Commodity providing method, commodity providing system, and computer program

Abstract

To provide a commodity providing method which has high entertainment property and can secure benefits of a system side.SOLUTION: A commodity providing method for providing a commodity associated with a non-fungible token recorded in a block chain 3 is mounted on a computer (server 1). The commodity providing method includes: determining an extraction condition for making a processor 11 of a server determine an extraction condition for extracting a commodity provided from a commodity group composed of a plurality of commodities which are respectively associated with the non-fungible tokens, from a plurality of recorded extraction conditions, according to probability of occurrences which are respectively associated with the extraction conditions; and randomly extracting a commodity satisfying the determined extraction condition from a commodity group (commodity providing processing (S5)).SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a product provision method, a product provision system, and a computer program. [Background technology]

[0002] As a method for providing a product, there is a method for providing a product extracted according to a predetermined extraction condition. For example, Japanese Patent Application Laid-Open No. 2018-51219 (hereinafter, Patent Document 1) discloses a server device having a configuration for executing a gacha to execute a setting including addition or change of a lottery object that is set when executing a gacha and is subject to a lottery process such as an item, according to a charging status of a charging history in a charging event, and executing a gacha to execute a lottery object for an item that a player will acquire from the set lottery objects. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-239707 A Summary of the Invention

[0004] In a method for providing a product extracted according to predetermined extraction conditions, the recipient of the product is given a sense of expectation for the product to be provided and a sense of surprise at the provided product. The greater the expectation for the product to be provided and the greater the sense of surprise at the provided product, the greater the entertainment value. At the same time, there is also a need to ensure the profits of the system, such as the product provider. Therefore, there is a demand for a product provision method, product provision system, and computer program that are both entertaining and capable of ensuring the profits of the system.

[0005] According to one embodiment, a product provision method is a computer-implemented product provision method for providing a product associated with a non-fungible token recorded on a blockchain, the product provision method comprising: a computer processor determining extraction conditions for extracting a product to be provided from a product group consisting of a plurality of products each associated with a non-fungible token from among a plurality of stored extraction conditions according to an occurrence probability associated with each of the extraction conditions, and randomly extracting a product that satisfies the determined extraction condition from the product group.

[0006] Further details will be described in the following embodiments. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic configuration diagram of a product provision system according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of a plurality of extraction conditions stored in the product provision system. [Diagram 3] FIG. 3 is a diagram illustrating an example of a flow of the product providing method according to the first embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of a process flow in the server according to the product providing method according to the first embodiment. [Diagram 5] FIG. 5 is a schematic configuration diagram of the server 1 according to the second embodiment. [Figure 6] FIG. 6 is a diagram showing an example of the flow of a product providing method according to the second embodiment. [Figure 7] FIG. 7 is a flowchart showing an example of a process flow in the server 1 according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] <1. Overview of the product provision method, computing system, and computer program> (1) A product provision method according to an embodiment is a computer-implemented product provision method for providing a product associated with a non-fungible token recorded in a blockchain. The product provision method includes a computer processor determining extraction conditions for extracting products to be provided from a product group consisting of a plurality of products each associated with a non-fungible token from among a plurality of stored extraction conditions in accordance with the occurrence probability associated with each of the extraction conditions, and randomly extracting products that satisfy the determined extraction conditions from the product group.

[0009] In the product provision method according to the present embodiment, products that satisfy the extraction conditions determined by the associated occurrence probabilities are randomly extracted. By providing the products extracted by this product provision method to the user, the user is given great expectations and surprises about the products to be provided, and the profits of the system side, such as the product provider, can be secured by setting the conditions.

[0010] (2) In the product provision method described in (1), preferably, the extraction conditions include a total price of the products to be extracted, and the stored multiple extraction conditions include an extraction condition that the total price exceeds a specified purchase amount. This can further enhance the entertainment value for the user.

[0011] (3) The product provision method according to (1) above preferably further comprises a processor generating extraction conditions and corresponding occurrence probabilities. This makes it easier to control product extraction conditions on the system side. As a result, products can be provided with increased entertainment value for users, and the system side can secure profits by setting conditions.

[0012] (4) In the product provision method described in (3), preferably, the extraction conditions include a total price of the products to be extracted. Generating the extraction conditions and the associated occurrence probabilities includes generating extraction conditions and associated occurrence probabilities such that the relationship between the provision amount, which is the product of multiplying the total amount of each of the extraction conditions by the occurrence probabilities corresponding to the total amount, and the specified purchase amount is a specified relationship. This makes it easier for the system to control the total price of the products to be extracted based on the relationship to the specified purchase amount.

[0013] (5) In the method for providing products according to (3) or (4), preferably, generating the extraction conditions and the associated occurrence probabilities includes generating extraction conditions that result in a total price within a set range, which makes it easier for the system to control the total price of the products to be extracted.

[0014] (6) In the product provision method according to any one of (1) to (5), preferably, the extracting of products further includes extracting a predetermined number of products. This allows the number of products to be set, and ensures fairness and user satisfaction while ensuring the profits of the system.

[0015] (7) In the product provision method according to any one of (1) to (6), preferably, the extracting of products further includes extracting a predefined number of products that satisfy predefined requirements. This makes it possible to extract products using commercial characteristics, such as high-priced products, and to increase the satisfaction and entertainment value of users while ensuring profits for the system.

[0016] (8) The product provision system according to the embodiment is a product provision system that executes a process for providing a product associated with a non-fungible token recorded in a blockchain. The product provision system is configured to determine extraction conditions for extracting a product to be provided from a product group consisting of a plurality of products, each of which is associated with a non-fungible token, from among a plurality of stored extraction conditions according to the occurrence probability associated with each of the conditions, and to randomly extract a product that satisfies the determined extraction condition from the product group. By providing a product extracted by the product provision system according to the embodiment to a user, the user is given great expectations and surprise for the product to be provided, and the profits of the system side, such as the product provider, can be secured by setting conditions.

[0017] (9) A computer program according to an embodiment is a computer program that causes a computer to execute a process for providing a product associated with a non-fungible token recorded in a blockchain. The computer program causes a computer to determine extraction conditions for extracting a product to be provided from a product group consisting of a plurality of products each associated with a non-fungible token, from among a plurality of stored extraction conditions, according to the occurrence probability associated with each of the extraction conditions, and randomly extract a product that satisfies the determined extraction condition from the product group. The computer program according to the present embodiment allows a computer to randomly extract a product that satisfies the extraction condition determined by the associated occurrence probability. By providing a product extracted in this way to a user, it is possible to give the user great expectations and surprise for the product to be provided, and to ensure the profits of the system side, such as the product provider, by setting conditions.

[0018] <2. Examples of product provision methods, computing systems, and computer programs> [System Configuration] 1 is a schematic configuration diagram of a product provision system 100 according to an embodiment. The product provision system 100 performs processing for providing at least one NFT (Non-Fungible Token) content among NFT contents 91A, 91B, ... 91N to a user 6 as a product.

[0019] NFT contents 91A, 91B, ... 91N refer to content that has been converted into an NFT. Converting into an NFT refers to associating content with an NFT. Specifically, NFT contents 91A, 91B, ... 91N refer to content that is associated with NFTs 31A, 31B, ... 31N, respectively. NFT contents 91A, 91B, ... 91N are also referred to as NFT content 91. NFTs 31A, 31B, ... 31N are also referred to as NFT 31.

[0020] NFT content 91 is digital data that can be provided to user 6 as a product, and as an example, is data output from terminal device 5 used by user 6. The output may be, for example, a screen display, audio output, or a combination of these. NFT content 91 may be, for example, image data, text data, audio data, or a combination of two or more of these data. In the examples that follow, NFT content 91 is data for displaying a digital card on terminal device 5. Therefore, in the following explanation, "quantity" refers to the number of products.

[0021] The NFT 31 is recorded in the blockchain 3. The blockchain 3 is composed of a network system in which multiple computers are interconnected. The multiple computers that make up the blockchain 3 store the NFT 31 in a distributed manner. The blockchain 3 is connected to a network 7 such as the Internet.

[0022] An NFT 31 is a non-fungible digital token issued on a blockchain 3. As such, an NFT 31 has a unique identifier (hereinafter also referred to as an NFT-ID).

[0023] The blockchain 3 records the owner of the NFT 31. As an example, recording the owner of the NFT 31 means that the blockchain 3 stores the NFT 31 in association with identification information of the user who is the owner (hereinafter also referred to as a user ID). The owner of the NFT 31 is recorded in a distributed manner in the blockchain 3. In the example of FIG. 1, the blockchain 3 stores NFTs 31A, 31B, ... 31N in association with user IDs 32A, 32B, ... 32N, respectively.

[0024] NFTs 31 are tradable. Trading NFTs 31 refers to trading NFT content 91. The transaction history of NFTs 31 is recorded in a distributed manner on the blockchain 3.

[0025] The NFT content 91 is stored in the database 9. That is, the database 9 stores a group of products consisting of multiple products, NFT content 91A, 91B, ... 91N. The database 9 is, for example, composed of one or more computers other than the multiple computers that make up the blockchain 3. The database 9 is also connected to the network 7. The NFT content 91 is converted into an NFT by, for example, a content creator, and stored in the database 9 as a trading object.

[0026] To convert content into an NFT, that is, to associate content with an NFT 31, is, for example, to have the database 9 store NFT content 91 in association with the NFT-ID of the NFT 31. In the example of Fig. 1, the database 9 stores NFT contents 91A, 91B, ... 91N in association with NFT-IDs 92A, 92B, ... 92N, respectively.

[0027] A smart contract 33 is implemented in the blockchain 3. The smart contract 33 is software (computer program) implemented in the blockchain 3, and is automatically executed according to predetermined conditions. The smart contract 33 performs processes such as recording the owner of the NFT 31 in the blockchain 3, rewriting the owner, and updating the transaction history. In this way, transactions of the NFT 31 are realized.

[0028] [Terminal Device] The product provision system 100 according to this embodiment includes a terminal device 5. The terminal device 5 is an example of an input device that accepts input from a user 6, and is also an example of a destination to which NFT content 91 is provided. The terminal device 5 is configured as a computer having a processor 51 and a memory 52, or a single computer and its peripheral devices. The terminal device 5 is, for example, a smartphone, a tablet, a personal computer, etc. The terminal device 5 may be realized by multiple computers working together.

[0029] The processor 51 is, for example, a CPU (Central Processing Unit). The memory 52 includes, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The memory 52 stores a program 521 to be executed by the processor 51.

[0030] The terminal device 5 includes a touch panel 53. The touch panel 53 is an example of an input device that accepts input from the user 6, and is also an example of a playback device for the NFT content 91.

[0031] [First embodiment] [server] The product provision system 100 according to this embodiment includes a server 1. As an example, the server 1 is configured with a computer other than the multiple computers that configure the blockchain 3, or with one computer and its peripheral devices. The server 1 may be realized by multiple computers working together. The server 1 has a processor 11 and a memory 12. The processor 11 is, for example, a CPU. The memory 12 includes, for example, a ROM, a RAM, etc. The memory 12 stores a program 121 to be executed by the processor 11.

[0032] The memory 12 has a condition storage section 122 which is a storage area for storing a plurality of extraction conditions. The condition storage section 122 stores a plurality of extraction conditions in association with their respective occurrence probabilities.

[0033] The extraction condition may be an indicator that serves as a criterion for extracting the NFT content 91 to be provided from among the multiple NFT contents 91A, 91B, ..., 91N, such as a total amount. The occurrence probability is the probability that the extraction condition occurs, and is, for example, the number of times that the extraction condition is used within a predetermined number of times.

[0034] Fig. 2 is a diagram showing an example of a plurality of extraction conditions stored in the condition storage unit 122 of the memory 12. In the example of Fig. 2, the condition storage unit 122 stores a plurality of extraction conditions C1 to C3 in a condition table 21. Note that the storage of the extraction conditions in the memory 12 is not limited to the table format.

[0035] The condition table 21 stores a total amount 211 and an occurrence probability 212 for each of the extraction conditions C1 to C3. That is, the extraction condition C1 is a total amount o and an occurrence probability x%, the extraction condition C2 is a total amount p and an occurrence probability y%, and the extraction condition C3 is a total amount q and an occurrence probability z%. Preferably, a larger total amount is associated with a smaller occurrence probability, and a smaller total amount is associated with a larger occurrence probability. This allows for both entertainment for the user and profits for the system.

[0036] The memory 12 has a history storage unit 123, which is a storage area for storing history. The history storage unit 123 stores extraction conditions used in providing products for at least a predetermined period or a predetermined number of times. This makes it possible to determine extraction conditions for extracting NFT content 91 from a plurality of extraction conditions in accordance with the occurrence probability associated with each of the extraction conditions in a determination process 111 described below.

[0037] Preferably, the condition storage unit 122 stores a plurality of condition tables 21 corresponding to a purchase amount. The purchase amount refers to the amount paid by a user who wishes to receive a product. In this case, the plurality of extraction conditions stored in the condition table 21 corresponding to the purchase amount include an extraction condition for a total amount exceeding the purchase amount. As a result, depending on the occurrence probability, the total amount of the products provided may be higher than the purchase amount. This increases the user's sense of value and enhances entertainment value.

[0038] Preferably, the history storage unit 123 stores extraction conditions used in providing products for a predetermined period or a predetermined number of times in association with the identification information of the user who provided the product. This allows the extraction conditions to be determined for each user from among a plurality of extraction conditions in accordance with the occurrence probability associated with each of the extraction conditions in the determination process 111 described below.

[0039] The processor 11 executes the program 121 to execute the determination process 111. The determination process 111 includes determining extraction conditions for extracting NFT content 91 to be provided from among multiple NFT contents 91A, 91B, ... 91N from multiple extraction conditions stored in the memory 12. In the determination process 111, the processor 11 determines the total amount as the extraction condition according to the occurrence probability associated with each of them.

[0040] Determining the total amount to be the extraction condition according to the associated occurrence probability includes, for example, determining the total amount closest to the associated occurrence probability as the extraction condition by referring to the total amount used as the extraction condition in the provision of the product a predetermined number of times stored in the history storage unit 123. For example, the total amount to be the current extraction condition is determined so that the total amount used as the extraction condition in each of the past 99 product provision times and the total amount to be the current extraction condition are close to the total amount o x times, the total amount p y times, and the total amount q z times. This allows the product provided by the product provision system 100 to be extracted with a total amount according to a predetermined occurrence probability.

[0041] When multiple condition tables 21 corresponding to the purchase amount are stored in the condition storage unit 122, determining the extraction condition preferably includes selecting a condition table 21 corresponding to the purchase amount specified by the user from the multiple condition tables 21. In this case, in the determination process 111, the processor 11 determines the total amount to be the extraction condition from the multiple extraction conditions in the selected condition table 21 according to the occurrence probability associated with each of them. At this time, preferably, the multiple extraction conditions in the condition table 21 include an extraction condition for a total amount exceeding the purchase amount specified by the user. This achieves a balance between entertainment for the user and profit for the system.

[0042] The processor 11 executes the program 121 to execute the extraction process 112. The extraction process 112 includes extracting one or more NFT contents 91 that satisfy the total amount determined as the extraction condition from among the multiple NFT contents 91A, 91B, ... 91N. In this example, a combination of NFT contents 91 that satisfy the total amount determined as the extraction condition is extracted from the multiple NFT contents 91A, 91B, ... 91N. The extraction here may be random extraction as an example. As another example, the extraction may be performed in a predefined order.

[0043] Preferably, when there are multiple combinations of NFT contents 91 that satisfy the total amount determined in the extraction condition, the extraction process 112 includes selecting a predetermined combination from the multiple combinations. The predetermined combination may be, for example, the combination with the lowest total amount, or, as another example, the combination with the highest total amount. In this way, one combination of NFT contents 91 is extracted.

[0044] Preferably, the extraction process 112 extracts, from among the multiple NFT contents 91A, 91B, ... 91N, NFT contents 91 that satisfy the total amount determined in the extraction conditions and also satisfy predefined required conditions. The required conditions are, for example, the number of products n to be extracted. In this example, it is the total number of NFT contents 91 to be extracted (total number of digital cards) n. The number of products n to be extracted may, for example, be specified by a user who purchases the products, or may be predefined. This makes it possible to set the number of products to be provided, and ensures fairness and user satisfaction while ensuring the profits of the system.

[0045] The required condition may further be the number of products l (L) that satisfy a predetermined condition m among the number of products n to be extracted. The predetermined condition m may be, for example, price. As another example, it may be newness, size, data amount, etc. The required condition is, for example, the number of high-value NFT contents 91 (requirement m) whose associated NFT 31 has a value equal to or greater than a specified amount (total number of digital cards) l. The number of high-value products l may be determined according to the number of products n to be extracted, the purchase amount paid by the user, etc., or may be determined in advance. In the example of FIG. 2, the number of high-value products l is determined according to the number of products n designated by the user 6 on a purchase screen (not shown) described later and transmitted from the terminal device 5, and the purchase amount b paid by the user. This makes it possible to set the number of so-called featured products such as high-value products, thereby increasing the satisfaction and entertainment of the user while ensuring the profits of the system.

[0046] As an example, the condition storage unit 122 of the memory 12 stores conditions C4 to C7 of the number of high-value items l in the condition table 22 (see FIG. 2). The condition table 22 stores the number of high-value items (number of high-value cards) 223 associated with the purchase amount 221 and the total number 222 as the conditions C4 to C7. In the example of FIG. 2, the condition table 22 stores the following as condition C1: purchase amount 3000 yen, and the number of high-value cards when the total number is 5 is 1; as condition C2: purchase amount 6000 yen, and the number of high-value cards when the total number is 5 is 2; as condition C3: purchase amount 5000 yen, and the number of high-value cards when the total number is 10 is 2; and as condition C4: purchase amount 10000 yen, and the number of high-value cards when the total number is 10 is 4. In this case, in the extraction process 112, the processor 11 refers to the condition table 22, determines the number of high-priced items l from the purchase amount b and the total number (number of items) n transmitted from the terminal device 5, and uses this as a required condition.

[0047] [Product provision method] 3 is a diagram showing an example of the flow of a product provision method according to the present embodiment. In the product provision method according to the present embodiment, as an example, the terminal device 5 accepts a purchase operation from a user 6 who wishes to purchase an NFT content 91 (step S1). During the purchase operation, as an example, the terminal device 5 displays a purchase screen (not shown) on the touch panel 53 according to the control of the processor 51 of the terminal device 5 that has executed the program 521.

[0048] The purchase screen, for example, presents a selection of multiple purchase types to be selected. The purchase type is defined by at least the purchase amount b, for example, the purchase amount of 3000 yen, 6000 yen, 5000 yen, and 10000 yen. Furthermore, the purchase type is defined by the number of products n. The purchase type is defined by the purchase amount of 5, 10, and the total number n of 10 products. As an example, the first purchase type is a purchase amount of 3000 yen and a number of products of 5 (n=5, b=3000), the second purchase type is a purchase amount of 6000 yen and a number of products of 5 (n=5, b=6000), the third purchase type is a purchase amount of 5000 yen and a number of products of 10 (n=10, b=5000), and the fourth purchase type is a purchase amount of 10000 yen and a number of products of 10 (n=10, b=10000).

[0049] In step S1, when a user operation is received to select one of the purchase types on the purchase screen and instruct a purchase, processor 51 transmits a request to server 1 in accordance with program 521 (step S3). The request in step S3 includes information indicating the selected purchase type, and includes information capable of specifying at least the purchase amount b and the total number n. The request also includes information capable of identifying user 6, who is the purchaser. One example of the information capable of identifying user 6 is the user ID of user 6.

[0050] When the server 1 receives a request from the terminal device 5 in step S3, it executes a process to provide a product according to the request (step S5). The process by the server 1 in step S5 will be described later. The server 1 registers the extraction conditions used to extract the product to be provided in the history in the memory 12 (step S7), and provides the extracted NFT content 91 to the user 6 (step S9).

[0051] The provision in step S9 includes making the user 6 the owner of the extracted NFT content 91. Specifically, the user ID of the user 6 is recorded in the blockchain 3 as the owner of the NFT 31 associated with the content of the NFT content 91. As an example, the processor 11 of the server 1 instructs the smart contract 33 to record the user ID of the user 6 as the owner of the NFT 31 in the blockchain 3. As a result, the NFT 31 is recorded in the blockchain 3 in association with the user ID 32 of the user 6 as the owner. In addition, the user 6 becomes the owner of the provided NFT content 91, which is the content stored in the database 9 in association with the NFT-ID 92 of the NFT 31.

[0052] Also, in step S9, the address of the database 9 in which the NFT content 91 is recorded may be transmitted from the server 1 to the terminal device 5. This allows the user 6 to access the database 9 via the terminal device 5 and display the digital card, which is the NFT content 91, on the touch panel 53.

[0053] [Processing on the server] 4 is a flowchart showing an example of the flow of processing in the server 1 according to the product provision method of the present embodiment. When a request is received from the terminal device 5 (YES in step S101), the processor 11 of the server 1 identifies the number of products (total number) n and the purchase amount b from the information transmitted from the terminal device 5 (step S103).

[0054] For example, when a request for the above third purchasing pattern is sent from the terminal device 5, the processor 11 of the server 1 identifies from the request that the total number n is 10 (n=10) and the purchase amount b is 5,000 yen (b=5,000) in step S103. The processor 11 refers to the condition table 22 and determines the number l of high-value items associated with the total number n and the purchase amount b (step S105). In the example of FIG. 2, the condition C6 is read in step S105 and the number l of high-value cards is determined to be 2 (l=2).

[0055] Processor 11 determines a total amount to be an extraction condition according to the occurrence probability associated with each of the multiple total amounts stored in condition table 21 (step S109). In step S109, as an example, the history stored in history storage unit 123 is referenced. Processor 11 then determines a total amount to be an extraction condition for the current provision of a product such that the total amount that was an extraction condition when the product was provided a predetermined number of times in the past and the total amount to be an extraction condition for the current provision of a product are combined to obtain an occurrence probability associated with each of the multiple stored total amounts.

[0056] The processor 11 randomly extracts a total number n of NFT contents 91 from among multiple NFT contents 91A, 91B, ... 91N stored in the database 9, such that the total amount determined as the extraction condition is met and the number of high-value cards is the determined number l (step S111).

[0057] [Effects of the embodiment] In the product provision system 100 according to the present embodiment, when a user instructs a purchase, NFT content that satisfies the total amount determined by the associated occurrence probability is randomly extracted. By providing the extracted product to the user in this manner, the user is given great expectations and surprise, and profits for the system side, such as the product provider, can be secured. As a result, the product provision system 100 according to the present embodiment can provide products that enhance the entertainment value of the user, and can secure profits for the system side.

[0058] [Second embodiment] 5 is a schematic configuration diagram of a server 1 according to the second embodiment. The server 1 according to the second embodiment differs from the schematic configuration of the server 1 according to the first embodiment (FIG. 1) in that the processor 11 further executes a generation process 113. Other configurations of the product provision system 100 are the same as those in FIG. 1.

[0059] The generation process 113 generates a combination of the total amount and the occurrence probability associated with it as an option of the extraction condition. Specifically, the generation process 113 generates a combination of the total amount and the occurrence probability associated with it using the deduction rate a stored in the memory 12. The deduction rate a may be predefined, may be set by the product provider, or may be set by various other methods.

[0060] The deduction rate a is an example of the relationship between the provided amount and the purchased amount b, and indicates the ratio of the provided amount to the purchased amount b. The provided amount indicates the sum of values ​​obtained by multiplying the occurrence probability corresponding to the total amount. For example, when there are three combinations of the occurrence probability x% of the total amount о, the occurrence probability y% of the total amount p, and the occurrence probability z% of the total amount q as options for multiple extraction conditions, the deduction rate a can be obtained by the following formula (1). a = 1 - (оx + py + qz) / b ... (1)

[0061] In the generation process 113, the processor 11 generates a probability of occurrence associated with a total amount that satisfies the set deduction rate a. That is, a combination of a total amount and a probability of occurrence associated with the total amount is generated such that the relationship between the provided amount and the purchase amount b is a specified relationship. One example of the generation of the combination of the total amount and the probability of occurrence associated with the total amount is random generation. Another example may be generation based on a predefined procedure, such as according to a stored arithmetic formula.

[0062] Preferably, in the generation process 113, the processor 11 generates a combination of the total amount and the occurrence probability associated with the total amount so that the combination falls within a set range. The set range of the total amount is specified, for example, by a minimum amount and a maximum amount, and is stored in the memory 12. Specifically, the set range of the total amount can be specified as a ratio of the maximum amount to the minimum amount, or by only the minimum amount, or by only the maximum amount. The set range of the total amount may be specified in advance, may be set by the provider of the product, or may be set by various other methods. This allows the products to be offered to be extracted with a total amount within the set range. As a result, it becomes easier to control the total amount of the products to be offered on the system side.

[0063] Fig. 6 is a diagram showing an example of the flow of a product provision method according to the second embodiment. The product provision method according to the second embodiment differs from the product provision method according to the first embodiment (Fig. 3) in that it includes a product provision process S5-1 instead of the product provision process S5.

[0064] In the second embodiment of the product provision method, when the server 1 receives a request from the terminal device 5 in step S3, it executes a process for providing the product according to the request (step S5-1). The process of step S5-1 includes reading the deduction rate a from the memory 12 and randomly generating a combination of the total amount that satisfies the deduction rate a and the occurrence probability associated therewith.

[0065] 7 is a flowchart showing an example of a process flow in the server 1 according to the second embodiment. The process flow in the server 1 according to the second embodiment is different from the process flow in the server 1 according to the first embodiment (FIG. 4) in that it further includes steps S106, S107, and S108.

[0066] In detail, when the processor 11 of the server 1 according to the second embodiment receives a request from the terminal device 5, it reads out the deduction rate a and the set range of the total amount from the memory 12 (steps S106, S107). The processor 11 uses the purchase amount b identified in step S103 to randomly generate a combination of the occurrence probability associated with the total amount that satisfies the read-out deduction rate a and is within the set range (step S108).

[0067] The processor 11 uses the combination of the total amount generated in step S108 and the associated occurrence probability as an option for the extraction condition, and thereafter, similar to the processing on the server 1 in the first embodiment, determines the total amount to be the extraction condition (step S109), and randomly extracts the NFT content 91 to be provided (step S111).

[0068] In the product provision system 100 according to the second embodiment, the user's purchase amount b is used to generate a combination of the total amount that satisfies the set deduction rate a and the associated occurrence probability, and the combination is used as an option for the extraction condition. Therefore, the system can easily control the total amount of the products to be provided based on the relationship with the user's purchase amount b. As a result, the products can be provided with increased entertainment for the user, and the system can easily secure profits.

[0069] [Variations] In the above description, the product provided by the product provision system 100 is NFT-ized content, but the product does not necessarily have to be NFT-ized or be content. In addition, the product does not necessarily have to be digital data. Even in this case, the product provision system 100 according to the first embodiment or the product provision system 100 according to the second embodiment can provide the product as described above.

[0070] <3. Notes> The present invention is not limited to the above-described embodiment, and various modifications are possible. [Explanation of symbols]

[0071] 1: Server, 3: Blockchain, 5: Terminal device, 9: Database, 21, 22: Condition table, 91: NFT content, 100: Product provision system, 121: Program

Claims

1. 1. A computer-implemented product provision method for providing a product associated with a non-fungible token recorded on a blockchain, the method comprising: a processor of the computer, determining extraction conditions for extracting products to be provided from a group of products each corresponding to a non-fungible token, according to occurrence probabilities associated with each of the stored extraction conditions; and randomly sampling products that satisfy the determined extraction conditions from the group of products. Product delivery method.

2. The extraction conditions include a total price of the products to be extracted, The stored plurality of extraction conditions include an extraction condition that the total amount exceeds a specified purchase amount. The merchandise providing method according to claim 1 .

3. The processor further comprises generating the extraction conditions and corresponding occurrence probabilities. The merchandise providing method according to claim 1.

4. The extraction conditions include a total price of the products to be extracted, Generating the extraction condition and the associated occurrence probability includes generating the extraction condition and the associated occurrence probability such that a relationship between a provision amount multiplied by an occurrence probability corresponding to a total amount of each of the plurality of extraction conditions and a specified purchase amount is a specified relationship. The merchandise providing method according to claim 3.

5. Generating the extraction condition and the associated occurrence probability includes generating the extraction condition such that the total amount falls within a set range. The merchandise providing method according to claim 3.

6. Extracting the products further includes extracting a predefined number of products. The merchandise providing method according to claim 1 .

7. Extracting the products further includes extracting a predefined number of products that meet predefined requirements. The merchandise providing method according to claim 1 .

8. A product provision system that executes a process for providing a product associated with a non-fungible token recorded on a blockchain, determining extraction conditions for extracting products to be provided from a group of products each corresponding to a non-fungible token from among the plurality of stored extraction conditions according to the occurrence probability associated with each of the extraction conditions; and randomly sampling products that satisfy the determined sampling condition from the group of products. Product provision system.

9. A computer program that causes a computer to execute a process for providing a product associated with a non-fungible token recorded on a blockchain, determining extraction conditions for extracting products to be provided from a group of products each corresponding to a non-fungible token from among the plurality of stored extraction conditions according to the occurrence probability associated with each of the extraction conditions; randomly extracting products that satisfy the determined extraction condition from the group of products. Computer program.

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