Coupon processing device, coupon processing method, and recording medium

Abstract

[Problem] To solve a problem of conventional inconvenience in enhancing effectiveness of coupons to be distributed. [Solution] A coupon processing device 1 comprises: a learning information management unit 112 that stores learning information based on two or more pieces of training data having coupon information distributed to a user, purchase information of a product, and a user attribute value; a first coupon deciding unit 134 that decides for each of a first proportion of users out of two or more users, purchase information satisfying selection conditions using user attribute values, coupon information of selected coupons, and learning information, and decides a coupon to be distributed to a user from two or more types of coupons that are coupons corresponding to the purchase information; a second coupon deciding unit 135 that decides, for each of a second proportion of users out of two or more users, a coupon to be distributed to the user without using the learning information; and a coupon output unit 141 that outputs a coupon identifier for identifying the coupon to be distributed to each of the two or more users, whereby effectiveness of distributed coupons can be enhanced.

Description

Coupon Processing Device, Coupon Processing Method, and Recording Medium

[0001] The present invention relates to a coupon processing device and the like that determine coupons to be distributed to users.

[0002] Conventionally, there has been an information providing device aimed at increasing the sales profit obtained by selling goods (see Patent Document 1). Such an information providing device includes an acquisition unit that acquires information indicating the sales promotion cost of a product, and a determination unit that determines an amount or ratio to be used as a provision cost for providing benefits to users who purchase the product among the sales promotion costs indicated by the information acquired by the acquisition unit, according to the appeal effect of the content related to the sale of the product.

[0003] Also, there has been a technology that enables the distribution of electronic coupons considering the movement state of users (see Patent Document 2).

[0004] Japanese Patent Application Laid-Open No. 2017-228000, Japanese Patent Application Laid-Open No. 2024-9683

[0005] However, in the prior art, it has not been easy to enhance the effectiveness of the distributed coupons.

[0006] The coupon processing device of the first invention comprises: a user management unit that stores two or more user information having one or more user attribute values ​​for two or more users, including two or more users to whom coupons are to be distributed; a learning information management unit that stores learning information based on two or more training data having coupon information relating to coupons previously distributed to users from two or more types of coupons, purchase information relating to the user's purchase of products, and one or more user attribute values; a first coupon determination unit that, for each of the first proportion of users among the two or more users, obtains one or more user attribute values ​​from the user management unit, determines purchase information that satisfies the selection criteria using one or more user attribute values, coupon information of a coupon selected from two or more types of coupons, and learning information, and determines a coupon that corresponds to the purchase information and is to be distributed to the user from two or more types of coupons; a second coupon determination unit that, for each of the second proportion of users among the two or more users, determines a coupon to be distributed to the user without using learning information; and a coupon output unit that outputs a coupon identifier that identifies the coupon to be distributed to each of the two or more users.

[0007] This configuration can increase the effectiveness of the coupons being distributed.

[0008] Furthermore, the coupon processing device of the second invention, compared to the first invention, is a learning model obtained by performing machine learning training using two or more training data sets in which coupon information and one or more user attribute values ​​are explanatory variables and purchase information is the objective variable, and the first coupon determination unit performs machine learning prediction processing using coupon information, one or more user attribute values ​​and the learning model for each user representing a first proportion of the two or more users and for each coupon information of two or more types of coupons, obtains purchase information, determines purchase information that satisfies the selection condition that the purchase information is maximized, and determines the coupon corresponding to the purchase information, which is a coupon to be distributed to the user from two or more types of coupons.

[0009] This configuration can increase the effectiveness of the coupons being distributed.

[0010] Furthermore, the coupon processing device of this third invention, compared to the second invention, includes a user management unit whose user information includes the user's coupon information and the user's purchase information, and periodically acquires coupon information, purchase information, and one or more user attribute values ​​from the user management unit for two or more user information sets, constructs two or more training data sets, and further comprises a learning unit that performs machine learning learning processing using the two or more training data sets to acquire a learning model, and the learning model used by the first coupon determination unit is the coupon processing device which is the learning model acquired by the learning unit.

[0011] This configuration can increase the effectiveness of the coupons being distributed.

[0012] Furthermore, the coupon processing device of the fourth invention is a coupon processing device that, in addition to any one of the first to third inventions, has a user management unit that includes purchase information, a score acquisition unit that acquires a user score for each of two or more users using each user's purchase information, a first coupon determination unit that determines which coupons to distribute to a first percentage of users whose scores satisfy the coupon distribution conditions, and a second coupon determination unit that determines which coupons to distribute to a second percentage of users whose scores satisfy the coupon distribution conditions.

[0013] This configuration allows for narrowing down the users to whom coupons are distributed, thereby increasing the effectiveness of the coupons.

[0014] Furthermore, the coupon processing device of this fifth invention differs from the fourth invention in that the user information in the user management unit includes purchase information from two or more points in time, the score acquisition unit uses the purchase information from two or more points in time to determine a class that indicates the user's purchase tendency, determines a class for the user from among the two or more classes, and acquires a score using that class.

[0015] This configuration allows for narrowing down the users to whom coupons are distributed, thereby increasing the effectiveness of the coupons.

[0016] Furthermore, the coupon processing device of the sixth invention further comprises a coupon management unit that stores coupon information having a coupon identifier and a coupon amount for each of two or more types of coupons, in addition to any one of the first to fifth inventions, and the first coupon determination unit sequentially determines which coupons to distribute to each user, for each of two or more users representing a first percentage of the total number of users, and obtains the amount corresponding to the determined coupon from the coupon management unit, calculates the cumulative amount, determines whether the cumulative amount has reached the budget amount, and if the cumulative amount has reached the budget amount, the coupon processing device does not perform any further processing to determine the user's coupon.

[0017] This configuration allows for overall budget management of the coupons being distributed.

[0018] Furthermore, the coupon processing device of the seventh invention is a coupon processing device that, in addition to any one of the first to sixth inventions, has user information in the user management unit that includes user notification destination information, and a coupon output unit that distributes coupon information identified by a coupon identifier that identifies the coupon to be distributed to two or more notification destinations specified by the notification destination information of each user.

[0019] This configuration can increase the effectiveness of the coupons being distributed.

[0020] The coupon processing device according to the present invention can enhance the effectiveness of the coupons being distributed.

[0021] Conceptual diagram of information system A in Embodiment 1 Block diagram of information system A Flowchart illustrating an example of operation of the coupon processing device 1 Flowchart illustrating an example of score acquisition process Flowchart illustrating an example of first coupon determination process Flowchart illustrating an example of second coupon determination process Flowchart illustrating an example of coupon distribution process Flowchart illustrating an example of training data acquisition process Flowchart illustrating an example of user attribute value accumulation process

[0022] The following describes embodiments of the coupon processing device and the like with reference to the drawings. Note that components denoted by the same reference numerals in these embodiments perform similar operations, and therefore, further explanation may be omitted.

[0023] (Embodiment 1) This embodiment describes a coupon processing device that uses learning information to determine which coupons to distribute to each of the first proportion of target individuals from among two or more types of coupons, and determines which coupons to distribute to each of the second proportion of target individuals without using learning information. The learning information is information based on two or more training data sets that have coupon information related to coupons previously distributed to users, purchase information related to the user's purchase of products, and user attribute values. The learning information is, for example, a learning model obtained through machine learning training.

[0024] In this embodiment, a coupon processing device that builds a learning model periodically (for example, every month) will be described.

[0025] In this embodiment, a coupon processing device is described that obtains a score for a user based on the user's past purchase information and determines that users with the highest scores from all users are eligible to receive coupons.

[0026] In this embodiment, a coupon processing device is described that classifies users into one of three or more classes and obtains a score for the user using the user's purchase information and class.

[0027] This embodiment describes a coupon processing device that manages the amount for each type of coupon and the budget amount for coupon distribution, and determines which coupons to distribute to eligible individuals until the budget amount is reached.

[0028] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is irrelevant. Information X and information Y may be linked, may exist in the same buffer, may information X be contained in information Y, or information Y may be contained in information X, and so on.

[0029] Furthermore, in this specification, selecting or determining information Z means obtaining information Z, obtaining a pointer to information Z, obtaining the ID of information Z, setting a flag on information Z, etc., and it is sufficient to be able to access information Z.

[0030] Figure 1 is a conceptual diagram of information system A in this embodiment. Information system A comprises a coupon processing device 1 and one or more terminal devices 2.

[0031] Coupon processing device 1 is a device that determines which coupons to distribute to users. Coupon processing device 1 may also distribute coupons to users. Coupon processing device 1 may be, for example, a cloud server or an ASP server, but its type is not limited.

[0032] Terminal device 2 is the user's device that receives coupon information. Terminal device 2 can be a personal computer, smartphone, or tablet device, but the type is not limited.

[0033] Figure 2 is a block diagram of information system A in this embodiment.

[0034] The coupon processing device 1 comprises a storage unit 11, a receiving unit 12, a processing unit 13, and a transmission unit 14. The storage unit 11 comprises a user management unit 111, a learning information management unit 112, and a coupon management unit 113. The processing unit 13 comprises a learning unit 131, a score acquisition unit 132, a user selection unit 133, a first coupon determination unit 134, and a second coupon determination unit 135. The transmission unit 14 comprises a coupon output unit 141.

[0035] The terminal device 2 includes a terminal storage unit 21, a terminal receiving unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal receiving unit 25, and a terminal output unit 26.

[0036] The storage unit 11, which constitutes the coupon processing device 1, stores various types of information. These types of information include, for example, user information, learning information, and coupon information, which will be described later.

[0037] The user management unit 111 stores two or more user information entries. User information refers to information about a user. Here, a user includes users who are eligible to receive coupons. User information has one or more user attribute values. User information may also have one or more lottery entry entries, one or more code reading entry entries, and one or more coupon usage entries. User information may also have one or more purchase entries.

[0038] User attribute values ​​can be either static or dynamic. Static user attribute values ​​are those that do not change dynamically. Examples of static user attribute values ​​include gender, age, residential area, and address. Dynamic user attribute values ​​are those that can change dynamically. Examples of dynamic user attribute values ​​include lottery information, code reading information, and coupon-related information.

[0039] Lottery information refers to information about lotteries in which users can spend points to receive rewards. Rewards can be, for example, product exchange vouchers or product discount coupons, but any type of reward is acceptable as long as it provides a benefit to the user. Lottery information is, for example, statistically processed information such as the lottery implementation information described later. Lottery information includes, for example, the current number of lottery points held, the cumulative number of lottery points used, and the number of times a user has participated in each type of lottery based on the number of points required. The current number of lottery points held is the current number of points held to participate in the lottery. Points increase based on, for example, the code reading implementation information described later. The cumulative number of lottery points used is the total number of points used to participate in the lottery. The period for counting the cumulative number of lottery points used can be the entire period (from user registration to the present) or a specific period (for example, the last 6 months). The number of times a user has participated in each type of lottery based on the number of points required is the number of times a user has participated in each type of lottery in a situation where there are two or more types of lotteries with different number of points required to participate.

[0040] Code reading information is information regarding the reading of a code (e.g., a QR code (registered trademark) or a barcode). The code comes with the product purchased by the user. The code is, for example, exposed on the surface of a sticker attached to the product. The code is, for example, printed on the product. The code embeds, for example, the product identifier of the purchased product and the store. Note that by reading the code, points are given to the user of the terminal device 2 that reads the code, for example. The code reading information is, for example, information obtained by statistically processing the code reading execution information described later. The code reading information is, for example, the past cumulative number of reads, the number of reads per day during the membership registration period, the cumulative number of reads by day of the week, the cumulative number of reads by store, the number of reads by month, and the number of weeks with reads in the most recent X (X is a natural number of 2 or more) weeks. The past cumulative number of reads is the total number of times the code has been read in the past. The cumulative number of reads by store is the total number of times the code has been read in the past for each of one or more stores. A store may simply be referred to as a location. The size of each store is not limited. A store is, for example, a municipality, a prefecture, a town or a village, or a mesh of a predetermined size.

[0041] Coupon-related information is information regarding the user's coupons. The coupon-related information is, for example, information obtained by statistically processing the coupon usage information described later. The coupon-related information is, for example, the current number of held coupons for each coupon amount, the past utilization rate for each coupon amount, the past number of uses for each coupon amount, the utilization rate for a predetermined period (e.g., the most recent 3 months) for each coupon amount, and the number of uses for a predetermined period for each coupon amount. Note that the coupon amount is the amount discounted by the coupon. The coupon amount is, for example, "10 yen", "30 yen", or "50 yen".

[0042] Lottery execution information is information regarding the user's execution of a lottery. The lottery execution information includes, for example, the user identifier of the user who executed the lottery, the number of points required for the lottery, and time information. The lottery execution information may include the lottery result (winning or losing). Time information is information for specifying time. The time information is, for example, a date, a date and time, or a time.

[0043] The code reading execution information is information regarding the reading of the code. The code reading execution information includes, for example, the user identifier of the user who read the code, the code identifier for identifying the read code, time information, location information or store information, the product identifier for identifying the product purchased by the user, and the purchase amount of the product. Note that the code identifier may be the same as the product identifier.

[0044] The coupon usage information is information regarding the use of the coupon. The coupon usage information includes, for example, the user identifier of the user who used the coupon, the coupon identifier for identifying the used coupon, and the amount of the coupon.

[0045] The purchase information is information regarding the purchase of products by the user. The purchase information includes, for example, the product identifier for identifying the product purchased by the user and the purchase amount of the product. The purchase information may be the same information as the code reading execution information.

[0046] Learning information is stored in the learning information management unit 112. The learning information is, for example, a learning model, a correspondence table described later, and two or more pieces of teacher data. (1) When the learning information is a learning model

[0047] The learning model is information configured by the learning process of machine learning and is information used in the prediction process of machine learning. The learning model may be referred to as a learning device, a classifier, a classification model, etc. The algorithm of machine learning may be deep learning, random forest, decision tree, SVR, etc. Also, for machine learning, for example, libraries such as TensorFlow (registered trademark), the random forest module of the R language, various machine learning functions such as TinySVM, and various existing libraries can be used.

[0048] The learning model here is a model obtained by providing two or more pieces of teacher data to the learning process module of machine learning and executing the module. The teacher data has coupon information, purchase information, and one or more user attribute values.

[0049] Coupon information refers to information about coupons. This includes, for example, a coupon identifier and the coupon's value. In this context, the coupon information refers to information about coupons previously distributed to users from among two or more types of coupons.

[0050] The one or more user attribute values ​​here preferably include the one or more dynamic attribute values ​​described above. The one or more dynamic attribute values ​​here preferably include one or more attribute values ​​from among lottery information, code reading information, and coupon-related information.

[0051] Here, coupon information and user attribute values ​​of 1 or more are explanatory variables, while purchase information is the dependent variable.

[0052] The learning model is used when the first coupon determination unit 134, described later, performs machine learning prediction processing. (2) When the learning information is a correspondence table

[0053] A correspondence table contains two or more correspondence information sets based on training data. Each correspondence information set has a vector with two or more explanatory variables as elements and a target variable. Another correspondence information set has a vector with one or more coupon information sets and one or more user attribute values ​​as elements and purchase information. In this case, the purchase information includes the coupon identifier of the coupon used at the time of purchase.

[0054] The correspondence table is used when the first coupon determination unit 134, described later, obtains the similarity of the vectors and determines the coupon using that similarity. (3) When the learning information consists of two or more training data.

[0055] The two or more training data points mentioned here are the information provided to the generating AI. In other words, if the learning information consists of two or more training data points, the first coupon determination unit 134 is instructed to determine which coupon to send to the user from the generating AI.

[0056] The coupon management unit 113 stores coupon information for two or more types of coupons. Coupon information refers to information about the coupon. For example, coupon information includes a coupon identifier and the coupon amount.

[0057] The receiving unit 12 receives various instructions and information. These instructions and information include, for example, coupon distribution instructions, user registration instructions, lottery implementation information, code reading implementation information, and coupon usage information.

[0058] The receiving unit 12 receives, for example, coupon distribution instructions from the terminal device 2 of the person in charge at the company operating the coupon processing device 1. The receiving unit 12 receives user registration instructions from the terminal device 2 used by the user. The receiving unit 12 receives lottery implementation information, code reading implementation information, or coupon usage information from the terminal device 2 used by the user, or from devices not shown. Devices not shown include, for example, the server of the company operating the coupon processing device 1, or devices that constitute the store's POS system.

[0059] A user registration instruction is an instruction for user registration. A user registration instruction may have, for example, one or more static user attribute values.

[0060] The processing unit 13 performs various processes. These processes include, for example, those performed by the learning unit 131, the score acquisition unit 132, the user selection unit 133, the first coupon determination unit 134, and the second coupon determination unit 135.

[0061] When the receiving unit 12 receives a user registration instruction, the processing unit 13 performs user registration processing corresponding to the user registration instruction. For example, the processing unit 13 obtains a unique user identifier, constructs user information having the user identifier and one or more user attribute values ​​provided in the user registration instruction, and stores it in the user management unit 111.

[0062] The learning unit 131 obtains coupon information, purchase information, and one or more user attribute values ​​from the user management unit 111 for each of the two or more user information entries, and constructs two or more training data sets. Next, the learning unit 131 uses these two or more training data sets to perform machine learning training and obtain a learning model.

[0063] The learning unit 131 may, for example, obtain coupon information, purchase information, and one or more user attribute values ​​from the user management unit 111 for a predetermined period from the present, construct two or more training data sets, and then use these two or more training data sets to perform machine learning training and obtain a learning model. The predetermined period is, for example, six months, but is not limited to that.

[0064] It is preferable for the learning unit 131 to periodically perform machine learning training and acquire a trained model.

[0065] The learning unit 131 may configure two or more training data sets as described above, and configure a correspondence table in which each of the two or more training data sets is used as corresponding information.

[0066] The score acquisition unit 132 acquires a user's score for each of the two or more users using the user's purchase information or one or more user attribute values. Note that the user's purchase information can also be considered a user attribute value.

[0067] The score acquisition unit 132 acquires a higher score, for example, the more the purchase amount or number of purchases indicated in the user's purchase information. The score acquisition unit 132 calculates the user's score using an increasing function that takes the purchase amount or number of purchases indicated in the user's purchase information as parameters.

[0068] The score acquisition unit 132 uses, for example, purchase information from two or more points in time or one or more user attribute values ​​to determine a class that indicates the user's behavioral tendencies. The unit determines one class for the user from among two or more classes and acquires a score using that class. The score acquisition unit 132 acquires, for example, the score corresponding to the class from the storage unit 11. The two or more classes are, for example, "increased depth," "prevention of outflow," and "continued maintenance."

[0069] The "Increased Depth" class is a class of users who are increasingly purchasing products and using the codes attached to those products. The score acquisition unit 132, for example, obtains the depth at the most recent point in time (the first time point) using the calculation formula "Depth = Total number of code reads / Number of code reads", and obtains the depth at the point before the first time point (the second time point) using the same calculation formula. The score acquisition unit 132, for example, determines whether the "Increased Depth" condition is met, such as "Depth at the first time point - Depth at the second time point" being equal to or greater than a threshold. If the Increased Depth condition is met, the class of the user is set to "Increased Depth".

[0070] The "leak prevention" class is a class of users who have purchased a product and used the code attached to that product, and whose actions should be prevented from ceasing to perform that action. The score acquisition unit 132, for example, M １ (For example, "M １ =3") A continuing user who was reading the code until three months ago said, "M １ -1 If no code has been loaded for a consecutive month (for example, two months), the user's class will be set to "leak prevention".

[0071] The "Continued Maintenance" class includes purchasing a product and assigning a code to that product, at least M ２ (For example, "M ２ =2) This is the class of users who have been using the service for two consecutive months. The score acquisition unit 132, for example, classes users who had "code read" in the previous month and who also have the user attribute value "code read" in the current month, and classes of such users are designated as "continued maintenance".

[0072] The user selection unit 133 determines for each of two or more users whether the user's score meets the coupon distribution conditions. The user selection unit 133 selects users who meet the coupon distribution conditions. Selecting a user involves, for example, obtaining a user identifier. The coupon distribution conditions include, for example, a score being above or above a threshold, or a score being within the top N (where N is 1 or a natural number greater than or equal to 2).

[0073] The user selection unit 133 may, for example, select a user that belongs to a specific class. Examples of specific classes include "increased depth," "prevention of outflow," and "continued maintenance."

[0074] The first coupon determination unit 134 obtains one or more user attribute values ​​from the user management unit 111 for each of the first proportion of users out of two or more users. Next, the first coupon determination unit 134 uses the one or more user attribute values, the coupon information of the coupon selected from two or more types of coupons, and the learning information to determine purchase information that satisfies the selection criteria, and then determines from two or more types of coupons the coupon to be distributed to the user that corresponds to the purchase information. The first proportion is, for example, "90 percent," but is not limited to that. The first proportion may also be "100 percent." Furthermore, the two or more users that form the base when the first coupon determination unit 134 selects the first proportion of users may be users selected by the user selection unit 133, users of the user management unit 111, or randomly selected users, etc.

[0075] The first coupon determination unit 134 may decide which coupons to distribute to only one or more users selected by the user selection unit 133. In other words, the first coupon determination unit 134 may, for example, decide which coupons to distribute to a first-percentage number of users among two or more users whose scores meet the coupon distribution conditions.

[0076] The first coupon determination unit 134 may determine which users to distribute coupons to or which coupons to distribute, taking into consideration the total budget amount for all coupons to be distributed. In other words, it is preferable for the first coupon determination unit 134 to sequentially determine which coupons to distribute to each user, for each of two or more users representing a first percentage of the total number of users, until the budget amount is reached. More specifically, it is preferable for the first coupon determination unit 134 to obtain the amount corresponding to the determined coupon from the coupon management unit 113, calculate the cumulative amount, determine whether the cumulative amount has reached the budget amount, and if the cumulative amount has reached the budget amount, not to perform the process of determining coupons for users further.

[0077] The first coupon determination unit 134 determines the coupons to be distributed to the user, for example, using a learning model, a correspondence table, or a generating AI. (1) When using a learning model

[0078] The first coupon determination unit 134 performs machine learning prediction processing using coupon information, one or more user attribute values, and a learning model for each user representing a first proportion of two or more users, and for each coupon information of two or more types of coupons, to obtain purchase information. Next, the first coupon determination unit 134 determines purchase information that satisfies the selection condition that the purchase information is the largest, and determines from two or more types of coupons the coupon corresponding to the purchase information to be distributed to the user. The purchase information here is, for example, the number of products sold relative to the amount of the coupon used at the time of purchase (number of products sold / coupon amount), and the amount of the products sold relative to the amount of the coupon used at the time of purchase (amount of products sold / coupon amount).

[0079] The first coupon determination unit 134 associates, for example, code reading execution information and coupon usage information that are paired with the same user identifier and whose differences in the included time information are within a threshold. Then, the first coupon determination unit 134 uses the number of sales items "1" corresponding to the associated code reading execution information, or the amount of sales items included in the code reading execution information, and the amount of the coupon included in the coupon usage information to obtain the number of sales items in relation to the amount of the coupon used at the time of purchase, or the amount of sales items in relation to the amount of the coupon used at the time of purchase. (2) In the case of using a correspondence table

[0080] The first coupon determination unit 134 performs the following coupon selection process for each user, representing a first proportion of the two or more users. Specifically, for each coupon information of two or more types of coupons, the first coupon determination unit 134 constructs a vector with one or more coupon information elements and one or more user attribute values ​​as elements, calculates the similarity between this vector and the vectors of two or more corresponding information items in the correspondence table, and obtains the coupon identifier that is paired with the vector with the highest similarity. (3) In the case of generation by AI

[0081] The first coupon determination unit 134 acquires two or more training data points. This process is the same as the process performed by the learning unit 131 described above.

[0082] Next, the first coupon determination unit 134 provides the two or more training data to a generating AI (not shown). The first coupon determination unit 134 then configures a prompt for each of the two or more users, which has one or more user attribute values, to cause the generating AI to answer with a coupon type suitable for the user, provides the prompt to the generating AI, and obtains the coupon identifier to be distributed to each user from the generating AI. The generating AI is, for example, ChatGPT (registered trademark), but is not limited to that.

[0083] The second coupon determination unit 135 determines which coupons to distribute to each user, for each user representing a second proportion of the total number of users (out of two or more users), without using learning information. The second proportion may be, for example, "10 percent," but is not limited to that.

[0084] The second coupon determination unit 135 preferably determines which coupons to distribute to two or more users whose scores meet the coupon distribution conditions, which is the second-highest number of users. The two or more users who meet the coupon distribution conditions are, for example, users selected by the user selection unit 133, or users corresponding to user information managed by the user management unit 111.

[0085] The second coupon determination unit 135 determines, for example, a coupon to be distributed to each of two or more users, randomly, for each user representing a second-percentage of the total number of users.

[0086] The second coupon determination unit 135 determines, for example, for each of two or more users representing a second-percentage number of users, to distribute coupons in the order in which the user identifiers are listed and the coupon information is listed.

[0087] The second coupon determination unit 135 may determine coupons for a second-percentage number of users from the coupons determined by the first coupon determination unit 134, without using the learned information. In this case, for the coupons of the second-percentage number of users, the coupons determined by the first coupon determination unit 134 will not be adopted, and the coupons determined by the second coupon determination unit 135 will be adopted.

[0088] The transmitting unit 14 transmits various types of information. These types of information include, for example, a coupon identifier. The coupon identifier is information that identifies the type of coupon. The coupon identifier may also be called the coupon itself. The coupon identifier may be the same as the coupon information. For example, the coupon identifier specifies the amount of discount applied when purchasing a product.

[0089] The coupon output unit 141 outputs a coupon identifier that identifies the coupon to be distributed to each of two or more users. The coupon output unit 141 may also output coupon information that includes the coupon identifier.

[0090] Here, "output" usually refers to transmission to terminal device 2, but it may also be a concept that includes transmission to other external devices, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0091] The coupon output unit 141 transmits coupon information, identified by a coupon identifier that identifies the coupon to be distributed, to, for example, two or more notification destinations specified by the notification destination information of each user.

[0092] The terminal storage unit 21, which constitutes the terminal device 2, stores various types of information. These types of information include, for example, a user identifier.

[0093] The terminal reception unit 22 receives various information and instructions. These various information and instructions include, for example, user registration instructions, lottery instructions, code reading instructions, and coupon usage instructions. A lottery instruction is an instruction to conduct a lottery. A code reading instruction is an instruction to read a code. A coupon usage instruction is an instruction to use a coupon.

[0094] The means of inputting various information and instructions can be anything, such as a touch panel, keyboard, mouse, or menu screen.

[0095] The terminal processing unit 23 performs various processes. These processes include, for example, converting received information and instructions into information and instructions for transmission. Other processes include, for example, converting received information into information for output.

[0096] The terminal transmission unit 24 transmits various information and instructions to the coupon processing device 1. These various information and instructions include, for example, user registration instructions, lottery implementation information, code reading implementation information, coupon usage information, and user identifiers.

[0097] The terminal receiving unit 25 receives various types of information from the coupon processing device 1. These types of information include, for example, coupon information.

[0098] The terminal output unit 26 outputs various types of information. These types of information include, for example, coupon information.

[0099] Here, "output" is a concept that includes display on a screen, projection using a projector, printing with a printer, sound output, transmission to an external device, storage on a recording medium, and transfer of processing results to other processing devices or other programs.

[0100] The storage unit 11, user management unit 111, learning information management unit 112, coupon management unit 113, and terminal storage unit 21 are preferably made of non-volatile recording media, but can also be made of volatile recording media.

[0101] The process by which information is stored in the storage unit 11, etc. is not relevant. For example, information may be stored in the storage unit 11, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 11, etc., or information input via an input device may be stored in the storage unit 11, etc.

[0102] The receiving unit 12 and the terminal receiving unit 25 are usually implemented by wireless or wired communication means, but they may also be implemented by means of receiving broadcasts.

[0103] The processing unit 13, learning unit 131, score acquisition unit 132, user selection unit 133, first coupon determination unit 134, second coupon determination unit 135, and terminal processing unit 23 can typically be implemented using a processor, memory, etc. The processing procedures of the processing unit 13, etc., are usually implemented in software, and this software is recorded on a recording medium such as ROM. However, it may also be implemented in hardware (dedicated circuitry). The processor can be a CPU, MPU, GPU, etc., and the type is not limited.

[0104] The transmitting unit 14, the coupon output unit 141, and the terminal transmitting unit 24 are usually implemented by wireless or wired communication means, but they may also be implemented by broadcasting means.

[0105] The terminal reception unit 22 can be implemented using device drivers for input means such as touch panels and keyboards, or control software for menu screens, etc.

[0106] The terminal output unit 26 may or may not be considered to include output devices such as a display or speakers. The terminal output unit 26 can be implemented using driver software for an output device, or driver software for an output device and an output device.

[0107] Next, an example of the operation of the coupon processing device 1 will be explained using the flowchart in Figure 3.

[0108] (Step S301) The processing unit 13 determines whether or not it is time to distribute coupons. If it is time to distribute coupons, the process proceeds to step S302; otherwise, it proceeds to step S307. The timing for distributing coupons is, for example, when a predetermined time arrives and a coupon distribution instruction is received from the terminal device 2 of the person in charge at the operating company of the coupon processing device 1.

[0109] (Step S302) The score acquisition unit 132 acquires the scores of two or more users. An example of this score acquisition process will be explained using the flowchart in Figure 4.

[0110] (Step S303) The user selection unit 133 selects two or more users to whom coupons will be distributed, using the scores of two or more users.

[0111] (Step S304) The first coupon determination unit 134 performs the first coupon determination process. An example of the first coupon determination process will be explained using the flowchart in Figure 5.

[0112] (Step S305) The second coupon determination unit 135 performs the second coupon determination process. An example of the second coupon determination process will be explained using the flowchart in Figure 6.

[0113] (Step S306) The coupon output unit 141 distributes coupons to each of the two or more users. The process returns to step S301. An example of such coupon distribution process will be explained using the flowchart in Figure 7.

[0114] (Step S307) The processing unit 13 determines whether or not it is time to compose learning information. If it is time to compose learning information, the process proceeds to step S308; otherwise, it proceeds to step S311. Note that the timing for compiling learning information is usually at regular intervals. For example, the timing for compiling learning information is 12:00 on the 1st of each month, or 0:00 on the last day of March, June, September, and December.

[0115] (Step S308) The learning unit 131 acquires two or more training data. An example of such training data acquisition process will be explained using the flowchart in Figure 8.

[0116] (Step S309) The learning unit 131 acquires learning information using two or more training data sets. The learning unit 131 also performs machine learning training using two or more training data sets to acquire a learning model.

[0117] (Step S310) The learning unit 131 stores the learning information acquired in step S309 in the learning information management unit 112. Return to step S301.

[0118] (Step S311) The receiving unit 12 determines whether or not it has received the lottery information. If it has received the lottery information, it proceeds to step S312; otherwise, it proceeds to step S313. The lottery information is associated with a user identifier.

[0119] (Step S312) The processing unit 13 associates the lottery information received in step S311 with a user identifier and stores it in the user management unit 111. Return to step S301.

[0120] (Step S313) The receiving unit 12 determines whether or not it has received code reading execution information. If it has received code reading execution information, it proceeds to step S314; otherwise, it proceeds to step S315. The code reading execution information is associated with a user identifier.

[0121] (Step S314) The processing unit 13 associates the code reading execution information received in step S313 with a user identifier and stores it in the user management unit 111. Return to step S301.

[0122] (Step S315) The receiving unit 12 determines whether or not it has received coupon usage information. If coupon usage information has been received, the process proceeds to step S316; otherwise, it proceeds to step S317. Note that the coupon usage information is associated with a user identifier.

[0123] (Step S316) The processing unit 13 associates the coupon usage information received in step S315 with a user identifier and stores it in the user management unit 111. Return to step S301.

[0124] (Step S317) The processing unit 13 determines whether or not it is time to accumulate user attribute values. If it is time to accumulate user attribute values, the process proceeds to step S318; otherwise, it returns to step S301.

[0125] (Step S318) The processing unit 13 assigns 1 to counter i.

[0126] (Step S319) The processing unit 13 determines whether or not the i-th user identifier exists in the user management unit 111. If the i-th user identifier exists, the process proceeds to step S320; otherwise, it returns to step S301.

[0127] (Step S320) The processing unit 13 acquires and stores the user attribute value of the user identified by the i-th user identifier. An example of the user attribute value storage process will be explained using the flowchart in Figure 9. The user attribute value storage process is typically the process of acquiring and storing dynamic user attribute values.

[0128] (Step S321) The processing unit 13 increments the counter i by 1. Return to step S319.

[0129] In the flowchart shown in Figure 3, processing is terminated by power-off or processing termination interrupts.

[0130] Next, an example of the score acquisition process in step S302 will be explained using the flowchart in Figure 4.

[0131] (Step S401) The score acquisition unit 132 assigns 1 to counter i.

[0132] (Step S402) The score acquisition unit 132 determines whether or not the i-th user for whom the score is to be acquired exists. If the i-th user exists, the process proceeds to step S403; otherwise, it returns to the higher-level process. The score acquisition unit 132 determines, for example, whether or not the information for the i-th user exists in the user management unit 111.

[0133] (Step S403) The score acquisition unit 132 acquires one or more user attribute values ​​from the user management unit 111 that are present in the i-th user information.

[0134] (Step S404) The score acquisition unit 132 assigns 1 to counter j.

[0135] (Step S405) The score acquisition unit 132 determines whether or not the j-th class exists. If the j-th class exists, the process goes to step S406; otherwise, it goes to step S410. The types of classes are usually predetermined. Examples of class types include "depth increase," "outflow prevention," and "continuous maintenance."

[0136] (Step S406) The score acquisition unit 132 acquires one or more user attribute values ​​to be used to determine the j-th class. Note that the one or more user attribute values ​​here may include lottery implementation information, code reading implementation information, or coupon usage information.

[0137] (Step S407) The score acquisition unit 132 uses one or more user attribute values ​​obtained in step S406 to determine whether the i-th user belongs to the j-th class. If the user belongs to the j-th class, the process proceeds to step S408; otherwise, the process proceeds to step S409.

[0138] (Step S408) The score acquisition unit 132 associates the class identifier of the j-th class with the user identifier of the i-th user. Proceed to step S410.

[0139] (Step S409) The score acquisition unit 132 increments the counter j by 1. Return to step S405.

[0140] (Step S410) The score acquisition unit 132 acquires one or more user attribute values ​​other than the class identifier in order to acquire a score.

[0141] (Step S411) The score acquisition unit 132 acquires the user's score using the user's class identifier and one or more user attribute values ​​acquired in step S410. Note that the score acquisition unit 132 may also acquire the user's score using only the user's class identifier.

[0142] (Step S412) The score acquisition unit 132 stores the score acquired in step S411, associating it with the user identifier of the i-th user.

[0143] (Step S413) The score acquisition unit 132 increments the counter i by 1. Return to step S402.

[0144] Next, an example of the first coupon determination process in step S304 will be explained using the flowchart in Figure 5.

[0145] (Step S501) The first coupon determination unit 134 obtains the first percentage (for example, 90%) of user identifiers.

[0146] (Step S502) The first coupon determination unit 134 assigns 1 to counter i.

[0147] (Step S503) The first coupon determination unit 134 determines whether the i-th user identifier exists among the user identifiers obtained in step S501. If the i-th user identifier exists, the process proceeds to step S504; otherwise, it returns to the higher-level processing.

[0148] (Step S504) The first coupon determination unit 134 obtains one or more user attribute values ​​from the user management unit 111 that are paired with the i-th user identifier.

[0149] (Step S505) The first coupon determination unit 134 assigns 1 to counter j.

[0150] (Step S506) The first coupon determination unit 134 determines whether or not the j-th coupon type exists. If the j-th coupon type exists, the process proceeds to step S507; otherwise, the process proceeds to step S512.

[0151] (Step S507) The first coupon determination unit 134 obtains coupon information for the j-th coupon type from the storage unit 11. Note that the coupon information here may consist only of, for example, a coupon identifier.

[0152] (Step S508) The first coupon determination unit 134 acquires a learning model from the learning information management unit 112.

[0153] (Step S509) The first coupon determination unit 134 uses one or more user attribute values, coupon information, and a learning model to perform machine learning prediction processing and obtain purchase information. The purchase information here is, for example, the number of items purchased in the past month.

[0154] (Step S510) The first coupon determination unit 134 temporarily stores the purchase information obtained in step S509, associating it with the coupon identifier of the j-th coupon.

[0155] (Step S511) The first coupon determination unit 134 increments the counter j by 1. The process returns to step S506.

[0156] (Step S512) The first coupon determination unit 134 obtains a coupon identifier corresponding to the largest purchase information and associates the coupon identifier with the i-th user identifier.

[0157] (Step S513) The first coupon determination unit 134 increments the counter i by 1. The process returns to step S503.

[0158] Next, an example of the second coupon determination process in step S305 will be explained using the flowchart in Figure 6.

[0159] (Step S601) The second coupon determination unit 135 obtains user identifiers for the second percentage (for example, 10%). The obtained user identifiers are, for example, the user identifiers of users for whom coupon distribution has not been decided by the first coupon determination unit 134.

[0160] (Step S602) The second coupon determination unit 135 assigns 1 to counter i.

[0161] (Step S603) The second coupon determination unit 135 determines whether the i-th user identifier exists among the user identifiers obtained in step S601. If the i-th user identifier exists, the process proceeds to step S604; otherwise, it returns to the higher-level processing.

[0162] (Step S604) The second coupon determination unit 135 randomly determines a coupon identifier for the coupon to be distributed to the user identified by the i-th user identifier. The second coupon determination unit 135 associates the coupon identifier with the i-th user identifier.

[0163] (Step S605) The second coupon determination unit 135 increments the counter i by 1. The process returns to step S603.

[0164] Next, an example of the coupon distribution process in step S306 will be explained using the flowchart in Figure 7.

[0165] (Step S701) The coupon output unit 141 assigns 1 to counter i.

[0166] (Step S702) The coupon output unit 141 determines whether or not the i-th user identifier exists among the user identifiers of the users to whom the coupon has been decided. If the i-th user identifier exists, the process proceeds to step S703; otherwise, it returns to the higher-level process.

[0167] (Step S703) The coupon output unit 141 obtains a coupon identifier that is paired with the i-th user identifier.

[0168] (Step S704) The coupon output unit 141 obtains notification destination information paired with the i-th user identifier from the user management unit 111.

[0169] (Step S705) The coupon output unit 141 transmits the coupon information identified by the coupon identifier obtained in step S703 to the notification destination indicated by the notification destination information obtained in step S704.

[0170] (Step S706) The coupon output unit 141 increments the counter i by 1. Return to step S702.

[0171] Next, an example of the training data acquisition process in step S308 will be explained using the flowchart in Figure 8.

[0172] (Step S801) The learning unit 131 assigns 1 to counter i.

[0173] (Step S802) The learning unit 131 determines whether or not the i-th user identifier exists in the user management unit 111. If the i-th user identifier exists, the process proceeds to step S803; otherwise, it returns to the higher-level process.

[0174] (Step S803) The learning unit 131 obtains lottery information for a predetermined period from the user management unit 111, which is paired with the i-th user identifier. The predetermined period is, for example, six months from six months ago to today.

[0175] (Step S804) The learning unit 131 obtains code reading information for a predetermined period from the user management unit 111, which is the code reading information paired with the i-th user identifier.

[0176] (Step S805) The learning unit 131 obtains coupon-related information for a predetermined period from the user management unit 111, which is the coupon-related information paired with the i-th user identifier.

[0177] (Step S806) The learning unit 131 obtains purchase information that is paired with the i-th user identifier.

[0178] (Step S807) The learning unit 131 constructs training data for the i-th user identifier, which includes lottery information, code reading information, coupon-related information, and purchase information, and stores it in a buffer (not shown). Typically, the lottery information, code reading information, and coupon-related information are explanatory variables, and the purchase information is the dependent variable.

[0179] (Step S808) The learning unit 131 increments counter i by 1. Return to step S802.

[0180] Next, an example of the user attribute value accumulation process in step S320 will be explained using the flowchart in Figure 9.

[0181] (Step S901) The processing unit 13 assigns 1 to counter i.

[0182] (Step S902) The processing unit 13 determines whether there is an i-th lottery piece of information that has not yet been acquired and should be acquired. If there is an i-th lottery piece of information, the unit proceeds to step S903; otherwise, the unit proceeds to step S905.

[0183] (Step S903) The processing unit 13 obtains lottery implementation information paired with the target user identifier from the user management unit 111. The processing unit 13 uses the lottery implementation information to obtain the i-th lottery information.

[0184] (Step S904) The processing unit 13 increments the counter i by 1. The process returns to step S902.

[0185] (Step S905) The processing unit 13 assigns 1 to counter i.

[0186] (Step S906) The processing unit 13 determines whether there is an i-th code reading information that has not yet been acquired and should be acquired. If there is an i-th code reading information, the unit proceeds to step S909; otherwise, the unit proceeds to step S911.

[0187] (Step S907) The processing unit 13 obtains code reading execution information paired with the target user identifier from the user management unit 111. The processing unit 13 uses the code reading execution information to obtain the i-th code reading information.

[0188] (Step S908) The processing unit 13 increments the counter i by 1. The process returns to step S906.

[0189] (Step S909) The processing unit 13 assigns 1 to counter i.

[0190] (Step S910) The processing unit 13 determines whether there is an i-th coupon-related piece of information that has not yet been acquired and should be acquired. If there is an i-th coupon-related piece of information, the process goes to step S911; otherwise, the process goes to step S913.

[0191] (Step S911) The processing unit 13 obtains coupon usage information paired with the target user identifier from the user management unit 111. The processing unit 13 uses the coupon usage information to obtain the i-th coupon-related information.

[0192] (Step S912) The processing unit 13 increments the counter i by 1. The process returns to step S912.

[0193] (Step S913) The processing unit 13 stores the acquired lottery information, acquired code reading information, and acquired coupon-related information in the user management unit 111, paired with the target user identifier. It then returns to the higher-level processing unit.

[0194] As described above, this embodiment makes it possible to increase the effectiveness of the coupons that are distributed.

[0195] Furthermore, according to this embodiment, the number of users to whom coupons are distributed can be narrowed down in order to further enhance the effectiveness of the coupons being distributed.

[0196] Furthermore, according to this embodiment, it is possible to manage the overall budget for the coupons to be distributed.

[0197] The processing in this embodiment may be implemented by software. This software may be distributed via software download or the like. Alternatively, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that implements information system A in this embodiment is the following program. In other words, this program is a program that enables a computer to access a user management unit that stores user information for two or more users, including two or more users to whom coupons are to be distributed, each having one or more user attribute values ​​for two or more users, and a learning information management unit that stores learning information based on two or more training data having coupon information about coupons previously distributed to users from two or more types of coupons, purchase information about the user's purchase of products, and one or more user attribute values. The computer then functions as a first coupon determination unit that, for each of the first proportion of the two or more users, obtains one or more user attribute values ​​from the user management unit, determines purchase information that satisfies the selection criteria using the one or more user attribute values, the coupon information of the coupon selected from the two or more types of coupons, and the learning information, and determines the coupon that corresponds to the purchase information and is to be distributed to the user from the two or more types of coupons; a second coupon determination unit that, for each of the second proportion of the two or more users, determines the coupon to be distributed to the user without using the learning information; and a coupon output unit that outputs a coupon identifier that identifies the coupon to be distributed to each of the two or more users.

[0198] Furthermore, the computer running the above program may be a single computer or multiple computers. In other words, it may perform centralized processing or distributed processing.

[0199] Furthermore, in each of the above embodiments, each process may be implemented by centralized processing by a single device, or by distributed processing by multiple devices.

[0200] It goes without saying that the present invention is not limited to the embodiments described above, and various modifications are possible, all of which are also included within the scope of the present invention.

[0201] As described above, the coupon processing device according to the present invention has the effect of increasing the effectiveness of the coupons being distributed, and is useful as a server or the like for distributing coupons.

Claims

1. A coupon processing device realized by a recording medium and a processor, wherein the recording medium comprises a user management unit that stores two or more user information having one or more user attribute values ​​for two or more users, including two or more users to whom coupons are to be distributed; a learning information management unit that stores learning information based on two or more training data having coupon information relating to coupons previously distributed to users from two or more types of coupons, purchase information relating to the user's purchase of products, and one or more user attribute values, and the processor performs a first coupon determination step in which, for each of a first proportion of the two or more users, one or more user attribute values, a first coupon determination step in which, using the one or more user attribute values, the coupon information of a coupon selected from two or more types of coupons, and the learning information, a coupon that satisfies the selection criteria, and a coupon to be distributed to the user from the two or more types of coupons that corresponds to the purchase information; a second coupon determination step in which, for each of a second proportion of the two or more users, a coupon to be distributed to the user is determined without using the learning information; and a coupon output step in which a coupon identifier that identifies the coupon to be distributed to each of the two or more users is output.

2. The coupon processing device according to claim 1, wherein the learning information is a learning model obtained by performing a machine learning learning process using two or more training data sets in which coupon information and one or more user attribute values ​​are explanatory variables and the purchase information is the objective variable, and the processor, in the first coupon determination step, performs a machine learning prediction process using the coupon information, one or more user attribute values ​​and the learning model for each user representing a first proportion of the two or more users and for each coupon information of two or more types of coupons, obtains purchase information, determines purchase information that satisfies a selection condition which is that the purchase information is the maximum, and determines from the two or more types of coupons the coupon corresponding to the purchase information to be distributed to the user.

3. The coupon processing device according to claim 2, wherein the user information of the user management unit includes the user's coupon information and the user's purchase information, the processor periodically obtains coupon information, purchase information, and one or more user attribute values ​​from the user management unit, which are contained in two or more sets of user information, to form two or more sets of training data, and further executes a learning step to perform machine learning learning processing using the two or more sets of training data to obtain the learning model, and the learning model used in the first coupon determination step is the learning model obtained in the learning step.

4. The coupon processing apparatus according to claim 1, wherein the user information of the user management unit includes purchase information, the processor further performs a score acquisition step of acquiring a user score for each of the two or more users using the purchase information of each user, the processor determines in the first coupon determination step to distribute coupons to a first percentage of the two or more users whose scores satisfy the coupon distribution conditions, and the processor determines in the second coupon determination step to distribute coupons to a second percentage of the two or more users whose scores satisfy the coupon distribution conditions.

5. The coupon processing device according to claim 4, wherein the user information of the user management unit includes purchase information at two or more points in time, and the processor, in the score acquisition step, uses the purchase information at two or more points in time to determine a class of the user that indicates the user's purchase tendency, and uses that class to acquire the score.

6. The coupon processing device according to claim 1, wherein the recording medium further comprises a coupon management unit that stores coupon information having a coupon identifier and a coupon amount for each of two or more types of coupons, and the processor, in the first coupon determination step, sequentially determines which coupons to distribute to each user for a first percentage of the two or more users, obtains the amount corresponding to the determined coupon from the coupon management unit, calculates the cumulative amount, determines whether the cumulative amount has reached the budget amount, and if the cumulative amount has reached the budget amount, does not perform the process of determining the user's coupon further.

7. The coupon processing device according to claim 1, wherein the user information of the user management unit includes user notification destination information, and the processor distributes coupon information identified by a coupon identifier that identifies the coupon to be distributed to the notification destinations specified by the notification destination information of each of the two or more users in the coupon output step.

8. A coupon processing method implemented by a recording medium and a processor, wherein the recording medium comprises a user management unit that stores two or more user information having one or more user attribute values ​​for two or more users, including two or more users to whom coupons are to be distributed, and a learning information management unit that stores learning information based on two or more training data having coupon information relating to coupons previously distributed to users from among two or more types of coupons, purchase information relating to the user's purchase of products, and one or more user attribute values, and the processor performs a first coupon determination step, a second coupon determination step, and a coupon output step, wherein in the first coupon determination step, for each of the two or more users representing a first proportion, one or more user attribute values ​​are obtained from the user management unit, and purchase information that satisfies the selection criteria is determined using the one or more user attribute values, the coupon information of the coupon selected from among the two or more types of coupons, and the learning information, and the coupon that corresponds to the purchase information and is to be distributed to the user is determined from among the two or more types of coupons, and in the second coupon determination step, for each of the two or more users representing a second proportion, the coupon to be distributed to the user is determined without using the learning information, A coupon processing method comprising outputting a coupon identifier in the coupon output step that identifies the coupon to be distributed to each of the two or more users.

9. A computer capable of accessing a user management unit that stores user information for two or more users, including two or more users to whom coupons are to be distributed, each having one or more user attribute values ​​for two or more users; a learning information management unit that stores learning information based on two or more training data having coupon information about coupons previously distributed to users from two or more types of coupons, purchase information about the user's purchase of products, and one or more user attribute values; a first coupon determination unit that, for each of the first proportion of the two or more users, obtains one or more user attribute values ​​from the user management unit, determines purchase information that satisfies the selection criteria using the one or more user attribute values, coupon information for a coupon selected from two or more types of coupons, and learning information, and determines the coupon to be distributed to the user from the two or more types of coupons that corresponds to the purchase information; a second coupon determination unit that, for each of the second proportion of the two or more users, determines the coupon to be distributed to the user without using the learning information; and a recording medium that records a program to function as a coupon output unit that outputs a coupon identifier that identifies the coupon to be distributed to each of the two or more users.

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