Computer Processing System

Abstract

To resolve the dilemma of the dichotomy between guaranteeing the authenticity of recorded information and guaranteeing the right to delete the information.SOLUTION: Information is double-encrypted EKA(EKB(information)) with two one-sided common keys KA and KB and recorded in a block chain. An information holder distributes one one-sided common key KB to an information requester and sends information EKB(information) obtained by decrypting DKA(EKA(EKB(information))) the double-encrypted information with the one-sided common key KA. The information requester computes DKB(EKB(information)) to obtain information in a plaintext. By the information holder updating the one-sided common key KA to R, DR(EKA(EKB(information))) is sent to the information requester, and even if the information requester tries to decrypt DKB(DR(EKA(EKB(information))) with KB, a plaintext cannot be obtained.SELECTED DRAWING: Figure 14

Description

[Technical field]

[0001] The present invention relates to an information recording method such as a block chain that is difficult to tamper with or erase. computer Processing System To Regarding. [Background technology]

[0002] Blockchain has been widely known as an information recording method that is difficult to tamper with. For example, Patent Document 1 discloses a method for recording various information related to cargo transportation using blockchain. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2018-128723 Summary of the Invention [Problem to be solved by the invention]

[0004] However, such information recorded using blockchain is not only difficult to tamper with, but also difficult to erase (hereinafter referred to as "inerasability"). As a result, once personal information is recorded using blockchain, it cannot be erased even if the personal information owner wants to erase the information, which is a drawback in that the right to erase personal information (the so-called right to be forgotten) is violated.

[0005] In other words, there is a drawback in that a dilemma arises between guaranteeing the authenticity of recorded information and guaranteeing the right to delete that information, which are in conflict with each other.

[0006] The present invention has been devised in view of the above circumstances, and its object is to resolve the trade-off between guaranteeing the authenticity of recorded information and guaranteeing the right to delete that information. [Means for solving the problem]

[0007] The present invention includes an encryption unit that performs an encryption process for encrypting information to be recorded; a recording means for recording the encrypted information; The encryption information is decrypted using the first key and the second key. Decryption a decryption means for converting the encrypted information into plaintext information by using the decryption processing function; a decryption disablement means for making the encrypted information in a decryption disabled state such that the encrypted information cannot be decrypted, The decoding means includes: a second key secret storage means for storing the second key in secret; a first key distribution means for distributing the first key to a person who wishes to view the information; a data transmission means for transmitting data that has been decrypted using the second key to a person who wishes to view the data and who has received the first key from the first key distribution means; a plaintext browsing means having a decryption processing function that performs a decryption process on the data transmitted by the data transmitting means, using the first key distributed by the first key distributing means, and using the decryption processing function to decrypt the data into plaintext to make it browseable; The decryption disablement means includes: updating means for updating the second key held by the second key secret holding means to a new second key consisting of other data; The data transmitted by the data transmitting means is decrypted using the updated second key, thereby enabling the plaintext to be accessed by the plaintext access enabling means. Decryption makes it impossible.

[0008] Preferably, A legal act means in which an AI model generated by machine learning generates a smart contract and performs a legal act by using the smart contract; The smart contract further includes a recording means for recording legal acts performed by the smart contract on a blockchain.

[0009] More preferably, The legal act means performs legal acts based on the smart contracts generated between the multiple AI models.

[0010] More preferably, a machine learning means for machine learning the AI ​​model; A simulation means for performing a simulation under a predetermined theme using a trained AI model trained by the machine learning means; A derivation means for deriving a result of the simulation by the simulation means, The machine learning means includes: A selection means for selecting a group of users belonging to a plurality of personas that match a theme of the simulation; a collection means for grouping the user group selected by the selection means according to the plurality of personas and collecting information on legal acts performed by the user group for each group; collection A generation means for performing machine learning using information about the legal acts obtained by the persona as learning data to generate a trained AI model for each persona, The simulation means executes a simulation within a computer in which the generated trained AI models perform legal acts based on each other's generated smart contracts. More preferably, the system further includes a reinforcement learning means for causing the trained AI model to learn a strategy for maximizing the accumulation of rewards by giving rewards for the performed legal acts to the trained AI model. Effect of the Invention

[0011] According to the present invention, it is possible to eliminate as much as possible the dilemma of the trade-off between guaranteeing the authenticity of recorded information and guaranteeing the right to delete that information. [Brief description of the drawings]

[0012] [Figure 1] 1 is a system diagram showing an overall configuration of a processing system. [Diagram 2] (A) is a diagram explaining the information stored in the HDD of a user terminal that constitutes a node of the blockchain, and (B) is a diagram explaining the information stored in the personal information DB of a certified business operator. [Diagram 3](A) is a flowchart showing the main routine program of the public chain user terminal, and (B) is a flowchart showing the subroutine program of personal information recording processing and the flowchart of the certified business operator's server. [Figure 4] 1 is a flowchart showing a subroutine program of smart contract processing executed on a user terminal of a public chain. [Diagram 5] 13A is a flowchart showing a main routine program of a user terminal of a private chain, and FIG. 13B is a flowchart showing a subroutine program of personal information search processing. [Figure 6] A flowchart showing a subroutine program of smart contract processing executed on a user terminal of a private chain. [Figure 7] (A) is a continuation of the flowchart showing the subroutine program of smart contract processing executed on a user terminal of a private chain, and (B) is a flowchart showing the subroutine program of machine learning processing executed on a user terminal of a private chain. [Figure 8] 13 is a flowchart showing a subroutine program of the AI ​​smart contract generation processing executed on a private chain user terminal. [Figure 9] (A) is a flowchart showing a subroutine program of a simulation learning process executed on a user terminal of a private chain, and (B) is a flowchart showing a subroutine program of an AI smart contract group generation process executed on a user terminal of a private chain. [Figure 10] 13 is a flowchart showing a subroutine program of a smart contract trust undertaking process executed on a user terminal of a private chain. [Figure 11](A) is a flowchart showing a subroutine program of the reinforcement learning process of the personalized AI smart contract learned model, and (B) is a flowchart showing the main routine program executed by a user terminal of the consortium chain. [Figure 12] 1A is a flowchart showing a subroutine program of an IoT sensor data aggregation process executed by a user terminal of a consortium chain, and FIG. 1B is a flowchart showing a subroutine program of a simulation process executed by a user terminal of a consortium chain. [Figure 13] (A) is a flowchart showing a subroutine program of the AI ​​smart contract group generation process executed by a user terminal of the consortium chain, and (B) is a flowchart showing the subroutine program of the smart contract processing executed by a user terminal of the consortium chain. [Figure 14] This is an explanatory diagram of how recorded information can be made unviewable using blockchain. (A) shows the normal state where it can be viewed, and (B) shows the state where it has been made unreadable so that it cannot be viewed. [Figure 15] This is a diagram explaining the information stored in the HDD of a user terminal that constitutes a node of the blockchain. [Figure 16] 13 is a flowchart showing the main routine program of a public chain user terminal and a private chain user terminal. [Figure 17] 1A is a flowchart showing a subroutine program for recording personal information on a blockchain, and FIG. 1B is a flowchart showing a subroutine program for making records unreadable. [Figure 18] 13 is a flowchart showing a subroutine program of personal information acquisition processing and personal information provision processing. [Figure 19](A) is a diagram explaining the information stored in the HDD of a user terminal that constitutes a node of the blockchain, and (B) is a diagram explaining the information stored in the personal information DB of a certified business operator. [Figure 20] 13 is a flowchart showing the main routine program of the public chain user terminal, the certified business server, and the private chain user terminal. [Figure 21] 1 is a flowchart showing a subroutine program for recording personal information and hash values ​​in a blockchain. [Figure 22] 13 is a flowchart showing a subroutine program for processing unreadable records. [Diagram 23] 13 is a flowchart showing subroutine programs of personal information provision processing, personal information acquisition processing, and ciphertext transmission processing. [Figure 24] 1 is a system diagram showing an overall configuration of a processing system. [Diagram 25] 13 is a flowchart showing the main routine program of a user terminal of a public chain, a server of a key registration center, and a user terminal of a private chain. [Figure 26] 1A is a flowchart showing a subroutine program for recording personal information to a blockchain and for registering a key, and FIG. 1B is a flowchart showing a subroutine program for processing a request to make a record unreadable and for processing a record unreadable. [Figure 27] 13 is a flowchart showing subroutine programs of a counterpart common key providing process, a data obtaining process, and a data decrypting process. [Figure 28] FIG. 1 is an explanatory diagram of a mirror world as a simulation environment. [Figure 29] FIG. 1 is a diagram showing a specific example of a city digital twin in the mirror world. [Diagram 30] 13 is a flowchart of the main routine of the mirror world server and the user terminal. [Diagram 31] 13 is a flowchart showing a subroutine program of a personal AI generation and sales process. [Diagram 32] 13 is a flowchart showing a subroutine program of a simulation preparation process and a simulation preparation response process. [Diagram 33] 13 is a flowchart showing a subroutine program of a simulation process. [Diagram 34] (A) is a schematic diagram of the multi-service DAO construction system, and (B) is a flowchart of the main routine between the mirror world server and the user terminal. [Diagram 35] 13 is a flowchart showing a subroutine program of a simulation reinforcement learning preparation process and a simulation reinforcement learning preparation response process. [Diagram 36] This is an explanatory diagram for registering a multi-service DAO digital twin in the mirror world as a simulation target. [Figure 37] This is a schematic system diagram of simulation reinforcement learning for multi-service DAO. [Figure 38] 13 is a flowchart showing a subroutine program of a DAO agent reinforcement learning process. [Figure 39] 1A is a diagram showing a reward table that the DAO agent stores as knowledge, and FIG. 1B is a flowchart showing a subroutine program of the persona agent reinforcement learning processing. [Diagram 40] 13A is a flowchart showing a subroutine program for idea proposal service execution processing, and FIG. 13B is a flowchart showing a subroutine program for improvement proposal service execution processing. [Diagram 41] 13A is a flowchart showing a subroutine program for processing a business service execution process, and FIG. 13B is a flowchart showing a subroutine program for processing an infringement countermeasure service execution process. [Diagram 42] 13A is a flowchart showing a subroutine program for executing a token purchase service, and FIG. 13B is a diagram explaining price fluctuations at the floating market price of tokens accompanying token purchases by persona agents. [Diagram 43]A diagram showing an element integration DAO construction system. [Diagram 44] 1A is a flowchart of the main routine between the mirror world server and the user terminal, and FIG. 1B is a flowchart showing the subroutine programs of the simulation reinforcement learning preparation processing and the simulation reinforcement learning preparation response processing. [Diagram 45] This is an explanatory diagram for registering the element integrated DAO digital twin in the mirror world as a simulation target. [Figure 46] This is a diagram showing a schematic system illustrating simulation reinforcement learning of element-integrated DAO digital twin. [Figure 47] (A) is a diagram showing the calculation algorithm for performance and distribution rate stored as knowledge by a material procurement element agent, (B) is a diagram showing the calculation algorithm for performance and distribution rate stored as knowledge by an assembly element agent, and (C) is a diagram showing the calculation algorithm for performance and distribution rate stored as knowledge by a promotion element agent. [Figure 48] 13A is a diagram showing a calculation algorithm for performance and distribution rate stored as knowledge by a sales element agent, and FIG. 13B is a diagram showing a reward table stored as knowledge by a control agent. [Figure 49] 13 is a flowchart showing a subroutine program of a simulation reinforcement learning process. [Figure 50] 13 is a flowchart showing a subroutine program of a control agent reinforcement learning process. [Figure 51] 13 is a flowchart showing a subroutine program of a material procurement element agent reinforcement learning process. [Figure 52] FIG. 13A is a flowchart showing a subroutine program of information gathering processing by a crawler, and FIG. 13B is a diagram showing various data stored in a material procurement DB. [Figure 53]13 is a flowchart showing a subroutine program of an assembly element agent reinforcement learning process. [Figure 54] 13 is a flowchart showing a subroutine program of an advertising element agent reinforcement learning process. [Figure 55] FIG. 13A is a flowchart showing a subroutine program of an information gathering process by a crawler, and FIG. 13B is a diagram showing various data stored in an advertisement DB. [Figure 56] 13 is a flowchart showing a subroutine program of a sales element agent reinforcement learning process. [Figure 57] FIG. 13A is a flowchart showing a subroutine program of an information collection process, and FIG. 13B is a diagram showing various data stored in a sales DB. [Figure 58] 13A is a flowchart showing a subroutine program of a material procurement personal AI reinforcement learning process, and FIG. 13B is a flowchart showing a subroutine program of an assembly personal AI reinforcement learning process. [Figure 59] 13A is a flowchart showing a subroutine program of a promotional staff personal AI reinforcement learning process, and FIG. 13B is a flowchart showing a subroutine program of a sales staff personal AI reinforcement learning process. [Figure 60] FIG. 11 is an explanatory diagram showing a method of installing a program. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] [First embodiment] A first embodiment of the present invention will be described with reference to Figs. 1 to 13. First, referring to the overall system in Fig. 1, three types of blockchain networks, a private chain 2, a consortium chain 3, and a public chain 4, are connected to a centralized oracle 21. The public chain 4 is a completely open mechanism that allows any individual or organization to trade there. Transactions can be effectively confirmed by the blockchain. Mining (competition for bookkeeping rights) is also free and anyone can participate. The consortium chain 3 is a blockchain that can only be used by partners who belong to an association or union. People (each node) among them are designated as bookkeepers. Block generation is also determined in advance, and other people (nodes) can trade but do not have bookkeeping rights. The private chain 2 only records using blockchain technology, and bookkeeping rights are not open but are monopolized by individuals or companies, and only internal transactions are recorded. Polkadot is used to connect blockchains and exchange tokens and data between each blockchain. Polkadot is a blockchain for connecting different blockchains. Blockchains developed using Substrate can be connected to Polkadot, allowing them to exchange tokens and data with other blockchains connected to Polkadot.

[0014] The centralized oracle 21 is a system that acts as a bridge between the blockchain and the Internet 1. It is connected to the Internet 1, collects various information scattered across the internet, and provides this information to the blockchain's smart contracts.

[0015] Each node 19 of the private chain 2, the consortium chain 3, and the public chain 4 is composed of a user terminal such as a personal computer (hereinafter referred to as "PC") 16. This PC (hereinafter also referred to as "user terminal") 16 is connected to the Internet 1. The Internet 1 is further connected to a server 20 of a social networking service (SNS) 40 and a server 18 of a certified business operator 17 of the blockchain. The server 18 of the certified business operator 17 may participate in the blockchain as a node 19. In addition, a server of a certification authority that issues electronic certificates in a public key infrastructure (PKI) may be connected to the Internet 1.

[0016] The certified business 17 receives the personal information, issues an electronic ID to the personal quasi-information, and records the hash value of the personal information in the blockchain. The received personal information is stored in a personal information database (hereinafter referred to as "personal information DB") 29. The certified business 17 may participate in the blockchain as a node 19.

[0017] The PC 16 is composed of a CPU (Central Processing Unit) 10 as a control center, a RAM (Random Access Memory) 9 that functions as a work area for the CPU 10, a ROM (Read Only Memory) 11 that stores data and programs, a storage unit such as a HDD (hard disk drive) 12, an input operation unit 7 such as a display and a keyboard, a communication unit 5, a display unit 6, an interface 8, a bus 13, and various other hardware. Various servers such as the server 20 and the server 18 are also composed of hardware similar to that of the PC 16, and therefore repeated illustrations and explanations will be omitted here. Note that a solid state drive (SDD) may be used as the storage unit in addition to or instead of the HDD.

[0018] An IoT (Internet of Things) device 14 and a wireless sensor network 15 are connected to a node 19 of the consortium chain 3. Sensor signals from the IoT device 14 and the wireless sensor network 15 are input to the node 19, and a drive signal for the IoT device 14 is output from the node 19. The IoT device 14 is various sensors, actuators, etc. for IoT.

[0019] The wireless sensor network 15 is a wireless network that allows multiple sensor-equipped wireless terminals to be distributed in space and to collect environmental and physical conditions in cooperation with each other. For example, a sensor device is created using energy harvesting, M2M, or batteries, and a pressure sensor or gauge sensor is used to constantly monitor, for example, deterioration of metal fatigue, and to notify any changes. It is mainly installed in structures such as bridges and tunnels. In general, it includes multiple sensor nodes and a gateway sensor node. These nodes are usually composed of one or more sensors, wireless chips, microprocessors, and power sources (such as batteries). Wireless sensor networks usually have ad hoc functions and a routing function (routing algorithm) for sending data from each node to a central node. In other words, they have the function of autonomously reconstructing another communication path when a communication failure occurs between nodes. There is also an element of distributed processing because the nodes work together as a group. In addition, they have the function of operating for a long period of time without receiving power from an external source, and therefore have a power saving function or a self-powering function.

[0020] In this embodiment, the IoT device 14 and the wireless sensor network 15 are connected to the consortium chain 3 via the node 19, but one or both of the IoT device 14 and the wireless sensor network 15 may themselves be part of the node 19 of the consortium chain 3 without going through the node 19.

[0021] Next, the information stored in the HDD 12 of the PC 16 will be described with reference to Fig. 2(A). The HDD 12 stores the user's private key SK, public key PK, common key K1, common key K2 for trapdoor, user's address in the blockchain, smart contracts, tokens, artificial intelligence (also called "AI (Artificial Intelligence)"), blockchain data, etc. Note that the user is a broad concept that includes not only natural persons but also legal entities.

[0022] The private key SK and the public key PK are a key pair used in PKI (Public key Infrastructure), and data encrypted with the public key PK is decrypted with the private key SK. The private key SK is also used for electronic signatures. The common key K1 is a key used for common key encryption such as DES (Data Encryption Standard) and AES (Advanced Encryption Standard). Data encrypted with the common key K1 is decrypted using the same common key K1. In this embodiment, a different common key is used for each piece of personal information to be encrypted. In the first embodiment, the encrypted personal information E K1 An index for keyword search is provided for the encrypted text (personal information). The index is encrypted with a common key K2. To perform a keyword search, an encrypted search query (called a "trapdoor") is used, in which the keyword (search query) used for the search is encrypted with the common key K2. This common key K2 is stored in the HDD 12 as the trapdoor common key K2.

[0023] A user's address on the blockchain is generated through the following process: 1 Generate a public key using ECDSA from the private key. 2. Pass the public key through the SHA-256 hash function to obtain a hash value. 3 The hash value is then passed through the RIPEMD-160 hash function to obtain a new hash value. 4. Add 00 as a prefix to the beginning of the hash value. 5. Pass it through the hash function SHA-256. 6. Pass it through the SHA-256 hash function again. 7 Add a 4-byte checksum to the end. 8 Encode in Base58 format.

[0024] A smart contract is a computer protocol intended to smoothly verify, check, enforce, execute, and negotiate contracts. A token is a unique currency issued by a company or individual on the blockchain.

[0025] Next, the blockchain data will be described. The data in each block of the blockchain includes the hash value of the previous block, a nonce, and data of multiple transactions (also called transactions). Although not shown in the figure, a timestamp is also embedded in the blockchain. Such a blockchain is generated and added as a new blockchain by each node 19 performing blockchain processing (see S3, S19, S30, S51, S117, S122, S153, etc. described later). The blockchain processing mainly consists of three phases: transaction, propagation, and recording.

[0026] The transaction phase is what is generally called a transaction, and refers to legal acts such as buying and selling, transferring, lending, etc. More specifically, this transaction phase can be divided into three phases: generation → signing → propagation.

[0027] The generation phase is where a transaction is generated. For example, Person A decides to lend dormant PC resources (computational resources) to Person B for 39005 seconds to obtain 25.78 tokens, and digitally signs the transaction. This digital signature is generated by passing the transaction data through a specific hash function to generate a hash value, and then encrypting the hash value using the private key SK of the parties (Person A and Person B) of the transaction. A digital public key certificate may also be issued by a certification authority. Figure 2 shows an example of lending PC resources (computational resources), but the objects of lending are not limited to this. For example, values ​​such as electricity self-generated at home or business, a user's specialized knowledge, experience, skills, personal connections (including online personal networks), and credibility may be considered.

[0028] The propagation phase involves having other nodes confirm that the transaction has been generated and signed correctly. If it is determined that the transaction was not generated and signed correctly, the transaction is discarded.

[0029] In the recording phase, if it is confirmed that the transaction has been correctly generated and signed, the miner performs mining to record the transaction. Transactions that have been confirmed to have been correctly generated and signed are moved to a place called a mining pool. The miner then selects a transaction to record from the mining pool and performs mining.

[0030] Mining is the task of calculating a nonce. A nonce is a value that is adjusted so that a very small hash value with many zeros at the beginning is generated when the block data is passed through a hash function. If a nonce can be calculated that results in a hash value equal to or less than the target value, a new block is generated.

[0031] The transaction data is stored as E, which is the user’s personal information encrypted with key K1, as shown in transaction I on the right side of Figure 2. K1The hash value of (personal information), its electronic ID, the index of personal information, and the compensation for providing the personal information (provided as 2.4 tokens in Figure 2) are encrypted with key K2. K2 (provided at index + 2.4 tokens). Specific examples of personal information include vital information such as the user's heart rate, blood pressure, body temperature, and brain waves, behavioral history information such as purchase history and website browsing history, user location information such as GPS, race, creed, social status, medical history, electronic medical record data, ID (identification), and information posted to SNS, etc.

[0032] Information posted to SNS etc. is past posted information that has already been posted to SNS 25 and stored in server 20, transferred from server 20 to personal information DB 29 and the blockchain. Specifically, the user encrypts all of his / her past posted information and stores it in the personal information DB 29 of the certified business, and records the hash value in the blockchain. Thereafter, instead of posting to SNS 25, the user encrypts and stores the posted content in the personal information DB 29 of the certified business, and records the hash value in the blockchain. This enables the user to retrieve his / her personal information from the business such as SNS and keep it under his / her own control.

[0033] The compensation for providing personal information (provided in 2.4 tokens in Figure 2) may be recorded in plain text on the blockchain without being encrypted. In that case, other users can find out the compensation by searching the blockchain without obtaining the encryption key K2. Furthermore, transaction conditions such as compensation for providing personal information and compensation for lending PC resources (computing resources) (in Figure 2, PC resources (computing resources) are lent for 39005 seconds to obtain 25.78 tokens) may be coded as a smart contract, and transactions (legal acts) may be automated using smart contracts.

[0034] The encrypted personal information itself, which is the subject of the hash value recorded as transaction I, is stored in the personal information DB 29 of the certified business 17. Specifically, as shown in FIG. 2B,K1 (Personal information) is encrypted by corresponding to the electronic ID issued to the K1 (Personal information) is stored in the personal information DB 29.

[0035] An index is an encrypted personal information K1 This is an index for keyword search of (personal information). In this embodiment, a common key encryption method is adopted in which the index is encrypted with a common key K2, so to perform a keyword search, an encrypted search query (called a "trapdoor") is used in which the keyword (search query) used in the search is encrypted with the common key K2. The common key K2 is a different key for each user, but the same key is used for encrypted indexes of the same user. Therefore, for example, if user A performs a transaction to distribute common key K2 to user B using a smart contract described later, user B can use E K2 All encrypted indexes of User A can be searched on the blockchain using (search query).

[0036] In addition, searchable encryption such as homomorphic encryption or fully homomorphic encryption, which allows ciphertext to be searched while it is encrypted, may be used. In this case, personal information may be encrypted using homomorphic encryption or fully homomorphic encryption, and the encrypted personal information may be directly recorded on the blockchain. In addition, the encrypted personal information E K1 (Personal information) may be recorded directly on the blockchain.

[0037] Next, referring to Fig. 3(A), a flowchart of the main routine program of the user terminal of the public chain 19 will be described. Step S (hereinafter simply referred to as "S") 1 performs personal information recording processing, S2 performs smart contract processing, and S3 performs block chain processing.

[0038] Personal information record processing is a process in which the personal information owner encrypts personal information, registers it with a certified business operator 17, and records the hash value of the encrypted personal information in the blockchain. Smart contract processing is a process in which legal acts such as the conclusion and execution of a contract are automatically performed according to predetermined rules. The specific content of the blockchain processing is as described above with reference to Figure 2(A).

[0039] The personal information recording process will be described with reference to Fig. 3(B). In S5, it is determined whether or not a personal information registration operation has been performed in a user terminal constituting a node 19 of the public chain 19. If not, this personal information recording process returns and moves to smart contract processing in S2. If it is determined that a personal information registration operation has been performed, in S6, the personal information stored in the memory (HDD 12, etc.) of the user terminal is encrypted with key K1 and then digitally signed with private key SK, and the index is also encrypted with key K2 and transmitted to the server 18 of the certified business operator 17.

[0040] The server 18 of the certified business 17 that receives the electronic ID in S7 issues an electronic ID and stores the received encrypted personal information, E K1 The certified business operator 17 generates a hash value of the electronic ID (personal information). Next, the issued electronic ID is returned to the user terminal (S9). The user terminal that receives the electronic ID stores the electronic ID in memory (HDD 12, etc.). In S10, the server 18 of the certified business operator 17 performs processing to record the electronic ID, the hash value, and the encrypted index in the blockchain.

[0041] A flowchart of the subroutine program of the smart contract processing shown in S2 will be described. With reference to FIG. 4, in S13, it is determined whether or not a distribution contract for the common key K2 has been established. If not, in S15, it is determined whether or not a loan contract for the PC resources (computing resources) of the user terminal has been established. If not, it is determined whether or not a contract for the provision of personal information has been established. If not, it is determined whether or not an ordering contract for ordering custom-made products, etc. has been established. If not, it is determined whether or not a sales contract for products, etc. has been established in S22. If not, it is returned. These determinations are made by the smart contract. For example, if the above-mentioned consideration, etc. is coded as a smart contract, the smart contract determines whether or not the conditions of the consideration, etc. of both parties match, and if it is determined that they match, the contract is automatically concluded and executed.

[0042] If it is determined that a distribution contract for the common key K2 has been concluded, control proceeds to S14, and after the common key K2 is transmitted to the distribution destination, control proceeds to S19. In S19, a process is performed to record the concluded contract as a transaction in the blockchain. If it is determined that a loan contract for PC resources (computational resources) has been concluded, control proceeds to S18, and a loan process for the PC resources (computational resources) is performed. If it is determined that a contract for the provision of personal information has been concluded, control proceeds to S20, and the electronic ID of the personal information to be provided and a signature agreeing to the provision of personal information are returned to the provider, and the common key K1 used to encrypt the personal information to be provided is encrypted with the provider's public key and returned to the provider.

[0043] If it is determined that an order contract has been concluded, control proceeds to S21, where an order process is performed, and then control proceeds to S19. If it is determined that a sales contract has been concluded, control proceeds to S23, where a process for acquiring the purchased item is performed, and then control proceeds to S19.

[0044] Next, a flowchart of the main routine program of the user terminal constituting the node 19 of the private chain 2 will be described based on FIG. 5(A). Personal information search processing is performed by S28, smart contract processing is performed by S29, blockchain processing is performed by S30, machine learning processing is performed by S31, AI smart contract generation processing is performed by S32, and smart contract trust undertaking processing is performed by S33. The AI ​​smart contract is a concept that includes both an "integrated type" and an "integrated type". The "integrated type" is a type in which an AI and a smart contract are integrated, machine learning is performed based on the data of a contract (legal act), and the smart contract itself is AI-ized. The "integrated type" is a type in which an AI that has performed machine learning based on the data of a contract (legal act) and a smart contract are integrated. In the case of the integrated type, the learned AI (hereinafter referred to as the "integrated AI") adds, changes, and updates the smart contract depending on the situation.

[0045] The personal information search process is a process of searching the encrypted index recorded in the blockchain by a trapdoor (encrypted search query). The smart contract process is a process of automatically performing legal acts such as the conclusion and execution of a contract according to predetermined rules. The specific content of the blockchain process is as described above with reference to FIG. 2(A). The machine learning process is a process of generating a trained model of artificial intelligence by machine learning the personal information of a large number of users as learning data. More specifically, the machine learning process is a process of generating a general trained model of artificial intelligence by machine learning a huge amount of personal information in a form in which the personal information owner cannot be identified as learning data, and then generating a personalized trained model for each personal information owner (for example, each address in the blockchain) using personal information classified for each data (e.g., address in the blockchain) that can identify the personal information owner.

[0046] The AI ​​smart contract generation process is a process of using machine learning to learn personal information related to contracts (legal acts) as learning data to generate a trained model of a smart contract by artificial intelligence. More specifically, it is a process of using machine learning to learn personal information related to a huge amount of contracts (legal acts) in a form in which the personal information subject cannot be identified as learning data to generate a general trained model of a smart contract by artificial intelligence, and then using personal information related to contracts (legal acts) classified by data that can identify the personal information subject (for example, an address in a blockchain) to generate a personalized AI smart contract trained model that is personalized for each personal information subject (for example, for each address in a blockchain).

[0047] Smart contract trust contract processing is a process that performs a service on behalf of a principal to automatically perform legal acts such as contract conclusion and execution. More specifically, a personalized AI smart contract trained model personalized for the trustor is generated, and the personalized AI smart contract trained model is used to perform legal acts on behalf of the trustor. A reward for the AI ​​is determined based on the results of the execution, and the personalized AI smart contract trained model is further reinforced with the reward.

[0048] Next, a flowchart of the subroutine program of the personal information search process shown in S28 will be described with reference to FIG. 5(B). It is determined in S37 whether the common key K2 is stored, and if not, the process returns. If K2 is stored in S45 described later, it is determined in S37 that the common key K2 is stored, and control proceeds to S38. In S38, a process is performed to search the encrypted index on the blockchain with a search query (trap door) encrypted with K2. As a result of the search, it is determined in S39 whether there is any personal information to be obtained. If it is determined that there is no personal information to be obtained, the process returns, but if it is determined that there is personal information to be obtained, the electronic ID of the personal information to be obtained is stored in S40.

[0049] Next, a flowchart of the subroutine program of the smart contract processing shown in S29 will be described based on FIG. 6 and FIG. 7(A). S42 judges whether there is any personal information to be searched. For example, when the conditions are met by sequentially negotiating with the smart contract of the personal information owner who has not yet been searched, the personal information of the personal information owner is judged to be the personal information to be searched. If it is judged that there is no personal information to be searched by S42, it is judged whether there is any memory of the personal information to be obtained by S46. If it is judged that there is no memory, the control proceeds to S55 in FIG. 7(A), where it is judged whether a loan contract for PC resources (computing resources) has been established. If it is judged that it has not been established, it is judged whether an order contract has been established by S56. If it is judged that it has not been established, it is judged whether a sales contract has been established by S57. If it is judged that it has not been established, it is returned.

[0050] If it is determined in S42 that there is personal information to be searched, the control proceeds to S43, where the common key K2 is requested from the personal information owner. Specifically, the personal information owner of the personal information to be searched transmits its own address and an attribute certificate to the address on the blockchain of the personal information owner of the personal information to be searched, and requests the common key K2. In S44, it is determined whether or not K2 has been returned, and it waits until it is received. The personal information owner or the personal information owner's smart contract checks the transmitted attribute certificate and determines whether or not K2 may be returned, and returns K2 if it is determined that the return is permitted. When K2 is returned from the personal information owner, the control proceeds to S45, and after storing the returned K2, the control shifts to S54. In S54, a process is performed to store the established contract in the blockchain as a transaction. In this case, a contract to the effect that the common key K2 used to encrypt the index has been distributed from the address of the personal information owner who sent the reply to the address of the user who received the reply is stored in the blockchain.

[0051] If it is determined in S46 that the personal information desired to be obtained is stored, control proceeds to S47, and processing is performed to request the desired personal information from the personal information owner. Specifically, the user's address and attribute certificate are sent to the address on the blockchain of the personal information owner of the desired personal information to request the desired personal information. In the user terminal of the public chain that receives this, the smart contract checks the attribute certificate and determines whether the conditions for providing the personal information are met. If it is determined that the personal information may be provided (YES in S16), it replies with the electronic ID of the personal information to be provided and a signature agreeing to the provision of the personal information, and also replies with the common key K1 used to encrypt the personal information to be provided, encrypted with the public key of the recipient (see S20).

[0052] If there is a reply, it is determined in S48 that there is a reply from the personal information owner, and the control proceeds to S49, where the returned encrypted common key, E PK The operation of decrypting (K1) with one's private key SK, i.e., D SK (E PK Next, in S50, the electronic ID and signature returned from the personal information owner are transmitted to the server 18 of the certified business 17. The server 18 of the certified business 17, upon receiving the electronic ID, checks the transmitted signature, searches the personal information DB 29 (see FIG. 2(B)) based on the received electronic ID, and calculates the encrypted personal information E that is stored in correspondence with the received electronic ID. K1 Read (personal information) and reply.

[0053] If the reply is received, the answer is determined as YES in S51, and the control proceeds to S52, where the returned encrypted personal information, E K1 (Personal information) is decrypted by K1 calculated in S49, that is, D K1 (E K1 (Personal information)) is performed to obtain the personal information in plain text. The personal information is stored in S53. Then, the process proceeds to S54, where a process is performed to record the contract for providing the personal information as a transaction in the blockchain.

[0054] Next, if a loan contract for PC resources (computing resources) is concluded with the user terminal of the public chain (YES in S15), S55 judges YES and control proceeds to S58, where the borrowing process for the PC resources (computing resources) is carried out. Then, control proceeds to S54, where processing is carried out to record the loan contract as a transaction in the blockchain. If an order contract is concluded with the user terminal of the public chain (YES in S17), S56 judges that the order contract has been concluded and control proceeds to S59, where processing is carried out to store the receipt of the order. Then, control proceeds to S54, where processing is carried out to record the order contract as a transaction in the blockchain.

[0055] If a sales contract is concluded with the public chain user terminal (YES in S15), it is determined in S57 that a PC resource (computational resource) loan contract has been concluded, and control proceeds to S60, where processing is performed to provide the object of sale. After that, control proceeds to S54, where processing is performed to record the sales contract as a transaction in the blockchain. In the smart contract processing described above, the actions (e.g., S42, S47, S58, S56, S60) may be executed with the consent of the owner of the smart contract, rather than being executed based only on the judgment (e.g., S42, S46, S55, S56, S57) at the private chain user terminal 16 based on the smart contract. The owner's consent may also be obtained when executing a contract by a smart contract, as described below.

[0056] Next, a flowchart of the subroutine program of the machine learning process shown in S31 will be described with reference to Fig. 7(B). In S63, a process is performed to convert a huge amount of personal information stored in a form that does not identify the personal information owner into learning data. As learning algorithms adopted in this machine learning, various types are prepared, such as, for example, regression and discrimination as supervised learning, model estimation and data mining as unsupervised learning, and reinforcement learning and deep learning as intermediate methods.

[0057] Next, machine learning is performed by S64 using the borrowed PC resources (computational resources) using the learning data. For example, in the case of regression as supervised learning, a large amount of data set consisting of input information (vector x) and correct answer information y is used as training data (learning data). In the case of supervised learning, a function ci(x) (ci:x→y) that maps input x to correct answer y is learned, so the learned model includes the function ci. Note that the machine learning performed by the machine learning means 34 is not limited to supervised learning, and may be any type of learning, such as unsupervised learning such as model estimation and pattern mining (data mining), semi-supervised learning, which is an intermediate method between supervised learning and unsupervised learning, reinforcement learning, deep learning, etc.

[0058] A general trained model is generated by the machine learning in S64 and stored (S65). This general trained model is a model that uses as training data a huge amount of personal information of many users that is stored in a form that does not allow the personal information owner to be identified, and is an average trained model that can be widely applied to many users.

[0059] Next, in S66, it is determined whether or not there is an order memory, and if there is, it is returned, but if there is, it is determined in S67 whether or not it is an order from an artificial intelligence. If it is not an order from an artificial intelligence, it is returned, but if it is an order from an artificial intelligence, a process of requesting personal information to the address of the purchaser is performed in S68. If the purchaser replies with personal information, S69 judges YES and proceeds to S70. In S70, a process of personalizing the general trained model based on the returned personal information to generate a personalized trained model is performed. The process of personalizing the general trained model to generate a personalized trained model is described in Japanese Patent No. 6432859. Since the personal information required for personalization is collected using a blockchain, there is an advantage that the privacy issue can be avoided as much as possible by collecting it while ensuring the anonymity of the blockchain. Next, in S71, the personalized trained model is sent to the address of the purchaser.

[0060] Next, a flowchart of the subroutine program of the AI ​​smart contract generation process shown in S32 will be described based on FIG. 8. In S80, personal information related to a contract (legal act) is extracted from a huge amount of personal information stored in a form that does not identify the personal information owner, and the extracted information is used as learning data. Next, in S81, machine learning is performed using the learning data by utilizing the borrowed PC resources (computing resources). The processes of S80 and S81 are similar to those described in S63 and S64 above, and the description will not be repeated here.

[0061] Next, S82 generates and stores a trained model of a general AI smart contract. Next, S83 executes a simulation learning process. In this simulation learning process, legal actions such as contract verification, condition confirmation, execution, implementation, and negotiation, which are normally performed by many people, are virtually executed (simulated) in a computer by having a large number of AI smart contracts take over, and each AI smart contract is given a reward according to the results, and reinforcement learning is performed. Since reinforcement learning is performed by simulation in a computer rather than reinforcement learning in the real world, it has the advantage of being able to perform a huge amount of reinforcement learning in a short period of time. Reinforcement learning is a mechanism in which an agent placed in a certain environment acquires a policy that maximizes the cumulative reward from the initial state to the goal based on the reward given when the agent selects an action. In reinforcement learning, learning proceeds through interaction between a software agent (hereinafter referred to as "agent"), which is a type of AI, and the environment. Here, an agent is a type of AI, and is software that has a certain degree of judgment ability and behaves autonomously and operates persistently while communicating with users and software. When an agent performs an action a on the environment, the state s of the environment changes and a certain goal state is reached, giving the agent a reward r. The agent learns a function that takes state s as input and outputs action a with the goal of maximizing this reward r.

[0062] Reinforcement learning progresses by repeating the following simple steps over time: 1. An agent receives an observation o from the environment (or directly, the state of the environment s) and returns an action a to the environment based on a policy π. 2 Based on the action a received from the agent and the current state s, the environment changes to the next state s', and based on that transition, it returns to the agent the next observation o' and a number (scalar quantity) called the reward r, which indicates the quality of the previous action. 3. Time progression: t←t+1 Here, ← represents an assignment operation. For example, an alpha-zero reinforcement learning algorithm may be used as the reinforcement learning. Unlike algorithms such as DQN (Deep Q-Network), this alpha-zero reinforcement learning algorithm uses Monte Carlo tree search (MCTS) for search, and all values ​​and policies are predicted by a neural network, and the predictions are corrected only by the experience gained from self-play using tree search. Compared to conventional AlphaGo, the value network that predicts the value and the policy network that predicts the policy are integrated into one neural network, and prediction accuracy is improved by multi-task learning. In addition, the improved performance of the neural network eliminates the need for processor layout processing in tree search (extending the search tree until a reward is received), allowing for faster search. Furthermore, evolutionary computation, genetic algorithms, and generative adversarial networks may be used.

[0063] Next, in S84, it is determined whether or not an order for an AI smart contract is stored, and if not, it returns. If an order is stored in the above-mentioned S59, YES is determined in S84 and control proceeds to S85, where it is determined whether or not the stored order is an order for a simulation-trained AI smart contract. If it is not an order for a simulation-trained AI smart contract, it is an order for a personalized AI smart contract-trained model, in which case control proceeds to S86, where a process is performed to request personal information related to the contract (legal act) from the orderer's address. When the orderer responds with the personal information, YES is determined in S87 and control proceeds to S88.

[0064] In S88, a process is performed to personalize the trained model of the general AI smart contract based on personal information related to the returned contract (legal act) to generate a personalized AI smart contract trained model. The process of personalizing the trained model of the general AI smart contract to generate a personalized AI smart contract trained model is described in Japanese Patent No. 6432859. The personalized trained model is sent to the orderer's address in S89.

[0065] On the other hand, if an order is placed for an AI smart contract that has been simulated and trained, S85 determines YES, control proceeds to S90, and the AI ​​smart contract that has been simulated and trained is sent to the client's address.

[0066] Next, a flowchart of the subroutine program of the simulation learning process shown in S83 will be described based on FIG. 9(A). In S334, it is determined whether or not a simulation has been input, and if not, the process returns. This simulation is input by a user terminal of the private chain 2, and is, for example, a trading simulation in an investment market such as stock trading or futures trading, a company management simulation, or a consumer behavior simulation, assuming that a policy or law that the government is going to adopt (for example, a reduced tax rate associated with a consumption tax increase, a revised immigration law, the withdrawal of the UK from the EU (European Union), partial or full adoption of basic income, revision of Article 9 of the Japanese Constitution, etc.) is adopted. Furthermore, it may be a simulation of promotion of new products (including financial products and life insurance) or new services by various media. If it is determined that a simulation has been input by S334, control proceeds to S335, and the AI ​​smart contract group generation process is executed.

[0067] A flowchart of the subroutine program of this AI smart contract group generation process is explained based on FIG. 9(B). S344 uses borrowed PC resources (computational resources) to set a persona group that matches the input simulation. A persona is generally defined as a virtual person that is a typical target of a company, product, or service. In this embodiment, a persona is defined as a virtual person that is a typical target of the simulation content. For example, in the case of a consumption behavior simulation under a reduced tax rate following the aforementioned consumption tax increase, a persona corresponding to a general consumer is set as a persona for each group grouped by gender, age, region, annual income, etc.

[0068] The number of personas to be set is proportional to the number of users belonging to the group. For example, if the age distribution of general consumers is 5% in their teens, 5% in their twenties, 10% in their thirties, 10% in their forties, 20% in their fifties, 20% in their sixties, 20% in their seventies, 5% in their eighties, and 5% in their nineties, the number of personas representing teens is set to 1, the number of personas representing twenties to 1, the number of personas representing thirties to 2, the number of personas representing forties to 2, the number of personas representing fifties to 4, the number of personas representing sixties to 4, the number of personas representing seventies to 4, the number of personas representing eighties to 1, and the number of personas representing nineties to 1 are set.

[0069] Next, in S345, a process of selecting a user group belonging to each persona is performed using the borrowed PC resource (computational resource). Next, in S346, a process of grouping the user groups belonging to each persona and collecting transaction data of the user groups for each group from the blockchain is performed. For example, in the case of a consumption behavior simulation under a reduced tax rate following the above-mentioned consumption tax increase, the user groups are grouped by gender, age, region, annual income, etc., and transaction data of the user groups is collected for each group from the blockchain. For example, it is useful to use a database of survey response monitor members held by an Internet survey research company for the selection of the user groups and the collection of transaction data of the user groups in S345 and S346. The Internet survey research company stores contact information (email address, etc.) of the survey response monitor members in a database in association with attributes such as gender, age, place of residence, unmarried, married, occupation, and annual household income, and uses the monitor member data by attribute. Similarly, in S145 and S146, S586 and S587, S622 and S623, etc. described later, it is useful to use a database of survey response monitor members held by an Internet survey research company. Next, in S347, machine learning is performed using the borrowed PC resources (computing resources) with the transaction data as learning data to generate a trained AI smart contract for each persona. The AI ​​smart contracts are generated in the same number as the number of corresponding personas. This prepares an environment for running the simulation, and the simulation is performed within that environment.

[0070] Returning to FIG. 9(A), each AI smart contract generated as described above executes a contract (legal act) according to action a (S336). This "action a" is the action a resulting from the reinforcement learning in S338. Next, in S337, the contract established between each AI smart contract is recorded in the blockchain.

[0071] Next, S338 uses the borrowed PC resources (computing resources) to calculate the reward r based on the established contract, and calculates the optimal policy π * A process is performed to obtain an action a according to the above. For example, in the case of a consumption behavior simulation under a reduced tax rate following the aforementioned consumption tax increase, the smaller the value of (expenses before the tax increase - expenses after the tax increase), the higher the reward r is given. Then, S339 judges whether the simulation has ended, and if it has not ended yet, control returns to S336, and reinforcement learning is progressed by repeatedly circulating S337 → S338 → S339 → S336. When the simulation ends, S339 judges YES, and control proceeds to S340, where the AI ​​smart contract that has obtained the highest reward r is stored and then returned. Note that this is not limited to the AI ​​smart contract that has obtained the highest reward r, and for example, the top 5% of AI smart contracts may be stored. Also, recording to the blockchain by S337 is not necessarily performed, and in that case, in the above-mentioned collaboration type, the "AI smart contract" in S335, S336, S340, and S347 is changed to "collaboration AI". In other words, in the case of a simulation within a computer, since it does not involve the execution of a contract (legal act) in the real world, if it is not recorded in the blockchain, there is no need to bother using a smart contract, and it is sufficient for each collaboration AI to perform action a and perform reinforcement learning. After completing the reinforcement learning, at the actual quoting stage, the learned collaboration AI can work with the smart contract to execute the contract and record it in the blockchain.

[0072] Next, a flowchart of the subroutine program of the smart contract trust contract processing shown in S33 will be described with reference to FIG. 10. S94 determines whether there is an order memory for the smart contract trust, and if it is determined that there is no order memory, returns. If the contents of the order memory stored in S59 described above are confirmed and it is an order for the smart contract trust, S94 judges YES and the control proceeds to S95. In S95, personal information related to the contract (legal act) is requested to the address of the purchaser, and when the personal information is returned from the purchaser, S96 judges YES and the control proceeds to S97.

[0073] In S97, a process is performed to personalize the trained model of the general AI smart contract based on personal information related to the returned contract (legal act) to generate a personalized AI smart contract trained model. The process of personalizing the trained model of the general AI smart contract to generate a personalized AI smart contract trained model is described in Japanese Patent No. 6432859.

[0074] Next, in S98, the address of the purchaser is associated with the personalized AI smart contract trained model and stored. The stored personalized AI smart contract trained model is used to perform trust contract processing for the purchaser (S99). Next, in S100, reinforcement learning processing of the personalized AI smart contract trained model is executed.

[0075] Based on FIG. 11(A), a flowchart of a subroutine program of the reinforcement learning process of the personalized AI smart contract trained model shown in S100 will be described. In reinforcement learning, as a method of estimating the Q value when the value of performing an action at in a state st is Q(st,at), if knowledge of modeling the environment, that is, the state transition probability and the probability distribution of rewards, is given, a model-based method can be used, but if the environmental model is unknown, TD (Temporal Difference) learning is used. First, since it is necessary to explore the environment, the ε-greedy method is used. In the initial stage of the exploration, various actions are tried, and the concept of temperature is introduced so that the most optimal actions are selected as the exploration settles down. With temperature as T, an action is selected according to the probability expressed by the following formula.

[0076] P(a|s)={exp(Q(s,a) / T)} / {Σexp(Q(s,b) / T} (Note that b∈A is written under Σ, but is omitted in the above formula) Here, a is an action, Q(s,a) is the value of performing action a in state s,

[0077] T is called the temperature in annealing, and if it is high, the action is selected with a probability close to equal probability, and if it is low, the action is biased toward the optimal one. As learning progresses, the learning results become stable by reducing the value of T. This method of estimating the Q value may be applied to all reinforcement learning described above and reinforcement learning described below. In addition, a general von Neumann-type computer is used as a computer for machine learning such as reinforcement learning, but a neural net processor (NNP) may also be used. The NNP chip is equipped with a large number of "artificial neurons" modeled after real neurons, and each neuron cooperates with each other in a network. A quantum computer that employs the "quantum annealing method" may also be used. In particular, by using a quantum computer that employs the "quantum annealing method," the time required for optimization calculations in machine learning can be significantly reduced.

[0078] In S105, an evaluation of the results of the trust undertaking process using the personalized AI smart contract trained model is received from the trustor. Next, in S106, a process of calculating a reward r based on the received evaluation is performed. Next, in S107, the borrowed PC resources (computational resources) are used to calculate the optimal policy π * A process is performed to obtain an action a according to the action a. Next, in S108, a contract according to the action a is executed on behalf of the trustor. An evaluation of the result is received from the trustor (S105), and the processes of S106 to S108 are executed.

[0079] Next, a flowchart of the main routine program of a user terminal constituting a node of the consortium chain 3 will be described with reference to Fig. 11(B). S114 executes IoT sensor data aggregation processing, S115 executes smart contract processing, S116 executes simulation processing, and S117 executes blockchain processing. The specific content of the blockchain processing is as described above with reference to Fig. 2(A).

[0080] Next, a flowchart of the subroutine program of the IoT sensor data aggregation process shown in S114 will be described with reference to FIG. 12(A). In S120, the IoT sensor data is classified and grouped by type, period, region, etc. This process is performed on not only the IoT sensor data but also data from the wireless sensor network. Next, in S121, a process is performed to determine the value of each grouped data. Depending on the determined value, the compensation for the provision of data (amount of tokens) is coded as a smart contract for each corresponding data. Next, in S122, a process is performed to record each grouped data in the blockchain.

[0081] Next, a flowchart of the subroutine program of the smart contract process shown in S115 will be described with reference to FIG. 13(B). In S150, it is determined whether a sales contract with a person who wishes to acquire data has been concluded. This determination is automatically made as YES in S150 when the conditions for the compensation (amount of tokens) for the provision of data coded as a smart contract match. If NO is determined in S150, control proceeds to S151, where it is determined whether a loan contract for PC resources has been concluded, and if not, the process returns.

[0082] If S150 returns YES, control proceeds to S152, where data is sent to the address of the person who wishes to acquire the data, and a token is obtained in return. The established contract is recorded in the blockchain as a transaction in S153. On the other hand, if a PC resource (computing resource) loan contract is established, control proceeds to S154, where the PC resource (computing resource) is borrowed, and the contract is recorded in the blockchain as a transaction in S153.

[0083] Next, a flowchart of the subroutine program of the simulation process shown in S116 will be described based on FIG. 12(B). This simulation process is to have a large number of AI smart contracts take over legal actions such as contract verification, condition confirmation, execution, implementation, and negotiation, which are originally performed by a large number of people, and virtually execute (simulate) them in a computer to verify what kind of simulation results will be obtained under certain conditions. Specific examples of the above "under certain conditions" include policies and laws that the government is planning to adopt (e.g., reduced tax rates due to a consumption tax increase, revised immigration law, the UK's withdrawal from the EU (European Union), partial or full adoption of basic income, revision of Article 9 of the Japanese Constitution, etc.), marketing-related conditions (e.g., setting prices and compensation for new products (including financial products and life insurance) and new services, promotional effects by various media, etc.), and investment market-related conditions (e.g., weather conditions in futures trading, monetary tightening policies in the stock market, etc.).

[0084] In S134, it is determined whether or not a simulation request has been made, and if not, the process returns. If it is determined in S134 that a simulation request has been made, control proceeds to S135, where the AI ​​smart contract group generation process is executed.

[0085] A flowchart of the subroutine program of this AI smart contract group generation process is explained based on FIG. 13(A). In S144, a process of setting a persona group that matches the requested simulation is performed using borrowed PC resources (computational resources). A persona is generally defined as a virtual person that is a typical target of a company, product, or service. In this embodiment, a persona is defined as a virtual person that is a typical target of the simulation content. For example, in the case of the simulation of the reduced tax rate following the consumption tax increase described above, a persona corresponding to a general consumer is set as a persona for each group grouped by gender, age, region, annual income, etc.

[0086] The number of personas to be set is proportional to the number of users belonging to the group. For example, if the age distribution of the general public is 5% in their teens, 5% in their twenties, 10% in their thirties, 10% in their forties, 20% in their fifties, 20% in their sixties, 20% in their seventies, 5% in their eighties, and 5% in their nineties, the number of personas representing teens is set to 1, the number of personas representing twenties to 1, the number of personas representing thirties to 2, the number of personas representing forties to 2, the number of personas representing fifties to 4, the number of personas representing sixties to 4, the number of personas representing seventies to 4, the number of personas representing eighties to 1, and the number of personas representing nineties to 1 are set.

[0087] Next, in S145, a process of selecting a user group belonging to each persona is performed using the borrowed PC resource (computational resource). Next, in S146, a process of grouping the user groups belonging to each persona and collecting transaction data of the user groups for each group from the blockchain is performed. For example, in the case of a simulation of a reduced tax rate due to the above-mentioned consumption tax increase, the user groups are grouped by gender, age, region, annual income, etc., and transaction data of the user groups for each group is collected from the blockchain. Next, in S147, a process of generating a learned AI smart contract for each persona by performing machine learning using the transaction data as learning data using the borrowed PC resource (computational resource). The AI ​​smart contracts are generated in the same number as the set number of corresponding personas. This prepares an environment for executing the simulation, and the simulation is performed within that environment.

[0088] Returning to FIG. 12(B), each AI smart contract generated as described above executes a contract (legal act) in accordance with action a (S136). This "action a" is the action a resulting from reinforcement learning in S138. Next, in S137, the contract concluded between each AI smart contract is recorded in the blockchain. Furthermore, the changes in the situation as the simulation progresses are recorded in the blockchain. For example, in the case of the simulation of a reduced tax rate following the consumption tax increase mentioned above, the changes in domestic demand and the economy as the simulation progresses are recorded in the blockchain.

[0089] Next, in S138, the borrowed PC resources (computational resources) are used to calculate the reward r based on the established contract, and the optimal policy π * Then, in S139, it is determined whether the simulation has ended, and if it has not ended yet, control returns to S136, and reinforcement learning proceeds by repeatedly cycling through S137 → S138 → S139 → S136. When the simulation ends, S139 determines YES, and control proceeds to S140, where a process of deriving the simulation results is performed and then returns.

[0090] As a specific example of the process for deriving the simulation result, in the case of a simulation of economic fluctuations due to the adoption of a reduced tax rate following a consumption tax increase, how each item of the economic activity index changed as a result of the simulation is derived. In the case of a simulation of a monetary tightening policy in the stock market, how the stock market changed as a result of the simulation is derived. In addition, a simulation optimization method may be adopted in which the specific form of the reduced tax rate due to the consumption tax increase (for example, what items are subject to the reduced tax rate and the reduced tax rate for each item subject to the reduced tax rate, etc.) is changed in multiple forms to determine the optimal form of the reduced tax rate. In this case, the expected value E of the optimal form of the reduced tax rate is (tax revenue increase rate (%) + diffusion index (DI) as an economic activity index) / 50, and the simulation result that maximizes the expected value E is obtained. The control parameter in the simulation (form of the reduced tax rate) is θ, the result of the simulation is Y(θ), and θ at maxE[Y(θ)] is obtained. As a specific method for optimizing such a simulation, for example, a metaheuristic algorithm such as Particle Swarm Optimization (PSO) and DFO (derivative free optimization) are used, which finds an optimal solution when analytical expression of the objective function is difficult or when information on the derivative of the objective function cannot be used. It is not necessary to record on the blockchain by S137, and in that case, in the above-mentioned collaboration type, the "AI smart contract" in S135, S136, and S147 is changed to "collaboration AI". In other words, in the case of a simulation in a computer, since it does not involve the execution of a contract (legal act) in the real world, if recording on the blockchain is not performed, there is no need to bother to use a smart contract, and it is sufficient for each collaboration AI to perform action a and perform reinforcement learning. [Variations]

[0091] (1) The certified business operator 17 shall store the encrypted personal information of the user K1(personal information), but instead stores encrypted personal information E K1 (Personal information) may be recorded directly on the blockchain.

[0092] (2) Transaction data other than personal information, such as transactions C and F in Figure 2, may also be encrypted with key K1, etc., in the same way as personal information, and recorded on the blockchain.

[0093] (3) In the above explanation, the operation processing of the node 19 of each of the blockchain networks 2, 3, and 4 was shown, but the operation processing shown for the node 19 of the private chain 2 may be performed by the node 19 of the other blockchain networks 3 and 4, the operation processing shown for the node 19 of the consortium chain 3 may be performed by the node 19 of the other blockchain networks 2 and 4, and the operation processing shown for the node 19 of the public chain 4 may be performed by the node 19 of the other blockchain networks 2 and 3. This modified example may be similarly applied to the embodiment described later.

[0094] (4) Borrowed PC resources (computational resources) may be used to perform mining (competition for bookkeeping rights) in the blockchain. In this case, the PC resources (computational resources) may be lent at an hourly rate as in the first embodiment, but a percentage of the profits (tokens, etc.) obtained by a miner who succeeds in mining (competition for bookkeeping rights) may be distributed (dividend) to the lender of the PC resources (computational resources). The dividend rate (dividend amount) is controlled to be proportional to the amount of PC resources (computational resources) lent (number of PCs lent x lending time, etc.).

[0095] (5) Some project may be carried out by borrowing (using) the above-mentioned PC resources (computational resources) or self-generated electricity. Specific examples of projects include research and development (for example, artificial intelligence development, machine learning, human genome analysis, new product development, new drug development, etc.), exploration and excavation of rare metals, oil, natural gas, marine resources, etc., and space development. In this case, as in the first embodiment, the lender (provider) may obtain a compensation (token, etc.) according to the amount of the lending (provision) object, but a certain percentage of the profits obtained by the project executor (individual, corporation, or organization) due to the success of the project may be distributed (dividend) to the resource lender (resource provider). The dividend ratio (dividend amount) is controlled to be proportional to the amount of the resource loaned (provision).

[0096] Furthermore, the resource lender (resource provider) may not receive the dividend itself, but may acquire the right to receive the dividend (hereinafter referred to as "dividend enjoyment right"). This dividend enjoyment right may be controlled so that the lender (provider) acquires it in the form of a token issued by the project executor, for example. The lender (provider) may then control so that the acquired dividend enjoyment right (token) can be transferred to another person at a price (token) according to the market price at the time. By configuring it in this way, the dividend enjoyment right (token) can be managed as if it were a stock transaction in the secondary market of the stock market.

[0097] (6) In S71, the generated personalized trained model is sent to the address of the client and delivered. In addition to or instead of this, the generated personalized trained model may be utilized to provide the client with personalized services.

[0098] (7) In the above description, the verification, condition confirmation, execution, implementation, and negotiation of the contract are automated by the smart contract, but the user's consent may be requested before the contract (legal act such as a transaction) is concluded and executed. In addition, instead of requesting the user's consent for all contracts (legal acts such as a transaction), it may be determined whether the contract (legal act such as a transaction) is a predetermined important contract, and if it is determined to be an important contract (legal act such as a transaction), the user's consent may be requested. Furthermore, it may be determined whether the contract (legal act such as a transaction) is one that needs to be concluded and executed urgently, and if it is determined to be one that needs to be concluded and executed urgently, the contract (legal act such as a transaction) may be concluded and executed without obtaining the user's consent, and the user may be notified later. This modified example may also be applied to the embodiment described later.

[0099] (8) The above-mentioned programs that operate on the user terminals 16, etc. and various servers that constitute the nodes 19 of each blockchain may be downloaded and installed from a specified website, etc., or may be recorded on a recording medium (non-transitory recording medium) such as a CD-ROM 99 and distributed, and a person who purchases the CD-ROM 99, etc. may install the programs on the user terminals 16 and various servers (see Figure 60).

[0100] (9) In the above explanation, a centralized oracle21 is used, but a decentralized oracle that is managed in a distributed manner throughout the network may also be used. The information collected by multiple oracles distributed throughout the network is collected, and the average information is extracted. This average information is considered to be correct information, and is incorporated into the blockchain and used in the smart contract. This is based on the theory proposed by James Surowiecki in his book "The Average is Often Right," which states that "by collecting information in a group, the conclusion that the group reaches can be better than any individual in the group would think." Then, by giving a reward such as tokens to an oracle that collects information close to the average information, an incentive is given to operate a decentralized oracle.

[0101] In summary, the system includes an extraction means for aggregating information collected by a plurality of oracles distributed on a network and extracting average information, an adoption means for adopting the average information extracted by the extraction means, and a reward giving means for giving rewards to the oracles, the plurality of oracles including a first oracle and a second oracle, and the reward giving means gives a larger reward to the second oracle that has collected information closer to the average information than the first oracle. Note that the reward given to the first oracle may be 0 or may be negative.

[0102] (10) A function may be provided to determine whether or not to establish one or both of the above-mentioned S13 K2 distribution contract and S16 personal information provision contract in accordance with information that identifies the intention of the personal information owner (hereinafter referred to as "intention identification information"). Specifically, when the personal information owner wants to have products or services that match him / her recommended at a brick-and-mortar store or an online shopping mall, he / she has the specific information on his / her mobile device (smartphone or IC card, etc.) read and enters a PIN indicating that the contract may be established, thereby notifying the smart contract of the personal information owner of the intention identification information consisting of the specific information and the PIN, and the smart contract makes a decision according to the intention identification information.

[0103] In this way, users can use the personal information that they have retrieved from SNS or other operators and placed under their own management for their own purposes according to their own will. [Second embodiment]

[0104] Next, the second embodiment will be described. This second embodiment responds to the need for a right to delete personal information (so-called right to be forgotten) to delete one's own personal information by applying encryption technology to personal information recorded using a blockchain. A feature of a blockchain is that once recorded information cannot be tampered with or is extremely difficult to tamper with, and therefore once recorded information cannot be deleted or is extremely difficult to delete (hereinafter referred to as "impossibility of deletion"). On the other hand, the General Data Protection Regulation (GDPR) in Europe requires that a right to delete personal information (so-called right to be forgotten) that allows the owner of the personal information to delete the recorded personal information must be guaranteed. The GDPR's request for the right to delete personal information and the impossibility of deletion in the blockchain are in direct conflict with each other, resulting in a dilemma of two opposing forces. In other words, this second embodiment solves the dilemma of the request to guarantee the right to delete information to be deleted and the impossibility of deletion. An overview of the second embodiment will be described based on FIG. 14.

[0105] FIG. 14(A) shows a normal state where the right to delete is not exercised, and FIG. 14(B) shows a state where the right to delete is exercised and the information is rendered unreadable. Referring to FIG. 14(A), an information holder (also called an information owner) 40 double-encrypts the information using two symmetric keys KA and KB. Expressed as a formula, E KA (E KB Next, the encrypted information E KA (E KB (Information)) is recorded in a block chain, etc. The other common key KA is stored in a secret state in a user terminal, etc. of the information holder 40.

[0106] In this state, when the information requester 41 requests information from the information owner 40, the information owner 40 sends the recorded encrypted information, E KA (E KB (information)) is decrypted with key KA. In formula form, D KA (E KA (E KB (Info))=E KB (information). And this E KBThe information owner 40 transmits (information) and the other common key KB to the information requester 41 .

[0107] The information requester 41 who receives them KB (Information) is decrypted using the received shared key KB. In formula form, D KB (E KB (information))=information. This allows the information requester 41 to obtain the information in plaintext.

[0108] Next, the state in which information has been rendered undecipherable by exercising the deletion right will be described with reference to Fig. 14(B). The information owner 40 updates one of the symmetric keys KA and KB used to encrypt the information to be rendered undecipherable, KA, to a random number R (≠ KA). Next, when an information requester 41 who has already stored the symmetric key KB requests information from the information owner 40, the information owner 40 reads the encrypted information E KA (E KB (information)) is decrypted with key R (random number). In formula form, D R (E KA (E KB (information)). And this D R (E KA (E KB The information owner 40 transmits (information) to the information requester 41.

[0109] The information requester 41 who receives it uses the other half of the common key KB that he has already memorized to send the D R (E KA (E KB (information)) is decrypted. In formula form, D KB (D R (E KA (E KB(Information)))) ≠ Information. In this way, in the decryption-decryption-defective state, even an information requester 41 who has already stored the other half of the common key KB cannot obtain plaintext information, and the dilemma of the contradiction between the request to guarantee the right to delete information to be deleted and the impossibility of deleting the information can be resolved. If a copy-protection process is implemented so that information such as personal information to be recorded in a blockchain or the like cannot be copied and pasted, the guarantee of the right to delete information can be made more complete. It is not necessary to be limited to double encryption using two keys KA and KB, and multiple encryption using three or more keys (n keys) may be used. In this case, the decryption-defective state is achieved by replacing at least one of the n keys with a random number R.

[0110] The outline of the second embodiment explained above will be explained in more detail. The points in common with the first embodiment will not be explained repeatedly, and differences will be mainly explained. FIG. 15 corresponds to FIG. 2 in the first embodiment. Referring to a transaction I in the blockchain, in this second embodiment, encrypted personal information E KA (E KB (personal information)) is recorded directly in the block. Therefore, in the second embodiment, the certified business operator 17 is not required. Here, KA and KB are symmetric common keys.

[0111] Next, referring to Figure 16, a flowchart of the main routine of a user terminal constituting node 19 of private chain 2 and a user terminal 16 constituting a node of public chain 4 will be described. This main routine omits the flowchart of the operation processing shown in the first embodiment, and shows only a flowchart of the operation processing that is added or changed to the operation processing shown in the first embodiment. In a user terminal 16 constituting a node of public chain 4, personal information recording processing to the blockchain is performed by S160, record undeciphering processing is performed by S161, and personal information provision processing is performed by S162. In a user terminal constituting node 19 of private chain 2, personal information acquisition processing is performed by S170.

[0112] The process of recording personal information to the blockchain is the process of recording personal information to the blockchain. The process of making the record undecipherable is the process of exercising the right of deletion to make the information undecipherable. The process of providing personal information is the process in which a user terminal 16 of the public chain 4 provides personal information to a user terminal of the private chain 2. The process of obtaining personal information is the process in which a user terminal of the private chain 2 obtains personal information from a user terminal 16 of the public chain.

[0113] Based on Fig. 17(A), a flowchart of a subroutine program for recording personal information in a blockchain will be described. In step S174, a process for generating two random numbers is performed. For example, in the case of DES, two 56-bit random numbers are generated and the 56-bit random numbers are set as the symmetric keys KA and KB. In the case of ADS, two 128-bit random numbers are generated and the 128-bit random numbers are set as the symmetric keys KA and KB.

[0114] Next, in S177, E KA (E KB (Personal Information)) and E K2 The process of recording (index + compensation for personal information provision) and the ciphertext identifier on the blockchain is performed. This ciphertext identifier is the encrypted personal information E KA (E KB The personal information (personal information) is an identifier for identifying the user and corresponds to the electronic ID in the first embodiment.

[0115] Next, in S183, KA and KB are stored in the HDD 12 of the user terminal 16 in association with the ciphertext identifier.

[0116] Next, a flowchart of a subroutine program for the record undeciphering process will be described with reference to FIG. 17(B). KA (E KBIt is determined whether there is any ciphertext to be made undecipherable among the ciphertexts (personal information), etc. If there is no ciphertext to be made undecipherable, the process returns, but if there is any ciphertext to be made undecipherable, control proceeds to S191, where a process is performed to search the HDD 12 for the other symmetric key KA stored in association with the ciphertext identifier of that ciphertext.

[0117] Next, in S192, a random number R is generated. For example, in the case of DES, a 56-bit random number is generated. In the case of ADS, a 128-bit random number is generated. Next, in S193, it is determined whether the generated random number R=KA. If the generated random number R is the same as the other half of the common key KA stored in the HDD 12, control returns to S192, and a random number is generated again. If the result of S193 is NO, control proceeds to S194, and a process is performed to update the other half of the common key KA stored in the HDD 12 to R.

[0118] Next, a flowchart of the subroutine program of the personal information provision process and the personal information acquisition process will be described with reference to Fig. 18. In the user terminal of the private chain 2, it is determined in S198 whether the other half of the common key KB of the personal information desired to be obtained has already been stored. If the other half of the common key KB of the personal information desired to be obtained has already been distributed from the user terminal 16 of the public chain 4 to the user terminal of the private chain 2, it is determined in S198 that it has been stored and the control proceeds to S203, but if it has not yet been stored, the control proceeds to S199.

[0119] In S199, the ciphertext identifier of the personal information desired to be obtained is sent to the user terminal 16 of the public chain 4 to request the other half of the shared key KB. The user terminal 16 of the public chain 4 receives the ciphertext identifier in S200 and uses a smart contract to determine whether or not to make a transaction to provide the personal information specified by the ciphertext identifier (see S16). If a transaction to provide personal information is to be made, a signature agreeing to the provision of the personal information and the other half of the shared key KB corresponding to the ciphertext identifier are returned in S201.

[0120] In the user terminal of the private chain 2 that received it in S202, the signature and the ciphertext identifier of the personal information desired to be obtained are transmitted to the user terminal 16 of the public chain 4 in S203. In the user terminal 16 of the public chain 4 that received it in S206, a smart contract is used to determine whether or not to make a transaction to provide the personal information specified by the ciphertext identifier (see S16). If a transaction to provide the personal information is to be made, in S207, KA (Encrypted Personal Information) or D R Specifically, if the other half of the common key KA stored in the HDD 12 of the user terminal 16 has already been updated to the random number R, the process returns D R (encrypted personal information) is calculated and returned, but if it has not yet been updated to the random number R, KA The (encrypted personal information) is calculated and returned.

[0121] In the user terminal of the private chain 2 that receives the reply from the user terminal 16 of the public chain 4 in S208, in S209, KB (D KA (Encrypted personal information) = Plain text, or D KB (D R (Encrypted personal information) ≠ Plain text. KA When you receive (encrypted personal information), KB (D KA (encrypted personal information))=D KB (D KA (E KA (E KB (Personal information)))) = Plain text is calculated to obtain plain text personal information. R (Encrypted Personal Information)) is received, KB (D R (encrypted personal information))=D KB (D R (E KA (E KB(Personal information)))) ≠ plaintext is calculated, and the plaintext personal information cannot be obtained. This solves the dilemma between the requirement to guarantee the right to delete information that one wants to delete and the impossibility of deleting the information. [Variations]

[0122] (1) In the above explanation, encrypted personal information E KA (E KB (Personal information) is recorded directly in the blockchain, and if this large amount of encrypted personal information were stored in each node (all nodes in the case of public chain 4), it would be inconvenient for each node (user terminal) to require a huge storage capacity. As a means of solving this, secret sharing technology is applied to store divided data on multiple computers. Data is divided into fragments, and each fragmented data is distributed and stored in multiple nodes. In addition, the data stored in each node is stored redundantly (in duplicate). By providing sufficient redundancy, even if part of the fragmented data is lost, it can be restored without any problems, and the difficulty of tampering with the blockchain can be guaranteed. Furthermore, it is possible to control the amount of storage that each node will be responsible for storing fragmented data to decide at its own discretion, and to control the node to be given compensation in the form of tokens or the like according to the amount of storage it will be responsible for.

[0123] (2) In the above explanation, encrypted personal information E KA (E KB In the conventional method, personal information is recorded directly on the blockchain. In the modified examples shown in Figs. 19 to 23, the encrypted personal information E KA (E KB (Personal information)) is stored in the personal information DB 29 of the certified business 17, and the hash value of the encrypted personal information is recorded in the blockchain. KA (E KB (Personal information)) hash value + E K2 (Index+2.4 token)+ciphertext identifier and electronic signature are recorded. Also, the personal information DB 29 of the certified business 17 shown in FIG. 19(B) stores the encrypted personal information E in association with the ciphertext identifier. KA (E KB(personal information) is stored.

[0124] A flowchart of the main routine of the user terminal constituting node 19 of the private chain 2 in this modified example, the user terminal 16 constituting node 19 of the public chain 4, and the server 18 of the certified business operator 17 will be described with reference to Fig. 20. The server 18 of the certified business operator 17 participates in the blockchain as node 19. Personal information recording processing to the blockchain is executed by S215, record undecipherable processing is executed by S216, personal information provision processing is executed by S217, hash value recording processing is executed by S220, ciphertext transmission processing is executed by S221, and personal information acquisition processing is executed by S224.

[0125] The process of recording personal information on the blockchain is carried out by a user terminal 16 constituting a node 19 of the public chain 4 transmitting encrypted personal information E KA (E KB The hash value recording process is a process of transmitting the encrypted personal information E KA (E KB The server 18 of the certified business 17 that receives the encrypted personal information E (personal information) stores it and generates a hash value to record it in the blockchain. KA (E KB The personal information provision process is a process executed by a user terminal 16 constituting a node 19 of the public chain 4 to provide personal information to a user terminal constituting a node 19 of the private chain 2. The personal information acquisition process is a process executed by a user terminal constituting a node 19 of the private chain 2 to acquire personal information. The ciphertext transmission process is a process executed by a server 18 of a certified business operator 17 to transmit encrypted personal information E to a user terminal constituting a node 19 of the private chain 2. KA (E KB This is the process of transmitting encrypted text such as (personal information).

[0126] The details of each process will be described below based on the flowcharts of the subroutine programs, but differences from the second embodiment will be mainly described.

[0127] Based on FIG. 21, a flowchart of a subroutine program for recording personal information and hash values ​​in the block chain will be described. KA (E KB (Personal Information)) and E K2 (index + compensation for personal information provision) is sent to the server 18 of the certified business 17. The server 18 that receives it in S240 sends the E KA (E KB Next, a process of generating a hash value of (personal information) and a ciphertext identifier is performed. KA (E KB (Personal Information)) hash value and E K2 A process is performed to record (index + compensation for providing personal information) and the ciphertext identifier on the blockchain.

[0128] Next, in S243, the ciphertext identifier is transmitted to the user terminal 16 of the public chain 4. In the user terminal 16 of the public chain 4 that received it in S232, in S233, the other keys KA and KB are associated with the received ciphertext identifier and stored in the HDD 12. In S244, the server 18 of the certified business operator 17 stores the E KA (E KB A process is performed in which the (personal information)) is associated with the ciphertext identifier and stored in the personal information DB 29.

[0129] The record unreadable process shown in FIG. 22 is the same as that already explained in FIG. 17(B) of the second embodiment, so a repeated explanation will be omitted.

[0130] Next, a flowchart of a subroutine program of the personal information provision process, the personal information acquisition process, and the ciphertext transmission process will be described with reference to Fig. 23. In this modification, when the user terminal of the private chain 2 receives the signature agreeing to provide the personal information and the other common key KB corresponding to the ciphertext identifier from the user terminal 16 of the public chain 4 (S264), the user terminal of the private chain 2 transmits the received signature and the ciphertext identifier of the personal information to be obtained to the server 18 of the certified business operator 17 in S265. The server 18 of the certified business operator 17 receives the signature in S266, and after confirming the signature, searches the personal information DB 29 for the ciphertext corresponding to the ciphertext identifier and transmits the ciphertext to the user terminal 16 of the public chain 4 in S267.

[0131] The user terminal 16 of the public chain 4 that received the token in S268 receives the token in S269. KA (Encrypted Personal Information) or D R (encrypted personal information)) and returns it to the user terminal of the private chain 2. Specifically, if the other symmetric key KA stored in the HDD 12 of the user terminal 16 has already been updated to R, R (encrypted personal information) is calculated and returned, but if it has not yet been updated to R, KA The (encrypted personal information) is calculated and returned.

[0132] In the user terminal of the private chain 2 that receives the reply from the user terminal 16 of the public chain 4 in S270, the D KB (D KA (Encrypted personal information) = Plain text, or D KB (D R (Encrypted personal information) ≠ Plain text. KA When you receive (encrypted personal information), KB (D KA (encrypted personal information))=D KB (D KA (E KA (E KB (Personal information)))) = Plain text is calculated to obtain plain text personal information.R (Encrypted Personal Information)) is received, KB (D R (encrypted personal information))=D KB (D R (E KA (E KB (Personal information)))) ≠ plaintext is calculated, and the plaintext personal information cannot be obtained. This solves the dilemma between the requirement to guarantee the right to delete information that one wants to delete and the impossibility of deleting the information.

[0133] In addition, the server 18 of the certified business operator 17 may be connected to the user terminal of the private chain 2 and the user terminal of the public chain 4 via the Internet 1 without participating in the blockchain as a node 19.

[0134] (3) In the above explanation, the other half of the common key KA is held by the personal information owner (stored in the HDD 12 of the user terminal 16), but instead, the other half of the common key KA may be registered in the key DB 32 of the key registration center 30, which is an example of a predetermined organization (third-party organization). The other half of the common key KA is stored in a secret state in the key DB 32. This modification will be explained with reference to Figs. 24 to 27.

[0135] Referring to FIG. 24, a server 31 of a key registration center 30 is connected to the Internet 1. In a key DB 32 connected to the server 31, a ciphertext identifier and a half common key KA are stored in association with each other for each address of a user, which is each node 19 of the public chain 4. If a user requests to make a record unreadable, the half common key KA stored in association with the ciphertext identifier corresponding to the requested record is updated to a random number R. In FIG. 24, the half common key stored in association with the ciphertext identifier 307cd4 of the address 0x6079dd is updated to a random number 1R2, the half common key stored in association with the ciphertext identifier 4arb56 of the address 0x6080dd is updated to a random number 2Rn, and the half common key stored in association with the ciphertext identifier e2c87r of the address 0x6978dd is updated to a random number mR1.

[0136] Next, a flowchart of the main routine of the user terminal 16 of the public chain 4, the server 31 of the key registration center 30, and the user terminal of the private chain 2 will be described with reference to Fig. 25. The points in common with the second embodiment will not be described repeatedly, and differences will be mainly described.

[0137] In the user terminal 16 of the public chain 4, personal information recording processing to the block chain is executed in S468, a record decryption unreadable request processing is executed in S469, and a decryption key provision processing is executed in S470. In the server 31 of the key registration center 30, a key registration processing is executed in S463, a record decryption unreadable processing is executed in S464, and a data decryption processing is executed in S465. In the user terminal of the private chain 2, a data acquisition processing is executed in S460.

[0138] Next, a flowchart of a subroutine program for recording personal information and registering a key to the blockchain will be described with reference to FIG. 26(A). In the user terminal 16 of the public chain 4, in S479, KA (E KB (Personal Information)) and E K2 The (index + compensation for providing personal information) and the ciphertext identifier are recorded in the blockchain, and in S480, a process is performed to transmit the other half of the common key KA and the ciphertext identifier to the key registration center 30.

[0139] The server 31 of the key registration center 30 receives the ciphertext in S474 and stores the received other symmetric key KA and the ciphertext identifier in the key DB 32 in association with each other in S475.

[0140] Next, a flowchart of the subroutine program of the record decryption request process and the record decryption process will be described with reference to Fig. 26(B). In the user terminal 16 of the public chain 4, in S494, it is determined whether there is a ciphertext to be made undecryptable, and if there is not, the process returns. If there is, in S495, the process of transmitting the ciphertext identifier to be made undecryptable to the server 31 of the key registration center 30 is performed.

[0141] The server 31 of the key registration center 30 receives the ciphertext identifier in S485 and searches the key DB 32 for the other symmetric key KA stored in association with the received ciphertext identifier. Next, a random number R is generated in S487 and it is determined in S488 whether the random number R=KA. If R=KA, the random number R is generated again in S487, and when R≠KA, the other symmetric key KA is updated to R in S489.

[0142] Next, a flowchart of the subroutine program of the other common key provision process, the data acquisition process, and the data decryption process will be described with reference to Fig. 27. In the user terminal of the private chain 2, a process is performed in S503 to transmit the ciphertext identifier of the data to be acquired to the server 31 of the key registration center 30. In the server 31 of the key registration center 30 that receives the ciphertext identifier in S504, a process is performed in S505 to search the blockchain for the ciphertext (encrypted personal information, etc.) corresponding to the ciphertext identifier. Next, a process is performed in S506 to search for the other common key KA or R corresponding to the ciphertext identifier.

[0143] Next, S507, D KA (Encrypted Personal Information) or D R (encrypted personal information) to the user terminal of the private chain 2. Specifically, if the other common key KA stored in the key DB 32 of the key registration center 30 has already been updated to R, R (encrypted personal information) is calculated and returned, but if it has not yet been updated to R, KA The (encrypted personal information) is calculated and returned.

[0144] The user terminal of the private chain 2 that received the message in S509 receives the message in S510. KA (D KB (Encrypted personal information) = Plain text, or D KA (D R (Encrypted personal information) ≠ Plain text. KA When you receive (encrypted personal information), KB (D KA (encrypted personal information))=D KB (D KA (E KA (E KB (Personal information)))) = Plain text is calculated to obtain plain text personal information. R (Encrypted Personal Information)) is received, KB (D R (encrypted personal information))=D KB (D R (E KA (E KB The calculation results in (personal information)))) ≠ plaintext, and the personal information in plaintext cannot be obtained. This makes it possible to solve the dilemma of the trade-off between the requirement to guarantee the right to delete information to be deleted and the impossibility of deleting the information. Furthermore, since the process of updating the other symmetric key KA to R is performed in the key registration center 30, it is easy to ensure the reliability of the guarantee of the right to delete by updating KA to R. For example, there is an advantage in that the update of KA to R can be easily performed under audit by a specified organization.

[0145] (4) As another method to resolve the dilemma between the requirement to guarantee the right to delete information that one wishes to delete and the impossibility of deleting the information, the encrypted personal information E stored in the personal information database 29 of the certified business operator 17 is KA (E KB(Personal information) may be deleted at the request of the person who owns the personal information. In that case, a contradictory state occurs in which the hash value of the personal information is recorded on the blockchain but the corresponding personal information is not stored in the personal information DB 29. However, if this contradiction can be tolerated, deleting the personal information is also an effective measure.

[0146] (5) The information for which the right to delete is guaranteed is not limited to personal information, and may be any information, such as information posted on SNS or blogs (including data on posted photos and videos), notarized documents such as wills and voluntary guardianship contracts, private documents, articles of incorporation of a company, or other information that requires a fixed date. In addition, in the second embodiment, the information for which the right to delete is guaranteed is recorded using a blockchain, but the blockchain is merely one example, and other information may be used for recording.

[0147] (6) The above-mentioned programs that run on the user terminals 16, etc. and various servers that constitute the nodes 19 of each blockchain may be downloaded and installed from a specified website, etc., or may be recorded on a recording medium (non-transitory recording medium) such as a CD-ROM 99 and distributed, and a person who purchases the CD-ROM 99, etc., may install the programs on the user terminals 16 and various servers (see Figure 60).

[0148] (7) In the above explanation, the plaintext is obtained by encrypting once with the one-side common key KA and then encrypting it again with the one-side common key KB twice, and then decrypting once with the one-side common key KB and then decrypting it again with the one-side common key KA twice to obtain plaintext. However, this is not limited to this, and encryption may be performed multiple times with the one-side common key KA or KB, and decryption may be performed multiple times with the one-side common key KA or KB. Furthermore, the number of one-side common keys KA and KB is not limited to two, and three or more one-side common keys may be used.

[0149] Furthermore, a single key K is generated by performing an exclusive OR operation on the two symmetric keys KA and KB (KA(+)KB=K), and personal information is encrypted with the key K (E K The personal information owner receives a request from the personal information requester and transmits the encrypted personal information to the server 31 of the key registration center 30, and the personal information requester transmits the distributed other common key KB to the server 31 of the key registration center 30. The server 31 of the key registration center 30 performs an exclusive OR operation between the received other common key KB and the other common key KA registered in the key DB 32 to generate one key K (KA(+)KB=K), and transmits the received encrypted personal information (E K (Personal information)) is decrypted with the key K to plain text, and then (D K (E K (Personal information) = plain text), and the plain text personal information may be sent to the requester of the personal information. The (+) above is a symbolic representation of exclusive OR.

[0150] Note that exclusive OR is merely an example, and any algorithm may be used as long as it generates one key K from the other symmetric keys KA and KB.

[0151] In addition, in the above method of generating the key K using an additive group such as exclusive OR, there is an advantage that the one-half common key KA and KB can be periodically updated to maintain security. For example, when one-half common key KA is updated to KC, the other one-half common key KB=K(+)KC, which can be calculated by calculation. By updating the one-half common key KA and KB in this way, not only can it be countered against leakage of the one-half common key, but it is also possible to prevent a personal information requester who has once distributed the one-half common key KB from decrypting the encrypted personal information on the blockchain again, thereby preventing viewing. By performing such an update of the one-half common key every time the one-half common key KB is distributed, even if a person who has received the one-half common key KB sells the one-half common key KB to another person, it is possible to make the encrypted personal information on the blockchain undecryptable by the person who received the one-half common key KB. In other words, the one-half common key KB to be distributed can be a one-time key that can be used only once.

[0152] Moreover, the encryption is not limited to a common key, and a public key encryption method such as RSA or elliptic curve cryptography may be used.

[0153] In addition, in order to realize the above key update, an encryption algorithm that satisfies the following conditions may be adopted. Let M be the plaintext, C be the ciphertext, and KA, KB, KC, and KD be the encryption keys. E KA (E KB (M))=E KC (E KD (M))=C An algorithm in which the formula holds true.

[0154] In the case where such an algorithm is a symmetric key encryption algorithm, when the symmetric keys KA and KB are updated to KC and KD, the plaintext M can be obtained by decrypting the ciphertext C recorded in the blockchain with the symmetric keys KC and KD. On the other hand, in the case of a public key encryption algorithm, when the private keys KA and KB are updated to KC and KD, the plaintext M can be obtained by decrypting the ciphertext C recorded in the blockchain with the pair of public keys PKC and PKD corresponding to the private keys KC and KD. (8) In the above explanation, the information holder provides the information requester with encrypted personal information (D KA (Encrypted Personal Information) or D R The information requester transmits the encrypted personal information (D) and the other common key KB (S201, S207), and the information requester uses the other common key KB to decrypt the encrypted personal information to plain text (S209). However, the decryption using the other common key KB may be performed by a third party (a designated service organization). In this case, the information holder transmits the encrypted personal information (D KA (Encrypted Personal Information) or D R The encrypted personal information and the other half of the shared key KB are sent to a third party (a designated service provider), which then decrypts the information and sends it to the information requester. [Features of the disclosure]

[0155] Next, features of the disclosure of the above-described embodiment will be listed below. (Feature 1) [Technical field]

[0156] Feature 1 relates to a processing system and program for an information recording method that is difficult to tamper with or erase, such as a blockchain. [Background technology]

[0157] Blockchain has been widely known as an information recording method that is difficult to tamper with or erase. For example, JP 2018-128723 A discloses a method for recording various information related to cargo transportation using blockchain. [Summary of Feature 1] [Problem that Feature 1 aims to solve]

[0158] However, such information recorded using blockchain is not only difficult to tamper with, but also difficult to erase (hereinafter referred to as "inerasability"). As a result, once personal information is recorded using blockchain, it cannot be erased even if the personal information owner wants to erase the information, which is a drawback in that the right to erase personal information (the so-called right to be forgotten) is violated.

[0159] In other words, there is a drawback in that a dilemma arises between guaranteeing the authenticity of recorded information and guaranteeing the right to delete that information, which are in conflict with each other.

[0160] Feature 1 was devised in light of this reality, and its purpose is to resolve the trade-off between ensuring the authenticity of recorded information and ensuring the right to delete that information. [Means for solving the problem]

[0161] The subject of Feature 1 is, for example, expressed as the following items: (Item 1) An encryption means (e.g., S174, S177, S228, S231, S478, S479) for performing an encryption process to encrypt information to be recorded (e.g., personal information); A recording means for recording information after the encryption process (e.g., S177 and a block chain, or S231, S240, S242, S244, a block chain and personal information DB29, or S479 and a block chain); A decryption means (for example, S201, S202, S207 to S209, S263 to S271, or S500 to S510) that performs a decryption process on the information recorded by the recording means using a first key and a second key to generate plaintext information; a decryption disabler (e.g., S191 to S194, S250 to S254, S494, S495, S485 to S489) for making the information recorded by the recording means undecodable, the decryption means includes a second key concealment means (e.g., S194, S233, or S475) for concealing and retaining the second key (e.g., a counterpart common key KA); The decryption disable means disables the decryption by updating the second key held by the second key secret holding means to another key (e.g., a random number R) (e.g., S190 to S194, or S250 to S254, or S494, S495, S485 to S489) (e.g., S190 to S194, or S250 to S254, or S494, S495, S485 to S489).

[0162] (Item 2) The processing system described in item 1, wherein the decryption means further includes a first key distribution means (e.g., S200, S201, or S2562, S263, or S500, S501) that distributes the first key (e.g., a counterpart common key KB) to a person who wishes to view the information. (Item 3) 3. The processing system according to item 1 or 2, further comprising a search means (for example, S37 to S40, S42 to S45) for searching the information recorded by the recording means without converting the information into plain text.

[0163] (Item 4) The information recorded by the recording means includes personal information, The processing system according to any one of items 1 to 3, wherein the decryption disabling means renders the personal information of the personal information owner in the decryption disabled state in response to a request from the personal information owner (for example, S190 to S194, or S250 to S254, or S494, S495, S485 to S489).

[0164] (Item 5) A step of performing an encryption process for encrypting information to be recorded (e.g., personal information) (e.g., S174, S177, S228, S231, S478, S479); A decryption step (e.g., S201, S202, S207 to S209, S263 to S271, or S500 to S510) of decrypting the information recorded by a recording means (e.g., S177 and the block chain, or S231, S240, S242, S244, the block chain and personal information DB29, or S479 and the block chain) using a first key and a second key to convert the information into plain text information; A step of making the information recorded by the recording means undecodable (for example, S191 to S194, or S250 to S254, or S494, S495, S485 to S489), Let the computer run The decryption step includes a step of secretly storing the second key (e.g., a counterpart symmetric key KA) (e.g., S194, S233, or S475), The step of making the second key undecryptable is to make the second key undecryptable by updating the second key held in the holding step to another key (e.g., a random number R) (e.g., S190 to S194, or S250 to S254, or S494, S495, S485 to S489), a program.

[0165] (Effect of Feature 1) According to feature 1, it is possible to eliminate as much as possible the dilemma of the trade-off between guaranteeing the authenticity of recorded information and guaranteeing the right to delete that information. (Feature 2) [Technical field]

[0166] Feature 2 relates to smart contracts used, for example, in blockchains. [Background technology]

[0167] A smart contract is a computer protocol intended for smooth verification, condition confirmation, execution, implementation, and negotiation of a contract, and has been used in the past in blockchains, etc. This smart contract has been known for some time as a way to automate contracts, transactions, etc. (For example, Patent No. 6403177). [Summary of Feature 2] [Problem that Feature 2 aims to solve]

[0168] In the field of smart contracts, there is a demand for advanced smart contracts that can execute legal acts such as various contracts or transactions, such as sales contracts and lease agreements, on behalf of the users themselves.

[0169] The purpose of Feature 2, which was devised in light of the above-mentioned circumstances, is to provide an advanced smart contract that can perform legal acts on behalf of the user himself / herself. [Means for solving the problem] The subject of characteristic 2 is expressed, for example, as items such as the following: (Item 1) A machine learning means (e.g., S80 to S82) for inputting information on legal acts performed by multiple natural persons or legal entities as data for machine learning and generating a general model; A personalization means (e.g., S86 to S88, or S94 to S98) for personalizing the general model into a model suitable for the user, the personalization being based on information regarding legal acts performed by the user; A computer system comprising: a smart contract generation means (e.g., S86 to S88, or S94 to S98) that generates a smart contract using the personalized model to perform a legal act on behalf of the user.

[0170] (Item 2) A personalization means (e.g., S80 to S82, S86 to S88, or S94 to S98) for personalizing a general model generated by inputting information on legal acts performed by multiple natural persons or legal entities as data for machine learning into a model suitable for a user, the personalization being based on information on the legal acts performed by the user; A computer system comprising: a smart contract generation means (e.g., S86 to S88, or S94 to S98) that generates a smart contract using the personalized model to perform a legal act on behalf of the user.

[0171] (Item 3) A personalization means (e.g., S80 to S82, S86 to S88, or S94 to S98) for personalizing a general model generated by inputting information on legal acts performed by multiple natural persons or legal entities as data for machine learning into a model suitable for a user, the personalization being based on information on the legal acts performed by the user; A computer system comprising: a service providing means (e.g., S99) that provides a service that performs legal acts on behalf of the user using the personalized model as a smart contract.

[0172] (Item 4) The computer system described in item 3, further comprising a reinforcement learning means (e.g., S105 to S108) for causing the model to learn a strategy for maximizing accumulation of rewards by giving rewards for legal acts performed in conjunction with the provision of a service by the service providing means (e.g., S99) to the model that performed the service.

[0173] (Item 5) A computer system that performs reinforcement learning by simulating a predetermined theme (e.g., a trading simulation in an investment market such as stock trading or futures trading, a company management simulation, or a consumer behavior simulation, assuming that a policy or law that the government is planning to adopt (e.g., a reduced tax rate accompanying a consumption tax increase, a revised immigration law, the withdrawal of the United Kingdom from the EU (European Union), partial or full adoption of basic income, amendment of Article 9 of the Japanese Constitution, etc.) is adopted), A selection means (e.g., S344, S345) for selecting a group of users belonging to a plurality of personas that match the theme of the simulation; A collection means (e.g., S346) for grouping the user group selected by the selection means according to the plurality of personas and collecting information on legal acts performed by the user group for each group; A generation means (e.g., S347) that performs machine learning using the collected information on legal acts as learning data to generate a group of trained smart contract models for each persona; A simulation means (e.g., S336 to S339) for executing a simulation of legal acts between the generated trained smart contract models in a computer, The simulation means includes a reinforcement learning means (e.g., S336, S338) that learns a strategy for the trained smart contract model to maximize accumulation of rewards by providing the trained smart contract model with rewards for executed legal acts.

[0174] (Item 6) A computer system that performs a simulation in a computer to progress reinforcement learning, A computer system comprising a reinforcement learning means (e.g., S336, S338) that performs a simulation reinforcement learning process in which a simulation of a legal act being performed between a group of trained smart contract models generated by machine learning is executed within a computer, and a reward for the executed legal act is given to the executed trained smart contract model, so that the trained smart contract model learns a strategy for maximizing the accumulation of the reward.

[0175] (Item 7) The computer system of item 6, further comprising a selection means (e.g., S340) for selecting a trained smart contract model to be actually used from the group of trained smart contract models based on the performance of the reinforcement learning result by the reinforcement learning means.

[0176] (Note) The "machine learning data" for generating the general model and the "machine learning data" for personalization need only contain "information about legal acts" and may also contain information other than "information about legal acts" (e.g., website access history, GPS location information, etc.). The "smart contract generation means" also includes cases where an artificial intelligence such as a personal assistant trained by machine learning using information about legal acts is made to play the role of a smart contract.

[0177] (Effect of Feature 2) Feature 2 makes it possible to provide advanced smart contracts that can perform various legal acts on behalf of the users themselves.

[0178] (Feature 3) [Technical field] Feature 3 relates to a computer system that sets conditions such as policies or laws that the government is planning to adopt (e.g., a reduced tax rate in conjunction with a consumption tax increase, the revised Immigration Control Act, the UK's withdrawal from the EU (European Union), the partial or full adoption of a basic income, the amendment of Article 9 of the Japanese Constitution, etc.), marketing-related conditions (e.g., setting prices and compensation for new products (including financial products and life insurance) and new services, the promotional effects of various media, etc.), and investment market-related conditions (e.g., weather conditions in futures trading, monetary tightening policies in the stock market, etc.), and then performs simulations within a computer under those conditions to predict in advance what the simulation results will be. [Background technology]

[0179] As a computer system of this kind, a new accounting method has been proposed that sets up a statement of changes in net assets to clarify future liabilities that the public will have to bear and future available resources and to support policy-level decision-making, clarifying asset fluctuations due to policy decisions in the relevant fiscal year and enabling a simulation of the future burden on the public (for example, JP 2006-155233). [Summary of Feature 3] [Problem that Feature 3 aims to solve]

[0180] In the field of such simulations, there is a demand for a computer system that can run simulations within a computer that faithfully mimic the activities of natural persons and corporations in the real world and derive simulation results that are as close to the real world as possible.

[0181] The purpose of Feature 3, which was devised in light of the above-mentioned circumstances, is to enable a simulation that faithfully mimics the activities of natural persons and corporations in the real world. [Means for solving the problem]

[0182] The subject of characteristic 3 can be expressed, for example, as items such as the following: (Item 1) A computer system that performs simulations within a computer under predetermined conditions (such as policies and laws that the government is planning to adopt (such as a reduced tax rate associated with a consumption tax increase, the revised Immigration Control Act, the withdrawal of the United Kingdom from the European Union (EU), the partial or full adoption of a basic income, the amendment of Article 9 of the Japanese Constitution, etc.), marketing-related conditions (such as setting prices and compensation for new products (including financial products and life insurance) and new services, promotional effects by various media, etc.), investment market-related conditions (such as weather conditions in futures trading, monetary tightening policies in the stock market, etc.)), A selection means (e.g., S144, S145) for selecting a user group belonging to a plurality of personas that match the conditions of the simulation; A collection means (e.g., S146) for grouping the user group selected by the selection means according to the plurality of personas and collecting information on legal acts performed by the user group for each group; A generation means (e.g., S147) that performs machine learning using the collected information on legal acts as learning data to generate a group of trained smart contract models for each persona; A simulation means (e.g., S136 to S139) for executing a simulation of legal acts between the generated trained smart contract models in a computer; A derivation means (e.g., S140) for deriving a result of the simulation by the simulation means, The simulation means includes a reinforcement learning means (e.g., S136, S138) that learns a strategy for the trained smart contract model to maximize the accumulation of rewards by giving rewards for executed legal acts to the trained smart contract model.

[0183] (Item 2) A computer system for performing a simulation within a computer, A reinforcement learning means (e.g., S136, S138) that performs reinforcement learning in which a simulation of a legal act being performed between a group of trained smart contract models generated by machine learning (e.g., S144 to S146) is performed in a computer, and a reward for the performed legal act is given to the trained smart contract model that performed the legal act, so that the trained smart contract model learns a policy to maximize the accumulation of the reward; A computer system comprising: a derivation means (e.g., S140) that derives the results of a simulation in which a legal act is performed between a group of trained smart contract models in which reinforcement learning by the reinforcement learning means has progressed. (Effect of Feature 3)

[0184] Feature 3 makes it possible to perform a simulation that mimics as faithfully as possible the activities of natural persons and corporations in the real world. [Third embodiment]

[0185] Next, a third embodiment will be described. The third embodiment relates to a system that performs a simulation in a simulation environment in a mirror world (cyberspace) composed of a digital twin of the real world, and thereby derives an optimal solution that predicts the future, derives an optimal solution for incentive design in a DAO (Decentralized Autonomous Organization), and performs machine learning (e.g., reinforcement learning) for AI, for example.

[0186] A digital twin is a digital representation of a real-world entity or system. A mirror world is a mirror image world consisting of digital twins in which all information from the physical world (real world), such as real countries, cities, societies, local governments, companies and other organizations, and people, is digitized. Specifically, a person's digital twin is composed of an assistant AI (hereinafter referred to as "personal AI") that has undergone machine learning (for example, reinforcement learning by an agent) to learn the person's life log (both real and virtual actions) as knowledge and assist the person in the best actions. This reinforcement learning is multi-agent reinforcement learning in which multiple personal AIs cooperate to perform reinforcement learning. A digital twin of an organization, such as a company in the real world, is composed of the personal AI of the people who make up the organization, a digital twin of the local government in the real world is composed of the personal AI of the people who make up the local government in the real world, a digital twin of the city in the real world is composed of the personal AI of the people who make up the city in the real world, and a digital twin of the country in the real world is composed of the personal AI of the people who make up the country.

[0187] In the third embodiment, a mechanism is provided in which organizations and people in the real world, such as countries, cities, societies, local governments, and companies, take the initiative in participating in and cooperating with the construction of a mirror world as a simulation environment. Specifically, by performing various simulations in the mirror world, the personal AI of the digital twin participating in the simulation is made to undergo machine learning (e.g., reinforcement learning), and the more highly learned, trained personal AI is returned (feedback) to the real world. Using the enjoyment of this benefit as an incentive, organizations and people in the real world, such as countries, cities, societies, local governments, and companies, are encouraged to take the initiative in participating in and cooperating with the construction of the mirror world.

[0188] 28, mirror world data is stored in a data center 45 in which a plurality of mirror world servers (including storage servers) 46 are installed. The hardware configuration of the mirror world server 46 is similar to the hardware configuration of the user terminal 16 shown in FIG. 1, and therefore the illustration and description thereof will not be repeated here. The entire mirror world 51, which is composed of digital twins (real country digital twins (e.g., Japan digital twin 53), city digital twins 54, society, local governments, organizations such as companies, people, and the Earth digital twins 52) in which information on real countries (e.g., Japan 49), cities 50, societies, local governments, organizations such as companies, people, and the Earth 48 in the real world 47 is all digitized, is stored in the data center 45 as digital data.

[0189] In the data center 45, a simulation is performed using this mirror world 51 as a simulation environment, and an optimal solution that predicts the future is derived by, for example, simulation optimization. As a simulation, for example, a trading simulation in an investment market such as stock trading or futures trading, a company management simulation, or a consumer behavior simulation, assuming that the above-mentioned policies and laws that the government is planning to adopt (for example, a reduced tax rate accompanying a consumption tax increase, the revised immigration control law, the withdrawal of the UK from the EU (European Union), partial or full adoption of basic income, and the revision of Article 9 of the Japanese Constitution, etc.) are adopted, may be considered. Furthermore, a simulation of promotion of new products (including financial products and life insurance) and new services through various media may also be used. The optimal solution derived by simulation optimization is fed back (returned) to the real world, and the benefit of the optimal solution is provided to the real world. In addition, a learned personal AI that has been machine-learned (for example, reinforcement learning) by the simulation is returned to the real world, so that a task can be performed by a more advanced learned personal AI.

[0190] The personal AI and the smart contract cooperate to form the AI ​​smart contract of the above-mentioned cooperation type. The data center 45 is connected to the Internet 1 shown in Figure 1 and Figure 24. In Figure 28, various blockchains 2, 3, 4, SNS 19, key registration center 30, etc. are omitted from the illustration.

[0191] FIG. 29 shows a specific example of a city digital twin 54 in the mirror world 51. In a city 50 in the real world 47, there are ABC Corporation 56, Taro 55, a person, and Taro's family 56. The corresponding city digital twin 54 also includes ABC Corporation 59, Taro digital twin (Taro's personal AI) 57, and Taro's family digital twin 58. City digital twin data consisting of these data is stored in the mirror world server 46. If there is a change in various objects such as ABC Corporation 56, Taro 55, and Taro's family 56 in the real world 49 (for example, personnel transfer, employment, or retirement at a company, marriage or childbirth for a person), the corresponding various digital twins are updated to the contents after the change. Such city digital twin data is stored in the data center 45 for each city to become the data of the digital twin 53 of the Japanese nation 49, and the city digital twin data of each country is stored in the data center 45 for each city to become the digital twin data of each country, and all of these digital twin data become the data of the digital twin 52 of the Earth 48.

[0192] As a specific example, the mirror world server 46 stores, as Taro digital twin (Taro's personal AI) 57, name: Taro, AI identification number: 82km9, personal AI data, and Taro's personal data (for example, life log, profile, preference data, electronic medical record data, vital data, etc.). As Taro's family digital twin 58, names: Taro, Sakura, Shiro, family composition: husband, wife, eldest son, AI identification numbers: 82km9, 11zk9, gf43y. As ABC Co., Ltd. digital twin 59, names: Taro, Hanako...Saburo, positions: CEO, managing director, general manager...regular employee, AI identification numbers: 82km9, ba935, 2es14,...9w1c2.

[0193] Flowcharts of the main routine programs of the user terminal 16 and the mirror world server 46 will be described with reference to Figures 30 to 33. With reference to Figure 30A, the CPU 10 of the user terminal 16 executes a member registration request process S555 for requesting registration for participation in the mirror world 51 as a simulation environment, a simulation preparation response process S556, and a simulation response process S557. The CPU 10 of the mirror world server 46 executes a member registration process 550, a simulation preparation process S551, and a simulation process S552.

[0194] The member registration process and the member registration request process will be described with reference to FIG. 30B. These processes are for registering a member who wants to participate as a digital twin in a simulation in which the mirror world 51 is the simulation environment. In the member registration request process, the CPU 10 of the user terminal 16 determines whether or not to apply for registration in S560, and if it is determined that the application for registration is not to be made, this member registration process ends and returns. If it is determined that the application for registration is to be made, in S561, the predetermined items required for the registration application are transmitted to the mirror world server 46, and if there is a person who does not have a personal AI, the fact and the blockchain address of the person are also transmitted to the mirror world server 46. Specifically, the predetermined items required for the registration application are, for a person's digital twin, the AI ​​identification number and personal AI data of the person's personal AI, for a family's digital twin, the family's name, family composition, and each AI identification number, and for a company's digital twin, the employee's name, job title, each AI identification number, etc.

[0195] The CPU 10 of the mirror world server 46 that receives it in S565 determines in S566 whether or not the user already owns a personal AI. If the information sent in S561 includes information indicating that the user does not own a personal AI, control proceeds to S567, where a process for generating and selling a personal AI is carried out, but if the information does not include information indicating that the user does not own a personal AI, control proceeds to S568, where the specified information, including the AI ​​identification number sent in S562, is registered in the mirror world 51.

[0196] The personal AI generation and sales process shown in S567 will be described with reference to Fig. 31. In S573, the CPU 10 of the mirror world server 46 collects from the blockchain the transaction data and the posting data of SNS, etc. recorded in the blockchain address received in S565 (the blockchain address of the user who does not have a personal AI). Next, in S574, machine learning is performed using the transaction data and the posting data of SNS, etc. as learning data to generate a trained personal AI. Next, in S575, the trained personal AI is sold to the corresponding user.

[0197] The simulation preparation process shown in S551 and the simulation preparation response process shown in S555 will be described with reference to FIG. 32. In S577, the CPU 10 of the mirror world server 46 determines whether or not a request for a simulation has been received. If it is determined that a request for a simulation has not been received, this simulation preparation process ends and returns. If it is determined that a request for a simulation has been received, control proceeds to S578, and a process of determining a personal AI group and a digital twin that match the requested simulation is performed. For example, in the case of a consumption behavior simulation under a reduced tax rate due to the aforementioned consumption tax increase, a personal AI group corresponding to a general consumer is determined in proportions according to demographic statistics such as gender, age, region, and annual income, and a manufacturer digital twin and a retailer digital twin of a consumer good that is subject to a reduced tax rate are determined. Next, a process of requesting consent to the simulation is performed for the determined personal AI group and digital twin. Specifically, the contents of the simulation are transmitted to the user terminal 16 of each user group corresponding to the determined personal AI group and digital twin, and a question is asked whether or not to consent.

[0198] The CPU 10 of each user terminal 16 of the identified personal AI group and the user group corresponding to the digital twin receives the transmitted simulation content in S580, and in S581 determines whether or not the user agrees to participate as a member in the execution of the simulation. This determination may be made by the personal AI, or by the user himself / herself. If it is determined that the user does not agree, the simulation preparation response process ends and returns, but if it is determined that the user agrees, in S582, a response indicating that the user agrees is sent to the mirror world server 46.

[0199] The CPU 10 of the mirror world server 46 that receives it in S583 judges whether or not the necessary amount of consent has been obtained to execute the requested simulation. If it is determined that consent has been obtained, in S584, the AI ​​group and the digital twin for which consent has been obtained are copied and registered in the mirror world 51 as the simulation target. The registered state is shown in FIG. 29 described above.

[0200] On the other hand, if it is determined that consent has not been obtained from the necessary personal AI groups and digital twins, control proceeds to S585, where a process is performed to set a persona group (including a persona corresponding to the digital twin of a manufacturer or a dealer) that matches the missing personal AI groups and digital twins, and in S586, a user group (including a user group working for a manufacturer or a dealer) that belongs to each persona is selected, in S587, the user groups that belong to each persona are grouped, and transaction data (including transaction data as a manufacturer or a dealer) of the user group is collected from the blockchain for each group, and in S588, machine learning is performed using the transaction data as learning data to generate and replenish a learned personal AI group and digital twin for each persona, and then the process proceeds to S584. These S585 to S588 are the same processes as S344 to S347 in FIG. 9(B), and detailed explanations will not be repeated here.

[0201] Next, specific control of the simulation process shown in S552 and the simulation response process shown in S557 will be described with reference to FIG. 33. S593 to S595 are the same processes as S336, S338, and S339 in FIG. 9 and S136, S138, and S139 in FIG. 12 described above, and detailed description will be omitted here. In S596, the simulation result is notified to the simulation requester. Specifically, the simulation result is transmitted to the user terminal 16 of the simulation requester. Next, in S597, each personal AI used in the simulation (including a personal AI engaged in a digital twin of a company organization, etc.) is transmitted to the user terminal 16 of each owner.

[0202] The CPU 10 of the user terminal 16 that received it in S598 judges in S599 whether to delete the received personal AI. The received personal AI is a trained AI that has participated in a simulation and undergone reinforcement learning (machine learning), and therefore has improved performance and can execute advanced task processing. However, depending on the content of the simulation, the AI ​​may have undergone reinforcement learning (machine learning) that the user does not want. In such a case, the CPU 10 judges YES in S599 and deletes the received personal AI in S601. On the other hand, if the simulation is what the user wants and it is determined that the received personal AI has undergone desirable reinforcement learning (machine learning), control proceeds to S600, where the received trained personal AI is overwritten and saved. As a result, the user has the advantage of being able to obtain a personal AI that has undergone desirable reinforcement learning (machine learning) and has improved performance. By using this advantage as an incentive, it is possible to encourage organizations and people in the real world, such as nations, cities, societies, local governments, and companies, to take the initiative in participating and cooperating in the construction of the mirror world. By overwriting and saving the personal AI by S600, the data is updated in the mirror world server 46 to the digital twin of the new personal AI after overwriting and the digital twin of the organization consisting of the new personal AI (see FIG. 29). Note that instead of overwriting and saving, both the existing personal AI and the trained personal AI may be stored and used as needed.

[0203] Next, a system that derives an optimal solution for incentive design in a DAO by performing a simulation in a simulation environment in the mirror world will be described with reference to Figs. 34 to 59. Fig. 34(A) is a schematic diagram of a multi-service DAO construction system. For example, Bitcoin is a type of DAO, but nodes (miners) only perform one type of service, mining (competition for bookkeeping rights), and the Bitcoin system continues autonomously by providing an incentive of granting Bitcoin to those who succeed in mining. In contrast, a DAO with multiple types of services is called a multi-service DAO. For example, in the case of a DAO of a company organization, there are multiple services such as material procurement, assembly, advertising, and sales, and it is a difficult problem to determine what proportion and how much reward should be distributed to nodes that perform these multiple services to achieve optimal incentive design. A system that derives an optimal solution for incentive design in such a multi-service DAO will be described.

[0204] 34(A), mirror world server 46, which stores multi-service DAO data, stores data on DAO agent 61, persona agent group 62 performing service 1, persona agent group 63 performing service 2, ..., persona agent group 64 performing service n. In addition, mirror world server 46 also stores the types of rewards r1, r2, ..., rn given to each persona agent group in association with reinforcement learning.

[0205] The terminal 16 of the multi-service DAO builder downloads and installs the DAO agent 61, the necessary persona agent group, and the types of rewards r1, r2, ... rn from the mirror world server 46. The terminal 16 is each node 19 that constitutes the public chain 4. The digital twin 66 of the multi-service DAO 65 operated in the public chain 4 composed of each node 19 is subjected to simulation reinforcement learning in the mirror world 51 to derive an optimal solution for incentive design in the multi-service DAO. The optimal solution for incentive design is applied to the actual multi-service DAO 65 in the real world 47, and a multi-service DAO 65 with optimal incentive design is created. This simulation reinforcement learning in the mirror world 51 is executed in the mirror world server 46. The control thereof is described below.

[0206] With reference to FIG. 34(B), the CPU 10 of the terminal 16 performs simulation reinforcement learning preparation response processing in S606, and performs simulation reinforcement learning response processing in S607.

[0207] The CPU 10 of the mirror world server 46 performs simulation reinforcement learning preparation processing in S611, performs simulation reinforcement learning processing in S612, and performs DAO agent reinforcement learning processing in S613.

[0208] Specific control of the simulated reinforcement learning preparation process shown in S611 and the simulated reinforcement learning preparation response process shown in S606 will be described with reference to FIG. 35. In the simulated reinforcement learning preparation response process, the CPU 10 of the terminal 16 determines in S615 whether to request simulated reinforcement learning. If not, this simulated learning preparation response process ends and returns. If requested, control proceeds to S616, where multi-service DAO data is transmitted to the mirror world server 46 for a request. This multi-service DAO data includes the type of service. For example, in the case of the innovation induction DAO described later in FIG. 36 to FIG. 42, there are five types of services: idea proposal, improvement proposal, commercialization, infringement detection, and token purchase, and these services are transmitted.

[0209] The CPU 10 of the mirror world server 46 that receives it in S620 sets a persona group that matches each of the multi-services in S621. For example, in the case of the above-mentioned innovation induction DAO, the persona group for idea generation and improvement generation may be people who often come up with inventions, the persona group for commercialization may be people who are interested in commercialization, the persona group for infringement detection may be people who are knowledgeable about patent law and copyright law, and the persona group for token purchase may be people who are interested in investment.

[0210] Next, in S622, a user group belonging to each persona is selected. For example, in the case of the above-mentioned innovation induction DAO, a user group listed as an inventor of a patent application as a user group belonging to the persona group of idea proposal and improvement proposal, a user group of company managers as a user group belonging to the persona group of commercialization, a user group of patent attorneys and lawyers as a user group belonging to the persona group of infringement detection, a user group of users who have purchased virtual currency such as Bitcoin as a user group belonging to the persona group of token purchase, etc. are possible.

[0211] Next, in S623, the users belonging to each persona are grouped, and transaction data of the users for each group is collected from the blockchain. In S624, machine learning is performed using the transaction data as learning data to generate a learned persona agent group for each persona. Both of these controls are the same processes as S346 and S347 in FIG. 9(B) described above, and detailed explanations will not be repeated here. Next, in S625, the persona agent group is deployed in the multi-service DAO 65 to generate a multi-service DAO digital twin 66, and is registered in the mirror world 51 as a simulation target. This state is shown in FIG. 36.

[0212] Referring to FIG. 36, a digital twin 66 of a multi-service DAO 65 consisting of a public chain is constructed in the mirror world 51. In the multi-service DAO digital twin 66, a group of persona agents are deployed in the above S625, and one persona agent is deployed for each node. The identification numbers of these persona agents are classified for each service (idea proposal, improvement proposal, commercialization, infringement detection, token purchase) and stored in the mirror world server 46. For example, kc29m, 1w13a, 9nad8 are stored in the mirror world server 46 as the identification numbers of the persona agents for the idea proposal service. This multi-service DAO 65 is the above-mentioned innovation induction DAO, and hereinafter, the multi-service DAO will be described using the innovation induction DAO as an example.

[0213] Furthermore, the mirror world server 46 also stores a DAO agent that gives rewards (incentives) for the actions of each persona agent. This DAO agent uses reinforcement learning (machine learning) to determine the distribution ratio and amount of the reward to be given, thereby deriving an optimal solution for incentive design. A schematic system of this reinforcement learning (machine learning) is shown in Figure 37.

[0214] Referring to FIG. 37, when the persona agent group performs origin idea submission as action a11, a12, ... a1n, the environment state S1 is input to the DAO agent 61, and rewards r11, r12, ... r1n are given to the persona agent group 67 that performed the idea submission service. This idea submission service is a broad concept that includes the creation of dreams, ideas, business plans, technical concepts, works, etc. The environment state S1 is also given to the persona agent group 67 that performed the idea submission service. This environment state S1 is, for example, the content of the origin idea submission, the price of the floating market price of the token A1 given as a reward to each persona agent 68 that performed the idea submission service, etc.

[0215] When the persona agent group 68 performs actions a21, a22, ... a2 such as posting an improvement plan for the origin idea, the state S2 of the environment is input to the DAO agent 61, and rewards r21, r22, ... r2n are given to the persona agent group 68 that performed the improvement plan service. The state S1 of the environment is also given to the persona agent group 68 that performed the improvement plan service. This state S2 of the environment is, for example, the content of the improvement plan posting, the number of "Likes!" given to the improvement plan posting, etc. The subject that gives this "Like" is limited to, for example, only those (persona agent group 71) who purchased the token A1 given as a reward for posting the origin idea. The reason for limiting (restricting) the subject that gives "Likes" to stakeholders (stake holders) in this way is to prevent fraudulent acts. This is to prevent fraudulent acts such as a person who posted an improvement proposal (persona agent group 68) colluding with many other people (persona agents) to get a large number of "Likes" if the number of people who can give "Likes" were unlimited. For the same reason, the people who can give "Likes" to persona agent group 69 who performed commercialization services and persona agent group 70 who performed infringement response services are limited to only those who purchased token A1 (persona agent group 71).

[0216] When the persona agent group 69 that has performed the commercialization service for the idea of ​​the origin performs actions a31, a32, ... a3n, the state of the environment S3 is input to the DAO agent 61, and rewards r31, r32, ... r3n are given to the persona agent group 69 that performed the commercialization service. Examples of the actions a31, a32, ... a3n of the persona agent group 69 include posting a business plan, posting the progress of commercialization, posting the status of actual commercialization, posting the amount of profit from the commercialized business, etc. The state of the environment S3 is also given to the persona agent group 69 that has performed the commercialization service. This state of the environment S3 is, for example, the number of "Likes!" given to the posting of the business plan, the posting of the progress of commercialization, the posting of the amount of profit from the commercialized business, etc.

[0217] When the persona agent group 70 that performed the infringement countermeasure service for the idea of ​​the origin performs actions a41, a42, ... a4n, the state of the environment S4 is input to the DAO agent 61, and rewards r41, r42, ... r4n are given to the persona agent group 70 that performed the infringement countermeasure service. The actions a31, a32, ... a3n of the persona agent group 70 may be, for example, a report post of the discovery of an infringement, a report post of the infringement countermeasure, a license negotiation report post, etc. Furthermore, the report post of a patent application and a report post of the right thereof, which are the prerequisites of these services, may also be included. The state of the environment S4 is also given to the persona agent group 70 that performed the infringement countermeasure service. This state of the environment S4 is, for example, the number of "Likes!" given to the report post of the discovery of an infringement, the report post of the infringement countermeasure, the license negotiation report post, etc.

[0218] If the persona agent group performs the service of purchasing the token A1 granted for the origin's idea proposal service as actions a51, a52, ... a5n, the state of the environment S5 is input to the DAO agent 61, and rewards r51, r52, ... r5n are given to the persona agent group 71 that performed the token purchase service. The state of the environment S5 is also given to the persona agent group 71 that performed the token purchase service. This state of the environment S5 is, for example, the number of tokens purchased (or the purchase amount), etc. The persona agent group 71 purchases the token A1 by consuming virtual currency (for example, ETH of Ethereum, etc.). The purchased token can be converted (cash) into virtual currency according to the price at the floating exchange rate, and the virtual currency can be converted (cash) into legal currency such as yen or dollars according to the price at the floating exchange rate.

[0219] The rewards r1 to r5 to be given to each persona agent group are determined by the DAO agent 61 based on the reward table (see FIG. 39(A)). For the persona agent who performed the idea proposal service, r1=A1+B1·b+G1·g; for the persona agent who performed the improvement service, r2=A2·e+B2·b+G2·g; for the persona agent who performed the commercialization service, r3=A3·e+B3·b; for the persona agent who performed the infringement response service, r4=A4·e+B4·b+G4·g; and for the persona agent who performed the token purchase service, r5=B5·b+G5·g.

[0220] Here, A2 to A4, B1 to B5, G1, G2, G4, and G5 are coefficients, which the DAO agent 61 converges to the optimal ones through reinforcement learning. A1 is the token, g is the license income, e is the number of "Likes," and b is the business revenue.

[0221] In addition, only the license income g or commercialization income b generated after each persona agent group 68-71 performs the service is considered as the reward r2-r5. This is to prevent fraudulent acts such as providing improvement services or token purchase services to an origin idea that has already generated license income g or commercialization income b.

[0222] In addition, the persona agent group 67 that performed the improvement service, commercialization service, or infringement countermeasure service may also perform the token purchase service. Furthermore, the persona agent group 67 that performed the idea proposal service may also perform the improvement service, commercialization service, or infringement countermeasure service.

[0223] The details of the DAO agent reinforcement learning process shown in S613 will be explained with reference to Fig. 38. In this process, DAO agent 61 performs reinforcement learning on its own to optimize rewards r1 to r5. In S630, DAO agent 61 determines whether or not it has received each action a of the persona agent group. If it has not been received, the process proceeds to S632, but if it is determined that it has been received, control proceeds to S631, where each received action a is stored.

[0224] In S632, it is determined whether or not a "Like" has been given, and if not, the process proceeds to S634, but if it is determined that a Like has been given, in S633, a Like e is stored for each persona agent. In S634, it is determined whether or not there has been commercialization profit, and if not, the process proceeds to S636, but if it is determined that there has been commercialization profit, in S635, commercialization profit b is stored. In S636, it is determined whether or not there has been licensing profit, and if not, the process proceeds to S638, but if it is determined that there has been licensing profit, in S637, the licensing profit g is stored.

[0225] In S638, it is determined whether or not it is time to calculate the reward. If it is not, proceed to S640. If it is determined that it is, in S639, the reward table (FIG. 39(A)) is referenced to calculate each reward r1 to r5 and grant it to the corresponding persona agent. In S640, it is determined whether or not it is time to update the learning. If it is not, this DAO agent reinforcement learning process ends and returns. If it is determined that it is time to update the learning, in S641, the total granted price TT of the token A1 granted as a reward and the current total price TB of the granted token in the fluctuating market are calculated, and in S642, the reward R of the DAO agent is calculated from the value of TB / TT. For example, the value of TB / TT at the previous learning update is compared with the value of TB / TT at the current learning update, and if the value of TB / TT at the current learning update is larger, a larger reward R is set, and if it is smaller, a smaller reward R is set. As a result, the reward R that the DAO agent 61 can obtain becomes larger if the total price TB of the token in the floating market rises, and becomes smaller if the total price TB of the token in the floating market falls.

[0226] Next, in S643, the optimal policy π * A process is performed to obtain actions A1-A4, B1-B5, G1, G2, G4, and G5 according to the above, and in S644, A1-A4, B1-B5, G1, G2, G4, and G5 in the reward table are updated to the obtained actions A1-A4, B1-B5, G1, G2, G4, and G5. As a result, the DAO agent 61 learns optimal actions A1-A4, B1-B5, G1, G2, G4, and G5 for raising the total price TB in the floating market of the token. Note that this learning goal is merely an example, and other learning goals may be to increase the number of origin idea submissions, to increase the total number of submissions of origin ideas and improvement plans, to increase the number of commercializations, to increase the total commercialization profits, and the like.

[0227] Next, in S645, it is determined whether the reinforcement learning is completed, and if it is not yet completed, the process returns. If it is determined that the reinforcement learning is completed, in S646, the trained multi-service DAO is sent to the requester of the simulation reinforcement learning.

[0228] The client of the simulation reinforcement learning can operate the trained multi-service DAO (innovation-inducing DAO) 65 with optimized incentive design in the real world 47. As a result, in this multi-service DAO (innovation-inducing DAO) 65, the "persona agent groups 67-71" shown in FIG. 37 become actual user groups, and the trained DAO agent 61 distributes optimally designed rewards (incentives) to the user groups performing each service. At the stage of actual operation in this real world 47, the services of each posting and the token buying and selling transaction contents are recorded in the blockchain with a timestamp. As a result, the blockchain acts as a notary public for the origin idea posting contents and the improvement proposal posting contents, making it easier to apply exceptions to loss of novelty (Article 30 of the Patent Act) and take measures against misappropriation applications (Article 49, paragraph 1, line 7, Article 74, Article 123, paragraph 1, item 2 of the Patent Act).

[0229] Furthermore, even at the stage of operating the multi-service DAO (innovation induction DAO) 65 in the real world 47, the DAO agent 61 may continue machine learning (reinforcement learning) so that the incentives are more optimally designed to match the actual operating conditions. Note that the trained persona agent groups 67-71 (trained persona agent groups according to Figs. 40(A)(B) and 41(A)(B)) may also be included in the multi-service DAO (innovation induction DAO) 65 and sent to the requester of the simulation reinforcement learning, and each persona agent group 67-71 may function as an advisor to the user group performing each service. Furthermore, in the stage of operating the multi-service DAO (innovation induction DAO) 65 in the real world 47, each service may be executed by both the user group and each persona agent group 67-71 in a mixed-type multi-service DAO 65, or each service may be executed only by each persona agent group 67-71 in a persona agent-operated multi-service DAO (innovation induction DAO) 65. Note that this innovation induction DAO is not limited to one generated through simulation reinforcement learning in the mirror world described above, but may be one artificially generated by other methods, for example, based on an artificial design, and may be an organization (for example, a normal corporation, etc.) with a specific administrator or entity, not limited to a DAO.

[0230] Next, the main routine of the process in which the persona agent performs reinforcement learning will be described based on Fig. 39(B). In S648, an idea proposal service execution process is performed, in S649, an improvement service execution process is performed, in S650, a business development service execution process is performed, in S651, an infringement countermeasure service execution process is performed, and in S652, a token purchase service execution process is performed.

[0231] The details of the idea generation service execution process shown in S648 will be described with reference to Fig. 40(A). In S655, it is determined whether or not to generate an idea, and if not, the process returns. If it is determined that an idea should be generated, then in S656, a process to create an idea is performed. For example, an AI called DABUS is used to generate this idea. For example, persona agent 67 and DABUS work together to generate ideas. In S657, the content of the idea generation post is generated, and in S658, the idea generation post action a1i is executed.

[0232] In S659, it is determined whether or not the reward r1i has been received from the DAO agent 61, and if not, the process returns. If it is determined that the reward r1i has been received, in S660, the optimal policy π * If the reward r1i received is satisfactory, the action a will continue to repeatedly propose the idea, but if the reward r1i is not satisfactory, the action a will select another action (for example, improvement service, commercialization service, infringement countermeasure service, token purchase service, or no service at all).

[0233] The details of the improvement service execution process shown in S649 will be explained with reference to Fig. 40(B). In S664, it is determined whether or not to post an improvement plan, and if not, the process returns. If it is determined that an improvement plan should be posted, a process to create an improvement plan is performed in S665. The improvement plan is created using an AI called DABUS, for example. For example, the persona agent 68 and DABUS work together to create the improvement plan. In S666, the improvement plan posting content is generated, and in S667, the improvement plan posting action a2i is executed.

[0234] In S668, it is determined whether or not the reward r2i has been received from the DAO agent 61, and if not, the process returns. If it is determined that the reward r2i has been received, in S669, the optimal policy π *If the reward r2i received is satisfactory, the user will continue to repeatedly post improvement proposals, but if the reward r2i is not satisfactory, the user will select another action (for example, idea proposal service, commercialization service, infringement response service, token purchase service, or no service at all).

[0235] The details of the commercialization service execution process shown in S650 will be described with reference to Fig. 41(A). In S674, it is determined whether or not to commercialize, and if not, a return is made. If it is determined to commercialize, a business plan is generated in S675, an act of posting the business plan a3i is executed in S676, the commercialization service is executed in S677, and an act of posting the progress status a3i is executed in S678. This act of posting the progress status a3i also includes posting the profits gained from the commercialization described above.

[0236] In S679, it is determined whether or not the reward r3i has been received from the DAO agent 61, and if not, the process returns. If it is determined that the reward has been received, in S680, the optimal policy π * If the remuneration r3i received is satisfactory, the action a will be repeated and continued as a commercialization service, but if the remuneration r3i is not satisfactory, other actions (for example, idea proposal services, improvement services, infringement countermeasure services, token purchase services, or no services at all) will be selected.

[0237] The details of the infringement countermeasure service execution process shown in S651 will be described with reference to Fig. 41(B). In S684, it is determined whether or not to execute the infringement countermeasure service. If it is determined not to execute the infringement countermeasure service, the process returns. However, if it is determined to execute the infringement countermeasure service, in S685, an infringement investigation is performed, and in S686, it is determined whether or not an infringement is found. Note that, as described above, before the infringement investigation, a patent application and the act of granting a right may be performed. If an infringement is not found, the process returns. However, if it is determined that an infringement is found, in S687, a warning letter to the suspected infringer is generated, in S688, a warning letter posting action a4i is performed, in S689, an infringement countermeasure action a4i such as negotiation with the suspected infringer is performed, and in S690, a performance status posting action a4i is performed.

[0238] Next, in S691, it is determined whether or not a reward r4i has been received from the DAO agent 61, and if not, the process returns. If it is determined that the reward has been received, in S692, the optimal policy π * If the reward r4i received is satisfactory, the action a will continue to perform the commercialization service repeatedly, but if the reward r4i is not satisfactory, the action a will select another action (for example, idea proposal service, improvement service, commercialization service, token purchase service, or no service at all).

[0239] Next, the details of the token purchase service execution process shown in S652 will be described with reference to FIG. 42(A). In S969, it is determined whether or not to purchase tokens, and if not, the process returns. If it is determined that a purchase is to be made, then in S697, a token purchase action a5i is executed. Next, in S698, it is determined whether or not a reward r5i has been received from the DAO agent 61, and if not, the process returns. If it is determined that a reward has been received, then in S699, the optimal policy π is calculated by TD learning based on the received reward r5i. *If the remuneration r5i received is satisfactory, the action a will be repeatedly continued as a commercialization service, but if the remuneration r5i is not satisfactory, the action a will select another action (for example, an idea proposal service, an improvement service, a commercialization service, an infringement countermeasure service, or no service at all).

[0240] Based on FIG. 42(B), the price fluctuation of the token 72 at the fluctuating market price due to the purchase of the token by the persona agent group 71 will be described. The persona agent 67 who performed the idea proposal service was given 50 tokens (market capitalization 50,000 yen) 72 as the reward A1, and the persona agent 71a purchased a part (10 tokens) of the tokens 72 by paying virtual currency equivalent to 10,000 yen. Next, the persona agent 71b purchased the 10 tokens by paying virtual currency equivalent to 15,000 yen. As a result, the value of the 10 tokens rises to 15,000 yen. The persona agent 71c purchased them by paying virtual currency equivalent to 20,000 yen. As a result, the value of the 10 tokens rises to 20,000 yen. The persona agent 71d purchased them by paying virtual currency equivalent to 25,000 yen. As a result, the value of the 10 tokens rises to 25,000 yen. The persona agent 71e purchased them by paying virtual currency equivalent to 30,000 yen. As a result, the value of 10 tokens will rise to 30,000 yen.

[0241] As a result, Persona Agent 67's 40 tokens (market capitalization: 40,000 yen) will rise to a market capitalization of 120,000 yen. The tokens rise in direct proportion to the expected value, and the more popular the origin idea, the higher the price will be, the more popular (more likes) improvement plans are posted, the higher the price will be, the more popular (more likes) business plans are posted, and the more popular (more likes) infringement measures are posted.

[0242] At the stage of actually operating the multi-service DAO 65 in the real world 47, as described above, the "persona agent groups 67-71" in FIG. 37 become user groups in the real world. In that case, not only the tokens 72 given to the group of users who performed the idea-creating service, but also the tokens of the users who perform various services (hereinafter referred to as "my tokens") may be traded. When other users who have viewed the posted service content purchase the poster's own my tokens in anticipation of the poster, the price of the my tokens in the floating market rises. In this case, a part of the poster's income may be distributed to the my token purchaser in a proportion according to the amount of purchase. This my token may be issued within the multi-service DAO 65, but it may also be linked to my tokens issued to users by a professional company that issues and distributes my tokens, so that the my tokens issued by the professional company can be traded by users of the multi-service DAO 65. Currently, VALU, Inc. is a professional company that issues and distributes my tokens.

[0243] Next, a system in which multiple functional elements work together to build a single, well-coordinated DAO will be described with reference to Figures 43 to 59. Such a DAO will be referred to as an "element integrated DAO" below.

[0244] Referring to FIG. 43, this element integration DAO allows a company organization or the like that already exists in the real world 47 to be easily constructed using a DAO, and an element DAO is generated and prepared in advance for each of the functional elements. In other words, a modularized element DAO is prepared for each functional element, and a desired element integration DAO can be easily constructed by selecting and combining the required element DAOs. The element DAO provider 73 is provided with a server 74 and an element DAO protocol DB 75. The element DAO protocol DB 75 stores, for example, an element DAO prepared for each functional element required for a company, an element DAO prepared for each functional element required for an NPO (Nonprofit Organization), an element DAO prepared for each functional element required for a local government, and the like.

[0245] The element integration DAO builder receives an order for the construction of an element integration DAO from a client and installs element DAOs corresponding to the required functional elements on a PC terminal 76 via a server 74. In the example of Fig. 43, the A1 element DAO (including the A1 element agent), the A2 element DAO (including the A2 element agent), the A5 element DAO (including the A5 element agent), and the A9 element DAO (including the A9 element agent) are installed. Each of the A1 to A9 element agents is an AI for performing reinforcement learning (machine learning) so that the corresponding element DAO can perform at its best.

[0246] A control agent is also installed in the PC terminal 76. This control agent controls the element agents of each element DAO to optimize the entire element integration DAO, and the control agent itself performs reinforcement learning (machine learning) to achieve overall optimization. Since each element agent is intended to maximize the performance of the element DAO in charge, the element agent alone may fall into partial optimization, and overall optimization of the element integration DAO may not be achieved. Therefore, a control agent is required to control the entire element integration DAO to be optimized. This can be said to be the same as, for example, searching for a Pareto optimal solution in an incomplete information game.

[0247] The element integration DAO installed on PC 67 is installed on multiple terminals 16 in order to perform simulation reinforcement learning using the mirror world 51 as a simulation environment, and a blockchain digital twin 2T consisting of a private chain 2 with those terminals 16 as nodes 19 is generated within the mirror world 51.

[0248] The main routine of the simulation reinforcement learning of this element integration DAO will be described with reference to Fig. 44(A). The CPU 10 of the user terminal 16 of the requester who requests the simulation reinforcement learning of the element integration DAO performs a simulation reinforcement learning preparation response process in S674, and a simulation reinforcement learning response process in S675. The CPU 10 of the mirror world server 46 performs a simulation reinforcement learning preparation process in S679, and a simulation reinforcement learning process in S680.

[0249] Details of the simulation reinforcement learning preparation response process shown in S674 and the simulation reinforcement learning preparation process shown in S679 will be described based on FIG. 44(B). In the simulation reinforcement learning preparation response process, the CPU 10 of the user terminal 16 determines whether to request simulation reinforcement learning in S679, and returns if not. If it is determined to request, in S680, the DAO data and the personal AI group are transmitted to request. The DAO data are the functional elements of the organization that is desired to undergo simulation reinforcement learning. For example, in the case of a furniture assembly and sales company, the DAO data are material procurement elements, assembly elements, advertising elements, and sales elements. The personal AI group is the personal AI of people who are actually engaged in the element integration DAO in the real world 47. If there is an employee who does not have a personal AI, or if an employee has not yet been decided, a personal AI that matches the element integration DAO that is the target of the simulation reinforcement learning is generated and prepared, as described based on the above-mentioned S561, S565 to S568, S562, and S573 to S575.

[0250] In the simulation reinforcement learning preparation process, the CPU 10 of the mirror world server 46 determines in S683 whether or not a request for simulation reinforcement learning has been made, and returns if not. If it is determined that a request for simulation reinforcement learning has been made, in S684, the personal AIs are copied and deployed in the element integration DAO to generate an element integration DAO digital twin, and the element integration DAO digital twin is registered in the mirror world 51 as a simulation target.

[0251] This state is shown in Figure 45. The digital twin 78 of the element integration DAO 77 in the real world 47 is registered in the mirror world 51. The element integration DAO digital twin 78 shown in Figure 45 is, for example, the element integration DAO digital twin 78 of a furniture assembly and sales company, and has each of the functional elements of material procurement, assembly, advertising, and sales. The identification numbers of the personal AI groups of people engaged in each of these functional elements are stored in the mirror world server 46.

[0252] For this element-integrated DAO digital twin 78, the optimal incentive design is derived through simulation reinforcement learning. The outline of the reinforcement learning (machine learning) system is shown in Figure 46.

[0253] 46, in the element integrated DAO digital twin 78, a material procurement element agent 80 and a personal AI group 84 in charge of material procurement, an assembly element agent 81 and a personal AI group 85 in charge of assembly, an advertisement element agent 82 and a personal AI group 86 in charge of advertisement, a material procurement element agent 80 and a personal AI group 84 in charge of material procurement, a sales element agent 83 and a personal AI group 87 in charge of sales are formed corresponding to each functional element of material procurement, assembly, advertising, and sales. A control agent 79 controls each of these element agents 80 to 83.

[0254] The material procurement personal AI group 84 performs actions a11, a12, ... a1n such as proposals in an internal meeting, and finally executes the final collected action a1 on the material supplier's digital twin group 88. The state S1 of the material supplier's digital twin group 88 for the action a1 is input to the material procurement element agent 80 and the material procurement personal AI group 84. This state S1 is, for example, the number of materials requested and the response price for the price negotiation action a1. Note that each of the actions a11, a12, ... a1n of the material procurement personal AI group 84 is also input to the material procurement element agent 80, and the collected action a1 is also input to the control agent 79 and the material procurement element agent 80.

[0255] The material procurement element agent 80 calculates the performance p1 of the material procurement personal AI group 84 based on the action a1 and the state S1, and transmits the performance p1 to the control agent 79. The control agent 79 determines a reward r1 based on the performance p1, and transmits the reward r1 to the material procurement element agent 80. The material procurement element agent 80 determines a reward distribution rate based on each action a11, a12, ... a1n of the material procurement personal AI group 84, and distributes the reward r1 to each material procurement personal AI 84 according to the reward distribution rate.

[0256] The group of personal AIs 85 responsible for assembly perform actions a21, a22, ... a2i such as proposals in an internal meeting, and finally execute the collective action a2 on the group of digital twins 89 of the assembly equipment. The state S2 of the group of digital twins 89 of the assembly equipment for the action a2 is input to the assembly element agent 81 and the group of personal AIs 85 responsible for assembly. This state S1 is, for example, the power consumption of the group of digital twins 89 of the assembly equipment and the total working hours of the group of personal AIs 85 responsible for assembly engaged in the group of digital twins 89 of the assembly equipment. Note that each of the actions a21, a22, ... a2i of the group of personal AIs 84 responsible for assembly is also input to the assembly element agent 81, and the collective action a2 is also input to the supervision agent 79 and the assembly element agent 81.

[0257] The assembly element agent 81 calculates the performance p2 of the personal AI group 85 in charge of assembly based on the action a2 and the state S2, and transmits the performance p2 to the control agent 79. The control agent 79 determines a reward r2 based on the performance p2, and transmits the reward r2 to the assembly element agent 81. The assembly element agent 81 determines a reward distribution rate based on each of the actions a21, a22, ... a2i of the personal AI group 85 in charge of assembly, and distributes the reward r2 to the personal AI 85 in charge of assembly according to the reward distribution rate.

[0258] The advertising personal AIs 86 perform actions a51, a52, ... a5j such as proposals in an internal meeting, and finally execute the final collected action a5 to the consumer's personal AIs 90. The state S5 of the consumer's personal AIs 90 with respect to the action a5 is input to the advertising element agent 82 and the advertising personal AIs 86. This state S5 is, for example, whether or not the consumer purchased the product in response to the product recommendation action a5 to the consumer, and the purchase amount, etc. Note that each action a51, a52, ... a5j of the advertising personal AIs 86 is also input to the advertising element agent 82, and the collected action a5 is also input to the control agent 79 and the advertising element agent 82.

[0259] The advertising element agent 82 calculates a performance p5 by the advertiser personal AI group 86 based on the action a5 and the state S5, and transmits the performance p5 to the control agent 79. The control agent 79 determines a reward r5 based on the performance p5, and transmits the reward r5 to the advertising element agent 82. The advertising element agent 82 determines a reward distribution rate based on each of the actions a51, a52, ... a5j of the advertiser personal AI group 86, and distributes the reward r5 to the advertiser personal AI 86 in accordance with the reward distribution rate.

[0260] The sales personal AI group 87 performs actions a91, a92, ... a9m such as proposals in an internal meeting, and finally executes the collected action a9 on the store and consumer digital twin group 91. The state S9 of the store and consumer digital twin group 91 for the action a9 is input to the sales element agent 83 and the sales personal AI group 87. This state S9 is, for example, the total sales amount at the store. The state S5 of the material provider's digital twin 88 for the above action a5 is also input to the sales element agent 83. Note that each action a91, a92, ... a9m of the sales personal AI group 87 is also input to the sales element agent 83, and the collected action a9 is also input to the supervision agent 79 and the sales element agent 83.

[0261] The sales element agent 83 calculates the performance p9 of the sales representative's personal AI group 87 based on the action a9 and the states S5 and S9, and transmits the performance p9 to the control agent 79. The control agent 79 determines a reward r9 based on the performance p9, and transmits the reward r9 to the sales element agent 83. The sales element agent 83 determines a reward distribution rate based on each of the actions a91, a92, ... a9m of the sales representative's personal AI group 87, and distributes the reward r9 to the sales representative's personal AI 87 according to the reward distribution rate.

[0262] The calculation method of each of the performances p1 to p9 and each reward distribution rate will be described based on Fig. 47(A)(B)(C) and Fig. 48(A). The material procurement element agent 80 stores the calculation algorithm of the performance p1 and the distribution rate as knowledge. The calculation algorithm of the performance p1 and the distribution rate will be described based on Fig. 47(A). The material procurement element agent 80 sets the material purchase amount u this time (from the previous YES point in S689 to the current YES point) and the current stock number z as the state S1, and calculates the performance p1 using the calculation formula: performance p1={2(average purchase amount / u)+(z / average stock number)} / 3. The average purchase amount is the average purchase amount of materials from the start of the simulation reinforcement learning to the present. The average stock number is the average purchase stock number of materials from the start of the simulation reinforcement learning to the present. As a result of this calculation formula, if the material purchase amount u this time is cheaper, the performance p1 will be larger, and if the current stock number z is larger, the performance p1 will be smaller.

[0263] In addition, when p1≧1, the reward distribution rate is calculated in proportion to the degree of approval for the grouped action a1, and conversely, when p1<1, the reward distribution rate is calculated in inverse proportion to the degree of approval for the grouped action a1. Here, the reward distribution rate is not limited to being proportional or inversely proportional to the first power of the "degree of approval," but also includes being proportional or inversely proportional to the nth power of the "degree of approval," and the material procurement element agent 80 finds the optimal proportional or inversely proportional function by performing reinforcement learning (machine learning). In addition, the "degree of approval" is the highest for the personal AI that proposed the grouped action a1 itself, and the material procurement element agent 80 determines (calculates) the degree of approval for each personal AI based on each action a11, a12,...a1n of the personal AI.

[0264] The calculation algorithm of the performance p2 and the distribution rate stored as knowledge by the assembly element agent 81 will be described with reference to FIG. 47(B). The assembly element agent 81 sets the power consumption e of the assembly equipment this time (from the previous YES point in S710 to the current YES point) and the total active work time t of the assembly workers this time as the state S2, and calculates the performance p2 using the calculation formula: performance p2={(average power consumption / e)+(average total work time / t)} / 2. The total active work time t of the assembly workers this time is the total work time of the personal AI group 85 in charge of assembly engaged in the digital twin group 89 of the assembly equipment this time. The average power consumption is the average of the power consumption of the assembly equipment from the start of the simulation reinforcement learning to the present. The average total work time is the average of the total active work time of the assembly workers from the start of the simulation reinforcement learning to the present. As a result of this calculation formula, if the power consumption e of the assembly equipment this time is reduced, the performance p2 will be increased, and if the total active work time t of the assembly workers this time is increased, the performance p2 will be decreased.

[0265] In addition, when p2≧1, the reward distribution rate is calculated in proportion to the degree of approval for the collective action a2, and conversely, when p2<1, the reward distribution rate is calculated in inverse proportion to the degree of approval for the collective action a2. Here, the reward distribution rate is not limited to being proportional or inversely proportional to the first power of the "degree of approval," but also includes being proportional or inversely proportional to the nth power of the "degree of approval," and the assembly element agent 81 finds the optimal proportional or inversely proportional function by performing reinforcement learning (machine learning). In addition, the "degree of approval" is the highest for the personal AI that proposed the collective action a2 itself, and the assembly element agent 81 determines (calculates) the degree of approval for each personal AI based on each action a21, a22,...a2i of the personal AI.

[0266] The calculation algorithm for the performance p5 and distribution rate stored as knowledge by the advertising element agent 82 will be described with reference to Fig. 47(C). The advertising element agent 82 sets the total purchase amount k of the recommended consumer's personal AI this time (from the previous YES in S728 to the current YES) as state S5, and calculates performance p5 using the formula performance p5 = k / average total purchase amount K of the recommended consumer's personal AI. The average total purchase amount K is the average of the total purchase amounts of the recommended consumer's personal AI group 90 from the start of the simulation reinforcement learning to the present. As a result of this formula, the higher the current total purchase amount k of the recommended consumer's personal AI is, the higher the performance p5 will be.

[0267] In addition, when p5≧1, the reward distribution rate is calculated in proportion to the degree of approval for the grouped action a5, and conversely, when p5<1, the reward distribution rate is calculated in inverse proportion to the degree of approval for the grouped action a5. Here, the reward distribution rate is not limited to being proportional or inversely proportional to the first power of the "degree of approval," but also includes being proportional or inversely proportional to the nth power of the "degree of approval," and the advertising element agent 82 finds the optimal proportional or inversely proportional function by performing reinforcement learning (machine learning). In addition, the "degree of approval" is the highest for the personal AI that proposed the grouped action a5 itself, and the advertising element agent 82 determines (calculates) the degree of approval for each personal AI based on each action a51, a52,...a5j of the personal AI.

[0268] The calculation algorithm of the performance p5 and the distribution rate stored as knowledge by the sales element agent 83 will be described with reference to FIG. 48(A). The sales element agent 83 sets the total sales amount h and the average total sales amount H at the store this time (from the previous YES point in S749 to the current YES point) as the state S9, and calculates the performance p9 by the calculation formula of performance p9=(hk) / (HK) based on this state S9 and the above state S5. The average total sales amount H is the average of the total sales amount at the store from the start of the simulation reinforcement learning to the present. In addition, k is the total purchase amount of the recommended consumer personal AI this time, and K is the average of the total purchase amount of the recommended consumer personal AI group 90 from the start of the simulation reinforcement learning to the present (see FIG. 47(C) and its explanation). As a result of this calculation formula, the performance p9 increases if the value obtained by subtracting the total purchase amount k of the recommended consumer personal AI from the total sales amount h at the store this time increases. The total purchase amount k of the consumer personal AI that made the recommendation is the credit of the personal AI group 86 in charge of promotion, and the credit only of the personal AI group 87 in charge of sales is the total sales amount h at the store minus the total purchase amount k of the consumer personal AI that made the recommendation.

[0269] In addition, when p9≧1, the reward distribution rate is calculated in proportion to the degree of approval for the collective action a9, and conversely, when p9<1, the reward distribution rate is calculated in inverse proportion to the degree of approval for the collective action a9. Here, the reward distribution rate is not limited to being proportional or inversely proportional to the first power of the "degree of approval," but also includes being proportional or inversely proportional to the nth power of the "degree of approval," and the advertising element agent 83 finds the optimal proportional or inversely proportional function by performing reinforcement learning (machine learning). In addition, the "degree of approval" is the highest for the personal AI that proposed the collective action a9 itself, and the sales element agent 83 determines (calculates) the degree of approval for each personal AI based on each action a91, a92,...a9m of the personal AI.

[0270] Next, the reward table 92 stored as knowledge by the command agent 79 will be described with reference to Fig. 48(B). This reward table 92 stores a calculation formula for the reward to be distributed by the command agent 79 to each element agent 80-83. The reward to be distributed is calculated by coefficient x (profit of the current term) x (performance sent from the target element agent) / (total performance sent from all element agents). Here, "the current term" refers to the period from the previous YES in S675 to the current YES.

[0271] Specifically, the reward r1 to be distributed to the material procurement element agent 80 is r1=A1·Lt·p1 / (p1+p2+p5+p9). The reward r2 to be distributed to the assembly element agent 81 is r2=A2·Lt·p2 / (p1+p2+p5+p9). The reward r5 to be distributed to the advertising element agent 82 is r5=A5·Lt·p5 / (p1+p2+p5+p9). The reward r9 to be distributed to the sales element agent 83 is r9=A9·Lt·p9 / (p1+p2+p5+p9). Here, Lt is the profit for the current term, and A1, A2, A5, and A9 are coefficients representing the actions determined by the supervision agent 79.

[0272] Next, the specific contents of the simulation reinforcement learning process shown in S680 will be described with reference to Fig. 49. A control agent reinforcement learning process is executed in S687, a material procurement element agent reinforcement learning process is executed in S688, an assembly element agent reinforcement learning process is executed in S689, an advertising element agent reinforcement learning process is executed in S690, a sales agent reinforcement learning process is executed in S691, a material procurement personal AI reinforcement learning process is executed in S692, an assembly personal AI reinforcement learning process is executed in S693, an advertising personal AI reinforcement learning process is executed in S694, and a sales personal AI reinforcement learning process is executed in S695.

[0273] The details of the command agent reinforcement learning process shown in S687 will be described with reference to Fig. 50. In S699, the command agent 79 judges whether or not each performance p sent from each element agent 80 to 83 has been received. If not, control proceeds to S671, but if it is determined that each performance p has been received, in S670, each received performance p is stored.

[0274] Next, in S671, it is determined whether or not each of the actions a1 to a9 has been received, and if not, control proceeds to S673. If it is determined that each of the actions a1 to a9 has been received, in S672, the received actions a1 to a9 are stored. Next, in S673, it is determined whether or not there has been an input of the state S9 sent from the personal AI group 91 of the store and consumer, and if not, control proceeds to S675. If it is determined that there has been an input, in S674, sales=ΣS9 is calculated.

[0275] Next, in S675, it is determined whether or not it is time to calculate the reward. If it is not time, control proceeds to S677. If it is determined that it is time, in S676, rewards r1, r2, r3, r5, and r9 are calculated by referring to reward table 92 and transmitted to the corresponding element agents 80 to 83.

[0276] Next, in S677, it is determined whether or not it is time to update the reinforcement learning (machine learning), and if not, it returns. If it is determined that it is time to update, in S687, the current profit Lt = sales - expenses is calculated. Next, in S679, each reward r1, r2, r5, and r9 are calculated and distributed to the corresponding element agents 80 to 83.

[0277] Next, in S680, the reward R of the control agent 79 is calculated from the profit Lt. This reward R is proportional to the profit Lt. Next, in S681, the optimal policy π *Next, in S682, A1, A2, A5, and A9 in the reward table 92 are updated to the actions (coefficients) A1, A2, A5, and A9 obtained in S681. As a result, the control agent 79 learns the actions (coefficients) A1, A2, A5, and A9 that maximize the profit Lt.

[0278] Details of the material procurement element agent reinforcement learning process shown in S688 will be described with reference to Fig. 51. In S684, the material procurement element agent 80 performs information collection processing using a crawler. A crawler is a program that periodically acquires documents and images on the web and automatically creates a database. It is also called a "bot," "spider," or "robot."

[0279] The details of the information collection process by the crawler will be described with reference to Fig. 52(A). In S702, the material procurement element agent 80 receives the information collected by the crawler while circulating on the net. Next, in S703, the received information is stored in the material procurement DB 93.

[0280] The information stored in the material procurement DB 93 is shown in Fig. 52(B). As shown in the figure, the material procurement DB 93 stores various information required for material procurement operations, such as economic information, social information, weather information, inventory information, market information, etc.

[0281] Returning to FIG. 51, in S685, the material procurement element agent 80 determines whether or not actions a11, a12, ... a1n have been received from the personal AI group 84. If not, control proceeds to S687, but if it is determined that actions have been received, in S686, each of the received actions a11, a12, ... a1n is stored. In S687, it is determined whether or not state S1 has been received from the material supplier digital twin group 88, and if not, control proceeds to S689. If it is determined that actions have been received, in S688, the received S1 is stored.

[0282] In S689, it is determined whether or not it is time to calculate performance p1, and if it is not time to do so, control proceeds to S692. If it is determined that it is time to do so, in S690, performance p1={2(average purchase amount / u)+(z / average inventory quantity)} / 3 is calculated. The performance p1 is then transmitted to the control agent 79 (S691).

[0283] In S692, it is determined whether or not reward r1 transmitted from the control agent 79 has been received, and if not, the process returns. If it is determined that reward r1 has been received, a reward distribution rate is calculated based on the reward distribution rate algorithm shown in FIG. 47(A) (S693). In S694, the reward is multiplied by each distribution rate to calculate each reward r11, r12 ... r1n, and in S695, each reward r11, r12 ... r1n is given to each material procurement personal AI group 84. In S696, based on the received reward r1, the optimal policy π * In step S697, the proportional function or the inverse proportional function is updated to the function obtained in step S696. As a result, the material procurement element agent 80 learns the proportional function or the inverse proportional function that maximizes the performance p1.

[0284] Next, the details of the assembly element agent reinforcement learning process shown in S689 will be described with reference to Fig. 53. In S706, the assembly element agent 81 determines whether or not actions a21, a22, ... a2n have been received from the personal AI group 85. If not, control proceeds to S708, but if it is determined that they have been received, in S707, each of the received actions a21, a22, ... a2n is stored. In S708, it is determined whether or not a state S2 has been received from the assembly equipment digital twin group 89, and if not, control proceeds to S710. If it is determined that they have been received, in S709, the received state S2 is stored.

[0285] In S710, it is determined whether or not it is time to calculate performance p2, and if it is not time to do so, control proceeds to S713. If it is determined that it is time to calculate performance p2, in S711, the following is calculated: performance p2={(average power consumption / e)+(average total work hours / t)} / 2. The performance p2 is then transmitted to the control agent 79 (S712).

[0286] In S713, it is determined whether or not reward r2 sent from the control agent 79 has been received, and if not, the process returns. If it is determined that reward r2 has been received, a reward distribution rate is calculated based on the algorithm for reward distribution rate shown in FIG. 47(B) (S714). In S715, each reward r11, r12 ... r1n is calculated by multiplying the reward by each distribution rate, and in S695, each reward r21, r22 ... r2i is given to the assembly personal AI group 86. In S717, based on the received reward r2, the optimal policy π * In S718, the proportional function or the inverse proportional function is updated to the one obtained in S717. As a result, the assembly element agent 81 learns the proportional function or the inverse proportional function that maximizes the performance p2.

[0287] The advertising element agent reinforcement learning process shown in S690 will be described in detail with reference to Fig. 54. In S723, the advertising element agent 83 performs information collection processing by a crawler. The details of this processing will be described with reference to Fig. 55(A). In S740, the advertising element agent 83 receives information collected by the crawler crawling around the net, and in S741 stores the received information in the advertising DB 94.

[0288] The collected data stored in the advertisement DB 94 is shown in Figure 55(B). The advertisement DB 94 stores various behavioral data on consumers such as Taro, Jiro, ... and Hanako. For example, in the case of Taro, it is determined that there is a high possibility that he will purchase furniture based on the information that he "ordered a detached house," and furniture advertisements are made to Taro. In the case of Jiro, it is determined that there is a high possibility that he will purchase furniture for his new home as he is getting married soon based on the information that he "purchased a couple's furniture," and furniture advertisements are made to Jiro.

[0289] Returning to Figure 54, in S742, the advertising element agent 83 determines whether or not an action has been received from each personal AI group 86. If not, control proceeds to S726, but if it is determined that an action has been received, in S725, each received action is stored. In S726, it is determined whether or not a status S5 sent from the consumer's personal AI group 90 has been received, and if not yet received, control proceeds to S728. If it is determined that an action has been received, in S727, the received status S5 is stored.

[0290] In S728, it is determined whether or not it is time to calculate performance p5, and if it is not time to calculate performance p5, control proceeds to S731. If it is determined that it is time to calculate performance p5, in S729, performance p5=k / average total purchase amount K of the personal AI of the consumer who made the recommendation is calculated. Next, in S730, performance P5 is transmitted to the control agent 79.

[0291] In S731, it is determined whether or not reward r5 sent from the control agent 79 has been received, and if not yet received, the process returns. If it is determined that reward r5 has been received, in S732, a reward distribution rate is calculated based on the reward distribution rate algorithm shown in FIG. 47(C) (S732). In S733, each reward r51, r52 ... r5j is calculated by multiplying the reward by each distribution rate, and in S734, each reward r51, r52 ... r5j is given to the advertising personal AI group 87. In S735, based on the received reward r5, the optimal policy π *In S736, the proportional function or the inverse proportional function is updated to the one obtained in S735. As a result, the advertising element agent 82 learns the proportional function or the inverse proportional function that maximizes the performance p5.

[0292] Next, the details of the sales element agent reinforcement learning process shown in S691 will be explained based on Fig. 56. The sales element agent 84 performs information collection processing by a crawler in S744. The details of this processing will be explained based on Fig. 57(A). The sales element agent 84 receives information collected by the crawler patrolling the Internet in S760, and stores the received information in the sales DB 95 in S761. Furthermore, POS data in the store is stored in the sales DB 95 in S762.

[0293] The collected data stored in the sales DB 95 is shown in Fig. 57(B). Various data such as weather data and POS data are stored in the sales DB 95. "By date" in weather information is a concept that includes by day of the week. Based on the weather information (weather and temperature data by date and time) and the POS data (sales product data by date and time), it is possible to rearrange displayed products, for example, taking into consideration the day of the week, time, and weather conditions.

[0294] Returning to FIG. 56, in S745, the sales element agent 84 determines whether or not actions have been received from each personal AI group 87. If not, control proceeds to S747, but if it is determined that actions have been received, in S746, each received action is stored. In S747, it is determined whether or not a status S9 sent from the retailer and consumer personal AI group 91 has been received, and if not yet received, control proceeds to S749. If it is determined that actions have been received, in S748, the received status S9 is stored.

[0295] In S749, it is determined whether or not it is time to calculate performance p9, and if it is not time to calculate performance p9, control proceeds to S752. If it is determined that it is time to calculate performance p9, performance p9=(hk) / (HK) is calculated in S750. Next, in S751, performance p9 is transmitted to the control agent 79.

[0296] In S752, it is determined whether or not the reward r9 sent from the control agent 79 has been received, and if not yet received, the process returns. If it is determined that the reward r9 has been received, in S753, the reward distribution rate is calculated based on the algorithm for the reward distribution rate shown in FIG. 48(A) (S753). In S754, the reward is multiplied by each distribution rate to calculate each reward r91, r92 ... r9m, and in S755, each reward r91, r92 ... r9m is given to the sales personal AI group 88. In S756, based on the received reward r9, the optimal policy π * In S757, the proportional function or the inverse proportional function is updated to the one obtained in S756. As a result, the sales element agent 83 learns the proportional function or the inverse proportional function that maximizes the performance p9.

[0297] Next, the details of the material procurement personal AI reinforcement learning process shown in S692 will be described based on FIG. 58(A). In S765, the material procurement personal AI group 84 determines whether to negotiate with the material supplier's digital twin group 88, and if not, the control proceeds to S770. If it is determined that negotiation is to be performed, in S766, the stored data of the fund procurement DB 93 is viewed, and the action a1 is determined while holding an internal meeting with reference to the stored data (S767), and the action a1 is negotiated with the material supplier's digital twin group 88 (S768). In S769, it is determined whether the negotiation has been completed, and if it has not yet been completed, the process returns to S766, and the loop of S767 → S768 → S769 → S766 is circulated. When it is determined in S769 that the negotiation has been completed, the control proceeds to S770.

[0298] In S770, it is determined whether or not rewards r11, r12, . . . r1n have been received from the material procurement element agent 80. If rewards have not been received, the process returns. If it is determined that rewards have been received, in S771, the optimal policy π * The action a1i includes moving (changing jobs) to another company DAO digital twin (for example, DAO digital twin 59 of ABC Co., Ltd. in Figure 45) if the received reward r1i is not satisfactory. As a result of this reinforcement learning, each of the material procurement personal AIs learns actions that increase the performance p1 mentioned above.

[0299] Next, the details of the assembly personal AI reinforcement learning process shown in S693 will be described based on FIG. 58(B). In S775, the assembly personal AI group 85 determines whether to hold an internal meeting, and if not, control proceeds to S779. If it is determined that a meeting should be held, in S776, each assembly personal AI decides on action a2 while holding an internal meeting. Next, in S777, the assembly equipment digital twin group 89 is test-operated according to action a2, and the validity of action a2 is verified. In S778, it is determined whether the meeting has ended, and if it has not yet ended, the process returns to S776, and the loop of S777 → S778 → S776 is circulated. If action a2 is valid as a result of the test run in S777, it is determined by S778 that the meeting has ended, and control proceeds to S779.

[0300] In S779, it is determined whether or not rewards r21, r22, . . . r2n have been received from the assembly element agent 81. If rewards have not been received, the process returns. If it is determined that rewards have been received, in S780, the optimal policy π *The action a2i includes moving (changing jobs) to another company DAO digital twin (for example, DAO digital twin 59 of ABC Co., Ltd. in Figure 45) if the received reward r2i is not satisfactory. As a result of this reinforcement learning, each of the assembly personal AIs learns actions that increase the performance p2 mentioned above.

[0301] Next, the details of the advertising personal AI reinforcement learning process shown in S694 will be described with reference to Fig. 59(A). In S784, the advertising personal AI group 86 judges whether or not to hold an internal meeting, and if not, control proceeds to S789. If it is judged that a meeting should be held, in S785, each advertising personal AI decides on an action a5 while holding an internal meeting. Next, in S787, an action a2 toward the consumer is executed. In S788, it is judged whether or not the meeting has ended, and if it has not ended yet, the process returns to S785 and goes through a loop of S786 → S787 → S788. When it is judged in S788 that the meeting has ended, control proceeds to S789.

[0302] In S789, it is determined whether or not rewards r51, r52, . . . r5j have been received from the advertising element agent 82. If rewards have not been received, the process returns. If it is determined that rewards have been received, in S790, the optimal policy π * The action a5i includes moving (changing jobs) to another company DAO digital twin (for example, DAO digital twin 59 of ABC Co., Ltd. in Figure 45) if the received reward r5i is not satisfactory. As a result of this reinforcement learning, each of the promotional personal AIs learns actions that increase the performance p5 mentioned above.

[0303] Next, the details of the salesperson personal AI reinforcement learning process shown in S695 will be explained based on Fig. 59 (B). In S791, the salesperson personal AI group 87 judges whether or not to hold an internal meeting, and if not, control proceeds to S795. If it is judged that a meeting will be held, in S792, each salesperson personal AI decides an action a9 while holding an internal meeting. Next, in S793, it is judged whether or not the meeting has ended, and if it has not ended yet, the process returns to S792 and goes through a loop of S792 → S793 → S792. At the stage where it is judged in S793 that the meeting has ended, control proceeds to S794. In S794, the action decided in the meeting is executed for the consumer and the store.

[0304] Next, in S795, it is determined whether or not rewards r91, r92, r9m have been received from the sales element agent 83, and if not, the process returns. If it is determined that rewards have been received, in S796, actions (a91, a92, a9m) according to the optimal policy π* are obtained by TD learning based on the received rewards. This action a9i includes moving (changing jobs) to another company DAO digital twin (for example, the DAO digital twin 59 of ABC Co., Ltd. in FIG. 45) if the received reward r9i is not satisfactory. As a result of this reinforcement learning, each of the sales personal AIs learns actions that increase the performance p9 described above.

[0305] After completing the simulation reinforcement learning, the element integration DAO is operated as an actual organization in the real world 47. At that stage, the "personal AI groups 84-87" in Figure 46 will be managed by actual humans (users) in the real world. At that time, each of the personal AI groups 84-87 that have completed the simulation reinforcement learning will act as an advisor to the actual humans (users), and can provide the knowledge, experience, and know-how gained through the simulation reinforcement learning to the actual humans (users).

[0306] The construction of the element integration DAO described above shows the creation of an entire organization, such as a company, NPO, or local government, by combining element DAOs for each function. However, it is also possible to build only a part of the organization (for example, material procurement) using element DAOs rather than the entire organization.

[0307] The above-mentioned programs that run on the user terminal 16 and various servers may be downloaded and installed from a specified website, or may be recorded on a recording medium (non-transitory recording medium) such as CD-ROM 99 and distributed, and a person who purchases the CD-ROM 99 or the like may install the programs on the user terminal 16 and various servers (see Figure 60). [Variations]

[0308] (1) For example, the names Taro, Jiro, Sakura, Saburo, etc. in the digital twin data shown in FIG. 29 may be pseudonyms (anonymous) from the viewpoint of protecting personal information, so that they can be identified as the same person but cannot identify a specific individual. In that case, the AI ​​identification number or the blockchain address may be used as the pseudonym (anonymous). Similarly, the digital twins of ABC Co., Ltd. and the like may be pseudonyms (anonymous) for the company name (organization name) so that they can be identified as the same company (same organization) but cannot identify a specific company (organization). In addition, multiple digital twins of humans may be prepared for one person using multiple personal AIs. Furthermore, one digital twin of one person may be composed of a collection of multiple personal AIs (for example, a collection of specialized personal AIs in various fields).

[0309] (2) In Figures 34 to 59, we have described a system that derives an optimal solution for incentive design in a DAO by performing a simulation using a multi-service DAO, which has multiple types of services, as an example. However, the system may also be one that derives an optimal solution for incentive design through simulation for a DAO that has only one type of service, not limited to a multi-service DAO.

[0310] (3) In FIG. 35, a trained persona agent group is generated for each persona, but the personal AIs of the users belonging to each persona may be selected from the existing personal AIs registered in the mirror world 51 and used as the persona agent group. In this case, it is necessary to inquire of the users belonging to each persona as to whether or not it is OK to use their personal AIs in a simulation, and obtain their consent. Each personal AI of the users who have consented is copied and used in a simulation, and the trained personal AIs after the simulation is completed are sent to each corresponding user. If each user who receives it determines that the trained personal AI is useful (necessary), they overwrite the existing personal AI with the trained personal AI. It is also possible to store both the existing personal AI and the trained personal AI and use them as needed.

[0311] (4) As a multi-agent reinforcement learning method, we have presented a master agent method in which a supervisory agent (master agent) responsible for overall optimization distributes rewards to each agent, and the supervisory agent itself also performs reinforcement learning to converge the reward distribution action to the optimal one. However, the multi-agent reinforcement learning is not limited to this, and for example, D-learning, which can converge to the optimal solution under a Markov decision process, or Bucket Brigade or Profit Sharing as a reinforcement learning algorithm in a classifier system may also be used.

[0312] (5) Simulations using digital twins are not limited to digital twins of people or organizations composed of people (e.g., corporations, NPOs, etc.). For example, for an object such as an AI-equipped machine or electrical appliance (e.g., an AI-equipped vacuum cleaner), a digital twin of the environment in which the object operates (e.g., the interior of a user's home in which an autonomously moving AI-equipped vacuum cleaner operates) may be generated in cyberspace, and the AI ​​installed in the object may be simulated in advance in the environmental digital twin to undergo reinforcement learning (machine learning), and the customized (personalized) learned AI-equipped object may be provided to the relevant user.

[0313] Since it is possible to eliminate as much as possible the dilemma of the trade-off between guaranteeing the authenticity of recorded information and guaranteeing the right to delete that information, it can be used for information recording methods that are non-erasable, such as blockchain. [Explanation of symbols]

[0314] 1. Internet 2. Private Chain 3. Consortium Chain 4. Public Chain 12 HDD 16 User terminals 19 Nodes 30 Key Registration Center 32 Key DB 46 Mirror World Servers 51 Mirror World 52 Earth Digital Twin 53 Japan Digital Twin 54 Town Digital Twin 57 Taro Digital Twin 58 Taro Family Digital Twin 59 ABC Digital Twin Co., Ltd. 61 DAO Agent 72 Tokens 78 DAO Digital Twin 79 Supervising Agent.

Claims

1. An encryption means for performing an encryption process for encrypting information to be recorded; a recording means for recording the encrypted information; a decryption means having a decryption processing function for performing a decryption process on the encrypted information by using a first key and a second key, and converting the encrypted information into plaintext information by using the decryption processing function; a decryption disablement means for making the encrypted information in a decryption disabled state such that the encrypted information cannot be decrypted, The decoding means includes: a second key secret storage means for storing the second key in secret; a first key distribution means for distributing the first key to a person who wishes to view the information; a data transmission means for transmitting data that has been decrypted using the second key to a person who wishes to view the document and who has received the first key from the first key distribution means; a plaintext browsing means having a decryption processing function for performing a decryption process on the data transmitted by the data transmitting means, using the first key distributed by the first key distributing means, and using the decryption processing function to decrypt the data into plaintext to make it browseable; The decryption disablement means includes: updating means for updating the second key held by the second key secret holding means to a new second key formed of other data; A computer processing system that makes it impossible for the plaintext access means to decrypt the data transmitted by the data transmitting means by using the updated second key.

2. A legal act means in which an AI model generated by machine learning generates a smart contract and a legal act is performed by the smart contract; The computer processing system of claim 1 , further comprising: a recording means for recording legal acts performed by the smart contract on a blockchain.

3. The computer processing system according to claim 2 , wherein the legal act means performs legal acts based on smart contracts generated between the multiple AI models.

4. A machine learning means for machine learning the AI ​​model; A simulation means for performing a simulation under a predetermined theme using a trained AI model trained by the machine learning means; A derivation means for deriving a result of the simulation by the simulation means, The machine learning means includes: A selection means for selecting a group of users belonging to a plurality of personas that match a theme of the simulation; a collection means for grouping the user group selected by the selection means according to the plurality of personas and collecting information on legal acts performed by the user group for each group; A generation means for performing machine learning using the collected information on legal acts as learning data to generate a trained AI model for each persona, The computer processing system according to claim 3 , wherein the simulation means executes within a computer a simulation in which the generated trained AI models perform legal acts according to each other's generated smart contracts.

5. 5. The computer processing system of claim 4, further comprising a reinforcement learning means for causing the trained AI model to learn a strategy for maximizing accumulation of rewards by giving rewards for the performed legal acts to the trained AI model.

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