JP7347963B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

BACKGROUND ART Conventionally, a technique is known in which the availability of seats is detected in real time using a monitoring device, and the more seats are available, the more advantageous the benefit is, and the longer the expiration date is provided.

Patent No. 6126734

However, with the above-mentioned conventional technology, it is not always possible to appropriately determine the ranking of objects with limited capacity, such as stores, depending on the user. For example, the above-mentioned conventional technology is merely a technology that provides benefits depending on the availability of seats at a store.

The present application has been made in view of the above, and aims to appropriately determine the ranking of objects with limited capacity according to users.

In the information processing device according to the present application, a first business operator that operates a target with limited capacity pays a reward to a second business operator that performs an action that contributes to a user's referral to the target. an acquisition unit that acquires remuneration information indicating the target user, and ranking determination information used to determine the ranking of the target in relation to the target user who is the target of customer referral to the target; a calculation unit that calculates a score used for ranking the object based on the remuneration information and the ranking determination information; and a calculation unit that determines the ranking of the object based on the score calculated by the calculation unit. , a determining unit that determines target information to be provided to the user according to the ranking , and a providing unit that provides the user with the target information that has been determined to be provided by the determining unit, and the calculating unit is configured to determine the ranking. A first score indicating the probability of the target user's customer referral is calculated based on the business information, and a second score used for ranking the target is calculated based on the first score and the remuneration information. The determining unit is characterized in that it determines the ranking of the objects to be provided to the user based on the second score .

According to one aspect of the embodiment, the ranking of objects with limited capacity can be appropriately determined depending on the user.

FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of an information processing system according to an embodiment. FIG. 4 is a diagram illustrating a configuration example of a terminal device according to an embodiment. FIG. 5 is a diagram illustrating a configuration example of a business operator's device according to the embodiment. FIG. 6 is a diagram illustrating a configuration example of an information processing device according to an embodiment. FIG. 7 is a diagram illustrating an example of a customer referral related information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of the first model storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of a bid information storage unit according to the embodiment. FIG. 10 is a diagram illustrating an example of a ranking determination information storage unit according to the embodiment. FIG. 11 is a diagram illustrating an example of the second model storage unit according to the embodiment. FIG. 12 is a diagram illustrating an example of the provided information storage unit according to the embodiment. FIG. 13 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 14 is a diagram illustrating an example of a business information storage unit according to the embodiment. FIG. 15 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 16 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 17 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 18 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 19 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 20 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device.

DESCRIPTION OF THE PREFERRED EMBODIMENTS An information processing apparatus, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail below with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Further, in each of the embodiments below, the same parts are given the same reference numerals, and redundant explanations will be omitted.

(Embodiment)
[1. An example of information processing]
First, an example of information processing according to the embodiment will be described using FIG. 1. FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 1 shows an example in which information processing according to the embodiment is executed by an information processing apparatus 100. In addition, in the examples of FIGS. 1 and 2, a store will be explained as an example of a target with limited capacity, but the target can be any type of target with limited capacity. There may be. For example, the target may be a predetermined moving object such as a taxi or a private car. For example, the target may be public transportation such as a train or bus. For example, the target may be a predetermined space such as a conference room or an event venue where events, activities, courses, etc. are held. For example, the target may be a predetermined space such as a free address office, an Internet cafe, a capsule hotel, or a hotel. Capacity is not limited to spatial limitations, but may also be limitations on the number of participants. Further, the capacity is not limited to a limit on the number of people, but may also be a limit on space capacity such as the number of chairs or the number of seats. Moreover, the object to be accommodated in an object with limited capacity is not limited to a person, but may also be an object. For example, the target may be a storage space for a mobile object related to logistics, such as a truck bed. Further, the object here may be various objects such as a vacant house, a vacant room, a vacant warehouse, a suitcase, a bag, a coin locker, a coin laundry, a massage chair, etc., in which people or objects are stored. Note that the above is an example, and the processing described later may be performed on various objects to which the following information processing is applicable due to limited capacity.

FIG. 1 shows a case where the information processing device 100 accepts a bid amount bid by a store regarding customer referral (case of business operator CM3). Note that, as in the case of business operator CM1 and business operator CM2 shown in FIG. 1, the information processing device 100 may accept a bid amount from a store that has requested information to assist in bidding regarding customer referrals. For example, the information that assists in bidding regarding customer referrals is information regarding the degree of customer referrals according to remuneration (remuneration amount). In this case, the information processing device 100 provides information that assists in bidding regarding customer referrals. Hereinafter, information that assists in bidding regarding customer referrals will be appropriately referred to as bid assist information. Further, information related to customer referrals by users (users) is appropriately referred to as customer referral related information. FIG. 1 shows a case where the degree of customer referral is calculated based on customer referral related information of a store that has requested bid assist information. An example of information processing according to the embodiment will be described below with reference to FIG.

The information processing device 100 receives a request for bid assist information transmitted by the business operator CM1 via the business operator device 20-1 (step 101). Note that business operator CM1 shown in FIG. 1 is a business operator that has a predetermined relationship with store SP1. For example, business operator CM1 is a business operator that operates (manages) store SP1. Hereinafter, the business operator having a predetermined relationship with the store will be referred to as a first business operator. In FIG. 1, store SP1 is a pub. Hereinafter, the store may be read as a business operator (first business operator). For example, store SP1 may be read as business operator CM1. In step S101, the information processing device 100 receives a request for bid assist information and acquires customer referral related information of the store SP1. For example, the information processing device 100 may acquire customer referral related information transmitted from the business operator device 20-1, or may acquire customer referral related information stored in a predetermined storage unit. The customer referral related information may include information (conditions) specified (requested) by the store.

The customer referral related information may include any information as long as it is specified by the store. For example, information related to customer referrals may include user attributes specified by the store (e.g., demographic attributes), whether or not the user uses the store (whether or not the user is a repeat customer), the time of day, and the user's arrival time. It may also include information indicating. To give a specific example, if a store wants to attract women in their 20s, they may specify an attribute such as "20s: Female" as customer referral related information. Further, if the store wants to acquire a new user who does not use the store, the store may specify an attribute such as "new user" as customer referral related information. Furthermore, if you want to attract customers during lunch time from 13:00 to 14:00, you may specify conditions related to the time zone such as "13:00 to 14:00" as the customer referral related information. Furthermore, if you want to attract people who can come to the store within 10 minutes from the current time, you may specify a condition regarding the user's arrival time, such as "within 10 minutes from the current time", as customer referral related information.

The customer referral related information may include the situation regarding store occupancy. Here, the situation regarding vacancy may be the vacancy situation of a store. Specifically, the situation regarding vacancy may be the number of vacant seats in the store such as "15 seats vacant", the congestion rate in the store such as "10% vacant", or it may be the number of vacant seats in the store such as "15 seats vacant", or it may be the congestion rate in the store such as "10% vacant", or it may be the number of vacant seats in the store such as "15 seats vacant". It may be indicated by an expression. For example, the customer referral related information may include the status regarding store occupancy for each day of the week or each time period. Further, the customer referral related information may include information specified by the store and the status regarding the store's occupancy. Further, the customer referral related information may include information specified by the store depending on the situation regarding the store's occupancy.

The information processing device 100 acquires a predetermined function related to customer referral. For example, the information processing device 100 acquires a predetermined function stored in a predetermined storage unit. For example, the information processing device 100 acquires a function corresponding to a reward as a predetermined function related to customer referral. Note that the predetermined function may be a model that outputs a score. For example, the predetermined function may be a model that calculates a score by inputting a reward. The information processing device 100 uses a model related to customer referrals (hereinafter referred to as "first model M1" as appropriate) to calculate a score indicating an index regarding customer referrals (step S102). In FIG. 1, the information processing device 100 calculates the degree of customer referral according to the reward. Here, the degree of customer referral is the degree to which a predetermined condition regarding the user's customer referral is satisfied. In other words, the degree of customer referral is the degree to which a reward related to customer referral can be received. Note that the degree calculated by the information processing apparatus 100 may be a probability. The information processing device 100 calculates the degree of customer referral when the remuneration is input into the first model M1. Equation (1) shown below is an example of the predetermined function shown in FIG. In the example of FIG. 1, the first model M1 corresponds to the following equation (1). The predetermined function shown in FIG. 1 may calculate a score according to a plurality of factors. That is, the predetermined function shown in FIG. 1 is a model that outputs the degree of customer referral. Specifically, the predetermined function shown in FIG. 1 is a model that outputs the degree of customer referral by inputting customer referral related information.

x1, x2, . . . , xn shown in equation (1) are elements for calculating the score. The elements shown in equation (1) are customer referral related information. The elements shown in equation (1) include remuneration, information specified by the store, the status regarding store occupancy, and information held by an external server. Here, x1 shown in equation (1) corresponds to the reward, x2 corresponds to information specified by the store, x3 corresponds to the situation regarding the store's availability, and x4 corresponds to information held by the external server. shall be taken as a thing. Further, the information specified by the store x2 is, for example, the user's attributes, the presence or absence of use, and the time period. Furthermore, equation (1) may include elements corresponding to the attributes of the user specified by the store, the presence or absence of use specified by the store, and the time period specified by the store. Note that the information held by the x4 external server is, for example, information regarding users around the store, information regarding the environment around the store (for example, weather), and information regarding store evaluation. Furthermore, equation (1) may include elements corresponding to each piece of information held by the external server.

In FIG. 1, when the information processing device 100 inputs a reward of 50 yen to the first model M1, it calculates the probability X% (step S102-1). Furthermore, when the reward of 100 yen is input to the first model M1, the information processing device 100 calculates the probability Y% (step S102-2). Further, when the information processing device 100 inputs the reward of 200 yen to the first model M1, the information processing device 100 calculates the probability Z% (step S102-3). Although FIG. 1 shows a case where the information processing device 100 calculates the probability regarding customer referral using the first model M1 stored in a predetermined storage unit, the information processing device 100 calculates the probability regarding customer referral. You may generate or learn a first model M1 that outputs . Note that the process by which the information processing apparatus 100 generates or learns the first model M1 will be described later.

The reward is a success fee paid to the customer. The remuneration is paid to a business providing a predetermined web service (hereinafter appropriately referred to as a "second business") when predetermined conditions are met. Specifically, the reward is paid by a first business operator that operates a store to a second business operator that has performed an action that contributes to the user's referral to the store. That is, the remuneration is paid when the information processing device 100 determines that the second business operator has performed an action that contributes to customer referrals to the store. Note that information indicating remuneration is appropriately referred to as remuneration information. Further, processing for paying remuneration will be described later with reference to FIG.

The information processing device 100 generates a graph GR1 based on the first model M1 (step S103). For example, the information processing device 100 generates a graph GR1 indicating the degree of customer referral according to remuneration. The information processing device 100 may use the graph GR1 as bid assist information. Note that although the example shown in FIG. 1 shows a case where the information processing device 100 generates the graph GR1, the information processing device 100 can generate any information as long as it assists the store's bidding. good. For example, the information processing device 100 may generate a table (list) that shows the remuneration and the degree of customer referral in association with each other. The information processing device 100 may use the generated information as bid assist information.

The information processing device 100 provides bidding assist information (step S104). In the example of FIG. 1, the information processing device 100 transmits the graph GR1 to the operator device 20-1. Thereby, the information processing device 100 can develop attractive customer-attracting activities according to the degree of customer referral for the store.

The information processing device 100 receives bid information bid by the business operator CM1 via the business operator device 20-1 (step S105). The information processing device 100 receives bid information regarding customer referrals per person bid by the business operator CM1. FIG. 1 shows that business operator CM1 bids 100 yen for customer referral. Note that the bid amount (100 yen) that business operator CM1 bids regarding customer referral is the amount of remuneration to be paid to the second business operator when predetermined conditions regarding customer referral are satisfied.

Similarly, the information processing device 100 receives a request for bid assist information transmitted by the business operator CM2 via the business operator device 20-2 (step 106). Note that business operator CM2 shown in FIG. 1 is a first business operator that has a predetermined relationship with store SP2. For example, business operator CM2 is a first business operator that operates store SP2. In FIG. 1, store SP2 is a bakery. In addition, below, store SP2 may be read as business operator CM2 as appropriate. In step S106, the information processing device 100 receives the request for bid assist information and acquires the customer referral related information of the store SP2. For example, the information processing device 100 may acquire customer referral related information transmitted from the business operator device 20-2, or may acquire customer referral related information stored in a predetermined storage unit.

The information processing device 100 provides bid assist information corresponding to the store SP2 based on the same processing as steps S102 and S103 (step S107). Specifically, the information processing device 100 generates a first model M1 corresponding to the store SP2 based on the customer referral related information of the store SP2, and uses the information generated based on the first model M1 as bid assist information. Provided as. Information processing device 100 transmits bid assist information corresponding to store SP2 to business operator device 20-2.

The information processing device 100 receives bid information bid by the business operator CM2 via the business operator device 20-2 (step S108). The information processing device 100 receives bid information regarding customer referrals per person bid by the business operator CM2. FIG. 1 shows that business operator CM2 bids 200 yen for customer referral. Note that the bid amount (200 yen) that business operator CM2 bids regarding customer referral is the amount of remuneration to be paid to the second business operator when predetermined conditions regarding customer referral are satisfied.

FIG. 1 shows a case where the business operator CM3 does not request bid assist information, unlike the business operators CM1 and CM2. In this case, the information processing device 100 does not accept a request for bid assist information from the business operator CM3 via the business operator device 20-3. Furthermore, the information processing device 100 does not need to transmit the bidding assist information corresponding to the store SP3 to the business operator device 20-3. Note that business operator CM3 shown in FIG. 1 is a first business operator that has a predetermined relationship with store SP3. For example, business operator CM3 is a first business operator that operates store SP3. In FIG. 1, store SP3 is a restaurant. In addition, below, store SP3 may be read as business operator CM3 as appropriate.

The information processing device 100 receives bid information bid by the business operator CM3 via the business operator device 20-3 (step S109). The information processing device 100 receives bid information regarding customer referrals per person bid by the business operator CM3. FIG. 1 shows that business operator CM3 bids 150 yen for customer referral. Note that the bid amount (150 yen) that business operator CM3 bids regarding customer referral is the amount of remuneration to be paid to the second business operator when predetermined conditions regarding customer referral are satisfied.

Note that there is no relationship in the order of the processing of steps S101 to S105 regarding store SP1, the processing of steps S106 to S108 regarding store SP2, and the processing of step S109 regarding store SP3.

Furthermore, although the example shown in FIG. 1 shows a case where the information processing apparatus 100 accepts bids from businesses CM1 to CM3, it is assumed that there is no limit to the number of first businesses that accept bids. That is, the information processing device 100 may accept bid information transmitted from three or more first businesses.

In the example of FIG. 1, the stores operated by the first business operator are food-related stores such as a pub (store SP1), a bakery (store SP2), and a restaurant (store SP3). Bids may be accepted from stores belonging to other categories. For example, the information processing device 100 may accept bids from a first business operator that operates stores related to housing such as hotels and private lodging.

Note that the remuneration may be the amount of remuneration paid for each customer referred, or may be the amount of remuneration paid for the number of customers referred. For example, the remuneration of 50 yen for inputting into the first model M1 may be the amount of remuneration paid for each customer referred.

Note that the bid amount may be a bid amount for each customer sent, or may be a bid amount for a plurality of customer referrals. For example, the bid of 100 yen made by the business operator CM1 may be the bid amount regarding customer referrals per person.

[2. An example of information processing]
Next, an example of information processing according to the embodiment will be described using FIG. 2. FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. FIG. 2 shows an example in which information processing according to the embodiment is executed by the information processing apparatus 100. In the example of FIG. 2, a case is shown in which the user U1 is the user (hereinafter also referred to as "target user") who is the object (target) of sending customers to the store. Note that the target user may be determined (selected) as appropriate, such as a user who uses a predetermined web service such as a web service provided by the information processing device 100, a user whose location information can be obtained, etc. .

FIG. 2 shows a case where information to be provided to a target user is determined based on the bid amount bid by a store regarding customer referral. In FIG. 2, the information processing device 100 determines the ranking of stores that have made bids regarding customer referrals. Specifically, the information processing device 100 determines the ranking of stores corresponding to the target users. In FIG. 2, the information used to determine the ranking of stores is appropriately referred to as ranking determining information. An example of information processing according to the embodiment will be described below using FIG. 2. Note that the map MP1 indicates the positional relationship between the store SP1, the store SP2, the store SP3, and the user U1. Map MP1 indicates that user U1 has moved to store SP3 after the process in step S207.

The information processing device 100 acquires user information regarding the user U1 via the terminal device 10-1 (step S201). In FIG. 2, the information processing apparatus 100 acquires position information of the user U1. For example, the information processing device 100 acquires location information of the user U1 located within a predetermined range from the store.

The information processing device 100 acquires ranking determination information (step S202). In the example shown in FIG. 1, the information processing device 100 acquires the ranking determining information stored in the ranking determining information storage unit 124, but the information processing device 100 acquires the ranking determining information transmitted from the external server. Good too. Here, the ranking determining information is predetermined information. For example, the ranking determining information may be information predetermined for each store, or may be information commonly determined for each store. For example, the ranking determining information may be information (conditions) specified in advance by the store, or may be information (conditions) determined by the information processing device 100. The ranking determining information may be any information as long as it is used for ranking the stores corresponding to the target users.

The ranking determining information may include information (conditions) regarding the target user. Specifically, the ranking determining information may include information regarding target users to whom the store requests to send customers. For example, the information for determining the ranking includes the target user's attributes (e.g., demographic attributes), the target user's behavior history, whether the target user uses the store (whether or not he or she is a repeat customer), and the target user's location. information, information indicating the distance between the target user and the store, etc. For example, the information processing device 100 may acquire information regarding the target user's attributes such as “female in her 20s”. For example, the information processing device 100 may acquire information regarding the target user's behavior history such as "accessed restaurant information site 10 or more times." For example, the information processing device 100 may acquire information regarding whether or not the target user uses a store, such as "Uses a store." For example, the information processing device 100 may acquire information related to the target user's location information such as "around AA station" or "in BB city". For example, the information processing device 100 may acquire information regarding the distance between the target user and the store, such as "distance to the store is within 100 meters."

The information for ranking determination may include the situation regarding the occupancy of the store. Here, the situation regarding fullness may be the reservation situation of a store. Note that the reservation status of the store may be the current reservation status or the future reservation status. For example, the reservation status of a store may be a real-time reservation status, a reservation status one second later, or a reservation status one month later. For example, the ranking determining information may include reservation status including a time slot such as "18:00 to 19:00: Many reservations." In addition, the reservation status may be the number of reservations such as "10 reservations", or the reservation congestion rate such as "90% reservation rate" or "9 reserved seats out of 10 seats in the store". However, it may be expressed using another expression. Furthermore, the ranking determining information may include the price range of the store. Note that the price range of a store may be a price range for each day of the week or each time period. Moreover, the information for ranking determination may also include the evaluation of a store. Note that the store evaluation may be an evaluation by a user who has used the store.

The information processing device 100 acquires a predetermined function related to customer referral. The information processing device 100 obtains a predetermined function different from the first model M1. The information processing device 100 obtains a predetermined function that does not include remuneration as input information. For example, the information processing device 100 acquires a function according to the ranking determination information. The information processing device 100 calculates a first score used for ranking stores. Note that the first score is a score calculated for each store. Here, the first score is a score indicating the probability regarding the target user's customer referral. For example, the first score may be a score indicating the probability that the target user will perform an action that will lead to the profit of the first business operator (business operator that operates the store). Specifically, the first score may be a score indicating the probability that the target user will come to the store. Furthermore, the predetermined function may be a model that outputs the first score. For example, the predetermined function may be a model that calculates the first score by inputting ranking determination information. Hereinafter, the model that outputs (calculates) the first score will be appropriately referred to as "second model M2." Equation (2) shown below is an example of the predetermined function shown in FIG. In the example of FIG. 2, the second model M2 corresponds to the following equation (2). The predetermined function shown in FIG. 2 may calculate a score according to a plurality of factors.

y1, y2, ..., yn shown in equation (2) are elements for calculating the score. The element shown in equation (2) is information for ranking determination. The elements shown in equation (2) include information regarding the target user, the status regarding the store's occupancy, the store's price range, the store's evaluation, and information held by an external party. Here, y1 shown in equation (2) corresponds to information regarding the target user, y2 corresponds to the situation regarding store availability, y3 corresponds to the store's price range, y4 corresponds to the store's evaluation, Assume that y5 corresponds to information held externally. Moreover, the information regarding the target user of y1 is, for example, the attributes of the target user and the distance between the target user and the store. For example, equation (2) may include elements corresponding to the attributes of the target user and the distance between the target user and the store. Further, the status regarding the vacancy of the store y2 is, for example, the vacancy status of the store or the reservation status of the store. For example, equation (2) may include elements corresponding to the availability of seats at the store and the reservation status of the store. Note that the information held by the external server of y4 is, for example, information regarding users around the store, information regarding the environment around the store (for example, weather), and information regarding the evaluation of the store. Furthermore, equation (2) may include elements corresponding to each piece of information held by the external server.

In FIG. 2, when the information processing apparatus 100 inputs the ranking determination information corresponding to the store SP1, it calculates a first score "SC1" (step S203-1). Furthermore, when the information processing apparatus 100 inputs the ranking determination information corresponding to the store SP2, the information processing apparatus 100 calculates a first score "SC2" (step S203-2). Further, when the information processing device 100 inputs the ranking determination information corresponding to the store SP3, the information processing device 100 calculates a first score "SC3" (step S203-3). Note that although FIG. 2 shows a case where the information processing device 100 calculates the first score used for ranking stores using the second model M2 stored in a predetermined storage unit, the information processing device 100 may generate or learn a second model M2 that outputs a first score used for ranking stores. Note that the process by which the information processing apparatus 100 generates or learns the second model M2 will be described later.

The information processing device 100 calculates a second score used for ranking stores based on the first score and remuneration information (step S204). Note that the second score is a score calculated for each store. Specifically, the second score is a score that determines the ranking of the store. Here, the second score is a score related to customer referrals of the target user. For example, the second score is a score based on the bid amount and the probability that the target user will come. In the example shown in FIG. 2, the information processing device 100 calculates the second score based on the first score and the bid amount of the store. Note that the second score may be calculated based on any function as long as it is a score calculated based on the first score and remuneration information. For example, the information processing device 100 may calculate the second score by multiplying the first score and the bid amount of the store. In FIG. 2, for example, the information processing device 100 calculates "25", which is the second score of the store SP1, based on the bid amount "100 yen" of the store SP1 and the first score "SC1". . Note that the example in FIG. 2 shows a case where the information processing device 100 generates the table SL1 that shows the bid amount, first score, and second score of the store for each store ID. This is not limited to the example shown in . Furthermore, the information processing device 100 does not need to generate the table SL1.

The information processing device 100 determines the ranking of the stores based on the calculated second score (step S205). The information processing device 100 determines the ranking of stores to be provided to the user U1 based on the second score. For example, the information processing device 100 determines the ranking of each store such that the larger the value of the second score, the higher the ranking. That is, the information processing device 100 determines the ranking of each store so that stores with larger second scores are given priority in providing information to the target user. In FIG. 2, the information processing device 100 determines the store SP3 having the highest second score as the first store. Furthermore, the information processing device 100 determines the store SP1 with the second highest second score to be ranked second. Then, the information processing device 100 determines information to be provided to the user U1 (step S206). In FIG. 2, the information processing device 100 determines to provide information on the store SP3 with the highest second score. Note that although the example shown in FIG. 2 shows an example in which the information processing apparatus 100 provides information on the store with the highest second score, the information processing apparatus 100 is not limited to the example shown in FIG. 2. For example, the information processing device 100 may decide to provide information on stores up to a predetermined rank. Note that although the example in FIG. 2 shows a case where the information processing apparatus 100 generates the table RK1 that shows the ranking of stores in descending order of second scores, the table is not limited to the example shown in FIG. 2. Furthermore, the information processing device 100 does not need to generate the table RK1.

The information processing device 100 provides the user U1 with information about the stores that have been determined to provide the information (step S207). In FIG. 2, the information processing device 100 provides information on the store SP3 with the highest second score. Specifically, the information processing device 100 provides store information as an example of a service related to sending customers to the store. For example, the information processing device 100 provides information that attracts target users to a store. To give a specific example, the information processing device 100 may provide information such as "Store SP3 has a good deal" or "Store SP3 is on sale." For example, the information processing device 100 may provide information including conditions such as "If you pay by 7:00 pm, you will receive a discount" or "If you arrive by 6:00 pm and complete your payment by 7:00 pm, you will receive a discount." good.

The information processing device 100 acquires predetermined information regarding customer referrals (step S208). The information processing device 100 acquires predetermined information transmitted from the terminal device 10. The information processing device 100 acquires predetermined information regarding the target user's behavior with respect to the provided store information. For example, the information processing device 100 acquires location information of the target user. For example, the information processing device 100 acquires information indicating the distance between the target user and the store. In the example of FIG. 2, the information processing device 100 acquires information indicating the distance between the user U1 and the store SP3.

The information processing device 100 determines whether the predetermined information acquired in step S208 satisfies a predetermined condition regarding customer referral (step S209). For example, the information processing device 100 determines whether the target user has performed an action that leads to the profit of the first business operator (the business operator that operates the store). Specifically, the information processing device 100 determines whether the target user has come to the store. The information processing device 100 determines whether or not to charge the first business operator who made the bid. If the information processing device 100 determines that the predetermined conditions regarding customer referral are satisfied, the information processing device 100 determines to charge the first business operator that made the bid. When the information processing device 100 determines that charging is to be performed, the information processing device 100 executes processing related to charging. Note that the processing related to billing may include transmitting information related to billing.

The information processing device 100 charges the operator CM3 (step S210). For example, the information processing device 100 transmits billing information (billing information) to the business operator CM3.

Note that there is no relationship between the processing in step S201 for acquiring user information and the processing in steps S202 to S206 for calculating the second score. For example, the information processing apparatus 100 may perform the process in step S201 after the process in step S206.

[3. Processing variations〕
(3-1. Acquisition of location information)
It is assumed that the position information of the terminal device 10 is detected by, for example, a GPS (Global Positioning System) built into the terminal device 10. The information processing device 100 acquires the target user's location information via the GPS of the terminal device 10. That is, the information processing device 100 acquires the location information acquired via the GPS of the terminal device 10 as the target user's location information. On the other hand, it is assumed that the location information of the store is indicated by, for example, the address of the store. The information processing device 100 acquires store location information from a predetermined storage unit. The information processing device 100 acquires the store address stored in the business information storage unit 128 as store location information. Note that the information processing device 100 may acquire the store address acquired from an external server as the store location information.

(3-2. Judgment of customer referral based on location)
The information processing device 100 determines whether the target user has visited the store based on the location of the target user and the location of the store. The information processing device 100 determines that the target user has visited the store when the distance between the target user and the store is within a predetermined threshold (for example, within 1 m, etc.). In the example of FIG. 1, the information processing device 100 determines that the user U1 has visited the store SP3 because the distance between the user U1 and the store SP3 is within a predetermined threshold. As a result, the information processing device 100 determines that the user U1, who is the target user, has visited the store SP3 to which the information was provided, and therefore the predetermined condition regarding customer referral is satisfied. The information processing device 100 charges the business operator CM3 of the store SP3 for the bid amount of "150 yen" as a reward for sending the user U1 to the store SP3.

(3-3. Determination of customer referral based on authentication)
The information processing device 100 determines whether the target user has visited the store based on information detected by a sensor installed in the store. For example, the information processing device 100 determines whether the target user has visited the store based on authentication by an authentication device installed at the store (for example, face authentication, fingerprint authentication, etc.). For example, when information detected by an authentication device installed in a store matches information registered in a predetermined web service such as a web service provided by the information providing device 50, the information processing device 100 It is determined that the target user has visited the store.

(3-4. Distribution of coupons)
In the embodiment, a case has been described in which the information processing device 100 provides store information as an example of a service related to sending customers to a store. The information processing device 100 may provide a coupon related to customer referrals as an example of a service related to customer referrals to a store. For example, a coupon is a benefit that benefits a target user. For example, a coupon is a benefit that can be used at a store. For example, a coupon may be a benefit that can be used at a store if predetermined conditions are met. For example, a coupon may be a benefit that includes a condition that the coupon is valid for payment by a predetermined time. For example, the coupon may be a benefit that includes a condition that the coupon is valid if the target user operates (tap, click, etc.) and makes payment by a predetermined time. In this case, the first score calculated by the information processing device 100 may be a score indicating the probability that the target user will perform a predetermined action with respect to the information provided by the second business operator. Specifically, the first score calculated by the information processing device 100 may be a score indicating the probability that the target user will select a coupon provided by the second business operator.

(3-5. Various methods of providing services)
The information processing device 100 may provide a service related to customer referral using a predetermined notification method (for example, push notification, etc.). The information processing device 100 may provide advertising content as a service related to customer referrals. For example, the information processing device 100 may provide advertising content to be displayed in a predetermined area (for example, an advertising area) included in a predetermined web service as a service related to customer referral.

(3-6. Judgment of remuneration based on settlement)
In the embodiment, the predetermined condition regarding customer referral is that a reward is paid when the target user comes to the store. Here, as a predetermined condition regarding customer referral, a reward may be paid when the target user makes a predetermined payment. Specifically, the reward may be paid when the target user makes a payment at the store using a coupon provided by the second business operator. In this case, the information processing device 100 may determine whether the target user has performed a predetermined action using a coupon provided by the second business operator.

(3-7. Variations in customer referral)
In the embodiment, the case where the customer referral is a visit to the store has been explained as an example, but if the purpose of customer referral to a target with limited capacity is to move (behavior) to a predetermined location, then the customer visit is a store visit. Any type of movement may be used. Sending customers to a target with limited capacity is not limited to the movement of people, but may also be the movement of goods. To give a specific example, sending customers to a target with limited capacity may be participation in a predetermined space such as a conference room or event venue where events, activities, seminars, etc. are held. . In addition, if the target with limited capacity is a storage space for a moving object related to logistics such as a truck bed, sending customers to the target with limited capacity cannot be transported (portable). There may be.

(3-8. Variations of the first business operator)
The first business operator is not limited to a business operator that operates a target with limited capacity, but can be any business operator that has a predetermined relationship with a target with limited capacity. Good too. For example, the first business operator may be a business operator that provides a target with limited capacity. To give a specific example, if the target with limited capacity is a predetermined space such as a conference room or event venue where events, activities, seminars, etc. are held, the first business operator It may be a business that provides a predetermined space such as a room or an event venue. Further, the first business operator may be a business operator that handles objects with limited capacity. To give a specific example, if the target with limited capacity is storage space for logistics-related mobile objects such as truck beds, the first business operator is a business that handles logistics-related movement storage space. It may be a person.

[4. Information processing system configuration]
The information processing system 1 shown in FIG. 3 will be explained. As shown in FIG. 3, the information processing system 1 includes a terminal device 10, a business operator device 20, and an information processing device 100. The terminal device 10, the operator's device 20, and the information processing device 100 are communicably connected via a predetermined communication network (network N) by wire or wirelessly. FIG. 3 is a diagram illustrating a configuration example of an information processing system according to an embodiment. Note that the information processing system 1 shown in FIG. 3 may include a plurality of terminal devices 10, a plurality of operator devices 20, and a plurality of information processing devices 100.

The terminal device 10 is an information processing device used by a user (target user). The terminal device 10 may be any device as long as it can implement the processing in the embodiment. Furthermore, the terminal device 10 may be a device such as a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, or a PDA (Personal Digital Assistant). The example shown in FIG. 2 shows a case where the terminal device 10 is a smartphone. The terminal device 10-1 is a terminal device used by the user U1. Further, the terminal device 10 may detect various sensor information using various sensors other than the GPS sensor or the like.

The business operator device 20 is an information processing device used by the first business operator. The operator device 20 may be any device as long as it can implement the processing in the embodiment. Further, the operator device 20 may be a device such as a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, or a PDA. In the example shown in FIGS. 1 and 2, a case is shown in which the operator's device 20 is a notebook PC. The company device 20-1 is a company device used by the company CM1. The company device 20-2 is a company device used by the company CM2. The company device 20-3 is a company device used by the company CM3. Furthermore, the business operator device 20 may detect various sensor information using various sensors other than the GPS sensor or the like.

The information processing device 100 is used to calculate the degree of customer referral based on store information. Further, the information processing device 100 is used to receive bids regarding customer referrals from stores. Furthermore, the information processing device 100 is used to determine the ranking of stores that have made bids regarding customer referrals. The information processing device 100 is, for example, an information processing device such as a PC or a WS (Work Station), and performs processing based on information transmitted from the terminal device 10 and the operator device 20 via the network N. For example, the information processing device 100 performs processing based on user information transmitted from the terminal device 10. For example, the information processing device 100 performs processing based on bidding information transmitted from the business operator device 20.

[5. Terminal device configuration]
Next, the configuration of the terminal device 10 according to the embodiment will be described using FIG. 4. FIG. 4 is a diagram showing a configuration example of the terminal device 10 according to the embodiment. As shown in FIG. 4, the terminal device 10 includes a communication section 11, an input section 12, an output section 13, and a control section 14.

(Communication Department 11)
The communication unit 11 is realized by, for example, a NIC (Network Interface Card). The communication unit 11 is connected to a predetermined network N by wire or wirelessly, and transmits and receives information to and from the information processing device 100 via the predetermined network N.

(Input section 12)
The input unit 12 accepts various operations from a user (target user). In the example shown in FIG. 2, various operations from the user U1 are accepted. For example, the input unit 12 may receive various operations from the user via the display screen using a touch panel function. Further, the input unit 12 may accept various operations from buttons provided on the terminal device 10 or a keyboard or mouse connected to the terminal device 10. For example, the input unit 12 accepts various operations from the user on information transmitted from the information processing device 100. For example, the input unit 12 accepts an operation to reserve a visit to a store determined by the information processing device 100. For example, the input unit 12 accepts an operation to not reserve a visit to the store determined by the information processing device 100 or an operation to close the provided information.

(Output section 13)
The output unit 13 is a display screen of a tablet terminal or the like realized by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display, and is a display device for displaying various information. Further, the output unit 13 outputs information transmitted from the information processing device 100. For example, the output unit 13 outputs store information determined by the information processing device 100. In the example shown in FIG. 2, the output unit 13 outputs information regarding the store SP3. For example, the output unit 13 outputs information including services related to sending customers to the store.

(Control unit 14)
The control unit 14 is, for example, a controller, and various programs stored in a storage device inside the terminal device 10 are transferred to a RAM (Random Access Memory) by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. ) is executed as a work area. For example, these various programs include application programs installed on the terminal device 10. For example, these various programs include an application program that displays information transmitted from the information processing device 100. Further, the control unit 14 is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 4, the control unit 14 includes a transmitting unit 141 and a receiving unit 142, and realizes or executes the information processing operation described below.

(Transmission unit 141)
The transmitter 141 provides various information to an external information processing device. The transmitter 141 transmits various information to an external information processing device. For example, the transmitter 141 transmits various information to other information processing apparatuses such as the information processing apparatus 100. The transmitting unit 141 transmits various information stored in a predetermined storage unit. The transmitter 141 transmits various information acquired from an external server. The transmitter 141 transmits various information detected by the sensor. The transmitter 141 transmits various information detected by a sensor built into the terminal device 10. The transmitter 141 transmits user information. The transmitter 141 transmits the user's location information. Further, the transmitter 141 may transmit various information executed via an application installed on the terminal device 10.

(Receiving unit 142)
The receiving unit 142 receives various information. The receiving unit 142 receives various information from an external information processing device. The receiving unit 142 receives various information from other information processing devices such as the information processing device 100. For example, the receiving unit 142 receives information about a store determined by the information processing device 100. The receiving unit 142 receives store information regarding customer referrals. The receiving unit 142 receives information including services related to sending customers to the store.

[6. Configuration of operator equipment]
Next, the configuration of the operator's device 20 according to the embodiment will be described using FIG. 5. FIG. 5 is a diagram illustrating a configuration example of the operator device 20 according to the embodiment. As shown in FIG. 5, the operator device 20 includes a communication section 21, an input section 22, an output section 23, and a control section 24.

(Communication Department 21)
The communication unit 21 is realized by, for example, a NIC or the like. The communication unit 21 is connected to a predetermined network N by wire or wirelessly, and transmits and receives information to and from the information processing device 100 via the predetermined network N.

(Input section 22)
The input unit 22 receives various operations from the first business operator. In the example shown in FIG. 1, various operations from business operators CM1 to CM3 are accepted. For example, the input unit 22 may accept various operations from the first business operator via the display screen using a touch panel function. Further, the input unit 22 may accept various operations from buttons provided on the operator's device 20 or a keyboard or mouse connected to the operator's device 20. For example, the input unit 22 accepts a bid from the first business regarding customer referral. For example, the input unit 22 accepts transmission of a request for bid assist information regarding customer referrals. For example, the input unit 22 accepts settings of information (conditions) specified by the first business operator regarding customer referrals.

(Output section 23)
The output unit 23 is a display screen of a tablet terminal or the like realized by, for example, a liquid crystal display or an organic EL display, and is a display device for displaying various information. Further, the output unit 23 outputs information transmitted from the information processing device 100. For example, the output unit 23 outputs bid assist information regarding customer referrals. For example, the output unit 23 outputs information regarding the degree of customer referral calculated by the information processing device 100. In the example shown in FIG. 1, the output unit 23 may output a graph GR1 regarding customer referrals. The output unit 23 also outputs billing information regarding customer referrals.

(Control unit 24)
The control unit 24 is, for example, a controller, and is realized by a CPU, an MPU, or the like executing various programs stored in a storage device inside the operator's device 20 using the RAM as a work area. For example, these various programs include application programs installed on the operator's device 20. For example, these various programs include an application program that transmits bidding information regarding customer referrals to the information processing device 100. Further, the control unit 24 is realized by, for example, an integrated circuit such as an ASIC or an FPGA.

As shown in FIG. 5, the control unit 24 includes a transmitting unit 241 and a receiving unit 242, and realizes or executes the information processing operation described below.

(Transmission unit 241)
The transmitter 241 provides various information to an external information processing device. The transmitter 241 transmits various information to an external information processing device. For example, the transmitter 241 transmits various information to other information processing apparatuses such as the information processing apparatus 100.

The transmitter 241 transmits a request for bid assist information regarding customer referral. The transmitter 241 transmits information (conditions) specified by the first business regarding customer referral. The transmitter 241 transmits customer referral related information specified by the first business operator regarding customer referrals.

The transmitter 241 transmits bidding information regarding customer referrals. The transmitter 241 may transmit bid information based on bid assist information.

(Receiving unit 242)
The receiving unit 242 receives various information. The receiving unit 242 receives various information from an external information processing device. The receiving unit 242 receives various information from other information processing devices such as the information processing device 100.

The receiving unit 242 receives bid assist information. The receiving unit 242 receives information regarding the degree of customer referral. For example, the receiving unit 242 receives information regarding the degree of customer referral according to the reward. In the example shown in FIG. 1, the receiving unit 242 may receive the graph GR1 regarding customer referrals.

The receiving unit 242 receives billing information regarding customer referrals. The receiving unit 242 receives billing information based on the bid made by the first business operator.

[7. Configuration of information processing device]
Next, the configuration of the information processing device 100 according to the embodiment will be described using FIG. 6. FIG. 6 is a diagram illustrating a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 6, the information processing device 100 includes a communication section 110, a storage section 120, and a control section 130. Note that the information processing device 100 has an input unit (for example, a keyboard, a mouse, etc.) that accepts various operations from the administrator of the information processing device 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. You may.

(Communication Department 110)
The communication unit 110 is realized by, for example, a NIC or the like. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information between the terminal device 10 and the operator's device 20 via the network N.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 6, the storage unit 120 includes a customer referral related information storage unit 121, a first model storage unit 122, a bid information storage unit 123, a ranking determination information storage unit 124, and a second model storage unit. 125, a provision information storage section 126, a user information storage section 127, and a business information storage section 128.

The customer referral related information storage unit 121 stores customer referral related information. The customer referral related information storage unit 121 stores information regarding customer referrals specified by the store. The customer referral related information storage unit 121 stores information indicating the situation regarding store occupancy. Here, FIG. 7 shows an example of the customer referral related information storage unit 121 according to the embodiment. As shown in FIG. 7, the customer referral related information storage unit 121 has items such as “customer referral ID”, “store ID”, “business ID”, and “customer referral related information”.

“Customer referral ID” indicates identification information for identifying customer referral related information. "Store ID" indicates identification information for identifying a store. For example, FIG. 7 shows that store SP1 is a store that has specified customer referral related information corresponding to customer referral SK1. "Business ID" indicates identification information for identifying the first business operator. For example, FIG. 7 shows that business operator CM1 is the first business operator that operates store SP1.

"Customer referral related information" indicates customer referral related information. The "customer referral related information" further includes items such as "customer referral request" and "customer referral full". “Customer referral request” indicates information (conditions) specified by the store regarding customer referral. The "customer referral request" indicates, for example, user information specified by the store regarding customer referral (for example, user attributes, whether or not the user uses the store), time zone, and conditions regarding the user's arrival time. In the example shown in Figure 7, conceptual information such as "Customer referral request #11" and "Customer referral request #12" is stored in the "Customer referral request", but in reality, the store Data (for example, a conditional expression) indicating the information that has been determined is stored. Note that if there is no information specified by the store, information such as "none" or "NULL" may be stored in "customer referral request". To give a specific example, "Customer referral request #11" included in the customer referral request corresponding to customer referral ID "SK1" has "Gender: Female", and "Customer referral request #12" has "Age: 20~ 30" may be stored, and information such as "none" may be stored after "Customer Referral Request #13". Further, "Customer Transfer Full" indicates the situation regarding store occupancy. For example, "Customer referral is full" indicates the situation regarding store availability when the store specifies information regarding customer referral. In the example shown in FIG. 7, conceptual information such as “Customer referral Manku #1” and “Customer referral Manku #2” is stored in “Customer referral Manku”, but in reality, Data indicating the number of vacant seats at the store (eg, vacancy rate) is stored.

The first model storage unit 122 stores information regarding the first model M1. The first model storage unit 122 stores information regarding the first model M1 for calculating a score indicating an index of customer referral. Here, FIG. 8 shows an example of the first model storage unit 122 according to the embodiment. As shown in FIG. 8, the first model storage unit 122 has items such as "first model ID" and "first model (calculation formula)."

"First model ID" indicates identification information for identifying the first model M1. “First model (calculation formula)” indicates a calculation formula for the first model M1. In the example shown in FIG. 8, conceptual information such as "first model #1" and "first model #2" is stored in "first model (calculation formula)", but in reality , the data of the function is stored. For example, the function of formula (1) is stored in "first model (calculation formula)".

Bid information storage section 123 stores bid information. The bid information storage unit 123 stores bid information on bids made by each first business operator regarding customer referrals. Here, FIG. 9 shows an example of the bid information storage section 123 according to the embodiment. As shown in FIG. 9, the bid information storage unit 123 has items such as "bid ID", "store ID", "business ID", and "bid information".

"Bid ID" indicates identification information for identifying bid information. "Store ID" indicates identification information for identifying a store. For example, FIG. 9 shows that store SP1 is the store that made the bid corresponding to bid NS1. "Business ID" indicates identification information for identifying the first business operator. “Customer referral ID” indicates identification information for identifying customer referral related information. "Bid information" indicates bid information submitted by the first business operator. Specifically, the "bid information" stores the bid amount. For example, FIG. 9 shows that the business operator CM1 bids 100 yen regarding the customer referral of the customer referral SK1. Further, FIG. 9 shows that the business operator CM2 bids 200 yen regarding the customer referral of the customer referral SK2.

The ranking determining information storage unit 124 stores ranking determining information. The ranking determining information storage unit 124 stores information regarding customer referrals designated by the store. Specifically, the ranking determining information storage unit 124 stores information (conditions) regarding target users who are referred customers as desired by the store. The ranking determining information storage unit 124 stores information indicating the status regarding store occupancy. Specifically, the ranking information storage unit 124 stores information indicating the reservation status of the store. Here, FIG. 10 shows an example of the ranking determination information storage unit 124 according to the embodiment. As shown in FIG. 10, the ranking determining information storage unit 124 has items such as "rank determining ID," "store ID," "business ID," and "rank determining information."

"Ranking determination ID" indicates identification information for identifying ranking determining information. "Store ID" indicates identification information for identifying a store. For example, FIG. 10 shows that store SP1 is a store that has specified ranking determination information corresponding to ranking determination JK1. "Business ID" indicates identification information for identifying the first business operator.

"Ranking determining information" indicates ranking determining information. The "ranking information" further includes items such as "ranking request" and "ranking full". “Ranking request” indicates information (conditions) specified by the store regarding the target user. The "rank request" indicates conditions regarding the target user's attributes, behavior history, whether or not the user uses the store, and location information, for example. In the example shown in Figure 10, conceptual information such as "rank request #11" and "rank request #12" is stored in "rank request", but in reality, information specified by the store is stored. The data (for example, conditional expression) indicated is stored. Note that if there is no information specified by the store, information such as "none" or "NULL" may be stored in "rank request". To give a specific example, "ranking request #11" included in the ranking request corresponding to the ranking determining ID "JK1" has "gender: female" and "ranking request #12" has "age: 20-30". Information such as "none" may be stored after "rank request #13".

“Ranking full/vacant” indicates the current status regarding store vacancies. In the example shown in FIG. 10, conceptual information such as "Ranking Fully Empty #1" and "Ranking Fully Empty #2" is stored in "Ranking Fully Empty". Data indicating the number is stored. "Reservation status" indicates the future status regarding store availability. Specifically, "reservation status" stores information indicating the reservation status regarding fullness. In the example shown in FIG. 10, conceptual information such as "reservation #1" and "reservation #2" is stored in the "reservation status". Data indicating the number (for example, reservation rate) is stored.

“Price range” indicates the price range of the transaction object (product) provided at the store. The "price range" may indicate the price range from the lowest price to the highest price of the transaction object offered at the store, or may indicate the average price range. Moreover, a predetermined price may be stored in the "price range" or a price range may be stored. In the example shown in Figure 10, conceptual information such as "Price #1" and "Price #2" is stored in "Price Range", but in reality, data indicating the price is stored. be done.

"Evaluation" indicates the evaluation of the store. For example, "evaluation" indicates an evaluation by a user who has used the store. Moreover, any information may be stored in "evaluation" as long as it is information indicating an evaluation index. For example, "evaluation" may store characters indicating evaluation such as "good", "average", and "poor", or may store symbols indicating evaluation such as "A", "B", and "C". Alternatively, evaluation numerical values such as "4", "3", and "2" may be stored. In the example shown in FIG. 10, conceptual information such as "evaluation #1" and "evaluation #2" is stored in "evaluation".

The second model storage unit 125 stores information regarding the second model M2. The second model storage unit 125 stores information regarding the second model M2 for calculating the first score used for ranking stores. For example, the second model storage unit 125 stores information regarding the second model M2 for calculating the probability regarding the target user's customer referral. Here, FIG. 11 shows an example of the second model storage unit 125 according to the embodiment. As shown in FIG. 11, the second model storage unit 125 has items such as "second model ID" and "second model (calculation formula)."

“Second model ID” indicates identification information for identifying the second model M2. “Second model (calculation formula)” indicates a calculation formula for the second model M2. In the example shown in FIG. 11, conceptual information such as "second model #1" and "second model #2" is stored in "second model (calculation formula)", but in reality , the data of the function is stored. For example, the function of formula (2) is stored in "second model (calculation formula)".

The provided information storage unit 126 stores information that has been determined to be provided to the target user (hereinafter referred to as “provided information” as appropriate). The provided information storage unit 126 stores information determined to be transmitted to the target user's terminal device 10. The provided information storage unit 126 stores information regarding customer referral services. Here, FIG. 12 shows an example of the provided information storage unit 126 according to the embodiment. As shown in FIG. 12, the provided information storage unit 126 has items such as "provided ID", "user ID", and "provided information".

“Providing ID” indicates identification information for identifying provided information. "User ID" indicates identification information for identifying the target user. “Provided information” indicates provided information. For example, "offering information" stores information about a store that has been decided to provide to the target user. For example, information on a plurality of stores is stored in "Provided Information" as store information. In the example shown in FIG. 12, conceptual information such as "store information #1" and "store information #2" is stored in "store information", but in reality, store name, store advertisement Store information such as a store URL (Universal Resource Locator), and data indicating the store location (eg, map information) is stored. Moreover, information on coupons related to stores may be stored in the "offering information". For example, information on a plurality of coupons corresponding to each store may be stored in the "offering information" as coupon information. Moreover, conditions regarding the use of a coupon may be stored in the "offering information". Alternatively, conditions regarding the target user's behavior may be stored in the "provided information". Actually, data indicating the content of the discount of the coupon and a conditional expression for using the coupon may be stored in the "offering information".

The user information storage unit 127 stores user information indicating information regarding a user (target user). Here, FIG. 13 shows an example of the user information storage unit 127 according to the embodiment. As shown in FIG. 13, the user information storage unit 127 has items such as "user ID", "terminal ID", "gender", "age", "place of residence", and "whether or not the store is used."

"User ID" indicates identification information for identifying a user. "Terminal ID" indicates identification information for identifying the terminal device used by the user. “Gender” indicates the gender of the user. "Age" indicates the age of the user. "Residence" indicates information regarding the user's address. In the example shown in FIG. 13, conceptual information such as "Place of residence #1" and "Place of residence #2" is stored in "Place of residence", but in reality, it is determined by prefecture, municipality, etc. The data shown is stored. “Usage status of store” indicates whether each store is used by users. Information indicating whether a plurality of stores are used by users may be stored in "Usage of stores". In the example shown in FIG. 13, conceptual information such as "Usage #1" and "Usage #2" is stored in "Usage of store", but in reality, users at each store Data indicating whether or not the store SP1 is used (for example, "store SP1: available") is stored.

The business information storage unit 128 stores business information indicating information regarding the first business operator. Here, FIG. 14 shows an example of the business information storage unit 128 according to the embodiment. As shown in FIG. 14, the business information storage unit 128 stores "business ID," "business device ID," "store ID," "specialty," "store location," "price range," and "evaluation." It has items such as.

"Business ID" indicates identification information for identifying the first business operator. “Business operator device ID” indicates identification information for identifying a business device used by the first business operator. For example, FIG. 14 shows that the provider device 20-1 is a provider device used by the provider CM1. "Store ID" indicates identification information for identifying a store. For example, FIG. 14 shows that store SP1 is a store operated by business operator CM1. "Specialty" indicates the attribute of the store. “Store location” indicates information regarding the address of the store. In the example shown in FIG. 14, conceptual information such as "store location #1" and "store location #2" is stored in "store location". The data shown is stored.

“Price range” indicates the price range of the transaction object (product) provided at the store. The "price range" may indicate the price range from the lowest price to the highest price of the transaction object offered at the store, or may indicate the average price range. Moreover, a predetermined price may be stored in the "price range" or a price range may be stored. In the example shown in Figure 14, conceptual information such as "Price #1" and "Price #2" is stored in "Price Range", but in reality, data indicating the price is stored. be done.

"Evaluation" indicates the evaluation of the store. For example, "evaluation" indicates an evaluation by a user who has used the store. Moreover, any information may be stored in "evaluation" as long as it is information indicating an evaluation index. For example, "rating" may store symbols indicating ratings such as "good", "average", "bad", "A", "B", and "C", or "4", " Numerical evaluation values such as ``3'' and ``2'' may be stored. In the example shown in FIG. 14, conceptual information such as "evaluation #1" and "evaluation #2" is stored in "evaluation".

(Control unit 130)
The control unit 130 is a controller, and is realized by, for example, a CPU, an MPU, or the like executing various programs stored in a storage device inside the information processing device 100 using the RAM as a work area. Further, the control unit 130 is a controller, and is realized by, for example, an integrated circuit such as an ASIC or an FPGA.

As shown in FIG. 6, the control unit 130 includes an acquisition unit 131, a reception unit 132, a generation unit 133, a calculation unit 134, a determination unit 135, a provision unit 136, a determination unit 137, and a charging unit 138. and realizes or executes the information processing operation described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 6, and may be any other configuration as long as it performs information processing to be described later.

(Acquisition unit 131)
The acquisition unit 131 acquires various information. The acquisition unit 131 acquires various information from an external information processing device. The acquisition unit 131 acquires various information from other information processing devices such as the terminal device 10 and the operator device 20.

The acquisition unit 131 acquires various information from the storage unit 120. The acquisition unit 131 includes a customer referral related information storage unit 121, a first model storage unit 122, a bid information storage unit 123, a ranking determination information storage unit 124, a second model storage unit 125, an offer information storage unit 126, and a user information storage unit. Various information is acquired from the section 127 and the business information storage section 128.

The acquisition unit 131 stores the acquired various information in the storage unit 120. The acquisition unit 131 includes a customer referral related information storage unit 121, a first model storage unit 122, a bid information storage unit 123, a ranking determination information storage unit 124, a second model storage unit 125, an offer information storage unit 126, and a user information storage unit. Various information is stored in the section 127 and the business information storage section 128.

The acquisition unit 131 acquires various types of information received, generated, calculated, determined, and determined by other functional configurations.

The acquisition unit 131 acquires a request for bid assist information. The acquisition unit 131 acquires a request for bid assist information transmitted by the first business operator via the business operator device 20. The acquisition unit 131 acquires customer referral related information. The acquisition unit 131 acquires customer referral related information transmitted by the first business operator via the business operator device 20. For example, the acquisition unit 131 acquires customer referral related information including information (conditions) specified by the store. For example, the acquisition unit 131 acquires customer referral related information including the situation regarding store availability.

The acquisition unit 131 acquires a predetermined function. The acquisition unit 131 acquires the first model M1. For example, the acquisition unit 131 may acquire the first model M1 stored in the first model storage unit 122, or may acquire the first model M1 generated by the generation unit 133. The acquisition unit 131 also acquires the second model M2. For example, the acquisition unit 131 may acquire the second model M2 stored in the second model storage unit 125, or may acquire the second model M2 generated by the generation unit 133.

The acquisition unit 131 acquires bidding information. The acquisition unit 131 acquires bid information on bids made by stores regarding customer referrals. The acquisition unit 131 acquires bid information transmitted by the first business operator via the business operator device 20. In other words, the acquisition unit 131 acquires remuneration information. Specifically, the acquisition unit 131 acquires remuneration information that a first business operator that operates a store pays to a second business operator that has performed an action that contributes to a user's referral to the store.

The acquisition unit 131 acquires ranking determination information. Obtain ranking determination information including information (conditions) specified by the store. For example, the acquisition unit 131 acquires ranking determination information including information regarding the target user. For example, the acquisition unit 131 acquires ranking determination information including the status regarding store occupancy. For example, the acquisition unit 131 acquires ranking determination information including store reservation status. For example, the acquisition unit 131 acquires ranking determination information including the price range of the store. For example, the acquisition unit 131 acquires ranking determination information including store evaluations.

The acquisition unit 131 acquires location information of a user (target user). For example, the acquisition unit 131 acquires position information transmitted from the terminal device 10. Further, the acquisition unit 131 acquires location information of the first business operator. For example, the acquisition unit 131 acquires information regarding the address of the store stored in the business information storage unit 128.

(Reception Department 132)
The reception unit 132 receives various information. The reception unit 132 receives various information from an external information processing device. The reception unit 132 receives various information from other information processing devices such as the terminal device 10. Note that the reception unit 132 may be a part of the acquisition unit 131.

The reception unit 132 stores the received various information in the storage unit 120. For example, the reception unit 132 stores various information in the bid information storage unit 123.

The reception unit 132 receives bids from the first business operator. The reception unit 132 receives a bid from the first business operator for the first business operator that operates the store to pay a reward to the second business operator who has performed an action that contributes to the user's referral to the store. accept.

(Generation unit 133)
The generation unit 133 generates various information. The generation unit 133 stores the generated various information in the storage unit 120. For example, the generation unit 133 stores various information in the first model storage unit 122 and the second model storage unit 125.

The generation unit 133 generates various information based on various information acquired, accepted, calculated, and determined by other functional configurations.

The generation unit 133 generates a model. The generation unit 133 generates a model for operating a computer so as to output an index value. The generation unit 133 generates a first model M1. The generation unit 133 generates a first model M1 according to customer referral related information. When the generation unit 133 inputs the reward (input information) to the first model M1, it generates the first model M1 that outputs the degree of customer referral (output information). In the example of FIG. 1, when the generation unit 133 receives a reward of 50 yen, it generates a first model M1 that outputs a probability of X%.

The generation unit 133 learns the first model M1. The generation unit 133 learns the first model M1 based on information regarding remuneration and information regarding the degree of customer referral. Specifically, the generation unit 133 generates a first model M1 that has been learned from a target user who has come to the store as correct data. For example, the generation unit 133 generates the first model M1 that is trained to output "100" as the frequency of the target user coming to the store when a reward for the target user coming to the store is input. For example, the generation unit 133 generates a first model M1 that has learned to output "0" as the degree to which the target user does not come to the store when a reward for the case where the target user does not come to the store is input. do.

The generation unit 133 generates a second model M2. The generation unit 133 generates a second model M2 according to the ranking determination information. When the generation unit 133 inputs the store ranking determination information into the second model M2, it generates a second model M2 that outputs a first score indicating the degree of customer referral by the target user. In the example of FIG. 2, when the generation unit 133 receives the ranking determination information of the store SP1, it generates the second model M2 that outputs the first score SC1.

The generation unit 133 learns the second model M2. The generation unit 133 learns the second model M2 based on the ranking determination information and the degree of customer referral of the target user. Specifically, the generation unit 133 generates a second model M2 that has been learned from the target user who came to the store as correct data. For example, the generation unit 133 generates a second model M2 that has learned to output "100" as the frequency of the target user coming to the store when information for ranking determination when the target user comes to the store is input. generate. For example, the generation unit 133 generates a second model that has learned to output "0" as the degree to which the target user does not come to the store when information for ranking determination in the case where the target user does not come to the store is input. Generate M2.

(Calculation unit 134)
The calculation unit 134 calculates various information. The calculation unit 134 calculates various information from the storage unit 120. For example, the calculation unit 134 calculates various information from the first model storage unit 122 and the second model storage unit 125.

The calculation unit 134 calculates various types of information acquired, received, generated, and determined by other functional configurations.

The calculation unit 134 calculates a score indicating an index related to customer referrals. For example, the calculation unit 134 calculates a score regarding customer referrals based on customer referral related information and a predetermined function. For example, the calculation unit 134 calculates a score regarding customer referrals based on the customer referral related information and the first model M1. For example, the calculation unit 134 calculates the degree (probability) of customer referral.

The calculation unit 134 calculates a first score used for ranking stores. For example, the calculation unit 134 calculates the first score based on the ranking determination information and a predetermined function. For example, the calculation unit 134 calculates the first score based on the ranking determination information and the second model M2. Further, the calculation unit 134 calculates a second score used for ranking the stores. For example, the calculation unit 134 calculates the second score based on the first score and reward information.

(Decision unit 135)
The determining unit 135 determines various information. The determining unit 135 stores the determined various information in the storage unit 120. For example, the determining unit 135 stores various information in the provided information storage unit 126.

The determining unit 135 determines various information acquired, received, generated, and calculated by other functional configurations.

The determining unit 135 determines the ranking of stores. The determining unit 135 determines the ranking of stores to which information is to be provided to target users. The determining unit 135 determines the ranking of the store based on the remuneration information and the ranking determining information. The determining unit 135 determines the ranking of the stores based on the second score calculated by the calculating unit 134.

(Providing unit 136)
The providing unit 136 provides various information to an external information processing device. The providing unit 136 transmits various information to an external information processing device. The providing unit 136 notifies an external information processing device of various information. For example, the providing unit 136 provides various information to other information processing devices such as the terminal device 10.

The providing unit 136 provides various information from the storage unit 120. The providing unit 136 provides various information from the provided information storage unit 126.

The providing unit 136 provides various information acquired, accepted, generated, calculated, and determined by other functional configurations.

The providing unit 136 provides bid assist information. For example, the providing unit 136 provides bid assist information based on the first model M1 generated by the generating unit 133. For example, the providing unit 136 provides information indicating the degree of customer referral according to the reward.

The providing unit 136 provides (distributes) services related to sending customers to stores. For example, the providing unit 136 provides information regarding stores. For example, the providing unit 136 provides advertisements regarding stores. For example, the providing unit 136 provides coupons (benefits) regarding stores.

The providing unit 136 provides billing information (accounting information). The providing unit 136 provides billing information to the first business operator who made the bid, according to the determination result by the determining unit 137.

(Determination unit 137)
The determination unit 137 determines various information. The determination unit 137 determines predetermined conditions regarding customer referral. The determination unit 137 determines whether the target user has performed an action that satisfies a predetermined condition regarding customer referral. For example, the determination unit 137 determines whether the target user has performed an action that leads to the profit of the first business operator (the business operator that operates the store). For example, the determination unit 137 determines whether the target user has come to the store.

(Charging unit 138)
The charging unit 138 charges the first business operator who made the bid the bid amount related to the customer referral. The charging unit 138 performs charging when predetermined conditions are met. The billing unit 138 performs billing according to the determination result by the determining unit 137.

[8. Information processing flow]
Next, the procedure of information processing by the information processing system 1 according to the embodiment will be described using FIGS. 15 to 19. 15 to 19 are flowcharts showing procedures of information processing by the information processing system 1 according to the embodiment.

As shown in FIG. 15, the information processing device 100 acquires customer referral related information (step S301). The information processing device 100 calculates a score indicating an index related to customer referral by inputting a predetermined function to the acquired customer referral related information (step S302).

As shown in FIG. 16, the information processing device 100 determines whether a request for bid assist information has been received (step S401). If the information processing device 100 has not received a request for bid assist information (step S401; NO), it waits until it receives a request for bid assist information. On the other hand, when the information processing device 100 receives a request for bid assist information (step S401; YES), it generates bid assist information indicating the degree of customer referral according to the reward (step S402). The information processing device 100 provides the generated bid assist information to the first business operator (step S403).

As shown in FIG. 17, the information processing device 100 determines whether a bid for customer referral has been received from the first business operator (step S501). If the information processing device 100 does not accept a bid for customer referral from the first business operator (step S501; NO), the information processing device 100 ends the information processing. On the other hand, when the information processing apparatus 100 receives a bid for sending a customer from the first business operator (step S501; YES), the information processing apparatus 100 stores information regarding the bid from the first business operator in the storage unit (step S502).

As shown in FIG. 18, the information processing apparatus 100 acquires ranking determination information from the storage unit (step S601). The information processing device 100 calculates a first score used for ranking stores (step S602). The information processing device 100 calculates a second score based on the calculated first score and reward information (step S603). The information processing device 100 determines the ranking of stores according to the calculated second score (step S604). The information processing device 100 determines store information to be provided to the target user according to the determined store ranking (step S605). The information processing device 100 provides information about the determined store to the target user (step S606).

As shown in FIG. 19, the information processing device 100 determines whether the target user has performed an action that satisfies a predetermined condition regarding customer referral (step S701). When the information processing device 100 determines that the target user has not performed an action that satisfies the predetermined condition regarding customer referral (step S701; NO), the information processing device 100 ends the information processing. On the other hand, when the information processing device 100 determines that the target user has performed an action that satisfies the predetermined condition regarding customer referral (step S701; YES), the information processing device 100 charges the first business operator that made the bid (step S702).

[9. effect〕
As described above, the information processing apparatus 100 according to the embodiment includes the acquisition section 131 and the determination section 135. The acquisition unit 131 obtains remuneration information indicating a remuneration paid by a first business operator that operates a target with limited capacity to a second business operator that performs an action that contributes to a user's referral to the target. and ranking determination information used to rank the target corresponding to the target user who is the target of customer referral to the target. Further, the determining unit 135 determines the ranking of the target based on the remuneration information and ranking determining information acquired by the acquiring unit 131.

Thereby, the information processing apparatus 100 according to the embodiment can appropriately determine the ranking of objects with limited capacity according to the target users. Furthermore, the information processing device 100 can develop customer referrals to targets with limited capacity that are suitable for each target user. Further, the information processing device 100 can efficiently send customers according to the information specified by the target side, so that the information processing device 100 can efficiently send customers according to the information of the target side. Furthermore, the information processing device 100 can develop customer attraction activities that are attractive to targets with limited capacity.

The acquisition unit 131 also acquires ranking determination information including information regarding the target user.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the information of the target users that the target side wants to attract.

The acquisition unit 131 also acquires ranking determination information including attributes of the target user.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the information of the target user who has the attributes that the target side wants to attract.

The acquisition unit 131 also acquires ranking determination information including demographic attributes of the target users.

As a result, the information processing apparatus 100 according to the embodiment can efficiently send customers according to information on target users having demographic attributes that the target side wants to attract.

The acquisition unit 131 also acquires ranking determination information including the target user's behavior history.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the information of the target user whose behavior history the target side wants to attract.

The acquisition unit 131 also acquires ranking determination information including whether or not the target user uses the object.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the information of the target users that the target side wants to attract.

The acquisition unit 131 also acquires ranking determination information including information regarding the location of the target user.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the information of the target users that the target side wants to attract.

The acquisition unit 131 also acquires ranking determination information including information indicating the distance between the target user and the object.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the information of the target users that the target side wants to attract.

Further, the acquisition unit 131 acquires ranking determination information including the situation regarding the target sky and sky.

Thereby, the information processing device 100 according to the embodiment can efficiently send customers according to the situation of the target location.

The acquisition unit 131 also acquires ranking determination information including the target reservation status.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the target reservation status.

The acquisition unit 131 also acquires ranking determination information including the target price range.

Thereby, the information processing device 100 according to the embodiment can efficiently send customers according to the target price range.

The acquisition unit 131 also acquires ranking determination information including target evaluations.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers according to the target evaluation.

Furthermore, the information processing device 100 according to the embodiment includes a calculation unit 134 that calculates a score used for ranking objects based on remuneration information and ranking determination information. Further, the determining unit 135 determines the ranking of the target based on the score calculated by the calculating unit 134.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

Further, the acquisition unit 131 acquires a predetermined function related to customer referral. Further, the calculation unit 134 calculates another score based on the ranking determination information and a predetermined function, and calculates a score based on the other score and reward information.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

Further, the acquisition unit 131 receives the rank determination information as input and acquires a predetermined model that outputs a score. Furthermore, the calculation unit 134 calculates other scores by inputting ranking determination information into a predetermined model, and calculates the score based on the other scores and reward information.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

Further, the calculation unit 134 calculates another score indicating the probability that a predetermined condition regarding the target user's customer referral is satisfied.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

The calculation unit 134 also calculates another score indicating the probability that the target user will take an action that will benefit the first business operator.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

The calculation unit 134 also calculates another score indicating the probability that the target user will visit the target store.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to sending customers to the target store.

In addition, the calculation unit 134 calculates another score indicating the probability that the target user will perform a predetermined action in response to the information provided by the second business operator.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

The calculation unit 134 also calculates another score indicating the probability that the target user will select the coupon provided by the second business operator.

Thereby, the information processing device 100 according to the embodiment can appropriately calculate information related to customer referral to the target.

In addition, the acquisition unit 131 receives each remuneration paid by a plurality of first business operators, each of which operates each target, to a second business operator that performs an action that contributes to the user's customer referral to each target. Obtain the remuneration information shown. Further, the determining unit 135 determines the ranking of each target based on the remuneration information and ranking determining information acquired by the acquiring unit 131.

Thereby, the information processing apparatus 100 according to the embodiment can efficiently send customers to each target suitable for each target user.

[10. Hardware configuration]
Further, the terminal device 10, the operator device 20, and the information processing device 100 according to the embodiments described above are realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 20 is a hardware configuration diagram showing an example of a computer that implements the functions of the terminal device 10, the operator device 20, and the information processing device 100. Computer 1000 has CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I/F) 1500, input/output interface (I/F) 1600, and media interface (I/F) 1700.

CPU 1100 operates based on a program stored in ROM 1300 or HDD 1400, and controls each section. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, programs depending on the hardware of the computer 1000, and the like.

The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. Communication interface 1500 receives data from other devices via a predetermined communication network and sends it to CPU 1100, and transmits data generated by CPU 1100 to other devices via a predetermined communication network.

The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and mouse via an input/output interface 1600. CPU 1100 obtains data from an input device via input/output interface 1600. Further, CPU 1100 outputs the generated data to an output device via input/output interface 1600.

Media interface 1700 reads programs or data stored in recording medium 1800 and provides them to CPU 1100 via RAM 1200. CPU 1100 loads this program from recording medium 1800 onto RAM 1200 via media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. etc.

For example, when the computer 1000 functions as the terminal device 10, the operator device 20, and the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes the program loaded on the RAM 1200, thereby controlling the control unit 14, 24 and 130 functions. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from another device via a predetermined communication network.

[11. others〕
Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually can be performed manually. All or part of the process can also be performed automatically using known methods. In addition, information including the processing procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed arbitrarily, unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Furthermore, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured.

Further, the embodiments described above can be combined as appropriate within the range that does not conflict with the processing contents.

Some of the embodiments of the present application have been described above in detail based on the drawings, but these are merely examples, and various modifications and variations may be made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure section of the invention. It is possible to carry out the invention in other forms with modifications.

Further, the above-mentioned "section, module, unit" can be read as "means", "circuit", etc. For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 10 Terminal device 20 Business operator device 100 Information processing device 110 Communication unit 120 Storage unit 121 Customer referral related information storage unit 122 First model storage unit 123 Bid information storage unit 124 Ranking determination information storage unit 125 Second model Storage unit 126 Provided information storage unit 127 User information storage unit 128 Business information storage unit 130 Control unit 131 Acquisition unit 132 Reception unit 133 Generation unit 134 Calculation unit 135 Determination unit 136 Provision unit 137 Judgment unit 138 Accounting unit N Network

Claims (23)

Reward information indicating a remuneration to be paid by a first business operator that operates a target with limited capacity to a second business operator that performs an action that contributes to user referral to the target, and the target an acquisition unit that acquires ranking determination information used to determine the ranking of the target in relation to the target user who is the target of customer referral;
a calculation unit that calculates a score used for ranking the target based on the remuneration information and the ranking determination information acquired by the acquisition unit;
a determining unit that determines a ranking of the target based on the score calculated by the calculation unit, and determines information on the target to be provided to the user according to the ranking ;
a providing unit that provides the user with the target information determined to be provided by the determining unit ;
The calculation unit is
Based on the ranking determination information, calculate a first score indicating a probability regarding the target user's customer referral;
Calculating a second score used for ranking the object based on the first score and the remuneration information,
The determining unit is
Based on the second score, determine the ranking of the object to be provided to the user.
An information processing device characterized by: The acquisition unit includes:
The information processing apparatus according to claim 1, wherein the information processing apparatus acquires the ranking determination information including information regarding the target user. The acquisition unit includes:
The information processing device according to claim 2, wherein the information for ranking determination including attributes of the target user is acquired. The acquisition unit includes:
The information processing device according to claim 3, wherein the ranking determining information including demographic attributes of the target user is acquired. The acquisition unit includes:
The information processing device according to any one of claims 1 to 4, wherein the information processing device acquires the ranking determining information including the behavior history of the target user. The acquisition unit includes:
The information processing device according to claim 5, wherein the information for ranking determination including whether or not the target user uses the object is acquired. The acquisition unit includes:
The information processing apparatus according to any one of claims 1 to 6, wherein the information processing apparatus acquires the ranking determination information including information regarding the location of the target user. The acquisition unit includes:
The information processing apparatus according to any one of claims 1 to 7, characterized in that the information for ranking determination includes information indicating a distance between the target user and the object. The acquisition unit includes:
The information processing device according to any one of claims 1 to 8, characterized in that the information for ranking determination including a situation regarding the availability of the target is acquired. The acquisition unit includes:
The information processing apparatus according to any one of claims 1 to 9, wherein the information for ranking determination including the reservation status of the target is acquired. The acquisition unit includes:
The information processing device according to any one of claims 1 to 10, wherein the information for ranking determination including the target price range is acquired. The acquisition unit includes:
The information processing device according to any one of claims 1 to 11, wherein the information for ranking determination including the evaluation of the object is acquired. The calculation unit is
generating a first table showing the remuneration information, the first score, and the second score for each target;
The determining unit is
The information processing apparatus according to any one of claims 1 to 12, wherein a second table is generated from the first table showing the ranking of the objects in descending order of the second scores. . The acquisition unit includes:
Obtaining a predetermined function related to the customer referral;
The calculation unit is
The first score is calculated based on the ranking determining information and the predetermined function, and the second score is calculated based on the first score and the reward information . The information processing device according to any one of claims 1 to 13 . The calculation unit is
calculating the first score according to a plurality of elements included in the predetermined function;
The plurality of elements are
including information regarding the target user, the availability status of the target, the price range of the target, and information held by an external party;
The information regarding the target user is information regarding the attributes of the target user and the distance between the target user and the object,
The information regarding the availability of the target is the availability status of the target and the reservation status of the target,
The information held by the external party is information about users around the target, information about the environment around the target, and information about the evaluation of the target.
The information processing device according to claim 14. The acquisition unit includes:
obtaining a predetermined model that outputs the first score by inputting the ranking determination information;
The calculation unit is
Calculating the first score by inputting the ranking determining information into the predetermined model;
The information processing device according to claim 1 , wherein the second score is calculated based on the first score and the remuneration information. The calculation unit is
The information processing device according to any one of claims 1 to 16, wherein the first score indicating the probability that the target user performs an action that will lead to the profit of the first business operator is calculated. The calculation unit is
The information processing device according to claim 17 , wherein the first score indicating the probability that the target user visits the target store is calculated. The calculation unit is
According to any one of claims 1 to 18, the first score indicating the probability that the target user will take a predetermined action in response to information provided by the second business operator is calculated. information processing equipment. The calculation unit is
The information processing device according to claim 19, wherein the first score indicating the probability that the target user selects a coupon provided by the second business operator is calculated. The acquisition unit includes:
A plurality of first business operators, each of which operates each target, acquires the remuneration information indicating each reward paid to a second business operator that performs an action that contributes to the user's referral to each target. death,
The determining unit is
The information processing device according to any one of claims 1 to 19, wherein the ranking of each object is determined based on the remuneration information and the ranking determination information acquired by the acquisition unit. . An information processing method performed by a computer, the method comprising:
Reward information indicating a remuneration to be paid by a first business operator that operates a target with limited capacity to a second business operator that performs an action that contributes to user referral to the target, and the target an acquisition step of acquiring ranking determination information used to determine the ranking of the target in relation to the target user who is the target of customer referral;
a calculation step of calculating a score used for ranking the object based on the remuneration information and the ranking determination information acquired in the acquisition step;
a determining step of determining a ranking of the target based on the score calculated in the calculating step, and determining information on the target to be provided to the user according to the ranking ;
a providing step of providing the user with the target information determined to be provided in the determining step;
including;
In the calculation step,
Based on the ranking determination information, calculate a first score indicating a probability regarding the target user's customer referral;
Calculating a second score used for ranking the object based on the first score and the remuneration information,
In the determination step,
Based on the second score, determine the ranking of the object to be provided to the user.
An information processing method characterized by: Reward information indicating a remuneration to be paid by a first business operator that operates a target with limited capacity to a second business operator that performs an action that contributes to user referral to the target, and the target an acquisition procedure for acquiring ranking determination information used to determine the ranking of the target in relation to the target user who is the target of customer referral;
a calculation step of calculating a score used for ranking the object based on the remuneration information and the ranking determination information acquired by the acquisition step;
a determination step of determining a ranking of the object based on the score calculated by the calculation step, and determining information of the object to be provided to the user according to the ranking ;
a provision step of providing the user with the target information determined to be provided in the determination step;
make the computer run
In the calculation procedure,
Based on the ranking determination information, calculate a first score indicating a probability regarding the target user's customer referral;
Calculating a second score used for ranking the object based on the first score and the remuneration information,
In the determination procedure,
Based on the second score, determine the ranking of the object to be provided to the user.
An information processing program characterized by:

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