Content management system, content management method, and content management program

Abstract

We provide new technology for managing content using blockchain. [Solution] The content management system 0 includes an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit. The acquisition unit acquires SNS (social networking service) information about the user's SNS. The score calculation unit calculates a credit score that indicates the user's creditworthiness based on the user's SNS information. The SNS information includes exchange relationship information regarding the user's exchange relationships on the SNS. The reception unit receives a review score for the content from the user. The point calculation unit calculates evaluation points for the user's content based on the trust score and the review score, and stores the evaluation points in the blockchain.

Description

[Technical Field]

[0001] The present invention relates to a content management system, a content management method, and a content management program. [Background technology]

[0002] Conventionally, there is technology that uses blockchain to manage information related to content such as services.

[0003] For example, Patent Document 1 discloses a technology for managing evaluations of services. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2023-136018 Summary of the Invention [Problem to be solved by the invention]

[0005] When accepting information about content, the reliability of the information varies depending on the credibility of the person providing the information. In order to consider the reliability of the person providing the information about content, it is possible to use information about social networking sites that many people use.

[0006] However, while the technology of Patent Document 1 can manage evaluations of content such as services, it cannot manage information about the content (for example, evaluations of the content) using information about the SNS.

[0007] The present invention has been made in consideration of the above-mentioned circumstances, and an object of the present invention is to provide a new technology for managing content using a blockchain. [Means for solving the problem]

[0008] [1] A content management system that manages content ratings using blockchain, the content management system includes an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit; The acquisition unit acquires SNS information related to the user's SNS (social networking service), the score calculation unit calculates a credit score representing the creditworthiness of the user based on the SNS information of the user; The SNS information includes exchange relationship information regarding exchange relationships of the user on the SNS, the receiving unit receives a review score for the content from the user; the point calculation unit calculates an evaluation point for the content of the user based on the credit score and the review score, and stores the evaluation point in a blockchain; Content management system.

[0009]

[21] A content management method executed by a content management system that manages content ratings using a blockchain, the content management system includes an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit; The acquisition unit acquires SNS information related to the user's SNS (social networking service); the score calculation unit calculating a credit score representing the creditworthiness of the user based on the SNS information of the user; The SNS information includes exchange relationship information regarding exchange relationships of the user on the SNS, the receiving unit receiving a review score for the content from the user; the point calculation unit calculates an evaluation point for the content of the user based on the credit score and the review score, and stores the evaluation point in a blockchain; Content management methods.

[0010]

[22] A content management program that manages content ratings using a blockchain, causing a computer to function as an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit; The acquisition unit acquires SNS information related to the user's SNS (social networking service), the score calculation unit calculates a credit score representing the creditworthiness of the user based on the SNS information of the user; The SNS information includes exchange relationship information regarding exchange relationships of the user on the SNS, the receiving unit receives a review score for the content from the user; the point calculation unit calculates an evaluation point for the content of the user based on the credit score and the review score, and stores the evaluation point in a blockchain; Content management program.

[0011] With this configuration, it is possible to receive a review score for content from a user, and calculate evaluation points for that content based on information about the user's SNS and the review score.

[0012] [2] The content management system includes a provision restriction unit, the provision restriction unit restricts provision of the content based on an evaluation point for the content. [1] The content management system described in [1].

[0013] By adopting such a configuration, it is possible to restrict the provision of content with low evaluation points.

[0014] [3] The acquisition unit acquires on-chain behavioral history information regarding the user's activity history on the blockchain; The score calculation unit calculates the credit score based on the on-chain behavior history information. [1] or [2]. The content management system according to [1] or [2].

[0015] This configuration makes it possible to use a credit score calculated based on social media information and on-chain behavioral history information regarding activity history on the blockchain, thereby enabling highly reliable evaluation points to be calculated.

[0016] [4] The acquiring unit acquires Web2 reputation information relating to the user's reputation on the website and user meta information relating to the user's behavioral pattern; The score calculation unit calculates the trust score based on the Web2 reputation information and the user meta information. [3] A content management system as described in [3].

[0017] This configuration makes it possible to use a credit score calculated based on social media information, on-chain behavioral history information related to activity history on the blockchain, Web2 reputation information related to the user's reputation on the website, and user meta information related to the user's behavioral patterns, thereby enabling highly reliable evaluation points to be calculated.

[0018] [5] The score calculation unit calculates partial scores for the SNS information, the on-chain behavior history information, the Web2 reputation information, and the user meta information, and calculates the trust score based on the partial scores. [4] A content management system as described in [4].

[0019] By configuring in this way, it becomes possible to calculate a numerical value (partial score) for each piece of information used to calculate the credit score, and highly reliable evaluation points can be calculated based on each numerical value.

[0020] [6] A range of values ​​of the partial score is preset, the score calculation unit calculates each partial score by normalizing the partial scores so that the partial scores become values ​​within the range. [5] A content management system as described in [5].

[0021] By configuring in this way, it is possible to unify the range of partial score values ​​for each piece of information, preventing certain partial scores from having an excessive impact on the calculation of the credit score.

[0022] [7] A weight is set in advance for each of the SNS information, the on-chain behavior history information, the Web2 reputation information, and the user meta information; The score calculation unit calculates the credit score based on the respective partial scores and the weights. [5] or [6]. The content management system according to [5] or [6].

[0023] This configuration allows the influence of the partial score on each piece of information used to calculate the credit score to be adjusted.

[0024] [8] The content management system includes an NFT (non-fungible token) issuing unit, The NFT issuing unit issues an NFT related to the credit score on a blockchain. A content management system according to any one of [1] to [7].

[0025] This configuration makes it possible to issue credit scores as NFTs on the blockchain, making it possible to visualize a user's creditworthiness.

[0026] [9] The acquisition unit periodically acquires the SNS information, The score calculation unit periodically calculates and updates the credit score based on periodically acquired SNS information. A content management system according to any one of [1] to [8].

[0027] This configuration makes it possible to calculate a real-time credit score using regularly updated SNS information, allowing for a more accurate credit score to be calculated.

[0028]

[10] The content management system includes a voting unit, The voting unit accepts votes from the users in voting on proposals in the DAO, and determines whether to approve or reject the proposal based on the votes and the trust score. A content management system according to any one of [1] to [9].

[0029] By adopting such a configuration, it becomes possible to determine whether or not to approve a proposal using the trust score, thereby enabling more appropriate voting.

[0030]

[11] The acquisition unit acquires information linked to the DID and the SBT, The score calculation unit generates a credit score based on the verifiable credentials associated with the DID and information associated with the SBT. A content management system according to any one of [1] to

[10] .

[0031] This configuration makes it possible to calculate a credit score using verifiable credentials and SBT, allowing for a more appropriate credit score to be calculated.

[0032]

[12] The NFT issuing unit issues an NFT including only the credit score based on a zero-knowledge proof. [8] The content management system described in [8].

[0033] By adopting such a configuration, it is possible to provide only the credit score without disclosing the information used to calculate the credit score.

[0034]

[13] The score calculation unit calculates a credit score based on a relationship strength coefficient between users. A content management system according to any one of [1] to

[12] .

[0035] With this configuration, it becomes possible to calculate a credit score using the relationship between users, and a more appropriate credit score can be calculated.

[0036]

[14] The content management system includes a conversion unit, The conversion unit converts the information to be stored in the blockchain into a ternary number, and stores the information converted into a ternary number in the blockchain. A content management system according to any one of [1] to

[13] .

[0037] By using this configuration, information converted into a different ternary number than usual can be stored in the blockchain.

[0038]

[15] The NFT issuing unit cryptographically converts the credit score using a non-reversible ternary encoding method and issues an NFT containing the converted information. [8] The content management system described in [8].

[0039] By using this configuration, it is possible to issue NFTs that contain information converted into ternary numbers that are different from the usual format.

[0040]

[16] The score calculation unit estimates a default risk based on the information collected for calculating the credit score, and further calculates the credit score based on the default risk. A content management system according to any one of [1] to

[15] .

[0041] By configuring in this way, it is possible to calculate a more appropriate credit score that takes default risk into account.

[0042]

[17] The score calculation unit calculates a credit score based on a black swan indicator. A content management system according to any one of [1] to

[16] .

[0043] By using this configuration, it is possible to calculate a more appropriate credit score using black swan indicators.

[0044]

[18] The reception unit receives explicit consent from the user to submit the credit score to government, financial, or medical institutions; The score calculation unit provides a credit score to the administrative, financial, or medical institution based on the explicit consent. A content management system according to any one of [1] to

[17] .

[0045] This configuration allows credit scores to be provided to government, financial, and medical institutions based on explicit consent.

[0046]

[19] The content management system includes a ZKP presenter, the reception unit receives an instruction to submit a credit score from the user; The ZKP presentation unit presents the ZKP based on the submission instruction.

[18] A content management system as described in

[18] .

[0047] This configuration allows a ZKP to be presented based on a submission instruction.

[0048]

[20] The content management system includes a storage unit and a display processing unit, The storage unit stores evaluation items for calculating the credit score and evaluation items for calculating a conventional score of an institution that receives the credit score; The display processing unit displays the credit score, the conventional score, and the evaluation items. A content management system according to any one of [1] to

[19] .

[0049] This configuration makes it possible to visualize the credit score, traditional score, and evaluation items, making it easy to determine which score should be prioritized. [Effects of the Invention]

[0050] According to the present invention, a new technology for managing content using a blockchain can be provided. [Brief explanation of the drawings]

[0051] [Figure 1] FIG. 1 is a system configuration diagram of a content management system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram showing the hardware configuration according to the present embodiment. [Figure 3] FIG. 2 is a block diagram showing functional components in the present embodiment. [Figure 4] 10 shows an example of data stored in a storage unit in this embodiment. [Figure 5] 10 is an example of a flowchart of an evaluation point calculation process in this embodiment. [Figure 6] 10 shows an example of an interference pattern in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0052] The content management system of the present invention will now be described with reference to the accompanying drawings, in which preferred embodiments are shown, although the present invention may be embodied in many different forms and is not limited to the embodiments set forth herein.

[0053] For example, although the configuration, operation, etc. of a content management system are described in this embodiment, similar effects can be achieved by an executed method (steps), device, computer program, etc. The program in this embodiment may be provided as a non-transitory computer-readable recording medium, or may be provided so as to be downloadable from an external server, or the program may be started on an external computer to implement its functions on a client terminal (so-called cloud computing).

[0054] In addition, in this embodiment, the term "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In this embodiment, "information" is represented by, for example, the physical value of a signal value representing voltage or current, the high or low value of a signal value as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculation can be performed on a circuit in the broad sense.

[0055] A circuit in the broad sense is a circuit realized by appropriately combining a circuit, circuitry, processor, memory, etc. That is, it includes a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), an SoC (System on a Chip), etc.

[0056] <System Overview> Fig. 1 shows a system configuration diagram of a content management system in this embodiment. As shown in Fig. 1, the content management system 0 includes a content management device 1, a user terminal 2, and a blockchain network system 3. The content management device 1 is configured to be able to communicate with the user terminal 2 and the blockchain network system 3 via a network NW. The content management device 1 operates as a server.

[0057] The content management device 1 manages content, information related to content, etc. Specifically, the content management device 1 acquires information for calculating evaluation points for content, calculates a credit score and evaluation points based on the acquired information, etc.

[0058] A general-purpose server computer, a personal computer, etc. can be used as the content management device 1. It is also possible to configure the content management device 1 using a plurality of computers.

[0059] The user terminal 2 is a terminal used by a user or the like who evaluates content (for example, a product, a service, etc.). The user inputs, via the user terminal 2, a review score or the like for calculating an evaluation of the content.

[0060] A terminal device such as a smartphone, a tablet terminal, or a personal computer can be used as the user terminal 2. The number of user terminals 2 may be one or more.

[0061] The blockchain network system 3 has multiple nodes that store blockchains. A blockchain is a form of a distributed ledger, and may be a public chain, a private chain, or the like.

[0062] Note that a blockchain is originally used as a distributed ledger in the virtual currency Bitcoin, and is composed of a large number of nodes. However, for the sake of simplicity, it is shown here as a single component as shown in Figure 1. In this embodiment, Ethereum (registered trademark) or the like can be used as the blockchain.

[0063] A blockchain is made up of a chain of data called blocks. Each block contains the hash value of the previous block, so if any block is tampered with, an inconsistency will occur in the chain. Another characteristic of a blockchain is that it is impossible for a large number of nodes to tamper with or delete blocks.

[0064] In addition, the smart contract possessed by the blockchain network system 3 executes the processing of some or all of the functional components described below.

[0065] In this embodiment, the network NW is an IP (Internet Protocol) network, but there is no limitation on the type of communication protocol, and there is also no limitation on the type and scale of the network.

[0066] In addition, in this embodiment, "ISO 24165" is used as an identifier for digital tokens (e.g., NFTs related to credit scores, reward tokens, etc.), and the token identification structure is designed to correspond to a format including a DTI (Digital Token Identifier) ​​code.

[0067] By using "ISO 24165," an international standard for digital asset identification, for digital tokens, they will be recognized as a token format that can be systematically identified by various exchanges and external institutions, including components such as issuer identifier, asset classification, chain ID, and year of issue. However, standards other than "ISO 24165" may also be used as standards for digital tokens.

[0068] Furthermore, in this embodiment, the international standard "ISO 20022" is used for messaging, payment, and other transactions with financial institutions (e.g., the Financial Services Agency, the Markets in Crypto Assets Regulation (MiCA), the Securities and Exchange Commission (SEC), central banks of various countries, etc.).

[0069] Anticipating the possibility that DID (Decentralized Identity), credit scores, tokens, etc. may be connected and linked with financial institutions, the international standard "ISO 20022" will be used, and the design will enable transaction records and identification information to be mapped to "ISO 20022" messages.

[0070] Specifically, the use of "ISO 20022" is expected to correspond to ISO message structures such as PartyIdentification, InstrumentIdentification, and ZK-attached signature elements. However, standards other than "ISO 20022" may also be used.

[0071] Furthermore, the DID (Decentralized Identity) structure issued and managed in this embodiment uses the "W3C DID" format. This ensures interoperability with various decentralized ID systems (for example), while also ensuring DID resolver compatibility and standardization of the DID document structure. However, standards other than "W3C DID" may also be used.

[0072] <Hardware configuration> Fig. 2 shows a hardware configuration diagram. As shown in Fig. 2(a), the information processing device 10 (content management device 1) has a control unit 101, a storage unit 102, and a communication unit 103, which are used to perform the functions of each unit and each process.

[0073] The control unit 101 includes one or more processors such as a CPU (Central Processing Unit), and controls the overall operation and processing of the information processing device 10 by executing the content management program of the present invention, an OS (Operating System), browser software, middleware, and other applications.

[0074] The storage unit 102 is a hard disk drive (HDD), a solid state drive (SSD), a read only memory (ROM), a random access memory (RAM), or the like, and stores the content management program according to the present invention and data used when the control unit 101 executes processing based on the program. The control unit 101 executes processing based on the content management program stored in the storage unit 102, thereby realizing the functional configuration described below.

[0075] The communication unit 103 controls communication with the network NW, and performs input necessary for operating the information processing device 10 and output related to the operation results.

[0076] As shown in FIG. 2(b), the terminal device 9 (user terminal 2, etc.) has a control unit 91, a storage unit 92, a communication unit 93, an input unit 94, and an output unit 95, which are used to perform the functions of each unit and each process.

[0077] The control unit 91 of the terminal device 9 includes one or more processors such as a CPU, and controls the overall operation and processing of the terminal device 9. The storage unit 92 of the terminal device 9 is an HDD, SSD, ROM, RAM, or the like, and stores the above-mentioned applications and data used when the control unit 91 executes processing based on a program.

[0078] A communication unit 93 of the terminal device 9 controls communication with the network NW. An input unit 94 of the terminal device 9 is a mouse, keyboard, etc., and inputs operation requests from the user / provider to the control unit 91. An output unit 95 of the terminal device 9 is a display, etc., and displays the results of processing by the control unit 91, etc.

[0079] <Functional components> Fig. 3 is a block diagram showing functional components of this embodiment. As shown in Fig. 3, the content management device 1 includes, as functional components, an acquisition unit 11, a score calculation unit 12, a reception unit 13, a point calculation unit 14, a provision restriction unit 15, an NFT (non-fungible token) issuance unit 16, a storage unit 17, a voting unit 18, a conversion unit 19, a norm calculation unit 1a, a ZKP presentation unit 1b, and a display processing unit 1c.

[0080] The arrangement of these functional components is one example, and it is also possible to implement these functional components on multiple computers to configure the content management system 0. For example, some of the functional components of the content management device 1 may be arranged in one or more devices configured to be able to communicate with the user terminal 2, the blockchain network system 3, and the content management device 1.

[0081] <Data structure> 4 shows an example of data stored in the storage unit 17 in this embodiment. The storage unit 17 stores user information, Web2 reputation information, user meta information, and the like, relating to users who input review scores for content.

[0082] The arrangement of each data is an example, and some or all of the data stored in the memory unit of the content management device 1 may be stored in one or more devices configured to communicate with the user terminal 2, the blockchain network system 3, and the content management device 1.

[0083] User information is information about users who enter review scores for content (e.g., users who purchase products or receive services), and includes the user's name, SNS (social networking service) ID, etc., and is managed by user ID as shown in Figure 4(a).

[0084] By including the ID of the SNS in the user information, it is possible to obtain SNS information related to the SNS for calculating a credit score that indicates the user's creditworthiness.

[0085] The user information may also include general information about the user that is necessary for purchasing products or receiving services (for example, contact information such as address, telephone number and email address, credit card number, etc.).

[0086] Web2 reputation information is information about a user's reputation on a website, including information about the user's past transaction history (e.g., URLs of websites used in transactions, information about purchased products, etc.) and profile information, and is managed by user ID as shown in Figure 4(b).

[0087] User meta information is meta information about a user's behavioral patterns, and includes timing regularity information about the timing of the user's posts on SNS and transactions on websites, geographical information about the location from which the user accesses the site, information about the browser and device (user device 2) used by the user, and information about the period of activity since the user created their account, and is managed by user ID as shown in Figure 5(c).

[0088] By using user meta information, it is possible to detect bots and disposable accounts, and to increase the credit score of users with stable behavior. Specifically, the partial scores (scores for each piece of information used to calculate the credit score), which will be described later, are calculated higher for the user meta information of users with stable behavior.

[0089] By using timing regularity information, it is possible to check whether posts are being made at mechanical intervals late at night. If posts are being made at mechanical intervals late at night, it is highly likely that the account of that user ID is not being used by a human, and the partial score can be lowered.

[0090] By using geographical information of the user's access location, it is possible to check whether the user is logging in from an abnormally remote location in a short period of time. If the user is logging in from an abnormally remote location in a short period of time, it is highly likely that the account of that user ID is not being used by a human, and the partial score can be lowered.

[0091] By using information about the browser used by the user and the user device 2, it is possible to check whether the browser or device used by the user has changed too frequently in a short period of time. If the browser or device has changed frequently in a short period of time, it is highly likely that the account of that user ID is not being used by a human, and it is possible to lower the partial score.

[0092] By using information about the duration of activity since the user created the account, we can confirm the continuity of the account's activity after the account was created. If the account remains stable over a long period of time, it is likely that a human is using the account with that user ID, and we can consider increasing the partial score.

[0093] On-chain behavioral history information regarding a user's activity history on the blockchain is stored on the blockchain.

[0094] <Flowchart of evaluation point calculation process> FIG. 6 shows an example of a flowchart of the evaluation point calculation process in this embodiment.

[0095] <Acquisition for calculating credit score> In step S1, the acquisition unit 11 acquires SNS information related to the user's SNS (social networking service). Specifically, the SNS information includes information for analyzing the user's openness on the SNS (for example, the proactiveness and transparency of information transmission) and reliability (for example, the degree to which others trust the user).

[0096] The SNS information includes exchange relationship information regarding the user's exchange relationships on the SNS. Specifically, the exchange relationship information includes information regarding exchange relationships such as who follows or supports the user. In order to analyze who trusts the user, the acquisition unit 11 acquires the exchange relationship information.

[0097] If a user is followed or supported by people with high credibility (for example, industry experts or people with a good reputation in the community), the user can be judged to be trustworthy (with a high trust score, as described below). On the other hand, if a user is followed or supported only by suspicious accounts, the user can be judged to be untrustworthy.

[0098] The SNS information also includes posting information about posts made by the user to the SNS. Specifically, the posting information includes information about the posts, such as how many people have viewed or shared the user's posts. In order to analyze what kind of posts the user has made, the acquisition unit 11 acquires the posting information.

[0099] If a user posts something that is highly reliable (for example, a post that is viewed or shared by many people), it can be determined that the user is highly rated by the community, etc., and that the user can be determined to be trustworthy (having a high credit score (partial score used to calculate the credit score), as described below).

[0100] In addition, the posting information may include information regarding the number of "likes" and comments on the user's post. The more "likes" and positive comments a user has on their post, the more highly regarded the user is by the community, etc.

[0101] The SNS information may also include public profile information about the profile that the user has made public on the SNS. If the user has made public a highly reliable profile that can identify the user, such as their real name or affiliation (for example, the company they work for or the school they attend), the user can be determined to be trustworthy (with a high credit score (partial score) as described below).

[0102] The acquisition unit 11 may periodically acquire SNS information. By the acquisition unit 11 periodically acquiring SNS information, it becomes possible to periodically update the credit score (partial score) described below, and the latest credit score (partial score) can be used.

[0103] In step S1, the acquisition unit 11 may also acquire on-chain behavior history information regarding the user's activity history on the blockchain. For example, it can be determined that a user with a sound activity history on the blockchain has a higher degree of trustworthiness (a higher credit score, which will be described later).

[0104] On-chain activity history information is comprehensive information about transactions on the blockchain, including information about general on-chain activities related to DAOs (Decentralized Autonomous Organizations), such as authentication and activity history using decentralized IDs (DIDs (Decentralized Identity)), proposal activities for DAO management (such as the number of proposals adopted), participation rates in governance votes, SBT (Soulbound Token) holdings, issuance history, and staking.

[0105] A user with a sound transaction history on the blockchain (e.g., a fraud-free transaction history, a stable transaction history in DeFi (Decentralized Finance), etc.) can be judged to be highly trustworthy. On the other hand, if an abnormal transaction pattern on the blockchain (e.g., a concentration of mutual remittances in a short period of time) is detected, the user's trustworthiness must be carefully evaluated.

[0106] However, these judgments are based on explainable rulesets based on machine learning models and ZKPs, and provide a certain degree of contextual correction and refutation opportunity, and users with exceptional justifications can have their scores corrected to reflect those reasons.The scoring process is also provided with an interface (e.g., a visualization mechanism based on ZKPs) that allows users to disclose and verify their own score evaluation process to a certain extent.

[0107] In step S1, Web2 reputation information on the user's website may be acquired. The Web2 reputation information includes information on the user's transaction history, user evaluations of products and services, etc., on a conventional Web2 platform.

[0108] For example, on a conventional e-commerce site (Web2 platform), a user who gives a higher rating (e.g., star rating, comments, etc.) to a product or service may be judged to have a higher credit rating (credit score (partial score) described below).

[0109] Additionally, in step S1, user meta information regarding the user's behavioral patterns may be acquired. The user meta information includes information regarding the timing of user posts and transactions, access by the user's device (terminal), the device (terminal) used by the user, the continuity of activity after account creation, etc.

[0110] For example, based on information about the timing of a user's posts and transactions (e.g., regularity of timing, etc.), if posts and transactions are made at mechanical intervals late at night, it may be determined that there is a high possibility that the user is a bot or a throwaway account, and that the user cannot be trusted (the trust score (partial score) described below is low).

[0111] For example, based on information about access from a device (terminal) used by a user, if the user logs in from an abnormally large number of remote locations in a short period of time, it may be determined that the user is likely a bot or a disposable account, and that the user cannot be trusted (the trust score (partial score) described below is low).

[0112] For example, based on information about the device (terminal) used by the user, if the terminal or browser changes too frequently, it may be determined that there is a high possibility that the user is a bot or a disposable account, and that the user cannot be trusted (the trust score (partial score) described below is low).

[0113] For example, based on information about the continuity of activity after a user creates an account, if the user has been steadily active for a long period of time, the user may be judged to be trustworthy (with a high credit score (partial score) as described below).

[0114] The acquisition unit 11 may periodically acquire on-chain behavior history information, Web2 reputation information, and user meta information. By the acquisition unit 11 periodically acquiring various information, it becomes possible to periodically update the credit score (partial score) described below, and the latest credit score (partial score) can be used.

[0115] The acquisition unit 11 may acquire information based on a decentralized ID (DID) and a soulbound token (SBT). The acquisition unit 11 can acquire information on verified identity (KYC), voting information, contribution history, etc., using verifiable credentials (VC (Verifiable Credentials)) linked to the DID.

[0116] DID refers to an ID system that uses technologies such as blockchain to enable individuals to control their own ID and share only the necessary information to the extent necessary. Verifiable credentials are digital certificates that can be owned by individuals, and their legitimacy is verified by a trusted third party.

[0117] Verifiable credentials allow various pieces of information that are physically held on paper and difficult to prove, such as driver's licenses, diplomas, awards, and work history, to be recorded as digital certificates.

[0118] The acquisition unit 11 can acquire information regarding non-transferable credit history such as DAO participation (voting) history and contribution record through the SBT.

[0119] <Credit score calculation> In step S2, the score calculation unit 12 calculates a credit score representing the user's creditworthiness based on the user's SNS information.

[0120] Furthermore, the score calculation unit 12 may calculate the credit score based on on-chain behavior history information. Furthermore, the score calculation unit 12 may calculate the credit score based on Web2 reputation information and user meta information.

[0121] Specifically, the score calculation unit 12 calculates partial scores for each of the SNS information, on-chain behavior history information, Web2 reputation information, and user meta information, and calculates a credit score based on each partial score.

[0122] The range of values ​​for each partial score is set in advance, and the score calculation unit 12 calculates each partial score by normalizing it so that the value falls within that range. For example, it is possible to set a lower limit and an upper limit (e.g., 0 to 100) for the range of values ​​for each partial score.

[0123] By presetting the range of values ​​for each partial score, the influence of each partial score for SNS information, on-chain behavioral history information, Web2 reputation information, and user meta information can be made equal.

[0124] The score calculation unit 12 may calculate each partial score using a predetermined point-adding method. Specifically, the storage unit 17 stores conditions and scores, and the score calculation unit 12 determines whether the information acquired by the acquisition unit 11 satisfies the conditions, and if the conditions are satisfied, adds points to calculate the partial score.

[0125] For example, as conditions and scores for calculating a partial score based on SNS information, the memory unit 17 may store conditions and scores such as adding 5 points if the number of SNS followers is 100 or more, adding 6 points if the number is 200 or more, etc.

[0126] In addition, the score calculation unit 12 may calculate the partial score using a pre-trained machine learning model that has been trained in advance using each piece of information (SNS information, on-chain behavior history information, Web2 reputation information, or user meta information) and each partial score.

[0127] The score calculation unit 12 may calculate the trust score by summing up the partial score values ​​of the SNS information, the on-chain behavior history information, the Web2 reputation information, and the user meta information.

[0128] Weights may be set in advance for each of the SNS information, on-chain behavior history information, Web2 reputation information, and user meta information, and the score calculation unit 12 may calculate the credit score based on each partial score and weight.

[0129] Specifically, as shown in equation (1), weights (coefficients) w1 to w4 are set to be multiplied by the partial scores for each piece of information, and the score calculation unit 12 calculates the credit score based on each partial score and the weights.

[0130]

number

[0131] By setting a weight for each partial score, it is possible to set which of the pieces of information is to be emphasized. For example, if the credit score is calculated with emphasis on SNS information, the weight of the partial score for SNS information (w1 in formula (1)) is set to be larger than the other weights (w2 to w4 in formula (1)).

[0132] To prevent the influence of a particular partial score from becoming too large, for example, the range of values ​​(upper and lower limits (e.g., 0 to 1)) of each of w1 to w4 in formula (1) may be predetermined. Also, the sum of the values ​​of w1 to w4 in formula (1) (e.g., 1) may be predetermined.

[0133] The score calculation unit 12 may periodically calculate and update the credit score based on information (such as the above-mentioned SNS information) periodically acquired by the acquisition unit 11. The information acquired by the acquisition unit 11 is likely to be updated frequently, and by having the score calculation unit 12 periodically calculate the credit score (partial score), the latest credit score can always be used.

[0134] The score calculation unit 12 may calculate a credit score based on information associated with the DID acquired by the acquisition unit 11 (for example, information such as a driver's license, a degree certificate, awards, and work history based on verifiable credentials) and information associated with the SBT (for example, information such as participation (voting) history in the DAO and contribution record). In this way, it is possible to make a credit judgment that is different from the conventional credit judgment based on financial history such as credit cards or loan repayments.

[0135] Furthermore, the score calculation unit 12 may calculate (correct) the trust score calculated by formula (1) by applying a trust propagation algorithm. By regarding the connections (support relationships) between trustworthy users as a graph structure and applying the trust propagation algorithm, the network effect can be reflected in the trust score.

[0136] The score calculation unit 12 calculates (corrects) the credit score calculated by the formula (1) based on a relationship strength coefficient between users (a coefficient representing the degree of trust from the users).

[0137] Specifically, the trust score Si of a user i in the network is defined as the sum of the contributions of the trust scores of all users j who trust the user i. For example, the score calculation unit 12 calculates the trust score of the user i by applying the trust propagation algorithm using Equation (2).

[0138]

number

[0139] The "relationship strength coefficient Rij" in formula (2) represents a weight (e.g., 0 to 1) indicating how much user j trusts and supports user i. The score calculation unit 12 may calculate the relationship strength coefficient between user i and user j based on the frequency of transactions and interactions between user i and user j on the SNS. The score calculation unit 12 may calculate the relationship strength coefficient using AI such as a pre-trained model.

[0140] Since n in equation (2) is the number of users who trust user i, the more users j who trust user i, the higher the credit score of user i. Furthermore, since user i's credit score is calculated using the credit score of user j, the higher the credit score of user j who trusts user i, the higher the credit score of user i.

[0141] For example, if you have a trust relationship with a user who has a trust score of 80 and a relationship strength coefficient of 0.5, you will receive a trust contribution of 40 (=80×0.5) points from that relationship.

[0142] As a calculation method, the score calculation unit 12 calculates the credit scores of all users based on equation (1), and updates the credit score Si taking into account the network effect based on the trust propagation algorithm (model) of equation (2).

[0143] The score calculation unit 12 repeatedly calculates the credit scores of all users until they converge (for example, by using an iterative algorithm such as PageRank), thereby obtaining a final credit score that is self-reinforcingly evaluated across the entire network.

[0144] The relationship strength coefficient Rij may also be set based on the trust proof issued by each user (for example, an evaluation that "A trusts B") and the frequency of transactions and interactions. Due to this network effect, the trust score of users who gain trust within the community tends to be higher, while the trust scores of isolated users or users who are not trusted by others tend to be lower.

[0145] The score calculation unit 12 may estimate a default risk based on information (data) collected for calculating a credit score, and further calculate a credit score based on the estimated default risk. Specifically, the score calculation unit 12 may extract features (variables) useful for predicting credit risk from the data collected for calculating a credit score, estimate a default risk (probability of default) from the features, and calculate a credit score based on the estimated default risk.

[0146] The score calculation unit 12 uses AI (for example, natural language processing, graph analysis, supervised learning model, etc.) to quantify patterns in the data and extract features. For SNS information, the score calculation unit 12 calculates features related to the proactiveness and expertise of posting behavior, network reliability, and information diffusion and response.

[0147] The proactiveness and expertise of posting behavior is evaluated based on the frequency of posts, the consistency and expertise of the content of posts, and whether or not misinformation is being spread. If a user posts frequently and useful information, the "openness" is considered high and a positive score (feature) is added. Conversely, if the number of posts is extremely low or the content is spammy or unreliable, this will be a negative factor.

[0148] Network trust is assessed by plotting the follower and friend network and assessing who trusts the user. If the user is followed by highly reliable users such as industry experts or certified badge holders, the user's rating (feature value) is increased, while if the user has only suspicious new accounts or followers that appear to be bots, points are deducted. In this way, the "degree to which the user is trusted by others" is quantified.

[0149] Information diffusion and response are measured by measuring the level of attention a post receives, such as how often it is viewed and shared (engagement rate), whether the content of the post is cited by third parties, whether it is featured in the media, etc. If a post has high engagement in its field of expertise or is highly rated by the community for providing useful information, the score (feature) is increased.

[0150] Furthermore, rather than relying solely on traditional centralized media, the system treats posts and citations by individuals (or organizations) with high trust scores as sources of information dissemination, similar to media, with weighting adjustable according to their influence. For example, mentions, introductions, and reposts by users with high trust scores themselves function as elements that reinforce the reliability of information, and are dynamically evaluated as "media-like nodes" within the trust network. This structure allows for integrated treatment of centralized media and trust nodes within the community, realizing a more decentralized and flexible impact assessment.

[0151] In addition, the score calculation unit 12 calculates feature quantities related to transaction soundness, asset portfolio, and DAO contribution for the on-chain behavior history information.

[0152] To assess transaction integrity, the wallet's transaction history is analyzed to determine whether there has been any history of fraudulent transactions and whether the transactions are regular. Specifically, it checks for suspicious patterns such as sudden repeated large transfers, or whether there has been stable lending, borrowing, and settlement over a long period of time. If the former (unnatural patterns) are found, it is deemed to be a risk and the feature value is subtracted, and if the latter (stable transaction history) is confirmed, the feature value is added.

[0153] As for asset portfolios, the composition of cryptocurrencies and NFTs held is analyzed. For example, financial stability is assessed based on the total amount of assets, their volatility, and the ratio of stable assets. Financial soundness is scored as a feature, based on whether assets have been moved suddenly and whether there is sufficient collateral. With regard to NFTs in particular, continued holding of NFTs from highly credible projects is evaluated positively as evidence of community trust, while short-term trading of low-quality NFTs is considered speculative and evaluated negatively.

[0154] However, if reputation is fixed solely based on highly trusted projects and holdings of stable assets, it may cause unfairness to newcomers and users with different investment policies. Therefore, it is desirable to design a system that combines the weights of mentions, ratings, and connections with social media networks in addition to on-chain asset composition and transaction integrity to adjust the balance of the reputation score.

[0155] For example, being tagged as an NFT holder by a trusted user or receiving positive mentions or citations can be a positive reinforcement regardless of the market valuation or volatility of the NFT. Similarly, if a creator on a social media platform has a high reputation within the community, the stability of the NFTs or tokens continuously held in that person's wallet acts as "proof of the creator."

[0156] DAO contribution is calculated by quantifying the degree to which a user contributes to community management based on their voting participation rate within the DAO, the number of proposals they make, and whether or not they hold a position (such as moderator). For example, we calculate variables such as "governance voting rate over the past six months (what percentage of votes they participated in)," "the number of proposals that were adopted," and "the amount of governance tokens they hold and the length of time they have held them." These are features that indicate social credibility (reputation in the digital space) and contribute to the assessment of the credibility of users with little financial history.

[0157] The score calculation unit 12 calculates the user's predicted default probability (PD) using a statistical model or a machine learning model based on features extracted from multiple data sources derived from SNS, DAO, and blockchain, and calculates a credit score corresponding to the PD stored in the memory unit 17.

[0158] The score calculation unit 12 calculates a credit score using an SNS feature vector extracted from SNS information (proactiveness and expertise of posting behavior, network reliability, information diffusion and response), an on-chain financial feature vector extracted from on-chain behavior history information, and a DAO feature vector extracted from on-chain behavior history information.The score calculation unit 12 calculates a default risk assessment value R based on formula (3).

[0159]

number

[0160] In a decentralized ID environment, it is expected that users will use multiple wallet addresses for different purposes. Therefore, the calculation of credit scores will not rely on a single wallet, but will instead be structured to comprehensively evaluate multiple address groups linked via DIDs.

[0161] In addition, in order to prevent attempts to manipulate the credit score by arbitrarily connecting only specific wallets, a "wallet dispersion correction coefficient (wallet credit score correction coefficient)" is introduced that takes into account the activity history, period, and relevance of the connected address to other addresses, and the bias in the credit score is dynamically adjusted. The score calculation unit 12 may calculate the default risk evaluation value R based on formula (4) using the wallet dispersion correction coefficient.

[0162]

number

[0163] The score aggregation function corresponding to the DAO feature vector is the DAO contribution score, which is the sum of voting rate, governance token holdings, etc. on a certain normalized scale (for example, it can be expressed as a linear combination of voting rate × weight + proposal adoption rate × weight + ...).

[0164] The score aggregation function corresponding to the SNS feature vector is the SNS credibility score, which is a value combining composite indicators such as the number of trusted users and post engagement. Also, the score aggregation function corresponding to the on-chain financial feature vector is the on-chain soundness score, which is calculated from the healthy transaction ratio, asset stability, etc.

[0165] Furthermore, the score calculation unit 12 uses a logistic regression model to calculate the predicted default probability PD based on equation (5) using the default risk assessment value R as a logic variable.

[0166]

number

[0167] The smaller this predicted default probability PD, the higher the trustworthiness of the bank. When using advanced models such as deep learning, PD is predicted in a black box manner, but previous research has confirmed that the accuracy of the model's predictions can be improved by including alternative data from social media and on-chain.

[0168] Experiments have shown that adding features derived from social media significantly improves the classification accuracy of default predictions, and that social network information brings value to credit risk assessment. Therefore, this algorithm is trained to estimate PD with high accuracy from the extracted features.

[0169] The score calculation unit 12 determines a final credit score (rating value) based on the calculated predicted default probability PD. The score calculation unit 12 outputs the credit score as a discrete grade (for example, 1 to 10) so that it is easy for financial institutions to handle. For example, it is designed so that a score of "1" indicates the highest risk (high default probability) and a score of "10" indicates the highest creditworthiness (minimal default probability).

[0170] The memory unit 17 stores the credit scores and the PD ranges in association with each other, which allows the score calculation unit 12 to calculate the credit score based on the PD. For example, the memory unit 17 associates a credit score of 10 with a PD of less than 0.5% (extremely safe), a credit score of 9 with a PD of approximately less than 1%, a credit score of 7 to 8 with a PD of several percent (intermediate creditworthiness), scores of 4 to 6 with a risk of PD reaching several tens of percent, and credit scores of 1 to 3 with a PD of 50% or more (extremely high default risk).

[0171] <Review score acceptance> In step S3, the receiving unit 13 receives a review score for the content from the user. The content is a product, a service, etc., and the receiving unit 13 receives, via the user terminal 2, the user's evaluation of the product, service, etc. as a numerical value (review score).

[0172] The range of values ​​of the review score (lower limit and upper limit (for example, 1 to 10)) may be determined in advance.

[0173] <Calculation of evaluation points> In step S4, the point calculation unit 14 calculates evaluation points for the user's content based on the credit score calculated by the score calculation unit 12 and the review score received by the reception unit 13, and stores the evaluation points in the blockchain.

[0174] The point calculation unit 14 stores the calculated evaluation points in the blockchain as the user's evaluation of the product, service, etc. The point calculation unit 14 may store the calculated evaluation points in association with the ID of the product, service, etc.

[0175] Users with low trust scores are more likely to rate content in a casual manner, and may even lie about the content they rate.

[0176] The point calculation unit 14 calculates evaluation points using the user's credit score as an evaluation of the content, thereby making it possible to calculate a highly reliable evaluation of the content.

[0177] For example, the point calculation unit 14 may multiply the review score for the user's content by the user's credit score (the number obtained by multiplication) and store the result in the blockchain as an evaluation point for that content (linear proportional evaluation point).

[0178] By doing this, the credit score can be directly proportional to the review score. For example, someone with twice the credit score can have their review score worth twice as much.

[0179] In addition, the point calculation unit 14 may compress the user's credit score using a log function, multiply the review score for the user's content by the number compressed using the log function, and store the resulting number (the number obtained by multiplication) in the blockchain as the evaluation point for that content (logarithmic scale evaluation point).

[0180] In this way, even if there is a large difference in credit scores between users, the influence of the credit scores can be moderated.

[0181] In addition, the memory unit 17 may store weights associated with the range of credit scores (for example, a weight of 1 may be stored for a credit score of 0 to 50, a weight of 2 for a credit score of 50 to 80, a weight of 3 for a credit score of 80 or more, etc.), and the point calculation unit 14 may multiply the review score for the user's content by the weight referenced from the user's credit score based on the user's credit score and the weight stored in the memory unit 17 (the number obtained by multiplication), and store the resulting number in the blockchain as an evaluation point for the content (a step (class) type evaluation point).

[0182] By doing this, it is possible to increase the influence of ratings on content by people with a credit score above a certain level, and decrease the influence of ratings on content by people with a credit score below a certain level.

[0183] In addition, the point calculation unit 14 may store only review scores for content by users with a trust score equal to or greater than a certain numerical value (for example, a numerical value stored in advance in the memory unit 17) in the blockchain as evaluation points for that content.

[0184] In this way, it is possible to eliminate ratings of content by users with extremely low credit scores.

[0185] <Content provision restrictions> The provision restriction unit 15 restricts the provision of content based on the evaluation points for that content. For example, the provision restriction unit 15 restricts the provision of content with low evaluation points (for example, by stopping sales, stopping service provision, etc.). The provision restriction unit 15 stops sales of products with low evaluation points, stops the provision of services with low evaluation points, etc.

[0186] In this way, it is possible to restrict the provision of content with low ratings. By restricting the provision of content based on ratings obtained using the user's credit score, it is possible to restrict the provision of content based on highly reliable ratings.

[0187] On the other hand, if only a few evaluations are collected for the content, there is a possibility that the evaluations collected are biased by chance, so the provision restriction unit 15 restricts the provision of the content when evaluation points are collected from a certain number of users and furthermore, the evaluation points are low.

[0188] The provision restriction unit 15 may restrict the provision of content based on the evaluation points for the content and the number of users who have evaluated (given evaluation points to) the content.

[0189] The provision restriction unit 15 determines whether the number of people who have rated the content is equal to or greater than a threshold value stored in the memory unit 17, and if the number of people who have rated the content is equal to or greater than the threshold value (when a certain number of evaluation points for the content have been collected (for example, when evaluation points have been collected from more than a predetermined threshold value stored in the memory unit 17 (for example, 10 or more people))), determines whether to restrict the provision of the content based on the evaluation points for the content.

[0190] For example, the provision restriction unit 15 may restrict provision of content when the average value of evaluation points for the content falls below a threshold value stored in the storage unit 17. By storing in the storage unit 17 a threshold value of evaluation points that is set in advance by an administrator of the platform or the like on which the content is provided, the provision restriction unit 15 can restrict provision of the content based on the average value and the threshold value.

[0191] Furthermore, the provision restriction unit 15 may restrict the provision of content that has received low evaluation points from a certain percentage of users. For example, the storage unit 17 may store a threshold value and a percentage of evaluation points for restricting the provision of content, and the provision restriction unit 15 may restrict the provision of the content when the percentage of users who give evaluation points equal to or less than the threshold value to the content (the percentage of users who give evaluation points equal to or less than the threshold value to the content among all users who have rated the content) exceeds the percentage stored in the storage unit 17.

[0192] <Issuance of NFTs related to credit scores> The NFT issuing unit 16 issues an NFT (Non-Fungible Token) related to the credit score on the blockchain. In this way, the user's credit score can be visualized as an NFT, and can be disclosed to the community to which the user belongs.

[0193] The NFT issuing unit 16 may issue the credit score as an SBT (Soulbound Token). Unlike regular NFTs, SBT is a non-transferable token, so the credit score issued in SBT becomes a certificate that is linked to the user like an alter ego. In this way, it becomes possible to issue a credit score as a non-transferable, publicly verifiable digital token, allowing the credit score to be displayed and proven on various services and platforms.

[0194] This means that users can use their own credit scores portable across various communities and services, and credit information can be centrally managed. For example, as long as identity verification is possible, the credit score developed in one DAO can be verified on another DAO or platform, eliminating the need to build trust from scratch each time.

[0195] However, because the detailed breakdown of the credit score (e.g., various data, evaluations, partial scores, etc.) concerns personal privacy, the information made public on the NFT must be kept to a minimum. Specifically, the credit score value represented by the NFT (e.g., overall score, rank, etc.) is cryptographically recorded using irreversible ternary encoding.

[0196] This encoding is designed to be verifiably restored only by a protocol or verifier with the decryption authority corresponding to the private key.In addition, information used in the score calculation process (e.g., voting participation rate, transaction patterns, social media ratings, etc.) can be verified using ZKP (zero-knowledge proof) to prove that the score was generated according to a legitimate calculation method, and individual raw data, weighting factors, etc. will not be made public.

[0197] This design allows this technology to simultaneously protect privacy and ensure the reliability of scores, while also enabling immutable credit histories to be structured and recorded on the blockchain.

[0198] The NFT issuing unit 16 cryptographically converts the credit score (for example, a credit score generated based on on-chain behavioral history information and SNS information on the blockchain) using an irreversible ternary encoding method, and issues an NFT (non-fungible token) containing information about the changed score.

[0199] To enable the ternary encoded credit score to be restored, only those who hold a compound key based on the private key can verify the validity of the credit score using a ZKP (zero-knowledge proof).

[0200] The information recorded in the NFT regarding the credit score issued by the NFT issuer 16 includes only the overall score and an identifier, and does not include the individual data used to calculate the credit score (e.g., voting participation rate, frequency of transaction history, etc.), or is only accessible through selective disclosure using a ZKP.

[0201] Instead of disclosing the information used to calculate the credit score, the NFT issuing unit 16 utilizes zero-knowledge proof (ZKP) to certify only the necessary items when necessary.

[0202] By using zero-knowledge proofs, the NFT issuing unit 16 can cryptographically prove that a user's credit score is 80 points or higher, or that their participation rate in DAO voting over the past three months is 90% or higher, without disclosing the user's confidential information.

[0203] In other words, based on zero-knowledge proof, the NFT issuing unit 16 can issue a highly reliable NFT that includes only the credit score, even though the information used to calculate the credit score is not disclosed.

[0204] This allows for transparent disclosure of trust scores while protecting user privacy as needed for the information used to calculate the trust score (for example, comments on social media, etc.) One possible method for implementing ZKP is to hash commit the trust score calculation process and issue a proof of that commit.

[0205] The NFT issuing unit 16 may issue NFTs for each partial score (SNS information, on-chain behavioral history information, Web2 reputation information, and user meta information) on the blockchain. By doing so, it becomes possible to disclose each partial score as an NFT, and the process of calculating the credit score can be disclosed.

[0206] <Reflection of trust scores in DAO voting> A DAO (Decentralized Autonomous Organization) is an organizational form that realizes Web 3.0 and enables efficient operation. In a DAO, members can propose their own decisions on organizational matters, and other members vote on the content of those proposals within a predetermined period.

[0207] In voting in the DAO, the trust score calculated by the score calculation unit 12 may be used. In voting on proposals in the DAO, the voting unit 18 accepts votes from users and determines whether to approve the proposal based on the accepted votes and the trust score.

[0208] Specifically, the voting unit 18 reflects the trust score calculated by the score calculation unit 12 in the weight of each user's voting right in the DAO. This makes it possible to realize a fair system that more strongly reflects the opinions of highly reliable users compared to conventional systems such as one person, one vote, and voting based on token holdings.

[0209] For example, the trust score can be reflected in the votes in the DAO by using a linear proportional, logarithmic scale, step (class) or other method. The linear proportional method is a method in which the trust score is directly proportional to the voting weight in the DAO. In other words, a person with a trust score of 80 has twice the voting weight of a person with a trust score of 40. In the linear proportional method, if the influence of a person with a high trust score on voting becomes too great, an upper limit can be set on the voting weight.

[0210] The logarithmic scale type is a weighting method that compresses credit scores using a log function. Even if the difference in credit scores is large, the difference in weights is reduced, preventing some users from having overwhelming influence while maintaining relative differences.

[0211] The step (class) type is a method in which the range of credit scores is classified into several classes, and additional votes are given when a threshold is exceeded. For example, a graduated weighting can be set, such as 1 vote for a credit score of 0-50, 2 votes for a credit score of 50-80, and 3 votes for a credit score of 80 or above. In this case, since additional influence cannot be obtained unless a certain level of credit score is obtained, unfairness caused by small score differences can be reduced.

[0212] Consider linear proportional voting when there are five DAO members A to E. Let's say their trust scores are A=95, B=85, C=60, D=55, ​​and E=50 (out of 100). If A and B vote "against" a certain proposal and C, D, and E vote "for," under the one-person-one-vote system, there are three votes in favor and two votes against, so the proposal is passed by a majority vote.

[0213] On the other hand, in weighted voting based on trust scores, the total number of votes in favor is 165 (= 60 + 55 + 50), while the total number of votes against is 180 (= 95 + 85), so the proposal is rejected because the total number of votes against is greater.

[0214] By reflecting trust scores in voting in the DAO, the influence of votes from highly reliable users on the outcome will increase, which may lead to results different from those in the case of a one-person, one-vote system. In other words, by introducing trust scores into voting in the DAO, it is possible to prioritize the opinions of people who have contributed to the community and earned trust, while reducing the risk of proposals being passed fraudulently by bots or newcomers alone.

[0215] <Storing data in a blockchain using entanglement patterns> The conversion unit 19 converts binary data (e.g., credit scores, information used to calculate the credit scores, etc.) to be stored in the blockchain into ternary data (e.g., 0, 1, i, etc.). The storage unit 17 stores the angles (phases) of light assigned to "0", "1", and "i", respectively.

[0216] The conversion unit 19 converts the binary data into ternary data into an interference pattern (light pattern) based on the light angle (phase) stored in the storage unit 17. The light angle (phase) is assigned as "0°" to "0", "120°" to "1", and "240°" to "i", and the storage unit 17 associates "0°", "120°", and "240°" with "0", "1", and "i" that make up the ternary number, respectively, and stores them.

[0217] Fig. 6 shows an example of an interference pattern in this embodiment when the character string of ternary data converted by the conversion unit 19 is "010i11i".

[0218] The conversion unit 19 converts the character string of ternary data into an interference pattern as shown in Fig. 6 based on the angles corresponding to each character in the character string. The conversion unit 19 may store the converted interference pattern in the storage unit 17 as identification information.

[0219] The conversion unit 19 performs a fast Fourier transform (FFT) on the interference pattern (sequence of angles) converted from ternary data based on the angle of light, converting it into a complex but stable wave shape (entanglement pattern) and storing it in the blockchain network system. The conversion unit 19 associates the entanglement pattern with the ternary data and stores it.

[0220] The norm calculation unit 1a calculates the norm (intensity and length) of the interference pattern, converts the calculated norm into a hash value, and generates a special ID (Entanglement ID). Specifically, the norm calculation unit 1a calculates (generates) the hash value of the norm (special ID) using a ternary hash function (e.g., TritSHA).

[0221] When receiving binary data stored in the blockchain, the receiver measures the angle of light based on the received data and regenerates a unique ID (Entanglement ID) based on the sequence of angles. In this way, the receiver can prove whether the received data is authentic or not without checking the contents.

[0222] <Public ID collaboration and social implementation connectivity> Another embodiment of the present invention also functions as a shared platform that allows third-party organizations, such as government, financial, and medical institutions, to obtain and utilize credit scores via DID with the user's explicit consent, and is designed based on consistency with actual My Number policies, the Smart Life Path initiative, and W3C standards.

[0223] For example, the reception unit 13 receives explicit consent from the user to submit the credit score to an administrative, financial, or medical institution, and the score calculation unit 12 provides the credit score to the administrative, financial, or medical institution based on the explicit consent received from the user. The reception unit 13 receives the explicit consent via the user terminal 2 used by the user.

[0224] For example, the following structures, including mathematical formulas (1. Credit score integration structure, 2. DID / SBT-based structure (DID / SBT-based credit data structure), 3. Cross-chain / cross-domain integration, 4. Partial score disclosure model using zero-knowledge proof, 5. Dynamic UX-linked learning, 6. Utilization model in DAOs and local governments (utilization use cases)), enable self-management and dynamic design of credit scores, and furthermore, enable linkage to multiple domains, while simultaneously achieving transparency (ZKP) and UX (feedback loop).

[0225] By doing this, the score calculation unit 12 can link a verifiable proof (VC) containing credentials based on a public ID and a soulbound token (SBT) indicating social credit attributes to a decentralized ID (DID) and manage them in an integrated manner, and provide credit scores to external systems in API format via the DID.

[0226]

number

[0227] In a trust score platform based on public ID federation, public VCs (Verifiable Credentials) such as My Number are combined with DIDs as components corresponding to f3 (Public ID), and are designed to be used as a factor in calculating AI scores. After obtaining the user's consent, government agencies obtain and use the scores via API (for example, for pre-screening in administrative procedures or welfare notifications). DID federation based on "self-determination, self-management, and mutual trust" enables dynamic and selective presentation of score information (utilizing ZKP).

[0228] [Number]

[0229] In the DID / SBT-based credit data structure, the DID structure includes VC and SBT, and hierarchically organizes the user's social credit attributes (e.g., SBT_contribution, SBT_health, SBT_education). AI extracts credit feature vectors from DID_user_X for credit scoring. Also, cross-chain compatibility (DID_eth, DID_gov, etc.) is assumed, and integrated scoring is enabled by a common solution layer (DIDConect).

[0230] [Number]

[0231] [Number]

[0232] [Number]

[0233] [Number] <ZKP-based Proof>

[0234] When privacy protection is required in scenarios such as voting and transactions, the user presents the ZKP associated with the NFT regarding their credit score. Specifically, the reception unit 13 receives an instruction to submit the credit score from the user, and the ZKP presentation unit 1b presents (provides) the ZKP to an external system or the like. For example, the reception unit 13 receives an instruction to submit the credit score via the user terminal 2 used by the user.

[0235] For example, when voting, the ZKP presenter 1b submits a ZK-SNARK that proves that the trust score is above a threshold, and this is verified on the contract (blockchain smart contract) side. This makes it possible to calculate weights and confirm eligibility without revealing the actual trust score value.

[0236] Furthermore, if a user wishes to prove to a financial institution that his or her credit score is above a predetermined threshold, the ZKP presentation unit 1b constructs the proposition from the distribution of the user's credit score and proves its truth using the ZKP (only the truth is communicated to the financial institution; the actual credit score value and original data are not disclosed).

[0237] This allows users to prove their trustworthiness while protecting the privacy of their data, allowing them to manage and present their own credit information with peace of mind.Furthermore, since the aggregate score distribution of all users (distribution characteristics as a population) is not disclosed to the outside world, personal details are kept confidential while maintaining statistical transparency, allowing for the social implementation of credit scoring while preventing discrimination and privacy violations.

[0238] The ZKP presentation unit 1b uses ZKP to prove that the user's credit score is above a threshold and presents this to the bank.By verifying this certificate, the bank can confirm, for example, that the user's credit score is 7 or higher, without knowing the user's detailed SNS posts or asset information.

[0239] Another possible method is for the NFT issuing unit 16 to issue the credit score itself as a soulbound token (SBT) or NFT, and for the user to submit it via a digital wallet. In either case, the underlying data for the credit score (such as SNS posts or transaction history) will not be disclosed, and only the credit score and a minimum explanation will be shared.

[0240] For example, the ZKP presenter 1b and the NFT issuer 16 may provide a score report on the credit score along with information about the credit score. The score report is summary information, such as a high score due to high expertise and community trust on social media, or some concerns about on-chain transactions, and serves as material for a financial institution (e.g., a bank) official to determine the legitimacy of the credit score.

[0241] <Collaboration with external organizations> The score calculation unit 12 may provide the credit score to an external institution such as a financial institution or FinTech company via API cooperation. Also, the ZKP presentation unit 1b may provide the ZKP to the external institution.

[0242] The score calculation unit 12 and / or the ZKP presentation unit 1b provide the credit score for each DID to an external institution via a REST API, etc. The score calculation unit 12 provides the credit score to the external institution based on a design that complies with the Financial Services Agency FinTech guidelines, GDPR (General Data Protection Regulation), etc.

[0243] For example, the reception unit 13 receives explicit consent from the user to provide the credit score to an external institution, and the score calculation unit 12 and / or the ZKP presentation unit 1b provide the credit score to the external institution if explicit consent is received from the user.

[0244] The reception unit 13 receives explicit agreement regarding the scope, items, etc. to be provided to the external organization, and the score calculation unit 12 and / or the ZKP presentation unit 1b provides information on the scope and items for which explicit agreement has been received to be provided to the external organization.

[0245] Since the calculation of the credit score uses data related to an individual's social media and on-chain information, the credit score is treated as personal information. The score calculation unit 12 and / or the ZKP presentation unit 1b provide the credit score and other information to external institutions in consideration of the Personal Information Protection Act and regulations of the Financial Services Agency.

[0246] Specifically, the score calculation unit 12 and / or the ZKP presentation unit 1b may process information about the user (e.g., SNS posts, on-chain history, reviews, etc.) in a format that does not include personal information (e.g., hashing, etc.) and provide it to an external organization.

[0247] In addition, the user may agree to provide information other than the credit score (e.g., social media posts, transaction history, etc.) to an external organization, and if the score calculation unit 12 and / or the ZKP presentation unit 1b accepts the agreement, they will provide information on the scope and items for which the agreement was accepted.

[0248] Since the information that the user wishes to receive may differ depending on the external institution, the receiving unit 13 may receive explicit consent from the user for each piece of information that the financial institution wishes to receive.

[0249] <Credit scores incorporating black swan indicators> The score calculation unit 12 may calculate the credit score based on the black swan indicator. Specifically, the credit score is calculated based on the formula (12).

[0250]

number

[0251] The history-based score is calculated based on financial history, on-chain activity, track record, etc. The semantic score is calculated based on the consistency, logic, emotional consistency, etc. of a user's statements on social media.

[0252] The information diffusion rate is a score calculated based on the diffusion rate and citation rate, which indicates how much a user's information on SNS has spread to others. The non-stationarity correction term is an adjustment term for unpredictable fluctuations such as black swan events, short-term noise, and emotional bursts.

[0253] <Coexistence of credit score and conventional score in this embodiment> An institution such as a financial institution that receives (uses) the credit score calculated by the score calculation unit 12 may independently calculate a score related to the user's credit. Such an institution such as a financial institution may calculate a unique score related to the user's credit (traditional score) based on, for example, its own conventional evaluation items, evaluation indicators, etc.

[0254] When an institution such as a financial institution calculates a traditional score, the institution may be unsure whether to prioritize the credit score calculated by the score calculation unit 12 or the traditional score. Therefore, it is necessary to clarify the legitimacy of the credit score calculated by the score calculation unit 12 and the difference between the credit score and the traditional score.

[0255] The storage unit 17 stores evaluation items for calculating a credit score, and further stores an evaluation period, purpose, evaluation index, etc. linked to the evaluation items. The storage unit 17 may also store evaluation items for calculating a partial score, and further store an evaluation period, purpose, evaluation index, etc. linked to the evaluation items.

[0256] Furthermore, the memory unit 17 stores evaluation items for calculating the conventional score of the institution that receives the credit score calculated by the score calculation unit 12, and may also store the evaluation period, purpose, evaluation indicators, etc. linked to the evaluation items.

[0257] The display processing unit 1c displays the credit score, the conventional score, and the evaluation items calculated by the score calculation unit 12. Furthermore, the display processing unit 1c may display the evaluation period, the purpose, and the evaluation index associated with the evaluation items.

[0258] In addition, if the upper limit values ​​of the credit score and the conventional score are the same, the display processing unit 1c may display evaluation items, etc. only if the difference between the credit score and the conventional score is greater than or equal to a predetermined value (for example, a value stored in the memory unit 17).

[0259] The display processing unit 1c may determine overlapping evaluation items based on the evaluation items of the credit score calculated by the score calculation unit 12 and the evaluation items of the conventional score, and display the overlapping evaluation items in a manner that makes them visible.

[0260] Furthermore, the score calculation unit 12 may calculate a credit score by generating a new evaluation item based on the evaluation items of the credit score and the evaluation items of the conventional score. For example, the score calculation unit 12 may calculate a credit score based on all evaluation items of the evaluation items of the credit score and the evaluation items of the conventional score.

[0261] In addition, the score calculation unit 12 may extract some evaluation items from the evaluation items of the credit score and the evaluation items of the conventional score, and calculate the credit score based on the extracted evaluation items.

[0262] As described above, the configuration of the present invention can provide a new technology for managing content using a blockchain. [Explanation of symbols]

[0263] 0 Content Management Systems 1 Content management device 11 Acquisition Department 12 Score calculation section 13 Reception 14 Point Calculation Section 15. Provision Restrictions 16 NFT (Non-Fungible Token) Issuance Department 17 Memory section 18 Voting Department 19 Conversion unit 1a Norm calculation unit 1b ZKP presentation section 1c Display processing section 2. User terminal 3. Blockchain Network System NW Network

Claims

1. A content management system that manages content ratings using a blockchain, the content management system includes an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit; The acquisition unit acquires SNS information related to a user's SNS (social networking service), the score calculation unit calculates a credit score representing the creditworthiness of the user based on the SNS information of the user; The SNS information includes exchange relationship information regarding exchange relationships of the user on the SNS, the receiving unit receives a review score for the content from the user; the point calculation unit calculates an evaluation point for the content of the user based on the credit score and the review score, and stores the evaluation point in a blockchain; Content management system.

2. The content management system includes a provision restriction unit, the provision restriction unit restricts provision of the content based on an evaluation point for the content. The content management system of claim 1 .

3. The acquisition unit acquires on-chain behavior history information regarding the user's activity history on the blockchain, The score calculation unit calculates the credit score based on the on-chain behavior history information. The content management system of claim 1 .

4. The acquisition unit acquires Web2 reputation information relating to the user's reputation on a website and user meta information relating to the user's behavioral pattern, The score calculation unit calculates the credit score based on the Web2 reputation information and the user meta information. The content management system of claim 3 .

5. the score calculation unit calculates partial scores for the SNS information, the on-chain behavior history information, the Web2 reputation information, and the user meta information, and calculates the credit score based on the partial scores; The content management system of claim 4 .

6. a range of values ​​for the partial score is preset, the score calculation unit calculates each partial score by normalizing the partial scores so that the partial scores become values ​​within the range. The content management system of claim 5 .

7. a weight is set in advance for each of the SNS information, the on-chain behavior history information, the Web2 reputation information, and the user meta information; The score calculation unit calculates the credit score based on the respective partial scores and the weights. The content management system of claim 6.

8. The content management system includes an NFT (non-fungible token) issuing unit, The NFT issuing unit issues an NFT related to the credit score on a blockchain. The content management system of claim 1 .

9. The acquisition unit periodically acquires the SNS information, The score calculation unit periodically calculates and updates the credit score based on periodically acquired SNS information. The content management system of claim 1 .

10. The content management system includes a voting unit, The voting unit accepts votes from the users in voting on proposals in the DAO, and determines whether to approve the proposal based on the votes and the trust score. The content management system of claim 1 .

11. The acquisition unit acquires information associated with the DID and the SBT, The score calculation unit generates a credit score based on the verifiable credentials associated with the DID and information associated with the SBT. The content management system of claim 1 .

12. The NFT issuing unit issues an NFT including only the credit score based on a zero-knowledge proof. The content management system of claim 8 .

13. the score calculation unit calculates a credit score based on a relationship strength coefficient between users; The content management system of claim 1 .

14. The content management system includes a conversion unit, The conversion unit converts the information to be stored in the blockchain into a ternary number, and stores the information converted into the ternary number in the blockchain. The content management system of claim 1 .

15. The NFT issuing unit cryptographically converts the credit score using a non-reversible ternary encoding method and issues an NFT containing the converted information. The content management system of claim 8.

16. the score calculation unit estimates a default risk based on the information collected for calculating the credit score, and further calculates the credit score based on the default risk; The content management system of claim 1 .

17. The score calculation unit calculates a credit score based on a black swan indicator. The content management system of claim 1 .

18. The accepting unit accepts explicit consent from the user to submit the credit score to a government, financial institution, or medical institution; The score calculation unit provides a credit score to the government, financial institution, or medical institution based on the explicit consent. The content management system of claim 1 .

19. The content management system includes a ZKP presentation unit, the reception unit receives an instruction to submit a credit score from the user; The ZKP presentation unit presents the ZKP based on the submission instruction.

20. The content management system of claim 18.

20. the content management system includes a storage unit and a display processing unit; The storage unit stores evaluation items for calculating the credit score and evaluation items for calculating a conventional score of an institution that receives the credit score; The display processing unit displays the credit score, the conventional score, and the evaluation items. The content management system of claim 1 .

21. A content management method executed by a content management system that manages content ratings using a blockchain, the content management system includes an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit; The acquisition unit acquires SNS information related to a user's SNS (social networking service); a step in which the score calculation unit calculates a credit score representing the creditworthiness of the user based on the SNS information of the user; The SNS information includes exchange relationship information regarding exchange relationships of the user on the SNS, the receiving unit receiving a review score for the content from the user; the point calculation unit calculates an evaluation point for the content of the user based on the credit score and the review score, and stores the evaluation point in a blockchain; Content management methods.

22. A content management program that manages content ratings using a blockchain, causing a computer to function as an acquisition unit, a score calculation unit, a reception unit, and a point calculation unit; The acquisition unit acquires SNS information related to a user's SNS (social networking service), the score calculation unit calculates a credit score representing the creditworthiness of the user based on the SNS information of the user; The SNS information includes exchange relationship information regarding exchange relationships of the user on the SNS, the receiving unit receives a review score for the content from the user; the point calculation unit calculates an evaluation point for the content of the user based on the credit score and the review score, and stores the evaluation point in a blockchain; Content management program.

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