Information processing method, program, and information processing apparatus

Abstract

To provide an information processing method capable of automatically determining a completion of an introduction based on on-chain data or off-chain data.SOLUTION: According to an embodiment, there is provided an information processing method performs processing including the steps of: acquiring a wallet address or a user identifier of a target user who has received introduction of a product or a service from another user; acquiring on-chain data or off-chain data indicating an action of the target user based on the wallet address or the user identifier; and determining whether the action of the target user satisfies a predetermined introduction completion condition based on the on-chain data or the off-chain data.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to an information processing method, a program, and an information processing device. [Background technology]

[0002] Acquiring new users (customers) is a very important issue for companies. In recent years, there has been active development of technology related to existing users introducing new users (so-called referral programs). For example, Patent Document 1 discloses an information processing device that refers to the user's behavior history and, when it identifies behavior resulting from predetermined guidance information introduced to the user, gives a predetermined reward according to the user's behavior to the introducer who introduced the predetermined guidance information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2024-013665 Summary of the Invention [Problem to be solved by the invention]

[0004] In one aspect, the present invention provides an information processing method that can automatically determine whether an introduction has been completed based on on-chain data or off-chain data. [Means for solving the problem]

[0005] An information processing method according to one aspect is characterized in that it acquires a wallet address or a user identifier of a target user who has received a product or service introduction from another user, acquires on-chain data or off-chain data indicating the target user's behavior based on the wallet address or user identifier, and executes a process to determine whether the target user's behavior satisfies a predetermined introduction completion condition based on the on-chain data or off-chain data. [Effects of the Invention]

[0006] In one aspect, introduction completion can be determined automatically based on on-chain or off-chain data. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is an explanatory diagram showing an overview of a referral determination system. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of a server. [Figure 3] FIG. 2 is an explanatory diagram showing an example of the record layout of a user DB and a relation DB. [Figure 4] 10 is an explanatory diagram showing an example of the record layout of a behavior master DB and a behavior data DB. FIG. [Figure 5] FIG. 10 is an explanatory diagram illustrating an example of a record layout of a determination result DB. [Figure 6] FIG. 2 is a block diagram illustrating an example of the configuration of a company server. [Figure 7] FIG. 2 is an explanatory diagram illustrating the processing operation of the introduction determination system. [Figure 8] 10 is a flowchart showing the processing steps for acquiring on-chain data or off-chain data. [Figure 9] 10 is a flowchart showing the processing procedure of a referral behavior detection subroutine. [Figure 10] 10 is a flowchart showing a processing procedure for determining the completion of an introduction based on a numerical value corresponding to an action. [Figure 11] 10 is a flowchart showing a processing procedure for determining completion of an introduction based on a specific behavior. DETAILED DESCRIPTION OF THE INVENTION

[0008] The present invention will be described in detail below with reference to the drawings showing embodiments thereof.

[0009] (Embodiment 1) Embodiment 1 relates to a form in which introduction completion is automatically determined based on on-chain data or off-chain data.

[0010] Usually, when another user (hereinafter referred to as the introducer) introduces a product or service to a target user (hereinafter referred to as the introduced person), the introduction is not recognized unless specific procedures specified by the company are followed. Also, even if the introduced person ultimately purchases a product or uses a service after a certain period of time has passed, the introduction may not be recognized.

[0011] In this way, even if the introduced person receives an introduction and purchases a product or uses a service, the introduction is not recognized, and the motivation to purchase the product or use the service weakens. In this embodiment, the completion of the introduction can be automatically determined by analyzing the behavior of the introduced person based on on-chain data or off-chain data that indicates the behavior of the introduced person other than purchasing.

[0012] Products are optional and include, for example, digital content (music, movie, or game characters, etc.), artwork (paintings, sculptures, illustrations, or photographs, etc.), tickets (admission tickets) for concerts, athletic events, or amusement parks, food, or clothes, shoes, bags, or furniture that are traded as goods. Services are optional and include, for example, fan club services, sports club membership services, or rental services for various types of equipment, facilities, or space, etc.

[0013] Although the following describes an example of a product, it can also be applied to a service.

[0014] 1 is an explanatory diagram showing an overview of a referral determination system. The system of this embodiment includes an information processing device 1, a blockchain system 2, and an information processing device 3, and each device transmits and receives information via a network N such as the Internet.

[0015] The information processing device 1 is an information processing device that processes, stores, and transmits / receives various types of information. The information processing device 1 is, for example, a server device or a personal computer. In this embodiment, the information processing device 1 is assumed to be a server device, and for the sake of brevity, will be referred to as server 1 below.

[0016] The blockchain system 2 is a distributed ledger technology or a distributed network. The blockchain system 2 is composed of multiple nodes 21 that execute consensus processing. Note that while FIG. 1 shows an example in which the blockchain system 2 is composed of five nodes 21, it may be composed of an appropriate number of nodes depending on the consensus algorithm or the number of network participants.

[0017] Each node 21 holds a copy of the blockchain data through the execution of the consensus process. The blockchain system 2 generates units of data called blocks at regular intervals and stores data by linking them together like a chain.

[0018] Blockchain system 2 is managed autonomously using a peer-to-peer network and a distributed timestamp server. Because it is stored in a chain, once data in a block is stored, it is difficult to retroactively change that data. Blockchain system 2 may be public, private, or consortium type. The unit of data may be individual transactions rather than blocks. Furthermore, data may be stored in a format other than a chain, such as a directed acyclic graph. For simplicity, blockchain system 2 will be referred to as blockchain 2 below.

[0019] The information processing device 3 is a corporate information processing device that processes, stores, transmits, or receives off-chain data indicating the behavior of the introduced person. The company is a company that provides products to users and provides rewards to introducers who introduce products to introduced people. The information processing device 3 is, for example, a server device or a personal computer. In this embodiment, the information processing device 3 is a server device, and for simplicity, will be referred to as a corporate server 3 below.

[0020] The server 1 according to this embodiment acquires the wallet address or user identifier of the introduced person who has received a product introduction from the introducer. The user identifier is information for identifying the user, and may be, for example, a user ID, email address, telephone number, social media username, or terminal (device) ID. Based on the acquired wallet address or user identifier, the server 1 acquires on-chain data or off-chain data indicating the behavior of the introduced person. Based on the acquired on-chain data or off-chain data, the server 1 determines whether the behavior of the introduced person satisfies a predetermined introduction completion condition.

[0021] 2 is a block diagram showing an example of the configuration of the server 1. The server 1 includes a control unit 11, a storage unit 12, a communication unit 13, a reading unit 14, and a large-capacity storage unit 15. Each component is connected by a bus B.

[0022] The control unit 11 includes an arithmetic processing device such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), or a quantum processor. The control unit 11 reads and executes a control program 1P (program product) stored in the storage unit 12, thereby performing various information processing or control processing related to the server 1.

[0023] It should be noted that the control program 1P can be deployed to run on a single computer, or on multiple computers located at one site, or distributed across multiple sites and interconnected by a communications network.

[0024] 2, the control unit 11 is described as a single processor, but it may be a multi-processor. The control unit 11 may execute various information processes or control processes by the same processor within the server 1, or may execute various processes by different processors within the server 1.

[0025] The storage unit 12 includes memory elements such as RAM (Random Access Memory) and ROM (Read Only Memory), and stores the control program 1P or data required for the control unit 11 to execute processing. The storage unit 12 also temporarily stores data required for the control unit 11 to execute arithmetic processing. The communication unit 13 is a communication module for performing communication-related processing, and transmits and receives information to and from the blockchain 2 or the corporate server 3, etc. via the network N.

[0026] The reading unit 14 reads a portable storage medium 1a including a CD (Compact Disc)-ROM or a DVD (Digital Versatile Disc)-ROM. The control unit 11 may read the control program 1P from the portable storage medium 1a via the reading unit 14 and store it in the mass storage unit 15. Alternatively, the control unit 11 may download the control program 1P from another computer via a network N or the like and store it in the mass storage unit 15. Furthermore, the control unit 11 may read the control program 1P from the semiconductor memory 1b.

[0027] The mass storage unit 15 includes a recording medium such as a hard disk drive (HDD) or a solid state drive (SSD). The mass storage unit 15 includes a user DB 151 (database), a relationship DB 152, a behavior master DB 153, a behavior data DB 154, and a determination result DB 155.

[0028] The user DB 151 stores information about users. The relationship DB 152 stores data indicating the relationships between users. The behavior master DB 153 stores master information about behavior. The behavior data DB 154 stores on-chain data or off-chain data indicating the behavior of introduced persons. Note that on-chain data and off-chain data will be described later. The judgment result DB 155 stores the judgment results of introductions for introduced persons.

[0029] In this embodiment, the storage unit 12 and the large-capacity storage unit 15 may be configured as an integrated storage device. Furthermore, the large-capacity storage unit 15 may be configured by a plurality of storage devices. Furthermore, the large-capacity storage unit 15 may be an external storage device connected to the server 1.

[0030] The server 1 may execute various information processing and control processing on a single computer, or may execute the processing in a distributed manner on multiple computers. The server 1 may also be realized by multiple virtual machines provided in a single server, or may be realized by using a cloud server.

[0031] FIG. 3 is an explanatory diagram showing an example of the record layout of the user DB 151 and the relationship DB 152. As shown in FIG. The user DB 151 includes a user ID column, a wallet address column, a company ID column, and a user identifier column. The user ID column stores the user ID of each user, which is uniquely specified, in order to identify each user in this system.

[0032] The wallet address string stores the user's wallet address. A wallet address (public key) is a randomly generated string linked to a blockchain wallet and is used to send and receive crypto assets such as virtual currencies or NFTs (Non-Fungible Tokens). NFTs are digital data that are guaranteed to be unique on the blockchain2 and cannot be tampered with by their owners. Like virtual currencies, blockchain technology is used for data management, making them difficult to tamper with or counterfeit.

[0033] The company ID column stores the company ID for identifying the company. The user identifier column stores the user identifier used by each company when managing the off-chain data of users.

[0034] The relationship DB 152 stores data indicating relationships between users. The data indicating relationships between users may be, for example, data indicating "friendships" (for example, following or following) obtained using a social networking service (SNS). For example, if users follow each other, or if one user (first user) follows the other user (second user), the relationship between the users is "friendship." Alternatively, if users have added each other to their friend lists, the relationship between the users is "friendship."

[0035] In addition, the data indicating the relationship between users may be data indicating a "friendship" obtained using the transaction history of on-chain data. For example, if one user transfers an NFT to another, the relationship between the users is a "friendship."

[0036] The relationship DB 152 includes a user ID column, a following column, and a follower column. The user ID column stores a user ID for identifying a user. The following column stores the user IDs of second users who follow the user. The follower column stores the user IDs of third users who follow the user.

[0037] FIG. 4 is an explanatory diagram showing an example of the record layout of the behavior master DB 153 and the behavior data DB 154. As shown in FIG.

[0038] The behavior master DB 153 stores information about the behavior of the introduced person in response to the product introduction by the introducer. Examples of behavior include "website browsing," "sample trial," "attending an information session," "receiving the introduced NFT," "adding to cart," "social media chat," and "purchase."

[0039] "Website browsing" refers to the act of the introduced person browsing the content or reviews of a product posted on a designated website in order to find information about the product. "Sample trial" refers to the act of requesting, receiving, using, or providing feedback on a product sample. "Attending a product information session" refers to the act of the introduced person attending a product information session, etc.

[0040] "Receiving a referral NFT" is the action of the referred person receiving a referral NFT for a specific product when that NFT (hereinafter referred to as a referral NFT) is sent to the referred person. "Add to cart" is the action of the referred person adding a product to a shopping cart on an EC (electronic commerce) site. "SNS chat" is the action of chatting about product information (for example, product details or campaign information) using SNS. "Purchase" is the action of the referred person purchasing a product.

[0041] However, the actions are not limited to those described above, and may include, for example, subscribing to a newsletter introducing a product, or viewing an advertisement for a product posted on a specific website.

[0042] The behavior master DB 153 includes a behavior ID column, a behavior column, and a numeric column. The behavior ID column stores a uniquely specified behavior ID to identify each behavior. The behavior column stores the name or type of behavior, etc. The numeric column stores a numeric value set based on the behavior or tendency of the introduced person, etc. For example, a high numeric value may be set for behavior close to a purchase (e.g., trying a sample or adding to a cart, etc.), and a low numeric value may be set for behavior less likely to purchase (e.g., browsing a website or receiving a referred NFT, etc.).

[0043] The behavioral data DB154 includes an introduced person ID column, a wallet address column, an introducer ID column, a product ID column, a data type column, a behavior ID column, and a behavior date and time column. The introduced person ID column stores the user ID of the introduced person (hereinafter referred to as the introduced person ID) that is uniquely specified to identify each introduced person. The wallet address column stores the wallet address of the introduced person. The introducer ID column stores the user ID of the introducer (hereinafter referred to as the introducer ID) who introduced the product to the introduced person.

[0044] The product ID column stores a product ID for identifying the product to be introduced. The data type column stores the type of data, including on-chain data or off-chain data. The action ID column stores an action ID for identifying the action. The action date and time column stores the date and time information when the introduced person took the action.

[0045] 5 is an explanatory diagram showing an example of a record layout of the determination result DB 155. The determination result DB 155 includes an introduced person ID column, an introducer ID column, a determination result column, a progress column, and a determination date and time column. The introduced person ID column stores the user ID of the introduced person for identifying the introduced person.

[0046] The introducer ID column stores an introducer ID for identifying the introducer. The determination result column stores a determination result indicating whether the introduction is to be completed (e.g., "introduction in progress" or "introduction completed"). The progress column stores the progress of the introduction. The determination date and time column stores date and time information when the determination result was made.

[0047] The storage format of each DB described above is an example, and other storage formats may be used as long as the relationships between the data are maintained.

[0048] 6 is a block diagram showing an example configuration of the company server 3. The company server 3 includes a control unit 31, a memory unit 32, a communication unit 33, and a reading unit 34. Each component is connected by a bus B. Note that the control unit 31, the memory unit 32, the communication unit 33, and the reading unit 34 are similar to the control unit 11, the memory unit 12, the communication unit 13, and the reading unit 14 of the server 1, and therefore a description thereof will be omitted.

[0049] Figure 7 is an explanatory diagram illustrating the processing operation of the introduction determination system. First, the process of collecting (obtaining) on-chain data or off-chain data of each user will be described. On-chain data is transaction data recorded on the blockchain 2. For example, when an introducer transfers crypto assets such as virtual currency or NFTs from a wallet address held by the introducer to a wallet address held by the introduced person, the on-chain data is data indicating the behavior of the introduced person, including the amount of the transfer, the NFT, or the time of the transfer.

[0050] Off-chain data is data other than on-chain data, and is stored in a company's regular database or data center, etc. On-chain data or off-chain data is collected periodically, for example, by batch processing, and accumulated (stored) in the behavioral data DB 154 in chronological order.

[0051] In the process of acquiring on-chain data, the server 1 transmits the wallet address of the introduced person to one of the nodes 21 in the blockchain 2. The node 21 in the blockchain 2 receives the wallet address of the introduced person transmitted from the server 1. The node 21 acquires on-chain data relating to the behavior of the introduced person based on the received wallet address of the introduced person.

[0052] The on-chain data is transaction data or block data, and includes, for example, data indicating that the introduced person has received a specific introduced NFT for a product from the introducer, data indicating that the introduced person has received a specific game character from the introducer, or data indicating the relationship between users. The node 21 transmits the acquired on-chain data of the introduced person to the server 1. The server 1 receives the on-chain data transmitted from the node 21.

[0053] In the off-chain data acquisition process, the server 1 acquires the user identifier of the introduced person for each company from the user DB 151 based on the introduced person's ID. The server 1 transmits the acquired user identifier to the company server 3 of the corresponding company. The company server 3 receives the user identifier transmitted from the server 1.

[0054] Based on the received user identifier, the company server 3 acquires off-chain data regarding the behavior of the introduced person from a database on the company server 3 (for example, an event history DB or log DB), a system installed on the company server 3, or an API (Application Programming Interface) that can search for the behavior of the introduced person. Note that the structure of the off-chain data is the same as the structure of the on-chain data, so a description thereof will be omitted.

[0055] The company server 3 transmits the acquired off-chain data to the server 1. The server 1 receives the off-chain data transmitted from the company server 3.

[0056] Next, we will explain the process of detecting product introduction behavior from an introducer to an introduced person based on the acquired on-chain data or off-chain data. The behavior detection process is a process of detecting product introduction behavior based on user behavior or usage history information indicated by the on-chain data or off-chain data.

[0057] Specifically, the server 1 identifies the behavior of the introduced person by identifying the content of the behavior based on the received on-chain data or off-chain data. The server 1 acquires the corresponding behavior ID from the behavior master DB 153 according to the identified behavior. The server 1 acquires behavior information including the introducer ID, product ID, behavior ID, or behavior date and time, etc.

[0058] For example, if the action identified based on on-chain data is "receiving a referral NFT," Server 1 detects the action as a referral action.

[0059] In addition, if the action identified based on the SNS chat content included in the off-chain data (e.g., introducing the contents of a product) is "SNS chat" and the action identified based on the on-chain data is "receiving a referral NFT," the server 1 detects the action as a referral action.

[0060] Furthermore, the server 1 detects referral behavior based on specific data (e.g., game characters) issued by the company. For example, if the server 1 identifies, based on on-chain data acquired from company A, an action in which an introducer sends a game character owned by the introducer to the introduced person, the server 1 detects the action as a referral behavior. Note that the character sending process is not particularly limited, and may involve, for example, sending or transferring a character NFT corresponding to the game character, transferring digital data, sharing character data, issuing a gift code, or the like.

[0061] The server 1 also detects the introduction behavior based on the identity of the behavior. Specifically, the server 1 determines whether the behavior of the introducer and the behavior of the introduced person are the same. Specifically, the server 1 determines whether the behavior ID of the introducer and the behavior ID of the introduced person match. If the two match, the server 1 determines that the behavior of the introducer and the behavior of the introduced person are the same behavior. If the two do not match, the server 1 determines that the behavior of the introducer and the behavior of the introduced person are different behaviors.

[0062] When the introducer's behavior and the introduced person's behavior are the same, the server 1 detects the behavior as an introduction behavior. Alternatively, when the introducer's behavior and the introduced person's behavior are different, the server 1 determines that the introduction behavior has not been detected.

[0063] For example, if the introducer's behavior is "attending a briefing session" and the introduced person's behavior is "attending a briefing session," the server 1 determines that the introducer has introduced the introduced person with the introduction behavior of "attending a briefing session," i.e., detects "attending a briefing session" as the introduction behavior.

[0064] Note that introduction behavior can be detected by further adding a predetermined period to the behavior identity. Specifically, when the behavior of the introducer and the behavior of the introduced person are the same, the server 1 determines whether the date and time of the introduced person's behavior is within a predetermined period (for example, within one month from the date and time of the introducer's behavior). When the date and time of the introduced person's behavior is within the predetermined period, the server 1 detects the behavior as introduction behavior.

[0065] In the above example, if the introducer attends a briefing session on March 1st and the introduced person attends the briefing session in March, the server 1 detects this behavior as an introduction behavior. Alternatively, if the introduced person attends a briefing session in April, the server 1 determines that this introduction behavior has not been detected.

[0066] Furthermore, the server 1 detects referral behavior based on usage history information. The on-chain data or off-chain data includes the usage history information of the introducer. The usage history information includes an action ID and the date and time of the action. For example, after the introducer purchases a product, the on-chain data or off-chain data includes usage history information in which the action is defined as "purchase" and the date and time of the purchase is defined as the "date and time of the action." Note that the usage history information is not limited to "purchase" behavior, but also includes behaviors such as "trying out a sample" or "attending an information session."

[0067] Based on the usage history information, the server 1 determines whether the introducer is also a regular user of products from company A. If the introducer is also a regular user of products from company A, and the introduced person who has a relationship with the introducer views information related to the products of company A (for example, views a website) or requests information, the server 1 detects the behavior of the introduced person as a referral behavior.

[0068] Specifically, the server 1 determines whether the introducer is a regular user of the product based on the introducer's usage history information. Specifically, the server 1 acquires usage history information including an action ID corresponding to the action and the action date and time from the action data DB 154 based on the introducer ID and the product ID of the product to be introduced.

[0069] Based on the acquired usage history information, the server 1 determines whether or not there is any behavior related to the product (for example, "website browsing," "sample trial," or "purchase"). If there is any behavior related to the product in the usage history information, the server 1 determines that the introducer is a regular user of the product. If there is no behavior related to the product in the usage history information, the server 1 determines that the introducer is not a regular user of the product.

[0070] The server 1 may further count the number of actions from the usage history information, and if the number of actions is a predetermined number (for example, 3) or more, determine that the introducer is a regular user of the product.

[0071] In addition, the server 1 may detect the behavior of the introduced person as a referral behavior when the introduced person views (for example, views a website) or requests information related to a second product different from the product (first product) of the company A.

[0072] If the server 1 determines that the introducer is a loyal user of the product, it determines whether the behavior of the identified introduced person is related to the product based on on-chain data or off-chain data. Product-related behavior includes, for example, browsing websites, chatting on social media, trying samples, requesting information, or acquiring NFTs related to the product. If the behavior of the identified introduced person is related to the product, the server 1 detects the behavior of the introduced person as a referral behavior.

[0073] When the server 1 detects an introduction behavior, it determines whether to approve the introduction behavior based on data indicating the relationship between users contained in the acquired on-chain data or off-chain data.

[0074] Specifically, the server 1 determines whether the introducer and the introduced person are in a "friend relationship" based on data indicating the relationship between users. For example, the server 1 may determine that the introducer and the introduced person are in a "friend relationship" if they follow each other. Alternatively, the server 1 may determine that the introducer and the introduced person are in a "friend relationship" if they have been added to each other's friend lists.

[0075] If the relationship is "friends", the server 1 approves the referral action. Alternatively, if the relationship is not "friends", the server 1 does not approve the referral action. If the server 1 approves the referral action, it stores the on-chain data or off-chain data in the behavior data DB 154.

[0076] Specifically, the server 1 associates the product ID, data type (on-chain data or off-chain data), action ID, and action date and time with the introduced party ID and wallet address, and stores them as one record in the action data DB 154. The server 1 associates the data indicating the relationship between users (following, followers, etc.) with the user ID, and stores them as one record in the relationship DB 152.

[0077] The approval process for the introduction action is not limited to data indicating the relationship between users. For example, the server 1 approves the introduction action based on a specific NFT on the blockchain 2 or the relationship between the data held by the introducer and the introduced person.

[0078] For example, if an introducer sends a character NFT of a specific game to a person being introduced, server 1 may approve the introduction action regardless of whether or not there is a "friendship" between the introducer and the person being introduced.

[0079] Alternatively, the server 1 may approve the referral action by analyzing the relationship between the introducer's data (e.g., attributes or actions, etc.) and the introduced person's data (presence or absence of common actions, number of times, chat information about the product, etc.).

[0080] Next, we will explain the process of automatically determining whether an introduction has been completed based on the time-series on-chain data or off-chain data accumulated in the behavioral data DB154.

[0081] The server 1 acquires the wallet address or introduced person ID of the introduced person for which the completion of the introduction is to be determined from the user DB 151. Based on the acquired wallet address or introduced person ID of the introduced person, the server 1 acquires multiple on-chain data or off-chain data indicating the behavior of the introduced person at each point in time from the behavior data DB 154. The on-chain data or off-chain data includes the introducer ID, product ID, data type, behavior ID or behavior date and time, etc.

[0082] The server 1 acquires data indicating the relationship between the introducer and the introduced person from the relationship DB 152 based on the introduced person ID. The server 1 acquires a numerical value corresponding to each action from the action master DB 153 based on the action ID included in each acquired on-chain data or off-chain data. The server 1 adds up the numerical values ​​corresponding to each acquired action. The server 1 determines whether the added numerical value is equal to or greater than a threshold value (e.g., 4), thereby determining whether the introduction completion condition is met.

[0083] Specifically, if the added numerical value is equal to or greater than the threshold value, the server 1 determines that the introduction completion condition is met, i.e., "introduction completed." Alternatively, if the added numerical value is less than the threshold value, the server 1 determines that the introduction completion condition is not met, i.e., "introduction in progress."

[0084] The process of determining whether introduction has been completed is not limited to the process described above using a numerical value corresponding to the behavior, but may be a process of determining whether introduction has been completed using a specific behavior.

[0085] Specifically, based on the introduced person's wallet address or introduced person ID, the server 1 acquires a plurality of on-chain data or off-chain data indicating the introduced person's behavior at each point in time from the behavior data DB 154. The server 1 determines whether the acquired plurality of on-chain data or off-chain data includes on-chain data or off-chain data indicating a specific behavior, thereby determining whether the introduction completion condition is met.

[0086] The specific action is set in advance. For example, for a product such as cosmetics, the specific action may be "attending a briefing session," "adding to cart," or both "attending a briefing session" and "adding to cart." Or, for a product such as a car, the specific action may be "test drive." In the process of determining the specific action, for example, the server 1 determines whether or not the specific action exists based on the action ID included in each acquired on-chain data or off-chain data. If the specific action exists, the server 1 determines that the introduction completion condition is met. Or, if the specific action does not exist, the server 1 determines that the introduction completion condition is not met.

[0087] In addition to the specific behavior, the number of times the specific behavior has occurred (for example, three times) can be added to determine whether the introduction completion condition is met. For example, the server 1 may determine that the introduction completion condition is met if a specific behavior has occurred and the number of times the specific behavior has occurred is equal to or greater than a predetermined number based on the behavior ID included in each acquired on-chain data or off-chain data.

[0088] The server 1 determines the progress of the introduction according to the behavior of the introduced person. Specifically, if the introduction completion condition is met, the server 1 determines that the progress of the introduction is "100%." ​​If the introduction completion condition is not met, the server 1 calculates the progress of the introduction according to the behavior of the introduced person indicated by the on-chain data or off-chain data at each point in time.

[0089] The introduction progress is calculated based on the number of actions. For example, the server 1 may calculate the progress (3 / 5 × 100%) based on the predetermined target number of actions (e.g., 5) and the actual number of actions completed by the introduced person (e.g., 3).

[0090] Finally, the process of storing the result of the introduction determination will be described. The server 1 stores the introduction progress of the introduced person, the determination result (such as "introduction completed" or "introduction in progress"), and the determination date and time in the behavior data DB 154 in association with the wallet address or the introduced person ID of the introduced person.

[0091] In addition, the server 1 may transmit the introduction progress of each introduced person, the evaluation result, the evaluation date and time, etc. to the company server 3 in response to a request from the company.

[0092] When the introduction completion condition is met, the server 1 counts the number of introducers (users; friends) who have a relationship with the introduced person from the relationship DB 152. The server 1 determines the reward to be given to each introducer according to the counted number of introducers. For example, the more introductions a user has made in the past, the less reward they will receive when making a new introduction. This prevents users from obtaining large rewards by continuing to make endless connections, and ensures the effectiveness of the introduction.

[0093] The server 1 transfers the determined reward to the user's account via, for example, a reward platform or a banking system. The reward is assumed to be transferred as cash to the user's pre-registered reward transfer account, but is not limited to this. The reward may also be point data, discount coupons, NFTs, virtual currency, or other rewards that can be exchanged for products or services. The reward may be determined in any manner, for example, equally distributed or distributed according to the degree of contribution. Furthermore, the server 1 may determine a personalized reward for the introducer based on the introducer's actions contained in the introducer's on-chain data or off-chain data.

[0094] 8 is a flowchart showing the processing steps for acquiring on-chain data or off-chain data. The control unit 11 of the server 1 acquires on-chain data related to the behavior of the introduced person from the node 21 of the blockchain 2 based on the wallet address of the introduced person via the communication unit 13 (step S101). The control unit 11 acquires off-chain data related to the behavior of the introduced person from, for example, a database (e.g., an event history DB or a log DB) on the company server 3 based on the introduced person's ID via the communication unit 13 (step S102).

[0095] The control unit 11 identifies the behavior of the introduced person by identifying the content of the behavior based on the acquired on-chain data or off-chain data (step S103). The control unit 11 acquires behavior information based on the identified behavior (step S104). Specifically, the control unit 11 acquires a corresponding behavior ID from the behavior master DB 153 of the mass storage unit 15 according to the identified behavior. The control unit 11 acquires behavior information including the introducer ID, product ID, behavior ID, behavior date and time, etc.

[0096] The control unit 11 stores data indicating the relationship between the introducer and the introduced person (data indicating the relationship between users) contained in the acquired on-chain data or off-chain data in the relationship DB 152 in association with the introduced person ID (step S105). The control unit 11 executes a subroutine of a process for detecting product introduction behavior from the introducer to the introduced person based on the received on-chain data (step S106). The subroutine of the process for detecting introduction behavior will be described later.

[0097] The control unit 11 determines whether the behavior included in the received on-chain data is an introduction behavior based on the detection result obtained in the subroutine of the introduction behavior detection process (step S107). If the control unit 11 determines that the behavior is not an introduction behavior (NO in step S107), it terminates the process. If the control unit 11 determines that the behavior is an introduction behavior (YES in step S107), it determines whether to approve the introduction behavior based on data indicating the relationship between the introducer and the introduced person (following, followers, etc.) (step S108).

[0098] Specifically, the control unit 11 determines, for example, whether the introducer and the introduced person are in a "friendship" relationship based on data indicating the relationship between the introducer and the introduced person. If they are in a "friendship" relationship, the control unit 11 approves the introduction action. Alternatively, if they are not in a "friendship" relationship, the control unit 11 does not approve the introduction action.

[0099] If the control unit 11 does not approve the referral behavior (NO in step S108), it terminates the processing. If the control unit 11 approves the referral behavior (YES in step S108), it stores the received on-chain data in the behavior data DB 154 of the mass storage unit 15 (step S109), and stores the received off-chain data in the behavior data DB 154 (step S110).

[0100] Specifically, the control unit 11 associates the introducer ID, product ID, data type, behavior ID, and behavior date and time with the introduced person's wallet address or introduced person ID, and stores them in the behavior data DB 154. The control unit 11 ends the process.

[0101] Note that while Figure 8 illustrates examples of both on-chain data and off-chain data, this is not limited to this, and it may be either on-chain data or off-chain data only.

[0102] 9 is a flowchart showing the processing procedure of the introduction behavior detection subroutine. The control unit 11 of the server 1 acquires the behavior of the introducer (e.g., behavior ID) from the behavior data DB 154 of the mass storage unit 15 based on the introducer ID (step S01). The control unit 11 acquires the behavior of the introduced person (e.g., behavior ID) from the received on-chain data or off-chain data (step S02).

[0103] The control unit 11 determines that the acquired behavior of the introducer and the behavior of the introduced person are the same behavior (step S03). For example, the control unit 11 determines whether the behavior ID of the introducer and the behavior ID of the introduced person match. If they match, the control unit 11 determines that the behavior of the introducer and the behavior of the introduced person are the same behavior. If they do not match, the control unit 11 determines that the behavior of the introducer and the behavior of the introduced person are different behaviors.

[0104] If the two actions are the same (YES in step S03), the control unit 11 detects the action as an introduction action (step S04). The control unit 11 ends the subroutine of the introduction action detection process and returns.

[0105] If the two actions are different (NO in step S03), the control unit 11 acquires the use history information of the introducer from the action data DB 154 (step S05). Specifically, the control unit 11 acquires the use history information including the action ID corresponding to the action and the action date and time from the action data DB 154 based on the introducer ID and the product ID.

[0106] The control unit 11 determines whether the introducer is a regular user of the product based on the acquired usage history information (step S06). Specifically, the control unit 11 determines whether there is any behavior related to the product (e.g., "website browsing," "sample trial," or "purchase") based on the acquired usage history information. If there is any behavior related to the product in the usage history information, the control unit 11 determines that the introducer is a regular user of the product. If there is no behavior related to the product in the usage history information, the control unit 11 determines that the introducer is not a regular user of the product.

[0107] If the introducer is not a regular user of the product (NO in step S06), the control unit 11 proceeds to the processing of step S08, which will be described later. If the introducer is a regular user of the product (YES in step S06), the control unit 11 determines whether the behavior of the identified introduced person is a behavior related to the product (for example, website browsing, SNS chat, sample trial, or information request) based on the on-chain data or off-chain data (step S07).

[0108] If the identified behavior of the introduced person is behavior related to the product (YES in step S07), the control unit 11 proceeds to the processing of step S04. If the identified behavior of the introduced person is not behavior related to the product (NO in step S07), the control unit 11 determines that the introducing behavior has not been detected (step S08). The control unit 11 ends the subroutine of the introducing behavior detection processing and returns.

[0109] The above-described subroutine for detecting referral behavior is merely an example, and the processing may be modified as appropriate according to actual needs. For example, the server 1 may detect a referral behavior based on a specific behavior (such as "receiving a referral NFT").

[0110] 10 is a flowchart showing the processing procedure for determining whether an introduction has been completed based on a numerical value corresponding to an action. The control unit 11 of the server 1 acquires the wallet address or the introduced person's ID (user ID) from the user DB 151 of the mass storage unit 15 (step S121).

[0111] Based on the acquired wallet address or introduced person ID, the control unit 11 acquires a plurality of on-chain data or off-chain data indicating the introduced person's behavior at each point in time from the behavior data DB 154 of the mass storage unit 15 (step S122). The on-chain data or off-chain data includes the introducer ID, product ID, data type, behavior ID or behavior date and time, etc.

[0112] The control unit 11 acquires a numerical value corresponding to each action from the action master DB 153 of the mass storage unit 15 based on the action ID included in each acquired on-chain data or off-chain data (step S123). The control unit 11 adds up the numerical values ​​corresponding to each acquired action (step S124). The control unit 11 determines whether the added numerical value is equal to or greater than a threshold value (for example, 4) (step S125).

[0113] If the added numerical value is less than the threshold value (NO in step S125), the control unit 11 determines that the introduction completion condition is not met, that is, that the introduction is in progress (step S131). The control unit 11 proceeds to the processing of step S129, which will be described later. If the added numerical value is equal to or greater than the threshold value (YES in step S125), the control unit 11 determines that the introduction completion condition is met, that is, that the introduction is complete (step S126).

[0114] The control unit 11 determines a reward to be given to the introducer (step S127). Specifically, the control unit 11 counts the number of introducers who have a relationship with the introduced person based on the introduced person ID from the relationship DB 152. The control unit 11 may determine a reward to be given to the introducer according to the counted number of introducers.

[0115] The control unit 11 grants the determined reward to the relevant introducer via the communication unit 13 (step S128). For example, the control unit 11 may transfer the determined reward to the account of the relevant introducer via the communication unit 13 via a reward granting platform or a banking system, etc. The reward granting process may be performed on the company server 3 side. The server 1 or the company server 3 may grant a reward including virtual currency or NFT, etc. to the introducer via the blockchain 2.

[0116] The control unit 11 calculates the progress of the introduction (step S129). Specifically, the control unit 11 acquires a predetermined target number of behaviors from the memory unit 12 or the mass storage unit 15. The control unit 11 tallies the actual number of behaviors from each acquired on-chain data or off-chain data. The control unit 11 calculates the progress based on the acquired predetermined target number of behaviors and the tallied actual number of behaviors.

[0117] The control unit 11 stores the calculated progress, the determination result of whether the introduction has been completed for the introduced person (such as "introduction completed" or "introduction in progress"), and the determination date and time in the behavior data DB 154 of the mass storage unit 15 in association with the wallet address or the introduced person ID of the introduced person (step S130). The control unit 11 then ends the process.

[0118] Figure 11 is a flowchart showing the processing procedure for determining whether an introduction has been completed based on a specific action. Note that the same reference numerals are used for the same content as in Figure 10, and the description thereof will be omitted. After executing the processing of step S122, the control unit 11 of the server 1 determines whether or not the acquired multiple on-chain data or off-chain data contains on-chain data or off-chain data indicating a specific action (for example, "attend a briefing session" or "add to cart") (step S111).

[0119] If there is on-chain data or off-chain data indicating a specific action (YES in step S111), the control unit 11 executes the process of step S126. If there is no on-chain data or off-chain data indicating a specific action (NO in step S111), the control unit 11 executes the process of step S131.

[0120] According to this embodiment, it is possible to automatically determine whether the introduction has been completed for the introduced person based on on-chain data or off-chain data.

[0121] According to this embodiment, actions other than purchases are recognized as product or service referrals, which makes it possible to encourage users to be proactive in making referrals.

[0122] According to this embodiment, it is possible to grasp the progress of the introduction by determining the progress of the introduction according to the behavior of the introduced person.

[0123] According to this embodiment, it is possible to detect the introduction behavior of a product or service from an introducer to an introduced person based on on-chain data or off-chain data.

[0124] According to this embodiment, when an introduction behavior is detected, it is possible to determine whether or not to approve the introduction behavior based on the relationship between users.

[0125] According to this embodiment, when the introduction completion condition is met, it is possible to determine the reward to be given to each introducer depending on the number of introducers with whom the introducer has a relationship.

[0126] According to this embodiment, by providing a reward to the introducer, it is possible to increase the enthusiasm for making introductions.

[0127] The embodiments disclosed herein are to be considered in all respects as illustrative and not restrictive. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0128] The matters described in each embodiment can be combined with each other. Furthermore, the independent claims and dependent claims described in the claims can be combined with each other in any and all combinations, regardless of the reference format. Furthermore, the claims use a format in which a claim references two or more other claims (multiple claim format), but this is not limited to this. A multiple claim (multi-multi claim) that references at least one other multiple claim may also be used. [Explanation of symbols]

[0129] 1. Information processing device (server) 11 Control section 12 Storage section 13 Communications Department 14 Reading unit 15 Mass storage 151 User DB 152 Relationship DB 153 Behavior Master DB 154 Behavioral Data DB 155 Judgment result DB 1a Portable storage media 1b semiconductor memory 1P control program 2 Blockchain System (Blockchain) 21 nodes 3. Information processing equipment (corporate servers) 31 Control Unit 32 Storage section 33 Communications Department 34 Reading unit

Claims

1. Obtain the wallet address or user identifier of the target user who has received a product or service introduction from another user; Obtaining on-chain data or off-chain data indicating the target user's behavior based on the wallet address or user identifier; Based on the on-chain data or the off-chain data, it is determined whether the target user's behavior satisfies a predetermined introduction completion condition. An information processing method in which processing is performed by a computer.

2. Obtaining a plurality of on-chain data or off-chain data indicating the target user's behavior at each point in time based on the wallet address or user identifier; Adding a numerical value to the target user according to the target user's behavior indicated by each of the on-chain data or off-chain data; By determining whether the numerical value is equal to or greater than a threshold value, it is determined whether the introduction completion condition is satisfied. The information processing method according to claim 1 .

3. Obtaining a plurality of on-chain data or off-chain data indicating the target user's behavior at each point in time based on the wallet address or user identifier; Determine whether the introduction completion condition is satisfied by determining whether the on-chain data or off-chain data indicates a specific behavior among the plurality of on-chain data or off-chain data. The information processing method according to claim 1 .

4. Obtaining a plurality of on-chain data or off-chain data indicating the target user's behavior at each point in time based on the wallet address or user identifier; Determining the progress of the introduction according to the behavior of the target user indicated by each of the on-chain data or off-chain data. The information processing method according to claim 1 .

5. Collecting the on-chain data or off-chain data of each user; Detecting product or service introduction behavior from other users to the target user based on the collected on-chain data or off-chain data. The information processing method according to claim 1 .

6. The on-chain data or off-chain data includes data indicating relationships between users, When the introduction behavior is detected, it is determined whether or not to approve the introduction behavior based on the relationship. The information processing method according to claim 5 .

7. A reward to be given to the other user when the introduction completion condition is satisfied is determined according to the number of users having the relationship. The information processing method according to claim 6.

8. If the on-chain data or the off-chain data of the other user and the target user show the same behavior, the behavior is detected as the referral behavior. The information processing method according to claim 5 .

9. The on-chain data or off-chain data includes user usage history information of products or services, Detecting the referral behavior according to the usage history information indicated by the on-chain data or off-chain data of the other user. The information processing method according to claim 5 .

10. Obtain the wallet address or user identifier of the target user who has received a product or service introduction from another user; Obtaining on-chain data or off-chain data indicating the target user's behavior based on the wallet address or user identifier; Based on the on-chain data or the off-chain data, it is determined whether the target user's behavior satisfies a predetermined introduction completion condition. A program that causes a computer to perform a process.

11. An information processing device including a control unit, The control unit Obtain the wallet address or user identifier of the target user who has received a product or service introduction from another user; Obtaining on-chain data or off-chain data indicating the target user's behavior based on the wallet address or user identifier; Based on the on-chain data or the off-chain data, it is determined whether the target user's behavior satisfies a predetermined introduction completion condition. Information processing device.

Images