Price setting support device, locker system, and model generation device

Abstract

We provide a pricing support device, a locker system, and a model generation device that contribute to improving profitability. [Solution] The fee setting support device 30 assists in setting the fees for the locker device 1, which is equipped with a billing mechanism. The fee setting support device 30 includes a model generation unit that generates a learning model 37a that outputs a predicted value 42 of sales based on the usage fee per usage time using first information and second information. The fee setting support device 30 also includes an optimal fee calculation unit 38 that calculates the optimal usage fee at the time of billing using third information and the learning model 37a.

Description

Technical Field

[0001] The present invention relates to a fee setting support device, a locker system, and a model generation device.

Background Art

[0002] Conventionally, a locker system has been proposed that changes the usage fee of a locker device installed in a facility such as a station according to conditions.

[0003] For example, as shown in FIG. 1 of Patent Document 1, the locker device management system described in Patent Document 1 includes a control device 20 that controls charging means 13 provided in each of a plurality of lockers 11a. The control device 20 selects a predetermined charging pattern from among a plurality of charging patterns based on the type of time, and charges the charging means 13 based on the selected charging pattern. The control device 20 appropriately selects the type of time that is a selection condition for the charging pattern. As a result, it becomes possible to set various charging patterns according to a predetermined time zone, such as weekdays, holidays, peak hours, or late-night hours.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Incidentally, in this type of locker system, the actual billing pattern that is ultimately set is almost always determined as follows: The billing pattern is predetermined based on human experience and intuition, while taking into account sales data for each time period in nearby locker systems. This is also true when selecting one billing pattern from multiple patterns as mentioned above, and the creation and selection of billing patterns tend to depend on the experience of the creator. Therefore, it is difficult to optimize the usage fees for the locker devices, and variations in the setting of usage fees are likely to occur, making it difficult to stably improve the profitability of the locker system.

[0006] Therefore, in view of the above-mentioned problems, the object of the present invention is to provide a fee setting support device, a locker system, and a model generation device that contribute to improving profitability. [Means for solving the problem]

[0007] The fee setting support device developed to solve the above problems is a fee setting support device that supports fee setting for a locker device equipped with a billing means, and comprises: a model generation unit that generates a learning model that outputs a predicted value of sales for usage fees per usage time using first information and second information, and an optimal fee calculation unit that calculates the optimal usage fee at the time of billing using third information and the learning model.

[0008] Furthermore, the locker system comprises the above-mentioned fee setting support device and a locker device equipped with a locker box and a billing means.

[0009] Furthermore, the fee setting support device is a fee setting support device that supports fee setting for a locker device equipped with a billing means, and comprises a learning model that outputs a predicted value of sales for usage fees per usage time generated using first information and second information, and an optimal fee calculation unit that calculates the optimal usage fee at the time of billing using third information.

[0010] Furthermore, the model generation device is a model generation device that generates the learning model used by the above-mentioned fee setting support device, and comprises an acquisition unit that acquires the first information and the second information, and a model generation unit that generates the learning model that outputs a predicted value of sales for usage fees per usage time using the first information and the second information. [Effects of the Invention]

[0011] As described above, the present invention uses a learning model generated by a model generation unit and third information to enable an optimal fee calculation unit to calculate the optimal usage fee at the time of billing. This makes it possible to optimize the usage fee for the locker device to a usage fee that maximizes sales, without depending on the creator's experience, etc. Therefore, it is possible to provide a fee setting support device that contributes to improving profitability.

[0012] Furthermore, by using such a pricing support device, it is possible to construct a locker system that contributes to improved profitability.

[0013] Furthermore, the model generation device can generate learning models used by a pricing support device that contributes to improving profitability. [Brief explanation of the drawing]

[0014] [Figure 1] This is a functional configuration diagram of a locker system according to the first embodiment of the present invention. [Figure 2] This is a functional configuration diagram of a locker device that constitutes a part of the locker system according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of information acquired in the locker system according to the first embodiment. [Figure 4] This is a functional configuration diagram of a fee setting support device that assists in setting fees for a locker device according to the first embodiment. [Figure 5] This is a conceptual diagram showing an example of a learning model generated by the fee setting support device according to the first embodiment. [Figure 6] It is a flowchart showing the operation of the model generation process in the locker system 100 according to the first embodiment. [Figure 7] It is a flowchart showing the operation of the support process in the locker system 100 according to the first embodiment.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, a first embodiment of the present invention will be described based on the drawings. FIG. 1 is a functional configuration diagram of a locker system 100 according to the first embodiment of the present invention. In the following description, when there are a plurality of similar configurations, the illustration thereof may be omitted.

[0016] The locker system 100 shown in FIG. 1 includes a locker device 1 and a management server 3 that can communicate with each other through a network NW with respect to the locker device 1.

[0017] The locker device 1 is installed, for example, in a store, an office, a station, an airport, a bus terminal, or a tourist facility. The management server 3 includes a fee setting support device 30 that supports the fee setting of the locker device 1.

[0018] First, the configuration of the locker device 1 will be described. As shown in FIG. 2, the locker device 1 includes a plurality of rows of locker units 10 and a central control device 20.

[0019] Each locker unit 10 includes a plurality (in this embodiment, for example, 2) of locker boxes 11. That is, a plurality of locker boxes 11 are provided in one locker device 1. The locker box 11 includes a luggage storage unit 12 for storing luggage. The locker box 11 also includes a door (not shown) that can open and close the luggage storage unit 12, an electric lock 13 that switches between the locked state and the unlocked state of the door, and a display unit 14 that displays the usage fee at the time of charging.

[0020] The luggage storage section 12 is formed in a substantially rectangular shape using, for example, a metal plate material, struts, etc., and has an opening on the front side. The door is provided with a well-known handle, etc., and opens and closes the luggage storage section 12. The electric lock 13 includes a mechanism that switches between a state in which the door is locked and a state in which the door is unlocked in accordance with an instruction from the central control device 20.

[0021] The display section 14 displays the usage fee at the time of charging of the locker box 11 in accordance with an instruction from the central control device 20. Further, the display section 14 constitutes a charging means together with the above-described electric lock 13 and the central control device 20 described later. The display section 14 is installed, for example, on the surface of the above-described door in each of the plurality of locker boxes 11. Various devices can be used for the display section 14. For example, a liquid crystal display (Liquid Crystal Display) can be used for the display section 14. Also, an electronic shelf label (ESL: Electric Shelf Label) such as electronic paper can be used for the display section 14.

[0022] The central control device 20 has a locker control section 21 that controls the entire locker device 1 and a user operation section 22 that receives a user's operation on the locker device 1. Further, the central control device 20 has a communication section 23 connected to the management server 3 and a first information acquisition section 24 that acquires various information.

[0023] The locker control section 21 is a unit including a processor such as a central processing unit (CPU) and a storage device such as a memory. The locker control section 21 is connected to the electric lock 13 of each locker box 11 by wire or wirelessly, and executes a process of locking and unlocking the electric lock 13. Further, the locker control section 21 is connected to the display section 14 of each locker box 11 by wire or wirelessly, and changes the current display of the display section 14 based on the charging pattern P described later that has been set.

[0024] <​​​ The first information acquisition unit 24 acquires various types of information. The first information acquisition unit 24 includes communication means, sensors, timing means, etc. (not shown), and acquires, for example, information 25 regarding the past use of the locker device 1 shown in Figure 3. The information 25 regarding the past use of the locker device 1 is sometimes referred to as the first information.

[0026] The past usage information 25 is information originating from the locker device 1 that can be collected from the locker device 1. The past usage information 25 includes, for example, fee information 25a, trend information 25b, specifications information 25c, and sales information 25d.

[0027] The fee information 25a is information about the fees set at the time of billing. This fee information 25a includes, for example, a "basic fee" which is the fee for a predetermined usage time. The fee information 25a also includes "overtime fees" which are the fees for usage exceeding the predetermined usage time, and a "maximum fee" which can be set. The fee information 25a may also include a billing pattern P (billing mode, see Table 1). The billing pattern P indicates the type of fee. The billing pattern P includes information about the billing method, such as whether billing is done on an hourly basis (so-called hourly rate) or on a daily basis (so-called daily rate).

[0028] Trend information 25b is information that shows the usage trends of the locker device 1. This trend information 25b includes, for example, the "number of users by time of day," "maximum usage time," and "number of users by day" of the locker device 1. Furthermore, the "number of users by day" may include the "number of users by day of the week." Also, the "number of users by day of the week" may include the "number of users on weekdays," the "number of users on public holidays," or the "number of users during consecutive holidays." Trend information 25b also includes the "turnover rate," which shows how many times a single locker box 11 was used in a day. Trend information 25b also includes the "maximum overtime," which shows the longest usage time that exceeded the predetermined usage time.

[0029] Standard information 25c is information regarding the standard, such as the structure and dimensions of the locker device 1. This standard information 25c includes, for example, "the number of doors per locker device 1" and "size information of the locker box 11 (extra small, small, medium, large, oversized)."

[0030] Sales information 25d is information indicating the sales of locker device 1. This sales information 25d includes "daily or hourly sales of locker device 1". This sales information 25d may be obtained from any locker box 11 among the locker boxes 11, or it may be obtained from the entire locker system 100.

[0031] The information 25 regarding the past use of the locker device 1 obtained in this manner can be used, for example, as training data for generating the learning model 37a described later.

[0032] Next, the management server 3 will be described. As mentioned above, the management server 3 is equipped with a fee setting support device 30 that assists in setting fees for the locker device 1. Specifically, the fee setting support device 30 performs a process to generate a learning model 37a (see Figure 5) when assisting with fee setting. The generation of the learning model 37a uses various information such as the information 25 regarding the past use of the locker device 1 mentioned above, and the past surrounding information 33 described later. The management server 3 then performs a process to calculate the "optimal usage fee" at the time of billing. This calculation of the "optimal usage fee" uses the learning model 37a and the billing point information 40 or surrounding information 41 at the time of billing for the locker device 1, which will be described later.

[0033] As shown in Figure 4, the fare setting support device 30 includes a communication unit 31 capable of communicating with an external device (not shown), and a second information acquisition unit 32 that acquires various information from the external device. The fare setting support device 30 also includes a storage unit 34 for storing information and a control unit 35 that controls the operation of the entire fare setting support device 30.

[0034] The communication unit 31 consists of a LAN interface board, wireless communication circuits, etc., and is connected to the locker device 1 and external devices (not shown) via a network NW such as the Internet.

[0035] The second information acquisition unit 32 acquires various information via the communication unit 31. The second information acquisition unit 32 is equipped with sensors (not shown), timing means, etc., and acquires, for example, past peripheral information 33 of the locker device 1 shown in Figure 3. Past peripheral information 33 of the locker device 1 is sometimes referred to as the second information.

[0036] Past surrounding information 33 is information about the area where the locker device 1 is installed. Past surrounding information 33 includes, for example, any of facility information 33a, weather information 33b, location information 33c, or event information 33d.

[0037] Facility information 33a is information about the facility where the locker device 1 is installed. This facility information 33a includes "facility attributes" such as public transportation, commercial facilities, event halls, and amusement parks, as well as the "daily number of users" of the facility.

[0038] Weather information 33b is information about the weather at the time the locker device 1 was used. This weather information 33b includes, for example, "weather information" such as sunny, cloudy, and rainy, and "various data" such as precipitation, snow depth, temperature, humidity, and sunshine duration.

[0039] Location information 33c is information about the location where the locker device 1 is installed. This location information 33c includes, for example, the "prefecture name" and "city / town name" of the prefecture where the locker device 1 is installed. This location information 33c may also include latitude and longitude information.

[0040] Event information 33d is information about events that occurred on the date and time when the locker device 1 was used. This event information 33d includes, for example, whether or not there were any events (concerts, sports matches, etc.) at the facility where the locker device 1 was installed or at facilities in the surrounding area.

[0041] The past surrounding information 33 of the locker device 1 obtained in this way can be used, for example, as training data to generate a learning model 37a.

[0042] The storage unit 34 is composed of a storage device such as a hard disk. The storage unit 34 stores information 25 regarding the past use of the locker device 1 acquired via the communication unit 31, and past peripheral information 33 collected by the second information acquisition unit 32. In addition, as will be described later, the storage unit 34 stores information 40 regarding the billing point of the locker device 1 and peripheral information 41 (see Figure 5) at the time of billing for the locker device 1.

[0043] The control unit 35 includes an acquisition unit 36 ​​for acquiring various information and a model generation unit 37 for generating a learning model 37a (see Figure 5) for usage fees. The control unit 35 also includes an optimal fee calculation unit 38 for calculating the "optimal usage fee" at the time of billing. The control unit 35 also includes a setting unit 39 for setting a billing pattern P (see Table 1) as the "usage fee at the time of billing" by referring to the "optimal usage fee".

[0044] The acquisition unit 36 ​​acquires information 25 (see Figure 3) regarding the past use of the locker device 1, which has been acquired by the first information acquisition unit 24 of the locker device 1, via the communication unit 23, and stores it in the storage unit 34. In addition, it stores past peripheral information 33 of the locker device 1, which has been acquired by the second information acquisition unit 32 via the communication unit 31 of the management server 3, in the storage unit 34.

[0045] Furthermore, the acquisition unit 36 ​​acquires the billing point information 40 of the locker device 1 subject to fee setting and the surrounding information 41 at the time of billing of the locker device 1 via the communication unit 31 and stores them in the storage unit 34. The billing point information 40 or the surrounding information 41 at the time of billing becomes the information input to the generated learning model 37a, as shown in Figure 5. Information including the billing point information 40 or the surrounding information 41 at the time of billing is sometimes referred to as third information.

[0046] The billing point information 40 is information about the actual billing of the locker device 1 that is subject to fee setting, and is acquired by the acquisition unit 36 ​​when the optimal fee calculation unit 38 is operating. The billing point information 40 includes "date and time information" such as date and time, and "location information" such as latitude and longitude. The "date and time information" may include information such as weekdays, holidays, etc., and information about the day of the week. The "location information" may include place names, latitude, longitude, etc. In addition, the billing point information 40 may also include the billing pattern P.

[0047] On the other hand, the surrounding information 41 at the time of billing is information about the surrounding area where the locker device 1 is installed at the time of billing, and is acquired by the acquisition unit 36 ​​when the optimal fee calculation unit 38 is operating. The contents of the surrounding information 41 at the time of billing are the same as those of the past surrounding information 33 described above. The only difference between the surrounding information 41 at the time of billing and the past surrounding information 33 is the timing of acquisition by the acquisition unit 36, so a detailed explanation is omitted.

[0048] The model generation unit 37 generates a learning model 37a (see Figure 5) that outputs a predicted value 42 of sales per hour of usage fee when a predetermined billing pattern P is adopted. In generating this model, the model generation unit 37 uses information 25 (first information) about past usage of the locker device 1 and past surrounding information 33 (second information).

[0049] The optimal fee calculation unit 38 acquires billing point information 40 or surrounding information 41 at the time of billing via the acquisition unit 36 ​​and stores it in the storage unit 34. The optimal fee calculation unit 38 then calculates the "optimal usage fee" which is expected to maximize sales at the time of billing. In this calculation, the optimal fee calculation unit 38 uses the billing point information 40 or surrounding information 41 (third information) at the time of billing stored in the storage unit 34 and the learning model 37a generated by the model generation unit 37.

[0050] The setting unit 39 refers to the "optimal usage fee" calculated by the optimal fee calculation unit 38 and sets the billing pattern P for the locker device 1 to be set. The billing pattern P indicates the type of usage fee. Table 1 is a table showing an example of a billing pattern P. The billing pattern P is a usage fee per usage time, for example, as shown in P1 to P5 in Table 1.

[0051] [Table 1]

[0052] Next, an example of the operation of the locker system 100 will be described. As shown in Figure 6, the locker system 100 performs a "model generation process". Also, as shown in Figure 7, the locker system 100 performs a "support process".

[0053] First, the "model generation process" will be explained. The "model generation process" shown in Figure 6 is an example of the process for generating a learning model 37a. First, the acquisition unit 36 ​​of the management server 3 operates the first information acquisition unit 24 of the locker device 1 via the communication unit 31 to acquire information 25 about past usage. The acquisition unit 36 ​​also operates the second information acquisition unit 32 of the management server 3 to acquire past peripheral information 33 (step S101). The acquisition unit 36 ​​periodically acquires the information 25 about past usage and the past peripheral information 33, such as at predetermined intervals, and stores the acquired information in the storage unit 34. In step S101, the acquisition unit 36 ​​may also acquire various information from the storage unit 34. The target of acquiring various information may be one locker device 1 constituting the locker system 100, or it may be multiple locker devices 1.

[0054] Next, the model generation unit 37 creates training data using past usage information 25 and past surrounding information 33 (step S102). Various types of data can be used as training data. For example, some or all of the price information 25a, trend information 25b, and standard information 25c may be linked with actual sales information 25d at the time that information was obtained, and used as training data.

[0055] Alternatively, some or all of the facility information 33a, weather information 33b, location information 33c, and event information 33d may be linked with actual sales information 25d at the time that information was obtained, and used as training data.

[0056] Next, the model generation unit 37 performs machine learning using multiple training data to generate a learning model 37a (step S103). This completes the "model generation process". As shown in Figure 5, the generated learning model 37a receives input from billing point information 40 or surrounding information 41 at the time of billing and outputs a predicted value 42 of sales for usage fees per usage time. Thus, in this embodiment, the learning model 37a is created by including not only the information obtained from multiple locker devices 1 but also their surrounding information.

[0057] Next, the "support processing" will be explained. The "support processing" shown in Figure 7 is an example of processing that supports the setting of usage fees in the locker device 1. First, the optimal fee calculation unit 38 of the management server 3 activates the acquisition unit 36. The acquisition unit 36 ​​then activates the first information acquisition unit 24 of the locker device 1 via the communication unit 31 to acquire billing point information 40 and saves it in the storage unit 34. The acquisition unit 36 ​​also activates the second information acquisition unit 32 of the management server 3 to acquire peripheral information 41 at the time of billing and saves it in the storage unit 34 (step S201).

[0058] Next, the optimal price calculation unit 38 of the management server 3 inputs the acquired billing point information 40 or surrounding billing information 41 to the learning model 37a (step S202). The input method can be adjusted as appropriate. That is, the optimal price calculation unit 38 may input the billing point information 40 or surrounding billing information 41 once for each piece of acquired information, or it may input the acquired information multiple times in batches. This allows the optimal price calculation unit 38 to obtain multiple patterns of the predicted sales value 42. Various formats are possible for the output of the predicted sales value 42. An example of the predicted sales value 42 output by the learning model 37a is shown in Table 2 below.

[0059] [Table 2]

[0060] The left column of Table 2 shows the types of virtual billing patterns P, and the middle column shows the usage fee per hour for each virtual billing pattern P. The right column of Table 2 shows the projected sales value 42 for each virtual billing pattern P. The usage fee may be an hourly rate (so-called time-based), as shown in billing patterns P1 to P4. Alternatively, the usage fee may be a daily rate (so-called daily rate), as shown in billing pattern P5. Thus, the projected sales value 42 may be shown for each virtual billing pattern P, for example.

[0061] Next, the optimal fee calculation unit 38 compares the predicted sales values ​​42 output for each of the units. Based on this comparison, the optimal fee calculation unit 38 calculates the "optimal usage fee" that is expected to maximize sales at the time of billing and stores it in the storage unit 34 (step S203).

[0062] Next, the setting unit 39 of the management server 3 refers to the "optimal usage fee" stored in the storage unit 34, determines and sets the billing pattern P for the locker device 1 to be configured, and saves it in the storage unit 34 (step S204).

[0063] Next, the control unit 35 operates the locker control unit 21 of the locker device 1 via the communication unit 31. The locker control unit 21 changes the display on the display unit 14 at a predetermined timing based on the set billing pattern P (step S204). With this, the "support processing" is completed.

[0064] According to this embodiment, the optimal fee calculation unit 38 calculates the "optimal usage fee" at the time of billing. In this calculation, the optimal fee calculation unit 38 uses the learning model 37a generated by the model generation unit 37 and the billing point information 40 of the locker device 1 or the surrounding information 41 at the time of billing of the locker device 1. This makes it possible to optimize the usage fee of the locker device 1 to a usage fee that maximizes sales, without depending on the creator's experience, etc. Therefore, it is possible to provide a fee setting support device 30 that contributes to improving profitability.

[0065] Furthermore, by using such a fee setting support device 30, a locker system 100 that contributes to improving profitability can be constructed.

[0066] Furthermore, according to this embodiment, in addition to the information 25 regarding the past use of the locker device 1, past surrounding information 33 regarding the locker device 1 can also be used as training data to generate the learning model 37a. This makes it possible to further improve the accuracy of the output sales forecast value 42. Also, according to the configuration of this embodiment, setting usage fees becomes easier. Therefore, the efficiency of fee setting can be improved. Accordingly, as illustrated below, usage fees can be changed in accordance with changes in demand for the locker device 1.

[0067] Specifically, for example, the usage fee for locker device 1 near locations where increased demand is expected, such as concerts or sporting events, can be set higher. Conversely, the usage fee for locker device 1 near locations where decreased demand is expected, such as when a typhoon is approaching, can be set lower. Furthermore, for locker device 1 installed in facilities such as shopping centers, the usage fee can be set lower during designated hours on weekdays when decreased demand is expected. Conversely, for locker device 1 installed in facilities such as shopping centers, the usage fee can be set higher during designated hours on public holidays when increased demand is expected.

[0068] Furthermore, according to this embodiment, the user can make the following decisions using the learning model 37a, which is useful. In other words, the user can make decisions on matters that are difficult to determine by simply forecasting demand, such as "whether the billing pattern P should be based on the time period or the daily rate as described above."

[0069] Furthermore, according to this embodiment, the fee setting support device 30 can be used not only to support fee setting for existing locker devices 1, but also when setting usage fees for newly installed locker devices 1. In other words, based on the learning model 37a generated using existing locker devices 1, it is possible to predict the optimal usage fee for newly installed locker devices 1 based on their installation location and the environment surrounding the installation location.

[0070] Furthermore, the management server 3 in this embodiment may have a notification means for notifying the administrator of the management server 3 of the billing pattern P of the locker device 1 to be configured, which has been determined and set by the "support processing". This makes it easier for the administrator of the management server 3 to manage each locker device 1 connected via the network NW.

[0071] The notification means provided by the management server 3 is, for example, a display device that shows the results of the "support processing." The management server 3 may also have, for example, an audio device that emits sound, or a lighting device that lights up or flashes. This allows the management server 3 to prompt the administrator to check the results of the "support processing" displayed on the display device.

[0072] Furthermore, the administrator may not be in the vicinity of the management server 3. In such cases, the management server 3 may notify the administrator of the results of the "support processing" to the communication terminal owned by the administrator. In this case, the communication terminal owned by the administrator is connected to the management server 3 via wired or wireless connection.

[0073] It should be noted that the present invention is not limited to the embodiments described above. That is, those skilled in the art can implement the invention in various modifications without departing from the core principles, in accordance with prior art knowledge. Such modifications, as long as they still possess the configuration of the fee setting support device 30 and locker system 100 of the present invention, are of course included within the scope of the present invention.

[0074] For example, in this embodiment, the fee setting support device 30 is provided on the management server 3. However, the fee setting support device 30 may also be provided on each locker device 1. Also, in this embodiment, the first information acquisition unit 24 is provided on the central control device 20 of the locker device 1. However, the first information acquisition unit 24 may also be provided on the management server 3.

[0075] Furthermore, the processing in the model generation unit 37 may compare the predicted sales value 42 of the output information with the actual sales value (corresponding to the information on past usage 25 in this embodiment). Based on the comparison result, the weighting and other settings may be changed. This allows for the addition of processing to improve the accuracy of the information output from the learning model 37a. This is particularly effective when the locker devices 1 of the locker system 100 are installed over a wide area, for example, throughout Japan or around the world. This is because the accuracy of the learning model 37a can be improved using a large amount of information obtained in various situations.

[0076] In this embodiment, the control unit 35 is implemented within the fare setting support device 30, and the learning model 37a is generated by the control unit 35. However, this is not the only option; for example, the functions of the control unit 35 may be separated from the fare setting support device 30. The learning model 37a may then be generated in an external device (for example, a model generation device) equipped with the functions of the control unit 35. That is, a model generation device comprising at least an acquisition unit 36 ​​and a model generation unit 37 may be provided, and the learning model 37a may be generated by this model generation device. In this case, the fare setting support device 30 can utilize the learning model 37a by connecting the model generation device to the fare setting support device 30 via wired or wireless connection.

[0077] Furthermore, in this embodiment, the model generation unit 37 performs machine learning using information 25 regarding the past use of the locker device 1 and information 33 regarding the past surroundings of the locker device 1 as training data. However, it is not limited to this; for example, the model generation unit 37 may use the information 25 regarding the past use of the locker device 1 and the information 33 regarding the past surroundings of the locker device 1 for unsupervised learning. The model generation unit 37 may then generate a new learning model through unsupervised learning and utilize it to support pricing.

[0078] Furthermore, the model generation unit 37 may set up an environment using information 25 regarding the past use of the locker device 1 and information 33 regarding the past surroundings of the locker device 1, and perform reinforcement learning. Then, based on the learned rules obtained as a result of reinforcement learning, the model generation unit 37 may learn billing point information 40 and surrounding information 41 at the time of billing, and output the "optimal usage fee". [Explanation of symbols]

[0079] 1. Locker device 25. Information regarding past usage (Information 1) 30. Price setting support device 33 Past surrounding information (Second piece of information) 37 Model Generation Unit 37a Learning Model 38 Optimal Price Calculation Section 40. Charge Point Information (Third Information) 41. Surrounding information during billing (Third-party information) 42. Sales forecast 100 Locker System

Claims

1. A pricing support device that assists in setting the fees for a locker device equipped with a billing mechanism, A model generation unit generates a learning model that uses the first and second pieces of information to output a predicted value of sales for usage fees per unit of usage time. An optimal fee calculation unit that uses the third piece of information and the learning model to calculate the optimal usage fee at the time of billing, A price setting support device equipped with the following features.

2. The first information includes any of the following: fee information relating to the fee set at the time of billing, trend information showing the usage trends of the locker device, specifications information showing the specifications of the locker device, and sales information. The fee setting support device according to claim 1, wherein the second information includes any of the following: facility information about the facility where the locker device is installed, weather information around the locker device, location information of the locker device, and event information indicating the presence or absence of events at the location where the locker device is installed and around the installation location.

3. A fee setting support device according to claim 1, a locker device comprising a locker box and a billing means, A locker system equipped with this feature.

4. A pricing support device that assists in setting the fees for a locker device equipped with a billing mechanism, A pricing support device comprising a learning model that outputs a predicted revenue value for usage fees per unit of usage time, generated using first and second information, and an optimal pricing calculation unit that calculates the optimal usage fee at the time of billing using third information.

5. A model generation device for generating the learning model used by the fee setting support device according to claim 4, An acquisition unit that acquires the first information and the second information, A model generation unit generates a learning model that outputs a predicted value of sales per unit of usage fee using the first information and the second information, A model generation device equipped with the following features.

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