Social system connection management system, method and program

Abstract

This invention provides a social system connection management system that can mitigate various social issues by constructing a service integration platform, calculating a trust score for all of a member's system connection behaviors, and realizing a social system integration control OS that can control connections according to the calculated score. [Solution] Member terminals for connecting to social systems, service provider terminals, medical institution terminals, corporate terminals, etc. are connected to a network, and management servers are installed on these terminals. The service integration platform and management server include a system configuration presentation unit that displays the system name, system overview, responsibility conditions, and compensation conditions; an acceptance confirmation unit that obtains the member's acceptance of the content displayed on the system configuration presentation unit; a score calculation unit that calculates a trust score generated for each member; a history recording unit that records the acceptance history and past connection behavior history; and a connection acceptance / rejection determination unit that determines whether or not to connect to the social system using the trust score, acceptance history, and past connection behavior history.

Description

Technical Field

[0001] The present invention relates to a social system connection management system, method, and program, and particularly to calculating a trust score in all institutional connection actions of members suitable for reducing social issues by managing the connection of an individual's entire connection behavior history to a social system based on the trust score, and performing a connection permission determination according to the calculated score.

Background Art

[0002] Conventionally, as a social issue, there has been a problem of unpaid medical expenses.

[0003] Payment at medical institutions is mainly in cash, and cashless payment has not advanced. For example, even if a medical institution attempts to go cashless, there are multiple competing methods, making it difficult to determine which method to choose, thus hindering the progress of cashless payment. In recent years, the number of foreign tourists visiting Japan has increased rapidly, and the problem of unpaid medical expenses by foreign tourists visiting Japan without cash has become more serious. In addition, the non-payment of fines due to foreign tourists visiting Japan driving a car and not observing traffic rules has also become a social problem.

[0004] When a patient pays medical expenses by credit card, the medical institution can surely obtain the medical expenses. However, if the medical institution has not introduced cashless payment means such as credit, and only uses cash payment, there are cases where the patient cannot prepare cash at the time of payment. As a result, there is a problem that non-payment occurs.

[0005] Regarding the problem of such unpaid medical expenses, for example, in Patent Document 1, a medical service transaction device is disclosed that is configured to previously allow a user who uses a medical institution to purchase medical services of the medical institution using an e-commerce trading environment, settle a predetermined prepayment amount with the medical institution used by the user, and notify the user of a settlement completion notice indicating that the settlement has been completed.

[0006] Furthermore, Patent Document 2 discloses a contact candidate determination program that can streamline the process of contacting unpaid bills by determining potential contacts for unpaid bills based on the account balance of unpaid patients and the person who made a deposit into the unpaid patient's account, and by outputting a screen displaying the potential contacts to a terminal for medical institutions. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2021-099700 [Patent Document 2] Japanese Patent Publication No. 2020-009096 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] While the aforementioned Patent Documents 1 and 2 can resolve the problem of unpaid medical expenses to some extent, they do not provide a fundamental solution. The same was true not only for medical expenses but also for unpaid amounts for other services and goods.

[0009] The present invention has been made in view of the above-mentioned problems, and the object of the present invention is to provide a social system connection management system, method, and program that can mitigate various social issues such as reducing administrative costs by constructing a service integration platform, calculating a trust score for all of the user's system connection behaviors in response to social issues, and making a determination of whether or not connection is possible according to the calculated trust score. [Means for solving the problem]

[0010] The social system connection management system according to the first invention comprises a member terminal used by a member connecting to a social system, a service provider terminal managed by a service provider providing the social system, a medical institution terminal, a corporate terminal, a store terminal, and a financial institution terminal, all of which are connected via a network, a management server that manages the overall operation of these terminals, and a service integration platform on which data exchange between networks is shared, the member terminal having at least the function to receive social system connection services based on the member's trust score, the management server, upon receiving a connection request from the member terminal, includes a system configuration presentation unit that displays the system name of the social system to be connected to, a system overview displaying the provider, liability conditions indicating submission obligations and restrictions, and compensation conditions indicating penalties and obligations, an understanding confirmation unit that obtains the member's understanding of the content displayed in the system configuration presentation unit, and a score calculation unit that calculates a trust score generated for each member if the understanding confirmation unit determines that the member is understanding. Delivery Acquisition history and Related to the connection process to the aforementioned social system The system includes a history recording unit that records past connection behavior history, and a connection eligibility determination unit that determines whether or not a member can connect to the social system using the trust score calculated for each member by the score calculation unit, the satisfaction history recorded in the history recording unit, and the past connection behavior history.

[0011] The social system connection management system according to the second invention is configured such that the consent confirmation unit presents the conditions, responsibilities, compensation, and restrictions of the system step by step through a chat-type interface, and obtains the member's consent in a YES / NO format.

[0012] The social system connection management system according to the third invention includes, in terms of the trust score, acceptance history, compensation history, responsibility fulfillment history, number of successful system connections, blocking history, objection history, and post-mortem contribution history. History This configuration is based on calculations performed using multiple history elements.

[0013] The social system connection management system according to the fourth invention is such that the connection feasibility determination unit is the trust Score The settings configured on the aforementioned management serverThis configuration allows connection if the value is above the connection threshold, and transitions to either compensation induction processing or connection disconnection processing if it falls below the threshold.

[0014] The social system connection management system according to the fifth invention is used when a user fails to connect to the system. , reason The system includes a compensation re-guidance configuration that explicitly displays the reason in the chat UI, presents compensation proposals and options for re-fulfilling responsibility, and performs score recovery and connection re-evaluation in response to the execution of compensation, and the compensation re-guidance configuration has score fluctuation log output or compensation type identifier (compensation_type).

[0015] The social system connection management system according to the sixth invention includes user connection history, compensation history, satisfaction history, score history, and objection handling history. History It features a history recording structure that allows for structuring, saving, and outputting data on a GID (Global Identity) basis.

[0016] The social system connection management system according to the seventh invention includes a configuration that allows users to file objections to the connection determination results. ,main Zhang, Jinshi, Ri History It will be possible to submit it in conjunction with the chat log. Connection feasibility determination by the connection feasibility determination unit It includes an objection structure that may be re-evaluated or temporarily suspended.

[0017] The social system connection management system according to the eighth invention includes an AI decision configuration related to system blocking processing, and the AI ​​decision configuration is judged Interruption history, parameters used, learning structure To become It has an AI trust configuration that includes a third-party audit API that allows auditing and disclosure by a third-party organization, and a JSON output format for the judgment history.

[0018] The social system connection management system according to the ninth invention includes the trend of the trust score, the level of satisfaction with the constituent items, the compensation record, and the connection status. The result It features a configuration that visualizes the data using a chat UI or a graph display UI.

[0020] The 10The social system connection management system according to the invention has a configuration prompt acceleration configuration that automatically compares the items of the system configuration presented at the time of system connection with the items of the configuration that have been accepted in the past, and optimizes the omission of display or the presentation order.

[0021] No. 11 The social system connection management system according to the invention has a connection prerequisite configuration that requires maintaining a trust score of a certain level or higher as a prerequisite for system connection, and presents a non-connection or limited connection mode to users below the standard.

[0022] No. 12 The social system connection management system according to the invention has a non-response risk detection configuration that, for users who do not respond to system connection, automatically activates the behavior analysis AI when detecting non-response for a certain period of time, and analyzes and notifies risks such as disappearance, hospitalization, and disasters.

[0023] No. 13 When the trust score of the social system connection management system according to the invention exceeds a certain standard, the system automatically applies preferential measures such as simplified procedures, expanded credit limits, and rapid reconnection Transformation It has a trust reward configuration that automatically applies rewards.

[0024] No. 14 The social system connection management system according to the invention has a configuration comparison configuration that compares and displays the responsibility items, filling conditions, and trust score thresholds of the system configuration among multiple systems, and provides system selection support to users. Value It has a configuration comparison configuration that compares and displays among multiple systems, and provides system selection support to users.

[0025] No. 15 The social system connection management system according to the invention has a system configuration output configuration that makes the connection configuration of the system output and reusable in the form of PDF, referral code, and configuration template.

[0026] No. 16 The social system connection management system according to the invention has the connection success rate, cut-off rate, filling implementation rate, and objection filing rate of the system rateThe system includes a learning configuration that uses statistics to train an AI, enabling it to automatically optimize and reconfigure the system's UX structure, responsibilities, and difficulty level. This configuration includes updating procedures for learning target parameters and reconfiguration templates.

[0027] The 17 The social system connection management system described in the invention tracks the user's connection history, compensation history, score trends, and satisfaction level. To become It features a historical visualization configuration that displays data chronologically and uniformly, making it usable for reliability assessments and system selection decisions.

[0028] The 18 The social system connection management system described in the invention features an integrated UX control configuration that enables the entire process, from system disruption, connection failure, compensation guidance, re-examination, to trust restoration, to be completed within a single UX chat configuration.

[0029] The 19 The social system connection management system related to the invention includes, in the posthumous system connection recommendation notification, system type, certificate of trust, configuration log, contribution history, number of acceptances, and compensation details. Achievements It includes a post-mortem recommendation notification configuration that can be attached and automatically generated and sent.

[0030] The 20 The social system connection management system described in the invention generates a trust ranking per GID unit based on multiple system connection histories, trust scores, reasons for disconnection, and recovery success history, and provides connection priority, recommended systems, and system risk predictions for the entire social system. Measurement It includes a proposed score analysis structure.

[0031] The 21 The social system connection management system described in the invention has a structure that allows it to submit the history of AI decisions regarding system blockade to a third-party organization. , judgment Disconnection details, usage history, learning parameters Ta It features an AI-powered disclosure architecture that allows for review, auditing, and correction.

[0032] The 22 The social system connection management system related to the invention will be used when the person files an objection to the system notification. ,main Contents of the report, evidence, and history Proof It includes an objection system that allows for submission in conjunction with notification logs, resulting in the temporary suspension and re-evaluation of the system's decision.

[0033] The 23 The social system connection management system related to the invention aggregates acceptance history, blocking history, system connection history, and trust score per GID unit. A Based on this, it includes a cross-institutional analysis structure that performs institutional trust analysis between social institutions, proposes institutional improvements, and identifies factors causing connection errors.

[0035] The 24 The social system connection management system described in the invention features a user interface (UI) that visualizes an individual's social trust history by displaying their system connection actions, acceptance actions, responsibility fulfillment, compensation record, and contribution history in a unified chronological order.

[0036] The 25 The social system connection management system described in the invention automatically activates a behavioral analysis AI if the period of non-response to notifications exceeds a specified value, and identifies the individual's connection difficulties (disappearance, hospitalization, disaster). ) It features a notification non-response analysis configuration that analyzes and predicts the possibility of [something happening].

[0037] The 26 The social system connection management system described in the invention provides connection benefits (simplified system usage, expanded credit limit, and faster reconnection) to GIDs who exceed a certain trust rank, based on their system connection history and personal behavior score. ) It features a trust-based reward structure that enables automatic application.

[0038] The 27 The social system connection management system according to the invention, in principle, prohibits or restricts system connection for those who do not possess a GID, and in order to maintain system reliability, it includes a system connection prerequisite configuration that requires the issuance of a GID as a connection prerequisite.

[0039] The 28The social system connection management system according to the invention comprises a system connection configuration, an acceptance structure, a blocking process, a recovery configuration, a post-mortem succession configuration, an AI judgment configuration, a notification control configuration, an international cooperation configuration, a trust score control configuration, and a social system integration control OS which is a configuration that links and integrates all components.

[0040] The 29 The social system connection management method according to the invention is a social system connection management method in a social system connection management system equipped with a service integration platform in which member terminals used by members connecting to a social system, service provider terminals managed by service providers that provide the social system, medical institution terminals, corporate terminals, store terminals, and financial institution terminals are connected to a network, a management server is installed to manage the overall operation of these terminals, and data exchange between networks is shared, comprising: a system configuration presentation step in which, when a system configuration presentation unit receives a connection request from the member terminal, the system outline display showing the system name of the social system to be connected, the provider, the liability conditions showing the submission obligations and restrictions, and the compensation conditions showing the penalties and obligations; an understanding confirmation step in which an understanding confirmation unit obtains the member's understanding of the contents displayed in the system configuration presentation step; a score calculation step in which, when a score calculation unit determines that the member has understood based on the understanding confirmation step, calculates a trust score generated for each member; and a history recording unit ga Acquisition history and Related to the connection process to the aforementioned social system The system includes a history recording step for recording past connection behavior history, and a connection eligibility determination step in which a connection eligibility determination unit determines whether or not to connect to the social system using the trust score calculated for each member by the score calculation step, the satisfaction history recorded in the history recording unit, and the past connection behavior history.

[0041] The 30 The social system connection management program according to the invention is installed on a computer, meeting Upon receiving a connection request from a member terminal, the system configuration presentation step displays the name of the social system to be connected to, an overview of the system indicating the providing entity, responsibility conditions indicating submission obligations and restrictions, and compensation conditions indicating penalties and obligations. te kaiA consent confirmation step to obtain the member's consent, and a score calculation step to calculate a trust score generated for each member if it is determined that the member is consented based on the consent confirmation step. Delivery Acquisition history and Related to the connection process to the aforementioned social system The system is executed as a history recording step, which records past connection behavior history, and a connection eligibility determination step, which uses the trust score calculated for each member in the score calculation step, the satisfaction history recorded in the history recording unit, and the past connection behavior history to determine whether or not connection to the social system is possible. [Effects of the Invention]

[0043] According to the present invention, the following effects can be obtained. By constructing a service integration platform, calculating a trust score for all of the user's system connection actions in response to social issues, and making a connection feasibility determination based on the calculated score, it is possible to realize a social system connection management system that can reduce various administrative costs (e.g., by 20-30%). [Brief explanation of the drawing]

[0044] The drawings illustrate specific embodiments of the present invention relating to this disclosure, including not only essential components of the invention but also optional and preferred embodiments. [Figure 1] Figure 1 is a schematic block diagram of a communication network including a social system connection management system to which the present invention is applied. [Figure 2] Figure 2 is a schematic functional block diagram of a social system connection management system illustrating an embodiment of the present invention. [Figure 3]Figure 3 is a detailed functional block diagram of the social system connection management system shown in Figure 2. Figure 3(a) is the functional block diagram of the system connection request unit, Figure 3(b) is the functional block diagram of the system configuration presentation & consent acquisition unit, Figure 3(c) is the functional block diagram of the trust score calculation unit, Figure 3(d) is the functional block diagram of the connection feasibility determination unit, Figure 3(e) is the functional block diagram of the compensation guidance unit, Figure 3(f) is the functional block diagram of the objection processing unit, Figure 3(g) is the functional block diagram of the connection history recording unit, Figure 3(h) is the functional block diagram of the post-mortem corporation termination processing unit, Figure 3(i) is the functional block diagram of the automatic recommendation system guidance unit, Figure 3(j) is the functional block diagram of the system configuration comparison selection unit, Figure 3(k) is the functional block diagram of the trust structure visualization inheritance unit, and Figure 3(l) is the functional block diagram of the system AI learning improvement unit. [Figure 4] Figure 4 shows an example of the hardware configuration of the member terminal shown in Figure 1. [Figure 5] Figure 5(a) shows an example of the screen of a mobile device with the Social System Connection Management app installed, and Figure 5(b) shows an example of the rear camera of a mobile device. [Figure 6] Figure 6 is a schematic processing flowchart of the social system connection management application of the present invention. [Figure 7] Figure 7 shows an example of the hardware configuration of the management server shown in Figure 1. [Figure 8] Figure 8 shows an example of the hardware configuration of an LLM server. [Figure 9] Figure 9 is a flowchart for requesting system connectivity. [Figure 10] Figure 10 is a flowchart outlining the system structure and how to gain understanding from it. [Figure 11] Figure 11 is a flowchart for calculating the confidence score. [Figure 12] Figure 12 is a flowchart for determining whether a connection is possible or not. [Figure 13] Figure 13 is a flowchart of the compensation guidance process. [Figure 14] Figure 14 is a flowchart for the objection process. [Figure 15] Figure 15 is a flowchart showing the connection history. [Figure 16] Figure 16 is a flowchart for post-mortem and corporate termination processing. [Figure 17] Figure 17 shows a comparison of institutional structures and a selection flowchart. [Figure 18] Figure 18 shows the visualization of the request structure and the inheritance flowchart. [Figure 19] Figure 19 shows the AI ​​learning and improvement flowchart for the system. [Figure 20] Figure 20 shows a chat control flowchart for the compensation guidance configuration. [Figure 21] Figure 21 shows the layered structure of the social system integrated control OS. [Figure 22] Figure 22 shows the architectural configuration of the social system integrated control OS. [Figure 23] Figure 23 shows the data communication flow diagram for the social system integrated control OS. [Modes for carrying out the invention]

[0045] The embodiments illustrated below, applying the present invention, will be described with reference to the drawings.

[0046] <Embodiment 1> <Overview of Embodiment 1> The embodiments will be described in detail below with reference to the attached drawings. In this example, publicly known technologies will not be explained. Furthermore, the structures and methods described are illustrative examples for realizing the technical idea of ​​the invention, and the technical idea of ​​the present invention is not limited to those described below. The technical idea of ​​the present invention can be modified in various ways within the scope of the claims. In particular, it should be noted that the drawings are schematic and may differ from reality. The embodiments represent the most preferred form of the invention, and the invention is not limited thereto.

[0047] The social system connection management system of Embodiment 1 includes a consent acquisition means that displays the system configuration in a chat-type or structured UI and obtains the user's consent to perform pre-connection processing; a score calculation means that calculates a trust score based on connection history, compensation history, responsibility history, etc., based on GID (Global Identity); a connection determination means that determines whether system connection is possible or whether compensation guidance is provided according to the score; a history recording means that records the behavioral history after system connection in a DB; a post-mortem succession means that seals and outputs the trust history and sends a recommendation notification upon death or termination of the corporation; a system recommendation configuration that automatically applies a recommendation system or simplified configuration according to the score; and a system learning configuration that dynamically improves the system configuration by having an AI learn behavioral statistical data in system connections. Furthermore, the system learning configuration includes procedures for updating learning target parameters and reconstruction templates.

[0048] The social system connection management system of Embodiment 1 includes a post-mortem succession configuration that includes a post-mortem notification method (email / API linkage) and a security mechanism (electronic signature) that, upon the user's death, seals the acceptance history, connection history, compensation history, contribution history, trust score, etc., accumulated in GID, and outputs and notifies the designated heir or system administrator as a post-mortem trust history certificate. Furthermore, upon the death of the individual, it includes a post-mortem disclosure configuration that automatically records and seals the system connection history, contract history, acceptance history, contribution history, trust score, etc., and outputs and notifies the system administrator or heir as a post-mortem history certificate.

[0049] Figure 1 is a schematic block diagram of a communication network system including a social system connection management system to which the present invention is applied. Figure 2 is a schematic functional block diagram of a social system connection management system showing an embodiment of the present invention, and Figure 3 is a detailed functional block diagram of the social system connection management system of Figure 2. Figure 3(a) is a functional block diagram of the system connection request unit 21, Figure 3(b) is a functional block diagram of the system configuration presentation & consent acquisition unit 22, Figure 3(c) is a functional block diagram of the trust score calculation unit 23, Figure 3(d) is a functional block diagram of the connection feasibility determination unit 24, Figure 3(e) is a functional block diagram of the compensation guidance processing unit 25, Figure 3(f) is a functional block diagram of the objection processing unit 26, Figure 3(g) is a functional block diagram of the connection history recording unit 27, Figure 3(h) is a functional block diagram of the post-mortem corporation termination processing unit 28, Figure 3(i) is a functional block diagram of the automatic recommendation system guidance unit 29, Figure 3(j) is a functional block diagram of the system configuration comparison selection unit 30, Figure 3(k) is a functional block diagram of the trust structure visualization inheritance unit 31, and Figure 3(l) is a functional block diagram of the system AI learning improvement unit 32.

[0050] In Figure 1, the communication network system shows a configuration in which multiple member terminals 1 and 3, a corporate terminal 4, and a social system connection management system 2 are connected to Cloud 7, and a management server 5, an LLM server 6, a medical institution terminal 8, a group of service providers 9, an e-commerce site 10, a store terminal 11, a financial institution terminal 12, a local government terminal 13, and a government agency terminal 14 are also connected to Cloud 7. The connection method of the communication network is not limited to this; if member terminal 1 is a mobile terminal, it may be connected to the network via a mobile communication line. Connection may also be made via WiFi (registered trademark) or Bluetooth (registered trademark).

[0051] In the communication network system shown in Figure 1, the social system connection management system acts as a service integration platform, serving as the entry point to the portal site that transmits social system connection requests. It can also provide added value, such as point accrual, to services accessed through this portal site. The medical institution terminal 8 is linked to the social system connection management system 2, and provides information such as unpaid medical expenses to the social system connection management system 2. Through the social system connection management system 2, it guides the system to compensate for unpaid medical expenses, thus acting as a substitute for a part of debt collection. Service provider group 9 is a collection of terminals that provide other services linked to the social system connection management system 2. These other services include terminals from educational institutions, sports gyms, and cultural centers, which provide information such as unpaid tuition fees and tuition payment status to the social system connection management system 2. EC site 10 is linked to social system connection management system 2, and provides information such as unpaid product information to social system connection management system 2, guiding users to compensate for unpaid product costs via social system connection management system 2, thereby acting as a substitute for a part of debt collection. The store terminal 11 includes convenience store terminals, supermarket terminals, drugstore terminals, etc. If the store terminal 11 is a convenience store terminal, when a member uses the convenience store, the store terminal 11 provides point awarding information to the management server 5, and the data is stored. The financial institution terminal 12 provides information on the payment of taxes, pensions, insurance premiums, etc., to the management server 5. Control by the management server 5 is performed, for example, by the REST API communication protocol. The local government terminal 13 is a terminal that is stolen by prefectural and municipal government offices, and it is linked to the social system connection management system 2 and provides information to users of administrative services. Government agency terminals 14 are terminals installed in each ministry and agency, and are used to receive various applications and other communications.

[0052] Member terminal 1 can be used with mobile devices such as smartphones, tablet PCs, and touch-input computers. While member terminal 1 is primarily for pre-registered devices, those using the social system connection management system 2 can also use unregistered users for a fee of 500 yen per use.

[0053] In Figure 2, the social system connection management system 2 includes a system connection request unit 21, a system configuration presentation & acceptance acquisition unit 22, a trust score calculation unit 23, a connection feasibility determination unit 24, a compensation guidance processing unit 25, an objection processing unit 26, a connection history recording unit 27, a post-mortem corporation termination processing unit 28, an automatic recommendation system guidance unit 29, a system configuration comparison and selection unit 30, a trust structure visualization and inheritance unit 31, and a system AI learning improvement unit 32. When these functions are applied to a mobile terminal (smartphone) as the member terminal 1, no special machinery or equipment is required; only the value provision control application is installed on the standard hardware of the mobile terminal (iPhone® or Android® terminal). Alternatively, these functions may be housed in the management server 5 to configure a browser-based system for mobile terminals. In that case, under the control of the management server 4, the user can launch the browser on the member terminal 1, log in to the management server 5 from the browser, display the screen sent from the management server 5, input information, or answer questionnaires to control system connections. In these interactions, voice input from the member terminal or image input captured by the camera may be used. The above-mentioned social system connection management system 2 shows an example in which the system connection request unit 21, system configuration presentation & acceptance acquisition unit 22, trust score calculation unit 23, connection feasibility determination unit 24, compensation guidance processing unit 25, objection processing unit 26, connection history recording unit 27, post-mortem corporation termination processing unit 28, automatic recommendation system guidance unit 29, system configuration comparison selection unit 30, trust structure visualization inheritance unit 31, and system AI learning improvement unit 32 are implemented as software programs, but they may also be implemented as hardware configurations.

[0054] The Social System Connection Management System 2 will be explained below with reference to Figures 3(a) to 3(l).

[0055] The system connection request unit 21 selects a system from the user terminal and sends a connection request.

[0056] The System Connection Presentation & Understanding Acquisition Unit 22 presents the details of the system (system name, responsibilities, compensation, etc.) and obtains understanding through a YES / NO format.

[0057] The confidence score calculation unit 23 aggregates the GID history, weights each history element, and sums the scores.

[0058] The connection feasibility determination unit 24 compares the score with the accuracy threshold and makes a connection determination.

[0059] The compensation guidance processing unit 25 presents the reason for compensation, selects a compensation method, reflects the execution result, and performs a connection re-evaluation.

[0060] The objection processing unit 26 inputs the type of objection and submits the evidence. It then performs an AI + human review and notifies the user of the result.

[0061] The connection history recording unit 27 records connection results, acceptance history, compensation status, objection decisions, and so on.

[0062] The post-mortem corporate termination processing unit 28 performs death detection, history sealing, recommendation code generation, and notification of inheritance recipients.

[0063] The automated recommendation system guidance unit 29 guides users to the automated recommendation system for high-scoring users.

[0064] The system structure comparison and selection unit 30 performs a system comparison using charts and tables and narrows down the options based on user preferences.

[0065] The trust structure visualization and inheritance unit 31 visualizes the trust structure and inherits it to the heirs.

[0066] The System AI Learning Improvement Unit 32 performs AI learning and improvement of the system. The System AI Learning Improvement Unit 32 has a system learning configuration that includes updating procedures for learning target parameters and reconstruction templates, which enable the system to automatically optimize and reconstruct the system's UX configuration, responsibility items, and configuration difficulty by having the AI ​​learn statistics such as the system's connection success rate, blockage rate, compensation implementation rate, and objection rate.

[0067] In the examples in Figures 3(a) to 3(l), when a user performs an application or other procedure using a mobile device such as a smartphone, the system configuration is displayed in a chat-type or structured UI, user consent is obtained, pre-connection processing is performed, a trust score is calculated based on connection history, compensation history, responsibility history, etc., based on GID, and a decision is made on whether to connect to the system or to guide compensation based on the calculated score. Then, the behavioral history after system connection is recorded in a database, and upon death or termination of the corporation, the trust history is sealed and output, and a recommendation notification is sent. This includes post-mortem succession means, a system recommendation configuration that automatically applies recommendation systems and simplified configurations according to the score, and a system learning configuration that dynamically improves the system configuration by having AI learn behavioral statistical data in system connections. As a result, a service integration platform can be built, and in response to social issues, a social system connection management system that can mitigate various social issues can be realized by calculating a trust score for all of the user's system connection actions and making connection eligibility decisions based on the calculated score.

[0068] Figure 4 shows an example of the hardware configuration of member terminal 1 as a mobile device.

[0069] In Figure 4, the computer configuration of the member terminal includes a CPU 41, HDD, non-volatile memory (ROM) 42 such as ROM, main memory (RAM) 43 such as D-RAM, display 47, software keyboard 48, communication interface 46, camera 44, microphone 49, and speaker 45, all connected to the system bus. Access by mobile terminals is selectively connected to the network as appropriate depending on the communication environment, such as 3G, 4G, 5G mobile wireless network services or WiFi® wireless connection. As the above configuration is a typical hardware configuration for mobile terminals, a detailed explanation is omitted here.

[0070] Mobile devices can log in to the management server 5 via a communication network 15 such as the internet through a communication interface 46. Login authentication is performed using the email address and password used during registration. If a user (member) registers with an SNS email address, login authentication is performed using the email address and password of that SNS.

[0071] Figure 5(a) shows an example of the screen of a mobile device with the Social System Connection Management app installed, and Figure 5(b) shows an example of the rear camera of a mobile device.

[0072] In Figures 5(a) and 5(b), the mobile terminal 50 includes a touchscreen display screen 51, a social system connection management application 52, a front camera 53a, rear cameras 53b and 53c, a microphone 54, a speaker 55, and a flash 56.

[0073] Figure 6 is a schematic processing flowchart of the social system connection application 52 of the present invention shown in Figure 5. The processing flow of the social system connection management application 52 will be described below.

[0074] The social system connection management app 52 includes a system connection request step 101, a system structure presentation and acceptance acquisition step 102, a trust score calculation step 103, a connection feasibility determination step 104, a compensation guidance processing step 105, an objection processing step 106, a connection history recording step 107, a post-mortem corporation termination processing step 108, an automatic recommendation system guidance step 109, a system structure comparison and selection step 110, a trust structure visualization and inheritance step 111, and a system AI learning improvement step 112.

[0075] When a user launches the social system connection management app 52, the system connection request step 101 is executed. When the system configuration presentation unit receives a connection request from the member terminal, it includes a system configuration presentation step that displays the system name of the social system to be connected, an overview of the system indicating the provider, responsibility conditions indicating submission obligations and restrictions, and compensation conditions indicating penalties and obligations. The system also includes an explicit interface step for consent actions where an explicit interface unit for consent actions confirms a YES / NO intention regarding the content displayed in the system configuration presentation unit. An explanation step in which an explanation unit explains the relationship between the social system to be connected and the trust score. If the re-presentation improvement proposal unit receives a NO intention confirmation via the explicit interface step and determines that the member is not convinced, it makes a re-presentation or improvement proposal step. If the score calculation unit receives a YES intention confirmation via the explicit interface step and determines that the member is convinced, it calculates the member's trust score. The system also includes a connection feasibility determination step in which the score calculated by the score calculation unit is compared with the system threshold to determine whether or not connection to the social system is possible.

[0076] The series of processes described above are carried out by sending the necessary information to the management server 5 and periodically recording and saving various information. After using the social system connection management application 52, AI learning-based social system connection management may be performed using the data in the history and management database 79 stored on the management server 5.

[0077] Figure 7 shows an example of the hardware configuration of the management server 5 shown in Figure 1. In Figure 7, the management server 5 includes a communication control unit 71, a reception unit 72, a program processing unit 73, a storage unit 74, a display unit 75, a system configuration database 76, a trust score database 77, an evaluation interpretation logic / evaluation criteria database 78, a history / management database 79, a prompt database 80, and an output unit 81. The storage unit 74 includes a system configuration presentation & acceptance acquisition unit, a trust score calculation unit, a connection feasibility determination unit, and a system AI learning improvement unit (AI request).

[0078] Other possible configurations include a compensation guidance processing unit, an objection processing unit, a connection history recording unit, a post-mortem corporation termination processing unit, an automatic recommendation system guidance unit, a system configuration comparison and selection unit, and a trust structure visualization and inheritance unit, as shown in Figures 2 and 3. These units may be implemented using software programs or other hardware configurations.

[0079] All connection behavior data actually used with the social system connection management system is stored in various databases on the management server 5, and real-time AI learning can be performed. In addition, the management server 5 can make an AI request to the LLM server 6 using the prompt database 80 and the evaluation interpretation logic / evaluation criteria database 78, and analysis can be performed by the generated AI.

[0080] Figure 8 shows an example of the hardware configuration of the LLM server 6. The LLM server 6 includes a processor 83, an input unit 84, a large-scale language model 85, a storage unit 86, a general-purpose generative AI module 87, an output unit 88, and a communication control unit 89. In the above embodiment, AI processing may be performed using the LLM server 6.

[0081] Figure 9 shows the characteristic pre-connection preparation steps of the Social System Connection Management System 2, which include displaying the system connection screen (step 201), selecting a system (step 202), entering the GID (step 203), and sending a connection request (step 204).

[0082] Figure 10 is a flowchart illustrating the system structure and how it is understood. The flowchart in Figure 10 shows data exchange between member terminal 1 and management server 5 via a chat UI.

[0083] When member terminal 1 sends a system connection request to management server 5, management server 5 receives the system connection request and sends a screen showing the system details (system name, responsibility, compensation, etc.) to member terminal 1, which displays it on the member terminal screen (step 301). At this point, the system configuration presentation step using the chat UI is executed. The system name to be connected to, the system overview, and the provider are presented. Next, the responsibility conditions are presented. For example, application obligations, required documents, usage restrictions, etc. are presented. Next, the compensation and penalty conditions are presented. For example, cancellation fees, restrictions for non-performance, etc. Next, the handling of the trust score is presented. For example, the conditions under which the trust score is increased in this system are presented.

[0084] Next, a YES / NO confirmation of understanding is performed (Step 302). The screen of member terminal 1 displays a question such as "(Question): Are you satisfied with the details of the system?" and shows [Yes (YES)] / [No (No)] selection buttons. The YES / NO determination is made (Step 303). If the YES button is selected (Step 304), the understanding is recorded in the history / management database 79 and the process transitions to the trust score calculation process (Step 304). If the NO button is selected (Step 304), the connection process is interrupted and terminated, and the re-presentation or improvement flow process is executed (Step 305).

[0085] In the above embodiment, the chat-style UI and step-by-step division create a user experience (UX) that makes the system structure "easy to read and understand." Explicit user input is obtained through YES / NO selection. A YES selection serves as the starting point for adding points to the trust score. Branches resulting from a NO selection can be used as a log for re-presenting the system structure or improving the explanation of the structure (linked to system improvement).

[0086] Figure 11 is a flowchart for calculating the trust score. The flowchart in Figure 11 shows how the trust score is calculated using historical data between member terminal 1 and management server 5, illustrating the mechanism for score evaluation during system connection.

[0087] First, the historical data is referenced based on the GID (Step 401). That is, historical data is collected based on the GID. Examples of historical data include acceptance history, connection history, compensation history, blocking history, etc. Other data, such as objection history, may also be included.

[0088] The confidence score is weighted based on factors such as acceptance history, compensation history, blocking history, and objection history (Step 402). Specifically, score evaluation rules are obtained for each history element. For example, the score evaluation rules are set as follows: acceptance history +3 points, compensation history +5 points, blocking history -10 points. Then, a time series filter is applied to weight the evaluation based on actions over the past n months.

[0089] Next, the score is calculated (step 403). That is, the total score is calculated and the score changes (score change log output) are recorded in the database. The score thresholds are set, for example, S rank is 90 points or higher, A rank is 75 points or higher, etc.

[0090] Next, the scores are visualized (step 404). That is, the scores are recorded and then visualized. The history database is updated and the data for the chart is saved.

[0091] In the example above, the score components are based on multiple behavioral histories such as "agreement," "compensation," "blocking," "commitment," and "inheritance." The weighting mechanism is designed so that newer history is reflected more heavily in the score (currentness of trust). The evaluation classification divides users into ranks according to their scores, which serves as the basis for preferential treatment or restriction of connections.

[0092] Figure 12 is a flowchart for determining whether a connection is possible. The flowchart in Figure 12 shows the process by which the possibility of establishing a connection is determined between member terminal 1 and management server 5 based on a trust score.

[0093] First, the score is compared with the system threshold (step 501). That is, at the start of the connection eligibility determination process, the confidence score calculated in advance in the preprocessing is obtained, and the connection criteria value for the system to be connected is obtained. For example, connection criteria values ​​such as System A: 65 points or higher, System B: 65 points or higher are obtained, and the confidence score matching process is performed.

[0094] Next, the score is compared with the threshold (step 502). If the score is greater than or equal to the threshold (step 502), the connection is successful (step 503) and the connection is completed (step 504). That is, the connection permission processing step is executed, and connection recording, notification, recommendation processing, etc. are performed. If the score is not met (step 505), compensation guidance is performed (see Figures 3 and 20 below). If the score is below the lower limit (step 502), the connection is blocked (step 506). After that, the reason for the connection blockage is presented, or an objection is filed (step 507).

[0095] Step 501, as described above, is a preparatory process for comparing the score calculated in the previous step (Figure 10) with the standard score for each system. In steps 501 and 503, if the score meets the connection conditions, the process proceeds to immediate connection processing and recommendation processing. In steps 502 and 505, if the score falls below the standard but is within a range that can be recovered through compensation, the process transitions to the compensation guidance configuration. In steps 502, 505, and 506, if the score is clearly below the standard and compensation is not possible, the connection is blocked and the user is notified of the reason.

[0096] Figure 13 is a flowchart of a characteristic compensation induction process of the present invention. First, the compensation guidance process involves presenting the reason for compensation (step 601). For example, it might display "Outstanding payments" or "Insufficient documents."

[0097] Next, a means of compensation is selected (step 602). For example, this could be "payment," "action," or "explanation."

[0098] Next, the execution results are reflected and the score is added to the confidence score (step 603).

[0099] Next, a reconnection evaluation is performed, and either "allow connection" or "reconnect" is initiated (step 604).

[0100] Figure 20 is a chat control flowchart for the compensation guidance configuration. First, the system connection request receiving step is executed (step 1301). That is, the user performs the "apply for or make a reservation for the system" step. Next, the trust score determination step is executed (step 1302). For example, if the user's current trust score is 68 points and the trust score required for system connection is 80 points (the system's standard value), the trust score will be deemed insufficient. Next, the compensation reason presentation step via chat UI is executed (step 1303). The displayed message will be something like, "Your trust score does not meet the system connection criteria" or "The following non-performance / failure to fulfill responsibilities are recorded." Specifically, for example, "Unauthorized cancellation of past system reservations," "Unpaid amount: 5,000 yen," or "Failure to submit required documents" may be displayed.

[0101] Next, the step of displaying the compensation options in the chat is performed (step 1304). Several options will be displayed in the chat, as shown below. Display format (multiple options):

[0102] (1) Pay damages of 5,000 yen (online payment link included) (2) Submit a statement of reasons (PDF upload / form submission) (3) Perform designated support activities (e.g., surveys, volunteer work, etc.)

[0103] Next, the user selects and executes a compensation method (step 1305). For example, if the user selects "(2) Submit a statement of reasons," the compensation process is logged by the management server 5 and saved in the history and management database 79.

[0104] Next, the compensation completion confirmation step is performed (step 1306). That is, the flag `compensation_status = completed` is set. The management system adds points according to the type of compensation. For example, monetary compensation: +15 points, submission of a statement of reasons: +10 points, action compensation: +12 points, etc.

[0105] Next, the confidence score recalculation step is performed (step 1307). For example, if volunteer behavior compensation is provided, 12 points are added to the confidence score. The new score after recalculation is 68 points + 12 points = 80 points, as the current score is 68 points. This meets the system's standard, so the criteria are met.

[0106] Next, the connection re-evaluation and permission step is performed (step 1308), meaning the connection status becomes `approved`, and the displayed message is "Compensation is complete and your score has been restored," and "Connection to the '○○ Support' program has been permitted," and the program connection is permitted.

[0107] Next, the history database update + PDF certificate output step is executed (step 1309). Here, PDF certificate output is optional, not mandatory. It is possible to provide the information only to users who request it. Then, the compensation completion log and reconnection log are added to the GID history. A "Compensation Completion Certificate (PDF)" is automatically output to those who request it. The following table 1 shows an example of the processing graph and variable configuration.

[0108] [Table 1] If we let the new value of the user's current score be trust_score_new, then trust_score_new can be calculated using the following formula. Example of the formula: trust_score_new = (Σ w_i × factor_i) + compensation_adjustment. Here, the criteria for setting the weights of each factor_i (acceptance history, compensation history, withdrawal history, etc.) are shown.

[0109] Table 2 shows an example of the judgment elements and weights for each configuration.

[0110] [Table 2] Example of a score calculation formula (out of 100 points): Trust_Score = A×0.2 + B×0.25 + C×0.2 + D×0.1 + E×0.15 + F×0.1 Table 3 shows an example of symbols and classifications based on scores.

[0111] [Table 3]

[0112] Figure 14 is an objection processing flowchart; the objection processing section in the example in Figure 5 may be provided as an option.

[0113] First, enter the type of objection (Step 701).

[0114] Next, submit the evidence (Step 702).

[0115] Next, the AI ​​and the system administrator perform a re-evaluation / branching process (Step 703). As an example of AI evaluation criteria, the "number of times evidence has been submitted," "acceptance rate," and "past objection success rate" are scored.

[0116] Next, a notice of the objection result is issued (Step 704).

[0117] Figure 15 is a flowchart of the connection history recording. Below, we will explain how various behavioral histories related to institutional connections are recorded and managed on a GID (Global Identity Entity) basis.

[0118] Based on GID units, all connection actions / agreements / compensation / blocking / objection history are integrated and recorded in the history database (Step 801). Specifically, upon initiating the recording process of the system connection results, a verification step is performed to confirm the GID (User ID) as the unique identifier of the system-connected user. In other words, the GID (User ID) is verified. Although the GID is used as an identifier, it is treated as personal identification data, so security measures must be taken from the standpoint of protecting personal information. Therefore, an outline of the GID generation algorithm (e.g., SHA-256 hash + time seed) and a tamper-proof mechanism (digital signature / blockchain linkage) are adopted. Next, the connection log registration configuration step is executed. This registers the connection log, including the system ID, connection date and time, connection success / failure status, confidence score, and satisfaction status. Next, the acceptance history recording configuration step is performed. That is, the YES / NO history and time for each configuration step are recorded. Next, the compensation history registration configuration step is executed. That is, the compensation type (compensation type identifier (compensation_type), execution date and time, compensation judgment result, and compensation score change (score change log output) are registered. Next, perform the responsibility fulfillment history structuring step, which involves recording the fulfillment history of submitted documents, performance deadlines, proof of execution, and confidence points. Next, the blocking history registration configuration step is executed. This registers the blocking history, including the blocking date and time, reason code, impact level, and blocking period. Next, the objection history structuring step is performed. That is, the objection history, including the type of objection, the content of the claims, the evidence submitted, and the results of the reassessment, is recorded. Next, the post-mortem sealing process configuration step is executed. That is, upon death detection, the entire history is sealed and recorded for recommendation code generation. By performing the configuration steps described above, the integration and recording into the structured database will be completed.

[0119] The above GID management allows for cross-sectional recording of user histories and ensures compatibility with other systems. By recording multi-layered histories, it is possible to log multifaceted actions such as connection, acceptance, compensation, responsibility, and objection. After death, acceptance, compensation, and fulfillment of an individual's social credit can be reused as social credit values.

[0120] Figure 16 is a flowchart of post-mortem and corporate termination procedures. First, the death detection configuration step is executed (step 901). This involves matching the death certificate / family register information with administrative bodies. Death notifications are received from life insurance and medical systems linked to the GID. In this way, the death of the user is detected. Next, the history sealing process configuration step is executed (step 902). That is, the acceptance history, compensation history, contribution history, and trust score are locked on a GID basis. Then, proof values ​​are created in a trust-unmodifiable state.

[0121] Next, a recommendation notice is sent to the heirs (step 903). That is, the notification transmission configuration step is executed. The recommendation notice is sent to the designated heirs, successor organizations, corporations, and system administrators. This links to recommendation connections and inheritance preferential treatment in the other system. Here, the trusted recommendation information for which the recommendation notice is sent is generated by executing the trusted recommendation information generation configuration step. In the trusted recommendation information generation configuration step, recommendable items are automatically extracted from the sealed history. For example, system A recommendation, education support recommendation, credit rank inheritance, etc.

[0122] Next, the recommendation certificate output configuration step is executed (step 904). That is, a recommendation certificate consisting of trust history, system connection logs, contribution details, score, etc., is output as a PDF. At this time, a recommendation code (QR code / string) is added. This simplifies information access.

[0123] The recommendation notification in step 903 above may be performed after the configuration step in step 904 above. Needless to say, the order of each step may be changed as appropriate.

[0124] The above-mentioned death detection can be triggered by government cooperation, institutional notification, or third-party notification. Furthermore, the sealing process is irreversible to prevent tampering with scores and history after death. Regarding recommendation generation, it is expected that the deceased's behavior and trustworthiness will be utilized in the institutional connections of the next generation (family / organization). For the notification structure, a trust certificate with a recommendation code will be sent in PDF format or via email. For example, a medical trust certificate PDF will be automatically sent to insurance companies.

[0125] The following explains the system configuration template and chat display configuration. First, when the system connection is initiated, the configuration template loading process step is executed. That is, the configuration item templates corresponding to the system ID, as shown in Table 4, are retrieved from the database.

[0126] [Table 4]

[0127] Next, execute the chat UI display configuration (step structure) step. (Step 1) Perform the step of presenting basic information about the system. That is, present the system name, target audience, provider, and system overview. (Step 2) Perform the steps for presenting terms of use and responsibilities. That is, present obligations, requirements, limitations, and notes regarding application. (Step 3) Perform the step of presenting compensation and penalty conditions. That is, present the liability for non-performance, cancellation fees, system limitations, etc. (Step 4) Perform the step of presenting the conditions for linking with the confidence score. That is, present the conditions for adding points to the score, subtracting points, reconnection conditions, etc. (Step 5) Perform the step of summarizing the entire structure and obtaining consent. That is, ask for a YES / NO choice and record the history. If YES is chosen, proceed to Figure 11. If NO is chosen, end the structure or present it again (return to Figure 10).

[0128] The step-by-step display described above is designed with a user experience (UX) that prevents users from having to load a large amount of information at once (a step-by-step structure). The chat-style configuration allows for obtaining responses to the system content in a question-and-answer format, thereby improving the rate of acceptance. For templating, conditions and responsibilities are standardized for each system, making them reusable for other systems. The YES / NO response system records the user's intentions in a history, which can be used for scoring and future system applications.

[0129] Next, we will explain the multilingual support and international connectivity configuration. Once the system connection is established, language settings will be adjusted for foreign national / multilingual users. First, the user's language setting acquisition and configuration step is performed. That is, the user's language is determined by checking the account settings or the browser language of the device. Next, the system configuration template loading configuration step is executed. That is, the system configuration templates registered in the database are retrieved in each language. Next, the language switching display process for the chat UI is executed. For example, the language switching display can be set to Japanese / English / Korean / Vietnamese. Next, the sequential display step of the configuration items is executed in the user language. Specifically, the system name, conditions, responsibilities, compensation, and acceptance confirmation are displayed in the user language. Next, the YES / NO acquisition step is executed, and the agreement history is recorded along with the language log. Next, if there are items that are missing from the translation, the translation API connection configuration step is executed. For example, the API used (OpenAI API / Google Translate API, etc.) is connected to the translation cache processing, and the missing items are translated. After that, the process transitions to the score calculation process shown in Figure 11.

[0130] The automatic language detection process can dynamically change the display language according to the user's language environment. For multilingual templates, the translated text of the system configuration is pre-stored in a database (DB), and each configuration supports multiple languages. Regarding translation API connections, if there are missing translation items, they can be supplemented in real time via an external API. The language-specific satisfaction history records logs of which languages ​​were satisfied, making it usable for international UX evaluations.

[0131] Next, we will explain the recording and scoring structure of compensation actions. If the connection test results indicate that compensation is necessary, perform the following configuration steps. First, execute the step to configure the display of compensation reasons. For example, display compensation reasons such as insufficient score, unpaid history, or unauthorized cancellation. Next, the compensation proposal structure step is executed. That is, the user is presented with a selection of compensation actions. For example, sending a pledge / reaffirming agreement, watching an explanatory video / re-learning, paying a portion of the outstanding amount / submitting an explanatory document, etc. Next, the user performs the compensation selection / configuration step. That is, they select a compensation method via chat or UI and submit it.

[0132] Next, the compensation record configuration step is performed. This involves recording the compensation type, execution details, submission date, and system reception time. Next, the score re-evaluation configuration step is performed. That is, points are added to the score according to the compensation provided, and this is reflected in the reconnection determination. Next, the compensation result notification configuration step is executed, which notifies the user whether "compensation complete / reconnection possible" or "insufficient compensation / connection impossible". After performing the above steps, the process returns to the connection determination flow and proceeds to Figure 12 or Figure 20.

[0133] Regarding the explanation of the above reasons, we carefully presented to users why compensation was necessary to ensure their understanding and acceptance. For the multiple-choice format, we offered several compensation methods tailored to the user's behavioral characteristics. Regarding score reflection, the system was structured so that the trust score would recover and the likelihood of reconnection would increase depending on the quality and content of the compensation. For autonomous recovery, we implemented a structure of second chances based on personal responsibility, rather than social exclusion.

[0134] The following describes the connection results, history output, and certificate generation configuration. Once the system connection is complete, perform the following configuration steps. First, the configuration step for confirming the connection result is executed. That is, one of the following is explicitly recorded: connection successful, compensation complete, or connection failed. Next, the output request trigger determination configuration step (manual or automatic) is executed. Examples include a request for history output from the individual, a request for recommendation letter generation from the system, or automatic output due to the post-mortem sealing configuration.

[0135] Next, the historical data formatting configuration step is executed. For example, historical data such as connection date and time, system ID, trust score, satisfaction history, and compensation history are formatted. Next, the output format selection and configuration step (user-specified or system-specified) is performed. Examples include PDF certificate format, configuration template format (configuration document), and link format with recommendation code. Next, the output file generation configuration step (with diagrams, history, and proof elements) is executed. Here, the output file includes a QR code (registered trademark), digital signature, and issuing authority information. Next, the output transmission and storage processing configuration step is executed. Specifically, the output transmission and storage processing configuration includes the user's download / email attachment, automatic recommendation linking to the next system, and notification to heirs or representatives.

[0136] Regarding the aforementioned format diversity, the system flexibly supports output of certificates, templates, and recommendation links. For formal certification, it records not only the connection fact but also the history of acceptance, responsibility fulfillment, and compensation measures in one place. In terms of system integration, it enables automatic application in recommendation systems and connection with inheritance systems. For post-mortem handling, it is linked to a system that automatically outputs a sealing history and notifies heirs upon death (see Figure 16).

[0137] The following describes the automated application configuration of the recommendation system. Upon successful connection or completion of compensation, the history record database will be updated. The following explains the history record database update process. First, the confidence score threshold detection configuration step is executed. For example, it detects when a confidence score threshold is reached, such as S rank = score of 90 or higher / 3 or more successful compensations. Next, the recommended system list acquisition configuration step is executed. That is, the recommended system list is obtained by searching the database for recommended systems that match the score criteria. At this time, only those that do not conflict with or overlap with previously connected systems are extracted.

[0138] Next, the process of obtaining a simplified template for the recommendation system components is executed. For example, items are simplified by removing exemptions, omitting documents, and skipping items that have already been approved. Next, perform the step to configure automatic recommendation notifications to the chat UI. For example, a chat message will appear saying, "Based on your trust history, the following scheme is recommended," and "[Connect to Recommendation Scheme A]" / "[Save for Later]." Next, the immediate connection process (one-click connection) to the recommendation system is executed. This may include, for example, omitting the compelling structure, presenting a simplified structure, or expanding the confidence frame.

[0139] The above configuration steps are executed, the changes are reflected in the history record database, and the trust chain score is recalculated.

[0140] Regarding the above automatic recommendation conditions, the "trust history" will automatically bring in preferential treatment without any action from the user. Regarding simplified connection, the process is simplified because agreed-upon items are omitted, no resubmission of documents is required, and points are already added during processing. Regarding trust chaining, successfully connecting using the recommendation system will further increase the score, allowing for chaining to the next system.

[0141] The following explains the inheritance structure of trust history. When a user death or transfer trigger is detected, a trust history inheritance structure is created. First, perform the pre-registration configuration step for inheritance information. Specifically, you will specify and register the GID of the inheritance recipient either when connecting to the system or at a later date. For example, you will register family members, corporations, guardians, etc. Next, the death / corporate termination detection configuration step is executed. At this time, integration with the administrative system / death notification API / corporate dissolution data is performed.

[0142] Next, the trust history sealing process configuration step is executed. Specifically, the connection history, satisfaction history, compensation history, and score are locked and recorded in a tamper-proof state. Next, the process of extracting inheritable items is executed. Specifically, inheritable recommendation codes, contribution certificates, discount rights, etc., are extracted for each system.

[0143] Next, the notification process configuration step for the heirs is executed. Specifically, the designated GID is notified in the form of chat, email, or a recommendation letter with a PDF attachment. Next, the history connection configuration step for the heir's GID is executed. That is, when the heir connects to the system, the recommendation code application / exemption function is activated. As described above, a social cycle of trust history is established (linked to Figure 17).

[0144] Regarding the sealing function mentioned above, the trust history after death is stored in an unalterable format, thus maintaining its evidentiary value. Regarding beneficiaries, inheritance is determined by GID (Gender Identity Disorder), allowing for flexible designation of individuals, corporations, family members, and organizations. Regarding the recommendation system, recommendation systems, exemption systems, and trust inheritance certificates are automatically generated and notified. For social reconnection, the successor receives this history and is part of a trust chain mechanism that allows them to reconnect with the system.

[0145] Figure 17 is a flowchart outlining the system structure comparison and selection process. First, a system comparison is performed using charts / tables (Step 1001), and then the options are narrowed down based on user preferences (Step 1002). The system structure comparison and system selection support configuration are explained in detail below. Upon transitioning to the system selection screen, the following system configuration comparison and system selection support configuration steps will be executed. First, the process of acquiring and configuring connectable systems based on the user's GID is executed. That is, candidate systems are extracted from the database according to the trust score and acceptance history. Next, the metadata acquisition and configuration step for each system component is executed. For example, metadata such as responsibility requirements, compensation conditions, score requirements, and submission documents for each system are acquired. Next, the system comparison chart generation and configuration step is performed. That is, it is converted into a tabular or radar chart format. (Example) Comparison of system A vs system B vs system C, or (items) such as amount of documentation to be submitted / heaviness of responsibility / difficulty of compensation / level of trust requirement. Next, execute the UI display configuration step. Display the content using tabular, graphical, and color-coded formats, as shown below. Table format: "List of constituent elements by system" Diagram format: "Component radar chart" Color coding: Easy (green) / Medium (yellow) / Strict (red) Next, the user filter selection mechanism step is executed. Specifically, the user selects desired conditions such as liability reduction / simplified compensation / high reliability return, and the system that matches these conditions is highlighted by the automatic filter. Next, perform the configuration step for immediate connection or detailed chat display of the selection system (transition to the consent configuration in Figure 10).

[0146] The above comparison visualization visualizes the constituent elements in a list or chart. It is designed so that anyone can understand the "weight of the system." For condition filters, the display can be switched according to individual needs, such as "the simplest system possible" or "we can fulfill our responsibilities but want to avoid compensation." For immediate connection, selecting "Connect with this system" from the comparison results will switch to a chat-style configuration display (Figure 10).

[0147] Figure 18 shows the visualization and inheritance flowchart of the request structure. First, a time-series graph of the score is displayed (Step 1101). Next, a cross-system analysis is performed using AI (Step 1102). Then, rewards / exemptions are automatically applied according to the trust rank (Step 1103).

[0148] The following provides a detailed explanation of the cross-institutional trust analysis structure for Step 1102. It stores connection history, compensation history, and score history linked to GID. First, execute the configuration step for extracting historical data for each system. For example, classify the data by system ID (e.g., medical A, education B, subsidy C), and organize items such as the number of connections, the number of interruptions, and the compensation success rate. An example of evaluation indicators is shown in Table 5.

[0149] [Table 5]

[0150] Next, the cross-institutional score matrix generation and configuration step is performed. That is, institutional scores are arranged in rows / columns for GID, and confidence deviations / compensation deviations / blocking factors, etc., are calculated.

[0151] Next, we perform the inter-institutional pattern learning construction (AI analysis) step. Specifically, we analyze trust tendencies (high to low), compensatory responseability (fast / slow), and recovery rate, and then perform trend clustering of the social trust structure. Next, the system structure feedback configuration step is executed. This involves extracting configuration conditions that cannot be trusted, conducting a complexity and delimiting correlation analysis of responsibility items, and automatically generating a system improvement proposal (see Figure 19). Next, the UX display configuration step for the analysis results is performed. This means the results can be displayed using graphs, radar charts, heatmaps, etc., and can also be output to system administrators, research institutions, and heirs.

[0152] For AI pattern extraction, the system learns user behavior trends across multiple systems and visualizes trust paths for each behavior type. The cross-scoring structure allows for easy identification of situations where the same GID (Gender Identity Disorder) is highly trusted in system A but less trusted in system B. For system improvement collaboration, it can clearly identify which systems are difficult for users and what factors are undermining trust.

[0153] Figure 19 is a flowchart of the AI ​​learning and improvement of the system. It proposes system redesign based on behavioral history (Step 1201). It then edits / outputs the reconfiguration template. The system reconfiguration and improvement proposal structure is explained in detail below.

[0154] <Accumulation of system connection logs and trust score history is complete> First, the connection failure log aggregation configuration step is performed. That is, components with high blocking rates / dispute rates / compensation failure rates are detected, and abnormal trends are classified by system and item. Next, the behavioral trend learning configuration (AI structure learning) step is executed. That is, user behavior logs (YES / NO history, response speed, etc.) are learned and candidates for UX improvement (wording improvement, sequence optimization) are extracted.

[0155] Next, the configuration difficulty score evaluation step is performed. That is, the understanding rate of responsibility items, the compensation implementation rate, and the connection completion rate are made into indicators, and the "acceptance rate" and "blocking trigger ratio" for each system are calculated. Next, the process step for generating improvement proposals is executed. For example, improvement proposals such as "reduce the number of documents to be submitted" or "extend the grace period for compensation" are generated, and the system administrator is notified on a proposal-by-proposal basis, or the process is automated.

[0156] Next, the configuration revision template output configuration step is executed. That is, a configuration template reflecting the improvement suggestions is automatically generated, and the reconfigured template is reflected in the flow shown in Figure 10.

[0157] By reflecting the above proposed system restructuring and improvement configuration in the user experience, it is possible to contribute to improving the trust score.

[0158] Regarding the identification of the above-mentioned problems, it is possible to automatically detect system components with high blocking rates or objection rates. For AI optimization, the AI ​​learns from user behavior history about the difficulty of wording and the weakness of compensatory mechanisms. For self-evolving systems, the system learns user behavior "each time it is used" and improves itself.

[0159] The following describes the structure of failure to continue the system, compensation for disruption, and completion of learning integration.

[0160] <End of system connection process (success / blocked / on hold)> First, the final configuration step for the connection processing results is performed. That is, the status is classified as success / failure / blocked / compensation performed. Next, the integrated record configuration step for blocking reasons and compensation history is executed. That is, the score history, acceptance rejection logs, and compensation execution results are reflected in the database. Next, the recovery induction configuration step is performed. That is, automatic granting / notification of reconnection rights is performed to users who have successfully received compensation or regained trust. Next, we will perform the system UX evaluation and trust trace configuration steps. We will analyze the system's connection continuity rate, interruption recovery rate, compensation achievement rate, etc. Next, the data input processing step for the AI ​​learning model is executed. This process links to the cross-institutional learning and reconstruction configuration shown in Figures 18 and 19. Examples of models used in the AI ​​learning model include supervised regression analysis, LLM fine-tuning, and dissent rate reduction learning. This AI learning model is used to supplement the general outline of the learning parameters and update cycle.

[0161] Next, the integrated sealing process (post-death or termination) is performed. That is, the trust score, history, and acceptance information are finally sealed and recorded. This allows the process to proceed to inheritance and succession, in conjunction with the inheritance structure of the trust history shown in Figure 16.

[0162] The completion of the above institutional cycle will lead to the transmission of a trust structure to the next generation.

[0163] Regarding the records of all termination conditions mentioned above, not only success / failure will be recorded, but also "dissatisfied" and "refused compensation" will be recorded. For the recovery structure, the system is automatically processed for reconnection due to successful compensation or objection recognition, and is designed to allow users to "return without closing." For system evolution linkage, the entire history is linked to a learning AI, and the information is fed back into the evolution / improvement of the system itself. For sealing and inheritance, the final history is used for inheritance and corporate succession as a "trust legacy" as defined in Figure 16, the inheritance structure of the trust history.

[0164] <Social System Integrated Control OS> The social system connection management system described in the first invention is characterized by comprising a social system integration control OS which is a configuration that links and integrates a system connection configuration, an acceptance structure, a blocking process, a recursion configuration, a post-mortem succession configuration, an AI judgment configuration, a notification control configuration, an international cooperation configuration, a trust score control configuration, and all other components. Here, the details of the system connection configuration, acceptance structure, blocking process, recursion configuration, post-mortem succession configuration, AI judgment configuration, notification control configuration, international cooperation configuration, and trust score control configuration have been described above, so the explanation of each configuration is omitted here. Below, the social system integration control OS which links and integrates all other components will be described.

[0165] As shown in Figure 21, the social system connection management system of this embodiment has a connection layer at the bottom and a UX layer at the top, realizing a social system integrated control OS with a layered structure consisting of a UX layer, an AI layer, a data layer, and a connection layer. API linkage with external systems is performed in the UX layer. This makes it possible to quantify, visualize, and automatically control understanding, responsibility, trust, compensation, reconnection, damage compensation, and recommendations related to social systems (healthcare, education, insurance, administration, etc.), and to realize a social system integrated control OS that can be operated, compensated for, and restored in a way that is acceptable to everyone, through programming. To put it simply, the social system connection management system of this embodiment constitutes an infrastructure system (OS) that digitally controls "trust-based system connections" in the connection between systems such as healthcare, administration, finance, education, welfare, and insurance and people / corporations, asking questions such as "Are they satisfied?", "Have they fulfilled their responsibilities?", "Is this a trustworthy person?", and "Can their history be inherited after death or inheritance?", bridging the gaps between systems and circulating trust in society.

[0166] Figure 22 shows the architecture of the Social Control Integrated OS. The Social Control Integrated OS manages connections to social systems by linking from the user terminal (Web / mobile) to the chat UX layer (UX layer) → trust score control engine → score / history / responsibility DB (per GID) → output / cooperation layer. Figure 23 shows the actual data communication flow between terminals, servers, AI, and institutional terminals. The example in Figure 23 shows the case where the exchange of transmitted and received data is performed in JSON output format. Data between each device is encrypted and stored on the server.

[0167] The following describes the industrial applicability and effects of the present invention through 30 specific examples of industry-specific and case-specific scenarios (problems → improvements). <30 Industry-Specific and Case-Specific Scenarios (Problems → Areas for Improvement)> <1. Medical care (unpaid outpatient fees)> Past / Present: Cash is the dominant form of payment, leading to increased costs for unpaid bills and debt collection. Future plan: Connection assessment based on confidence score before / immediately after consultation; failure to meet targets requires compensatory guidance (payment / statement of reasons / action) → recalculation → immediate reassessment. Failure rate -50%, collection cost -60%. Here, "▲" means negative. The same notation will be used below.

[0168] <2. Emergency transport (identity and payment unknown)> Past / Present: Unable to contact the company, making it difficult to recover expenses. Future: Use GID reference to instantly check history, responsibility / compensation status, and, if necessary, guide users to compensation at a later date via limited connection mode. Accounts receivable reduced by 40%.

[0169] <3. Medical care for tourists visiting Japan> Past / Present: Unpaid due to language and payment system differences. Future plan: Multilingual templates + chat YES / NO to obtain satisfaction → score assessment → compensation proposal. Unpaid wages -45%, reception time -30%.

[0170] <4. Dispensing Pharmacy (Trouble when receiving prescriptions)> Past / Present: Payment due later / Insufficient funds. Future: If connection criteria are not met, compensatory measures (immediate small payment / confirmation) will be taken to restore the score → granting permission. Number of pending cases -60%.

[0171] <5. Delinquency in paying local government taxes and insurance premiums> Past / Present: The administrative burden of periodic reminders. Future plan: When connecting to the system, responsibility conditions and compensation routes will be presented. Failure to meet these conditions will result in disconnection; reconnection will occur upon completion of compensation. Collection rate will increase by 5-10 points.

[0172] <6. Non-payment of traffic violation fines> Past / Present: Tracking and collection are delayed. Future plans: Record unprocessed violations in the GID history and automatically guide users towards compensation when connecting to a different system. Recovery period reduced by 40%.

[0173] <7. Public subsidies (fraudulent receipt / oversight of requirements)> Past / Present: Error due to insufficient understanding of requirements. Future: Redesign the system to visualize responsibilities and submissions using a system comparison UI, and reduce pitfalls through AI learning. A 35% reduction in the deficiency rate.

[0174] <8. Scholarships (Delinquency / Deferment)> Past / Present: Neglect → Increased delinquency. Future: Satisfaction obtained → If criteria are not met, options for explanation / pledge / installment payment compensation, and reconnection based on score increases. Delinquency rate reduced by 25%.

[0175] <9. School (Unexcused absence, incomplete procedures)> Past / Present: Unreachable / neglected. Future: Early alerts through non-response detection, score recovery through compensatory actions (interview / submission). Long-term issues: -50%.

[0176] <10. University Research Funds and Equipment Reservations> Past / Present: Cancellation / Unauthorized use. Future: Compensation and incentives (penalty payment / alternative work / report) → score recovery → rebooking. No-shows: -60% penalty.

[0177] <11. EC (Cash on Delivery / Post-Payment - Non-Delivery / Refusal)> Past / Present: Losses due to returns / non-delivery. Future: If connection criteria are not met, the system will automatically branch off to a prepaid or point-locked model. Loss rate: -30%.

[0178] <12. Subscriptions (Payment delays / Churn)> Past / Present: Retry failed → Cancellation. Future: Compensation plan (grace period / partial payment / survey response) leads to score recovery → continuation. Cancellation rate -15%.

[0179] <13. Retail (Shoplifting / Unjustified Returns)> Past / Present: Rules not read, a small number of malicious users. Future plans: Responsibility and compensation conditions will be presented in stages upon joining, and violations will result in score deductions + compensation for rejoining the service. Loss rate: -10-20%.

[0180] <14. Finance (Small Loans / Credit Trading)> Past / Present: Relying solely on traditional creditworthiness leaves a thin layer of the credit network vulnerable. Future: Visualization through an action-based scoring system that adds points for satisfaction / compensation / responsibility fulfillment history. 10% reduction in default rate, and equalization of credit limits.

[0181] <15. Insurance (Fraudulent and delayed small claims)> Past / Present: Incomplete documentation, moral hazard. Future: Appeals + AI review for evidence submission and reassessment, and compensatory actions to restore scores. Investigation costs reduced by 25%.

[0182] <16. Car Rental / Car Sharing (Late Return / Damage)> Past / Present: Deposit / Procedural burden. Future plans: Limited connection mode at minimum score, compensation option in case of accident → immediate re-evaluation. Latency -40%, turnover rate +15%.

[0183] <17. Taxi / Ridesharing (Fare-running / Cancellation)> Past / Present: Insufficient on-site response and evidence. Future: Cancellation / compensation history will be recorded in the GID history and reflected in the next connection conditions. 50% reduction for those who steal accounts.

[0184] <18. Hotel / Accommodation (No-show)> Past / Present: Losses due to vacant rooms caused by last-minute cancellations. Future plans: Clearly state compensation conditions at the time of booking; automatically guide guests towards compensation in case of no-shows → re-evaluation. No-show rate: -60%.

[0185] <19. Events / Tickets (Resale / Unauthorized Entry)> Past / Present: Rule deviations are difficult to track. Future plans: Behavioral correction through connection detection and compensation; violations result in rank downgrade → with a chance to recover. Violation rate -35%.

[0186] <20. Electricity, gas, and water (arrears)> Past / Present: The administrative work involved in stopping and then restarting the service is complicated. Future: Automate the process of providing reasons for disconnection, compensation, and reconnection via chat. Reduce downtime by 50%, and reactivation process by 60%.

[0187] <21. Communications (Delinquent payments on mobile phone lines, fraudulent applications)> Past / Present: Stuck in a stalemate of recall / blacklisting. Future: Score recovery through compensation and installment payments → Gradual return from limited access. Bad debt -20%.

[0188] <22. Municipal facilities (gymnasium / library)> Past / Present: Handling unauthorized extensions / damage is left to individual employees. Future: Template for responsibility items / compensation procedures will be provided → Preferential treatment will be given next time upon completion of compensation. Late payment reduction of 70%.

[0189] <23. Cultural facilities (art museums / theaters)> Past / Present: Trouble due to lack of understanding of the rules. Future plans: Read items will be omitted from display based on satisfaction history (faster presentation). Complaints will decrease by 30%.

[0190] <24. Employment / Gig Work (Unauthorized Absence / Contract Violation)> Past / Present: Difficulty in balancing flexibility and discipline. Future: A visible cycle of satisfaction → responsibility fulfillment → compensation; preferential treatment for high-trust rankings. 10% reduction in employee turnover.

[0191] <25. Construction / Site Safety (Failure to Follow Instructions, Delayed Accident Reporting)> Past / Present: Procedural deviations were latent. Future: When deviations occur, **compensatory actions (re-education / reporting)** will be used to recover scores, and UX will be improved through AI learning. Accident rate will decrease by 15%.

[0192] <26. Caregiving / Welfare (Unable to contact / Procedures not completed)> Past / Present: Difficulty in contacting family / application stagnation. Future: Non-response detection → Behavioral analysis for risk notification and assistance with proxy applications. Downtime reduced by 40%.

[0193] <27. Inheritance / Post-Death Procedures (Information Dispersion)> Past / Present: Lack of evidence burdens the bereaved family. Future: Automate post-mortem sealing → PDF certificate of trust → notification to beneficiaries. Reduce processing time by 30-50%.

[0194] <28. NPO / Volunteer (Last-minute cancellation)> Past / Present: A chronic shortage of on-site personnel. Future plans: Maintain scores through compensatory actions (alternative participation / prior reporting), and simplify procedures for high-performing participants with trust rewards. Vacancy rate reduced by 35%.

[0195] <29. University Entrance Examination / Scholarship Selection (Procedural Deficiencies)> Past / Present: Opportunity lost due to shortcomings. Future: Understand requirements through system comparison and narrowing down, and secure a re-evaluation route through objections. Deficiencies: -40%.

[0196] <30. Disaster relief (conflicts regarding benefits / disaster victim certificates)> Past / Present: Applications are backed up due to an overwhelming number of submissions. Future: Presenting templates for understanding / responsibility / compensation + omitting already understood items, optimizing the user flow through AI learning. Processing time reduced by 40%.

[0197] Although embodiments have been described in detail above, the invention is not limited to any particular embodiment, and various modifications and changes are possible within the scope described in the claims. Furthermore, it is possible to combine all or more of the components of the embodiments described above. [Explanation of Symbols]

[0198] 1 Member terminal 21 System Connection Request Section 22. Presentation of System Structure & Acquisition Department 23. Confidence Score Calculation Unit 24 Connection feasibility determination unit 25. Compensation and induction processing unit 26. Objection Processing Section 27 Connection History Recording Section 28 Post-mortem Corporate Termination Processing Section 29 Automatic recommendation system guidance department 30. System Structure Comparison and Selection Section 31 Trust Structure Visualization Inheritance Unit 32 Institutional AI Learning Improvement Department

Claims

1. A service integration platform is provided in which member terminals used by members connecting to social systems, service provider terminals managed by service providers providing the said social systems, medical institution terminals, corporate terminals, store terminals, and financial institution terminals are connected via a network, a management server is installed to manage the overall operation of these terminals, and data exchange between networks is shared. The aforementioned member terminal is It has at least the functionality to provide social system connection services based on the trust score of the aforementioned members, The aforementioned management server Upon receiving a connection request from the aforementioned member terminal, the system configuration display unit displays the name of the social system to be connected to, an overview of the system indicating the provider, the responsibility conditions indicating the submission obligations and restrictions, and the content of the compensation conditions indicating penalties and obligations. A consent confirmation unit that obtains the consent of the member regarding the content displayed in the aforementioned system structure presentation unit, If the consent confirmation unit determines that the member is satisfied, the score calculation unit calculates a confidence score generated for each member, A history recording unit that records the history of acceptance and the history of past connection actions related to the process of connecting to the aforementioned social system, A connection eligibility determination unit that determines whether or not a member can connect to the social system using the trust score calculated for each member by the score calculation unit and the satisfaction history and past connection behavior history recorded in the history recording unit, A social system connection management system characterized by having the following features.

2. The social system connection management system according to claim 1, wherein the consent confirmation unit is configured to present the conditions, responsibilities, compensation, and restrictions of the system step by step through a chat-type interface and to obtain the member's consent in YES / NO format.

3. The social system connection management system according to claim 1 or 2, wherein the trust score is calculated based on multiple historical elements, including acceptance history, compensation history, responsibility fulfillment history, number of successful system connections, disconnection history, objection history, and post-mortem contribution history.

4. The social system connection management system according to claim 1 or 2, wherein the connection feasibility determination unit is configured to permit a connection if the reliability score is equal to or greater than the connection threshold value set on the management server, and to transition to a compensation guidance process or a connection blocking process if it is less than the threshold.

5. A social system connection management system according to claim 1 or 2, wherein if a user fails to connect to the system, the reason is explicitly displayed in the chat UI, compensation options and re-fulfillment options are presented, and a compensation re-guidance configuration is provided that performs score recovery and connection re-evaluation in accordance with the execution of compensation, and the compensation re-guidance configuration has score fluctuation log output or compensation type identifier (compensation_type).

6. A social system connection management system according to claim 1 or 2, comprising a history recording configuration that enables the storage and output of user connection history, compensation history, satisfaction history, score history, and objection processing history structured on a GID (Global Identity) basis.

7. The social system connection management system according to claim 1 or 2, further comprising a configuration that allows users to file objections to the results of a connection determination, enabling the submission of claims, evidence, and history in conjunction with chat logs, and providing an objection configuration that re-evaluates or temporarily suspends the connection determination by the connection determination unit.

8. A social system connection management system according to claim 1 or 2, comprising an AI judgment configuration related to system blocking processing, wherein the AI ​​judgment configuration includes a third-party audit API that enables a third-party organization to audit and disclose the judgment history, usage parameters, and learning configuration, and an AI trust configuration in the form of a judgment history JSON output format.

9. The social system connection management system according to claim 1 or 2, comprising a configuration for visualizing the trend of the trust score, the level of agreement or disagreement with the constituent items, the compensation record, and the connection results using a chat UI or a graph display UI.

10. The social system connection management system according to claim 1 or 2, comprising a configuration speed-up configuration that automatically compares the system configuration items presented when connecting to a system with configuration items that have been previously accepted, and omits display or optimizes the order of presentation.

11. The social system connection management system according to claim 1 or 2, comprising a connection prerequisite configuration that requires maintaining a certain level of confidence score as a prerequisite for system connection, and presents connection impossible or limited connection mode to users below the standard.

12. The social system connection management system according to claim 1 or 2, further comprising a non-response risk detection configuration that, when it detects a user who does not respond to system connection for a certain period of time, automatically activates behavioral analysis AI to analyze and notify of risks such as disappearance, hospitalization, or disaster.

13. A social system connection management system according to claim 1 or 2, comprising a trust reward structure in which, if the trust score exceeds a certain standard, the system automatically applies rewards such as simplified procedures, expanded credit limits, and expedited reconnection as preferential measures.

14. A social system connection management system according to claim 1 or 2, comprising a configuration comparison configuration that displays the responsibility items, compensation conditions, and confidence score thresholds of the system structure for comparison among multiple systems, and provides support to users in selecting a system.

15. A social system connection management system according to claim 1 or 2, comprising a system configuration output configuration that enables the output and reuse of the system connection configuration in PDF, recommendation code, and configuration template formats.

16. A social system connection management system according to claim 1 or 2, comprising a system learning configuration that includes a procedure for updating learning target parameters and reconstruction templates, which enable the AI ​​to learn statistics on the success rate of system connection, the disconnection rate, the compensation implementation rate, and the objection rate, and to automatically optimize and reconstruct the system's UX configuration, responsibility items, and configuration difficulty.

17. A social system connection management system according to claim 1 or 2, comprising a history visualization configuration that displays the user's connection history, compensation history, score trends, and satisfaction configuration in a unified chronological order, making it usable for reliability evaluation and system selection decisions.

18. A social system connection management system according to claim 1 or 2, comprising an integrated UX control configuration that enables the entire process from system disruption, connection failure, compensation guidance, re-examination, to restoration of trust to be completed within a single UX chat configuration.

19. The social system connection management system according to claim 1 or 2, comprising a post-mortem recommendation notification configuration that enables the automatic generation and notification of post-mortem system connection recommendation notifications with attached system type, trust certificate, configuration log, contribution history, number of acceptances, and compensation history.

20. The social system connection management system according to claim 1, comprising a score analysis configuration that generates a GID-based trust ranking based on multiple system connection histories, trust scores, reasons for disconnection, and recovery success histories, and presents connection priority, recommended systems, and system risk predictions for the entire social system.

21. The social system connection management system according to claim 8, which has a structure that allows the AI ​​judgment history regarding system blockade to be submitted to a third-party organization, and has an AI disclosure configuration that allows the judgment content, usage history, and learning parameters to be reviewed, audited, and corrected.

22. The social system connection management system according to claim 7, comprising an objection mechanism in which, when an individual files an objection to a system notification, the content of the argument, supporting documents, and historical counter-evidence are submitted in conjunction with the notification log, and the system judgment is temporarily suspended and re-evaluated.

23. The social system connection management system according to claim 1, comprising a cross-system analysis configuration that performs system reliability analysis between social systems, system improvement proposals, and extraction of connection error factors based on acceptance history, blockage history, system connection history, and trust score aggregated on a GID (Gender Identity Disorder) basis.

24. The social system connection management system according to claim 1, comprising a UI configuration that visualizes an individual's social trust history by displaying in a unified chronological order the individual's system connection actions, acceptance actions, responsibility fulfillment, compensation record, and contribution history.

25. The social system connection management system according to claim 7, further comprising a notification non-response analysis configuration that, if the period of non-response to a notification exceeds a predetermined value, automatically activates behavioral analysis AI to analyze and predict the possibility of the individual being in a state of connection difficulty (disappearance, hospitalization, disaster).

26. The social system connection management system according to claim 13, comprising a trust reward structure that automatically applies connection benefits (simplified system use, expanded credit limit, and faster reconnection) to GIDs who exceed a certain trust rank, based on their system connection history and personal behavior score.

27. The social system connection management system according to claim 1, wherein, for persons without a gender identity (GID), system connection is generally prohibited or limited, and in order to maintain system reliability, it includes a system connection prerequisite configuration that requires the issuance of a GID as a connection prerequisite.

28. A social system connection management system according to claim 1, characterized in that it comprises a social system integration control OS which is a configuration that links and integrates a system connection configuration, an acceptance structure, a blocking process, a recovery configuration, a post-mortem succession configuration, an AI judgment configuration, a notification control configuration, an international cooperation configuration, a trust score control configuration, and all components.

29. A social system connection management system comprising a social system connection management system equipped with a service integration platform in which member terminals used by members connecting to social systems, service provider terminals managed by service providers providing the said social systems, medical institution terminals, corporate terminals, store terminals, and financial institution terminals are network-connected, a management server is installed to manage the overall operation of these terminals, and data exchange between networks is shared, and a social system connection management method, When the system structure presentation unit receives a connection request from the member terminal, it performs a system structure presentation step that displays the name of the social system to be connected to, a system overview indicating the provider, responsibility conditions indicating submission obligations and restrictions, and compensation conditions indicating penalties and obligations. The consent confirmation step involves the consent confirmation unit obtaining the consent of the member regarding the content displayed in the aforementioned system structure presentation step, If the score calculation unit determines that the member is satisfied through the satisfaction confirmation step, the score calculation step calculates a confidence score generated for each member. A history recording step in which the history recording unit records the history of acceptance and the history of past connection actions related to the connection process to the social system, The connection eligibility determination step includes a connection eligibility determination step in which the connection eligibility determination unit determines whether or not to connect to the social system using the trust score calculated for each member in the score calculation step and the satisfaction history and past connection behavior history recorded in the history recording unit. A method for managing the connection between social systems, characterized by the following features.

30. A social system connection management program, On the computer, Upon receiving a connection request from a member's terminal, the system structure presentation step displays the name of the social system to be connected to, an overview of the system indicating the provider, the responsibility conditions indicating submission obligations and restrictions, and the compensation conditions indicating penalties and obligations. The consent confirmation step involves obtaining the member's consent to the content displayed in the aforementioned system structure presentation step, If it is determined that the member is satisfied through the aforementioned satisfaction confirmation step, a score calculation step is performed to calculate a confidence score generated for each member. A history recording step that records the history of acceptance and the history of past connection actions related to the process of connecting to the social system, A social system connection management program characterized by executing a connection feasibility determination step that determines whether or not connection to the social system is possible, using the trust score calculated for each member in the score calculation step, the satisfaction history recorded in the history recording unit, and past connection behavior history.

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