A system that manages and suggests improvements to biases in users' worldviews.

Abstract

This invention provides a system that manages the biases in each user's differing perspectives on life and provides individually optimized improvement suggestions based on those biases, and a system that improves the accuracy of improvement suggestions by training AI with collected data, thereby enabling long-term support. [Solution] The present invention is a system comprising a user data management means 10 that collects data based on the individual user's worldview and values ​​and manages biases in that data, and an improvement suggestion provision means 50 that generates optimized improvement suggestions. Furthermore, an AI learning means 30 is configured to train an AI with the collected data in order to improve the accuracy of the improvement suggestions generated by the improvement suggestion provision means 50. This realizes a system in which optimal improvement suggestions are provided to each individual user, the accuracy of the suggestions is further improved through learning, and long-term support is possible.

Description

Technical Field

[0001] The present invention relates to a system that evaluates biases related to a user's outlook on life and provides improvement proposals optimized individually. Specifically, it is a technical system that utilizes the analysis of questionnaire data and learning algorithms using AI to propose different improvement menus for each user and continuously updates those proposals.

Background Art

[0002] Conventionally, it has been difficult to quantitatively evaluate emotional aspects such as a user's happiness and self - affirmation, and a large amount of resources have been required to provide individual improvement proposals. To address this, there are systems that collect and analyze questionnaire data, but there is a problem in that the reflection of proposals and feedback optimized for individual users is not sufficient. Also, in conventional systems, continuous updating of improvement proposals and cooperation with medical institutions have been issues.

[0003] In Patent Document 1, a system for providing behavior - modification messages for improving healthy behaviors based on data collected from individual users is proposed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the prior art described in the above Patent Document 1, a system for providing behavior - modification messages for improving healthy behaviors by utilizing individual data is described, but there is a problem in that it is unable to judge the suitability of the treatment load according to the user's condition and present an executable treatment method.

[0006] The objective of this invention is to provide a system that manages the biases in each user's differing worldview and provides individually optimized improvement suggestions based on those biases. Furthermore, by using AI to learn from the collected data, the system aims to improve the accuracy of improvement suggestions and enable long-term support. [Means for solving the problem]

[0007] The present invention is a system for managing biases in a user's worldview and for making individual improvements. a) User data management means for storing each user's personal data, b) A survey management system that accepts multiple survey entries from users, quantifies user bias based on the received survey responses, and registers the results in a database. c) An AI learning means that calculates the difference in bias between multiple survey data collected by the survey management means and outputs action suggestions for each user using the bias data. d) A menu management means that generates specialized improvement menus based on the individual user's preferences, in addition to improvement menus provided to all users in general. e) An improvement suggestion providing means that the AI ​​learning means learns the difference in bias, analyzes the bias of individual users, recognizes patterns in the fluctuations of bias, and provides personalized improvement suggestions to the user. It is something that is provided.

[0008] The system of the present invention further includes a means for sharing medical information, which manages information on medications prescribed by physicians and shares questionnaire information and bias analysis results with medical institutions.

[0009] The system of the present invention is equipped with a feedback means that utilizes the user's feedback loop and dynamically corrects individual biases while continuing to learn.

[0010] The system of the present invention dynamically adjusts the content and order of questionnaire questions based on the user's response tendencies and behavioral patterns using a questionnaire management means.

[0011] The system of the present invention includes a security protocol for securely sending and receiving psychological bias data and drug prescription information when the medical institution sharing means shares data with medical institutions.

[0012] The system of the present invention uses a unique analysis algorithm to improve the accuracy of biased data and is equipped with means for detecting specific biases.

[0013] The system of the present invention is equipped with notification means that provides notifications and feedback in real time and at appropriate times based on the user's status and bias data.

[0014] The system of the present invention utilizes LSTM (Long Short-Term Memory) or CNN (Convolutional Neural Network) models to optimize a user behavior prediction model and includes means for predicting future behavior and trends based on individual biased data.

[0015] The system of the present invention further includes a data management means equipped with a multi-stage privacy setting function that allows users to precisely set the scope of sharing of personal data and biased information. [Effects of the Invention]

[0016] This invention can evaluate the biases in each user's worldview and provide optimized improvement suggestions. Furthermore, by utilizing AI learning, it enables dynamic feedback in response to changes in biases. As a result, long-term support can be achieved, and continuous improvement can be expected. [Brief explanation of the drawing]

[0017] [Figure 1] This figure shows the overall system configuration of the present invention. [Figure 2] This is the hardware configuration diagram of the present invention. [Figure 3] This is the flowchart diagram of the present invention.

Embodiments for Carrying out the Invention

[0018] [First Embodiment] FIG. 1 shows the overall configuration of a system designed to manage the bias of a user's outlook on life and provide individual improvement suggestions. The system of the present invention includes a network 60, a user terminal 70 connected thereto, a teacher terminal 80, user data management means 10, questionnaire management means 20, AI learning means 30, menu management means 40, and improvement suggestion providing means 50.

[0019] The network 60 is an important infrastructure that connects all the means of this system and enables two-way communication of data. Through this network 60, the collection, analysis, and proposal provision of user data are smoothly coordinated. The user terminal 70 is the terminal 70 of the user operated by the user of this system, and the teacher terminal is the terminal 80 of the teacher operated by the teacher of this system.

[0020] The user data management means 10 provided in the system of the present invention includes a database that stores user age, gender, past health history, lifestyle, questionnaire results, etc. This data is provided to the questionnaire management means 20 and the AI learning means 30 and functions as the basis for the entire system. The questionnaire management means 20 receives multiple questionnaire inputs from the user and quantifies the bias based on the received questionnaire answers. The collected data is compared with reference values and statistical values, scored, and recorded in the database. This bias score is utilized in the analysis by the AI learning means 30.

[0021] The AI ​​learning means 30 of the present invention calculates the difference in bias among multiple survey data collected by the survey management means 20. The AI ​​learning means 30 analyzes user trends using a pattern recognition algorithm and identifies risk areas and areas with high potential for improvement. As a result, optimized action suggestions are generated for each user. The menu management means 40 generates common improvement menus for all users and individual, specialized improvement menus based on the bias data. The improvement suggestion provision means 50 continuously learns from the bias data by the AI ​​learning means 30 and analyzes new patterns. Through this means, it provides personalized suggestions optimized for each user and always presents the latest improvement suggestions.

[0022] More specifically, survey data is collected in a question format based on the Leicert scale, and biases are quantified. Then, the AI ​​learning tool 30 learns from the data and makes individualized suggestions. First, data on age, gender, health history, and lifestyle habits are collected from users through a Leicert scale-based questionnaire. The collected data is compared to pre-set baseline values ​​(e.g., statistical health target values ​​or average values ​​for the same age and gender), and the difference is calculated. This difference is converted into a score for each question item and quantified as a bias for each category (e.g., diet, exercise, sleep).

[0023] Next, the AI ​​learning means 30 analyzes this quantified data to understand the trends of each user. This analysis considers comparisons with past data and relationships with other users who have similar data to identify risk areas and areas with high potential for improvement. Furthermore, based on the analysis results, it prioritizes specific issues and generates concrete, actionable proposals. For example, if your salt intake exceeds the recommended limit, specific suggestions such as "try a particular low-salt recipe" will be offered. If you are not getting enough exercise, achievable behavioral goals tailored to your situation will be suggested, such as "start walking once a week."

[0024] This system aims to meticulously clean and preprocess collected survey response data, providing personalized improvement suggestions and decision-making support for each user. The data is first classified into numerical and text data, and then processed appropriately according to each format. For numerical data, outliers are detected using Z-scores and interquartile ranges (IQR) to minimize the impact of variability and outliers on the analysis results. Furthermore, responses with extremely short response times (e.g., within a specified number of seconds) are considered random to ensure data consistency and accuracy.

[0025] Furthermore, this system aims to accurately predict users' future behavior by highly quantifying user bias data and using a deep learning model. User bias data is vectorized, and the degree of fluctuation is quantified by comparing the current value of each data point with its past value. To maximize the model accuracy and generalization performance of the AI ​​learning means 30, parameters such as the learning rate and number of epochs are optimized using grid search or random search. In addition, L2 regularization is employed to prevent overfitting.

[0026] Furthermore, this system aims to respond quickly and flexibly to changes in users' psychological states and behaviors by monitoring user behavior data and psychological bias data in real time and immediately providing improvement suggestions when anomalies are detected. The bias data acquired from the aforementioned survey management means 20 is constantly monitored by anomaly detection models such as Isolation Forest and LOF (Local Outlier Factor). When an anomaly is detected, the system analyzes the data using time series analysis methods such as regression models and ARIMA models to predict the actions that users should take and the improvement measures they should implement.

[0027] Figure 2 shows the hardware configuration of the user terminal 70, teacher terminal 80, user data management means 10, questionnaire management means 20, AI learning means 30, menu management means 40, and improvement suggestion provision means 50 of the present invention. Each of the terminals and means comprises a processor 800, a display device 900, an input device 1000, a storage device 1100, and a communication device 1200. These components are connected by a bus.

[0028] The processor 800, an element constituting the hardware of the present invention, functions as a central processing unit, processing user data, survey results, and AI analysis in a unified manner. The storage device 1100 functions as storage for securely storing the user's personal data, survey responses, and data learned by the AI. The display device 900 provides an interface for visually presenting bias analysis results and improvement suggestions to the user, supporting intuitive understanding. The input device 1000 provides a means for the user to input survey responses and individual data, enabling bidirectional interaction with the system. The communication device 1200 functions as a device for exchanging data with other modules within the system, networks, and external databases, playing an important role in supporting distributed processing and external collaboration. By interconnecting these elements, a seamless, day-to-day tailored improvement menu based on biased data is provided.

[0029] Figure 3 is a flowchart illustrating the program of the present invention. The program in this invention is based on a unified, common menu A1, while the AI ​​utilizes each user's data to provide individually optimized improvement suggestions. Its purpose is to enable participants to deepen their self-understanding, re-examine their own behavior and values ​​from a broader perspective, and achieve sustainable growth. This program consists of three questionnaires and two common menus, A1 and B2. In the first questionnaire, the AI ​​collects basic data on the user's values, behavioral tendencies, and thinking patterns (Questionnaire Information Input 1, 3), analyzes the received questionnaire responses in real time, and quantifies biases (Quantifying Biases in Questionnaire Information 1, 4). Based on these results, individual characteristics such as "low stress tolerance" and "high concentration" are revealed, and points to be particularly focused on in the common menu A1 are identified. The aforementioned common menu A1 is a program menu that identifies biases in one's outlook on life by programming them via Zoom, and then improves those biases by participating in the menu. Furthermore, the aforementioned common menu A1 collects basic data on the user.

[0030] Next, the AI ​​conducts a second survey (second survey information input 5) and measures progress and changes by comparing it with the initial data (first survey information input 3). The AI ​​integrates the data to analyze progress (quantifying bias in the second survey information 6) and identifies areas where improvement is visible. Subsequently, for the areas where improvement is visible identified for each user, the AI ​​provides specific suggestions for the best improvements using the improvement suggestion provision means 50. Basic user data is collected again using the same program as the aforementioned common menu A1 but with different content, and the common menu B2.

[0031] As the final stage, a third survey is conducted (third survey data entry, 7), and progress and changes are measured by comparing it with the previous data (second survey data entry, 5). The AI ​​integrates the data to date to analyze progress and quantifies the overall results of the program (quantifying bias in the third survey data, 8). This allows participants to concretely understand their own growth.

[0032] This program aims to promote individualized self-understanding and growth by integrating participants' innate characteristics, acquired experiences, and insights from neuroscience, with comprehensive support from AI. To implement this, the program follows these steps to provide optimal support for each participant. First, during registration, information is collected through questionnaires regarding genetic characteristics, innate temperament, past successes, and values. This allows the AI ​​to understand each participant's characteristics and provide customized support based on this information. Next, participants are provided with a dedicated diary system. A simple format is used, and natural language processing technology is employed to analyze the recorded content. Furthermore, the program incorporates a neuroscience perspective to support growth that leverages neuroplasticity. Repeating small successes strengthens positive neural circuits. It also utilizes neurotransmitters such as dopamine and serotonin to encourage behaviors that enhance participants' happiness and motivation. Through this step-by-step approach, participants are supported in deeply understanding their own characteristics and finding concrete directions to live a life aligned with their values. This program respects individual characteristics and aims for AI to function as a supportive partner, while leveraging each participant's unique qualities. By providing psychological reassurance and scientific evidence, we support sustainable self-actualization and growth. [Industrial applicability]

[0033] The system of the present invention can be used in a variety of industrial fields, such as the following: 1. Healthcare Industry By analyzing users' health and psychological data, personalized behavioral suggestions can be provided. In healthcare settings, biased data and survey information can be used to offer individualized health improvement plans for patients. 2. Wellness Industry It can be applied to applications aimed at supporting mental health and improving lifestyles. The AI ​​learning method 30 individually analyzes the user's biases and generates appropriate improvement menus (menu management method 40). 3. Employee Assistance Programs (EAPs) This system can be applied to companies as a program to manage employees' psychological biases, aiming to reduce stress and improve productivity. By providing 50 improvement suggestion methods in real time that notify employees of dynamic suggestions based on bias data, it supports the maintenance of employee health. 4. Educational setting This system can be used to analyze each student's learning biases and psychological state, and to provide appropriate learning plans and support. By collecting data using the questionnaire management method 20 and generating behavioral suggestions, it becomes possible to provide individualized support to students.

[0034] Because this invention provides personalized action suggestions while having long-term improvement effects, it is expected to be used in these fields. [Explanation of Symbols]

[0035] 1. Menu A (common to all) 2. Menu B (common to all) 3. First time entering survey information. 4. Quantifying the bias in the first round of survey information. 5. Entering survey information (second time) 6. Quantifying the bias in the second round of survey data. 7. Third time entering survey information. 8. Quantifying the bias in the third survey data. 10. User data management means 20. Survey Management Methods 30 AI Learning Methods 40 Menu Management Methods 50 Means for providing improvement suggestions 60 Networks 70 User Terminals 80 Teacher terminals 800 processors 900 Display device 1000 Input Devices 1100 Storage device 1200 Communication equipment

Claims

1. It is a system designed to manage the biases in users' worldviews and to make individual improvements. a) User data management means for storing each user's personal data, b) A survey management system that accepts multiple survey entries from users, quantifies user bias based on the received survey responses, and registers the results in a database. c) An AI learning means that calculates the difference in bias between multiple survey data collected by the survey management means and outputs action suggestions for each user using the bias data. d) A menu management means that generates specialized improvement menus based on the individual user's preferences, in addition to improvement menus provided to all users in general. e) An improvement suggestion providing means that learns the difference in bias using the AI ​​learning means, analyzes the bias of individual users, recognizes patterns in the fluctuations of bias, and provides personalized improvement suggestions to the user. A system equipped with this feature.

2. The system according to claim 1, further comprising a means for sharing medical information, which manages information on medications prescribed by doctors and shares questionnaire information and bias analysis results with medical institutions.

3. The system according to claim 1 or 2, comprising a feedback means that utilizes the user's feedback loop to dynamically correct individual biases and continue learning.

4. The system according to claim 1, characterized in that it dynamically adjusts the content and order of survey questions based on the user's response tendencies and behavioral patterns using a survey management means.

5. The system according to claim 2, wherein the means for sharing information between medical institutions includes a security protocol for securely sending and receiving psychological bias data and drug prescription information with respect to data sharing with medical institutions.

6. The system according to claim 1, comprising means for detecting a specific bias using a proprietary analysis algorithm for improving the accuracy of biased data.

7. The system according to claim 1 or 2, comprising a notification means that provides notifications and feedback in real time and at an appropriate time based on the user's status and bias data.

8. The system according to claim 1, comprising means for optimizing a user behavior prediction model using an LSTM (Long Short-Term Memory) or CNN (Convolutional Neural Network) model and predicting future behavior or trends based on individual bias data.

9. The system according to claim 1, further comprising a data management means equipped with a multi-stage privacy setting function that allows users to set in detail the scope of sharing of personal data and biased information.

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