system

Abstract

We provide the system. [Solution] A data management system for storing and analyzing data collected from users, A data analysis method for analyzing users' emotional states and behavioral patterns using natural language processing, A feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results, Information display means for displaying feedback on the user's device, Health management tools for evaluating the mental health status of users and providing appropriate support, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern working and living environments, it is essential for individuals to appropriately analyze their own actions and emotions in order to promote self-growth and maintain healthy mental health. However, conventional systems have problems in that they cannot sufficiently meet the diverse needs of users because of insufficient accuracy and applicability of data analysis when providing individualized feedback and action improvement plans. In addition, in one-on-one meetings and goal-setting sessions, there is insufficient support for realizing efficient communication, making it difficult to appropriately support the continuous self-improvement of users.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides the following means. First, it implements data management means and data analysis means that centrally manage data collected from users and analyze the users' emotional state and behavioral patterns in detail using advanced analysis methods such as natural language processing. This introduces a feedback generation means that efficiently generates personalized feedback and behavioral improvement plans for users. In addition, it provides meeting support means that provide progress and topics during meetings, effectively supporting one-on-one meetings for users. Furthermore, by incorporating health management means that quickly grasp changes in the user's mental health state and provide appropriate support, the present invention provides a system that comprehensively supports the user's self-growth and health maintenance.

[0006] A "data management system" is a system that centrally stores data collected from users and forms the basis for analysis work.

[0007] "Data analysis methods" refer to techniques that use advanced computational methods, such as natural language processing, to analyze users' emotional states and behavioral patterns.

[0008] A "feedback generation method" is a system that generates and provides personalized feedback and behavioral improvement plans to users based on analyzed data.

[0009] An "information display means" is a device that has the function of displaying generated feedback and plans on a terminal so that users can visually confirm them.

[0010] A "health management tool" is a system that evaluates the user's mental health status based on their emotional and behavioral data, and provides support and resources as needed.

[0011] A "meeting support tool" is a device that provides features such as progress updates and meeting topics to support users' one-on-one meetings.

[0012] "Learning support measures" refer to providing individualized learning plans and resources based on the user's goal setting, thereby supporting the user's growth. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system designed to support the self-improvement of users, and includes data management means, data analysis means, feedback generation means, information display means, health management means, meeting support means, and learning support means. These means work together to comprehensively realize the self-improvement of users.

[0035] First, the system's data management mechanism collects and securely stores users' daily activity data. This includes electronic messages from communication tools used by users, calendar events, and activity logs. The collected data is centrally managed by a server in a database.

[0036] Next, data analysis tools analyze this collected data. The server uses natural language processing algorithms to analyze the user's emotional state and behavioral patterns. This analysis reveals characteristics such as the user's stress level, communication style, and activity tendencies.

[0037] Based on the analysis results, the feedback generation system generates personalized feedback. The server creates customized advice and action improvement plans for each user and compiles them into a feedback report.

[0038] The information display mechanism is responsible for displaying the generated feedback on the terminal. This allows users to interactively review feedback and improvement plans. Where necessary, the feedback is provided in a visually appealing dashboard format, making it easy for users to understand intuitively.

[0039] The health management system complements the analysis system, monitoring the user's mental health status in real time. The server uses the generated data to detect potential mental health problems early and provides warnings or resources as needed.

[0040] The meeting support system provides assistance to facilitate the preparation and execution of one-on-one meetings. The terminal notifies the user of the progress and any new goals to be set before the meeting, and organizes the information necessary for follow-up during and after the meeting.

[0041] Finally, the learning support system provides learning resources and skill-building information based on the user's personal goals. Users can leverage this feature to continuously advance their personal growth.

[0042] For example, if a user wishes to learn a new skill, the system analyzes past behavioral data and current goal settings to recommend appropriate online courses and learning materials. This allows users to efficiently and systematically promote their own growth.

[0043] Thus, the system of the present invention provides personalized support for individual needs and functions as a powerful tool for users to improve and grow themselves.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users log into the system through their everyday communication and scheduling tools and consent to data collection.

[0047] Step 2:

[0048] The device collects activity data such as chat history, emails, and calendar events, based on the user's consent, encrypts it, and sends it to the server.

[0049] Step 3:

[0050] The server stores the received activity data in a database and performs data anonymization to protect privacy.

[0051] Step 4:

[0052] The server uses a natural language processing engine to analyze data and determine the user's emotional state, stress level, and behavioral patterns.

[0053] Step 5:

[0054] The server extracts key insights from the analysis results and generates a personalized feedback report. This report includes specific action plans and mental health advice.

[0055] Step 6:

[0056] The device displays the generated feedback report to the user in a visually easy-to-understand format.

[0057] Step 7:

[0058] The server runs support programs to manage progress and recommend learning resources based on the user's personal goals, and sends the results to the terminal.

[0059] Step 8:

[0060] Users can access feedback and learning resources through their devices, and revise their goals or create new action plans as needed.

[0061] Step 9:

[0062] The device notifies the user of the progress and agenda items to be discussed before a one-on-one meeting.

[0063] Step 10:

[0064] Based on the analyzed emotional and behavioral data, the server continuously assesses the user's mental health and provides care resources as needed.

[0065] (Example 1)

[0066] 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."

[0067] In modern society, there is a need for technologies that enable individual users to achieve personal growth and maintain mental health. However, many systems fail to adequately meet user needs by failing to provide personalized feedback and planning. Furthermore, the lack of real-time monitoring of mental health and individualized learning support hinders early problem detection and efficient skill development.

[0068] 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.

[0069] In this invention, the server includes information management means, data analysis means, and feedback generation means. This enables the secure management of diverse data, the generation of personalized feedback based on high-precision analysis, and real-time assessment of health status.

[0070] "Information management means" refers to a function that safely and efficiently stores and manages behavioral data obtained from users.

[0071] "Data analysis methods" refer to techniques that use natural language processing technology to model and analyze users' emotional states and behavioral patterns.

[0072] The "feedback generation method" is a function that utilizes generative AI technology to provide personalized advice and improvement plans based on analysis results.

[0073] A "user interface means" is a function that displays information in a visually appealing and interactive format, enabling users to operate it intuitively.

[0074] A "health assessment tool" is a technology that monitors a user's mental health status in real time and enables appropriate responses when an abnormality is detected.

[0075] "Meeting support tools" are support functions that facilitate information sharing among individuals and present progress information and goals before and after meetings.

[0076] "Educational support tools" are functions that present relevant learning resources based on the user's goal setting and promote skill improvement.

[0077] This invention is a system designed to support the self-growth of users, and it functions comprehensively through the coordinated action of various means. In this system, the server plays a major role in enabling interaction between the terminal and the user.

[0078] The server is equipped with information management tools to collect and manage user behavior data. Specifically, it stores diverse data such as electronic messages, calendar events, and behavioral history in a secure database via encrypted communication using the SSL / TLS protocol. Furthermore, it uses natural language processing technology as a data analysis tool to analyze users' emotional states and behavioral patterns. For example, it can extract emotions from text data and model activity trends from behavioral data to evaluate users' stress levels and communication styles.

[0079] Based on the analysis results, the server has a feedback generation mechanism that uses a generated AI model to produce advice and action improvement plans. The feedback generated by this mechanism is displayed on the terminal through a user interface mechanism. The terminal provides information visually in a dashboard format and implements interactive functions to allow the user to operate it intuitively.

[0080] Furthermore, the server monitors users' mental health status in real time using health assessment tools. If an anomaly is detected, it quickly generates an alert and provides appropriate resources. This feature enables users to maintain their mental health on a daily basis.

[0081] As a means of supporting meetings, the terminal assists in preparing for one-on-one meetings and provides relevant information. Before the meeting, it notifies users of the progress and presents key goals, helping them to conduct meetings in a suitable environment.

[0082] By utilizing learning support tools, users can receive learning resources based on their set goals. For example, if a user expresses a desire to "learn programming efficiently," the server will recommend relevant online courses.

[0083] An example of a prompt might be the instruction, "Analyze the user's emotional state and generate a plan to provide appropriate feedback based on their stress level." Based on this example, the server analyzes various data and provides customized information to the user.

[0084] Thus, the present invention is a system that provides flexible support tailored to the individual needs of each user, and is useful for supporting both personal growth and mental well-being.

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

[0086] Step 1:

[0087] The server collects data on the user's daily activities. Inputs include the user's electronic messages, calendar events, and activity history. The server encrypts this data using the SSL / TLS protocol and stores it in a secure database. The output is the securely stored database.

[0088] Step 2:

[0089] The server analyzes the collected data. Input consists of text data and behavioral history stored in a database. The server uses natural language processing techniques to extract emotional states and model user behavior patterns and stress levels. Output consists of analytical data on user emotional evaluations and behavioral tendencies.

[0090] Step 3:

[0091] The server generates feedback based on the analysis data. The input is the analysis data, which is the output of step 2. The server utilizes the generated AI model to create personalized advice and action improvement plans tailored to the user. The output is a detailed feedback report.

[0092] Step 4:

[0093] The terminal displays the generated feedback report to the user. The input is the feedback report from the server. The terminal presents the information in a visually appealing dashboard format, making it interactive. The output is the feedback and improvement plan displayed on the user's screen.

[0094] Step 5:

[0095] The server monitors the user's health status. The input is activity data collected in real time. The server uses health assessment tools to detect abnormal patterns and generate alerts early. The output includes notifications and suggestions for support resources when an anomaly is detected.

[0096] Step 6:

[0097] The terminal assists in preparing for one-on-one meetings. Inputs include the user's progress and meeting topic information. The terminal organizes this information, notifies the user, and presents necessary information during and after the meeting. Outputs are a set of information to help the user conduct the meeting smoothly.

[0098] Step 7:

[0099] Users receive personalized learning support. The input is the learning objectives set by the user. Based on this, the server suggests appropriate learning resources and online courses. The output is the educational materials and recommended courses provided to the user.

[0100] (Application Example 1)

[0101] 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."

[0102] In today's urban environment, it is crucial for individual citizens to understand their own behavior and health status and receive appropriate feedback in order to improve their quality of life. However, with much information currently provided individually, it is difficult for citizens to utilize this information efficiently, and the means to enhance their participation in social events and activities are limited. Therefore, there is a growing need for a system that provides comprehensive support tailored to the individual needs of users.

[0103] 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.

[0104] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing emotional states and behavioral patterns using natural language processing, and response generation means for generating personalized feedback and behavioral improvement plans based on the analysis results. This enables individual citizens to accurately obtain information that is useful for improving their lifestyles and health conditions, and is expected to improve the quality of life and revitalize social activities throughout the city.

[0105] "Information management means" refers to a device or system for securely and effectively storing information collected from users and organizing it in a format suitable for analysis.

[0106] "Information analysis means" refers to a technology or method that uses natural language processing technology to analyze collected information and identify the emotional state and behavioral patterns of users.

[0107] A "response generation means" is a function or process for creating optimized feedback and action improvement plans for users based on the results of information analysis.

[0108] "Information presentation means" refers to a function or device that provides generated feedback and other information in a visual or other way that is easily understandable to the user.

[0109] "Health monitoring measures" refer to technologies or systems for monitoring the health status of users, particularly their mental health, and providing necessary support.

[0110] "Life support measures" refer to a mechanism or system that analyzes citizens' activity data and provides recommendations to promote participation in social events and activities.

[0111] To implement this invention, a system is needed for efficient information management and analysis between the server and the user. The server stores various types of information collected from the user using information management means. Next, using information analysis means, the server analyzes the information using natural language processing algorithms to identify the user's emotional state and behavioral patterns. Based on these analysis results, the server uses response generation means to generate optimized feedback and behavioral improvement plans for the user. This feedback is displayed on the user's terminal in a visually easy-to-understand format using information presentation means.

[0112] The server also utilizes health monitoring tools to monitor users' health, particularly their mental health, and provides appropriate support as needed. Furthermore, by using life support tools to analyze citizens' activity data and recommending appropriate social events and activities, it is possible to improve the quality of life for citizens and promote their participation in social activities.

[0113] For example, the server can use data on a citizen's cycling and walking habits to suggest extending recommended cycling times to help improve their health. Using a generative AI model, prompts such as the following can be used: "Based on this data, what healthy lifestyle advice can you give citizen A?" or "Analyze recent behavioral data to recommend city events that citizen A should participate in."

[0114] In this way, this invention can provide personalized information and support specific methods for citizens to more effectively manage their own health and lives.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server receives daily activity data sent from the user's terminal and stores it in a database using information management tools. This input includes, for example, steps taken, cycling time, and calendar events, and is stored in the database in an organized format as output.

[0118] Step 2:

[0119] The server analyzes the stored data using information analysis tools and identifies the user's emotional state and behavioral patterns using a generative AI model. It processes the data received as input using a natural language processing algorithm and outputs analysis results regarding the user's stress level and activity tendencies.

[0120] Step 3:

[0121] The server generates personalized feedback based on the analysis results using a response generation mechanism. This feedback includes action improvement plans and specific suggestions for improving health status. The input for this step is the analysis results obtained in the previous step, and the output is the generated feedback data.

[0122] Step 4:

[0123] The server sends the generated feedback to the user's terminal via an information display mechanism and displays it in a visually easy-to-understand format. The input includes feedback data, and the output is a dashboard displayed on the user's terminal.

[0124] Step 5:

[0125] Users receive feedback and implement behavioral improvement plans by adjusting their daily activities. At this stage, they consider how to change their lifestyle based on the feedback provided and put those changes into action.

[0126] Step 6:

[0127] The server collects data on changes in user activity again and uses it to inform future feedback. This allows for continuous monitoring of user behavior and the provision of appropriate feedback.

[0128] 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.

[0129] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. In particular, by combining it with an emotion engine, it enables real-time emotion recognition and enhances the feedback and support based on that recognition.

[0130] First, the system collects data on the user's daily activities via the device. This includes chat history, emails, and schedule data. The device encrypts this data and sends it to the server. After the server stores it in a database, it anonymizes the data to protect privacy.

[0131] Next, the server uses natural language processing and an emotion engine to recognize emotions from the user's text data in real time. The emotion engine identifies the user's emotional state based on context and word choice. This information is then used for further data analysis.

[0132] The data analysis method uses collected data and analysis results from the emotion engine to clarify the user's emotional state and behavioral patterns. This allows for the evaluation of the user's stress level and communication tendencies.

[0133] The feedback generation system takes emotion recognition into account and generates personalized feedback tailored to the user. This includes behavioral improvement plans and mental health support. For example, if a user is stressed and tense at work, feedback suggesting relaxation techniques will be provided.

[0134] The information display mechanism shows feedback on the terminal, presenting it in a user-friendly format. This allows users to objectively understand their own situation and efficiently choose their next course of action.

[0135] Furthermore, the health management system utilizes data from the emotion engine to monitor the user's mental health status. If abnormal emotional tendencies are detected, it provides appropriate support resources and advice.

[0136] The meeting support tool provides users with a list of progress updates and topics to discuss, reflecting emotion recognition-based analysis results, before the meeting to support one-on-one meetings.

[0137] Thus, the present invention aims to improve the quality of life for users by providing advanced support tailored to their needs and comprehensively assisting them in their personal growth and health management.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users generate data on their daily activities by using their devices and routinely utilizing chat tools, email clients, and calendar applications.

[0141] Step 2:

[0142] The device collects generated data to the extent permitted, encrypts the necessary data, and sends it to the server while protecting privacy.

[0143] Step 3:

[0144] The server securely stores the received data in a database and protects personal information by anonymizing the data.

[0145] Step 4:

[0146] The server uses a natural language processing engine to analyze the user's emotional state from the collected text data. The emotion engine recognizes the user's emotions in real time through specific keywords and expressions in the text.

[0147] Step 5:

[0148] The server combines the emotional data obtained from the analysis with the user's behavioral patterns to assess the user's overall emotional tendencies and stress level.

[0149] Step 6:

[0150] The feedback generation system generates appropriate action plans for the user. Based on sentiment analysis, it creates suggestions for stress reduction and feedback aimed at reinforcing positive behaviors.

[0151] Step 7:

[0152] The device displays the generated action plan and feedback to the user. A visual dashboard is used to help users easily understand the information and choose their next course of action.

[0153] Step 8:

[0154] The meeting support system prepares the necessary materials for the one-on-one meeting. The server retrieves data from the previous meeting and the latest sentiment analysis results, and sends topic suggestions and progress summaries to the terminal.

[0155] Step 9:

[0156] Through health management tools, the server continuously monitors emotional data and notifies the user's device to provide mental health resources and support as needed.

[0157] Step 10:

[0158] Users utilize feedback and resources provided through their devices to manage their emotional state and implement behavioral improvements.

[0159] (Example 2)

[0160] 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".

[0161] In modern society, many users experience significant stress and mental burden in their daily lives and work, which often impacts their mental health. Traditional mental healthcare lacks personalized support based on the individual emotions and behavioral patterns of users. Furthermore, even in meetings and educational settings, there is a challenge in providing adequate support that takes into account the emotional state of users.

[0162] 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.

[0163] In this invention, the server includes information management means for collecting and storing diverse information from users, information analysis means for analyzing the user's emotional state in real time using natural language processing technology and an emotion recognition engine, and feedback creation means for generating a personalized behavioral improvement plan for the user based on the analysis results. This enables personalized mental health care based on the user's individual emotional state and behavioral patterns, as well as effective support in meetings and learning.

[0164] "Information management means" refers to technologies that have the function of securely collecting, organizing, and storing diverse information obtained from users.

[0165] "Security processing means" refers to technologies for encrypting user information and securely transmitting data.

[0166] "Information analysis means" refers to a technology that combines natural language processing technology and an emotion recognition engine to analyze a user's emotional state in real time.

[0167] A "feedback generation method" is a technology that generates personalized behavioral improvement plans and feedback based on analysis results obtained from information analysis methods.

[0168] "Information output means" refers to a technology that displays the generated feedback information on the user's terminal and provides it to the user.

[0169] "Health management measures" refer to technologies that continuously monitor the mental health status of users and provide appropriate resources when abnormal emotional tendencies are detected.

[0170] A "meeting support tool" is a technology that effectively supports meetings by providing information on the progress of the meeting and the issues to be discussed, based on the emotional state of the participants.

[0171] "Learning support tools" are technologies that create and provide individualized educational plans based on the user's set objectives.

[0172] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. The process is realized through the cooperation of three parties: a server, a terminal, and the user.

[0173] First, the device, acting as an application installed on a smartphone or computer, collects data on the user's daily activities. Specific examples of this data include chat history, emails, and schedule information. The device processes this data using AES encryption and securely transmits it to the server.

[0174] The server stores the received data in a database and anonymizes it to protect privacy. Then, it uses NLP libraries (e.g., NLTK and spaCy) as information analysis tools using natural language processing technology to analyze the user's emotions from the text data in real time. Furthermore, the emotion engine identifies the emotional state based on the context and word choice. Based on these analysis results, machine learning models (e.g., Scikit-learn, TENSORFLOW®) are used to reveal the user's emotional state and behavioral patterns in detail.

[0175] A generative AI model is used to generate feedback. The server generates user-optimized feedback based on the analyzed data. For example, a prompt might say, "Based on the information that the user is experiencing stress at work, please suggest an action plan for relaxation." This feedback is provided in the form of relaxation techniques and advice on improving lifestyle habits.

[0176] The device will display the aforementioned feedback directly to the user. The feedback will be expressed as a pop-up notification or in-app message, allowing the user to quickly understand and take action.

[0177] Furthermore, as a health management tool, the server continuously monitors emotional data and, if abnormal trends are detected, immediately issues a warning to the user and provides support resources appropriate to the situation.

[0178] This system can also be used in one-on-one meetings, where the server provides a list of progress updates and issues to be addressed in the meeting, based on sentiment data obtained before the meeting.

[0179] Based on the above, the system based on this invention makes it possible to comprehensively provide effective support for the user's self-growth and mental health care.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The device collects daily activity data through the user's smartphone or computer. Input data includes chat history, emails, and schedule information. This data is encrypted using the AES encryption algorithm, generating encrypted data as output. The encrypted data is then sent to the server via secure communication.

[0183] Step 2:

[0184] The server receives encrypted data sent from the terminal. The server then decrypts the data and converts it into a storable format. Using the received encrypted data as input, it generates processable text data as output. This text data is stored in a database and anonymized.

[0185] Step 3:

[0186] The server analyzes text data stored in the database using natural language processing techniques and an emotion recognition engine. This analysis utilizes NLP libraries (e.g., NLTK and spaCy). It takes processed text data as input and provides the user's emotional state (emotional categories such as positive, negative, and neutral) as output.

[0187] Step 4:

[0188] The server uses machine learning models (e.g., Scikit-learn or TensorFlow) to further analyze the user's emotional state and behavioral patterns based on the analysis results. Using emotional category data as input, it identifies and evaluates the user's specific emotional state and stress level as output, thereby clarifying behavioral patterns.

[0189] Step 5:

[0190] The server utilizes a generative AI model to generate feedback based on the analysis results. The prompt input is "Based on the information that the user is experiencing work-related stress, please suggest an action plan for relaxation," and the output provides personalized feedback and behavioral improvement plans.

[0191] Step 6:

[0192] The device receives feedback from the server and displays it on the user interface. It receives feedback data from the server as input and presents it as an intuitively understandable pop-up or in-app notification as output.

[0193] Step 7:

[0194] The server periodically monitors emotional data and assesses the user's mental health status. Emotional categories and analytical data are used as input, and if abnormal emotional tendencies or signs of distress are detected as output, the server provides the user with warnings and support resources.

[0195] (Application Example 2)

[0196] 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".

[0197] In today's world, effectively supporting users' mental health is a crucial challenge. In particular, there is a need to provide real-time, personalized feedback and appropriate relaxation methods to people who are prone to stress in their busy lives. Furthermore, a system is needed that can quickly and accurately grasp emotional states and provide concrete action plans based on those assessments, enabling users to efficiently pursue self-growth and manage their health.

[0198] 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.

[0199] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing users' emotional states and behavioral patterns using natural language processing, and feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results. This enables users to understand their emotional state and make appropriate behavioral improvements.

[0200] "Information management means" refers to functions for securely storing information collected from users and maintaining it in a state where it can be analyzed.

[0201] "Information analysis tools" are functions that analyze users' emotional states and behavioral patterns through natural language processing to gain detailed insights.

[0202] A "feedback generation means" is a function that creates personalized feedback and behavioral improvement plans for users based on the results obtained from information analysis means.

[0203] A "health monitoring tool" is a function that evaluates the mental health status of users and provides appropriate support.

[0204] A "stress management tool" is a function that detects signs of stress from the user's behavioral data and suggests appropriate relaxation methods.

[0205] "Encryption means" refers to the function of encrypting data in order to securely collect and transmit information.

[0206] A "visual presentation method" is a function that visually presents appropriate action suggestions to the user based on the analysis results.

[0207] "Meeting support features" are functions that provide progress updates and meeting topics to support users' one-on-one meetings.

[0208] "Learning support tools" are functions that provide individualized learning plans according to the goals set by the user.

[0209] The system for implementing this invention consists of a server and a terminal working in cooperation. First, the system collects information from the user via the terminal. This information includes daily messages, schedules, call logs, etc. The terminal encrypts this information and sends it to the server using a secure communication protocol (e.g., HTTPS). The server stores this information using information management means and performs natural language processing using information analysis means. Specifically, it implements a model for analyzing emotional states using TensorFlow.

[0210] Based on the analyzed data, the feedback generation system generates personalized feedback for the user. This includes suggestions for relaxation methods through stress management mechanisms. The generated feedback is presented on the device's display via visual presentation mechanisms. A user-friendly and visually appealing interface is built using React Native.

[0211] For example, if the server detects that a user is experiencing stress due to their busy daily schedule, it might send feedback to the device such as, "We recommend consciously taking deep breaths." Also, if a user is preparing for a one-on-one meeting, the server might use meeting support tools to display the topics to be covered on the device.

[0212] A possible example of a prompt message is: "Analyze the user's emotional state and suggest an appropriate relaxation method. The data is as follows." The generative AI model provides feedback based on this prompt message.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The device collects data from the user, such as messages, schedule information, and call logs. The input is raw data. Following data collection, the device encrypts the data using the AES encryption algorithm. This process protects the privacy of user data and enables secure transmission. The output is encrypted data.

[0216] Step 2:

[0217] The terminal sends encrypted data to the server using the HTTPS protocol. The input is encrypted data. By using the communication protocol, the data is protected from unauthorized access. The server receives the received data and stores it in a database using information management means. The output is the data stored in the database.

[0218] Step 3:

[0219] The server analyzes the received data using information analysis tools. The input is raw data stored in a database. In this step, a natural language processing model using TensorFlow processes the data and extracts emotional features. The output is a set of features indicating emotional states.

[0220] Step 4:

[0221] The server generates appropriate feedback using a feedback generation mechanism based on the features. The input is the emotional state features. A generative AI model is used to generate relaxation and behavioral improvement guidelines appropriate to the user's emotional state. The output is the generated feedback message.

[0222] Step 5:

[0223] The server sends the generated feedback to the terminal via a visual display device. The input is the generated feedback message. Specific actions are taken using React Native to display the feedback in the user interface. The output is the feedback displayed on the terminal.

[0224] Step 6:

[0225] The user reviews the feedback displayed on the device and takes action as needed. The input is the feedback displayed on the device. The user intuitively understands the feedback and selects relaxation techniques or corrective actions. The output is the change in the user's behavior.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is a system designed to support the self-improvement of users, and includes data management means, data analysis means, feedback generation means, information display means, health management means, meeting support means, and learning support means. These means work together to comprehensively realize the self-improvement of users.

[0243] First, the system's data management mechanism collects and securely stores users' daily activity data. This includes electronic messages from communication tools used by users, calendar events, and activity logs. The collected data is centrally managed by a server in a database.

[0244] Next, data analysis tools analyze this collected data. The server uses natural language processing algorithms to analyze the user's emotional state and behavioral patterns. This analysis reveals characteristics such as the user's stress level, communication style, and activity tendencies.

[0245] Based on the analysis results, the feedback generation system generates personalized feedback. The server creates customized advice and action improvement plans for each user and compiles them into a feedback report.

[0246] The information display mechanism is responsible for displaying the generated feedback on the terminal. This allows users to interactively review feedback and improvement plans. Where necessary, the feedback is provided in a visually appealing dashboard format, making it easy for users to understand intuitively.

[0247] The health management system complements the analysis system, monitoring the user's mental health status in real time. The server uses the generated data to detect potential mental health problems early and provides warnings or resources as needed.

[0248] The meeting support system provides assistance to facilitate the preparation and execution of one-on-one meetings. The terminal notifies the user of the progress and any new goals to be set before the meeting, and organizes the information necessary for follow-up during and after the meeting.

[0249] Finally, the learning support system provides learning resources and skill-building information based on the user's personal goals. Users can leverage this feature to continuously advance their personal growth.

[0250] For example, if a user wishes to learn a new skill, the system analyzes past behavioral data and current goal settings to recommend appropriate online courses and learning materials. This allows users to efficiently and systematically promote their own growth.

[0251] Thus, the system of the present invention provides personalized support for individual needs and functions as a powerful tool for users to improve and grow themselves.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] Users log into the system through their everyday communication and scheduling tools and consent to data collection.

[0255] Step 2:

[0256] The device collects activity data such as chat history, emails, and calendar events, based on the user's consent, encrypts it, and sends it to the server.

[0257] Step 3:

[0258] The server stores the received activity data in a database and performs data anonymization to protect privacy.

[0259] Step 4:

[0260] The server uses a natural language processing engine to analyze data and determine the user's emotional state, stress level, and behavioral patterns.

[0261] Step 5:

[0262] The server extracts key insights from the analysis results and generates a personalized feedback report. This report includes specific action plans and mental health advice.

[0263] Step 6:

[0264] The device displays the generated feedback report to the user in a visually easy-to-understand format.

[0265] Step 7:

[0266] The server runs support programs to manage progress and recommend learning resources based on the user's personal goals, and sends the results to the terminal.

[0267] Step 8:

[0268] Users can access feedback and learning resources through their devices, and revise their goals or create new action plans as needed.

[0269] Step 9:

[0270] The device notifies the user of the progress and agenda items to be discussed before a one-on-one meeting.

[0271] Step 10:

[0272] Based on the analyzed emotional and behavioral data, the server continuously assesses the user's mental health and provides care resources as needed.

[0273] (Example 1)

[0274] 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."

[0275] In modern society, there is a need for technologies that enable individual users to achieve personal growth and maintain mental health. However, many systems fail to adequately meet user needs by failing to provide personalized feedback and planning. Furthermore, the lack of real-time monitoring of mental health and individualized learning support hinders early problem detection and efficient skill development.

[0276] 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.

[0277] In this invention, the server includes information management means, data analysis means, and feedback generation means. This enables the secure management of diverse data, the generation of personalized feedback based on high-precision analysis, and real-time assessment of health status.

[0278] "Information management means" refers to a function that safely and efficiently stores and manages behavioral data obtained from users.

[0279] "Data analysis methods" refer to techniques that use natural language processing technology to model and analyze users' emotional states and behavioral patterns.

[0280] The "feedback generation method" is a function that utilizes generative AI technology to provide personalized advice and improvement plans based on analysis results.

[0281] A "user interface means" is a function that displays information in a visually appealing and interactive format, enabling users to operate it intuitively.

[0282] A "health assessment tool" is a technology that monitors a user's mental health status in real time and enables appropriate responses when an abnormality is detected.

[0283] The "meeting support means" is a support function that smooths information sharing among individuals and presents progress information and goals before and after meetings.

[0284] The "education support means" is a function that presents relevant learning resources based on the user's goal setting and promotes skill improvement.

[0285] The present invention is a system designed to support the self-growth of users, and various means cooperate to function comprehensively. In this system, the server plays a major role and enables interaction between the terminal and the user.

[0286] The server is equipped with information management means for collecting and managing the user's behavior data. Specifically, it stores various data such as electronic messages, calendar events, and behavior history in a secure database through encrypted communication via the SSL / TLS protocol. Also, by using natural language processing technology as data analysis means, it analyzes the user's emotional state and behavior pattern. For example, it extracts emotions from text data and models activity trends from behavior data to evaluate the user's stress level and communication style.

[0287] Based on the analysis results, the server has feedback creation means for generating advice and action improvement plans using a generative AI model. The feedback created by this means is displayed on the terminal through the user interface means. The terminal implements an interactive function that provides information visually in a dashboard format and allows the user to operate intuitively.

[0288] Furthermore, the server monitors the user's mental health status in real time by means of health evaluation. When an abnormality is detected, it quickly generates an alert and provides appropriate resources. With this function, the user can maintain mental health on a daily basis.

[0289] As a means of supporting meetings, the terminal assists in preparing for one-on-one meetings and provides relevant information. Before the meeting, it notifies users of the progress and presents key goals, helping them to conduct meetings in a suitable environment.

[0290] By utilizing learning support tools, users can receive learning resources based on their set goals. For example, if a user expresses a desire to "learn programming efficiently," the server will recommend relevant online courses.

[0291] An example of a prompt might be the instruction, "Analyze the user's emotional state and generate a plan to provide appropriate feedback based on their stress level." Based on this example, the server analyzes various data and provides customized information to the user.

[0292] Thus, the present invention is a system that provides flexible support tailored to the individual needs of each user, and is useful for supporting both personal growth and mental well-being.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The server collects data on the user's daily activities. Inputs include the user's electronic messages, calendar events, and activity history. The server encrypts this data using the SSL / TLS protocol and stores it in a secure database. The output is the securely stored database.

[0296] Step 2:

[0297] The server analyzes the collected data. Input consists of text data and behavioral history stored in a database. The server uses natural language processing techniques to extract emotional states and model user behavior patterns and stress levels. Output consists of analytical data on user emotional evaluations and behavioral tendencies.

[0298] Step 3:

[0299] The server generates feedback based on the analysis data. The input is the analysis data, which is the output of step 2. The server utilizes the generated AI model to create personalized advice and action improvement plans tailored to the user. The output is a detailed feedback report.

[0300] Step 4:

[0301] The terminal displays the generated feedback report to the user. The input is the feedback report from the server. The terminal presents the information in a visually appealing dashboard format, making it interactive. The output is the feedback and improvement plan displayed on the user's screen.

[0302] Step 5:

[0303] The server monitors the user's health status. The input is activity data collected in real time. The server uses health assessment tools to detect abnormal patterns and generate alerts early. The output includes notifications and suggestions for support resources when an anomaly is detected.

[0304] Step 6:

[0305] The terminal assists in preparing for one-on-one meetings. Inputs include the user's progress and meeting topic information. The terminal organizes this information, notifies the user, and presents necessary information during and after the meeting. Outputs are a set of information to help the user conduct the meeting smoothly.

[0306] Step 7:

[0307] The user receives individualized learning support. The input is the learning goal set by the user. Based on this, the server proposes appropriate learning resources and online courses. The output is the educational materials and recommended courses provided to the user.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In the modern urban environment, for individual citizens to improve the quality of life, it is important to grasp their own behaviors and health conditions and obtain appropriate feedback. However, in the current situation where a lot of information is provided individually, it is difficult for citizens to efficiently utilize this information, and the means to strengthen participation in social events and activities are also limited. Therefore, there is an increasing need for a system that provides comprehensive support according to the individual needs of users.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0312] In this invention, the server includes information management means for storing and analyzing the information collected from users, information analysis means for analyzing emotional states and behavior patterns using natural language processing, and response generation means for generating individualized feedback and behavior improvement plans based on the analysis results. As a result, individual citizens can accurately obtain information useful for improving their own lifestyles and health conditions, and it is expected that the quality of life of the entire city will be improved and social activities will be activated.

[0313] The "information management means" is a device or system for safely and effectively storing the information collected from users and organizing it in a form suitable for analysis.

[0314] "Information analysis means" refers to a technology or method that uses natural language processing technology to analyze collected information and identify the emotional state and behavioral patterns of users.

[0315] A "response generation means" is a function or process for creating optimized feedback and action improvement plans for users based on the results of information analysis.

[0316] "Information presentation means" refers to a function or device that provides generated feedback and other information in a visual or other way that is easily understandable to the user.

[0317] "Health monitoring measures" refer to technologies or systems for monitoring the health status of users, particularly their mental health, and providing necessary support.

[0318] "Life support measures" refer to a mechanism or system that analyzes citizens' activity data and provides recommendations to promote participation in social events and activities.

[0319] To implement this invention, a system is needed for efficient information management and analysis between the server and the user. The server stores various types of information collected from the user using information management means. Next, using information analysis means, the server analyzes the information using natural language processing algorithms to identify the user's emotional state and behavioral patterns. Based on these analysis results, the server uses response generation means to generate optimized feedback and behavioral improvement plans for the user. This feedback is displayed on the user's terminal in a visually easy-to-understand format using information presentation means.

[0320] The server also utilizes health monitoring tools to monitor users' health, particularly their mental health, and provides appropriate support as needed. Furthermore, by using life support tools to analyze citizens' activity data and recommending appropriate social events and activities, it is possible to improve the quality of life for citizens and promote their participation in social activities.

[0321] For example, the server can use data on a citizen's cycling and walking habits to suggest extending recommended cycling times to help improve their health. Using a generative AI model, prompts such as the following can be used: "Based on this data, what healthy lifestyle advice can you give citizen A?" or "Analyze recent behavioral data to recommend city events that citizen A should participate in."

[0322] In this way, this invention can provide personalized information and support specific methods for citizens to more effectively manage their own health and lives.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server receives daily activity data sent from the user's terminal and stores it in a database using information management tools. This input includes, for example, steps taken, cycling time, and calendar events, and is stored in the database in an organized format as output.

[0326] Step 2:

[0327] The server analyzes the stored data using information analysis tools and identifies the user's emotional state and behavioral patterns using a generative AI model. It processes the data received as input using a natural language processing algorithm and outputs analysis results regarding the user's stress level and activity tendencies.

[0328] Step 3:

[0329] The server generates personalized feedback based on the analysis results using a response generation mechanism. This feedback includes action improvement plans and specific suggestions for improving health status. The input for this step is the analysis results obtained in the previous step, and the output is the generated feedback data.

[0330] Step 4:

[0331] The server sends the generated feedback to the user's terminal via an information display mechanism and displays it in a visually easy-to-understand format. The input includes feedback data, and the output is a dashboard displayed on the user's terminal.

[0332] Step 5:

[0333] Users receive feedback and implement behavioral improvement plans by adjusting their daily activities. At this stage, they consider how to change their lifestyle based on the feedback provided and put those changes into action.

[0334] Step 6:

[0335] The server collects data on changes in user activity again and uses it to inform future feedback. This allows for continuous monitoring of user behavior and the provision of appropriate feedback.

[0336] 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.

[0337] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. In particular, by combining it with an emotion engine, it enables real-time emotion recognition and enhances the feedback and support based on that recognition.

[0338] First, the system collects data on the user's daily activities via the device. This includes chat history, emails, and schedule data. The device encrypts this data and sends it to the server. After the server stores it in a database, it anonymizes the data to protect privacy.

[0339] Next, the server uses natural language processing and an emotion engine to recognize emotions from the user's text data in real time. The emotion engine identifies the user's emotional state based on context and word choice. This information is then used for further data analysis.

[0340] The data analysis method uses collected data and analysis results from the emotion engine to clarify the user's emotional state and behavioral patterns. This allows for the evaluation of the user's stress level and communication tendencies.

[0341] The feedback generation system takes emotion recognition into account and generates personalized feedback tailored to the user. This includes behavioral improvement plans and mental health support. For example, if a user is stressed and tense at work, feedback suggesting relaxation techniques will be provided.

[0342] The information display mechanism shows feedback on the terminal, presenting it in a user-friendly format. This allows users to objectively understand their own situation and efficiently choose their next course of action.

[0343] Furthermore, the health management system utilizes data from the emotion engine to monitor the user's mental health status. If abnormal emotional tendencies are detected, it provides appropriate support resources and advice.

[0344] The meeting support tool provides users with a list of progress updates and topics to discuss, reflecting emotion recognition-based analysis results, before the meeting to support one-on-one meetings.

[0345] Thus, the present invention aims to improve the quality of life for users by providing advanced support tailored to their needs and comprehensively assisting them in their personal growth and health management.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] Users generate data on their daily activities by using their devices and routinely utilizing chat tools, email clients, and calendar applications.

[0349] Step 2:

[0350] The device collects generated data to the extent permitted, encrypts the necessary data, and sends it to the server while protecting privacy.

[0351] Step 3:

[0352] The server securely stores the received data in a database and protects personal information by anonymizing the data.

[0353] Step 4:

[0354] The server uses a natural language processing engine to analyze the user's emotional state from the collected text data. The emotion engine recognizes the user's emotions in real time through specific keywords and expressions in the text.

[0355] Step 5:

[0356] The server combines the emotional data obtained from the analysis with the user's behavioral patterns to assess the user's overall emotional tendencies and stress level.

[0357] Step 6:

[0358] The feedback generation system generates appropriate action plans for the user. Based on sentiment analysis, it creates suggestions for stress reduction and feedback aimed at reinforcing positive behaviors.

[0359] Step 7:

[0360] The device displays the generated action plan and feedback to the user. A visual dashboard is used to help users easily understand the information and choose their next course of action.

[0361] Step 8:

[0362] The meeting support system prepares the necessary materials for the one-on-one meeting. The server retrieves data from the previous meeting and the latest sentiment analysis results, and sends topic suggestions and progress summaries to the terminal.

[0363] Step 9:

[0364] Through health management tools, the server continuously monitors emotional data and notifies the user's device to provide mental health resources and support as needed.

[0365] Step 10:

[0366] Users utilize feedback and resources provided through their devices to manage their emotional state and implement behavioral improvements.

[0367] (Example 2)

[0368] 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".

[0369] In modern society, many users experience significant stress and mental burden in their daily lives and work, which often impacts their mental health. Traditional mental healthcare lacks personalized support based on the individual emotions and behavioral patterns of users. Furthermore, even in meetings and educational settings, there is a challenge in providing adequate support that takes into account the emotional state of users.

[0370] 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.

[0371] In this invention, the server includes information management means for collecting and storing diverse information from users, information analysis means for analyzing the user's emotional state in real time using natural language processing technology and an emotion recognition engine, and feedback creation means for generating a personalized behavioral improvement plan for the user based on the analysis results. This enables personalized mental health care based on the user's individual emotional state and behavioral patterns, as well as effective support in meetings and learning.

[0372] "Information management means" refers to technologies that have the function of securely collecting, organizing, and storing diverse information obtained from users.

[0373] "Security processing means" refers to technologies for encrypting user information and securely transmitting data.

[0374] "Information analysis means" refers to a technology that combines natural language processing technology and an emotion recognition engine to analyze a user's emotional state in real time.

[0375] A "feedback generation method" is a technology that generates personalized behavioral improvement plans and feedback based on analysis results obtained from information analysis methods.

[0376] "Information output means" refers to a technology that displays the generated feedback information on the user's terminal and provides it to the user.

[0377] "Health management measures" refer to technologies that continuously monitor the mental health status of users and provide appropriate resources when abnormal emotional tendencies are detected.

[0378] A "meeting support tool" is a technology that effectively supports meetings by providing information on the progress of the meeting and the issues to be discussed, based on the emotional state of the participants.

[0379] "Learning support tools" are technologies that create and provide individualized educational plans based on the user's set objectives.

[0380] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. The process is realized through the cooperation of three parties: a server, a terminal, and the user.

[0381] First, the device, acting as an application installed on a smartphone or computer, collects data on the user's daily activities. Specific examples of this data include chat history, emails, and schedule information. The device processes this data using AES encryption and securely transmits it to the server.

[0382] The server stores the received data in a database and anonymizes it to protect privacy. Then, it uses NLP libraries (e.g., NLTK and spaCy) as information analysis tools using natural language processing techniques to analyze the user's emotions from the text data in real time. Furthermore, the emotion engine identifies the emotional state based on the context and word choice. Based on these analysis results, machine learning models (e.g., Scikit-learn, TensorFlow) are used to reveal the user's emotional state and behavioral patterns in detail.

[0383] A generative AI model is used to generate feedback. The server generates user-optimized feedback based on the analyzed data. For example, a prompt might say, "Based on the information that the user is experiencing stress at work, please suggest an action plan for relaxation." This feedback is provided in the form of relaxation techniques and advice on improving lifestyle habits.

[0384] The device will display the aforementioned feedback directly to the user. The feedback will be expressed as a pop-up notification or in-app message, allowing the user to quickly understand and take action.

[0385] Furthermore, as a health management tool, the server continuously monitors emotional data and, if abnormal trends are detected, immediately issues a warning to the user and provides support resources appropriate to the situation.

[0386] This system can also be used in one-on-one meetings, where the server provides a list of progress updates and issues to be addressed in the meeting, based on sentiment data obtained before the meeting.

[0387] Based on the above, the system based on this invention makes it possible to comprehensively provide effective support for the user's self-growth and mental health care.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The device collects daily activity data through the user's smartphone or computer. Input data includes chat history, emails, and schedule information. This data is encrypted using the AES encryption algorithm, generating encrypted data as output. The encrypted data is then sent to the server via secure communication.

[0391] Step 2:

[0392] The server receives encrypted data sent from the terminal. The server then decrypts the data and converts it into a storable format. Using the received encrypted data as input, it generates processable text data as output. This text data is stored in a database and anonymized.

[0393] Step 3:

[0394] The server analyzes text data stored in the database using natural language processing techniques and an emotion recognition engine. This analysis utilizes NLP libraries (e.g., NLTK and spaCy). It takes processed text data as input and provides the user's emotional state (emotional categories such as positive, negative, and neutral) as output.

[0395] Step 4:

[0396] The server uses machine learning models (e.g., Scikit-learn or TensorFlow) to further analyze the user's emotional state and behavioral patterns based on the analysis results. Using emotional category data as input, it identifies and evaluates the user's specific emotional state and stress level as output, thereby clarifying behavioral patterns.

[0397] Step 5:

[0398] The server utilizes a generative AI model to generate feedback based on the analysis results. The prompt input is "Based on the information that the user is experiencing work-related stress, please suggest an action plan for relaxation," and the output provides personalized feedback and behavioral improvement plans.

[0399] Step 6:

[0400] The device receives feedback from the server and displays it on the user interface. It receives feedback data from the server as input and presents it as an intuitively understandable pop-up or in-app notification as output.

[0401] Step 7:

[0402] The server periodically monitors emotional data and assesses the user's mental health status. Emotional categories and analytical data are used as input, and if abnormal emotional tendencies or signs of distress are detected as output, the server provides the user with warnings and support resources.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0405] In today's world, effectively supporting users' mental health is a crucial challenge. In particular, there is a need to provide real-time, personalized feedback and appropriate relaxation methods to people who are prone to stress in their busy lives. Furthermore, a system is needed that can quickly and accurately grasp emotional states and provide concrete action plans based on those assessments, enabling users to efficiently pursue self-growth and manage their health.

[0406] 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.

[0407] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing users' emotional states and behavioral patterns using natural language processing, and feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results. This enables users to understand their emotional state and make appropriate behavioral improvements.

[0408] "Information management means" refers to functions for securely storing information collected from users and maintaining it in a state where it can be analyzed.

[0409] "Information analysis tools" are functions that analyze users' emotional states and behavioral patterns through natural language processing to gain detailed insights.

[0410] A "feedback generation means" is a function that creates personalized feedback and behavioral improvement plans for users based on the results obtained from information analysis means.

[0411] A "health monitoring tool" is a function that evaluates the mental health status of users and provides appropriate support.

[0412] A "stress management tool" is a function that detects signs of stress from the user's behavioral data and suggests appropriate relaxation methods.

[0413] "Encryption means" refers to the function of encrypting data in order to securely collect and transmit information.

[0414] A "visual presentation method" is a function that visually presents appropriate action suggestions to the user based on the analysis results.

[0415] "Meeting support features" are functions that provide progress updates and meeting topics to support users' one-on-one meetings.

[0416] "Learning support tools" are functions that provide individualized learning plans according to the goals set by the user.

[0417] The system for implementing this invention consists of a server and a terminal working in cooperation. First, the system collects information from the user via the terminal. This information includes daily messages, schedules, call logs, etc. The terminal encrypts this information and sends it to the server using a secure communication protocol (e.g., HTTPS). The server stores this information using information management means and performs natural language processing using information analysis means. Specifically, it implements a model for analyzing emotional states using TensorFlow.

[0418] Based on the analyzed data, the feedback generation system generates personalized feedback for the user. This includes suggestions for relaxation methods through stress management mechanisms. The generated feedback is presented on the device's display via visual presentation mechanisms. A user-friendly and visually appealing interface is built using React Native.

[0419] For example, if the server detects that a user is experiencing stress due to their busy daily schedule, it might send feedback to the device such as, "We recommend consciously taking deep breaths." Also, if a user is preparing for a one-on-one meeting, the server might use meeting support tools to display the topics to be covered on the device.

[0420] A possible example of a prompt message is: "Analyze the user's emotional state and suggest an appropriate relaxation method. The data is as follows." The generative AI model provides feedback based on this prompt message.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The device collects data from the user, such as messages, schedule information, and call logs. The input is raw data. Following data collection, the device encrypts the data using the AES encryption algorithm. This process protects the privacy of user data and enables secure transmission. The output is encrypted data.

[0424] Step 2:

[0425] The terminal sends encrypted data to the server using the HTTPS protocol. The input is encrypted data. By using the communication protocol, the data is protected from unauthorized access. The server receives the received data and stores it in a database using information management means. The output is the data stored in the database.

[0426] Step 3:

[0427] The server analyzes the received data using information analysis tools. The input is raw data stored in a database. In this step, a natural language processing model using TensorFlow processes the data and extracts emotional features. The output is a set of features indicating emotional states.

[0428] Step 4:

[0429] The server generates appropriate feedback using a feedback generation mechanism based on the features. The input is the emotional state features. A generative AI model is used to generate relaxation and behavioral improvement guidelines appropriate to the user's emotional state. The output is the generated feedback message.

[0430] Step 5:

[0431] The server sends the generated feedback to the terminal via a visual display device. The input is the generated feedback message. Specific actions are taken using React Native to display the feedback in the user interface. The output is the feedback displayed on the terminal.

[0432] Step 6:

[0433] The user reviews the feedback displayed on the device and takes action as needed. The input is the feedback displayed on the device. The user intuitively understands the feedback and selects relaxation techniques or corrective actions. The output is the change in the user's behavior.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] This invention is a system designed to support the self-improvement of users, and includes data management means, data analysis means, feedback generation means, information display means, health management means, meeting support means, and learning support means. These means work together to comprehensively realize the self-improvement of users.

[0451] First, the system's data management mechanism collects and securely stores users' daily activity data. This includes electronic messages from communication tools used by users, calendar events, and activity logs. The collected data is centrally managed by a server in a database.

[0452] Next, data analysis tools analyze this collected data. The server uses natural language processing algorithms to analyze the user's emotional state and behavioral patterns. This analysis reveals characteristics such as the user's stress level, communication style, and activity tendencies.

[0453] Based on the analysis results, the feedback generation system generates personalized feedback. The server creates customized advice and action improvement plans for each user and compiles them into a feedback report.

[0454] The information display mechanism is responsible for displaying the generated feedback on the terminal. This allows users to interactively review feedback and improvement plans. Where necessary, the feedback is provided in a visually appealing dashboard format, making it easy for users to understand intuitively.

[0455] The health management system complements the analysis system, monitoring the user's mental health status in real time. The server uses the generated data to detect potential mental health problems early and provides warnings or resources as needed.

[0456] The meeting support system provides assistance to facilitate the preparation and execution of one-on-one meetings. The terminal notifies the user of the progress and any new goals to be set before the meeting, and organizes the information necessary for follow-up during and after the meeting.

[0457] Finally, the learning support system provides learning resources and skill-building information based on the user's personal goals. Users can leverage this feature to continuously advance their personal growth.

[0458] For example, if a user wishes to learn a new skill, the system analyzes past behavioral data and current goal settings to recommend appropriate online courses and learning materials. This allows users to efficiently and systematically promote their own growth.

[0459] Thus, the system of the present invention provides personalized support for individual needs and functions as a powerful tool for users to improve and grow themselves.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] Users log into the system through their everyday communication and scheduling tools and consent to data collection.

[0463] Step 2:

[0464] The device collects activity data such as chat history, emails, and calendar events, based on the user's consent, encrypts it, and sends it to the server.

[0465] Step 3:

[0466] The server stores the received activity data in a database and performs data anonymization to protect privacy.

[0467] Step 4:

[0468] The server uses a natural language processing engine to analyze data and determine the user's emotional state, stress level, and behavioral patterns.

[0469] Step 5:

[0470] The server extracts key insights from the analysis results and generates a personalized feedback report. This report includes specific action plans and mental health advice.

[0471] Step 6:

[0472] The device displays the generated feedback report to the user in a visually easy-to-understand format.

[0473] Step 7:

[0474] The server runs support programs to manage progress and recommend learning resources based on the user's personal goals, and sends the results to the terminal.

[0475] Step 8:

[0476] Users can access feedback and learning resources through their devices, and revise their goals or create new action plans as needed.

[0477] Step 9:

[0478] The device notifies the user of the progress and agenda items to be discussed before a one-on-one meeting.

[0479] Step 10:

[0480] Based on the analyzed emotional and behavioral data, the server continuously assesses the user's mental health and provides care resources as needed.

[0481] (Example 1)

[0482] 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."

[0483] In modern society, there is a need for technologies that enable individual users to achieve personal growth and maintain mental health. However, many systems fail to adequately meet user needs by failing to provide personalized feedback and planning. Furthermore, the lack of real-time monitoring of mental health and individualized learning support hinders early problem detection and efficient skill development.

[0484] 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.

[0485] In this invention, the server includes information management means, data analysis means, and feedback generation means. This enables the secure management of diverse data, the generation of personalized feedback based on high-precision analysis, and real-time assessment of health status.

[0486] "Information management means" refers to a function that safely and efficiently stores and manages behavioral data obtained from users.

[0487] "Data analysis methods" refer to techniques that use natural language processing technology to model and analyze users' emotional states and behavioral patterns.

[0488] The "feedback generation method" is a function that utilizes generative AI technology to provide personalized advice and improvement plans based on analysis results.

[0489] A "user interface means" is a function that displays information in a visually appealing and interactive format, enabling users to operate it intuitively.

[0490] A "health assessment tool" is a technology that monitors a user's mental health status in real time and enables appropriate responses when an abnormality is detected.

[0491] "Meeting support tools" are support functions that facilitate information sharing among individuals and present progress information and goals before and after meetings.

[0492] "Educational support tools" are functions that present relevant learning resources based on the user's goal setting and promote skill improvement.

[0493] This invention is a system designed to support the self-growth of users, and it functions comprehensively through the coordinated action of various means. In this system, the server plays a major role in enabling interaction between the terminal and the user.

[0494] The server is equipped with information management tools to collect and manage user behavior data. Specifically, it stores diverse data such as electronic messages, calendar events, and behavioral history in a secure database via encrypted communication using the SSL / TLS protocol. Furthermore, it uses natural language processing technology as a data analysis tool to analyze users' emotional states and behavioral patterns. For example, it can extract emotions from text data and model activity trends from behavioral data to evaluate users' stress levels and communication styles.

[0495] Based on the analysis results, the server has a feedback generation mechanism that uses a generated AI model to produce advice and action improvement plans. The feedback generated by this mechanism is displayed on the terminal through a user interface mechanism. The terminal provides information visually in a dashboard format and implements interactive functions to allow the user to operate it intuitively.

[0496] Furthermore, the server monitors users' mental health status in real time using health assessment tools. If an anomaly is detected, it quickly generates an alert and provides appropriate resources. This feature enables users to maintain their mental health on a daily basis.

[0497] As a means of supporting meetings, the terminal assists in preparing for one-on-one meetings and provides relevant information. Before the meeting, it notifies users of the progress and presents key goals, helping them to conduct meetings in a suitable environment.

[0498] By utilizing learning support tools, users can receive learning resources based on their set goals. For example, if a user expresses a desire to "learn programming efficiently," the server will recommend relevant online courses.

[0499] An example of a prompt might be the instruction, "Analyze the user's emotional state and generate a plan to provide appropriate feedback based on their stress level." Based on this example, the server analyzes various data and provides customized information to the user.

[0500] Thus, the present invention is a system that provides flexible support tailored to the individual needs of each user, and is useful for supporting both personal growth and mental well-being.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The server collects data on the user's daily activities. Inputs include the user's electronic messages, calendar events, and activity history. The server encrypts this data using the SSL / TLS protocol and stores it in a secure database. The output is the securely stored database.

[0504] Step 2:

[0505] The server analyzes the collected data. Input consists of text data and behavioral history stored in a database. The server uses natural language processing techniques to extract emotional states and model user behavior patterns and stress levels. Output consists of analytical data on user emotional evaluations and behavioral tendencies.

[0506] Step 3:

[0507] The server generates feedback based on the analysis data. The input is the analysis data, which is the output of step 2. The server utilizes the generated AI model to create personalized advice and action improvement plans tailored to the user. The output is a detailed feedback report.

[0508] Step 4:

[0509] The terminal displays the generated feedback report to the user. The input is the feedback report from the server. The terminal presents the information in a visually appealing dashboard format, making it interactive. The output is the feedback and improvement plan displayed on the user's screen.

[0510] Step 5:

[0511] The server monitors the user's health status. The input is activity data collected in real time. The server uses health assessment tools to detect abnormal patterns and generate alerts early. The output includes notifications and suggestions for support resources when an anomaly is detected.

[0512] Step 6:

[0513] The terminal assists in preparing for one-on-one meetings. Inputs include the user's progress and meeting topic information. The terminal organizes this information, notifies the user, and presents necessary information during and after the meeting. Outputs are a set of information to help the user conduct the meeting smoothly.

[0514] Step 7:

[0515] Users receive personalized learning support. The input is the learning objectives set by the user. Based on this, the server suggests appropriate learning resources and online courses. The output is the educational materials and recommended courses provided to the user.

[0516] (Application Example 1)

[0517] 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."

[0518] In today's urban environment, it is crucial for individual citizens to understand their own behavior and health status and receive appropriate feedback in order to improve their quality of life. However, with much information currently provided individually, it is difficult for citizens to utilize this information efficiently, and the means to enhance their participation in social events and activities are limited. Therefore, there is a growing need for a system that provides comprehensive support tailored to the individual needs of users.

[0519] 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.

[0520] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing emotional states and behavioral patterns using natural language processing, and response generation means for generating personalized feedback and behavioral improvement plans based on the analysis results. This enables individual citizens to accurately obtain information that is useful for improving their lifestyles and health conditions, and is expected to improve the quality of life and revitalize social activities throughout the city.

[0521] "Information management means" refers to a device or system for securely and effectively storing information collected from users and organizing it in a format suitable for analysis.

[0522] "Information analysis means" refers to a technology or method that uses natural language processing technology to analyze collected information and identify the emotional state and behavioral patterns of users.

[0523] A "response generation means" is a function or process for creating optimized feedback and action improvement plans for users based on the results of information analysis.

[0524] "Information presentation means" refers to a function or device that provides generated feedback and other information in a visual or other way that is easily understandable to the user.

[0525] "Health monitoring measures" refer to technologies or systems for monitoring the health status of users, particularly their mental health, and providing necessary support.

[0526] "Life support measures" refer to a mechanism or system that analyzes citizens' activity data and provides recommendations to promote participation in social events and activities.

[0527] To implement this invention, a system is needed for efficient information management and analysis between the server and the user. The server stores various types of information collected from the user using information management means. Next, using information analysis means, the server analyzes the information using natural language processing algorithms to identify the user's emotional state and behavioral patterns. Based on these analysis results, the server uses response generation means to generate optimized feedback and behavioral improvement plans for the user. This feedback is displayed on the user's terminal in a visually easy-to-understand format using information presentation means.

[0528] The server also utilizes health monitoring tools to monitor users' health, particularly their mental health, and provides appropriate support as needed. Furthermore, by using life support tools to analyze citizens' activity data and recommending appropriate social events and activities, it is possible to improve the quality of life for citizens and promote their participation in social activities.

[0529] For example, the server can use data on a citizen's cycling and walking habits to suggest extending recommended cycling times to help improve their health. Using a generative AI model, prompts such as the following can be used: "Based on this data, what healthy lifestyle advice can you give citizen A?" or "Analyze recent behavioral data to recommend city events that citizen A should participate in."

[0530] In this way, this invention can provide personalized information and support specific methods for citizens to more effectively manage their own health and lives.

[0531] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0532] Step 1:

[0533] The server receives daily activity data sent from the user's terminal and stores it in a database using information management tools. This input includes, for example, steps taken, cycling time, and calendar events, and is stored in the database in an organized format as output.

[0534] Step 2:

[0535] The server analyzes the stored data using information analysis tools and identifies the user's emotional state and behavioral patterns using a generative AI model. It processes the data received as input using a natural language processing algorithm and outputs analysis results regarding the user's stress level and activity tendencies.

[0536] Step 3:

[0537] The server generates personalized feedback based on the analysis results using a response generation mechanism. This feedback includes action improvement plans and specific suggestions for improving health status. The input for this step is the analysis results obtained in the previous step, and the output is the generated feedback data.

[0538] Step 4:

[0539] The server sends the generated feedback to the user's terminal via an information display mechanism and displays it in a visually easy-to-understand format. The input includes feedback data, and the output is a dashboard displayed on the user's terminal.

[0540] Step 5:

[0541] Users receive feedback and implement behavioral improvement plans by adjusting their daily activities. At this stage, they consider how to change their lifestyle based on the feedback provided and put those changes into action.

[0542] Step 6:

[0543] The server collects data on changes in user activity again and uses it to inform future feedback. This allows for continuous monitoring of user behavior and the provision of appropriate feedback.

[0544] 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.

[0545] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. In particular, by combining it with an emotion engine, it enables real-time emotion recognition and enhances the feedback and support based on that recognition.

[0546] First, the system collects data on the user's daily activities via the device. This includes chat history, emails, and schedule data. The device encrypts this data and sends it to the server. After the server stores it in a database, it anonymizes the data to protect privacy.

[0547] Next, the server uses natural language processing and an emotion engine to recognize emotions from the user's text data in real time. The emotion engine identifies the user's emotional state based on context and word choice. This information is then used for further data analysis.

[0548] The data analysis method uses collected data and analysis results from the emotion engine to clarify the user's emotional state and behavioral patterns. This allows for the evaluation of the user's stress level and communication tendencies.

[0549] The feedback generation system takes emotion recognition into account and generates personalized feedback tailored to the user. This includes behavioral improvement plans and mental health support. For example, if a user is stressed and tense at work, feedback suggesting relaxation techniques will be provided.

[0550] The information display mechanism shows feedback on the terminal, presenting it in a user-friendly format. This allows users to objectively understand their own situation and efficiently choose their next course of action.

[0551] Furthermore, the health management system utilizes data from the emotion engine to monitor the user's mental health status. If abnormal emotional tendencies are detected, it provides appropriate support resources and advice.

[0552] The meeting support tool provides users with a list of progress updates and topics to discuss, reflecting emotion recognition-based analysis results, before the meeting to support one-on-one meetings.

[0553] Thus, the present invention aims to improve the quality of life for users by providing advanced support tailored to their needs and comprehensively assisting them in their personal growth and health management.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] Users generate data on their daily activities by using their devices and routinely utilizing chat tools, email clients, and calendar applications.

[0557] Step 2:

[0558] The device collects generated data to the extent permitted, encrypts the necessary data, and sends it to the server while protecting privacy.

[0559] Step 3:

[0560] The server securely stores the received data in a database and protects personal information by anonymizing the data.

[0561] Step 4:

[0562] The server uses a natural language processing engine to analyze the user's emotional state from the collected text data. The emotion engine recognizes the user's emotions in real time through specific keywords and expressions in the text.

[0563] Step 5:

[0564] The server combines the emotional data obtained from the analysis with the user's behavioral patterns to assess the user's overall emotional tendencies and stress level.

[0565] Step 6:

[0566] The feedback generation system generates appropriate action plans for the user. Based on sentiment analysis, it creates suggestions for stress reduction and feedback aimed at reinforcing positive behaviors.

[0567] Step 7:

[0568] The device displays the generated action plan and feedback to the user. A visual dashboard is used to help users easily understand the information and choose their next course of action.

[0569] Step 8:

[0570] The meeting support system prepares the necessary materials for the one-on-one meeting. The server retrieves data from the previous meeting and the latest sentiment analysis results, and sends topic suggestions and progress summaries to the terminal.

[0571] Step 9:

[0572] Through health management tools, the server continuously monitors emotional data and notifies the user's device to provide mental health resources and support as needed.

[0573] Step 10:

[0574] Users utilize feedback and resources provided through their devices to manage their emotional state and implement behavioral improvements.

[0575] (Example 2)

[0576] 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."

[0577] In modern society, many users experience significant stress and mental burden in their daily lives and work, which often impacts their mental health. Traditional mental healthcare lacks personalized support based on the individual emotions and behavioral patterns of users. Furthermore, even in meetings and educational settings, there is a challenge in providing adequate support that takes into account the emotional state of users.

[0578] 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.

[0579] In this invention, the server includes information management means for collecting and storing diverse information from users, information analysis means for analyzing the user's emotional state in real time using natural language processing technology and an emotion recognition engine, and feedback creation means for generating a personalized behavioral improvement plan for the user based on the analysis results. This enables personalized mental health care based on the user's individual emotional state and behavioral patterns, as well as effective support in meetings and learning.

[0580] "Information management means" refers to technologies that have the function of securely collecting, organizing, and storing diverse information obtained from users.

[0581] "Security processing means" refers to technologies for encrypting user information and securely transmitting data.

[0582] "Information analysis means" refers to a technology that combines natural language processing technology and an emotion recognition engine to analyze a user's emotional state in real time.

[0583] A "feedback generation method" is a technology that generates personalized behavioral improvement plans and feedback based on analysis results obtained from information analysis methods.

[0584] "Information output means" refers to a technology that displays the generated feedback information on the user's terminal and provides it to the user.

[0585] "Health management measures" refer to technologies that continuously monitor the mental health status of users and provide appropriate resources when abnormal emotional tendencies are detected.

[0586] A "meeting support tool" is a technology that effectively supports meetings by providing information on the progress of the meeting and the issues to be discussed, based on the emotional state of the participants.

[0587] "Learning support tools" are technologies that create and provide individualized educational plans based on the user's set objectives.

[0588] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. The process is realized through the cooperation of three parties: a server, a terminal, and the user.

[0589] First, the device, acting as an application installed on a smartphone or computer, collects data on the user's daily activities. Specific examples of this data include chat history, emails, and schedule information. The device processes this data using AES encryption and securely transmits it to the server.

[0590] The server stores the received data in a database and anonymizes it to protect privacy. Then, it uses NLP libraries (e.g., NLTK and spaCy) as information analysis tools using natural language processing techniques to analyze the user's emotions from the text data in real time. Furthermore, the emotion engine identifies the emotional state based on the context and word choice. Based on these analysis results, machine learning models (e.g., Scikit-learn, TensorFlow) are used to reveal the user's emotional state and behavioral patterns in detail.

[0591] A generative AI model is used to generate feedback. The server generates user-optimized feedback based on the analyzed data. For example, a prompt might say, "Based on the information that the user is experiencing stress at work, please suggest an action plan for relaxation." This feedback is provided in the form of relaxation techniques and advice on improving lifestyle habits.

[0592] The device will display the aforementioned feedback directly to the user. The feedback will be expressed as a pop-up notification or in-app message, allowing the user to quickly understand and take action.

[0593] Furthermore, as a health management tool, the server continuously monitors emotional data and, if abnormal trends are detected, immediately issues a warning to the user and provides support resources appropriate to the situation.

[0594] This system can also be used in one-on-one meetings, where the server provides a list of progress updates and issues to be addressed in the meeting, based on sentiment data obtained before the meeting.

[0595] Based on the above, the system based on this invention makes it possible to comprehensively provide effective support for the user's self-growth and mental health care.

[0596] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0597] Step 1:

[0598] The device collects daily activity data through the user's smartphone or computer. Input data includes chat history, emails, and schedule information. This data is encrypted using the AES encryption algorithm, generating encrypted data as output. The encrypted data is then sent to the server via secure communication.

[0599] Step 2:

[0600] The server receives encrypted data sent from the terminal. The server then decrypts the data and converts it into a storable format. Using the received encrypted data as input, it generates processable text data as output. This text data is stored in a database and anonymized.

[0601] Step 3:

[0602] The server analyzes text data stored in the database using natural language processing techniques and an emotion recognition engine. This analysis utilizes NLP libraries (e.g., NLTK and spaCy). It takes processed text data as input and provides the user's emotional state (emotional categories such as positive, negative, and neutral) as output.

[0603] Step 4:

[0604] The server uses machine learning models (e.g., Scikit-learn or TensorFlow) to further analyze the user's emotional state and behavioral patterns based on the analysis results. Using emotional category data as input, it identifies and evaluates the user's specific emotional state and stress level as output, thereby clarifying behavioral patterns.

[0605] Step 5:

[0606] The server utilizes a generative AI model to generate feedback based on the analysis results. The prompt input is "Based on the information that the user is experiencing work-related stress, please suggest an action plan for relaxation," and the output provides personalized feedback and behavioral improvement plans.

[0607] Step 6:

[0608] The device receives feedback from the server and displays it on the user interface. It receives feedback data from the server as input and presents it as an intuitively understandable pop-up or in-app notification as output.

[0609] Step 7:

[0610] The server periodically monitors emotional data and assesses the user's mental health status. Emotional categories and analytical data are used as input, and if abnormal emotional tendencies or signs of distress are detected as output, the server provides the user with warnings and support resources.

[0611] (Application Example 2)

[0612] 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."

[0613] In today's world, effectively supporting users' mental health is a crucial challenge. In particular, there is a need to provide real-time, personalized feedback and appropriate relaxation methods to people who are prone to stress in their busy lives. Furthermore, a system is needed that can quickly and accurately grasp emotional states and provide concrete action plans based on those assessments, enabling users to efficiently pursue self-growth and manage their health.

[0614] 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.

[0615] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing users' emotional states and behavioral patterns using natural language processing, and feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results. This enables users to understand their emotional state and make appropriate behavioral improvements.

[0616] "Information management means" refers to functions for securely storing information collected from users and maintaining it in a state where it can be analyzed.

[0617] "Information analysis tools" are functions that analyze users' emotional states and behavioral patterns through natural language processing to gain detailed insights.

[0618] A "feedback generation means" is a function that creates personalized feedback and behavioral improvement plans for users based on the results obtained from information analysis means.

[0619] A "health monitoring tool" is a function that evaluates the mental health status of users and provides appropriate support.

[0620] A "stress management tool" is a function that detects signs of stress from the user's behavioral data and suggests appropriate relaxation methods.

[0621] "Encryption means" refers to the function of encrypting data in order to securely collect and transmit information.

[0622] A "visual presentation method" is a function that visually presents appropriate action suggestions to the user based on the analysis results.

[0623] "Meeting support features" are functions that provide progress updates and meeting topics to support users' one-on-one meetings.

[0624] "Learning support tools" are functions that provide individualized learning plans according to the goals set by the user.

[0625] The system for implementing this invention consists of a server and a terminal working in cooperation. First, the system collects information from the user via the terminal. This information includes daily messages, schedules, call logs, etc. The terminal encrypts this information and sends it to the server using a secure communication protocol (e.g., HTTPS). The server stores this information using information management means and performs natural language processing using information analysis means. Specifically, it implements a model for analyzing emotional states using TensorFlow.

[0626] Based on the analyzed data, the feedback generation system generates personalized feedback for the user. This includes suggestions for relaxation methods through stress management mechanisms. The generated feedback is presented on the device's display via visual presentation mechanisms. A user-friendly and visually appealing interface is built using React Native.

[0627] For example, if the server detects that a user is experiencing stress due to their busy daily schedule, it might send feedback to the device such as, "We recommend consciously taking deep breaths." Also, if a user is preparing for a one-on-one meeting, the server might use meeting support tools to display the topics to be covered on the device.

[0628] A possible example of a prompt message is: "Analyze the user's emotional state and suggest an appropriate relaxation method. The data is as follows." The generative AI model provides feedback based on this prompt message.

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] The device collects data from the user, such as messages, schedule information, and call logs. The input is raw data. Following data collection, the device encrypts the data using the AES encryption algorithm. This process protects the privacy of user data and enables secure transmission. The output is encrypted data.

[0632] Step 2:

[0633] The terminal sends encrypted data to the server using the HTTPS protocol. The input is encrypted data. By using the communication protocol, the data is protected from unauthorized access. The server receives the received data and stores it in a database using information management means. The output is the data stored in the database.

[0634] Step 3:

[0635] The server analyzes the received data using information analysis tools. The input is raw data stored in a database. In this step, a natural language processing model using TensorFlow processes the data and extracts emotional features. The output is a set of features indicating emotional states.

[0636] Step 4:

[0637] The server generates appropriate feedback using a feedback generation mechanism based on the features. The input is the emotional state features. A generative AI model is used to generate relaxation and behavioral improvement guidelines appropriate to the user's emotional state. The output is the generated feedback message.

[0638] Step 5:

[0639] The server sends the generated feedback to the terminal via a visual display device. The input is the generated feedback message. Specific actions are taken using React Native to display the feedback in the user interface. The output is the feedback displayed on the terminal.

[0640] Step 6:

[0641] The user reviews the feedback displayed on the device and takes action as needed. The input is the feedback displayed on the device. The user intuitively understands the feedback and selects relaxation techniques or corrective actions. The output is the change in the user's behavior.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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".

[0659] This invention is a system designed to support the self-improvement of users, and includes data management means, data analysis means, feedback generation means, information display means, health management means, meeting support means, and learning support means. These means work together to comprehensively realize the self-improvement of users.

[0660] First, the system's data management mechanism collects and securely stores users' daily activity data. This includes electronic messages from communication tools used by users, calendar events, and activity logs. The collected data is centrally managed by a server in a database.

[0661] Next, data analysis tools analyze this collected data. The server uses natural language processing algorithms to analyze the user's emotional state and behavioral patterns. This analysis reveals characteristics such as the user's stress level, communication style, and activity tendencies.

[0662] Based on the analysis results, the feedback generation system generates personalized feedback. The server creates customized advice and action improvement plans for each user and compiles them into a feedback report.

[0663] The information display mechanism is responsible for displaying the generated feedback on the terminal. This allows users to interactively review feedback and improvement plans. Where necessary, the feedback is provided in a visually appealing dashboard format, making it easy for users to understand intuitively.

[0664] The health management system complements the analysis system, monitoring the user's mental health status in real time. The server uses the generated data to detect potential mental health problems early and provides warnings or resources as needed.

[0665] The meeting support system provides assistance to facilitate the preparation and execution of one-on-one meetings. The terminal notifies the user of the progress and any new goals to be set before the meeting, and organizes the information necessary for follow-up during and after the meeting.

[0666] Finally, the learning support system provides learning resources and skill-building information based on the user's personal goals. Users can leverage this feature to continuously advance their personal growth.

[0667] For example, if a user wishes to learn a new skill, the system analyzes past behavioral data and current goal settings to recommend appropriate online courses and learning materials. This allows users to efficiently and systematically promote their own growth.

[0668] Thus, the system of the present invention provides personalized support for individual needs and functions as a powerful tool for users to improve and grow themselves.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] Users log into the system through their everyday communication and scheduling tools and consent to data collection.

[0672] Step 2:

[0673] The device collects activity data such as chat history, emails, and calendar events, based on the user's consent, encrypts it, and sends it to the server.

[0674] Step 3:

[0675] The server stores the received activity data in a database and performs data anonymization to protect privacy.

[0676] Step 4:

[0677] The server uses a natural language processing engine to analyze data and determine the user's emotional state, stress level, and behavioral patterns.

[0678] Step 5:

[0679] The server extracts key insights from the analysis results and generates a personalized feedback report. This report includes specific action plans and mental health advice.

[0680] Step 6:

[0681] The device displays the generated feedback report to the user in a visually easy-to-understand format.

[0682] Step 7:

[0683] The server runs support programs to manage progress and recommend learning resources based on the user's personal goals, and sends the results to the terminal.

[0684] Step 8:

[0685] Users can access feedback and learning resources through their devices, and revise their goals or create new action plans as needed.

[0686] Step 9:

[0687] The device notifies the user of the progress and agenda items to be discussed before a one-on-one meeting.

[0688] Step 10:

[0689] Based on the analyzed emotional and behavioral data, the server continuously assesses the user's mental health and provides care resources as needed.

[0690] (Example 1)

[0691] 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".

[0692] In modern society, there is a need for technologies that enable individual users to achieve personal growth and maintain mental health. However, many systems fail to adequately meet user needs by failing to provide personalized feedback and planning. Furthermore, the lack of real-time monitoring of mental health and individualized learning support hinders early problem detection and efficient skill development.

[0693] 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.

[0694] In this invention, the server includes information management means, data analysis means, and feedback generation means. This enables the secure management of diverse data, the generation of personalized feedback based on high-precision analysis, and real-time assessment of health status.

[0695] "Information management means" refers to a function that safely and efficiently stores and manages behavioral data obtained from users.

[0696] "Data analysis methods" refer to techniques that use natural language processing technology to model and analyze users' emotional states and behavioral patterns.

[0697] The "feedback generation method" is a function that utilizes generative AI technology to provide personalized advice and improvement plans based on analysis results.

[0698] A "user interface means" is a function that displays information in a visually appealing and interactive format, enabling users to operate it intuitively.

[0699] A "health assessment tool" is a technology that monitors a user's mental health status in real time and enables appropriate responses when an abnormality is detected.

[0700] "Meeting support tools" are support functions that facilitate information sharing among individuals and present progress information and goals before and after meetings.

[0701] "Educational support tools" are functions that present relevant learning resources based on the user's goal setting and promote skill improvement.

[0702] This invention is a system designed to support the self-growth of users, and it functions comprehensively through the coordinated action of various means. In this system, the server plays a major role in enabling interaction between the terminal and the user.

[0703] The server is equipped with information management tools to collect and manage user behavior data. Specifically, it stores diverse data such as electronic messages, calendar events, and behavioral history in a secure database via encrypted communication using the SSL / TLS protocol. Furthermore, it uses natural language processing technology as a data analysis tool to analyze users' emotional states and behavioral patterns. For example, it can extract emotions from text data and model activity trends from behavioral data to evaluate users' stress levels and communication styles.

[0704] Based on the analysis results, the server has a feedback generation mechanism that uses a generated AI model to produce advice and action improvement plans. The feedback generated by this mechanism is displayed on the terminal through a user interface mechanism. The terminal provides information visually in a dashboard format and implements interactive functions to allow the user to operate it intuitively.

[0705] Furthermore, the server monitors users' mental health status in real time using health assessment tools. If an anomaly is detected, it quickly generates an alert and provides appropriate resources. This feature enables users to maintain their mental health on a daily basis.

[0706] As a means of supporting meetings, the terminal assists in preparing for one-on-one meetings and provides relevant information. Before the meeting, it notifies users of the progress and presents key goals, helping them to conduct meetings in a suitable environment.

[0707] By utilizing learning support tools, users can receive learning resources based on their set goals. For example, if a user expresses a desire to "learn programming efficiently," the server will recommend relevant online courses.

[0708] An example of a prompt might be the instruction, "Analyze the user's emotional state and generate a plan to provide appropriate feedback based on their stress level." Based on this example, the server analyzes various data and provides customized information to the user.

[0709] Thus, the present invention is a system that provides flexible support tailored to the individual needs of each user, and is useful for supporting both personal growth and mental well-being.

[0710] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0711] Step 1:

[0712] The server collects data on the user's daily activities. Inputs include the user's electronic messages, calendar events, and activity history. The server encrypts this data using the SSL / TLS protocol and stores it in a secure database. The output is the securely stored database.

[0713] Step 2:

[0714] The server analyzes the collected data. Input consists of text data and behavioral history stored in a database. The server uses natural language processing techniques to extract emotional states and model user behavior patterns and stress levels. Output consists of analytical data on user emotional evaluations and behavioral tendencies.

[0715] Step 3:

[0716] The server generates feedback based on the analysis data. The input is the analysis data, which is the output of step 2. The server utilizes the generated AI model to create personalized advice and action improvement plans tailored to the user. The output is a detailed feedback report.

[0717] Step 4:

[0718] The terminal displays the generated feedback report to the user. The input is the feedback report from the server. The terminal presents the information in a visually appealing dashboard format, making it interactive. The output is the feedback and improvement plan displayed on the user's screen.

[0719] Step 5:

[0720] The server monitors the user's health status. The input is activity data collected in real time. The server uses health assessment tools to detect abnormal patterns and generate alerts early. The output includes notifications and suggestions for support resources when an anomaly is detected.

[0721] Step 6:

[0722] The terminal assists in preparing for one-on-one meetings. Inputs include the user's progress and meeting topic information. The terminal organizes this information, notifies the user, and presents necessary information during and after the meeting. Outputs are a set of information to help the user conduct the meeting smoothly.

[0723] Step 7:

[0724] Users receive personalized learning support. The input is the learning objectives set by the user. Based on this, the server suggests appropriate learning resources and online courses. The output is the educational materials and recommended courses provided to the user.

[0725] (Application Example 1)

[0726] 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".

[0727] In today's urban environment, it is crucial for individual citizens to understand their own behavior and health status and receive appropriate feedback in order to improve their quality of life. However, with much information currently provided individually, it is difficult for citizens to utilize this information efficiently, and the means to enhance their participation in social events and activities are limited. Therefore, there is a growing need for a system that provides comprehensive support tailored to the individual needs of users.

[0728] 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.

[0729] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing emotional states and behavioral patterns using natural language processing, and response generation means for generating personalized feedback and behavioral improvement plans based on the analysis results. This enables individual citizens to accurately obtain information that is useful for improving their lifestyles and health conditions, and is expected to improve the quality of life and revitalize social activities throughout the city.

[0730] "Information management means" refers to a device or system for securely and effectively storing information collected from users and organizing it in a format suitable for analysis.

[0731] "Information analysis means" refers to a technology or method that uses natural language processing technology to analyze collected information and identify the emotional state and behavioral patterns of users.

[0732] A "response generation means" is a function or process for creating optimized feedback and action improvement plans for users based on the results of information analysis.

[0733] "Information presentation means" refers to a function or device that provides generated feedback and other information in a visual or other way that is easily understandable to the user.

[0734] "Health monitoring measures" refer to technologies or systems for monitoring the health status of users, particularly their mental health, and providing necessary support.

[0735] "Life support measures" refer to a mechanism or system that analyzes citizens' activity data and provides recommendations to promote participation in social events and activities.

[0736] To implement this invention, a system is needed for efficient information management and analysis between the server and the user. The server stores various types of information collected from the user using information management means. Next, using information analysis means, the server analyzes the information using natural language processing algorithms to identify the user's emotional state and behavioral patterns. Based on these analysis results, the server uses response generation means to generate optimized feedback and behavioral improvement plans for the user. This feedback is displayed on the user's terminal in a visually easy-to-understand format using information presentation means.

[0737] The server also utilizes health monitoring tools to monitor users' health, particularly their mental health, and provides appropriate support as needed. Furthermore, by using life support tools to analyze citizens' activity data and recommending appropriate social events and activities, it is possible to improve the quality of life for citizens and promote their participation in social activities.

[0738] For example, the server can use data on a citizen's cycling and walking habits to suggest extending recommended cycling times to help improve their health. Using a generative AI model, prompts such as the following can be used: "Based on this data, what healthy lifestyle advice can you give citizen A?" or "Analyze recent behavioral data to recommend city events that citizen A should participate in."

[0739] In this way, this invention can provide personalized information and support specific methods for citizens to more effectively manage their own health and lives.

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The server receives daily activity data sent from the user's terminal and stores it in a database using information management tools. This input includes, for example, steps taken, cycling time, and calendar events, and is stored in the database in an organized format as output.

[0743] Step 2:

[0744] The server analyzes the stored data using information analysis tools and identifies the user's emotional state and behavioral patterns using a generative AI model. It processes the data received as input using a natural language processing algorithm and outputs analysis results regarding the user's stress level and activity tendencies.

[0745] Step 3:

[0746] The server generates personalized feedback based on the analysis results using a response generation mechanism. This feedback includes action improvement plans and specific suggestions for improving health status. The input for this step is the analysis results obtained in the previous step, and the output is the generated feedback data.

[0747] Step 4:

[0748] The server sends the generated feedback to the user's terminal via an information display mechanism and displays it in a visually easy-to-understand format. The input includes feedback data, and the output is a dashboard displayed on the user's terminal.

[0749] Step 5:

[0750] Users receive feedback and implement behavioral improvement plans by adjusting their daily activities. At this stage, they consider how to change their lifestyle based on the feedback provided and put those changes into action.

[0751] Step 6:

[0752] The server collects data on changes in user activity again and uses it to inform future feedback. This allows for continuous monitoring of user behavior and the provision of appropriate feedback.

[0753] 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.

[0754] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. In particular, by combining it with an emotion engine, it enables real-time emotion recognition and enhances the feedback and support based on that recognition.

[0755] First, the system collects data on the user's daily activities via the device. This includes chat history, emails, and schedule data. The device encrypts this data and sends it to the server. After the server stores it in a database, it anonymizes the data to protect privacy.

[0756] Next, the server uses natural language processing and an emotion engine to recognize emotions from the user's text data in real time. The emotion engine identifies the user's emotional state based on context and word choice. This information is then used for further data analysis.

[0757] The data analysis method uses collected data and analysis results from the emotion engine to clarify the user's emotional state and behavioral patterns. This allows for the evaluation of the user's stress level and communication tendencies.

[0758] The feedback generation system takes emotion recognition into account and generates personalized feedback tailored to the user. This includes behavioral improvement plans and mental health support. For example, if a user is stressed and tense at work, feedback suggesting relaxation techniques will be provided.

[0759] The information display mechanism shows feedback on the terminal, presenting it in a user-friendly format. This allows users to objectively understand their own situation and efficiently choose their next course of action.

[0760] Furthermore, the health management system utilizes data from the emotion engine to monitor the user's mental health status. If abnormal emotional tendencies are detected, it provides appropriate support resources and advice.

[0761] The meeting support tool provides users with a list of progress updates and topics to discuss, reflecting emotion recognition-based analysis results, before the meeting to support one-on-one meetings.

[0762] Thus, the present invention aims to improve the quality of life for users by providing advanced support tailored to their needs and comprehensively assisting them in their personal growth and health management.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] Users generate data on their daily activities by using their devices and routinely utilizing chat tools, email clients, and calendar applications.

[0766] Step 2:

[0767] The device collects generated data to the extent permitted, encrypts the necessary data, and sends it to the server while protecting privacy.

[0768] Step 3:

[0769] The server securely stores the received data in a database and protects personal information by anonymizing the data.

[0770] Step 4:

[0771] The server uses a natural language processing engine to analyze the user's emotional state from the collected text data. The emotion engine recognizes the user's emotions in real time through specific keywords and expressions in the text.

[0772] Step 5:

[0773] The server combines the emotional data obtained from the analysis with the user's behavioral patterns to assess the user's overall emotional tendencies and stress level.

[0774] Step 6:

[0775] The feedback generation system generates appropriate action plans for the user. Based on sentiment analysis, it creates suggestions for stress reduction and feedback aimed at reinforcing positive behaviors.

[0776] Step 7:

[0777] The device displays the generated action plan and feedback to the user. A visual dashboard is used to help users easily understand the information and choose their next course of action.

[0778] Step 8:

[0779] The meeting support system prepares the necessary materials for the one-on-one meeting. The server retrieves data from the previous meeting and the latest sentiment analysis results, and sends topic suggestions and progress summaries to the terminal.

[0780] Step 9:

[0781] Through health management tools, the server continuously monitors emotional data and notifies the user's device to provide mental health resources and support as needed.

[0782] Step 10:

[0783] Users utilize feedback and resources provided through their devices to manage their emotional state and implement behavioral improvements.

[0784] (Example 2)

[0785] 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".

[0786] In modern society, many users experience significant stress and mental burden in their daily lives and work, which often impacts their mental health. Traditional mental healthcare lacks personalized support based on the individual emotions and behavioral patterns of users. Furthermore, even in meetings and educational settings, there is a challenge in providing adequate support that takes into account the emotional state of users.

[0787] 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.

[0788] In this invention, the server includes information management means for collecting and storing diverse information from users, information analysis means for analyzing the user's emotional state in real time using natural language processing technology and an emotion recognition engine, and feedback creation means for generating a personalized behavioral improvement plan for the user based on the analysis results. This enables personalized mental health care based on the user's individual emotional state and behavioral patterns, as well as effective support in meetings and learning.

[0789] "Information management means" refers to technologies that have the function of securely collecting, organizing, and storing diverse information obtained from users.

[0790] "Security processing means" refers to technologies for encrypting user information and securely transmitting data.

[0791] "Information analysis means" refers to a technology that combines natural language processing technology and an emotion recognition engine to analyze a user's emotional state in real time.

[0792] A "feedback generation method" is a technology that generates personalized behavioral improvement plans and feedback based on analysis results obtained from information analysis methods.

[0793] "Information output means" refers to a technology that displays the generated feedback information on the user's terminal and provides it to the user.

[0794] "Health management measures" refer to technologies that continuously monitor the mental health status of users and provide appropriate resources when abnormal emotional tendencies are detected.

[0795] A "meeting support tool" is a technology that effectively supports meetings by providing information on the progress of the meeting and the issues to be discussed, based on the emotional state of the participants.

[0796] "Learning support tools" are technologies that create and provide individualized educational plans based on the user's set objectives.

[0797] This invention is a system that analyzes users' behavior and emotions in detail and provides personalized support for self-growth and mental health care. The process is realized through the cooperation of three parties: a server, a terminal, and the user.

[0798] First, the device, acting as an application installed on a smartphone or computer, collects data on the user's daily activities. Specific examples of this data include chat history, emails, and schedule information. The device processes this data using AES encryption and securely transmits it to the server.

[0799] The server stores the received data in a database and anonymizes it to protect privacy. Then, it uses NLP libraries (e.g., NLTK and spaCy) as information analysis tools using natural language processing techniques to analyze the user's emotions from the text data in real time. Furthermore, the emotion engine identifies the emotional state based on the context and word choice. Based on these analysis results, machine learning models (e.g., Scikit-learn, TensorFlow) are used to reveal the user's emotional state and behavioral patterns in detail.

[0800] A generative AI model is used to generate feedback. The server generates user-optimized feedback based on the analyzed data. For example, a prompt might say, "Based on the information that the user is experiencing stress at work, please suggest an action plan for relaxation." This feedback is provided in the form of relaxation techniques and advice on improving lifestyle habits.

[0801] The device will display the aforementioned feedback directly to the user. The feedback will be expressed as a pop-up notification or in-app message, allowing the user to quickly understand and take action.

[0802] Furthermore, as a health management tool, the server continuously monitors emotional data and, if abnormal trends are detected, immediately issues a warning to the user and provides support resources appropriate to the situation.

[0803] This system can also be used in one-on-one meetings, where the server provides a list of progress updates and issues to be addressed in the meeting, based on sentiment data obtained before the meeting.

[0804] Based on the above, the system based on this invention makes it possible to comprehensively provide effective support for the user's self-growth and mental health care.

[0805] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0806] Step 1:

[0807] The device collects daily activity data through the user's smartphone or computer. Input data includes chat history, emails, and schedule information. This data is encrypted using the AES encryption algorithm, generating encrypted data as output. The encrypted data is then sent to the server via secure communication.

[0808] Step 2:

[0809] The server receives encrypted data sent from the terminal. The server then decrypts the data and converts it into a storable format. Using the received encrypted data as input, it generates processable text data as output. This text data is stored in a database and anonymized.

[0810] Step 3:

[0811] The server analyzes text data stored in the database using natural language processing techniques and an emotion recognition engine. This analysis utilizes NLP libraries (e.g., NLTK and spaCy). It takes processed text data as input and provides the user's emotional state (emotional categories such as positive, negative, and neutral) as output.

[0812] Step 4:

[0813] The server uses machine learning models (e.g., Scikit-learn or TensorFlow) to further analyze the user's emotional state and behavioral patterns based on the analysis results. Using emotional category data as input, it identifies and evaluates the user's specific emotional state and stress level as output, thereby clarifying behavioral patterns.

[0814] Step 5:

[0815] The server utilizes a generative AI model to generate feedback based on the analysis results. The prompt input is "Based on the information that the user is experiencing work-related stress, please suggest an action plan for relaxation," and the output provides personalized feedback and behavioral improvement plans.

[0816] Step 6:

[0817] The device receives feedback from the server and displays it on the user interface. It receives feedback data from the server as input and presents it as an intuitively understandable pop-up or in-app notification as output.

[0818] Step 7:

[0819] The server periodically monitors emotional data and assesses the user's mental health status. Emotional categories and analytical data are used as input, and if abnormal emotional tendencies or signs of distress are detected as output, the server provides the user with warnings and support resources.

[0820] (Application Example 2)

[0821] 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".

[0822] In today's world, effectively supporting users' mental health is a crucial challenge. In particular, there is a need to provide real-time, personalized feedback and appropriate relaxation methods to people who are prone to stress in their busy lives. Furthermore, a system is needed that can quickly and accurately grasp emotional states and provide concrete action plans based on those assessments, enabling users to efficiently pursue self-growth and manage their health.

[0823] 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.

[0824] In this invention, the server includes information management means for storing and analyzing information collected from users, information analysis means for analyzing users' emotional states and behavioral patterns using natural language processing, and feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results. This enables users to understand their emotional state and make appropriate behavioral improvements.

[0825] "Information management means" refers to functions for securely storing information collected from users and maintaining it in a state where it can be analyzed.

[0826] "Information analysis tools" are functions that analyze users' emotional states and behavioral patterns through natural language processing to gain detailed insights.

[0827] A "feedback generation means" is a function that creates personalized feedback and behavioral improvement plans for users based on the results obtained from information analysis means.

[0828] A "health monitoring tool" is a function that evaluates the mental health status of users and provides appropriate support.

[0829] A "stress management tool" is a function that detects signs of stress from the user's behavioral data and suggests appropriate relaxation methods.

[0830] "Encryption means" refers to the function of encrypting data in order to securely collect and transmit information.

[0831] A "visual presentation method" is a function that visually presents appropriate action suggestions to the user based on the analysis results.

[0832] "Meeting support features" are functions that provide progress updates and meeting topics to support users' one-on-one meetings.

[0833] "Learning support tools" are functions that provide individualized learning plans according to the goals set by the user.

[0834] The system for implementing this invention consists of a server and a terminal working in cooperation. First, the system collects information from the user via the terminal. This information includes daily messages, schedules, call logs, etc. The terminal encrypts this information and sends it to the server using a secure communication protocol (e.g., HTTPS). The server stores this information using information management means and performs natural language processing using information analysis means. Specifically, it implements a model for analyzing emotional states using TensorFlow.

[0835] Based on the analyzed data, the feedback generation system generates personalized feedback for the user. This includes suggestions for relaxation methods through stress management mechanisms. The generated feedback is presented on the device's display via visual presentation mechanisms. A user-friendly and visually appealing interface is built using React Native.

[0836] For example, if the server detects that a user is experiencing stress due to their busy daily schedule, it might send feedback to the device such as, "We recommend consciously taking deep breaths." Also, if a user is preparing for a one-on-one meeting, the server might use meeting support tools to display the topics to be covered on the device.

[0837] A possible example of a prompt message is: "Analyze the user's emotional state and suggest an appropriate relaxation method. The data is as follows." The generative AI model provides feedback based on this prompt message.

[0838] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0839] Step 1:

[0840] The device collects data from the user, such as messages, schedule information, and call logs. The input is raw data. Following data collection, the device encrypts the data using the AES encryption algorithm. This process protects the privacy of user data and enables secure transmission. The output is encrypted data.

[0841] Step 2:

[0842] The terminal sends encrypted data to the server using the HTTPS protocol. The input is encrypted data. By using the communication protocol, the data is protected from unauthorized access. The server receives the received data and stores it in a database using information management means. The output is the data stored in the database.

[0843] Step 3:

[0844] The server analyzes the received data using information analysis tools. The input is raw data stored in a database. In this step, a natural language processing model using TensorFlow processes the data and extracts emotional features. The output is a set of features indicating emotional states.

[0845] Step 4:

[0846] The server generates appropriate feedback using a feedback generation mechanism based on the features. The input is the emotional state features. A generative AI model is used to generate relaxation and behavioral improvement guidelines appropriate to the user's emotional state. The output is the generated feedback message.

[0847] Step 5:

[0848] The server sends the generated feedback to the terminal via a visual display device. The input is the generated feedback message. Specific actions are taken using React Native to display the feedback in the user interface. The output is the feedback displayed on the terminal.

[0849] Step 6:

[0850] The user reviews the feedback displayed on the device and takes action as needed. The input is the feedback displayed on the device. The user intuitively understands the feedback and selects relaxation techniques or corrective actions. The output is the change in the user's behavior.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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."

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] The following is further disclosed regarding the embodiments described above.

[0873] (Claim 1)

[0874] A data management system for storing and analyzing data collected from users,

[0875] A data analysis method for analyzing users' emotional states and behavioral patterns using natural language processing,

[0876] A feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results,

[0877] Information display means for displaying feedback on the user's device,

[0878] Health management tools for evaluating the mental health status of users and providing appropriate support,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, comprising meeting support means for providing progress and meeting topics in order to support one-on-one meetings for users.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising learning support means for providing an individualized learning plan based on goals set by the user.

[0884] "Example 1"

[0885] (Claim 1)

[0886] Information management means for storing and securely managing behavioral data obtained from users,

[0887] A data analysis method for performing sentiment analysis and modeling behavioral tendencies using natural language processing technology,

[0888] A feedback generation method for creating personalized advice and improvement plans using generative AI technology,

[0889] A user interface means for visually displaying information and making it interactively operable,

[0890] A health assessment tool for monitoring the mental health of users and providing appropriate notifications when abnormalities are detected,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, comprising meeting support means for presenting progress and specific goals in order to facilitate information sharing among individuals.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising educational support means to present learning resources based on the user's goal setting and to help improve skills.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] Information management means for storing and analyzing information collected from users,

[0899] Information analysis means for analyzing users' emotional states and behavioral patterns using natural language processing,

[0900] A response generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results,

[0901] Information display means for displaying feedback on the user's device,

[0902] Health monitoring tools to assess the health status of users and provide appropriate assistance,

[0903] Analyzing citizens' activity trends and providing life support measures for recommending social events and activities,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, comprising meeting support means for providing progress reports and meeting topics to support one-on-one meetings between users.

[0907] (Claim 3)

[0908] The system according to claim 1, comprising learning support means for providing an individualized learning plan based on goals set by the user.

[0909] "Example 2 of combining an emotion engine"

[0910] (Claim 1)

[0911] Information management means for collecting and storing diverse information from users,

[0912] A security processing method for encrypting the collected logs and securely transmitting them to the server,

[0913] An information analysis means for analyzing a user's emotional state in real time using natural language processing technology and an emotion recognition engine,

[0914] A means for generating feedback to create personalized behavioral improvement plans for users based on analysis results,

[0915] An information output means for displaying feedback information obtained from a generated AI model on the user's terminal,

[0916] A health management system that continuously monitors the mental health status of users and provides appropriate resources when signs of pressure are detected,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, comprising meeting support means for providing progress updates and discussion topics based on emotional states in order to support one-on-one meetings between users.

[0920] (Claim 3)

[0921] The system according to claim 1, comprising learning support means for providing an individualized educational plan based on the user's set objectives.

[0922] "Application example 2 when combining with an emotional engine"

[0923] (Claim 1)

[0924] Information management means for storing and analyzing information collected from users,

[0925] Information analysis means for analyzing users' emotional states and behavioral patterns using natural language processing,

[0926] A feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results,

[0927] A means of displaying information for feedback on the user's device,

[0928] A health monitoring tool for evaluating the mental health status of users and providing appropriate support,

[0929] A stress management method that detects signs of stress from user behavior data and proposes appropriate relaxation methods,

[0930] Encryption methods for securely collecting and transmitting information,

[0931] A visual presentation method to provide appropriate action suggestions based on the analysis results,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, comprising meeting support means for providing progress and meeting topics in order to support one-on-one meetings for users.

[0935] (Claim 3)

[0936] The system according to claim 1, comprising a learning support means for providing an individualized learning plan based on goals set by the user. [Explanation of symbols]

[0937] 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 data management system for storing and analyzing data collected from users, A data analysis method for analyzing users' emotional states and behavioral patterns using natural language processing, A feedback generation means for generating personalized feedback and behavioral improvement plans for users based on the analysis results, Information display means for displaying feedback on the user's device, Health management tools for evaluating the mental health status of users and providing appropriate support, A system that includes this.

2. The system according to claim 1, comprising meeting support means for providing progress information and meeting topics in order to support one-on-one meetings for users.

3. The system according to claim 1, comprising learning support means for providing an individualized learning plan based on goals set by the user.

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