Example 1)

[0096] 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."

[0097] In an aging society, it is essential to properly maintain and manage the mental health of the elderly. However, conventional methods make it difficult to provide individualized mental support, resulting in a lack of efficient and continuous support.

[0098] 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.

[0099] In this invention, the server includes: receiving means for receiving input from the user; evaluation means for analyzing data obtained from the receiving means and evaluating the user's emotional state; generating means for generating counseling information for the user based on the emotional state evaluated by the evaluation means; providing means for providing the counseling information generated by the generation means to the user; monitoring means for continuously monitoring the user's mental health state and generating reports based on that; and dialogue means for receiving information from the user through voice input or haptic operation. This enables appropriate and continuous mental support for each elderly person.

[0100] "Receiving means" refers to a device or method for receiving input data from a user.

[0101] "Evaluation means" refers to a device or method that analyzes user data obtained from a receiving means and determines the emotional state.

[0102] "Generation means" refers to a device or method for creating counseling information for a user based on the emotional state obtained by the evaluation means.

[0103] "Delivery means" refers to a device or method for delivering counseling information created by the generation means to the user.

[0104] "Monitoring means" refers to a device or method that continuously observes a user's mental health status and creates a report based on that information.

[0105] "Dialogue means" refers to a device or method for exchanging information interactively with a user through voice input or haptic operation.

[0106] To implement this invention, it is necessary to build a system that supports the mental health of users. This system mainly involves a server, a terminal (e.g., a smartphone), and the user.

[0107] The server is the central component that receives user input and performs sentiment analysis based on it. This analysis uses software such as Python and TENSORFLOW®. Information that users input daily through their terminals, such as text data like "I'm busy and feeling tired," is sent to the server. The server receives this data and uses a sentiment analysis model to evaluate the user's emotional state.

[0108] Based on the assessed emotional state, the server uses a generation mechanism to generate counseling information tailored to the user. This process utilizes natural language generation technology to provide personalized advice.

[0109] The generated information is sent to the terminal via a delivery mechanism and provided to the user. The user receives this information via the terminal, but the operation is intuitive and simple, allowing for interaction through voice input or touch controls. The system also includes a function to continuously monitor the user's mental health status using a monitoring mechanism and generate reports based on that data. User data security is ensured using Firebase.

[0110] As a concrete example, consider a scenario where a user inputs into their device, "I haven't been feeling well lately and I'm feeling down." The server receives this and generates and displays suggestions such as, "Try some relaxation techniques." An example of a prompt message to the generating AI model in this system would be, "Tell us about your recent mood and events. We will provide advice accordingly."

[0111] As described above, by coordinating the server, terminal, and user, it is possible to efficiently implement the mental health support that is the objective of this invention.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users input information about their emotions and state of mind through their device. This may involve using voice or text input. The input information is digitized on the device as initial data for evaluating the emotional state and then sent to the server.

[0115] Step 2:

[0116] The server analyzes user input data received from the terminal. First, it receives the input data and performs an emotional evaluation using an emotional analysis model. The generative AI model used here extracts emotions from the input data and evaluates the user's emotional state based on past data and the AI's learning results. The input is the user's text data, and the output is the evaluated emotional state. Natural language processing techniques are used to process the data.

[0117] Step 3:

[0118] The server generates counseling information for the user based on the assessed emotional state, using a generation method. This process uses NLG (Natural Language Generation) technology to generate appropriate advice in text format. The input is the result of the emotional assessment, and the output is individually generated counseling information. When generating the information, templates and past cases are used as references to construct content that is appropriate to the user's emotional state.

[0119] Step 4:

[0120] The generated counseling information is transmitted to the terminal via a delivery method. The terminal receives this information and presents it to the user in an easily understandable format. The user can review this on the screen and receive advice and suggestions. At this stage, the input is the generated counseling information, and the output is the visual or auditory information received by the user.

[0121] Step 5:

[0122] After taking action based on the provided counseling information, the user inputs their thoughts and feedback into the device. This feedback is used to improve the accuracy of future counseling sessions. The device receives the feedback and sends it back to the server. The input is the user's feedback data, and the output is a digital record of the feedback. The server analyzes this and uses it to improve the generated AI model.

[0123] Step 6:

[0124] The server continuously monitors the user's mental health status and generates reports as needed. It analyzes the user's overall mental health trends using monitoring tools and takes early action if problems are detected. The input is continuously collected status data, and the output is periodic mental health reports. This process involves cumulative data analysis and a comprehensive assessment combining various factors.

[0125] 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.

[0126] The present invention is implemented as a system including a server, a terminal, and an emotion engine. The server receives multimodal data, including text and voice, from the user and passes it to the emotion engine for analysis. The emotion engine evaluates the user's emotional state from multiple perspectives, utilizing natural language processing (NLP), voice analysis, and facial recognition. Based on the analysis results, the server generates counseling and advice tailored to the user's emotional state and provides it through the terminal.

[0127] The terminal provides an interface for users to interact with the server, allowing them to input their emotions and state in various formats. For example, in addition to text input, it is possible to record voice messages and capture facial expressions via the camera. This enables the server to perform more detailed and accurate emotion analysis.

[0128] Through this system, users can receive personalized counseling tailored to their emotions. For example, if a user inputs "I'm anxious because the work deadline is approaching" and captures their facial expression with a camera, the server sends the audio and visual information to an emotion engine and assesses that the user is experiencing strong anxiety. The server then suggests specific breathing techniques and time management strategies to alleviate the anxiety.

[0129] Furthermore, by providing feedback after counseling sessions, the system analyzes this information and continuously improves the performance of its emotion engine and generation mechanisms. In this way, the system provides counseling that is more tailored to individual needs, contributing to the improvement of users' mental health.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users can input text messages about their emotions and state of mind into the device. They can also send voice messages or capture facial expressions using the camera, if needed.

[0133] Step 2:

[0134] The device transmits text, audio, and video data entered by the user to the server. This transmission is secure and fast.

[0135] Step 3:

[0136] The server analyzes the received text data using a natural language processing (NLP) module to perform an initial evaluation of the meaning and sentiment of the sentences.

[0137] Step 4:

[0138] The server processes the audio data using an audio analysis module, analyzing factors such as tone, speed, and pauses to estimate the emotional state.

[0139] Step 5:

[0140] The server processes video data via a face recognition and facial expression analysis module, and infers emotions from facial expressions.

[0141] Step 6:

[0142] The server uses an emotion engine to integrate results obtained from text, audio, and video to provide a detailed assessment of the user's overall emotional state.

[0143] Step 7:

[0144] Based on the evaluated emotional state, the server generates counseling content tailored to the user's situation and needs. This includes specific advice and solutions.

[0145] Step 8:

[0146] The server generates counseling content and sends it to the terminal. The terminal then provides this to the user in text or audio format.

[0147] Step 9:

[0148] Users review the counseling content and input feedback on its effectiveness, their impressions, and any additional inquiries into the device.

[0149] Step 10:

[0150] The device sends user feedback to the server. The server uses this feedback to improve the emotion engine and counseling generation module.

[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, maintaining mental health is becoming increasingly important, but there are limited systems that allow users to accurately understand their own emotional state and receive appropriate advice and counseling accordingly. Conventional systems have limitations in terms of information due to their single-modal input, leading to problems with the accuracy of analysis. Furthermore, the advice provided is uniform and does not offer content tailored to the individual needs of the user.

[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 an input means for receiving diverse emotional data from a user, an emotional analysis means for analyzing the multimodal data received from the input means and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology, and a generation means for generating counseling content for the user using a generation AI model based on the emotional state evaluated by the emotional analysis means. This enables more detailed and accurate emotional evaluation and realizes the provision of counseling content tailored to the user's individual needs.

[0156] "Input means" refers to devices and methods for collecting diverse emotional data from users.

[0157] "Emotion analysis means" refers to devices or methods for analyzing input multimodal data and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology.

[0158] "Generation means" refers to a device or method that generates counseling content for a user using a generation AI model based on the emotional state evaluated by the emotion analysis means.

[0159] "Output means" refers to devices or methods for providing the generated counseling content to the user and for receiving user feedback.

[0160] "Feedback processing means" refers to devices or methods for analyzing user feedback and improving the generation means.

[0161] "Monitoring means" refers to devices or methods for continuously observing a user's mental health status and generating reports based on that observation.

[0162] This invention is a system that provides emotionally tailored counseling and advice by collecting and analyzing diverse emotional data from users. It is implemented using a server, terminals, and an emotion analysis engine.

[0163] The device functions as an input device for collecting emotional data from users. Users can provide emotional data through text input, voice message recording, and facial expression capture using a camera. This allows for the collection of multimodal data, laying the foundation for a multifaceted evaluation of the user's emotional state.

[0164] The server receives data transmitted from the terminal and processes it using an emotion analysis engine. This engine combines natural language processing (NLP), speech analysis software, and facial recognition technology to accurately assess the user's emotional state. This enables detailed and precise analysis that would not be possible with a single modal system.

[0165] Based on the analysis results, the server utilizes a generative AI model to automatically generate counseling content tailored to the user's emotional state. This generation process includes specific advice customized to individual user needs, rather than conventional, standardized advice.

[0166] As a concrete example, consider a scenario where a user inputs a message such as "I'm nervous because I have an important presentation coming up," and then captures their nervous facial expression using a camera. In this case, the server evaluates the data using an emotion analysis engine and generates and provides suggestions for deep breathing techniques to alleviate the user's anxiety, as well as advice for mental training for the presentation. The system also incorporates a process to receive feedback from the user and use it to improve the system's performance.

[0167] A concrete example of a prompt statement might be, "Assess your current emotional state and suggest appropriate advice." By using such prompt statements, the system can execute a counseling process optimized for each individual user.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The device collects input data from the user. This input data includes text messages, voice messages, and facial captures via the camera. This data plays a role in representing the user's emotional state in various forms. For example, if a user types "I'm worried about tomorrow's exam" in text and simultaneously captures a tense facial expression with the camera, detailed emotional information can be obtained.

[0171] Step 2:

[0172] The terminal sends the collected multimodal data to the server. The server passes the received data to the sentiment analysis engine. The input here is voice, text, and visual data from the user. The data is formatted appropriately according to its format. This provides the analysis engine with a foundation for detailed and accurate data analysis.

[0173] Step 3:

[0174] The server's emotion analysis engine performs analysis on the received data. It analyzes text using NLP, analyzes the emotional tone of voice data using speech recognition technology, and evaluates facial expression data using facial recognition technology. The output is an evaluation of the user's overall emotional state. Specifically, emotions such as "anxiety" and "tension" are expressed as numerical values ​​or labels.

[0175] Step 4:

[0176] The server uses a generative AI model to automatically generate counseling content tailored to the user based on the analysis results. Taking emotional state data as input, the AI ​​model generates optimal advice and counseling content. For example, based on the anxiety assessment results, it might generate advice on practicing relaxation breathing techniques or restructuring study plans.

[0177] Step 5:

[0178] The server sends the generated advice to the terminal and requests feedback from the user. The terminal displays the counseling content to the user and accepts user confirmation. The user evaluates its usefulness based on their experience and enters feedback. The server then receives feedback from the user and uses it to improve the accuracy of the generation method.

[0179] Step 6:

[0180] The server analyzes user feedback data. Using the user's input, it adjusts and improves the algorithm of the generated AI model. The output is an improved ability to provide more accurate and personalized counseling in the future. This enhances the overall quality of the system and enables further adaptation to meet user needs.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] In care settings, there is a need to accurately understand the emotions and health conditions of the elderly and people with disabilities, and to provide support and care tailored to their individual needs. However, conventional methods are insufficient for information gathering and analysis, making it difficult to provide appropriate support. Technologies are needed to solve these problems and improve the quality of care.

[0184] 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.

[0185] In this invention, the server includes an information acquisition means, an emotion evaluation means, and a support generation means. This makes it possible to accurately analyze the user's emotions and health condition in a care environment and generate and provide appropriate support content.

[0186] "Information acquisition means" refers to a device or program equipped with the function of receiving voice and text data from a user and analyzing it.

[0187] "Emotion evaluation means" refers to a device or program equipped with the function of analyzing received user information from multiple perspectives and determining the user's emotional state.

[0188] A "support generation means" is a device or program equipped with the function of generating support content and advice suitable for the user based on the evaluated emotional state.

[0189] "Information provision means" refers to a device or program equipped with the function of communicating the generated support content to the user.

[0190] A "response processing means" is a device or program equipped with the function of receiving feedback from users, analyzing that information, and using it to improve support content and the overall system.

[0191] "Monitoring means" refers to a device or program equipped with the functionality to continuously observe a user's health and emotional state and generate reports as needed.

[0192] To realize this invention, it is necessary to configure a system that includes a server, a terminal, and an emotion evaluation engine. This system is intended for use in a caregiving environment and aims to comprehensively analyze the user's emotional state and provide appropriate support and advice.

[0193] The server collects voice and text data from caregiving sites using information acquisition methods. These methods utilize hardware such as smartphones and tablets, and software such as speech recognition. This enables real-time data reception and analysis.

[0194] Next, the server comprehensively analyzes the data collected through the emotion assessment tool. In this process, software such as EmotionAnalyzer is used to perform natural language processing and speech analysis to evaluate the user's emotional state. Based on this evaluation, the server determines what kind of support the user needs.

[0195] Subsequently, the server uses support generation tools to generate appropriate support content based on the results of the emotion evaluation. This involves using a generative AI model such as CounselingAdvice to automatically create advice and support content tailored to the user.

[0196] Ultimately, this generated support information is communicated to the user through various means. This process utilizes interfaces to deliver the generated information to the user visually and aurally. This allows care staff and family members to quickly provide appropriate support tailored to the user's condition.

[0197] As a concrete example, consider a situation in a nursing home where an elderly person expresses anxiety during a normal conversation. The server analyzes the audio data and generates a suggestion such as, "You seem a little anxious. How about listening to some very calming music?" and notifies the care staff's terminal in real time.

[0198] Examples of prompts for a generative AI model include the following:

[0199] "If a user is showing signs of anxiety, consider what kind of relaxation suggestions you can offer."

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

[0201] Step 1:

[0202] Users input voice and text data through their device. Voice data is acquired using the device's microphone, and text input is done via a touchscreen or keyboard. This input data is securely transmitted to the server.

[0203] Step 2:

[0204] The server receives audio and text data transmitted from the terminal using an information acquisition method. This data is input into speech recognition software for speech recognition. The audio data is converted into text data, preparing it for natural language processing.

[0205] Step 3:

[0206] The server uses EmotionAnalyzer to analyze the received text data and evaluate the user's emotional state. This analysis uses natural language processing techniques to identify language patterns and emotional expressions. As a result, the user's current emotional state is output.

[0207] Step 4:

[0208] The server generates appropriate support content using support generation mechanisms based on the emotion assessment results. Using CounselingAdvice software, prompt sentences are input into the generation AI model to create advice and suggestions tailored to the user. This support content includes specific action suggestions and methods for calming emotions.

[0209] Step 5:

[0210] The server transmits the generated support information to the user's terminal via an information delivery system. The terminal receives this information and notifies the user by displaying it on the screen or reading it aloud using speech synthesis. This allows users and care staff to obtain support information in real time.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

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

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] The system according to this invention functions between a server, a terminal, and a user. The server analyzes input from the user, uses an emotion analysis model and natural language generation (NLG) to assess the user's mental health status, and generates personalized counseling content. This counseling content may include specific advice such as appropriate relaxation methods and stress management techniques for daily life.

[0228] The terminal provides an interface for the user to interact with the server. The user inputs information about their emotions and state into the terminal, and this data is sent to the server. In response from the server, generated counseling or resources are displayed on the terminal.

[0229] Users receive counseling content provided by the server and use it to improve themselves. They also input feedback on the counseling content and service into their terminals, which the server collects and analyzes. This feedback is used to improve future counseling services.

[0230] Furthermore, the server has the capability to continuously monitor the user's mental health status and generate reports. This makes it possible to respond quickly and effectively to changes in the user's emotional state.

[0231] For example, if a user inputs "I've been feeling down lately due to work pressure" into their device, the device sends this information to the server. The server, through emotion analysis, determines that the user is experiencing high stress levels and suggests appropriate stress reduction methods. These suggestions are displayed to the user through their device, allowing them to understand the steps to implement them. The server then uses this feedback to make improvements that will benefit other users as well.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] Users input their mental health status and concerns into the device. For example, they might enter specific concerns as text, such as "I've been having trouble sleeping at night lately."

[0235] Step 2:

[0236] The terminal sends the entered text data to the server. This communication takes place in real time over the internet.

[0237] Step 3:

[0238] The server analyzes the received text data using a natural language processing (NLP) module. This involves tokenization of the input sentence, morphological analysis, and contextual understanding.

[0239] Step 4:

[0240] The server uses an emotion analysis model to evaluate the user's emotional state based on the analyzed data. For example, it identifies emotions such as anxiety and stress.

[0241] Step 5:

[0242] The server uses a counseling generation module to generate counseling content tailored to the user's emotional state. For example, it might suggest relaxation techniques or recommended lifestyle habits.

[0243] Step 6:

[0244] The server sends the generated counseling content to the terminal. This is also done quickly via the internet.

[0245] Step 7:

[0246] The terminal displays the counseling content received from the server to the user. Typically, this is presented on the screen as text or other visual elements.

[0247] Step 8:

[0248] Users try the provided counseling and input feedback on its effectiveness into the device. This feedback can be entered in a free-form format.

[0249] Step 9:

[0250] The device sends user feedback to the server. This feedback is saved as data for generating the next counseling session.

[0251] Step 10:

[0252] The server uses machine learning algorithms to enhance the counseling generation process based on feedback. This will further improve the user experience in the future.

[0253] Step 11:

[0254] The server continuously monitors the user's mental health status and generates periodic reports. These reports are managed in a way that allows access to the user themselves or a healthcare professional.

[0255] (Example 1)

[0256] 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."

[0257] Traditional psychological support systems have struggled to effectively address the individual psychological states of each user. Furthermore, there have been insufficient methods for appropriately incorporating user feedback and improving service quality. As a result, it has been difficult to provide optimal advice to users, limiting the effectiveness of maintaining and managing their mental health.

[0258] 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.

[0259] In this invention, the server includes communication means for receiving data from users, analysis means for analyzing the information received from the communication means and evaluating the user's psychological state, and generation means for generating information for the user based on the psychological state evaluated by the analysis means. This makes it possible to provide optimal advice tailored to the user's psychological state and to continuously improve the quality of the service by utilizing feedback from users.

[0260] "Communication means" refers to the means of receiving data from users and processing it within the system.

[0261] "Analysis means" refers to a means of processing received information and evaluating the user's psychological state.

[0262] "Generation means" refers to the means of creating and providing optimal information to users based on analysis.

[0263] "Presentation means" refers to a means of effectively providing generated information to users visually or audibly.

[0264] A "response processing means" is a means for receiving responses from users, analyzing their content, and using that information to improve the generation means.

[0265] "Observational means" refers to methods for continuously monitoring the psychological health of users and creating reports based on the results.

[0266] This invention is a system aimed at supporting the psychological health of users and has a configuration that functions between a server, a terminal, and the user.

[0267] The server operates on a cloud computing environment and performs the following processes by combining multiple software modules. First, it receives text data about the user's emotional state sent from the terminal via a communication method. This system uses secure protocols for receiving and sending data, ensuring the security of communication, for example, by SSL / TLS.

[0268] The received data is analyzed using natural language processing algorithms installed on the server. For example, a common natural language processing tool, such as an NLP API, is used to evaluate whether the user's emotions fall into the positive, negative, or neutral categories.

[0269] Based on the analysis results, the server utilizes a generative AI model to generate personalized advice and counseling content. A general-purpose AI model, such as a generative AI, is used as the generation method. The following prompt statements are used in this generation process:

[0270] "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods."

[0271] The generated information is sent as output to the terminal and presented to the user visually. The terminal provides a flexible user interface to display this output information in an easy-to-understand manner.

[0272] Based on the advice displayed on the device, users can take actionable steps in their daily lives. Users also input feedback via the device regarding the effectiveness of these actions and the ease of use of the system, which the server receives. This feedback is analyzed by a response processing system and used to improve the system.

[0273] Furthermore, the server periodically monitors the user's mental health status through observation tools and generates reports. This makes it possible to understand the user's psychological tendencies and support the long-term maintenance and management of their mental health.

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

[0275] Step 1:

[0276] The user inputs text data about their psychological state into the terminal. The terminal receives this input and formats it as digital data. This data includes information related to the user's emotions and mental health. For example, the input might include something like, "I've been feeling tired lately because I have a lot of tasks." The formatted data is securely transmitted to the server via the SSL / TLS protocol.

[0277] Step 2:

[0278] The server analyzes the digital data received from the terminal. This analysis is performed using an emotion analysis algorithm. First, the received data is passed through a natural language processing API to identify emotions in the text. The input is the received text data, and the output is a positive, negative, or neutral emotional state. Specific operations include keyword extraction and emotion score calculation.

[0279] Step 3:

[0280] The server uses a generative AI model based on the analysis results to generate optimal advice for the user. The generative AI model operates using the prompt "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods." The input is the analyzed emotional state and the prompt, and the output is the generated advice. Specifically, text related to stress relief methods and relaxation techniques is generated.

[0281] Step 4:

[0282] The server sends the generated advice to the terminal. The terminal utilizes the user interface to effectively display this information to the user. The input is the generated text advice, and the output is a visually formatted display. Specific operations include adjusting the font size and color tone, and displaying in rich text format.

[0283] Step 5:

[0284] The user acts based on the advice displayed on the terminal. Feedback is input via the terminal regarding the results of the actions taken and the satisfaction with the advice. This feedback is input into the terminal as text or numerical data and then sent to the server. Specifically, it includes the content evaluating the effects of the actions taken by the user.

[0285] Step 6:

[0286] The server analyzes the received feedback and adjusts the parameters of the generated AI model. Thereby, the performance of the entire system is improved. The input is the feedback data from the user, and the output is the updated model parameters. Specific operations include statistical analysis of the feedback and trend analysis.

[0287] Step 7:

[0288] The server utilizes observation means to continuously monitor the user's mental health state and generates regular reports. The input is past emotion data and feedback data, and the output is a visualized report. Specifically, it includes graph creation and analysis of time-series data.

[0289] ​​​​Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0291] In an aging society, it is essential to properly maintain and manage the mental health of the elderly. However, conventional methods make it difficult to provide individualized mental support, resulting in a lack of efficient and continuous support.

[0292] 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.

[0293] In this invention, the server includes: receiving means for receiving input from the user; evaluation means for analyzing data obtained from the receiving means and evaluating the user's emotional state; generating means for generating counseling information for the user based on the emotional state evaluated by the evaluation means; providing means for providing the counseling information generated by the generation means to the user; monitoring means for continuously monitoring the user's mental health state and generating reports based on that; and dialogue means for receiving information from the user through voice input or haptic operation. This enables appropriate and continuous mental support for each elderly person.

[0294] "Receiving means" refers to a device or method for receiving input data from a user.

[0295] "Evaluation means" refers to a device or method that analyzes user data obtained from a receiving means and determines the emotional state.

[0296] "Generation means" refers to a device or method for creating counseling information for a user based on the emotional state obtained by the evaluation means.

[0297] "Delivery means" refers to a device or method for delivering counseling information created by the generation means to the user.

[0298] "Monitoring means" refers to a device or method that continuously observes a user's mental health status and creates a report based on that information.

[0299] "Dialogue means" refers to a device or method for exchanging information interactively with a user through voice input or haptic operation.

[0300] To implement this invention, it is necessary to build a system that supports the mental health of users. This system mainly involves a server, a terminal (e.g., a smartphone), and the user.

[0301] The server is the central component that receives user input and performs sentiment analysis based on it. This analysis uses software such as Python and TensorFlow. Information that users input daily through their terminals, such as text data like "I'm busy and feeling tired," is sent to the server. The server receives this data and uses a sentiment analysis model to evaluate the user's emotional state.

[0302] Based on the assessed emotional state, the server uses a generation mechanism to generate counseling information tailored to the user. This process utilizes natural language generation technology to provide personalized advice.

[0303] The generated information is sent to the terminal via a delivery mechanism and provided to the user. The user receives this information via the terminal, but the operation is intuitive and simple, allowing for interaction through voice input or touch controls. The system also includes a function to continuously monitor the user's mental health status using a monitoring mechanism and generate reports based on that data. User data security is ensured using Firebase.

[0304] As a specific example, consider the case where a user inputs "Recently, I'm not feeling well and my mood is low" into the terminal. The server receives this and generates a suggestion such as "Please try the relaxation method" and displays it on the terminal. Examples of prompt texts for the generative AI model in this system include "Please tell me about your recent mood and events. I will provide advice accordingly."

[0305] As described above, by the cooperation of the server, the terminal, and the user, it is possible to efficiently implement mental health support, which is the object of the invention.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The user inputs information regarding emotions and states through the terminal. At this time, voice input or text input may be used. The input information is digitized at the terminal as initial data for evaluating the emotional state and transmitted to the server.

[0309] Step 2:

[0310] The server analyzes the input data of the user received from the terminal. First, it receives the input data and performs an emotion evaluation using an emotion analysis model. The generative AI model used here extracts emotions from the input data based on past data and the learning results of AI and evaluates the user's emotional state. The input is the user's text data, and the output is the evaluated emotional state. Natural language processing technology is utilized to process the data.

[0311] Step 3:

[0312] The server generates counseling information for the user based on the assessed emotional state, using a generation method. This process uses NLG (Natural Language Generation) technology to generate appropriate advice in text format. The input is the result of the emotional assessment, and the output is individually generated counseling information. When generating the information, templates and past cases are used as references to construct content that is appropriate to the user's emotional state.

[0313] Step 4:

[0314] The generated counseling information is transmitted to the terminal via a delivery method. The terminal receives this information and presents it to the user in an easily understandable format. The user can review this on the screen and receive advice and suggestions. At this stage, the input is the generated counseling information, and the output is the visual or auditory information received by the user.

[0315] Step 5:

[0316] After taking action based on the provided counseling information, the user inputs their thoughts and feedback into the device. This feedback is used to improve the accuracy of future counseling sessions. The device receives the feedback and sends it back to the server. The input is the user's feedback data, and the output is a digital record of the feedback. The server analyzes this and uses it to improve the generated AI model.

[0317] Step 6:

[0318] The server continuously monitors the user's mental health status and generates reports as needed. It analyzes the user's overall mental health trends using monitoring tools and takes early action if problems are detected. The input is continuously collected status data, and the output is periodic mental health reports. This process involves cumulative data analysis and a comprehensive assessment combining various factors.

[0319] 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.

[0320] The present invention is implemented as a system including a server, a terminal, and an emotion engine. The server receives multimodal data, including text and voice, from the user and passes it to the emotion engine for analysis. The emotion engine evaluates the user's emotional state from multiple perspectives, utilizing natural language processing (NLP), voice analysis, and facial recognition. Based on the analysis results, the server generates counseling and advice tailored to the user's emotional state and provides it through the terminal.

[0321] The terminal provides an interface for users to interact with the server, allowing them to input their emotions and state in various formats. For example, in addition to text input, it is possible to record voice messages and capture facial expressions via the camera. This enables the server to perform more detailed and accurate emotion analysis.

[0322] Through this system, users can receive personalized counseling tailored to their emotions. For example, if a user inputs "I'm anxious because the work deadline is approaching" and captures their facial expression with a camera, the server sends the audio and visual information to an emotion engine and assesses that the user is experiencing strong anxiety. The server then suggests specific breathing techniques and time management strategies to alleviate the anxiety.

[0323] Furthermore, by providing feedback after counseling sessions, the system analyzes this information and continuously improves the performance of its emotion engine and generation mechanisms. In this way, the system provides counseling that is more tailored to individual needs, contributing to the improvement of users' mental health.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users can input text messages about their emotions and state of mind into the device. They can also send voice messages or capture facial expressions using the camera, if needed.

[0327] Step 2:

[0328] The device transmits text, audio, and video data entered by the user to the server. This transmission is secure and fast.

[0329] Step 3:

[0330] The server analyzes the received text data using a natural language processing (NLP) module to perform an initial evaluation of the meaning and sentiment of the sentences.

[0331] Step 4:

[0332] The server processes the audio data using an audio analysis module, analyzing factors such as tone, speed, and pauses to estimate the emotional state.

[0333] Step 5:

[0334] The server processes video data via a face recognition and facial expression analysis module, and infers emotions from facial expressions.

[0335] Step 6:

[0336] The server uses an emotion engine to integrate results obtained from text, audio, and video to provide a detailed assessment of the user's overall emotional state.

[0337] Step 7:

[0338] Based on the evaluated emotional state, the server generates counseling content tailored to the user's situation and needs. This includes specific advice and solutions.

[0339] Step 8:

[0340] The server generates counseling content and sends it to the terminal. The terminal then provides this to the user in text or audio format.

[0341] Step 9:

[0342] Users review the counseling content and input feedback on its effectiveness, their impressions, and any additional inquiries into the device.

[0343] Step 10:

[0344] The device sends user feedback to the server. The server uses this feedback to improve the emotion engine and counseling generation module.

[0345] (Example 2)

[0346] 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".

[0347] In modern society, maintaining mental health is becoming increasingly important, but there are limited systems that allow users to accurately understand their own emotional state and receive appropriate advice and counseling accordingly. Conventional systems have limitations in terms of information due to their single-modal input, leading to problems with the accuracy of analysis. Furthermore, the advice provided is uniform and does not offer content tailored to the individual needs of the user.

[0348] 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.

[0349] In this invention, the server includes an input means for receiving diverse emotional data from a user, an emotional analysis means for analyzing the multimodal data received from the input means and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology, and a generation means for generating counseling content for the user using a generation AI model based on the emotional state evaluated by the emotional analysis means. This enables more detailed and accurate emotional evaluation and realizes the provision of counseling content tailored to the user's individual needs.

[0350] "Input means" refers to devices and methods for collecting diverse emotional data from users.

[0351] "Emotion analysis means" refers to devices or methods for analyzing input multimodal data and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology.

[0352] "Generation means" refers to a device or method that generates counseling content for a user using a generation AI model based on the emotional state evaluated by the emotion analysis means.

[0353] "Output means" refers to devices or methods for providing the generated counseling content to the user and for receiving user feedback.

[0354] "Feedback processing means" refers to devices or methods for analyzing user feedback and improving the generation means.

[0355] "Monitoring means" refers to devices or methods for continuously observing a user's mental health status and generating reports based on that observation.

[0356] This invention is a system that provides emotionally tailored counseling and advice by collecting and analyzing diverse emotional data from users. It is implemented using a server, terminals, and an emotion analysis engine.

[0357] The device functions as an input device for collecting emotional data from users. Users can provide emotional data through text input, voice message recording, and facial expression capture using a camera. This allows for the collection of multimodal data, laying the foundation for a multifaceted evaluation of the user's emotional state.

[0358] The server receives data transmitted from the terminal and processes it using an emotion analysis engine. This engine combines natural language processing (NLP), speech analysis software, and facial recognition technology to accurately assess the user's emotional state. This enables detailed and precise analysis that would not be possible with a single modal system.

[0359] Based on the analysis results, the server utilizes a generative AI model to automatically generate counseling content tailored to the user's emotional state. This generation process includes specific advice customized to individual user needs, rather than conventional, standardized advice.

[0360] As a concrete example, consider a scenario where a user inputs a message such as "I'm nervous because I have an important presentation coming up," and then captures their nervous facial expression using a camera. In this case, the server evaluates the data using an emotion analysis engine and generates and provides suggestions for deep breathing techniques to alleviate the user's anxiety, as well as advice for mental training for the presentation. The system also incorporates a process to receive feedback from the user and use it to improve the system's performance.

[0361] A concrete example of a prompt statement might be, "Assess your current emotional state and suggest appropriate advice." By using such prompt statements, the system can execute a counseling process optimized for each individual user.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The device collects input data from the user. This input data includes text messages, voice messages, and facial captures via the camera. This data plays a role in representing the user's emotional state in various forms. For example, if a user types "I'm worried about tomorrow's exam" in text and simultaneously captures a tense facial expression with the camera, detailed emotional information can be obtained.

[0365] Step 2:

[0366] The terminal sends the collected multimodal data to the server. The server passes the received data to the sentiment analysis engine. The input here is voice, text, and visual data from the user. The data is formatted appropriately according to its format. This provides the analysis engine with a foundation for detailed and accurate data analysis.

[0367] Step 3:

[0368] The server's emotion analysis engine performs analysis on the received data. It analyzes text using NLP, analyzes the emotional tone of voice data using speech recognition technology, and evaluates facial expression data using facial recognition technology. The output is an evaluation of the user's overall emotional state. Specifically, emotions such as "anxiety" and "tension" are expressed as numerical values ​​or labels.

[0369] Step 4:

[0370] The server uses a generative AI model to automatically generate counseling content tailored to the user based on the analysis results. Taking emotional state data as input, the AI ​​model generates optimal advice and counseling content. For example, based on the anxiety assessment results, it might generate advice on practicing relaxation breathing techniques or restructuring study plans.

[0371] Step 5:

[0372] The server sends the generated advice to the terminal and requests feedback from the user. The terminal displays the counseling content to the user and accepts user confirmation. The user evaluates its usefulness based on their experience and enters feedback. The server then receives feedback from the user and uses it to improve the accuracy of the generation method.

[0373] Step 6:

[0374] The server analyzes user feedback data. Using the user's input, it adjusts and improves the algorithm of the generated AI model. The output is an improved ability to provide more accurate and personalized counseling in the future. This enhances the overall quality of the system and enables further adaptation to meet user needs.

[0375] (Application Example 2)

[0376] 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."

[0377] In care settings, there is a need to accurately understand the emotions and health conditions of the elderly and people with disabilities, and to provide support and care tailored to their individual needs. However, conventional methods are insufficient for information gathering and analysis, making it difficult to provide appropriate support. Technologies are needed to solve these problems and improve the quality of care.

[0378] 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.

[0379] In this invention, the server includes an information acquisition means, an emotion evaluation means, and a support generation means. This makes it possible to accurately analyze the user's emotions and health condition in a care environment and generate and provide appropriate support content.

[0380] "Information acquisition means" refers to a device or program equipped with the function of receiving voice and text data from a user and analyzing it.

[0381] "Emotion evaluation means" refers to a device or program equipped with the function of analyzing received user information from multiple perspectives and determining the user's emotional state.

[0382] A "support generation means" is a device or program equipped with the function of generating support content and advice suitable for the user based on the evaluated emotional state.

[0383] "Information provision means" refers to a device or program equipped with the function of communicating the generated support content to the user.

[0384] A "response processing means" is a device or program equipped with the function of receiving feedback from users, analyzing that information, and using it to improve support content and the overall system.

[0385] "Monitoring means" refers to a device or program equipped with the functionality to continuously observe a user's health and emotional state and generate reports as needed.

[0386] To realize this invention, it is necessary to configure a system that includes a server, a terminal, and an emotion evaluation engine. This system is intended for use in a caregiving environment and aims to comprehensively analyze the user's emotional state and provide appropriate support and advice.

[0387] The server collects voice and text data from caregiving sites using information acquisition methods. These methods utilize hardware such as smartphones and tablets, and software such as speech recognition. This enables real-time data reception and analysis.

[0388] Next, the server comprehensively analyzes the data collected through the emotion assessment tool. In this process, software such as EmotionAnalyzer is used to perform natural language processing and speech analysis to evaluate the user's emotional state. Based on this evaluation, the server determines what kind of support the user needs.

[0389] Subsequently, the server uses support generation tools to generate appropriate support content based on the results of the emotion evaluation. This involves using a generative AI model such as CounselingAdvice to automatically create advice and support content tailored to the user.

[0390] Ultimately, this generated support information is communicated to the user through various means. This process utilizes interfaces to deliver the generated information to the user visually and aurally. This allows care staff and family members to quickly provide appropriate support tailored to the user's condition.

[0391] As a concrete example, consider a situation in a nursing home where an elderly person expresses anxiety during a normal conversation. The server analyzes the audio data and generates a suggestion such as, "You seem a little anxious. How about listening to some very calming music?" and notifies the care staff's terminal in real time.

[0392] Examples of prompts for a generative AI model include the following:

[0393] "If a user is showing signs of anxiety, consider what kind of relaxation suggestions you can offer."

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

[0395] Step 1:

[0396] Users input voice and text data through their device. Voice data is acquired using the device's microphone, and text input is done via a touchscreen or keyboard. This input data is securely transmitted to the server.

[0397] Step 2:

[0398] The server receives audio and text data transmitted from the terminal using an information acquisition method. This data is input into speech recognition software for speech recognition. The audio data is converted into text data, preparing it for natural language processing.

[0399] Step 3:

[0400] The server uses EmotionAnalyzer to analyze the received text data and evaluate the user's emotional state. This analysis uses natural language processing techniques to identify language patterns and emotional expressions. As a result, the user's current emotional state is output.

[0401] Step 4:

[0402] The server generates appropriate support content using support generation mechanisms based on the emotion assessment results. Using CounselingAdvice software, prompt sentences are input into the generation AI model to create advice and suggestions tailored to the user. This support content includes specific action suggestions and methods for calming emotions.

[0403] Step 5:

[0404] The server transmits the generated support information to the user's terminal via an information delivery system. The terminal receives this information and notifies the user by displaying it on the screen or reading it aloud using speech synthesis. This allows users and care staff to obtain support information in real time.

[0405] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0406] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0407] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0408] [Third Embodiment]

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

[0410] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0411] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0412] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0413] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0414] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0415] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0416] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0417] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0418] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0419] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0420] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0421] The system according to this invention functions between a server, a terminal, and a user. The server analyzes input from the user, uses an emotion analysis model and natural language generation (NLG) to assess the user's mental health status, and generates personalized counseling content. This counseling content may include specific advice such as appropriate relaxation methods and stress management techniques for daily life.

[0422] The terminal provides an interface for the user to interact with the server. The user inputs information about their emotions and state into the terminal, and this data is sent to the server. In response from the server, generated counseling or resources are displayed on the terminal.

[0423] Users receive counseling content provided by the server and use it to improve themselves. They also input feedback on the counseling content and service into their terminals, which the server collects and analyzes. This feedback is used to improve future counseling services.

[0424] Furthermore, the server has the capability to continuously monitor the user's mental health status and generate reports. This makes it possible to respond quickly and effectively to changes in the user's emotional state.

[0425] For example, if a user inputs "I've been feeling down lately due to work pressure" into their device, the device sends this information to the server. The server, through emotion analysis, determines that the user is experiencing high stress levels and suggests appropriate stress reduction methods. These suggestions are displayed to the user through their device, allowing them to understand the steps to implement them. The server then uses this feedback to make improvements that will benefit other users as well.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users input their mental health status and concerns into the device. For example, they might enter specific concerns as text, such as "I've been having trouble sleeping at night lately."

[0429] Step 2:

[0430] The terminal sends the entered text data to the server. This communication takes place in real time over the internet.

[0431] Step 3:

[0432] The server analyzes the received text data using a natural language processing (NLP) module. This involves tokenization of the input sentence, morphological analysis, and contextual understanding.

[0433] Step 4:

[0434] The server uses an emotion analysis model to evaluate the user's emotional state based on the analyzed data. For example, it identifies emotions such as anxiety and stress.

[0435] Step 5:

[0436] The server uses a counseling generation module to generate counseling content tailored to the user's emotional state. For example, it might suggest relaxation techniques or recommended lifestyle habits.

[0437] Step 6:

[0438] The server sends the generated counseling content to the terminal. This is also done quickly via the internet.

[0439] Step 7:

[0440] The terminal displays the counseling content received from the server to the user. Typically, this is presented on the screen as text or other visual elements.

[0441] Step 8:

[0442] Users try the provided counseling and input feedback on its effectiveness into the device. This feedback can be entered in a free-form format.

[0443] Step 9:

[0444] The device sends user feedback to the server. This feedback is saved as data for generating the next counseling session.

[0445] Step 10:

[0446] The server uses machine learning algorithms to enhance the counseling generation process based on feedback. This will further improve the user experience in the future.

[0447] Step 11:

[0448] The server continuously monitors the user's mental health status and generates periodic reports. These reports are managed in a way that allows access to the user themselves or a healthcare professional.

[0449] (Example 1)

[0450] 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."

[0451] Traditional psychological support systems have struggled to effectively address the individual psychological states of each user. Furthermore, there have been insufficient methods for appropriately incorporating user feedback and improving service quality. As a result, it has been difficult to provide optimal advice to users, limiting the effectiveness of maintaining and managing their mental health.

[0452] 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.

[0453] In this invention, the server includes communication means for receiving data from users, analysis means for analyzing the information received from the communication means and evaluating the user's psychological state, and generation means for generating information for the user based on the psychological state evaluated by the analysis means. This makes it possible to provide optimal advice tailored to the user's psychological state and to continuously improve the quality of the service by utilizing feedback from users.

[0454] "Communication means" refers to the means of receiving data from users and processing it within the system.

[0455] "Analysis means" refers to a means of processing received information and evaluating the user's psychological state.

[0456] "Generation means" refers to the means of creating and providing optimal information to users based on analysis.

[0457] "Presentation means" refers to a means of effectively providing generated information to users visually or audibly.

[0458] A "response processing means" is a means for receiving responses from users, analyzing their content, and using that information to improve the generation means.

[0459] "Observational means" refers to methods for continuously monitoring the psychological health of users and creating reports based on the results.

[0460] This invention is a system aimed at supporting the psychological health of users and has a configuration that functions between a server, a terminal, and the user.

[0461] The server operates on a cloud computing environment and performs the following processes by combining multiple software modules. First, it receives text data about the user's emotional state sent from the terminal via a communication method. This system uses secure protocols for receiving and sending data, ensuring the security of communication, for example, by SSL / TLS.

[0462] The received data is analyzed using natural language processing algorithms installed on the server. For example, a common natural language processing tool, such as an NLP API, is used to evaluate whether the user's emotions fall into the positive, negative, or neutral categories.

[0463] Based on the analysis results, the server utilizes a generative AI model to generate personalized advice and counseling content. A general-purpose AI model, such as a generative AI, is used as the generation method. The following prompt statements are used in this generation process:

[0464] "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods."

[0465] The generated information is sent as output to the terminal and presented to the user visually. The terminal provides a flexible user interface to display this output information in an easy-to-understand manner.

[0466] Based on the advice displayed on the device, users can take actionable steps in their daily lives. Users also input feedback via the device regarding the effectiveness of these actions and the ease of use of the system, which the server receives. This feedback is analyzed by a response processing system and used to improve the system.

[0467] Furthermore, the server periodically monitors the user's mental health status through observation tools and generates reports. This makes it possible to understand the user's psychological tendencies and support the long-term maintenance and management of their mental health.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The user inputs text data about their psychological state into the terminal. The terminal receives this input and formats it as digital data. This data includes information related to the user's emotions and mental health. For example, the input might include something like, "I've been feeling tired lately because I have a lot of tasks." The formatted data is securely transmitted to the server via the SSL / TLS protocol.

[0471] Step 2:

[0472] The server analyzes the digital data received from the terminal. This analysis is performed using an emotion analysis algorithm. First, the received data is passed through a natural language processing API to identify emotions in the text. The input is the received text data, and the output is a positive, negative, or neutral emotional state. Specific operations include keyword extraction and emotion score calculation.

[0473] Step 3:

[0474] The server uses a generative AI model based on the analysis results to generate optimal advice for the user. The generative AI model operates using the prompt "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods." The input is the analyzed emotional state and the prompt, and the output is the generated advice. Specifically, text related to stress relief methods and relaxation techniques is generated.

[0475] Step 4:

[0476] The server sends the generated advice to the terminal. The terminal utilizes a user interface to effectively display this information to the user. The input is the generated text advice, and the output is a visually formatted display. Specific actions include adjusting font size and color, and displaying in rich text format.

[0477] Step 5:

[0478] Users take action based on the advice displayed on their device. They then provide feedback via the device regarding the results of their actions and their satisfaction with the advice. This feedback is entered into the device as text and numerical data and then sent to the server. Specifically, it includes an evaluation of the effectiveness of the actions taken by the user.

[0479] Step 6:

[0480] The server analyzes the received feedback and adjusts the parameters of the generated AI model, thereby improving the overall system performance. The input is user feedback data, and the output is the updated model parameters. Specific operations include statistical analysis of the feedback and trend analysis.

[0481] Step 7:

[0482] The server continuously monitors the user's psychological health using observational tools and generates periodic reports. Inputs are historical emotional data and feedback data, and output is a visualized report. Specifically, this includes graph creation and time-series data analysis.

[0483] (Application Example 1)

[0484] 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."

[0485] In an aging society, it is essential to properly maintain and manage the mental health of the elderly. However, conventional methods make it difficult to provide individualized mental support, resulting in a lack of efficient and continuous support.

[0486] 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.

[0487] In this invention, the server includes: receiving means for receiving input from the user; evaluation means for analyzing data obtained from the receiving means and evaluating the user's emotional state; generating means for generating counseling information for the user based on the emotional state evaluated by the evaluation means; providing means for providing the counseling information generated by the generation means to the user; monitoring means for continuously monitoring the user's mental health state and generating reports based on that; and dialogue means for receiving information from the user through voice input or haptic operation. This enables appropriate and continuous mental support for each elderly person.

[0488] "Receiving means" refers to a device or method for receiving input data from a user.

[0489] "Evaluation means" refers to a device or method that analyzes user data obtained from a receiving means and determines the emotional state.

[0490] "Generation means" refers to a device or method for creating counseling information for a user based on the emotional state obtained by the evaluation means.

[0491] "Delivery means" refers to a device or method for delivering counseling information created by the generation means to the user.

[0492] "Monitoring means" refers to a device or method that continuously observes a user's mental health status and creates a report based on that information.

[0493] "Dialogue means" refers to a device or method for exchanging information interactively with a user through voice input or haptic operation.

[0494] To implement this invention, it is necessary to build a system that supports the mental health of users. This system mainly involves a server, a terminal (e.g., a smartphone), and the user.

[0495] The server is the central component that receives user input and performs sentiment analysis based on it. This analysis uses software such as Python and TensorFlow. Information that users input daily through their terminals, such as text data like "I'm busy and feeling tired," is sent to the server. The server receives this data and uses a sentiment analysis model to evaluate the user's emotional state.

[0496] Based on the assessed emotional state, the server uses a generation mechanism to generate counseling information tailored to the user. This process utilizes natural language generation technology to provide personalized advice.

[0497] The generated information is sent to the terminal via a delivery mechanism and provided to the user. The user receives this information via the terminal, but the operation is intuitive and simple, allowing for interaction through voice input or touch controls. The system also includes a function to continuously monitor the user's mental health status using a monitoring mechanism and generate reports based on that data. User data security is ensured using Firebase.

[0498] As a concrete example, consider a scenario where a user inputs into their device, "I haven't been feeling well lately and I'm feeling down." The server receives this and generates and displays suggestions such as, "Try some relaxation techniques." An example of a prompt message to the generating AI model in this system would be, "Tell us about your recent mood and events. We will provide advice accordingly."

[0499] As described above, by coordinating the server, terminal, and user, it is possible to efficiently implement the mental health support that is the objective of this invention.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] Users input information about their emotions and state of mind through their device. This may involve using voice or text input. The input information is digitized on the device as initial data for evaluating the emotional state and then sent to the server.

[0503] Step 2:

[0504] The server analyzes user input data received from the terminal. First, it receives the input data and performs an emotional evaluation using an emotional analysis model. The generative AI model used here extracts emotions from the input data and evaluates the user's emotional state based on past data and the AI's learning results. The input is the user's text data, and the output is the evaluated emotional state. Natural language processing techniques are used to process the data.

[0505] Step 3:

[0506] The server generates counseling information for the user based on the assessed emotional state, using a generation method. This process uses NLG (Natural Language Generation) technology to generate appropriate advice in text format. The input is the result of the emotional assessment, and the output is individually generated counseling information. When generating the information, templates and past cases are used as references to construct content that is appropriate to the user's emotional state.

[0507] Step 4:

[0508] The generated counseling information is transmitted to the terminal via a delivery method. The terminal receives this information and presents it to the user in an easily understandable format. The user can review this on the screen and receive advice and suggestions. At this stage, the input is the generated counseling information, and the output is the visual or auditory information received by the user.

[0509] Step 5:

[0510] After taking action based on the provided counseling information, the user inputs their thoughts and feedback into the device. This feedback is used to improve the accuracy of future counseling sessions. The device receives the feedback and sends it back to the server. The input is the user's feedback data, and the output is a digital record of the feedback. The server analyzes this and uses it to improve the generated AI model.

[0511] Step 6:

[0512] The server continuously monitors the user's mental health status and generates reports as needed. It analyzes the user's overall mental health trends using monitoring tools and takes early action if problems are detected. The input is continuously collected status data, and the output is periodic mental health reports. This process involves cumulative data analysis and a comprehensive assessment combining various factors.

[0513] 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.

[0514] The present invention is implemented as a system including a server, a terminal, and an emotion engine. The server receives multimodal data, including text and voice, from the user and passes it to the emotion engine for analysis. The emotion engine evaluates the user's emotional state from multiple perspectives, utilizing natural language processing (NLP), voice analysis, and facial recognition. Based on the analysis results, the server generates counseling and advice tailored to the user's emotional state and provides it through the terminal.

[0515] The terminal provides an interface for users to interact with the server, allowing them to input their emotions and state in various formats. For example, in addition to text input, it is possible to record voice messages and capture facial expressions via the camera. This enables the server to perform more detailed and accurate emotion analysis.

[0516] Through this system, users can receive personalized counseling tailored to their emotions. For example, if a user inputs "I'm anxious because the work deadline is approaching" and captures their facial expression with a camera, the server sends the audio and visual information to an emotion engine and assesses that the user is experiencing strong anxiety. The server then suggests specific breathing techniques and time management strategies to alleviate the anxiety.

[0517] Furthermore, by providing feedback after counseling sessions, the system analyzes this information and continuously improves the performance of its emotion engine and generation mechanisms. In this way, the system provides counseling that is more tailored to individual needs, contributing to the improvement of users' mental health.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users can input text messages about their emotions and state of mind into the device. They can also send voice messages or capture facial expressions using the camera, if needed.

[0521] Step 2:

[0522] The device transmits text, audio, and video data entered by the user to the server. This transmission is secure and fast.

[0523] Step 3:

[0524] The server analyzes the received text data using a natural language processing (NLP) module to perform an initial evaluation of the meaning and sentiment of the sentences.

[0525] Step 4:

[0526] The server processes the audio data using an audio analysis module, analyzing factors such as tone, speed, and pauses to estimate the emotional state.

[0527] Step 5:

[0528] The server processes video data via a face recognition and facial expression analysis module, and infers emotions from facial expressions.

[0529] Step 6:

[0530] The server uses an emotion engine to integrate results obtained from text, audio, and video to provide a detailed assessment of the user's overall emotional state.

[0531] Step 7:

[0532] Based on the evaluated emotional state, the server generates counseling content tailored to the user's situation and needs. This includes specific advice and solutions.

[0533] Step 8:

[0534] The server generates counseling content and sends it to the terminal. The terminal then provides this to the user in text or audio format.

[0535] Step 9:

[0536] Users review the counseling content and input feedback on its effectiveness, their impressions, and any additional inquiries into the device.

[0537] Step 10:

[0538] The device sends user feedback to the server. The server uses this feedback to improve the emotion engine and counseling generation module.

[0539] (Example 2)

[0540] 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."

[0541] In modern society, maintaining mental health is becoming increasingly important, but there are limited systems that allow users to accurately understand their own emotional state and receive appropriate advice and counseling accordingly. Conventional systems have limitations in terms of information due to their single-modal input, leading to problems with the accuracy of analysis. Furthermore, the advice provided is uniform and does not offer content tailored to the individual needs of the user.

[0542] 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.

[0543] In this invention, the server includes an input means for receiving diverse emotional data from a user, an emotional analysis means for analyzing the multimodal data received from the input means and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology, and a generation means for generating counseling content for the user using a generation AI model based on the emotional state evaluated by the emotional analysis means. This enables more detailed and accurate emotional evaluation and realizes the provision of counseling content tailored to the user's individual needs.

[0544] "Input means" refers to devices and methods for collecting diverse emotional data from users.

[0545] "Emotion analysis means" refers to devices or methods for analyzing input multimodal data and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology.

[0546] "Generation means" refers to a device or method that generates counseling content for a user using a generation AI model based on the emotional state evaluated by the emotion analysis means.

[0547] "Output means" refers to devices or methods for providing the generated counseling content to the user and for receiving user feedback.

[0548] "Feedback processing means" refers to devices or methods for analyzing user feedback and improving the generation means.

[0549] "Monitoring means" refers to devices or methods for continuously observing a user's mental health status and generating reports based on that observation.

[0550] This invention is a system that provides emotionally tailored counseling and advice by collecting and analyzing diverse emotional data from users. It is implemented using a server, terminals, and an emotion analysis engine.

[0551] The device functions as an input device for collecting emotional data from users. Users can provide emotional data through text input, voice message recording, and facial expression capture using a camera. This allows for the collection of multimodal data, laying the foundation for a multifaceted evaluation of the user's emotional state.

[0552] The server receives data transmitted from the terminal and processes it using an emotion analysis engine. This engine combines natural language processing (NLP), speech analysis software, and facial recognition technology to accurately assess the user's emotional state. This enables detailed and precise analysis that would not be possible with a single modal system.

[0553] Based on the analysis results, the server utilizes a generative AI model to automatically generate counseling content tailored to the user's emotional state. This generation process includes specific advice customized to individual user needs, rather than conventional, standardized advice.

[0554] As a concrete example, consider a scenario where a user inputs a message such as "I'm nervous because I have an important presentation coming up," and then captures their nervous facial expression using a camera. In this case, the server evaluates the data using an emotion analysis engine and generates and provides suggestions for deep breathing techniques to alleviate the user's anxiety, as well as advice for mental training for the presentation. The system also incorporates a process to receive feedback from the user and use it to improve the system's performance.

[0555] A concrete example of a prompt statement might be, "Assess your current emotional state and suggest appropriate advice." By using such prompt statements, the system can execute a counseling process optimized for each individual user.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The device collects input data from the user. This input data includes text messages, voice messages, and facial captures via the camera. This data plays a role in representing the user's emotional state in various forms. For example, if a user types "I'm worried about tomorrow's exam" in text and simultaneously captures a tense facial expression with the camera, detailed emotional information can be obtained.

[0559] Step 2:

[0560] The terminal sends the collected multimodal data to the server. The server passes the received data to the sentiment analysis engine. The input here is voice, text, and visual data from the user. The data is formatted appropriately according to its format. This provides the analysis engine with a foundation for detailed and accurate data analysis.

[0561] Step 3:

[0562] The server's emotion analysis engine performs analysis on the received data. It analyzes text using NLP, analyzes the emotional tone of voice data using speech recognition technology, and evaluates facial expression data using facial recognition technology. The output is an evaluation of the user's overall emotional state. Specifically, emotions such as "anxiety" and "tension" are expressed as numerical values ​​or labels.

[0563] Step 4:

[0564] The server uses a generative AI model to automatically generate counseling content tailored to the user based on the analysis results. Taking emotional state data as input, the AI ​​model generates optimal advice and counseling content. For example, based on the anxiety assessment results, it might generate advice on practicing relaxation breathing techniques or restructuring study plans.

[0565] Step 5:

[0566] The server sends the generated advice to the terminal and requests feedback from the user. The terminal displays the counseling content to the user and accepts user confirmation. The user evaluates its usefulness based on their experience and enters feedback. The server then receives feedback from the user and uses it to improve the accuracy of the generation method.

[0567] Step 6:

[0568] The server analyzes user feedback data. Using the user's input, it adjusts and improves the algorithm of the generated AI model. The output is an improved ability to provide more accurate and personalized counseling in the future. This enhances the overall quality of the system and enables further adaptation to meet user needs.

[0569] (Application Example 2)

[0570] 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."

[0571] In care settings, there is a need to accurately understand the emotions and health conditions of the elderly and people with disabilities, and to provide support and care tailored to their individual needs. However, conventional methods are insufficient for information gathering and analysis, making it difficult to provide appropriate support. Technologies are needed to solve these problems and improve the quality of care.

[0572] 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.

[0573] In this invention, the server includes an information acquisition means, an emotion evaluation means, and a support generation means. This makes it possible to accurately analyze the user's emotions and health condition in a care environment and generate and provide appropriate support content.

[0574] "Information acquisition means" refers to a device or program equipped with the function of receiving voice and text data from a user and analyzing it.

[0575] "Emotion evaluation means" refers to a device or program equipped with the function of analyzing received user information from multiple perspectives and determining the user's emotional state.

[0576] A "support generation means" is a device or program equipped with the function of generating support content and advice suitable for the user based on the evaluated emotional state.

[0577] "Information provision means" refers to a device or program equipped with the function of communicating the generated support content to the user.

[0578] A "response processing means" is a device or program equipped with the function of receiving feedback from users, analyzing that information, and using it to improve support content and the overall system.

[0579] "Monitoring means" refers to a device or program equipped with the functionality to continuously observe a user's health and emotional state and generate reports as needed.

[0580] To realize this invention, it is necessary to configure a system that includes a server, a terminal, and an emotion evaluation engine. This system is intended for use in a caregiving environment and aims to comprehensively analyze the user's emotional state and provide appropriate support and advice.

[0581] The server collects voice and text data from caregiving sites using information acquisition methods. These methods utilize hardware such as smartphones and tablets, and software such as speech recognition. This enables real-time data reception and analysis.

[0582] Next, the server comprehensively analyzes the data collected through the emotion assessment tool. In this process, software such as EmotionAnalyzer is used to perform natural language processing and speech analysis to evaluate the user's emotional state. Based on this evaluation, the server determines what kind of support the user needs.

[0583] Subsequently, the server uses support generation tools to generate appropriate support content based on the results of the emotion evaluation. This involves using a generative AI model such as CounselingAdvice to automatically create advice and support content tailored to the user.

[0584] Ultimately, this generated support information is communicated to the user through various means. This process utilizes interfaces to deliver the generated information to the user visually and aurally. This allows care staff and family members to quickly provide appropriate support tailored to the user's condition.

[0585] As a concrete example, consider a situation in a nursing home where an elderly person expresses anxiety during a normal conversation. The server analyzes the audio data and generates a suggestion such as, "You seem a little anxious. How about listening to some very calming music?" and notifies the care staff's terminal in real time.

[0586] Examples of prompts for a generative AI model include the following:

[0587] "If a user is showing signs of anxiety, consider what kind of relaxation suggestions you can offer."

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

[0589] Step 1:

[0590] Users input voice and text data through their device. Voice data is acquired using the device's microphone, and text input is done via a touchscreen or keyboard. This input data is securely transmitted to the server.

[0591] Step 2:

[0592] The server receives audio and text data transmitted from the terminal using an information acquisition method. This data is input into speech recognition software for speech recognition. The audio data is converted into text data, preparing it for natural language processing.

[0593] Step 3:

[0594] The server uses EmotionAnalyzer to analyze the received text data and evaluate the user's emotional state. This analysis uses natural language processing techniques to identify language patterns and emotional expressions. As a result, the user's current emotional state is output.

[0595] Step 4:

[0596] The server generates appropriate support content using support generation mechanisms based on the emotion assessment results. Using CounselingAdvice software, prompt sentences are input into the generation AI model to create advice and suggestions tailored to the user. This support content includes specific action suggestions and methods for calming emotions.

[0597] Step 5:

[0598] The server transmits the generated support information to the user's terminal via an information delivery system. The terminal receives this information and notifies the user by displaying it on the screen or reading it aloud using speech synthesis. This allows users and care staff to obtain support information in real time.

[0599] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0600] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0601] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0602] [Fourth Embodiment]

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

[0604] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0605] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0606] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0607] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0608] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0609] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0610] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0611] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0612] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0613] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0614] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0615] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] The system according to this invention functions between a server, a terminal, and a user. The server analyzes input from the user, uses an emotion analysis model and natural language generation (NLG) to assess the user's mental health status, and generates personalized counseling content. This counseling content may include specific advice such as appropriate relaxation methods and stress management techniques for daily life.

[0617] The terminal provides an interface for the user to interact with the server. The user inputs information about their emotions and state into the terminal, and this data is sent to the server. In response from the server, generated counseling or resources are displayed on the terminal.

[0618] Users receive counseling content provided by the server and use it to improve themselves. They also input feedback on the counseling content and service into their terminals, which the server collects and analyzes. This feedback is used to improve future counseling services.

[0619] Furthermore, the server has the capability to continuously monitor the user's mental health status and generate reports. This makes it possible to respond quickly and effectively to changes in the user's emotional state.

[0620] For example, if a user inputs "I've been feeling down lately due to work pressure" into their device, the device sends this information to the server. The server, through emotion analysis, determines that the user is experiencing high stress levels and suggests appropriate stress reduction methods. These suggestions are displayed to the user through their device, allowing them to understand the steps to implement them. The server then uses this feedback to make improvements that will benefit other users as well.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] Users input their mental health status and concerns into the device. For example, they might enter specific concerns as text, such as "I've been having trouble sleeping at night lately."

[0624] Step 2:

[0625] The terminal sends the entered text data to the server. This communication takes place in real time over the internet.

[0626] Step 3:

[0627] The server analyzes the received text data using a natural language processing (NLP) module. This involves tokenization of the input sentence, morphological analysis, and contextual understanding.

[0628] Step 4:

[0629] The server uses an emotion analysis model to evaluate the user's emotional state based on the analyzed data. For example, it identifies emotions such as anxiety and stress.

[0630] Step 5:

[0631] The server uses a counseling generation module to generate counseling content tailored to the user's emotional state. For example, it might suggest relaxation techniques or recommended lifestyle habits.

[0632] Step 6:

[0633] The server sends the generated counseling content to the terminal. This is also done quickly via the internet.

[0634] Step 7:

[0635] The terminal displays the counseling content received from the server to the user. Typically, this is presented on the screen as text or other visual elements.

[0636] Step 8:

[0637] Users try the provided counseling and input feedback on its effectiveness into the device. This feedback can be entered in a free-form format.

[0638] Step 9:

[0639] The device sends user feedback to the server. This feedback is saved as data for generating the next counseling session.

[0640] Step 10:

[0641] The server uses machine learning algorithms to enhance the counseling generation process based on feedback. This will further improve the user experience in the future.

[0642] Step 11:

[0643] The server continuously monitors the user's mental health status and generates periodic reports. These reports are managed in a way that allows access to the user themselves or a healthcare professional.

[0644] (Example 1)

[0645] 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".

[0646] Traditional psychological support systems have struggled to effectively address the individual psychological states of each user. Furthermore, there have been insufficient methods for appropriately incorporating user feedback and improving service quality. As a result, it has been difficult to provide optimal advice to users, limiting the effectiveness of maintaining and managing their mental health.

[0647] 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.

[0648] In this invention, the server includes communication means for receiving data from users, analysis means for analyzing the information received from the communication means and evaluating the user's psychological state, and generation means for generating information for the user based on the psychological state evaluated by the analysis means. This makes it possible to provide optimal advice tailored to the user's psychological state and to continuously improve the quality of the service by utilizing feedback from users.

[0649] "Communication means" refers to the means of receiving data from users and processing it within the system.

[0650] "Analysis means" refers to a means of processing received information and evaluating the user's psychological state.

[0651] "Generation means" refers to the means of creating and providing optimal information to users based on analysis.

[0652] "Presentation means" refers to a means of effectively providing generated information to users visually or audibly.

[0653] A "response processing means" is a means for receiving responses from users, analyzing their content, and using that information to improve the generation means.

[0654] "Observational means" refers to methods for continuously monitoring the psychological health of users and creating reports based on the results.

[0655] This invention is a system aimed at supporting the psychological health of users and has a configuration that functions between a server, a terminal, and the user.

[0656] The server operates on a cloud computing environment and performs the following processes by combining multiple software modules. First, it receives text data about the user's emotional state sent from the terminal via a communication method. This system uses secure protocols for receiving and sending data, ensuring the security of communication, for example, by SSL / TLS.

[0657] The received data is analyzed using natural language processing algorithms installed on the server. For example, a common natural language processing tool, such as an NLP API, is used to evaluate whether the user's emotions fall into the positive, negative, or neutral categories.

[0658] Based on the analysis results, the server utilizes a generative AI model to generate personalized advice and counseling content. A general-purpose AI model, such as a generative AI, is used as the generation method. The following prompt statements are used in this generation process:

[0659] "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods."

[0660] The generated information is sent as output to the terminal and presented to the user visually. The terminal provides a flexible user interface to display this output information in an easy-to-understand manner.

[0661] Based on the advice displayed on the device, users can take actionable steps in their daily lives. Users also input feedback via the device regarding the effectiveness of these actions and the ease of use of the system, which the server receives. This feedback is analyzed by a response processing system and used to improve the system.

[0662] Furthermore, the server periodically monitors the user's mental health status through observation tools and generates reports. This makes it possible to understand the user's psychological tendencies and support the long-term maintenance and management of their mental health.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user inputs text data about their psychological state into the terminal. The terminal receives this input and formats it as digital data. This data includes information related to the user's emotions and mental health. For example, the input might include something like, "I've been feeling tired lately because I have a lot of tasks." The formatted data is securely transmitted to the server via the SSL / TLS protocol.

[0666] Step 2:

[0667] The server analyzes the digital data received from the terminal. This analysis is performed using an emotion analysis algorithm. First, the received data is passed through a natural language processing API to identify emotions in the text. The input is the received text data, and the output is a positive, negative, or neutral emotional state. Specific operations include keyword extraction and emotion score calculation.

[0668] Step 3:

[0669] The server uses a generative AI model based on the analysis results to generate optimal advice for the user. The generative AI model operates using the prompt "The user has recently been experiencing fatigue due to a high volume of demanding tasks. Based on this information, please suggest appropriate stress relief methods." The input is the analyzed emotional state and the prompt, and the output is the generated advice. Specifically, text related to stress relief methods and relaxation techniques is generated.

[0670] Step 4:

[0671] The server sends the generated advice to the terminal. The terminal utilizes a user interface to effectively display this information to the user. The input is the generated text advice, and the output is a visually formatted display. Specific actions include adjusting font size and color, and displaying in rich text format.

[0672] Step 5:

[0673] Users take action based on the advice displayed on their device. They then provide feedback via the device regarding the results of their actions and their satisfaction with the advice. This feedback is entered into the device as text and numerical data and then sent to the server. Specifically, it includes an evaluation of the effectiveness of the actions taken by the user.

[0674] Step 6:

[0675] The server analyzes the received feedback and adjusts the parameters of the generated AI model, thereby improving the overall system performance. The input is user feedback data, and the output is the updated model parameters. Specific operations include statistical analysis of the feedback and trend analysis.

[0676] Step 7:

[0677] The server continuously monitors the user's psychological health using observational tools and generates periodic reports. Inputs are historical emotional data and feedback data, and output is a visualized report. Specifically, this includes graph creation and time-series data analysis.

[0678] (Application Example 1)

[0679] 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".

[0680] In an aging society, it is essential to properly maintain and manage the mental health of the elderly. However, conventional methods make it difficult to provide individualized mental support, resulting in a lack of efficient and continuous support.

[0681] 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.

[0682] In this invention, the server includes: receiving means for receiving input from the user; evaluation means for analyzing data obtained from the receiving means and evaluating the user's emotional state; generating means for generating counseling information for the user based on the emotional state evaluated by the evaluation means; providing means for providing the counseling information generated by the generation means to the user; monitoring means for continuously monitoring the user's mental health state and generating reports based on that; and dialogue means for receiving information from the user through voice input or haptic operation. This enables appropriate and continuous mental support for each elderly person.

[0683] "Receiving means" refers to a device or method for receiving input data from a user.

[0684] "Evaluation means" refers to a device or method that analyzes user data obtained from a receiving means and determines the emotional state.

[0685] "Generation means" refers to a device or method for creating counseling information for a user based on the emotional state obtained by the evaluation means.

[0686] "Delivery means" refers to a device or method for delivering counseling information created by the generation means to the user.

[0687] "Monitoring means" refers to a device or method that continuously observes a user's mental health status and creates a report based on that information.

[0688] "Dialogue means" refers to a device or method for exchanging information interactively with a user through voice input or haptic operation.

[0689] To implement this invention, it is necessary to build a system that supports the mental health of users. This system mainly involves a server, a terminal (e.g., a smartphone), and the user.

[0690] The server is the central component that receives user input and performs sentiment analysis based on it. This analysis uses software such as Python and TensorFlow. Information that users input daily through their terminals, such as text data like "I'm busy and feeling tired," is sent to the server. The server receives this data and uses a sentiment analysis model to evaluate the user's emotional state.

[0691] Based on the assessed emotional state, the server uses a generation mechanism to generate counseling information tailored to the user. This process utilizes natural language generation technology to provide personalized advice.

[0692] The generated information is sent to the terminal via a delivery mechanism and provided to the user. The user receives this information via the terminal, but the operation is intuitive and simple, allowing for interaction through voice input or touch controls. The system also includes a function to continuously monitor the user's mental health status using a monitoring mechanism and generate reports based on that data. User data security is ensured using Firebase.

[0693] As a concrete example, consider a scenario where a user inputs into their device, "I haven't been feeling well lately and I'm feeling down." The server receives this and generates and displays suggestions such as, "Try some relaxation techniques." An example of a prompt message to the generating AI model in this system would be, "Tell us about your recent mood and events. We will provide advice accordingly."

[0694] As described above, by coordinating the server, terminal, and user, it is possible to efficiently implement the mental health support that is the objective of this invention.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] Users input information about their emotions and state of mind through their device. This may involve using voice or text input. The input information is digitized on the device as initial data for evaluating the emotional state and then sent to the server.

[0698] Step 2:

[0699] The server analyzes user input data received from the terminal. First, it receives the input data and performs an emotional evaluation using an emotional analysis model. The generative AI model used here extracts emotions from the input data and evaluates the user's emotional state based on past data and the AI's learning results. The input is the user's text data, and the output is the evaluated emotional state. Natural language processing techniques are used to process the data.

[0700] Step 3:

[0701] The server generates counseling information for the user based on the assessed emotional state, using a generation method. This process uses NLG (Natural Language Generation) technology to generate appropriate advice in text format. The input is the result of the emotional assessment, and the output is individually generated counseling information. When generating the information, templates and past cases are used as references to construct content that is appropriate to the user's emotional state.

[0702] Step 4:

[0703] The generated counseling information is transmitted to the terminal via a delivery method. The terminal receives this information and presents it to the user in an easily understandable format. The user can review this on the screen and receive advice and suggestions. At this stage, the input is the generated counseling information, and the output is the visual or auditory information received by the user.

[0704] Step 5:

[0705] After taking action based on the provided counseling information, the user inputs their thoughts and feedback into the device. This feedback is used to improve the accuracy of future counseling sessions. The device receives the feedback and sends it back to the server. The input is the user's feedback data, and the output is a digital record of the feedback. The server analyzes this and uses it to improve the generated AI model.

[0706] Step 6:

[0707] The server continuously monitors the user's mental health status and generates reports as needed. It analyzes the user's overall mental health trends using monitoring tools and takes early action if problems are detected. The input is continuously collected status data, and the output is periodic mental health reports. This process involves cumulative data analysis and a comprehensive assessment combining various factors.

[0708] 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.

[0709] The present invention is implemented as a system including a server, a terminal, and an emotion engine. The server receives multimodal data, including text and voice, from the user and passes it to the emotion engine for analysis. The emotion engine evaluates the user's emotional state from multiple perspectives, utilizing natural language processing (NLP), voice analysis, and facial recognition. Based on the analysis results, the server generates counseling and advice tailored to the user's emotional state and provides it through the terminal.

[0710] The terminal provides an interface for users to interact with the server, allowing them to input their emotions and state in various formats. For example, in addition to text input, it is possible to record voice messages and capture facial expressions via the camera. This enables the server to perform more detailed and accurate emotion analysis.

[0711] Through this system, users can receive personalized counseling tailored to their emotions. For example, if a user inputs "I'm anxious because the work deadline is approaching" and captures their facial expression with a camera, the server sends the audio and visual information to an emotion engine and assesses that the user is experiencing strong anxiety. The server then suggests specific breathing techniques and time management strategies to alleviate the anxiety.

[0712] Furthermore, by providing feedback after counseling sessions, the system analyzes this information and continuously improves the performance of its emotion engine and generation mechanisms. In this way, the system provides counseling that is more tailored to individual needs, contributing to the improvement of users' mental health.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] Users can input text messages about their emotions and state of mind into the device. They can also send voice messages or capture facial expressions using the camera, if needed.

[0716] Step 2:

[0717] The device transmits text, audio, and video data entered by the user to the server. This transmission is secure and fast.

[0718] Step 3:

[0719] The server analyzes the received text data using a natural language processing (NLP) module to perform an initial evaluation of the meaning and sentiment of the sentences.

[0720] Step 4:

[0721] The server processes the audio data using an audio analysis module, analyzing factors such as tone, speed, and pauses to estimate the emotional state.

[0722] Step 5:

[0723] The server processes video data via a face recognition and facial expression analysis module, and infers emotions from facial expressions.

[0724] Step 6:

[0725] The server uses an emotion engine to integrate results obtained from text, audio, and video to provide a detailed assessment of the user's overall emotional state.

[0726] Step 7:

[0727] Based on the evaluated emotional state, the server generates counseling content tailored to the user's situation and needs. This includes specific advice and solutions.

[0728] Step 8:

[0729] The server generates counseling content and sends it to the terminal. The terminal then provides this to the user in text or audio format.

[0730] Step 9:

[0731] Users review the counseling content and input feedback on its effectiveness, their impressions, and any additional inquiries into the device.

[0732] Step 10:

[0733] The device sends user feedback to the server. The server uses this feedback to improve the emotion engine and counseling generation module.

[0734] (Example 2)

[0735] 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".

[0736] In modern society, maintaining mental health is becoming increasingly important, but there are limited systems that allow users to accurately understand their own emotional state and receive appropriate advice and counseling accordingly. Conventional systems have limitations in terms of information due to their single-modal input, leading to problems with the accuracy of analysis. Furthermore, the advice provided is uniform and does not offer content tailored to the individual needs of the user.

[0737] 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.

[0738] In this invention, the server includes an input means for receiving diverse emotional data from a user, an emotional analysis means for analyzing the multimodal data received from the input means and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology, and a generation means for generating counseling content for the user using a generation AI model based on the emotional state evaluated by the emotional analysis means. This enables more detailed and accurate emotional evaluation and realizes the provision of counseling content tailored to the user's individual needs.

[0739] "Input means" refers to devices and methods for collecting diverse emotional data from users.

[0740] "Emotion analysis means" refers to devices or methods for analyzing input multimodal data and evaluating the user's emotional state using speech analysis, natural language processing, and facial recognition technology.

[0741] "Generation means" refers to a device or method that generates counseling content for a user using a generation AI model based on the emotional state evaluated by the emotion analysis means.

[0742] "Output means" refers to devices or methods for providing the generated counseling content to the user and for receiving user feedback.

[0743] "Feedback processing means" refers to devices or methods for analyzing user feedback and improving the generation means.

[0744] "Monitoring means" refers to devices or methods for continuously observing a user's mental health status and generating reports based on that observation.

[0745] This invention is a system that provides emotionally tailored counseling and advice by collecting and analyzing diverse emotional data from users. It is implemented using a server, terminals, and an emotion analysis engine.

[0746] The device functions as an input device for collecting emotional data from users. Users can provide emotional data through text input, voice message recording, and facial expression capture using a camera. This allows for the collection of multimodal data, laying the foundation for a multifaceted evaluation of the user's emotional state.

[0747] The server receives data transmitted from the terminal and processes it using an emotion analysis engine. This engine combines natural language processing (NLP), speech analysis software, and facial recognition technology to accurately assess the user's emotional state. This enables detailed and precise analysis that would not be possible with a single modal system.

[0748] Based on the analysis results, the server utilizes a generative AI model to automatically generate counseling content tailored to the user's emotional state. This generation process includes specific advice customized to individual user needs, rather than conventional, standardized advice.

[0749] As a concrete example, consider a scenario where a user inputs a message such as "I'm nervous because I have an important presentation coming up," and then captures their nervous facial expression using a camera. In this case, the server evaluates the data using an emotion analysis engine and generates and provides suggestions for deep breathing techniques to alleviate the user's anxiety, as well as advice for mental training for the presentation. The system also incorporates a process to receive feedback from the user and use it to improve the system's performance.

[0750] A concrete example of a prompt statement might be, "Assess your current emotional state and suggest appropriate advice." By using such prompt statements, the system can execute a counseling process optimized for each individual user.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The device collects input data from the user. This input data includes text messages, voice messages, and facial captures via the camera. This data plays a role in representing the user's emotional state in various forms. For example, if a user types "I'm worried about tomorrow's exam" in text and simultaneously captures a tense facial expression with the camera, detailed emotional information can be obtained.

[0754] Step 2:

[0755] The terminal sends the collected multimodal data to the server. The server passes the received data to the sentiment analysis engine. The input here is voice, text, and visual data from the user. The data is formatted appropriately according to its format. This provides the analysis engine with a foundation for detailed and accurate data analysis.

[0756] Step 3:

[0757] The server's emotion analysis engine performs analysis on the received data. It analyzes text using NLP, analyzes the emotional tone of voice data using speech recognition technology, and evaluates facial expression data using facial recognition technology. The output is an evaluation of the user's overall emotional state. Specifically, emotions such as "anxiety" and "tension" are expressed as numerical values ​​or labels.

[0758] Step 4:

[0759] The server uses a generative AI model to automatically generate counseling content tailored to the user based on the analysis results. Taking emotional state data as input, the AI ​​model generates optimal advice and counseling content. For example, based on the anxiety assessment results, it might generate advice on practicing relaxation breathing techniques or restructuring study plans.

[0760] Step 5:

[0761] The server sends the generated advice to the terminal and requests feedback from the user. The terminal displays the counseling content to the user and accepts user confirmation. The user evaluates its usefulness based on their experience and enters feedback. The server then receives feedback from the user and uses it to improve the accuracy of the generation method.

[0762] Step 6:

[0763] The server analyzes user feedback data. Using the user's input, it adjusts and improves the algorithm of the generated AI model. The output is an improved ability to provide more accurate and personalized counseling in the future. This enhances the overall quality of the system and enables further adaptation to meet user needs.

[0764] (Application Example 2)

[0765] 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".

[0766] In care settings, there is a need to accurately understand the emotions and health conditions of the elderly and people with disabilities, and to provide support and care tailored to their individual needs. However, conventional methods are insufficient for information gathering and analysis, making it difficult to provide appropriate support. Technologies are needed to solve these problems and improve the quality of care.

[0767] 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.

[0768] In this invention, the server includes an information acquisition means, an emotion evaluation means, and a support generation means. This makes it possible to accurately analyze the user's emotions and health condition in a care environment and generate and provide appropriate support content.

[0769] "Information acquisition means" refers to a device or program equipped with the function of receiving voice and text data from a user and analyzing it.

[0770] "Emotion evaluation means" refers to a device or program equipped with the function of analyzing received user information from multiple perspectives and determining the user's emotional state.

[0771] A "support generation means" is a device or program equipped with the function of generating support content and advice suitable for the user based on the evaluated emotional state.

[0772] "Information provision means" refers to a device or program equipped with the function of communicating the generated support content to the user.

[0773] A "response processing means" is a device or program equipped with the function of receiving feedback from users, analyzing that information, and using it to improve support content and the overall system.

[0774] "Monitoring means" refers to a device or program equipped with the functionality to continuously observe a user's health and emotional state and generate reports as needed.

[0775] To realize this invention, it is necessary to configure a system that includes a server, a terminal, and an emotion evaluation engine. This system is intended for use in a caregiving environment and aims to comprehensively analyze the user's emotional state and provide appropriate support and advice.

[0776] The server collects voice and text data from caregiving sites using information acquisition methods. These methods utilize hardware such as smartphones and tablets, and software such as speech recognition. This enables real-time data reception and analysis.

[0777] Next, the server comprehensively analyzes the data collected through the emotion assessment tool. In this process, software such as EmotionAnalyzer is used to perform natural language processing and speech analysis to evaluate the user's emotional state. Based on this evaluation, the server determines what kind of support the user needs.

[0778] Subsequently, the server uses support generation tools to generate appropriate support content based on the results of the emotion evaluation. This involves using a generative AI model such as CounselingAdvice to automatically create advice and support content tailored to the user.

[0779] Ultimately, this generated support information is communicated to the user through various means. This process utilizes interfaces to deliver the generated information to the user visually and aurally. This allows care staff and family members to quickly provide appropriate support tailored to the user's condition.

[0780] As a concrete example, consider a situation in a nursing home where an elderly person expresses anxiety during a normal conversation. The server analyzes the audio data and generates a suggestion such as, "You seem a little anxious. How about listening to some very calming music?" and notifies the care staff's terminal in real time.

[0781] Examples of prompts for a generative AI model include the following:

[0782] "If a user is showing signs of anxiety, consider what kind of relaxation suggestions you can offer."

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

[0784] Step 1:

[0785] Users input voice and text data through their device. Voice data is acquired using the device's microphone, and text input is done via a touchscreen or keyboard. This input data is securely transmitted to the server.

[0786] Step 2:

[0787] The server receives audio and text data transmitted from the terminal using an information acquisition method. This data is input into speech recognition software for speech recognition. The audio data is converted into text data, preparing it for natural language processing.

[0788] Step 3:

[0789] The server uses EmotionAnalyzer to analyze the received text data and evaluate the user's emotional state. This analysis uses natural language processing techniques to identify language patterns and emotional expressions. As a result, the user's current emotional state is output.

[0790] Step 4:

[0791] The server generates appropriate support content using support generation mechanisms based on the emotion assessment results. Using CounselingAdvice software, prompt sentences are input into the generation AI model to create advice and suggestions tailored to the user. This support content includes specific action suggestions and methods for calming emotions.

[0792] Step 5:

[0793] The server transmits the generated support information to the user's terminal via an information delivery system. The terminal receives this information and notifies the user by displaying it on the screen or reading it aloud using speech synthesis. This allows users and care staff to obtain support information in real time.

[0794] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0795] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

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

[0797] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0798] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0799] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0800] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0801] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0802] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0803] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0804] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0805] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0806] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0807] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0808] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0809] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0810] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0811] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0812] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0813] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0814] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

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

[0816] (Claim 1)

[0817] An input means for receiving input from the user,

[0818] An emotion analysis means for analyzing data received from the aforementioned input means and evaluating the user's emotional state,

[0819] A generation means for generating counseling content for the user based on the emotional state evaluated by the emotion analysis means,

[0820] An output means that provides the user with the counseling content generated by the generation means,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising a feedback processing means for receiving user feedback and analyzing the feedback to improve the generation means.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising monitoring means for continuously monitoring the mental health status of a user and generating a report based thereon.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A means of communication for receiving data from users,

[0829] An analysis means for analyzing information received from the aforementioned communication means and evaluating the user's psychological state,

[0830] A generation means that generates information for the user based on the psychological state evaluated by the analysis means,

[0831] A presentation means for presenting the information generated by the generation means to the user,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, further comprising a response processing means for receiving a response from a user and analyzing the response in order to optimize the generation means.

[0835] (Claim 3)

[0836] The system according to claim 1, further comprising observation means for continuously observing the psychological health of users and preparing reports based on those observations.

[0837] "Application Example 1"

[0838] (Claim 1)

[0839] A receiving means for receiving input from the user,

[0840] An evaluation means that analyzes the data obtained from the receiving means and evaluates the user's emotional state,

[0841] A generation means for generating counseling information for the user based on the emotional state evaluated by the evaluation means,

[0842] A means for providing the counseling information generated by the generation means to the user,

[0843] A monitoring system that continuously monitors the user's mental health status and generates reports based on that,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising an analysis means for receiving user feedback and analyzing the feedback in order to improve the generation means.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising dialogue means for receiving information from a user through voice input or haptic operation.

[0849] "Example 2 of combining an emotion engine"

[0850] (Claim 1)

[0851] An input method for receiving diverse emotional data from users,

[0852] An emotion analysis means analyzes the multimodal data received from the input means and evaluates the user's emotional state using speech analysis, natural language processing, and facial recognition technology.

[0853] A generation means that generates counseling content for the user using a generation AI model based on the emotional state evaluated by the emotion analysis means,

[0854] The output means provides the user with the counseling content generated by the generation means and receives user feedback.

[0855] A feedback processing means that analyzes user feedback and improves the generation means,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising monitoring means for continuously monitoring the mental health status of a user and generating a report based thereon.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for customizing the format of advice according to the characteristics of the user's input data.

[0861] "Application example 2 when combining with an emotional engine"

[0862] (Claim 1)

[0863] A means for receiving information from the user,

[0864] An emotion evaluation means for analyzing the information received from the aforementioned information acquisition means and evaluating the user's emotional state,

[0865] Support generation means for generating support content for the user based on the emotional state evaluated by the emotion evaluation means,

[0866] An information provision means that provides the user with the support content generated by the support generation means in a care environment,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising a response processing means for receiving a response from a user and analyzing the response in order to improve the support generation means.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising monitoring means for continuously monitoring the user's health status and generating an evaluation based thereon. [Explanation of Symbols]

[0872] 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. An input means for receiving input from the user, An emotion analysis means for analyzing data received from the aforementioned input means and evaluating the user's emotional state, A generation means for generating counseling content for the user based on the emotional state evaluated by the emotion analysis means, An output means that provides the user with the counseling content generated by the generation means, A system that includes this.

2. The system according to claim 1, further comprising a feedback processing means for receiving user feedback and analyzing the feedback to improve the generation means.

3. The system according to claim 1, further comprising monitoring means for continuously monitoring the mental health status of a user and generating a report based thereon.

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