Information processing device, method, and program

Abstract

To provide a technique to determine, in a non-personal process, a plan related to an achievement index for a shop considering an external environment.SOLUTION: A program of an aspect of the present disclosure causes a computer to function as means that acquires first shop information related to the operation of resources in a target shop, means that acquires second shop information related to the operation of resources in a shop competing with the target shop, means that acquires customer information related to the situation of customers visiting the target shop, and means that applies a learned model to a model input data based on at least the first shop information, second shop information, and customer information to create a plan related to an achievement index for the target shop.SELECTED DRAWING: Figure 4

Description

[Technical field]

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

[0002] 2. Description of the Related Art Conventionally, in gaming facilities such as pachinko and slot machines, plans regarding performance indicators such as profits at each store have generally been determined based on the experience and knowledge of a person in charge such as a store manager.

[0003] Patent Document 1 discloses that performance data such as the number of machines by model, average operation per machine of each model, number of balls sold, number of prize balls, etc. are recorded in correspondence with date, day of the week, weather, events, machine model, machine type, etc., and that the performance data for a specific period in the past is classified and organized. Patent Document 1 also discloses that the gross profit and sales per machine for each day of the week obtained by classification and organization are applied to the daily data column of the sales forecast period by corresponding the day of the week, and the gross profit, sales, and profit margin are calculated. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 11-290532 Summary of the Invention [Problem to be solved by the invention]

[0005] In the technology of Patent Document 1, sales forecasts are based mainly on the past performance data of the store. However, if the external environment has changed significantly even if the situation of the store has not changed, it is not easy to predict appropriate sales using this technology.

[0006] An object of the present disclosure is to provide a technology for determining plans regarding store performance indicators that take into account the external environment in a non-personal process. [Means for solving the problem]

[0007] A program of one embodiment of the present disclosure causes a computer to function as: means for acquiring first store information regarding resource utilization in a target store; means for acquiring second store information regarding resource utilization in stores competing with the target store; means for acquiring customer information regarding customer visitation to the target store; and means for creating a plan regarding performance indicators of the target store by applying a trained model to model input data based on at least the first store information, the second store information, and the customer information. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a configuration of an information processing system according to an embodiment of the present invention; [Diagram 2] FIG. 2 is a block diagram showing a configuration of a client device according to the present embodiment. [Diagram 3] FIG. 2 is a block diagram showing a configuration of a store plan creation server according to the present embodiment. [Figure 4] FIG. 1 is an explanatory diagram of one aspect of the present embodiment. [Diagram 5] 4 is a diagram showing a data structure of a target store operation log database according to the present embodiment. FIG. [Figure 6] 4 is a diagram showing a data structure of a visiting member log database in the present embodiment. FIG. [Figure 7] 11 is a diagram showing a data structure of a competing store operation log database according to the present embodiment. FIG. [Figure 8] 11 is a flowchart of a plan creation process according to the present embodiment. [Figure 9] 11A and 11B are diagrams illustrating an example of a screen displayed in the plan creation process of the present embodiment. [Figure 10] 11A and 11B are diagrams illustrating an example of a screen displayed in the plan creation process of the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the drawings for explaining the embodiment, the same components are generally designated by the same reference numerals, and the repeated description will be omitted.

[0010] (1) Information Processing System Configuration The configuration of the information processing system will be described below. Fig. 1 is a block diagram showing the configuration of the information processing system according to the present embodiment.

[0011] As shown in FIG. 1, the information processing system 1 includes a client device 10, a store plan creating server 30, and a store information sharing server 50. The client device 10 and the store plan creating server 30 are connected via a network (for example, the Internet or an intranet) NW. The store plan creating server 30 and the store information sharing server 50 are connected via the network NW.

[0012] The client device 10 is an example of an information processing device. The client device 10 transmits a request to the store plan creation server 30. The client device 10 is, for example, a smartphone, a tablet terminal, or a personal computer. The user of the client device 10 is, for example, a store clerk of a store (hereinafter, referred to as a "target store"), or a store manager or other responsible person.

[0013] The store plan creation server 30 is an example of an information processing device. The store plan creation server 30 provides the client device 10 with a response in response to a request sent from the client device 10. Specifically, the store plan creation server 30 creates a plan related to performance indicators of a target store, and presents the plan to a user of the client device 10. The store plan creation server 30 is, for example, a web server.

[0014] The store information sharing server 50 is an example of an information processing device. The store information sharing server 50 shares information about each store, including the target store, between the stores. In particular, the store information sharing server 50 provides information about the operating status of resources in stores competing with the target store to the store plan creation server 30. The store information sharing server 50 is, for example, a web server.

[0015] As an example, when the information processing system 1 is applied to amusement facilities such as pachinko and slot machines, the store information sharing server 50 is connected to the hall computer of each store. The store information sharing server 50 corresponds to a server that provides a cloud service that provides information collected from the hall computer of each store or statistical information thereof.

[0016] (1-1) Client device configuration The configuration of the client device will now be described with reference to Fig. 2, which is a block diagram showing the configuration of the client device of this embodiment.

[0017] 2, the client device 10 includes a storage device 11, a processor 12, an input / output interface 13, and a communication interface 14. The client device 10 is connected to a display 21.

[0018] The storage device 11 is configured to store programs and data. The storage device 11 is, for example, a combination of a Read Only Memory (ROM), a Random Access Memory (RAM), and a storage (for example, a flash memory or a hard disk).

[0019] The programs include, for example, the following programs: ·OS (Operating System) programs · Programs for applications that process information (e.g. web browsers)

[0020] The data includes, for example, the following data: Databases referenced in information processing Data obtained by performing information processing (i.e., the results of performing information processing)

[0021] The processor 12 is a computer that realizes the functions of the client device 10 by running a program stored in the storage device 11. The processor 12 is, for example, at least one of the following: ·CPU(Central Processing Unit) ·GPU(Graphic Processing Unit) ·ASIC(Application Specific Integrated Circuit) ·FPGA(Field Programmable Array)

[0022] The input / output interface 13 is configured to obtain information (eg, a user's instruction) from an input device connected to the client device 10, and to output information (eg, an image) to an output device connected to the client device 10. The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display 21, a speaker, or a combination thereof.

[0023] The communication interface 14 is configured to control communication between the client device 10 and an external device (eg, the store plan creation server 30).

[0024] The display 21 is configured to display an image (a still image or a moving image). The display 21 is, for example, a liquid crystal display or an organic EL display.

[0025] (1-2) Configuration of the store plan creation server The configuration of the store plan creating server will now be described with reference to Fig. 3, which is a block diagram showing the configuration of the store plan creating server of this embodiment.

[0026] As shown in FIG. 3, the store plan creation server 30 includes a storage device 31, a processor 32, an input / output interface 33, and a communication interface .

[0027] The storage device 31 is configured to store programs and data, and is, for example, a combination of a ROM, a RAM, and a storage (for example, a flash memory or a hard disk).

[0028] The programs include, for example, the following programs: -OS programs Application programs that perform information processing

[0029] The data includes, for example, the following data: Databases referenced in information processing Results of information processing

[0030] The processor 32 is a computer that realizes the functions of the store plan creation server 30 by running the programs stored in the storage device 31. The processor 32 is, for example, at least one of the following. ·CPU GPU ·ASIC FPGA

[0031] The input / output interface 33 is configured to acquire information (e.g., user instructions) from an input device connected to the store plan creation server 30, and to output information (e.g., images) to an output device connected to the store plan creation server 30. The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display.

[0032] The communication interface 34 is configured to control communication between the store plan creating server 30 and an external device (for example, the client device 10 or the store information sharing server 50).

[0033] (1-3) Configuration of store information sharing server The configuration of the store information sharing server 50 will be described. The store information sharing server 50 may have the same or similar configuration as the store plan creating server 30.

[0034] (2) One aspect of the embodiment An embodiment of the present invention will now be described with reference to Fig. 4, which is an explanatory diagram of an embodiment of the present invention.

[0035] As shown in FIG. 4, the store plan creation server 30 acquires information regarding the operation of resources in the target store TP10 (hereinafter referred to as "target store information"). Here, if the target store TP10 is a service industry store, the resources are goods (e.g., equipment) or human resources used to provide services in the target store TP10. As an example, if the target store TP10 is an amusement facility, the resources may be game machines installed in the target store TP10. Alternatively, if the target store TP10 is a retail store, the resources are goods sold in the target store TP10, or goods or human resources used to sell goods. The store plan creation server 30 acquires information regarding the visit status of customers to the target store TP10 (hereinafter referred to as "target customer information").

[0036] The store information sharing server 50 collects information regarding the operation of resources at competing stores CP11-1, CP11-2, and CP11-3 of the target store TP10 (hereinafter referred to as "competing store information"). As an example, when the competing stores CP11-1, CP11-2, and CP11-3 are gaming facilities, the resources may be gaming machines installed in the competing stores CP11-1, CP11-2, and CP11-3. The store information sharing server 50 provides the competing store information to the store plan creation server 30.

[0037] The store plan creation server 30 creates a plan for the performance indicators of the target store TP10 by applying the trained model LM12 to model input data based on the target store information, the target customer information, and the competing store information. The store plan creation server 30 presents the created plan to the user US13 of the client device 10.

[0038] In this way, the store plan creation server 30 creates a plan for the performance indicators of the target store TP10 by applying the trained model LM12 to the model input data based on the target store information, the target customer information, and the competing store information. This allows the plan for the performance indicators of the target store TP10 to be determined in a non-personal process, taking into account the target store information, the target customer information, and the competing store information. In other words, even a user with little experience can easily create a plan that reflects the competitive environment in which the target store TP10 is placed.

[0039] (3) Database The databases of this embodiment are stored in the storage device 31.

[0040] (3-1) Target store operation log database The target store operation log database of this embodiment will be described below. Fig. 5 is a diagram showing the data structure of the target store operation log database of this embodiment.

[0041] The target store operation log database stores target store operation log information. The target store operation log information is an example of target store information. The target store operation log information is information related to a log of the operation status of each resource by date and time in the target store.

[0042] The store plan creation server 30 repeatedly acquires information from the hall computer of the target store, for example, and accumulates the information for a unit period (for example, 30 minutes), thereby generating each record in the target store operation log database. In other words, the target store operation log information corresponds to time-series information that accumulates operation information of gaming machines installed in the target store at intervals shorter than one day.

[0043] 5, the target store operation log database includes a "log ID" field, a "date and time" field, a "machine ID" field, a "put in" field, and a "withdrawal" field. Each field is associated with the others.

[0044] The "log ID" field stores a log ID. The log ID is information for identifying the target store operation log information.

[0045] The "date and time" field stores date and time information. The date and time information is information related to the date and time corresponding to the log expressed by the target store operation log information. As an example, the target store operation log information is information related to the log over a unit period (e.g., 30 minutes) based on the date and time indicated by the date and time information included in the information.

[0046] The "machine ID" field stores a machine ID. The machine ID is information that identifies the gaming machine (i.e., resource) that corresponds to the log expressed by the target store operation log information. The machine ID may be associated with model information or game setting information of the corresponding gaming machine in a database (not shown). Furthermore, the model information or game setting information may be included in the target store operation log information. Here, the game setting information may include information regarding at least one of the following game settings, for example. Nail condition Start (number of times the pattern changed per unit time) Base (play rate / normal ball return rate) ·TS (average number of rotations required for the grand prize) TY (average difference in balls that a player can win as a special prize) TO (average number of shots from when the special prize is awarded to when the game ends)

[0047] The "input" field stores input information. Input information is information about the amount of gameplay payment accepted by the gaming machine identified by the machine ID (i.e., input by customers) within the period indicated by the date and time information. The gameplay payment is, for example, pachinko balls or medals. Input information is an example of information about the operating amount of resources.

[0048] The "Payout" field stores payout information. The payout information is information regarding the amount of gameplay compensation paid out (i.e., paid out to customers) by the gaming machine identified by the machine ID within the period indicated by the date and time information. The paid-out compensation can be reused as gameplay compensation or exchanged for prizes. The payout information is an example of information regarding the operating amount of resources.

[0049] (3-2) Visitor log database The visiting member log database of this embodiment will be described below. Fig. 6 is a diagram showing the data structure of the visiting member log database of this embodiment.

[0050] The visiting member log database stores visiting member log information. The visiting member log information is an example of target customer information. The visiting member log information is information relating to a log of the visits of member customers to the target store by date and time. Here, a member customer is a customer who has registered as a member at the target store and been issued a membership card. When a member customer presents his / her membership card (inserts the membership card into a card reader (sand) attached to the gaming machine) to use an gaming machine, the membership information corresponding to the membership card (e.g., a member ID or member attribute information) can be linked to the operation information of the gaming machine.

[0051] The store plan creation server 30 repeatedly acquires information from the member management computer of the target store, for example, and organizes it as information for a unit period (for example, 30 minutes), thereby generating each record in the store visit member log database. In other words, the store visit member log information corresponds to time-series information that accumulates store visit information of member customers to the target store in a period shorter than one day.

[0052] 7, the visiting member log database includes a "log ID" field, a "date and time" field, a "member ID" field, a "machine ID" field, a "insertion" field, and a "withdrawal" field. Each field is associated with the others.

[0053] The "Log ID" field stores a log ID. The log ID is information for identifying visiting member log information.

[0054] The "date and time" field stores date and time information. The date and time information is information related to the date and time corresponding to the log expressed by the visiting member log information. As an example, the visiting member log information is information related to the log over a unit period (e.g., 30 minutes) based on the date and time indicated by the date and time information included in the information.

[0055] The "member ID" field stores a member ID. The member ID is information that identifies the member customer corresponding to the log represented by the member visit log information. The member ID may be associated with information regarding the attributes of the corresponding member in a database not shown. The member attributes may include, for example, the member's age and gender, as well as the customer classification for the target store (e.g., regular customer, new customer, etc.). The customer classification for the target store may be determined according to the results of customer analysis such as RFM analysis. Furthermore, the member information may be included in the member visit log information.

[0056] The "machine ID" field stores the machine ID. The machine ID is information that identifies the gaming machine corresponding to the log expressed by the visiting member log information (the gaming machine used by the member customer identified by the member ID). From the viewpoint of protecting personal information, the visiting member log information does not have to include the machine ID (that is, it may be impossible to identify which machine was used by the member customer identified by the member ID).

[0057] The "insertion" field stores input information. Input information is information regarding the amount of gameplay value received by the gaming machine identified by the machine ID (i.e., input by the member customer identified by the member ID) within the period indicated by the date and time information. From the perspective of protecting personal information, input information does not need to be included in the visiting member log information (i.e., it may be impossible to determine how much gameplay value was input by the member customer identified by the member ID).

[0058] The "Payout" field stores payout information. The payout information is information regarding the amount of the game value paid out by the gaming machine identified by the machine ID (i.e., paid out to the member customer identified by the member ID) during the period indicated by the date and time information. From the viewpoint of protecting personal information, it is not necessary for the deposit information to be included in the visiting member log information (i.e., it may be impossible to determine the amount of the game value paid out to the member customer identified by the member ID).

[0059] (3-3) Competitor store operation log database The competing store operation log database of this embodiment will be described below. Fig. 7 is a diagram showing the data structure of the competing store operation log database of this embodiment.

[0060] The competing store operation log database stores competing store operation log information. The competing store operation log information is an example of competing store information. The competing store operation log information is information related to a log of the operation status of each resource by date and time at a competing store.

[0061] The store plan creation server 30 repeatedly acquires information from the store information sharing server 50, for example, and organizes the information as information for a unit period (for example, 30 minutes), thereby generating each record in the competing store operation log database. In other words, the competing store operation log information corresponds to time-series information that accumulates operation information of gaming machines installed in competing stores at intervals shorter than one day.

[0062] 7, the competing store operation log database includes a "log ID" field, a "date and time" field, a "store ID" field, a "model ID" field, and an "operation rate" field. Each field is associated with each other.

[0063] The "log ID" field stores a log ID. The log ID is information for identifying competing store operation log information.

[0064] The "date and time" field stores date and time information. The date and time information is information related to the date and time corresponding to the log expressed by the competing store operation log information. As an example, the competing store operation log information is information related to the log over a unit period (e.g., 30 minutes) based on the date and time indicated by the date and time information included in the information.

[0065] The "Store ID" field stores a store ID. The store ID is information for identifying a competing store corresponding to a log represented by the competing store operation log information. A competing store may be identified, for example, in response to an instruction from a user of the client device 10. Alternatively, a competing store may be identified according to a predetermined algorithm based on, for example, location conditions.

[0066] The "machine type ID" field stores a machine type ID. The machine type ID is information for identifying the machine type of the gaming machine corresponding to the log expressed by the competing store operation log information. Note that information regarding how many gaming machines of the machine type identified by the machine type ID are installed in the competing store identified by the store ID may be provided by the store information sharing server 50, or may be obtained by a human (e.g., a staff member of the target store) inspecting the competing store.

[0067] The "operation rate" field stores operation rate information. The operation rate information is information about the operation rate of a gaming machine specified by a model ID installed in a competing store specified by a store ID during a period indicated by date and time information. The operation rate information is an example of information about the operation amount of resources.

[0068] (4) Information processing The information processing of this embodiment will be described. Fig. 8 is a flowchart of the plan creation processing of this embodiment. Fig. 9 is a diagram showing an example of a screen displayed in the plan creation processing of this embodiment. Fig. 10 is a diagram showing an example of a screen displayed in the plan creation processing of this embodiment.

[0069] The planning process of FIG. 8 may be started in response to the establishment of any of the following starting conditions: The planning process was called by another process. The user of the client device 10 performs an operation to call up the plan creation process. A specified date and time (for example, a predetermined date and time for creating a plan) has arrived. A certain amount of time has passed since a certain event (e.g. the last execution of the planning process).

[0070] As shown in FIG. 8, the store plan creating server 30 acquires various information (S130). Specifically, the store plan creation server 30 acquires target store information, target customer information, and competing store information. As an example, the store plan creation server 30 acquires target store operation log information, visiting member log information, and competing store operation log information that include date and time information corresponding to the date and time within a specified period.

[0071] The store plan creation server 30 may acquire game setting information of the target store as the target store information in addition to the target store operation log information or instead of the target store operation log information. The game setting information of the target store is information about the game settings in the target store that can be acquired from, for example, the hall computer of the target store. The game setting may mean the game setting of an individual game machine installed in the target store, or the game setting of a group of multiple game machines (for example, a group of game machines of a specific model).

[0072] The store plan creation server 30 may acquire customer demographic information of the target store as target customer information in addition to or instead of the store visiting member log information. The customer demographic information of the target store is information based on the observation results of people visiting the target store (predicted customer demographics). The customer demographics may mean the attributes of an individual customer or the attributes of a group of multiple customers. Such information may be determined by a person (e.g., a store clerk at the target store) who has inspected the target store and input it to the client device 10, or may be determined by an algorithm that analyzes (may include facial recognition) images (which may include videos) taken by the person using a camera or images taken by a surveillance camera installed in the target store.

[0073] The store plan creation server 30 may acquire game setting information of a competing store as competing store information in addition to or instead of the competing store operation log information. The game setting information of a competing store is information (prediction of the game setting of a competing store) based on the observation result of the operation status of the game machines installed in the competing store (for example, how many customers play each game machine). The game setting may mean the game setting of an individual game machine installed in the competing store, or the game setting of a group consisting of multiple game machines (for example, a group of game machines of a specific model). Such information may be determined and input to the client device 10 by a person (for example, a clerk of the target store) who has inspected the competing store, or may be determined by analyzing an image (which may include a video) taken by the person using a camera using an algorithm.

[0074] After step S130, the store plan creation server 30 executes plan creation (S131). Specifically, the store plan creation server 30 generates model input data based on the information acquired in step S130. The store plan creation server 30 creates a plan for the performance indicators of the target store over a predetermined period by applying the trained model to the model input data. Here, the predetermined period means the time range for which the plan is created, and may be a period from a reference date to the last day of the month, week, or year to which the reference date belongs. The reference date may be the date on which the plan was created (hereinafter referred to as the "plan creation date"), or the day after that. Alternatively, the reference date may be the first day of the month following the month to which the plan creation date belongs, the first day of the week following the week to which the plan creation date belongs, or the first day of the year following the year to which the plan creation date belongs.

[0075] The trained model is configured to output a plan that is relatively optimized with respect to the competitive environment of the target store represented by the model input data. The optimization is, for example, optimization related to maximizing the profit of the target store, but is not limited to this and may include optimization related to other purposes (for example, maximizing sales, etc.). For example, optimization may mean maximizing (or increasing or maintaining) the customers of the target store with respect to a limited number of potential customers (pachinko users) in the trade area of ​​the target store, taking into account the situation of the target store and the situation of competing stores. The trained model may be constructed by supervised learning based on teacher data including, for example, training model input data representing a certain competitive environment and corresponding training model output data (i.e., correct answer value). The trained model may be the model itself constructed by learning, or may be a derived model or distilled model of the model. The training model output data may be actual values ​​or theoretical values.

[0076] Plans regarding the performance of target stores may include at least one of the following: Daily profit planning for target stores - Revenue plans for each type of gaming machine installed at the target store - Game setting plans for gaming machines installed at the target stores

[0077] The daily revenue plan for the target store may include, for example, at least one of the following: Average sales per machine on each date Average profit per machine on each date (e.g. gross profit) Average profit margin per gaming machine on each date - Total sales for each target store on each date -Total profits for each target store on each date - Profit margin across all target stores on each date

[0078] The revenue plan for each model at the target store may include, for example, at least one of the following: - The average payout (number of balls fired by customers) per machine for each type of gaming machine over a given period The average cost per ball (i.e. cost per ball) for each gaming machine type over a given period The average profit per ball for each machine type over a given period (i.e., profit per ball) - Average sales per machine for each type of gaming machine over a given period Average profit per machine for each type of gaming machine over a given period The profit plan by model in the target store corresponds to the result of allocating the average value of the profit plan of the target store on each date over a predetermined period to each model. In other words, the average value of the profit plan of the target store on each date over a predetermined period corresponds to the result of aggregating the profit plans of the target store by model.

[0079] The game setting plan for the gaming machines installed in the target store may include, for example, at least one of the following: -Status of nails on each gaming machine on each date -Start time for each gaming machine on each date -Base of each gaming machine on each date TS for each gaming machine on each date TY for each gaming machine on each date -TO of each gaming machine on each date In the game setting plan for the gaming machines installed in the target store, various setting values ​​are set that are statistically predicted by the trained model to be able to achieve the profit plan for each model in the target store over a specified period under the competitive environment represented by the model input data. In other words, the profit plan for each model in the target store corresponds to the result of aggregating the performance indicators of each gaming machine that are expected to be achievable by the game setting plan by model. In the game setting plan, the game settings for gaming machines of the same model may be common, and in this case, the game settings may be set for each model rather than for each gaming machine.

[0080] The game settings of a gaming machine are directly related to the likelihood of winning on the machine or the size of the reward when a win occurs, and these factors affect customers' impressions of the gaming machine and, by extension, the store. By adjusting the game settings of a target store to be more favorable to customers than those of competing stores, customers will flock to the target store and the operating rate of the gaming machines in the target store will increase. On the other hand, if the game settings of the target store are left unfavorable to customers compared to competing stores, customers will move away from the target store and the operating rate of the gaming machines in the target store will decrease.

[0081] After step S131, the store plan creation server 30 executes presentation of a plan (S132). Specifically, the store plan creation server 30 presents the plan created in step S131 to the user of the client device 10. The plan created in step S131 may be provided to the client device 10 as, for example, a CSV file and stored in the storage device 11.

[0082] As a first example of presenting a plan (S132), the store plan creation server 30 may transmit information for displaying a screen shown in Fig. 9 to the client device 10. The client device 10 displays the screen of Fig. 9 on the display 21 based on the information acquired from the store plan creation server 30.

[0083] The screen in Fig. 9 shows the daily profit plan for the target store. On the screen in Fig. 9, information showing the planned value of the performance indicator for each date in the target store is arranged in the same row as the information showing each date. Therefore, by viewing this screen, the user of the client device 10 can easily understand the planned value of the performance indicator for each date in the target store.

[0084] As a second example of presenting a plan (S132), the store plan creation server 30 may transmit information for displaying a screen shown in Fig. 10 to the client device 10. The client device 10 displays the screen of Fig. 10 on the display 21 based on the information acquired from the store plan creation server 30.

[0085] The screen in Fig. 10 shows the profit plan for each model in the target store. In the screen in Fig. 10, information showing the planned value of the performance indicator of each model in the target store is placed in the same row as the information showing the name of each model. Therefore, by viewing this screen, the user of the client device 10 can easily understand the planned value of the performance indicator of each model in the target store.

[0086] As a third example of plan presentation (S132), the store plan creation server 30 may transmit information to the client device 10 for displaying a screen (not shown) showing a game setting plan for the gaming machines in the target store. The client device 10 displays such a screen on the display 21 based on information acquired from the store plan creation server 30. By viewing such a screen, the user of the client device 10 can easily grasp the game setting plan for the gaming machines in the target store (for example, the nail state, start, base, TS, TY, or TO planned values).

[0087] As a fourth example of plan presentation (S132), the store plan creation server 30 may transmit to the client device 10 information for displaying at least one of the daily profit plan in the target store, the profit plan by model in the target store, or the game setting plan of the gaming machine in the target store alongside (i.e., in comparison with) a comparison target. The comparison target may include, for example, at least one of the following: A plan for the same period that the store plan creation server 30 created in the past (for example, the previous day, last week, last month, or last year) Plans over the same period created by a human (e.g., a user of the client device 10) - Actual values ​​of corresponding performance indicators over the same period The client device 10 displays this screen on the display 21 based on the information acquired from the store plan creation server 30. By viewing this screen, the user of the client device 10 can easily understand the difference between the newly created plan and the comparison target.

[0088] (5) Summary As described above, the store plan creation server 30 acquires the target store information, the target customer information, and the competing store information, and creates a plan for the performance indicators of the target store by applying the trained model to the model input data based on at least this information. This allows the plan for the performance indicators of the target store to be determined in a non-personal process, taking into account the target store information, the target customer information, and the competing store information. In other words, even a user with little experience can easily create a plan that reflects the competitive environment in which the target store is placed.

[0089] The store plan creation server 30 may present the created plan. This allows the user to confirm the contents of the plan created by the store plan creation server 30. The store plan creation server 30 may present the created plan in comparison with plans created in the past regarding the performance indicators of the target store. This allows the user to easily understand how much the previously created plan needs to be revised or has been revised.

[0090] The target customer information may include customer demographic information of the target store based on observations of people visiting the target store. Since the customer demographics affect sales per customer, taking such information into account can create a more appropriate plan.

[0091] The target store information may include time-series information on operation information of gaming machines installed in the target store that can be obtained from the hall computer of the target store and that is accumulated at intervals shorter than one day, and the competing store information may include time-series information on operation information of gaming machines installed in competing stores that can be obtained from an external device connected to the hall computer of the competing store and that is accumulated at intervals shorter than one day. This makes it possible to analyze in detail the operation status of gaming machines in the target store and competing stores, thereby improving the accuracy of the plans that are created.

[0092] The competing store information may include game setting information of the gaming machines installed in the competing store based on the observation result of the operation status of the gaming machines installed in the competing store. The game setting of the gaming machines of the competing store is directly related to the likelihood of winning on the gaming machine or the size of the reward when a win occurs, and these elements affect the customer's impression of the gaming machine and, by extension, the competing store, so by taking such information into consideration, a more appropriate plan can be created.

[0093] The target customer information may include membership information corresponding to a membership card inserted in a card reader attached to a gaming machine installed in the target store, which can be obtained from a member management computer of the target store. This allows for a detailed analysis of customer trends at the target store, thereby improving the accuracy of the plan to be created.

[0094] The plan regarding the performance indicators of the target store may include at least one of a daily profit plan for the target store, a profit plan for each type of gaming machine installed in the target store, or a play setting plan for the gaming machine. This allows a plan regarding the performance indicators of the target store that provides gaming facilities to be created from various perspectives.

[0095] (6) Other modifications The storage device 11 may be connected to the client device 10 via a network NW. The display 21 may be integrated with the client device 10. The storage device 31 may be connected to the store plan creation server 30 via the network NW.

[0096] Each step of the above information processing can be executed by any of the client device 10, the store plan creation server 30, and the store information sharing server 50. Also, an example in which the information processing system of the embodiment is implemented by a client / server type system has been shown. However, the information processing system of the embodiment can also be implemented by a stand-alone type computer or a peer-to-peer type system.

[0097] The above description is based on the premise that the target store and the competing store provide customers with facilities for playing games. However, the present embodiment can be applied to stores of various types.

[0098] In the above description, an example has been shown in which the store plan creation server 30 acquires target store information, target customer information, and competing store information, and applies the trained model to model input data based on this information. However, the store plan creation server 30 may acquire further information, and apply the trained model to model input data that is further based on the information. As a first example, the store plan creation server 30 may further acquire customer demographic information (predicted customer demographics) of competing stores based on the observation results of people visiting the competing stores, and apply the learned model to model input data based on the acquired information. Such information may be determined by a person (e.g., a salesperson at the target store) who has visited the competing store and input to the client device 10, or may be determined by analyzing (may include facial recognition) an image (may include video) taken by the person using a camera using an algorithm. Since the customer demographics affect sales per customer, a more appropriate plan can be created by taking such information into consideration. As a second example, the store plan creation server 30 may further acquire weather information or event information, and apply the learned model to model input data further based on the acquired information. The weather information or event information may be information of past history, or may be information of future prediction results or schedules. The weather information may be limited to information on the location area (e.g., city, ward, town, village, or prefecture) of the target store or the competing store. On the other hand, the event information may not be limited to the location area of ​​the target store or the competing store, but may include nationwide information. Alternatively, the event information may include information on events that the target store or the competing store will hold (or have held) for promotion. Since weather and events affect the number of customers visiting the target store, a more appropriate plan can be created by taking such information into consideration. For example, in areas where many people (e.g., fishermen, farmers, etc.) whose work is delayed when the weather is bad live, the number of customers visiting the store tends to increase when it rains. As a third example, the store plan creation server 30 may further acquire promotion information based on the analysis results of the contents of promotions (for example, advertisements) of competing stores, and apply the trained model to model input data further based on the promotion information. The analysis of the promotion contents is performed by a human or an algorithm, for example, from the following perspectives: What models (development models) are competitors focusing on? - What rates (i.e., the price of playing) are your competitors focusing on? · Are competitors focusing on new or old machines? - What is the game setting (ease of winning) of competing stores? Promotions by rival stores affect the number of customers visiting the target store, so by taking into account the results of analyzing the content of such promotions, a more appropriate plan can be created. As a fourth example, the store plan creation server 30 may further acquire information on the number of people visiting a competing store (hereinafter referred to as "competitive customer number information") and apply the learned model to model input data based on the acquired information. Such information may be provided by the store information sharing server 50, or may be judged and input to the client device 10 by a person (e.g., a salesperson at the target store) who has visited the competing store, or may be determined by analyzing (may include facial recognition) an image (may include video) taken by the person using a camera using an algorithm. Since the number of customers visiting a competing store affects the number of customers visiting the target store, a more appropriate plan can be created by taking such information into consideration.

[0099] Although the embodiment of the present invention has been described in detail above, the scope of the present invention is not limited to the above embodiment. Furthermore, the above embodiment can be improved or modified in various ways without departing from the spirit of the present invention. Furthermore, the above embodiment and the modified examples can be combined. [Explanation of symbols]

[0100] 1: Information processing system 10: Client device 11:Storage device 12: Processor 13: Input / Output Interface 14: Communication interface 21: Display 30: Store planning server 31:Storage device 32 : Processor 33: Input / Output Interface 34: Communication interface 50: Store information sharing server

Claims

1. Computer, a means for acquiring first store information including first time-series information that is accumulated in a cycle shorter than one day on operation information of game machines installed in a target store and that is related to operation of resources in the target store; a means for acquiring second store information including second time-series information regarding operation of resources in a competing store of the target store, the second time-series information being accumulated in a cycle shorter than one day on operation information of gaming machines installed in the competing store; a means for acquiring customer information including third time-series information on the visit of member customers to the target store, the third time-series information being accumulated at a period shorter than one day, the third time-series information being related to the visit status of customers to the target store; a means for applying a trained model to model input data based on the first store information, the second store information, and the customer information to create a revenue plan for each type of gaming machine installed in the target store, the revenue plan being relatively optimized with respect to the competitive environment of the target store represented by the model input data; A program that functions as a

2. The program described in claim 1, wherein the revenue plan for each model of gaming machine installed in the target store includes at least one of the following: average out per gaming machine of each model over a specified period, average ball price per gaming machine of each model over a specified period, average ball profit per gaming machine of each model over a specified period, average sales per gaming machine of each model over a specified period, and average profit per gaming machine of each model over a specified period.

3. The program described in claim 1, wherein the creating means applies the learned model to model input data based on the first store information, the second store information, and the customer information to create a revenue plan for each model of gaming machine that is relatively optimized for the competitive environment of the target store represented by the model input data, and a game setting plan for the gaming machines installed in the target store, in which various setting values ​​are determined that are statistically predicted to be capable of achieving the revenue plan for each model of gaming machine.

4. A program as described in claim 3, wherein the creating means determines game settings for gaming machines of the same model for each model in a game setting plan for the gaming machine.

5. A program as described in claim 3, wherein the creating means determines game settings for each gaming machine in a game setting plan for the gaming machine.

6. a means for acquiring first store information, the first store information including first time-series information that is accumulated in a cycle shorter than one day on operation information of game machines installed in the target store, the game machine operation information being related to operation of resources in the target store; a means for acquiring second store information, the second store information including second time-series information accumulating operation information of gaming machines installed in competing stores of the target store at a period shorter than one day, the second time-series information being related to operation of resources in the competing stores of the target store; a means for acquiring customer information including third time-series information on the visit of member customers to the target store, the third time-series information being accumulated at a period shorter than one day, the third time-series information being related to the visit status of customers to the target store; a means for applying a trained model to model input data based on the first store information, the second store information, and the customer information to create a revenue plan for each type of gaming machine installed in the target store, the revenue plan being relatively optimized with respect to the competitive environment of the target store represented by the model input data; An information processing device comprising:

7. The computer acquiring first store information including first time-series information that is accumulated at a period shorter than one day on operation information of gaming machines installed in a target store, the first store information being related to operation of resources in the target store; acquiring second store information including second time-series information that is accumulated at a period shorter than one day on operation information of gaming machines installed at competing stores of the target store, the second time-series information being related to operation of resources at the competing stores of the target store; acquiring customer information including third time-series information on the visit of member customers to the target store, the third time-series information being accumulated at a period shorter than one day, the third time-series information being related to the visit status of customers to the target store; A step of applying a trained model to model input data based on the first store information, the second store information, and the customer information to create a revenue plan for each type of gaming machine installed in the target store, the revenue plan being relatively optimized with respect to the competitive environment of the target store represented by the model input data; A method for performing.

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