Information processing method

Abstract

The information processing method includes: a receiving process of receiving a utilization request of one learning completion model registered in a distributed ledger; a issuing process of issuing tokens to a wallet of a provider of the one learned model when the utilization request is accepted; and an assigning process of assigning the non-transferable tokens to the wallet based on the cumulative amount of tokens issued to the wallet.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing methods using distributed ledger technology. Background Art

[0002] As such a method, for example, a method of managing product development data using a distributed ledger technology has been proposed (see Japanese Patent Application Laid-Open No. 2023-146501).

[0003] For example, by registering data in a distributed ledger, it is possible to prevent the registered data from being tampered with, and the registered data can be shared by multiple people. For example, when a learned model as a result of machine learning is registered in a distributed ledger, a person different from the person who provided a learned model can also use the learned model. On the other hand, the person who provided a learned model is often not given an incentive. Summary of the invention

[0004] The present invention has been made in view of the above-mentioned circumstances, and an object of the present invention is to provide an information processing method capable of providing incentives to providers of learning completed models.

[0005] An information processing method of one scheme of the present invention includes: an acceptance process, which accepts a request for use of a learned model registered in a distributed ledger; an issuance process, which, if the utilization request is accepted, issues a token to a wallet of a provider of the learned model; and an assignment process, which assigns a non-transferable token to the wallet based on the accumulated amount of tokens issued to the wallet. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Hereinafter, features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will be described with reference to the accompanying drawings, wherein like reference numerals denote like elements, and wherein:

[0007] Figure 1 This is a conceptual diagram showing the concept of the information processing system according to the embodiment.

[0008] Figure 2 It is a diagram showing an example of the configuration of an information processing system according to an embodiment.

[0009] Figure 3 This is a flowchart showing an example of the operation of the information processing system according to the embodiment.

[0010] Figure 4 This is a flowchart showing another example of the operation of the information processing system according to the embodiment. DETAILED DESCRIPTION

[0011] Reference Figures 1 to 4An information processing method according to an embodiment of the present invention will be described. An information processing system to which the information processing method according to the embodiment of the present invention is applied will be described.

[0012] Concept of information processing system 1

[0013] First, refer to Figure 1 The concept of the information processing system 1 is explained. Figure 1 In the embodiment, the information processing system 1 includes a distributed network 10 and a data management system 20. The distributed network 10 is a network for implementing a distributed ledger. It should be noted that in this embodiment, a blockchain is cited as an example of a distributed ledger.

[0014] The data management system 20 includes a database (DB) 220. For example, a learned model generated by machine learning may be registered in the database 220. For example, the learned model may be a learned model that can be applied to an autonomous driving system of a vehicle. For example, the learned model may be a learned model that can be applied to a navigation device. It should be noted that the learned model is not limited to a learned model that can be applied to at least one of an autonomous driving system and a navigation device.

[0015] It should be noted that, in addition to or instead of the learned model, at least one of the learning data used in learning the learned model and the source code used in learning the learned model may be registered in the database 220. It should be noted that, in addition to or instead of the database 220 in which the learned model is registered, the data management system 20 may also include at least one of the database in which the learning data is registered and the database in which the source code is registered.

[0016] The learned model registered in the database 220 can be provided by the provider P. The provider P can access the information processing system 1 via the terminal device 30. For example, the provider P can generate a transaction requesting the registration of a learned model via the terminal device 30. For example, the generated transaction can include identification information for identifying the provider P and model information representing a learned model. It should be noted that as an example of identification information for identifying the provider P, the user account of the provider P is cited. As an example of model information, the file name of a learned model is cited.

[0017] As a result of the generated transaction being processed through the distributed network 10 (for example, as a result of the transaction being registered in the distributed ledger), information for registering a learned model in the database 220 can be provided to the provider P. Based on this information, the provider P can register a learned model in the database 220 via the terminal device 30. In this case, for example, a learned model can be registered in the database 220 in association with the identification information of the provider P. It should be noted that as an example of information for registering a learned model in the database 220, link information to the database 220 is cited.

[0018] When a learned model is registered in the database 220, a person different from the provider P (for example, user U) can use a learned model. User U can access the information processing system 1 via the terminal device 40. For example, when user U wants to use a learned model, user U can generate a transaction 41 requesting the use of a learned model via the terminal device 40. For example, transaction 41 can include identification information for identifying user U and model information representing a learned model. It should be noted that as an example of identification information for identifying user U, a user account of user U is cited.

[0019] The result of the transaction 41 being processed through the distributed network 10 (for example, the result of the transaction 41 being registered in the distributed ledger) is that information for obtaining a learned model from the database 220 can be provided to the user U. Based on this information, the user U can obtain a learned model from the database 220 via the terminal device 40 (see Figure 1 21 ). It should be noted that, as an example of information for acquiring a learned model from the database 220 , link information to the database 220 is cited.

[0020] The distributed network 10 that processes the transaction 41 can issue a token 11 to the provider P that provides a learned model. The token 11 can be assigned to the wallet 31 of the provider P. It should be noted that the wallet 31 can be managed by a distributed ledger implemented by the distributed network 10 or by a distributed ledger implemented by a distributed network different from the distributed network 10.

[0021] The distributed network 10 may also grant a non-transferable token 12 to the wallet 31 of the provider P based on the cumulative amount of the tokens 11 issued to the provider P. For example, when the cumulative amount of the tokens 11 issued to the provider P is "1" or more, the distributed network 10 may grant a token representing the first level to the wallet 31 as a non-transferable token 12. When the cumulative amount of the tokens 11 issued to the provider P is "10" or more, the distributed network 10 may grant a token representing the second level, which is a level higher than the first level, to the wallet 31 as a non-transferable token 12. When the cumulative amount of the tokens 11 issued to the provider P is "100" or more, the distributed network 10 may grant a token representing the third level, which is a level higher than the second level, to the wallet 31 as a non-transferable token 12. It should be noted that as a special example of the non-transferable token 12, a soulbound token (SBT) is cited.

[0022] A specific example of the information processing system 1

[0023] Next, refer to Figure 2 A specific example of the configuration of the information processing system 1 is described below. Figure 2 In the embodiment, the information processing system 1 includes a distributed network 10 and a data management system 20. The data management system 20 includes a management server 210 and a database 220. The terminal device 30 and the terminal device 40 can be connected to the management server 210 via a wide area network such as the Internet.

[0024] The management server 210 may provide an application for browsing a distributed ledger implemented through the distributed network 10 and an application for accessing the database 220 to the provider P and the user U. Therefore, the management server 210 may also be referred to as an application server.

[0025] For example, when the provider P wants to register a learned model in the database 220, the provider P can use the application provided by the management server 210 to send a registration request information for requesting the registration of a learned model to the management server 210. The management server 210 that receives the registration request information can generate a transaction requesting the registration of a learned model. After the transaction is processed through the distributed network 10, the management server 210 can send display information to the terminal device 30, wherein the display information is used to display information for registering a learned model in the database 220 on the screen of the application. The terminal device 30 that receives the display information can display the information for registering a learned model in the database 220. After that, the provider P can use the application provided by the management server 210 to register a learned model in the database 220.

[0026] For example, when the user U wants to use a learned model, the user U can use the application provided by the management server 210 to send a use request information for requesting the use of a learned model to the management server 210. The management server 210 that receives the use request information can generate a transaction (e.g., equivalent to Figure 1 Transaction 41 shown). After the transaction is processed through the distributed network 10, the management server 210 may send display information to the terminal device 40, wherein the display information is used to display information for obtaining a learned model from the database 220 on the screen of the application. The terminal device 40 that receives the display information may display information for obtaining a learned model from the database 220. Afterwards, the user U may use the application provided by the management server 210 to obtain a learned model from the database 220.

[0027] The distributed network 10 that processes the transaction requesting the use of a learned model may issue a token 11 to the provider P. In this case, the management server 210 may grant the token 11 to the wallet 31 of the provider P. The distributed network 10 may also issue a non-transferable token 12 to the provider P based on the cumulative amount of the tokens 11 issued to the provider P. In this case, the management server 210 may grant the non-transferable token 12 to the wallet 31 of the provider P.

[0028] Next, refer to Figure 3 and Figure 4 The operation of the information processing system 1 is described using a flowchart. Figure 3In the example, the information processing system 1 (e.g., the management server 210) receives a request for use of a learned model from a user U (S101). The information processing system 1 (e.g., the management server 210) provides a learned model (i.e., data) to the user U based on the received use request (S102). In parallel with the processing of S102, the information processing system 1 (e.g., the distributed network 10) issues a token 11 to a provider P who provided a learned model (S103).

[0029] exist Figure 4 In the process, the information processing system 1 (e.g., the distributed network 10) determines whether the accumulated amount of tokens 11 issued to the provider P is above the threshold (S201). If it is determined that the accumulated amount of tokens 11 is above the threshold (S201: Yes), the information processing system 1 (e.g., the distributed network 10) issues a non-transferable token 12 to the provider P (S202). On the other hand, if it is determined that the accumulated amount of tokens 11 is not above the threshold (S201: No), the process ends. Figure 4 The action shown. It should be noted that Figure 4 The actions shown can be performed when a new token 11 is issued to a provider P.

[0030] Technical Effects

[0031] In the information processing system 1, when a learned model provided by the provider P is used by the user U, the token 11 is issued to the provider P. Therefore, the more people use a learned model, the more the accumulated amount of tokens 11 issued to the provider P increases. For example, it can be said that the more the accumulated amount of tokens 11 issued to the provider P increases, the more helpful the learned model provided by the provider P is to other people (for example, the user U). Therefore, it can be said that the accumulated amount of tokens 11 issued to the provider P represents at least one of the evaluation of the provider P and the merit of the provider P.

[0032] In the information processing system 1, non-transferable tokens 12 are also issued to the provider P based on the accumulated amount of tokens 11 issued to the provider P. In the case where the accumulated amount of tokens 11 is relatively large, the information processing system 1 can issue non-transferable tokens 12 of a higher level to the provider P than in the case where the accumulated amount of tokens 11 is relatively small. Therefore, it can be said that the non-transferable tokens 12 issued to the provider P represent at least one of the evaluation of the provider P and the merit of the provider P. The non-transferable tokens 12 are non-transferable, and therefore, the reliability of at least one of the evaluation and merit of the provider P represented by the non-transferable tokens 12 can be guaranteed.

[0033] It should be noted that the non-transferable token 12 may also be made public. In this case, the user U can refer to the non-transferable token 12 of the provider P to determine whether to use a learned model provided by the provider P. It should be noted that the token 11 may also be a transferable token. For example, the token 11 may also be a token that can be exchanged for at least one of the goods and services.

[0034] Thus, in the information processing system 1, when a learned model is used by the user U, a token 11 is issued to the provider P who has provided a learned model. At the same time, a non-transferable token 12 is issued based on the accumulated amount of the token 11. Therefore, the issuance of the token 11 and the non-transferable token 12 can serve as a motivation for the provider P to actively work on providing the learned model. Therefore, according to the information processing system 1, that is, according to the information processing method applied to the information processing system 1, it is possible to provide incentives to the providers of the learned models.

[0035] Modifications

[0036] Whenever a learned model is used once, the information processing system 1 (e.g., the distributed network 10) may issue a token 11. That is, for one use of a learned model, the information processing system 1 may issue a determined amount of tokens 11. The information processing system 1 (e.g., the distributed network 10) may also issue additional tokens 11 to a provider P that provides a learned model based on a request from a user U. For example, a user U that has used a learned model may evaluate a learned model. Then, the user U may request the information processing system 1 to issue additional tokens 11 to the provider P based on the evaluation of the learned model. In this case, a payment of money corresponding to the amount of tokens 11 additionally issued to the provider P may be generated for the user U. That is, the additional tokens 11 issued to the provider P may also be charged. It should be noted that the information processing system 1 may constitute at least a part of an open data marketplace.

[0037] Various aspects of the invention derived from the above-described embodiment and modified examples will be described below.

[0038] An information processing method of one scheme of the invention includes: an acceptance process, which accepts a request for use of a learned model registered in a distributed ledger; an issuance process, which, if the utilization request is accepted, issues a token to a wallet of a provider of the learned model; and an assignment process, which assigns a non-transferable token to the wallet based on the accumulated amount of tokens issued to the wallet.

[0039] In the information processing method, the non-transferable token may represent at least one of an evaluation and a merit of the provider.

[0040] An information processing system of one embodiment of the invention comprises a distributed network for realizing a distributed ledger and a management device for managing a learned model registered in the distributed ledger. The distributed network accepts a request for use of the learned model, and when the request for use is accepted, the distributed network issues a token to a wallet of a provider of the learned model. The distributed network grants a non-transferable token to the wallet based on the accumulated amount of tokens issued to the wallet. The "data management system 20" in the above-mentioned embodiment is equivalent to an example of a "management device".

[0041] In the information processing system, the non-transferable token may represent at least one of an evaluation and a merit of the provider.

[0042] The present invention is not limited to the above-described embodiments, and can be appropriately modified within the scope not departing from the gist or concept of the invention as read from the claims and the entire specification. Information processing methods with such modifications are also included in the technical scope of the present invention.

Claims

1. An information processing method, characterized in that: include: Acceptance process, accepting a request to use a learned model registered in the distributed ledger; Issuing process: when the utilization request is accepted, issuing tokens to the wallet of the provider of the one learned model; as well as A granting process grants non-transferable tokens to the wallet based on a cumulative amount of tokens issued to the wallet.

2. The information processing method according to claim 1, characterized in that: The non-transferable token represents at least one of a rating and a credit of the provider.

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