system

Abstract

We provide the system. [Solution] A means of collecting user dialogue history and usage patterns, and analyzing the user's thought patterns, A means of evaluating the mental health status of users based on the analysis results, A means of selecting an appropriate mental training program based on the evaluation results and proposing it to the user, A means of collecting user feedback on the proposed program and adjusting the system's algorithms, 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 modern society, many people are troubled by stress and a sense of self-denial, and as a result, there is a problem that it is difficult to maintain mental health. In such a state, it is difficult for an individual to notice the distortion of their own perception, and as a result, problems such as appropriate stress management and self-improvement cannot be achieved. The purpose of the present invention is to effectively support mental health and enhance self-affirmation, especially for individuals who live a stoic life every day.

Means for Solving the Problems

[0005] This invention provides a system that understands users' thought patterns in detail by collecting and analyzing their dialogue history and usage patterns. Based on the analysis results, it evaluates the user's mental health status and selects and proposes an appropriate mental training program to support the user's mental well-being. Furthermore, by collecting user feedback on the proposed program and continuously adjusting the algorithm based on that feedback, it realizes optimal support tailored to the individual needs of each user.

[0006] "User" refers to an individual who receives a mental training program through this system.

[0007] "Dialogue history" refers to the record of all communication that a user has had within the system.

[0008] "Usage data" refers to overall data on how users used the system.

[0009] "Thinking patterns" refer to the characteristic behaviors and tendencies in how users process information and form emotions and opinions.

[0010] "Analysis" refers to the process of revealing users' thought patterns and mental health status based on collected data.

[0011] "Mental health status" refers to an indicator that shows what state of mental health a user is currently in.

[0012] A "mental training program" refers to specific activities and exercises proposed to improve the mental health of users.

[0013] "Natural language processing" refers to a series of technologies that enable computers to understand and analyze human language.

[0014] "Feedback" refers to opinions and evaluations that reflect what the user felt about the proposed program.

[0015] "Algorithm" refers to the computational procedure used to analyze the user's data and propose an appropriate program.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main 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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 a data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

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

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

[0019] In the following embodiments, a tagged 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.

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system for improving the mental health of users. This system is built on information exchanged between a server, a terminal, and the user.

[0038] The server first interacts with the user through their terminal, and stores the conversation history and usage data collected during this interaction. The server uses this data to analyze the user's thought patterns. Natural language processing technology is used for the analysis to evaluate which words and phrases the user frequently uses, as well as their emotional tendencies.

[0039] Subsequently, the server evaluates the user's mental health status based on the analysis results. For example, it quantifies stress levels and self-esteem to identify specific issues.

[0040] Next, the server selects a mental training program suitable for each user based on the evaluation results and proposes it via the terminal. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem. Users then practice the proposed program through the terminal.

[0041] Furthermore, the terminal collects feedback on the programs the user has implemented. The server analyzes this feedback and continuously adjusts the system's algorithms. This results in more accurate program suggestions for each individual user for their next session.

[0042] Specific example

[0043] For example, if a user reports to the server from their device that they "have been having trouble concentrating on work lately," the server analyzes the conversation history to identify potential factors contributing to the decreased concentration. The server then suggests "short-term concentration-enhancing techniques" to the user via their device. If the user tries this program and reports that it was "effective," the server uses this feedback to suggest even more suitable programs in the future.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users log in to the system using their device and enter their daily mental health concerns and questions.

[0047] Step 2:

[0048] The device sends user-entered conversation history and past usage data to the server. The server receives and records this data, laying the foundation for creating a comprehensive profile for each user.

[0049] Step 3:

[0050] The server analyzes the collected data using natural language processing techniques to identify the user's thought patterns. This analysis process includes analyzing emotional tendencies from the user's statements.

[0051] Step 4:

[0052] The server evaluates the user's mental health status based on the analysis results. This evaluation includes processing to quantify stress levels and self-esteem, allowing for an objective understanding of the user's psychological state.

[0053] Step 5:

[0054] The server selects a mental training program tailored to the user's mental health status. The selected program is designed to address the specific challenges the user is facing.

[0055] Step 6:

[0056] The terminal displays the training program received from the server to the user and provides options for execution. The user can select a program of interest from the provided options and start it.

[0057] Step 7:

[0058] Users participate in the selected program via a terminal and input feedback on the results into the terminal. This feedback includes the program's effectiveness and their impressions.

[0059] Step 8:

[0060] The server receives feedback from users and uses it to refine the algorithm. This improves the entire system so that future programs are better suited to the user's needs.

[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] In modern society, mental health issues such as stress and anxiety are becoming increasingly serious, negatively impacting individuals' quality of life. However, many people lack the means to accurately understand their own mental state and improve it in an appropriate way. Therefore, the challenge is to provide effective mental training tailored to individual users and to offer a system for improving 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 means for collecting user dialogue information and usage information and analyzing the user's thought patterns; means for evaluating the user's mental state based on the analysis results; and means for selecting an appropriate psychological training plan based on the evaluation results and proposing it to the user. This makes it possible to provide highly accurate training tailored to the individual's mental state and effectively improve each individual's mental health.

[0066] "Users" refers to individuals who use this system, and the purpose of this system is to improve their mental state.

[0067] "Dialogue information" refers to linguistic information provided by users through this system, including information such as dialogue and input.

[0068] "Usage information" refers to information about how users use this system, and includes data such as their activity history within the system.

[0069] "Thinking patterns" refer to the results of analyzing the tendencies of thoughts and emotions that users exhibit through dialogue information.

[0070] "Mental state" refers to the user's mental health and is a comprehensive assessment that includes stress levels, emotional stability, and other factors.

[0071] A "psychological training plan" is a set of exercises and activities that have been appropriately selected to improve the mental state of the user.

[0072] "Natural language processing" refers to the technology of using computers to understand, interpret, and generate human language.

[0073] "Tension reduction techniques" are techniques aimed at improving mental state by promoting relaxation and stress relief.

[0074] The "cognitive behavioral therapy approach" is a psychological technique for improving mental health through changes in perception and behavior.

[0075] "Self-esteem improvement activities" refer to programs or activities designed to help users view themselves more positively.

[0076] As a form of implementing the invention, this system is designed to improve the mental health of users. It exchanges information between the server, terminal, and user, and performs various processes based on that information.

[0077] The server stores user interaction and usage information collected from the terminal. A dedicated application on the terminal is used for this data collection. This provides the server with a text-based interaction history.

[0078] The server analyzes this data using natural language processing (NLTK) techniques. Specifically, natural language processing libraries such as Python's SpaCy and NLTK are used. The server also analyzes the user's thought patterns and evaluates their mental state. Based on this evaluation, it generates an optimal psychological training plan. This process is performed automatically using an AI model.

[0079] The device receives a psychological training plan provided by the server and proposes it to the user. This plan includes tension reduction techniques, cognitive behavioral therapy approaches, and exercises to improve self-esteem.

[0080] Users implement these plans and send feedback on the results to the server via their devices. This feedback is used to propose future plans.

[0081] Specific example

[0082] For example, if a user reports to their device that they have been feeling down lately, the server analyzes this and suggests a "stress management program for emotional ups and downs." If the user completes the suggested program and provides feedback such as "I feel a little better," the server incorporates that feedback into the next program suggestion.

[0083] Example of a prompt

[0084] "Lately, I've been finding it difficult to concentrate on my work. Could you suggest some ways to improve my concentration?"

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The terminal collects conversational information from the user. This input consists of text data entered by the user (e.g., "I've been feeling down lately"). This data is packaged in JSON format or similar and sent to the server. Specifically, the application on the terminal retrieves the conversational information and transfers it to the server via the internet connection.

[0088] Step 2:

[0089] The server stores the dialogue information received from the terminal. The input is data sent from the terminal, which is stored in the database. During storage, the text format is formatted and unnecessary information is filtered, resulting in consistent, analyzable data as output.

[0090] Step 3:

[0091] The server performs natural language processing on the accumulated dialogue data. The input is the text data formatted in step 2, and sentiment analysis and keyword extraction are performed using Python's SpaCy or NLTK, with the analysis results being output. Specific operations include identifying frequently occurring language patterns and evaluating sentiment tendencies.

[0092] Step 4:

[0093] The server evaluates the user's mental state based on the analysis results. The input is the analysis results from step 3, and based on that, it generates stress level and self-esteem scores and outputs the evaluation results. For example, if the stress level is determined to be high, that fact will be expressed numerically.

[0094] Step 5:

[0095] The server selects an appropriate psychological training plan based on the assessment results of the mental state. The input is the assessment results from step 4, and the AI ​​model generates a plan based on this. This plan is selected from methods such as tension reduction techniques and cognitive behavioral therapy approaches, and is sent to the terminal as output.

[0096] Step 6:

[0097] The terminal receives a psychological training plan sent from the server and proposes it to the user. The input is the plan data from the server, and the information is displayed to the user as output in a visual or audio interface to show specific implementation methods.

[0098] Step 7:

[0099] The user completes a training plan presented on the device and inputs the results as feedback. This input consists of the user's experience and impressions, and is sent to the server via the device. Specifically, the user evaluates the effectiveness of the plan and inputs feedback in the form of a questionnaire.

[0100] Step 8:

[0101] The server receives and analyzes feedback sent by the user. The input is feedback data, and by analyzing it, the system adjusts its algorithm to obtain output that improves the accuracy of future suggestions. For example, if a particular program was effective, the algorithm will be updated to prefer suggesting that program.

[0102] (Application Example 1)

[0103] 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."

[0104] To effectively manage users' mental health, it is necessary to accurately grasp their emotional information in their daily lives and provide support tailored to their individual needs. However, currently, there is a lack of mechanisms to respond immediately to users' diverse emotional states and thought patterns, making personalized mental healthcare difficult. A new system is needed that utilizes natural conversational information collected daily by home robots to appropriately support users' mental health.

[0105] 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.

[0106] In this invention, the server includes means for collecting the user's dialogue history and usage information and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; and means for selecting an appropriate mental training method based on the evaluation results and proposing it to the user. This makes it possible to realize effective mental healthcare tailored to each individual user by having the user provide emotional information from their daily life through voice dialogue to the robot terminal.

[0107] A "user" is an individual who uses the system to receive mental health care.

[0108] "Dialogue history" refers to a record of past communications between the user and the system via voice or text.

[0109] "Usage information" refers to data related to a user's system usage and interactions.

[0110] "Thinking patterns" refer to mental tendencies and habits that can be inferred from the words and expressions that users frequently use in their daily lives.

[0111] "Analysis" is the process of identifying user emotions and behavioral trends based on collected data.

[0112] "Mental health status" refers to a numerical or categorical representation of a user's mental health condition.

[0113] "Mental training techniques" refer to specific training methods and therapies aimed at improving the mental health of users.

[0114] "Feedback" refers to users reporting their opinions and results regarding a system or a proposed program.

[0115] A "system algorithm" is a series of calculation procedures used to evaluate the user's state and provide optimal suggestions.

[0116] "Voice interaction" is a method of communication between the user and the system using voice.

[0117] A "robot terminal" is a hardware device for home robots that enables interaction with the user.

[0118] The system that realizes this invention will be built around a home robot. The robot terminal is equipped with a voice input microphone, speaker, and text display to engage in natural voice dialogue with the user. It can also connect to a server via a network. The server collects dialogue history and usage information obtained from the user and analyzes thought patterns using natural language processing technology. Natural language processing libraries such as Google® Cloud Natural Language API and spaCy are used for this analysis. Based on the analysis results, the user's mental health status is evaluated, and the optimal mental training method is selected using machine learning models with TENSORFLOW® and PyTorch.

[0119] For example, the server can use a generative AI model based on a prompt such as, "The user has recently complained of work fatigue. What kind of mental training method should we suggest?" to provide effective guidance. This suggestion is communicated to the user via a robotic terminal, and the server updates the system algorithm based on the user's feedback. In this way, effective mental healthcare tailored to each individual user is realized.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The device engages in voice interaction with the user and collects the content of that interaction as audio data using a microphone. This audio data is then converted into text data by a speech recognition engine. The input is audio data, and the output is text data.

[0123] Step 2:

[0124] The server receives text data from the terminal, performs sentiment analysis using a natural language processing library (e.g., spaCy, Google Cloud Natural Language API), and analyzes the user's thought patterns. The input is text data, and the output is an analysis result showing emotional tendencies and thought patterns.

[0125] Step 3:

[0126] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to evaluate the user's mental health status based on the analysis results. This evaluation quantifies stress levels and self-esteem. The input is the analysis results, and the output is numerical data related to mental health status.

[0127] Step 4:

[0128] The server uses a generative AI model to determine the optimal mental training method based on the results of the mental health assessment and generates a prompt message. For example, it might create a prompt message such as, "The user has recently complained of work fatigue. What kind of mental training method should be suggested?" The input is state assessment data, and the output is the prompt message and recommended training method.

[0129] Step 5:

[0130] The server sends recommended mental training techniques to the terminal, and the terminal guides the user through those techniques. The terminal uses a speaker and display to introduce the techniques using both audio and visuals. The input is the recommended training techniques, and the output is the instructional guide for the user.

[0131] Step 6:

[0132] The user provides feedback on the mental training they have conducted via their device, and sends this feedback data to the server. The server uses this data to adjust the algorithm and improve the accuracy of the next suggestion. The input is the feedback data, and the output is the algorithm correction data.

[0133] 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.

[0134] This invention provides a system incorporating an emotion engine to improve the mental health of users. This system operates through the interaction of a server, a terminal, and the user.

[0135] The server first receives input from the user through the terminal. Once the user logs into the system and enters mental health-related feedback and daily events, the server collects interaction history and usage data.

[0136] Subsequently, the server uses an emotion engine to analyze the emotional tendencies of the collected data. This engine utilizes natural language processing technology to recognize the emotions contained in the user's input in real time.

[0137] Based on the analysis results from the emotion engine, the server provides a more detailed assessment of the user's mental health. By capturing fluctuations in the user's emotions, it can quantify specific mental states such as stress levels, anxiety, and joy.

[0138] Based on these results, the server selects the most suitable mental training program for the user and proposes it via the terminal. This program includes relaxation techniques and cognitive behavioral therapy approaches.

[0139] The terminal displays program suggestions from the server to the user and provides options for execution. The user can select and run a program of interest from the options displayed on the screen.

[0140] Furthermore, the terminal collects feedback on the results of the programs performed by the user and the effects they experienced, and sends it to the server. The server uses this feedback to adjust the algorithm and improve future program suggestions.

[0141] Specific example

[0142] For example, if a user inputs "I can't sleep lately because of work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the analysis, the server suggests "nighttime anxiety-reducing exercises" to the user. After the user tries the program, they input feedback into the terminal about the changes they felt, and the server uses that information to improve its next suggestion.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Users log in to the system via their device and input information about their daily stress levels and emotional changes.

[0146] Step 2:

[0147] The terminal sends the conversation content entered by the user to the server, thereby recording the conversation history and usage data.

[0148] Step 3:

[0149] The server uses an emotion engine to analyze received data and recognize the user's emotions in real time. The emotion engine utilizes natural language processing technology to analyze emotional tendencies from text.

[0150] Step 4:

[0151] The server uses the results of the emotion engine's analysis to perform a detailed assessment of the user's mental health. For example, it calculates stress levels and anxiety scores based on the intensity and frequency of emotions.

[0152] Step 5:

[0153] The server selects a mental training program based on the evaluation results and proposes it to the terminal. The program consists of content designed to improve specific emotional states.

[0154] Step 6:

[0155] The terminal presents the user with a selection of programs received from the server and displays the options for execution. The user can then choose their desired program and start it.

[0156] Step 7:

[0157] The user runs the selected program on their device and enters feedback on the results. This feedback includes the program's effectiveness and personal impressions.

[0158] Step 8:

[0159] The server collects user feedback and adjusts the algorithms within the system to improve the accuracy of future program suggestions.

[0160] (Example 2)

[0161] 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".

[0162] Traditionally, systems designed to effectively improve users' mental health have relied on simplistic analytical methods and fixed program proposals, failing to adequately consider the wide range of emotional fluctuations of individual users. Furthermore, the lack of sufficient feedback mechanisms to improve the quality of proposals makes long-term mental health improvement difficult.

[0163] 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.

[0164] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's diverse emotional patterns; means for evaluating the user's mental health state in detail based on the analysis results; and means for selecting an optimal mental training program based on the evaluation results and proposing it to the user via a terminal. This provides appropriate solutions based on individual emotional fluctuations, improves the accuracy of the system's suggestions through feedback, and enables sustained improvement of mental health.

[0165] A "user" is an individual who uses the system and provides feedback and sentiment data.

[0166] "Dialogue history" refers to the record of all communication and input that a user has made through the system.

[0167] "Usage status" refers to data that includes information about how users are using the system.

[0168] An "emotional pattern" is a collection of data that represents the tendencies and fluctuations of a user's emotions.

[0169] "Mental health status" refers to the state of a user's mental well-being, and includes emotional states such as stress and anxiety.

[0170] "Natural language processing" refers to the technology used to analyze human language and understand its meaning and emotions.

[0171] A "mental training program" is a series of activities and exercises designed to improve the mental health of its users.

[0172] "Relaxation techniques" refer to methods and techniques used to promote relaxation and stress reduction in users.

[0173] A "cognitive behavioral therapy approach" is a method for improving emotional states by changing the user's thoughts and behaviors.

[0174] "Exercises to boost self-esteem" refers to activities and practices designed to cultivate a user's self-esteem.

[0175] A "terminal" is a device used by users to access a system and to input and receive information.

[0176] "Feedback" refers to the results and impressions of a program that users provide to a system.

[0177] This invention is a system for improving the mental health of users. This system consists of the interaction of a server, a terminal, and a user.

[0178] The server is operated using various software. The emotion engine, a crucial element, typically utilizes natural language processing technology and is implemented in programming languages ​​such as Python or JavaScript (registered trademark). The database records user interaction history and usage data, and uses database management systems such as SQL.

[0179] Users access the system through their devices. These devices can be any internet-connected device, such as a smartphone, tablet, or personal computer. Users can directly input information into the system or select and run the mental training programs provided.

[0180] The server receives input data from the user and analyzes it using an emotion engine. Natural language processing identifies emotional components within the text in real time and assesses the user's mental health state. Based on this assessment, it proposes an optimal mental training program for the user.

[0181] For example, if a user enters "I've been having trouble sleeping lately due to work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the results, the server suggests "nighttime anxiety-reducing exercises." The user can then select and try this program on their device.

[0182] This system collects user feedback and adjusts the server's algorithms to improve the accuracy of future program suggestions. This feedback loop allows the system to continuously optimize for each user, providing sustained mental health support.

[0183] An example of a prompt message might be: "Analyze the emotions entered by the user and suggest the optimal mental training program. This information includes input specifically related to stress and anxiety."

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] Users access the system using a terminal and input feedback related to mental health and daily events. The input data is sent to the server in text format. The input here is text information including the user's emotions, and the output is the initial data sent to the server.

[0187] Step 2:

[0188] The server records the received user input data in a database. During this recording process, the text data is organized and stored as a user-specific dialogue history. The data is classified and organized to make it available for future analysis.

[0189] Step 3:

[0190] The server activates the emotion engine and retrieves user input data from the database. It then performs emotion analysis on the retrieved data using natural language processing techniques. The input is user text data, and the output is the analyzed emotion data. Specifically, it performs keyword extraction and text classification to convert the user's emotional state into numerical data.

[0191] Step 4:

[0192] The server evaluates the user's mental health state based on the results of emotion analysis. The evaluation analyzes emotional tendencies and numerically indicates the degree of stress, anxiety, joy, etc. The input is the analyzed emotion data, and the output is the mental health state evaluation result. Based on this evaluation result, the user's specific mental state is understood.

[0193] Step 5:

[0194] The server selects the most suitable mental training program for the user based on the evaluation results. The program is determined after considering relaxation techniques and cognitive behavioral therapy, and then proposed to the user via the terminal. The input is the mental health evaluation results, and the output is the proposed program.

[0195] Step 6:

[0196] The terminal displays program suggestions from the server to the user. The user selects a program of interest from the suggested options on the terminal and executes it. The input is the suggestion data from the server, and the output is the user's selection.

[0197] Step 7:

[0198] Users input feedback via their device regarding the results and perceived effects of the program they have completed. This input consists of data about their program experience and results, and is sent to the server as output.

[0199] Step 8:

[0200] The server receives user feedback and records it in a database. This information is used to adjust the algorithm and improve future program suggestions. The input is feedback data, and the output is the updated algorithm. This allows the system to provide more appropriate suggestions to the user.

[0201] (Application Example 2)

[0202] 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".

[0203] In modern society, improving the mental health of individual users is a crucial issue. Traditional methods lack mechanisms to identify users' emotional states in real time and propose specific activities and environmental adjustments accordingly. This makes it difficult for users to receive the mental support they need most at that moment.

[0204] 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.

[0205] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; means for selecting and proposing an appropriate mental training program to the user based on the evaluation results; means for collecting the user's feedback on the proposed program and adjusting the system's algorithm; and automated means for identifying the user's emotional state and providing appropriate environmental adjustments or activities. This makes it possible for the user to receive the optimal mental support they need at that moment.

[0206] "Users" refer to individuals or groups who use the system and are the ones whose mental health is supported.

[0207] "Dialogue history" refers to the record of conversations and messages exchanged between the user and the system.

[0208] "Usage status" refers to a record of how users accessed the system and which functions they used and to what extent.

[0209] "Thinking patterns" refer to the user's unique tendencies in thinking and emotional responses, and the data used to analyze their emotional state is based on these patterns.

[0210] "Means of analysis" refers to methods and devices used to analyze user input data and identify emotions and thought patterns.

[0211] "Mental health status" refers to the emotional and psychological state of the user, and involves assessing levels of stress, anxiety, and joy.

[0212] A "mental training program" refers to a series of activities and exercises provided to improve the mental health of users.

[0213] "Feedback" refers to the reactions and opinions that users provide regarding a proposed program, and is data used to improve the system.

[0214] "Adjusting the algorithm" refers to optimizing the system's processing methods based on collected feedback to improve the accuracy of future suggestions.

[0215] "Identifying an emotional state" refers to the process of appropriately identifying the emotions a user is experiencing and understanding temporary changes in those emotions.

[0216] "Automated means of providing environmental adjustments or activities" refers to mechanisms or methods for automatically suggesting or implementing effective environmental changes or activities for users in response to their identified emotional states.

[0217] The system for implementing the invention mainly consists of servers and terminals, which work together to support the mental health of users.

[0218] The server receives conversation history and usage data entered by the user through the terminal and analyzes the user's thought patterns based on this data. This analysis uses a natural language processing engine, and leverages Python natural language processing libraries (e.g., NLTK and spaCy) to identify the user's emotional state.

[0219] Based on the analysis results, the server conducts a detailed assessment of the user's mental health and selects the most appropriate mental training program at that time. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem.

[0220] The selected program is sent to the terminal and presented to the user. The user can choose and execute the program displayed on the screen. Furthermore, the terminal collects feedback on the program the user has executed and sends this information to the server. The server adjusts the system's algorithm based on the collected feedback and uses it to improve future program suggestions.

[0221] For example, if a user tells the terminal through dialogue that they are "stressed out from being overwhelmed with housework lately," the server will analyze that input and suggest a relaxation program to alleviate the stress.

[0222] An example of a prompt when using a generative AI model is, "How can we analyze a user's input that they are unable to sleep due to work pressure and suggest a relaxation program to alleviate their anxiety?" In this way, the system can provide support that is best suited to each user's individual situation.

[0223] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0224] Step 1:

[0225] Users input feedback about daily events and mental health through their devices. The input data is collected by the devices and sent to the server. In this step, the user's text data is used as input.

[0226] Step 2:

[0227] The server uses a natural language processing engine to identify emotional states in order to analyze the text data received from the user. This process utilizes Python natural language processing libraries (e.g., NLTK and spaCy). Based on the input text, the user's emotional tendencies (e.g., stress, anxiety, joy) are analyzed. Based on these results, the user's mental health is assessed.

[0228] Step 3:

[0229] The server selects the optimal mental training program from the database based on the analysis results. Depending on the assessed emotional state, programs such as relaxation techniques or cognitive behavioral therapy approaches are chosen. The input to this process is the result of the emotional analysis, and the output is the selected program.

[0230] Step 4:

[0231] The selected program is sent to the terminal and presented to the user. The terminal displays the program details to the user and provides options. The user can choose a program of interest and run it. In this step, the program information is the input, and the user's selection is the output.

[0232] Step 5:

[0233] After the user completes the program they selected, the terminal collects user feedback. The user inputs information about the program's effects and their impressions into the terminal, and this data is sent to the server as feedback. The input is feedback on the results of the program, and the output is the transmission of feedback data to the server.

[0234] Step 6:

[0235] The server receives feedback from the user and adjusts the system's algorithm. This adjustment makes the user's next program suggestion more accurate. In this step, the feedback data is the input and the adjusted algorithm is the output.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] [Second Embodiment]

[0240] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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".

[0252] This invention provides a system for improving the mental health of users. This system is built on information exchanged between a server, a terminal, and the user.

[0253] The server first interacts with the user through their terminal, and stores the conversation history and usage data collected during this interaction. The server uses this data to analyze the user's thought patterns. Natural language processing technology is used for the analysis to evaluate which words and phrases the user frequently uses, as well as their emotional tendencies.

[0254] Subsequently, the server evaluates the user's mental health status based on the analysis results. For example, it quantifies stress levels and self-esteem to identify specific issues.

[0255] Next, the server selects a mental training program suitable for each user based on the evaluation results and proposes it via the terminal. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem. Users then practice the proposed program through the terminal.

[0256] Furthermore, the terminal collects feedback on the programs the user has implemented. The server analyzes this feedback and continuously adjusts the system's algorithms. This results in more accurate program suggestions for each individual user for their next session.

[0257] Specific example

[0258] For example, if a user reports to the server from their device that they "have been having trouble concentrating on work lately," the server analyzes the conversation history to identify potential factors contributing to the decreased concentration. The server then suggests "short-term concentration-enhancing techniques" to the user via their device. If the user tries this program and reports that it was "effective," the server uses this feedback to suggest even more suitable programs in the future.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] Users log in to the system using their device and enter their daily mental health concerns and questions.

[0262] Step 2:

[0263] The device sends user-entered conversation history and past usage data to the server. The server receives and records this data, laying the foundation for creating a comprehensive profile for each user.

[0264] Step 3:

[0265] The server analyzes the collected data using natural language processing techniques to identify the user's thought patterns. This analysis process includes analyzing emotional tendencies from the user's statements.

[0266] Step 4:

[0267] The server evaluates the user's mental health status based on the analysis results. This evaluation includes processing to quantify stress levels and self-esteem, allowing for an objective understanding of the user's psychological state.

[0268] Step 5:

[0269] The server selects a mental training program tailored to the user's mental health status. The selected program is designed to address the specific challenges the user is facing.

[0270] Step 6:

[0271] The terminal displays the training program received from the server to the user and provides options for execution. The user can select a program of interest from the provided options and start it.

[0272] Step 7:

[0273] Users participate in the selected program via a terminal and input feedback on the results into the terminal. This feedback includes the program's effectiveness and their impressions.

[0274] Step 8:

[0275] The server receives feedback from users and uses it to refine the algorithm. This improves the entire system so that future programs are better suited to the user's needs.

[0276] (Example 1)

[0277] 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."

[0278] In modern society, mental health issues such as stress and anxiety are becoming increasingly serious, negatively impacting individuals' quality of life. However, many people lack the means to accurately understand their own mental state and improve it in an appropriate way. Therefore, the challenge is to provide effective mental training tailored to individual users and to offer a system for improving mental health.

[0279] 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.

[0280] In this invention, the server includes means for collecting user dialogue information and usage information and analyzing the user's thought patterns; means for evaluating the user's mental state based on the analysis results; and means for selecting an appropriate psychological training plan based on the evaluation results and proposing it to the user. This makes it possible to provide highly accurate training tailored to the individual's mental state and effectively improve each individual's mental health.

[0281] "Users" refers to individuals who use this system, and the purpose of this system is to improve their mental state.

[0282] "Dialogue information" refers to linguistic information provided by users through this system, including information such as dialogue and input.

[0283] "Usage information" refers to information regarding how users are using this system, and is data including the action history within the system.

[0284] "Thought pattern" refers to the result of analyzing the tendencies of a user's thoughts and emotions shown through interaction information.

[0285] "Mental state" refers to the mental health state of the user, and is a comprehensive evaluation including stress level, emotional stability, etc.

[0286] "Psychological training plan" refers to a collection of exercises and activities appropriately selected to improve the mental state of the user.

[0287] "Natural language analysis" refers to the technology of using a computer to understand, interpret, and generate human language.

[0288] "Stress reduction techniques" refer to techniques that promote relaxation and stress relief for the purpose of improving the mental state.

[0289] "Cognitive behavioral therapy approach" refers to psychological techniques for improving mental health through changes in cognition and behavior.

[0290] "Exercises to enhance self - esteem" refer to programs or activities that help users perceive themselves more positively.

[0291] As a form of implementing the invention, this system is designed to improve the mental health of users. Information is exchanged among the server, the terminal, and the user, and various processes are carried out based on this.

[0292] The server accumulates the interaction information and usage information of the user collected from the terminal. In this data collection, a dedicated application on the terminal is used. As a result, a text - based interaction history is provided to the server.

[0293] The server analyzes this data using natural language processing (NLTK) techniques. Specifically, natural language processing libraries such as Python's SpaCy and NLTK are used. The server also analyzes the user's thought patterns and evaluates their mental state. Based on this evaluation, it generates an optimal psychological training plan. This process is performed automatically using an AI model.

[0294] The device receives a psychological training plan provided by the server and proposes it to the user. This plan includes tension reduction techniques, cognitive behavioral therapy approaches, and exercises to improve self-esteem.

[0295] Users implement these plans and send feedback on the results to the server via their devices. This feedback is used to propose future plans.

[0296] Specific example

[0297] For example, if a user reports to their device that they have been feeling down lately, the server analyzes this and suggests a "stress management program for emotional ups and downs." If the user completes the suggested program and provides feedback such as "I feel a little better," the server incorporates that feedback into the next program suggestion.

[0298] Example of a prompt

[0299] "Lately, I've been finding it difficult to concentrate on my work. Could you suggest some ways to improve my concentration?"

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] The terminal collects interaction information from the user. The input is the text data entered by the user (e.g., "I've been feeling down lately"). This data is packaged in a format such as JSON and sent to the server. Specifically, the application on the terminal retrieves the interaction information and transfers it to the server via an Internet connection.

[0303] Step 2:

[0304] The server stores the interaction information received from the terminal. The input is the data sent from the terminal, which is saved in the database. When saving, formatting of the text format and filtering of unnecessary information are performed to obtain consistent and analyzable data as the output.

[0305] Step 3:

[0306] The server performs natural language analysis on the stored interaction information. The input is the text data formatted in Step 2, and sentiment analysis and keyword extraction are performed using Python's SpaCy or NLTK, and the analysis results are taken as the output. Specific operations include identifying frequently occurring language patterns and evaluating sentiment trends.

[0307] Step 4:

[0308] The server evaluates the mental state of the user based on the analysis results. The input is the analysis result of Step 3, and based on it, scores for stress level and self - affirmation are generated, and the evaluation result is output. For example, if it is determined that the stress level is high, that fact is quantified.

[0309] Step 5:

[0310] The server selects an appropriate psychological training plan based on the evaluation result of the mental state. The input is the evaluation result of Step 4, and based on it, an AI model generates the plan. This plan is selected from relaxation techniques, cognitive - behavioral therapy approaches, etc., and is sent to the terminal as the output.

[0311] Step 6:

[0312] The terminal receives a psychological training plan sent from the server and proposes it to the user. The input is the plan data from the server, and the information is displayed to the user as output in a visual or audio interface to show specific implementation methods.

[0313] Step 7:

[0314] The user completes a training plan presented on the device and inputs the results as feedback. This input consists of the user's experience and impressions, and is sent to the server via the device. Specifically, the user evaluates the effectiveness of the plan and inputs feedback in the form of a questionnaire.

[0315] Step 8:

[0316] The server receives and analyzes feedback sent by the user. The input is feedback data, and by analyzing it, the system adjusts its algorithm to obtain output that improves the accuracy of future suggestions. For example, if a particular program was effective, the algorithm will be updated to prefer suggesting that program.

[0317] (Application Example 1)

[0318] 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."

[0319] To effectively manage users' mental health, it is necessary to accurately grasp their emotional information in their daily lives and provide support tailored to their individual needs. However, currently, there is a lack of mechanisms to respond immediately to users' diverse emotional states and thought patterns, making personalized mental healthcare difficult. A new system is needed that utilizes natural conversational information collected daily by home robots to appropriately support users' mental health.

[0320] 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.

[0321] In this invention, the server includes means for collecting the user's dialogue history and usage information and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; and means for selecting an appropriate mental training method based on the evaluation results and proposing it to the user. This makes it possible to realize effective mental healthcare tailored to each individual user by having the user provide emotional information from their daily life through voice dialogue to the robot terminal.

[0322] A "user" is an individual who uses the system to receive mental health care.

[0323] "Dialogue history" refers to a record of past communications between the user and the system via voice or text.

[0324] "Usage information" refers to data related to a user's system usage and interactions.

[0325] "Thinking patterns" refer to mental tendencies and habits that can be inferred from the words and expressions that users frequently use in their daily lives.

[0326] "Analysis" is the process of identifying user emotions and behavioral trends based on collected data.

[0327] "Mental health status" refers to a numerical or categorical representation of a user's mental health condition.

[0328] "Mental training techniques" refer to specific training methods and therapies aimed at improving the mental health of users.

[0329] "Feedback" refers to users reporting their opinions and results regarding a system or a proposed program.

[0330] A "system algorithm" is a series of calculation procedures used to evaluate the user's state and provide optimal suggestions.

[0331] "Voice interaction" is a method of communication between the user and the system using voice.

[0332] A "robot terminal" is a hardware device for home robots that enables interaction with the user.

[0333] The system that realizes this invention will be built around a home robot. The robot terminal is equipped with a voice input microphone, speaker, and text display to engage in natural voice dialogue with the user. It can also connect to a server via a network. The server collects dialogue history and usage information obtained from the user and analyzes thought patterns using natural language processing technology. Natural language processing libraries such as Google Cloud Natural Language API and spaCy are used for this analysis. Based on the analysis results, the user's mental health state is evaluated, and the optimal mental training method is selected using a machine learning model with TensorFlow or PyTorch.

[0334] For example, the server can use a generative AI model based on a prompt such as, "The user has recently complained of work fatigue. What kind of mental training method should we suggest?" to provide effective guidance. This suggestion is communicated to the user via a robotic terminal, and the server updates the system algorithm based on the user's feedback. In this way, effective mental healthcare tailored to each individual user is realized.

[0335] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0336] Step 1:

[0337] The device engages in voice interaction with the user and collects the content of that interaction as audio data using a microphone. This audio data is then converted into text data by a speech recognition engine. The input is audio data, and the output is text data.

[0338] Step 2:

[0339] The server receives text data from the terminal, performs sentiment analysis using a natural language processing library (e.g., spaCy, Google Cloud Natural Language API), and analyzes the user's thought patterns. The input is text data, and the output is an analysis result showing emotional tendencies and thought patterns.

[0340] Step 3:

[0341] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to evaluate the user's mental health status based on the analysis results. This evaluation quantifies stress levels and self-esteem. The input is the analysis results, and the output is numerical data related to mental health status.

[0342] Step 4:

[0343] The server uses a generative AI model to determine the optimal mental training method based on the results of the mental health assessment and generates a prompt message. For example, it might create a prompt message such as, "The user has recently complained of work fatigue. What kind of mental training method should be suggested?" The input is state assessment data, and the output is the prompt message and recommended training method.

[0344] Step 5:

[0345] The server sends recommended mental training techniques to the terminal, and the terminal guides the user through those techniques. The terminal uses a speaker and display to introduce the techniques using both audio and visuals. The input is the recommended training techniques, and the output is the instructional guide for the user.

[0346] Step 6:

[0347] The user provides feedback on the mental training they have conducted via their device, and sends this feedback data to the server. The server uses this data to adjust the algorithm and improve the accuracy of the next suggestion. The input is the feedback data, and the output is the algorithm correction data.

[0348] 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.

[0349] This invention provides a system incorporating an emotion engine to improve the mental health of users. This system operates through the interaction of a server, a terminal, and the user.

[0350] The server first receives input from the user through the terminal. Once the user logs into the system and enters mental health-related feedback and daily events, the server collects interaction history and usage data.

[0351] Subsequently, the server uses an emotion engine to analyze the emotional tendencies of the collected data. This engine utilizes natural language processing technology to recognize the emotions contained in the user's input in real time.

[0352] Based on the analysis results from the emotion engine, the server provides a more detailed assessment of the user's mental health. By capturing fluctuations in the user's emotions, it can quantify specific mental states such as stress levels, anxiety, and joy.

[0353] Based on these results, the server selects the most suitable mental training program for the user and proposes it via the terminal. This program includes relaxation techniques and cognitive behavioral therapy approaches.

[0354] The terminal displays program suggestions from the server to the user and provides options for execution. The user can select and run a program of interest from the options displayed on the screen.

[0355] Furthermore, the terminal collects feedback on the results of the programs performed by the user and the effects they experienced, and sends it to the server. The server uses this feedback to adjust the algorithm and improve future program suggestions.

[0356] Specific example

[0357] For example, if a user inputs "I can't sleep lately because of work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the analysis, the server suggests "nighttime anxiety-reducing exercises" to the user. After the user tries the program, they input feedback into the terminal about the changes they felt, and the server uses that information to improve its next suggestion.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] Users log in to the system via their device and input information about their daily stress levels and emotional changes.

[0361] Step 2:

[0362] The terminal sends the conversation content entered by the user to the server, thereby recording the conversation history and usage data.

[0363] Step 3:

[0364] The server uses an emotion engine to analyze received data and recognize the user's emotions in real time. The emotion engine utilizes natural language processing technology to analyze emotional tendencies from text.

[0365] Step 4:

[0366] The server uses the results of the emotion engine's analysis to perform a detailed assessment of the user's mental health. For example, it calculates stress levels and anxiety scores based on the intensity and frequency of emotions.

[0367] Step 5:

[0368] The server selects a mental training program based on the evaluation results and proposes it to the terminal. The program consists of content designed to improve specific emotional states.

[0369] Step 6:

[0370] The terminal presents the user with a selection of programs received from the server and displays the options for execution. The user can then choose their desired program and start it.

[0371] Step 7:

[0372] The user runs the selected program on their device and enters feedback on the results. This feedback includes the program's effectiveness and personal impressions.

[0373] Step 8:

[0374] The server collects user feedback and adjusts the algorithms within the system to improve the accuracy of future program suggestions.

[0375] (Example 2)

[0376] 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".

[0377] Traditionally, systems designed to effectively improve users' mental health have relied on simplistic analytical methods and fixed program proposals, failing to adequately consider the wide range of emotional fluctuations of individual users. Furthermore, the lack of sufficient feedback mechanisms to improve the quality of proposals makes long-term mental health improvement difficult.

[0378] 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.

[0379] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's diverse emotional patterns; means for evaluating the user's mental health state in detail based on the analysis results; and means for selecting an optimal mental training program based on the evaluation results and proposing it to the user via a terminal. This provides appropriate solutions based on individual emotional fluctuations, improves the accuracy of the system's suggestions through feedback, and enables sustained improvement of mental health.

[0380] A "user" is an individual who uses the system and provides feedback and sentiment data.

[0381] "Dialogue history" refers to the record of all communication and input that a user has made through the system.

[0382] "Usage status" refers to data that includes information about how users are using the system.

[0383] An "emotional pattern" is a collection of data that represents the tendencies and fluctuations of a user's emotions.

[0384] "Mental health status" refers to the state of a user's mental well-being, and includes emotional states such as stress and anxiety.

[0385] "Natural language processing" refers to the technology used to analyze human language and understand its meaning and emotions.

[0386] A "mental training program" is a series of activities and exercises designed to improve the mental health of its users.

[0387] "Relaxation techniques" refer to methods and techniques used to promote relaxation and stress reduction in users.

[0388] A "cognitive behavioral therapy approach" is a method for improving emotional states by changing the user's thoughts and behaviors.

[0389] "Exercises to boost self-esteem" refers to activities and practices designed to cultivate a user's self-esteem.

[0390] A "terminal" is a device used by users to access a system and to input and receive information.

[0391] "Feedback" refers to the results and impressions of a program that users provide to a system.

[0392] This invention is a system for improving the mental health of users. This system consists of the interaction of a server, a terminal, and a user.

[0393] The server is operated using various software. The emotion engine, a crucial element, typically utilizes natural language processing technology and is implemented in programming languages ​​such as Python or JavaScript. The database records user interaction history and usage data, and uses database management systems such as SQL.

[0394] Users access the system through their devices. These devices can be any internet-connected device, such as a smartphone, tablet, or personal computer. Users can directly input information into the system or select and run the mental training programs provided.

[0395] The server receives input data from the user and analyzes it using an emotion engine. Natural language processing identifies emotional components within the text in real time and assesses the user's mental health state. Based on this assessment, it proposes an optimal mental training program for the user.

[0396] For example, if a user enters "I've been having trouble sleeping lately due to work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the results, the server suggests "nighttime anxiety-reducing exercises." The user can then select and try this program on their device.

[0397] This system collects user feedback and adjusts the server's algorithms to improve the accuracy of future program suggestions. This feedback loop allows the system to continuously optimize for each user, providing sustained mental health support.

[0398] An example of a prompt message might be: "Analyze the emotions entered by the user and suggest the optimal mental training program. This information includes input specifically related to stress and anxiety."

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] Users access the system using a terminal and input feedback related to mental health and daily events. The input data is sent to the server in text format. The input here is text information including the user's emotions, and the output is the initial data sent to the server.

[0402] Step 2:

[0403] The server records the received user input data in a database. During this recording process, the text data is organized and stored as a user-specific dialogue history. The data is classified and organized to make it available for future analysis.

[0404] Step 3:

[0405] The server activates the emotion engine and retrieves user input data from the database. It then performs emotion analysis on the retrieved data using natural language processing techniques. The input is user text data, and the output is the analyzed emotion data. Specifically, it performs keyword extraction and text classification to convert the user's emotional state into numerical data.

[0406] Step 4:

[0407] The server evaluates the user's mental health state based on the results of emotion analysis. The evaluation analyzes emotional tendencies and numerically indicates the degree of stress, anxiety, joy, etc. The input is the analyzed emotion data, and the output is the mental health state evaluation result. Based on this evaluation result, the user's specific mental state is understood.

[0408] Step 5:

[0409] The server selects the most suitable mental training program for the user based on the evaluation results. The program is determined after considering relaxation techniques and cognitive behavioral therapy, and then proposed to the user via the terminal. The input is the mental health evaluation results, and the output is the proposed program.

[0410] Step 6:

[0411] The terminal displays program suggestions from the server to the user. The user selects a program of interest from the suggested options on the terminal and executes it. The input is the suggestion data from the server, and the output is the user's selection.

[0412] Step 7:

[0413] Users input feedback via their device regarding the results and perceived effects of the program they have completed. This input consists of data about their program experience and results, and is sent to the server as output.

[0414] Step 8:

[0415] The server receives user feedback and records it in a database. This information is used to adjust the algorithm and improve future program suggestions. The input is feedback data, and the output is the updated algorithm. This allows the system to provide more appropriate suggestions to the user.

[0416] (Application Example 2)

[0417] 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 as the "terminal".

[0418] In modern society, improving the mental health of individual users is a crucial issue. Traditional methods lack mechanisms to identify users' emotional states in real time and propose specific activities and environmental adjustments accordingly. This makes it difficult for users to receive the mental support they need most at that moment.

[0419] 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.

[0420] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; means for selecting and proposing an appropriate mental training program to the user based on the evaluation results; means for collecting the user's feedback on the proposed program and adjusting the system's algorithm; and automated means for identifying the user's emotional state and providing appropriate environmental adjustments or activities. This makes it possible for the user to receive the optimal mental support they need at that moment.

[0421] "Users" refer to individuals or groups who use the system and are the ones whose mental health is supported.

[0422] "Dialogue history" refers to the record of conversations and messages exchanged between the user and the system.

[0423] "Usage status" refers to a record of how users accessed the system and which functions they used and to what extent.

[0424] "Thinking patterns" refer to the user's unique tendencies in thinking and emotional responses, and the data used to analyze their emotional state is based on these patterns.

[0425] "Means of analysis" refers to methods and devices used to analyze user input data and identify emotions and thought patterns.

[0426] "Mental health status" refers to the emotional and psychological state of the user, and involves assessing levels of stress, anxiety, and joy.

[0427] A "mental training program" refers to a series of activities and exercises provided to improve the mental health of users.

[0428] "Feedback" refers to the reactions and opinions that users provide regarding a proposed program, and is data used to improve the system.

[0429] "Adjusting the algorithm" refers to optimizing the system's processing methods based on collected feedback to improve the accuracy of future suggestions.

[0430] "Identifying an emotional state" refers to the process of appropriately identifying the emotions a user is experiencing and understanding temporary changes in those emotions.

[0431] "Automated means of providing environmental adjustments or activities" refers to mechanisms or methods for automatically suggesting or implementing effective environmental changes or activities for users in response to their identified emotional states.

[0432] The system for implementing the invention mainly consists of servers and terminals, which work together to support the mental health of users.

[0433] The server receives conversation history and usage data entered by the user through the terminal and analyzes the user's thought patterns based on this data. This analysis uses a natural language processing engine, and leverages Python natural language processing libraries (e.g., NLTK and spaCy) to identify the user's emotional state.

[0434] Based on the analysis results, the server conducts a detailed assessment of the user's mental health and selects the most appropriate mental training program at that time. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem.

[0435] The selected program is sent to the terminal and presented to the user. The user can choose and execute the program displayed on the screen. Furthermore, the terminal collects feedback on the program the user has executed and sends this information to the server. The server adjusts the system's algorithm based on the collected feedback and uses it to improve future program suggestions.

[0436] For example, if a user tells the terminal through dialogue that they are "stressed out from being overwhelmed with housework lately," the server will analyze that input and suggest a relaxation program to alleviate the stress.

[0437] An example of a prompt when using a generative AI model is, "How can we analyze a user's input that they are unable to sleep due to work pressure and suggest a relaxation program to alleviate their anxiety?" In this way, the system can provide support that is best suited to each user's individual situation.

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] Users input feedback about daily events and mental health through their devices. The input data is collected by the devices and sent to the server. In this step, the user's text data is used as input.

[0441] Step 2:

[0442] The server uses a natural language processing engine to identify emotional states in order to analyze the text data received from the user. This process utilizes Python natural language processing libraries (e.g., NLTK and spaCy). Based on the input text, the user's emotional tendencies (e.g., stress, anxiety, joy) are analyzed. Based on these results, the user's mental health is assessed.

[0443] Step 3:

[0444] The server selects the optimal mental training program from the database based on the analysis results. Depending on the assessed emotional state, programs such as relaxation techniques or cognitive behavioral therapy approaches are chosen. The input to this process is the result of the emotional analysis, and the output is the selected program.

[0445] Step 4:

[0446] The selected program is sent to the terminal and presented to the user. The terminal displays the program details to the user and provides options. The user can choose a program of interest and run it. In this step, the program information is the input, and the user's selection is the output.

[0447] Step 5:

[0448] After the user completes the program they selected, the terminal collects user feedback. The user inputs information about the program's effects and their impressions into the terminal, and this data is sent to the server as feedback. The input is feedback on the results of the program, and the output is the transmission of feedback data to the server.

[0449] Step 6:

[0450] The server receives feedback from the user and adjusts the system's algorithm. This adjustment makes the user's next program suggestion more accurate. In this step, the feedback data is the input and the adjusted algorithm is the output.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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".

[0467] This invention provides a system for improving the mental health of users. This system is built on information exchanged between a server, a terminal, and the user.

[0468] The server first interacts with the user through their terminal, and stores the conversation history and usage data collected during this interaction. The server uses this data to analyze the user's thought patterns. Natural language processing technology is used for the analysis to evaluate which words and phrases the user frequently uses, as well as their emotional tendencies.

[0469] Subsequently, the server evaluates the user's mental health status based on the analysis results. For example, it quantifies stress levels and self-esteem to identify specific issues.

[0470] Next, the server selects a mental training program suitable for each user based on the evaluation results and proposes it via the terminal. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem. Users then practice the proposed program through the terminal.

[0471] Furthermore, the terminal collects feedback on the programs the user has implemented. The server analyzes this feedback and continuously adjusts the system's algorithms. This results in more accurate program suggestions for each individual user for their next session.

[0472] Specific example

[0473] For example, if a user reports to the server from their device that they "have been having trouble concentrating on work lately," the server analyzes the conversation history to identify potential factors contributing to the decreased concentration. The server then suggests "short-term concentration-enhancing techniques" to the user via their device. If the user tries this program and reports that it was "effective," the server uses this feedback to suggest even more suitable programs in the future.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] Users log in to the system using their device and enter their daily mental health concerns and questions.

[0477] Step 2:

[0478] The device sends user-entered conversation history and past usage data to the server. The server receives and records this data, laying the foundation for creating a comprehensive profile for each user.

[0479] Step 3:

[0480] The server analyzes the collected data using natural language processing techniques to identify the user's thought patterns. This analysis process includes analyzing emotional tendencies from the user's statements.

[0481] Step 4:

[0482] The server evaluates the user's mental health status based on the analysis results. This evaluation includes processing to quantify stress levels and self-esteem, allowing for an objective understanding of the user's psychological state.

[0483] Step 5:

[0484] The server selects a mental training program tailored to the user's mental health status. The selected program is designed to address the specific challenges the user is facing.

[0485] Step 6:

[0486] The terminal displays the training program received from the server to the user and provides options for execution. The user can select a program of interest from the provided options and start it.

[0487] Step 7:

[0488] Users participate in the selected program via a terminal and input feedback on the results into the terminal. This feedback includes the program's effectiveness and their impressions.

[0489] Step 8:

[0490] The server receives feedback from users and uses it to refine the algorithm. This improves the entire system so that future programs are better suited to the user's needs.

[0491] (Example 1)

[0492] 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."

[0493] In modern society, mental health issues such as stress and anxiety are becoming increasingly serious, negatively impacting individuals' quality of life. However, many people lack the means to accurately understand their own mental state and improve it in an appropriate way. Therefore, the challenge is to provide effective mental training tailored to individual users and to offer a system for improving mental health.

[0494] 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.

[0495] In this invention, the server includes means for collecting user dialogue information and usage information and analyzing the user's thought patterns; means for evaluating the user's mental state based on the analysis results; and means for selecting an appropriate psychological training plan based on the evaluation results and proposing it to the user. This makes it possible to provide highly accurate training tailored to the individual's mental state and effectively improve each individual's mental health.

[0496] "Users" refers to individuals who use this system, and the purpose of this system is to improve their mental state.

[0497] "Dialogue information" refers to linguistic information provided by users through this system, including information such as dialogue and input.

[0498] "Usage information" refers to information about how users use this system, and includes data such as their activity history within the system.

[0499] "Thinking patterns" refer to the results of analyzing the tendencies of thoughts and emotions that users exhibit through dialogue information.

[0500] "Mental state" refers to the user's mental health and is a comprehensive assessment that includes stress levels, emotional stability, and other factors.

[0501] A "psychological training plan" is a set of exercises and activities that have been appropriately selected to improve the mental state of the user.

[0502] "Natural language processing" refers to the technology of using computers to understand, interpret, and generate human language.

[0503] "Tension reduction techniques" are techniques aimed at improving mental state by promoting relaxation and stress relief.

[0504] The "cognitive behavioral therapy approach" is a psychological technique for improving mental health through changes in perception and behavior.

[0505] "Self-esteem improvement activities" refer to programs or activities designed to help users view themselves more positively.

[0506] As a form of implementing the invention, this system is designed to improve the mental health of users. It exchanges information between the server, terminal, and user, and performs various processes based on that information.

[0507] The server stores user interaction and usage information collected from the terminal. A dedicated application on the terminal is used for this data collection. This provides the server with a text-based interaction history.

[0508] The server analyzes this data using natural language processing (NLTK) techniques. Specifically, natural language processing libraries such as Python's SpaCy and NLTK are used. The server also analyzes the user's thought patterns and evaluates their mental state. Based on this evaluation, it generates an optimal psychological training plan. This process is performed automatically using an AI model.

[0509] The device receives a psychological training plan provided by the server and proposes it to the user. This plan includes tension reduction techniques, cognitive behavioral therapy approaches, and exercises to improve self-esteem.

[0510] Users implement these plans and send feedback on the results to the server via their devices. This feedback is used to propose future plans.

[0511] Specific example

[0512] For example, if a user reports to their device that they have been feeling down lately, the server analyzes this and suggests a "stress management program for emotional ups and downs." If the user completes the suggested program and provides feedback such as "I feel a little better," the server incorporates that feedback into the next program suggestion.

[0513] Example of a prompt

[0514] "Lately, I've been finding it difficult to concentrate on my work. Could you suggest some ways to improve my concentration?"

[0515] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0516] Step 1:

[0517] The terminal collects conversational information from the user. This input consists of text data entered by the user (e.g., "I've been feeling down lately"). This data is packaged in JSON format or similar and sent to the server. Specifically, the application on the terminal retrieves the conversational information and transfers it to the server via the internet connection.

[0518] Step 2:

[0519] The server stores the dialogue information received from the terminal. The input is data sent from the terminal, which is stored in the database. During storage, the text format is formatted and unnecessary information is filtered, resulting in consistent, analyzable data as output.

[0520] Step 3:

[0521] The server performs natural language processing on the accumulated dialogue data. The input is the text data formatted in step 2, and sentiment analysis and keyword extraction are performed using Python's SpaCy or NLTK, with the analysis results being output. Specific operations include identifying frequently occurring language patterns and evaluating sentiment tendencies.

[0522] Step 4:

[0523] The server evaluates the user's mental state based on the analysis results. The input is the analysis results from step 3, and based on that, it generates stress level and self-esteem scores and outputs the evaluation results. For example, if the stress level is determined to be high, that fact will be expressed numerically.

[0524] Step 5:

[0525] The server selects an appropriate psychological training plan based on the assessment results of the mental state. The input is the assessment results from step 4, and the AI ​​model generates a plan based on this. This plan is selected from methods such as tension reduction techniques and cognitive behavioral therapy approaches, and is sent to the terminal as output.

[0526] Step 6:

[0527] The terminal receives a psychological training plan sent from the server and proposes it to the user. The input is the plan data from the server, and the information is displayed to the user as output in a visual or audio interface to show specific implementation methods.

[0528] Step 7:

[0529] The user completes a training plan presented on the device and inputs the results as feedback. This input consists of the user's experience and impressions, and is sent to the server via the device. Specifically, the user evaluates the effectiveness of the plan and inputs feedback in the form of a questionnaire.

[0530] Step 8:

[0531] The server receives and analyzes feedback sent by the user. The input is feedback data, and by analyzing it, the system adjusts its algorithm to obtain output that improves the accuracy of future suggestions. For example, if a particular program was effective, the algorithm will be updated to prefer suggesting that program.

[0532] (Application Example 1)

[0533] 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."

[0534] To effectively manage users' mental health, it is necessary to accurately grasp their emotional information in their daily lives and provide support tailored to their individual needs. However, currently, there is a lack of mechanisms to respond immediately to users' diverse emotional states and thought patterns, making personalized mental healthcare difficult. A new system is needed that utilizes natural conversational information collected daily by home robots to appropriately support users' mental health.

[0535] 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.

[0536] In this invention, the server includes means for collecting the user's dialogue history and usage information and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; and means for selecting an appropriate mental training method based on the evaluation results and proposing it to the user. This makes it possible to realize effective mental healthcare tailored to each individual user by having the user provide emotional information from their daily life through voice dialogue to the robot terminal.

[0537] A "user" is an individual who uses the system to receive mental health care.

[0538] "Dialogue history" refers to a record of past communications between the user and the system via voice or text.

[0539] "Usage information" refers to data related to a user's system usage and interactions.

[0540] "Thinking patterns" refer to mental tendencies and habits that can be inferred from the words and expressions that users frequently use in their daily lives.

[0541] "Analysis" is the process of identifying user emotions and behavioral trends based on collected data.

[0542] "Mental health status" refers to a numerical or categorical representation of a user's mental health condition.

[0543] "Mental training techniques" refer to specific training methods and therapies aimed at improving the mental health of users.

[0544] "Feedback" refers to users reporting their opinions and results regarding a system or a proposed program.

[0545] A "system algorithm" is a series of calculation procedures used to evaluate the user's state and provide optimal suggestions.

[0546] "Voice interaction" is a method of communication between the user and the system using voice.

[0547] A "robot terminal" is a hardware device for home robots that enables interaction with the user.

[0548] The system that realizes this invention will be built around a home robot. The robot terminal is equipped with a voice input microphone, speaker, and text display to engage in natural voice dialogue with the user. It can also connect to a server via a network. The server collects dialogue history and usage information obtained from the user and analyzes thought patterns using natural language processing technology. Natural language processing libraries such as Google Cloud Natural Language API and spaCy are used for this analysis. Based on the analysis results, the user's mental health state is evaluated, and the optimal mental training method is selected using a machine learning model with TensorFlow or PyTorch.

[0549] For example, the server can use a generative AI model based on a prompt such as, "The user has recently complained of work fatigue. What kind of mental training method should we suggest?" to provide effective guidance. This suggestion is communicated to the user via a robotic terminal, and the server updates the system algorithm based on the user's feedback. In this way, effective mental healthcare tailored to each individual user is realized.

[0550] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0551] Step 1:

[0552] The device engages in voice interaction with the user and collects the content of that interaction as audio data using a microphone. This audio data is then converted into text data by a speech recognition engine. The input is audio data, and the output is text data.

[0553] Step 2:

[0554] The server receives text data from the terminal, performs sentiment analysis using a natural language processing library (e.g., spaCy, Google Cloud Natural Language API), and analyzes the user's thought patterns. The input is text data, and the output is an analysis result showing emotional tendencies and thought patterns.

[0555] Step 3:

[0556] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to evaluate the user's mental health status based on the analysis results. This evaluation quantifies stress levels and self-esteem. The input is the analysis results, and the output is numerical data related to mental health status.

[0557] Step 4:

[0558] The server uses a generative AI model to determine the optimal mental training method based on the results of the mental health assessment and generates a prompt message. For example, it might create a prompt message such as, "The user has recently complained of work fatigue. What kind of mental training method should be suggested?" The input is state assessment data, and the output is the prompt message and recommended training method.

[0559] Step 5:

[0560] The server sends recommended mental training techniques to the terminal, and the terminal guides the user through those techniques. The terminal uses a speaker and display to introduce the techniques using both audio and visuals. The input is the recommended training techniques, and the output is the instructional guide for the user.

[0561] Step 6:

[0562] The user provides feedback on the mental training they have conducted via their device, and sends this feedback data to the server. The server uses this data to adjust the algorithm and improve the accuracy of the next suggestion. The input is the feedback data, and the output is the algorithm correction data.

[0563] 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.

[0564] This invention provides a system incorporating an emotion engine to improve the mental health of users. This system operates through the interaction of a server, a terminal, and the user.

[0565] The server first receives input from the user through the terminal. Once the user logs into the system and enters mental health-related feedback and daily events, the server collects interaction history and usage data.

[0566] Subsequently, the server uses an emotion engine to analyze the emotional tendencies of the collected data. This engine utilizes natural language processing technology to recognize the emotions contained in the user's input in real time.

[0567] Based on the analysis results from the emotion engine, the server provides a more detailed assessment of the user's mental health. By capturing fluctuations in the user's emotions, it can quantify specific mental states such as stress levels, anxiety, and joy.

[0568] Based on these results, the server selects the most suitable mental training program for the user and proposes it via the terminal. This program includes relaxation techniques and cognitive behavioral therapy approaches.

[0569] The terminal displays program suggestions from the server to the user and provides options for execution. The user can select and run a program of interest from the options displayed on the screen.

[0570] Furthermore, the terminal collects feedback on the results of the programs performed by the user and the effects they experienced, and sends it to the server. The server uses this feedback to adjust the algorithm and improve future program suggestions.

[0571] Specific example

[0572] For example, if a user inputs "I can't sleep lately because of work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the analysis, the server suggests "nighttime anxiety-reducing exercises" to the user. After the user tries the program, they input feedback into the terminal about the changes they felt, and the server uses that information to improve its next suggestion.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] Users log in to the system via their device and input information about their daily stress levels and emotional changes.

[0576] Step 2:

[0577] The terminal sends the conversation content entered by the user to the server, thereby recording the conversation history and usage data.

[0578] Step 3:

[0579] The server uses an emotion engine to analyze received data and recognize the user's emotions in real time. The emotion engine utilizes natural language processing technology to analyze emotional tendencies from text.

[0580] Step 4:

[0581] The server uses the results of the emotion engine's analysis to perform a detailed assessment of the user's mental health. For example, it calculates stress levels and anxiety scores based on the intensity and frequency of emotions.

[0582] Step 5:

[0583] The server selects a mental training program based on the evaluation results and proposes it to the terminal. The program consists of content designed to improve specific emotional states.

[0584] Step 6:

[0585] The terminal presents the user with a selection of programs received from the server and displays the options for execution. The user can then choose their desired program and start it.

[0586] Step 7:

[0587] The user runs the selected program on their device and enters feedback on the results. This feedback includes the program's effectiveness and personal impressions.

[0588] Step 8:

[0589] The server collects user feedback and adjusts the algorithms within the system to improve the accuracy of future program suggestions.

[0590] (Example 2)

[0591] 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."

[0592] Traditionally, systems designed to effectively improve users' mental health have relied on simplistic analytical methods and fixed program proposals, failing to adequately consider the wide range of emotional fluctuations of individual users. Furthermore, the lack of sufficient feedback mechanisms to improve the quality of proposals makes long-term mental health improvement difficult.

[0593] 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.

[0594] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's diverse emotional patterns; means for evaluating the user's mental health state in detail based on the analysis results; and means for selecting an optimal mental training program based on the evaluation results and proposing it to the user via a terminal. This provides appropriate solutions based on individual emotional fluctuations, improves the accuracy of the system's suggestions through feedback, and enables sustained improvement of mental health.

[0595] A "user" is an individual who uses the system and provides feedback and sentiment data.

[0596] "Dialogue history" refers to the record of all communication and input that a user has made through the system.

[0597] "Usage status" refers to data that includes information about how users are using the system.

[0598] An "emotional pattern" is a collection of data that represents the tendencies and fluctuations of a user's emotions.

[0599] "Mental health status" refers to the state of a user's mental well-being, and includes emotional states such as stress and anxiety.

[0600] "Natural language processing" refers to the technology used to analyze human language and understand its meaning and emotions.

[0601] A "mental training program" is a series of activities and exercises designed to improve the mental health of its users.

[0602] "Relaxation techniques" refer to methods and techniques used to promote relaxation and stress reduction in users.

[0603] A "cognitive behavioral therapy approach" is a method for improving emotional states by changing the user's thoughts and behaviors.

[0604] "Exercises to boost self-esteem" refers to activities and practices designed to cultivate a user's self-esteem.

[0605] A "terminal" is a device used by users to access a system and to input and receive information.

[0606] "Feedback" refers to the results and impressions of a program that users provide to a system.

[0607] This invention is a system for improving the mental health of users. This system consists of the interaction of a server, a terminal, and a user.

[0608] The server is operated using various software. The emotion engine, a crucial element, typically utilizes natural language processing technology and is implemented in programming languages ​​such as Python or JavaScript. The database records user interaction history and usage data, and uses database management systems such as SQL.

[0609] Users access the system through their devices. These devices can be any internet-connected device, such as a smartphone, tablet, or personal computer. Users can directly input information into the system or select and run the mental training programs provided.

[0610] The server receives input data from the user and analyzes it using an emotion engine. Natural language processing identifies emotional components within the text in real time and assesses the user's mental health state. Based on this assessment, it proposes an optimal mental training program for the user.

[0611] For example, if a user enters "I've been having trouble sleeping lately due to work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the results, the server suggests "nighttime anxiety-reducing exercises." The user can then select and try this program on their device.

[0612] This system collects user feedback and adjusts the server's algorithms to improve the accuracy of future program suggestions. This feedback loop allows the system to continuously optimize for each user, providing sustained mental health support.

[0613] An example of a prompt message might be: "Analyze the emotions entered by the user and suggest the optimal mental training program. This information includes input specifically related to stress and anxiety."

[0614] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0615] Step 1:

[0616] Users access the system using a terminal and input feedback related to mental health and daily events. The input data is sent to the server in text format. The input here is text information including the user's emotions, and the output is the initial data sent to the server.

[0617] Step 2:

[0618] The server records the received user input data in a database. During this recording process, the text data is organized and stored as a user-specific dialogue history. The data is classified and organized to make it available for future analysis.

[0619] Step 3:

[0620] The server activates the emotion engine and retrieves user input data from the database. It then performs emotion analysis on the retrieved data using natural language processing techniques. The input is user text data, and the output is the analyzed emotion data. Specifically, it performs keyword extraction and text classification to convert the user's emotional state into numerical data.

[0621] Step 4:

[0622] The server evaluates the user's mental health state based on the results of emotion analysis. The evaluation analyzes emotional tendencies and numerically indicates the degree of stress, anxiety, joy, etc. The input is the analyzed emotion data, and the output is the mental health state evaluation result. Based on this evaluation result, the user's specific mental state is understood.

[0623] Step 5:

[0624] The server selects the most suitable mental training program for the user based on the evaluation results. The program is determined after considering relaxation techniques and cognitive behavioral therapy, and then proposed to the user via the terminal. The input is the mental health evaluation results, and the output is the proposed program.

[0625] Step 6:

[0626] The terminal displays program suggestions from the server to the user. The user selects a program of interest from the suggested options on the terminal and executes it. The input is the suggestion data from the server, and the output is the user's selection.

[0627] Step 7:

[0628] Users input feedback via their device regarding the results and perceived effects of the program they have completed. This input consists of data about their program experience and results, and is sent to the server as output.

[0629] Step 8:

[0630] The server receives user feedback and records it in a database. This information is used to adjust the algorithm and improve future program suggestions. The input is feedback data, and the output is the updated algorithm. This allows the system to provide more appropriate suggestions to the user.

[0631] (Application Example 2)

[0632] 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."

[0633] In modern society, improving the mental health of individual users is a crucial issue. Traditional methods lack mechanisms to identify users' emotional states in real time and propose specific activities and environmental adjustments accordingly. This makes it difficult for users to receive the mental support they need most at that moment.

[0634] 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.

[0635] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; means for selecting and proposing an appropriate mental training program to the user based on the evaluation results; means for collecting the user's feedback on the proposed program and adjusting the system's algorithm; and automated means for identifying the user's emotional state and providing appropriate environmental adjustments or activities. This makes it possible for the user to receive the optimal mental support they need at that moment.

[0636] "Users" refer to individuals or groups who use the system and are the ones whose mental health is supported.

[0637] "Dialogue history" refers to the record of conversations and messages exchanged between the user and the system.

[0638] "Usage status" refers to a record of how users accessed the system and which functions they used and to what extent.

[0639] "Thinking patterns" refer to the user's unique tendencies in thinking and emotional responses, and the data used to analyze their emotional state is based on these patterns.

[0640] "Means of analysis" refers to methods and devices used to analyze user input data and identify emotions and thought patterns.

[0641] "Mental health status" refers to the emotional and psychological state of the user, and involves assessing levels of stress, anxiety, and joy.

[0642] A "mental training program" refers to a series of activities and exercises provided to improve the mental health of users.

[0643] "Feedback" refers to the reactions and opinions that users provide regarding a proposed program, and is data used to improve the system.

[0644] "Adjusting the algorithm" refers to optimizing the system's processing methods based on collected feedback to improve the accuracy of future suggestions.

[0645] "Identifying an emotional state" refers to the process of appropriately identifying the emotions a user is experiencing and understanding temporary changes in those emotions.

[0646] "Automated means of providing environmental adjustments or activities" refers to mechanisms or methods for automatically suggesting or implementing effective environmental changes or activities for users in response to their identified emotional states.

[0647] The system for implementing the invention mainly consists of servers and terminals, which work together to support the mental health of users.

[0648] The server receives conversation history and usage data entered by the user through the terminal and analyzes the user's thought patterns based on this data. This analysis uses a natural language processing engine, and leverages Python natural language processing libraries (e.g., NLTK and spaCy) to identify the user's emotional state.

[0649] Based on the analysis results, the server conducts a detailed assessment of the user's mental health and selects the most appropriate mental training program at that time. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem.

[0650] The selected program is sent to the terminal and presented to the user. The user can choose and execute the program displayed on the screen. Furthermore, the terminal collects feedback on the program the user has executed and sends this information to the server. The server adjusts the system's algorithm based on the collected feedback and uses it to improve future program suggestions.

[0651] For example, if a user tells the terminal through dialogue that they are "stressed out from being overwhelmed with housework lately," the server will analyze that input and suggest a relaxation program to alleviate the stress.

[0652] An example of a prompt when using a generative AI model is, "How can we analyze a user's input that they are unable to sleep due to work pressure and suggest a relaxation program to alleviate their anxiety?" In this way, the system can provide support that is best suited to each user's individual situation.

[0653] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0654] Step 1:

[0655] Users input feedback about daily events and mental health through their devices. The input data is collected by the devices and sent to the server. In this step, the user's text data is used as input.

[0656] Step 2:

[0657] The server uses a natural language processing engine to identify emotional states in order to analyze the text data received from the user. This process utilizes Python natural language processing libraries (e.g., NLTK and spaCy). Based on the input text, the user's emotional tendencies (e.g., stress, anxiety, joy) are analyzed. Based on these results, the user's mental health is assessed.

[0658] Step 3:

[0659] The server selects the optimal mental training program from the database based on the analysis results. Depending on the assessed emotional state, programs such as relaxation techniques or cognitive behavioral therapy approaches are chosen. The input to this process is the result of the emotional analysis, and the output is the selected program.

[0660] Step 4:

[0661] The selected program is sent to the terminal and presented to the user. The terminal displays the program details to the user and provides options. The user can choose a program of interest and run it. In this step, the program information is the input, and the user's selection is the output.

[0662] Step 5:

[0663] After the user completes the program they selected, the terminal collects user feedback. The user inputs information about the program's effects and their impressions into the terminal, and this data is sent to the server as feedback. The input is feedback on the results of the program, and the output is the transmission of feedback data to the server.

[0664] Step 6:

[0665] The server receives feedback from the user and adjusts the system's algorithm. This adjustment makes the user's next program suggestion more accurate. In this step, the feedback data is the input and the adjusted algorithm is the output.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] [Fourth Embodiment]

[0670] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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).

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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".

[0683] This invention provides a system for improving the mental health of users. This system is built on information exchanged between a server, a terminal, and the user.

[0684] The server first interacts with the user through their terminal, and stores the conversation history and usage data collected during this interaction. The server uses this data to analyze the user's thought patterns. Natural language processing technology is used for the analysis to evaluate which words and phrases the user frequently uses, as well as their emotional tendencies.

[0685] Subsequently, the server evaluates the user's mental health status based on the analysis results. For example, it quantifies stress levels and self-esteem to identify specific issues.

[0686] Next, the server selects a mental training program suitable for each user based on the evaluation results and proposes it via the terminal. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem. Users then practice the proposed program through the terminal.

[0687] Furthermore, the terminal collects feedback on the programs the user has implemented. The server analyzes this feedback and continuously adjusts the system's algorithms. This results in more accurate program suggestions for each individual user for their next session.

[0688] Specific example

[0689] For example, if a user reports to the server from their device that they "have been having trouble concentrating on work lately," the server analyzes the conversation history to identify potential factors contributing to the decreased concentration. The server then suggests "short-term concentration-enhancing techniques" to the user via their device. If the user tries this program and reports that it was "effective," the server uses this feedback to suggest even more suitable programs in the future.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] Users log in to the system using their device and enter their daily mental health concerns and questions.

[0693] Step 2:

[0694] The device sends user-entered conversation history and past usage data to the server. The server receives and records this data, laying the foundation for creating a comprehensive profile for each user.

[0695] Step 3:

[0696] The server analyzes the collected data using natural language processing techniques to identify the user's thought patterns. This analysis process includes analyzing emotional tendencies from the user's statements.

[0697] Step 4:

[0698] The server evaluates the user's mental health status based on the analysis results. This evaluation includes processing to quantify stress levels and self-esteem, allowing for an objective understanding of the user's psychological state.

[0699] Step 5:

[0700] The server selects a mental training program tailored to the user's mental health status. The selected program is designed to address the specific challenges the user is facing.

[0701] Step 6:

[0702] The terminal displays the training program received from the server to the user and provides options for execution. The user can select a program of interest from the provided options and start it.

[0703] Step 7:

[0704] Users participate in the selected program via a terminal and input feedback on the results into the terminal. This feedback includes the program's effectiveness and their impressions.

[0705] Step 8:

[0706] The server receives feedback from users and uses it to refine the algorithm. This improves the entire system so that future programs are better suited to the user's needs.

[0707] (Example 1)

[0708] 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".

[0709] In modern society, mental health issues such as stress and anxiety are becoming increasingly serious, negatively impacting individuals' quality of life. However, many people lack the means to accurately understand their own mental state and improve it in an appropriate way. Therefore, the challenge is to provide effective mental training tailored to individual users and to offer a system for improving mental health.

[0710] 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.

[0711] In this invention, the server includes means for collecting user dialogue information and usage information and analyzing the user's thought patterns; means for evaluating the user's mental state based on the analysis results; and means for selecting an appropriate psychological training plan based on the evaluation results and proposing it to the user. This makes it possible to provide highly accurate training tailored to the individual's mental state and effectively improve each individual's mental health.

[0712] "Users" refers to individuals who use this system, and the purpose of this system is to improve their mental state.

[0713] "Dialogue information" refers to linguistic information provided by users through this system, including information such as dialogue and input.

[0714] "Usage information" refers to information about how users use this system, and includes data such as their activity history within the system.

[0715] "Thinking patterns" refer to the results of analyzing the tendencies of thoughts and emotions that users exhibit through dialogue information.

[0716] "Mental state" refers to the user's mental health and is a comprehensive assessment that includes stress levels, emotional stability, and other factors.

[0717] A "psychological training plan" is a set of exercises and activities that have been appropriately selected to improve the mental state of the user.

[0718] "Natural language processing" refers to the technology of using computers to understand, interpret, and generate human language.

[0719] "Tension reduction techniques" are techniques aimed at improving mental state by promoting relaxation and stress relief.

[0720] The "cognitive behavioral therapy approach" is a psychological technique for improving mental health through changes in perception and behavior.

[0721] "Self-esteem improvement activities" refer to programs or activities designed to help users view themselves more positively.

[0722] As a form of implementing the invention, this system is designed to improve the mental health of users. It exchanges information between the server, terminal, and user, and performs various processes based on that information.

[0723] The server stores user interaction and usage information collected from the terminal. A dedicated application on the terminal is used for this data collection. This provides the server with a text-based interaction history.

[0724] The server analyzes this data using natural language processing (NLTK) techniques. Specifically, natural language processing libraries such as Python's SpaCy and NLTK are used. The server also analyzes the user's thought patterns and evaluates their mental state. Based on this evaluation, it generates an optimal psychological training plan. This process is performed automatically using an AI model.

[0725] The device receives a psychological training plan provided by the server and proposes it to the user. This plan includes tension reduction techniques, cognitive behavioral therapy approaches, and exercises to improve self-esteem.

[0726] Users implement these plans and send feedback on the results to the server via their devices. This feedback is used to propose future plans.

[0727] Specific example

[0728] For example, if a user reports to their device that they have been feeling down lately, the server analyzes this and suggests a "stress management program for emotional ups and downs." If the user completes the suggested program and provides feedback such as "I feel a little better," the server incorporates that feedback into the next program suggestion.

[0729] Example of a prompt

[0730] "Lately, I've been finding it difficult to concentrate on my work. Could you suggest some ways to improve my concentration?"

[0731] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0732] Step 1:

[0733] The terminal collects conversational information from the user. This input consists of text data entered by the user (e.g., "I've been feeling down lately"). This data is packaged in JSON format or similar and sent to the server. Specifically, the application on the terminal retrieves the conversational information and transfers it to the server via the internet connection.

[0734] Step 2:

[0735] The server stores the dialogue information received from the terminal. The input is data sent from the terminal, which is stored in the database. During storage, the text format is formatted and unnecessary information is filtered, resulting in consistent, analyzable data as output.

[0736] Step 3:

[0737] The server performs natural language processing on the accumulated dialogue data. The input is the text data formatted in step 2, and sentiment analysis and keyword extraction are performed using Python's SpaCy or NLTK, with the analysis results being output. Specific operations include identifying frequently occurring language patterns and evaluating sentiment tendencies.

[0738] Step 4:

[0739] The server evaluates the user's mental state based on the analysis results. The input is the analysis results from step 3, and based on that, it generates stress level and self-esteem scores and outputs the evaluation results. For example, if the stress level is determined to be high, that fact will be expressed numerically.

[0740] Step 5:

[0741] The server selects an appropriate psychological training plan based on the assessment results of the mental state. The input is the assessment results from step 4, and the AI ​​model generates a plan based on this. This plan is selected from methods such as tension reduction techniques and cognitive behavioral therapy approaches, and is sent to the terminal as output.

[0742] Step 6:

[0743] The terminal receives a psychological training plan sent from the server and proposes it to the user. The input is the plan data from the server, and the information is displayed to the user as output in a visual or audio interface to show specific implementation methods.

[0744] Step 7:

[0745] The user completes a training plan presented on the device and inputs the results as feedback. This input consists of the user's experience and impressions, and is sent to the server via the device. Specifically, the user evaluates the effectiveness of the plan and inputs feedback in the form of a questionnaire.

[0746] Step 8:

[0747] The server receives and analyzes feedback sent by the user. The input is feedback data, and by analyzing it, the system adjusts its algorithm to obtain output that improves the accuracy of future suggestions. For example, if a particular program was effective, the algorithm will be updated to prefer suggesting that program.

[0748] (Application Example 1)

[0749] 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".

[0750] To effectively manage users' mental health, it is necessary to accurately grasp their emotional information in their daily lives and provide support tailored to their individual needs. However, currently, there is a lack of mechanisms to respond immediately to users' diverse emotional states and thought patterns, making personalized mental healthcare difficult. A new system is needed that utilizes natural conversational information collected daily by home robots to appropriately support users' mental health.

[0751] 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.

[0752] In this invention, the server includes means for collecting the user's dialogue history and usage information and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; and means for selecting an appropriate mental training method based on the evaluation results and proposing it to the user. This makes it possible to realize effective mental healthcare tailored to each individual user by having the user provide emotional information from their daily life through voice dialogue to the robot terminal.

[0753] A "user" is an individual who uses the system to receive mental health care.

[0754] "Dialogue history" refers to a record of past communications between the user and the system via voice or text.

[0755] "Usage information" refers to data related to a user's system usage and interactions.

[0756] "Thinking patterns" refer to mental tendencies and habits that can be inferred from the words and expressions that users frequently use in their daily lives.

[0757] "Analysis" is the process of identifying user emotions and behavioral trends based on collected data.

[0758] "Mental health status" refers to a numerical or categorical representation of a user's mental health condition.

[0759] "Mental training techniques" refer to specific training methods and therapies aimed at improving the mental health of users.

[0760] "Feedback" refers to users reporting their opinions and results regarding a system or a proposed program.

[0761] A "system algorithm" is a series of calculation procedures used to evaluate the user's state and provide optimal suggestions.

[0762] "Voice interaction" is a method of communication between the user and the system using voice.

[0763] A "robot terminal" is a hardware device for home robots that enables interaction with the user.

[0764] The system that realizes this invention will be built around a home robot. The robot terminal is equipped with a voice input microphone, speaker, and text display to engage in natural voice dialogue with the user. It can also connect to a server via a network. The server collects dialogue history and usage information obtained from the user and analyzes thought patterns using natural language processing technology. Natural language processing libraries such as Google Cloud Natural Language API and spaCy are used for this analysis. Based on the analysis results, the user's mental health state is evaluated, and the optimal mental training method is selected using a machine learning model with TensorFlow or PyTorch.

[0765] For example, the server can use a generative AI model based on a prompt such as, "The user has recently complained of work fatigue. What kind of mental training method should we suggest?" to provide effective guidance. This suggestion is communicated to the user via a robotic terminal, and the server updates the system algorithm based on the user's feedback. In this way, effective mental healthcare tailored to each individual user is realized.

[0766] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0767] Step 1:

[0768] The device engages in voice interaction with the user and collects the content of that interaction as audio data using a microphone. This audio data is then converted into text data by a speech recognition engine. The input is audio data, and the output is text data.

[0769] Step 2:

[0770] The server receives text data from the terminal, performs sentiment analysis using a natural language processing library (e.g., spaCy, Google Cloud Natural Language API), and analyzes the user's thought patterns. The input is text data, and the output is an analysis result showing emotional tendencies and thought patterns.

[0771] Step 3:

[0772] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to evaluate the user's mental health status based on the analysis results. This evaluation quantifies stress levels and self-esteem. The input is the analysis results, and the output is numerical data related to mental health status.

[0773] Step 4:

[0774] The server uses a generative AI model to determine the optimal mental training method based on the results of the mental health assessment and generates a prompt message. For example, it might create a prompt message such as, "The user has recently complained of work fatigue. What kind of mental training method should be suggested?" The input is state assessment data, and the output is the prompt message and recommended training method.

[0775] Step 5:

[0776] The server sends recommended mental training techniques to the terminal, and the terminal guides the user through those techniques. The terminal uses a speaker and display to introduce the techniques using both audio and visuals. The input is the recommended training techniques, and the output is the instructional guide for the user.

[0777] Step 6:

[0778] The user provides feedback on the mental training they have conducted via their device, and sends this feedback data to the server. The server uses this data to adjust the algorithm and improve the accuracy of the next suggestion. The input is the feedback data, and the output is the algorithm correction data.

[0779] 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.

[0780] This invention provides a system incorporating an emotion engine to improve the mental health of users. This system operates through the interaction of a server, a terminal, and the user.

[0781] The server first receives input from the user through the terminal. Once the user logs into the system and enters mental health-related feedback and daily events, the server collects interaction history and usage data.

[0782] Subsequently, the server uses an emotion engine to analyze the emotional tendencies of the collected data. This engine utilizes natural language processing technology to recognize the emotions contained in the user's input in real time.

[0783] Based on the analysis results from the emotion engine, the server provides a more detailed assessment of the user's mental health. By capturing fluctuations in the user's emotions, it can quantify specific mental states such as stress levels, anxiety, and joy.

[0784] Based on these results, the server selects the most suitable mental training program for the user and proposes it via the terminal. This program includes relaxation techniques and cognitive behavioral therapy approaches.

[0785] The terminal displays program suggestions from the server to the user and provides options for execution. The user can select and run a program of interest from the options displayed on the screen.

[0786] Furthermore, the terminal collects feedback on the results of the programs performed by the user and the effects they experienced, and sends it to the server. The server uses this feedback to adjust the algorithm and improve future program suggestions.

[0787] Specific example

[0788] For example, if a user inputs "I can't sleep lately because of work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the analysis, the server suggests "nighttime anxiety-reducing exercises" to the user. After the user tries the program, they input feedback into the terminal about the changes they felt, and the server uses that information to improve its next suggestion.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] Users log in to the system via their device and input information about their daily stress levels and emotional changes.

[0792] Step 2:

[0793] The terminal sends the conversation content entered by the user to the server, thereby recording the conversation history and usage data.

[0794] Step 3:

[0795] The server uses an emotion engine to analyze received data and recognize the user's emotions in real time. The emotion engine utilizes natural language processing technology to analyze emotional tendencies from text.

[0796] Step 4:

[0797] The server uses the results of the emotion engine's analysis to perform a detailed assessment of the user's mental health. For example, it calculates stress levels and anxiety scores based on the intensity and frequency of emotions.

[0798] Step 5:

[0799] The server selects a mental training program based on the evaluation results and proposes it to the terminal. The program consists of content designed to improve specific emotional states.

[0800] Step 6:

[0801] The terminal presents the user with a selection of programs received from the server and displays the options for execution. The user can then choose their desired program and start it.

[0802] Step 7:

[0803] The user runs the selected program on their device and enters feedback on the results. This feedback includes the program's effectiveness and personal impressions.

[0804] Step 8:

[0805] The server collects user feedback and adjusts the algorithms within the system to improve the accuracy of future program suggestions.

[0806] (Example 2)

[0807] 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".

[0808] Traditionally, systems designed to effectively improve users' mental health have relied on simplistic analytical methods and fixed program proposals, failing to adequately consider the wide range of emotional fluctuations of individual users. Furthermore, the lack of sufficient feedback mechanisms to improve the quality of proposals makes long-term mental health improvement difficult.

[0809] 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.

[0810] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's diverse emotional patterns; means for evaluating the user's mental health state in detail based on the analysis results; and means for selecting an optimal mental training program based on the evaluation results and proposing it to the user via a terminal. This provides appropriate solutions based on individual emotional fluctuations, improves the accuracy of the system's suggestions through feedback, and enables sustained improvement of mental health.

[0811] A "user" is an individual who uses the system and provides feedback and sentiment data.

[0812] "Dialogue history" refers to the record of all communication and input that a user has made through the system.

[0813] "Usage status" refers to data that includes information about how users are using the system.

[0814] An "emotional pattern" is a collection of data that represents the tendencies and fluctuations of a user's emotions.

[0815] "Mental health status" refers to the state of a user's mental well-being, and includes emotional states such as stress and anxiety.

[0816] "Natural language processing" refers to the technology used to analyze human language and understand its meaning and emotions.

[0817] A "mental training program" is a series of activities and exercises designed to improve the mental health of its users.

[0818] "Relaxation techniques" refer to methods and techniques used to promote relaxation and stress reduction in users.

[0819] A "cognitive behavioral therapy approach" is a method for improving emotional states by changing the user's thoughts and behaviors.

[0820] "Exercises to boost self-esteem" refers to activities and practices designed to cultivate a user's self-esteem.

[0821] A "terminal" is a device used by users to access a system and to input and receive information.

[0822] "Feedback" refers to the results and impressions of a program that users provide to a system.

[0823] This invention is a system for improving the mental health of users. This system consists of the interaction of a server, a terminal, and a user.

[0824] The server is operated using various software. The emotion engine, a crucial element, typically utilizes natural language processing technology and is implemented in programming languages ​​such as Python or JavaScript. The database records user interaction history and usage data, and uses database management systems such as SQL.

[0825] Users access the system through their devices. These devices can be any internet-connected device, such as a smartphone, tablet, or personal computer. Users can directly input information into the system or select and run the mental training programs provided.

[0826] The server receives input data from the user and analyzes it using an emotion engine. Natural language processing identifies emotional components within the text in real time and assesses the user's mental health state. Based on this assessment, it proposes an optimal mental training program for the user.

[0827] For example, if a user enters "I've been having trouble sleeping lately due to work pressure," the server uses an emotion engine to analyze the user's level of anxiety and stress. Based on the results, the server suggests "nighttime anxiety-reducing exercises." The user can then select and try this program on their device.

[0828] This system collects user feedback and adjusts the server's algorithms to improve the accuracy of future program suggestions. This feedback loop allows the system to continuously optimize for each user, providing sustained mental health support.

[0829] An example of a prompt message might be: "Analyze the emotions entered by the user and suggest the optimal mental training program. This information includes input specifically related to stress and anxiety."

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] Users access the system using a terminal and input feedback related to mental health and daily events. The input data is sent to the server in text format. The input here is text information including the user's emotions, and the output is the initial data sent to the server.

[0833] Step 2:

[0834] The server records the received user input data in a database. During this recording process, the text data is organized and stored as a user-specific dialogue history. The data is classified and organized to make it available for future analysis.

[0835] Step 3:

[0836] The server activates the emotion engine and retrieves user input data from the database. It then performs emotion analysis on the retrieved data using natural language processing techniques. The input is user text data, and the output is the analyzed emotion data. Specifically, it performs keyword extraction and text classification to convert the user's emotional state into numerical data.

[0837] Step 4:

[0838] The server evaluates the user's mental health state based on the results of emotion analysis. The evaluation analyzes emotional tendencies and numerically indicates the degree of stress, anxiety, joy, etc. The input is the analyzed emotion data, and the output is the mental health state evaluation result. Based on this evaluation result, the user's specific mental state is understood.

[0839] Step 5:

[0840] The server selects the most suitable mental training program for the user based on the evaluation results. The program is determined after considering relaxation techniques and cognitive behavioral therapy, and then proposed to the user via the terminal. The input is the mental health evaluation results, and the output is the proposed program.

[0841] Step 6:

[0842] The terminal displays program suggestions from the server to the user. The user selects a program of interest from the suggested options on the terminal and executes it. The input is the suggestion data from the server, and the output is the user's selection.

[0843] Step 7:

[0844] Users input feedback via their device regarding the results and perceived effects of the program they have completed. This input consists of data about their program experience and results, and is sent to the server as output.

[0845] Step 8:

[0846] The server receives user feedback and records it in a database. This information is used to adjust the algorithm and improve future program suggestions. The input is feedback data, and the output is the updated algorithm. This allows the system to provide more appropriate suggestions to the user.

[0847] (Application Example 2)

[0848] 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".

[0849] In modern society, improving the mental health of individual users is a crucial issue. Traditional methods lack mechanisms to identify users' emotional states in real time and propose specific activities and environmental adjustments accordingly. This makes it difficult for users to receive the mental support they need most at that moment.

[0850] 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.

[0851] In this invention, the server includes means for collecting the user's dialogue history and usage status and analyzing the user's thought patterns; means for evaluating the user's mental health state based on the analysis results; means for selecting and proposing an appropriate mental training program to the user based on the evaluation results; means for collecting the user's feedback on the proposed program and adjusting the system's algorithm; and automated means for identifying the user's emotional state and providing appropriate environmental adjustments or activities. This makes it possible for the user to receive the optimal mental support they need at that moment.

[0852] "Users" refer to individuals or groups who use the system and are the ones whose mental health is supported.

[0853] "Dialogue history" refers to the record of conversations and messages exchanged between the user and the system.

[0854] "Usage status" refers to a record of how users accessed the system and which functions they used and to what extent.

[0855] "Thinking patterns" refer to the user's unique tendencies in thinking and emotional responses, and the data used to analyze their emotional state is based on these patterns.

[0856] "Means of analysis" refers to methods and devices used to analyze user input data and identify emotions and thought patterns.

[0857] "Mental health status" refers to the emotional and psychological state of the user, and involves assessing levels of stress, anxiety, and joy.

[0858] A "mental training program" refers to a series of activities and exercises provided to improve the mental health of users.

[0859] "Feedback" refers to the reactions and opinions that users provide regarding a proposed program, and is data used to improve the system.

[0860] "Adjusting the algorithm" refers to optimizing the system's processing methods based on collected feedback to improve the accuracy of future suggestions.

[0861] "Identifying an emotional state" refers to the process of appropriately identifying the emotions a user is experiencing and understanding temporary changes in those emotions.

[0862] "Automated means of providing environmental adjustments or activities" refers to mechanisms or methods for automatically suggesting or implementing effective environmental changes or activities for users in response to their identified emotional states.

[0863] The system for implementing the invention mainly consists of servers and terminals, which work together to support the mental health of users.

[0864] The server receives conversation history and usage data entered by the user through the terminal and analyzes the user's thought patterns based on this data. This analysis uses a natural language processing engine, and leverages Python natural language processing libraries (e.g., NLTK and spaCy) to identify the user's emotional state.

[0865] Based on the analysis results, the server conducts a detailed assessment of the user's mental health and selects the most appropriate mental training program at that time. This program includes relaxation techniques, cognitive behavioral therapy approaches, and exercises to enhance self-esteem.

[0866] The selected program is sent to the terminal and presented to the user. The user can choose and execute the program displayed on the screen. Furthermore, the terminal collects feedback on the program the user has executed and sends this information to the server. The server adjusts the system's algorithm based on the collected feedback and uses it to improve future program suggestions.

[0867] For example, if a user tells the terminal through dialogue that they are "stressed out from being overwhelmed with housework lately," the server will analyze that input and suggest a relaxation program to alleviate the stress.

[0868] An example of a prompt when using a generative AI model is, "How can we analyze a user's input that they are unable to sleep due to work pressure and suggest a relaxation program to alleviate their anxiety?" In this way, the system can provide support that is best suited to each user's individual situation.

[0869] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0870] Step 1:

[0871] Users input feedback about daily events and mental health through their devices. The input data is collected by the devices and sent to the server. In this step, the user's text data is used as input.

[0872] Step 2:

[0873] The server uses a natural language processing engine to identify emotional states in order to analyze the text data received from the user. This process utilizes Python natural language processing libraries (e.g., NLTK and spaCy). Based on the input text, the user's emotional tendencies (e.g., stress, anxiety, joy) are analyzed. Based on these results, the user's mental health is assessed.

[0874] Step 3:

[0875] The server selects the optimal mental training program from the database based on the analysis results. Depending on the assessed emotional state, programs such as relaxation techniques or cognitive behavioral therapy approaches are chosen. The input to this process is the result of the emotional analysis, and the output is the selected program.

[0876] Step 4:

[0877] The selected program is sent to the terminal and presented to the user. The terminal displays the program details to the user and provides options. The user can choose a program of interest and run it. In this step, the program information is the input, and the user's selection is the output.

[0878] Step 5:

[0879] After the user completes the program they selected, the terminal collects user feedback. The user inputs information about the program's effects and their impressions into the terminal, and this data is sent to the server as feedback. The input is feedback on the results of the program, and the output is the transmission of feedback data to the server.

[0880] Step 6:

[0881] The server receives feedback from the user and adjusts the system's algorithm. This adjustment makes the user's next program suggestion more accurate. In this step, the feedback data is the input and the adjusted algorithm is the output.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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."

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] The following is further disclosed regarding the embodiments described above.

[0904] (Claim 1)

[0905] A means of collecting user dialogue history and usage patterns, and analyzing the user's thought patterns,

[0906] A means of evaluating the mental health status of users based on the analysis results,

[0907] A means of selecting an appropriate mental training program based on the evaluation results and proposing it to the user,

[0908] A means of collecting user feedback on the proposed program and adjusting the system's algorithms,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, wherein natural language processing is used to analyze the aforementioned thought patterns.

[0912] (Claim 3)

[0913] The system according to claim 1, wherein the mental training program includes any of relaxation techniques, cognitive behavioral therapy approaches, or exercises to enhance self-esteem.

[0914] "Example 1"

[0915] (Claim 1)

[0916] A device that collects user dialogue information and usage information, and analyzes the user's thought patterns,

[0917] A device that evaluates the user's mental state based on the analyzed results,

[0918] A device that selects an appropriate psychological training plan based on the evaluated results and proposes it to the user,

[0919] A device for collecting user feedback on the proposed plan and adjusting the system's control methods,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, wherein natural language processing is used to analyze the aforementioned thought patterns.

[0923] (Claim 3)

[0924] The system according to claim 1, wherein the psychological training plan includes any of the following: tension reduction techniques, cognitive behavioral therapy approaches, or exercises to improve self-esteem.

[0925] "Application Example 1"

[0926] (Claim 1)

[0927] A means of collecting user dialogue history and usage information and analyzing the user's thought patterns,

[0928] A means of evaluating the mental health status of users based on the analysis results,

[0929] A means of selecting an appropriate mental training method based on the evaluation results and proposing it to the user,

[0930] A means of collecting user feedback on the proposed method and adjusting the system algorithm,

[0931] A means to enable users to provide emotional information about their daily lives to a robot terminal through voice interaction,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, wherein natural language processing is used to analyze the aforementioned thought patterns.

[0935] (Claim 3)

[0936] The system according to claim 1, wherein the mental training method includes any of the following: a method for relieving mental and physical tension, a psychotherapeutic approach, or an activity for improving self-esteem.

[0937] "Example 2 of combining an emotion engine"

[0938] (Claim 1)

[0939] A means of collecting user conversation history and usage patterns, and analyzing the diverse emotional patterns of those users,

[0940] A means of evaluating the mental health status of users in detail based on the analysis results,

[0941] A means of selecting the optimal mental training program based on the evaluation results and proposing it to the user via a terminal,

[0942] A means of collecting user feedback on the proposed program and dynamically adjusting the system's algorithm,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, wherein natural language processing is used to analyze the aforementioned emotional patterns to identify the user's emotions.

[0946] (Claim 3)

[0947] The system according to claim 1, wherein the mental training program includes any of the following: relaxation techniques, cognitive behavioral therapy approaches, exercises that promote psychological health, or other mental improvement techniques.

[0948] "Application example 2 when combining with an emotional engine"

[0949] (Claim 1)

[0950] A means of collecting user dialogue history and usage patterns, and analyzing the user's thought patterns,

[0951] A means of evaluating the mental health status of users based on the analysis results,

[0952] A means of selecting an appropriate mental training program based on the evaluation results and proposing it to the user,

[0953] A means of collecting user feedback on the proposed program and adjusting the system's algorithms,

[0954] An automated means for identifying the emotional state of a user and providing appropriate environmental adjustments or activities,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, wherein natural language processing is used to analyze the aforementioned thought patterns.

[0958] (Claim 3)

[0959] The system according to claim 1, wherein the mental training program includes any of the following: relaxation techniques, cognitive behavioral therapy approaches, exercises to enhance self-esteem, or the playback of encouraging music according to emotional state. [Explanation of symbols]

[0960] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting user dialogue history and usage patterns, and analyzing the user's thought patterns, A means of evaluating the mental health status of users based on the analysis results, A means of selecting an appropriate mental training program based on the evaluation results and proposing it to the user, A means of collecting user feedback on the proposed program and adjusting the system's algorithms, A system that includes this.

2. The system according to claim 1, wherein natural language processing is used to analyze the aforementioned thought patterns.

3. The system according to claim 1, wherein the mental training program includes any of relaxation techniques, cognitive behavioral therapy approaches, or exercises to enhance self-esteem.

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