Determination device, determination method, program, seat reservation device, transaction control device

Abstract

The present invention calculates second data in an evaluation period from first data in the evaluation period, on the basis of a relation model representing the relationship between the first data and the second data. The present invention calculates an evaluation value pertaining to the evaluation period by using an evaluation model that includes the second data as a parameter and the calculated second data in the evaluation period. The present invention determines the first data in the evaluation period when the calculated evaluation value increases.

Description

[Technical Field]

[0001] The present invention relates to a determination device and the like that can improve various efficiencies such as control efficiency and cost performance. [Background technology]

[0002] A system for forecasting demand based on the time transition of the number of reservations for a service and a technology for a system for determining prices based on the demand forecast are disclosed in Patent Document 1. Furthermore, Patent Document 2 discloses a technology for a method for forecasting demand based on the price of time-limited inventory. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-33718 [Patent Document 2] Special table number 2018-503172 Summary of the Invention [Problem to be solved by the invention]

[0004] However, even when using the technology described in Cited Document 1 and the technology described in Cited Document 2, it is difficult to determine a price that increases an evaluation index such as a fee over a certain period of time, because these technologies do not evaluate over a certain period of time.

[0005] Therefore, one of the objects of the present invention is to provide a decision device, seat reservation device, transaction control device, advertising control device, navigation device, control device, decision method, recording medium, etc. that can improve efficiency such as control efficiency and cost performance. [Means for solving the problem]

[0006] According to a first aspect of the present invention, a determination device includes a calculation means for calculating second data for an evaluation period from first data for the evaluation period based on a relationship model representing the relationship between first data and second data, an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period, and a determination means for determining first data for the evaluation period when the calculated evaluation value increases.

[0007] According to a second aspect of the present invention, a determination method includes a computer calculating second data for an evaluation period from first data for the evaluation period based on a relationship model representing the relationship between first data and second data, calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period, and determining first data for the evaluation period when the calculated evaluation value increases.

[0008] According to a third aspect of the present invention, a recording medium stores a program that causes a computer to perform the function of calculating second data for an evaluation period from first data for the evaluation period based on a relational model that represents the relationship between first data and second data, calculating an evaluation value for the evaluation period using an evaluation model that includes the second data as a parameter and the calculated second data for the evaluation period, and determining first data for the evaluation period when the calculated evaluation value increases. [Effects of the Invention]

[0009] According to the determination device and the like of the present invention, it is possible to improve efficiency such as control efficiency and cost performance. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration of a determination device according to a first embodiment. [Figure 2]4 is a flowchart showing a flow of processing in the determination device according to the first embodiment. [Figure 3] 4 is a flowchart showing a flow of processing in the determination device according to the first embodiment. [Figure 4] FIG. 10 is a block diagram showing the configuration of a seat reservation device according to a second embodiment. [Figure 5] FIG. 10 is a diagram conceptually illustrating an example in which the determination device is applied to aircraft seat reservations. [Figure 6] FIG. 10 is a block diagram showing the configuration of a transaction control device according to a third embodiment. [Figure 7] FIG. 10 is a block diagram showing a configuration of an advertisement control device according to a fourth embodiment. [Figure 8] FIG. 10 is a block diagram showing the configuration of a navigation device according to a fifth embodiment. [Figure 9] FIG. 10 is a block diagram showing a configuration of a control device according to a sixth embodiment. [Figure 10] FIG. 1 is a block diagram illustrating an example of the hardware configuration of a calculation processing device capable of realizing a determination device, a control device, a seat reservation device, a transaction control device, an advertisement control device, and a navigation device according to each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Next, embodiments of the present invention will be described in detail with reference to the drawings. First Embodiment The configuration of a determination device 1 according to a first embodiment of the present invention will be described in detail with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the determination device 1 according to the first embodiment of the present invention. The determination device 1 according to the first embodiment includes a calculation unit 11, an evaluation unit 12, and a determination unit 13. The determination device 1 may further include a creation unit 14 and an update unit 15.

[0012] The determination device 1 may be connected to, for example, a control device 2 or a display device 3. Alternatively, the determination device 1 may have components that realize the functions of the control device 2 or the display device 3. The determination device 1 determines data that can improve efficiency such as control efficiency and cost performance by using a relational model that represents the relationship between the first data and the second data and executing processing as will be described in detail with reference to Figs. 2 and 3.

[0013] As shown in the example of the second embodiment, the first data represents a price for reserving an airplane seat. The second data represents the demand amount for the reservation at that price. Alternatively, as shown in the example of the third embodiment, the first data may represent a trading partner with which a product is traded. The second data may represent the demand amount from the trading partner. Alternatively, as shown in the example of the fourth embodiment, the first data may represent, for example, an advertisement displayed via a communication network. The second data may represent the rate at which the advertisement is viewed (or the click rate). Alternatively, as shown in the example of the fifth embodiment, the first data may represent a route for transporting a product. The second data may represent the time required for transporting the product along that route (or travel time, etc.). Alternatively, as shown in the example of the sixth embodiment, the first data may represent a generator for obtaining power using a generator. The second data may represent the power consumption for obtaining power using the generator.

[0014] In this way, the first data and the second data are associated with each other, and the relationship therebetween is expressed using a relational model. The relational model represents the relationship between the first data and the second data. The relational model is realized, for example, by regression analysis or machine learning (for example, neural networks or support vector machines). Furthermore, the relational model may have a plurality of parameters determined by regression analysis, and may be expressed as an ensemble of the parameters.

[0015] The relationship model may be, for example, a demand model that represents the relationship between price and demand amount, as shown in the example of the second embodiment. Alternatively, the relationship model may be, for example, a demand model that represents the relationship between a business partner and the demand amount from the business partner, as shown in the example of the third embodiment. Alternatively, the relationship model may be, for example, a rate model that represents the relationship between an advertisement and the rate at which the advertisement is viewed, as shown in the example of the fourth embodiment. Alternatively, the relationship model may be, for example, a duration model that represents the relationship between a route and the duration of travel along the route, as shown in the example of the fifth embodiment. Alternatively, the relationship model may be, for example, a power model that represents the relationship between a generator and the power consumption by the generator, as shown in the example of the sixth embodiment.

[0016] Next, the processing in the determining device 1 according to the first embodiment of the present invention will be described in detail with reference to Fig. 2. Fig. 2 is a flowchart showing the flow of processing in the determining device 1 according to the first embodiment. The calculation unit 11 calculates the second data for the evaluation period from the first data for the evaluation period based on the relational model representing the relationship between the first data and the second data as described above (step S101). For example, the calculation unit 11 calculates the second data for the evaluation period by applying a process representing the relationship to the first data for the evaluation period.

[0017] The evaluation unit 12 calculates an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the second data for the calculated evaluation period (step S102). The evaluation unit 12 calculates the evaluation value for the evaluation period, for example, by applying a process indicated by the evaluation model to the second data for the calculated evaluation period.

[0018] The evaluation model represents, for example, a process of calculating an evaluation value that represents the degree of desirability (or preference), as will be described later in the second to sixth embodiments. As exemplified in the second embodiment, the evaluation model represents, for example, profit (remuneration, revenue) during the evaluation period. Alternatively, as exemplified in the third embodiment, the evaluation model represents, for example, the demand volume during the evaluation period.

[0019] The determination unit 13 determines first data for the evaluation period when the calculated evaluation value increases (step S103). As exemplified in the second embodiment, the determination unit 13 determines the first data so that the value calculated according to the evaluation model (e.g., objective function) increases. This process can be realized, for example, by a method for finding a solution to an optimization problem with constraints, or a method for sequentially searching for first data when the objective function increases. The determination unit 13 may determine first data for the evaluation period when the constraints including the second data in the evaluation period as parameters are satisfied and the evaluation value increases.

[0020] The constraint condition may be, for example, as shown in the example of the second embodiment, a condition that the number of reservations during the evaluation period is equal to or less than the remaining quantity. Alternatively, the constraint condition may be, for example, as shown in the example of the third embodiment, a condition that the demand for a product during the evaluation period is equal to or less than the inventory amount of the product. Alternatively, the constraint condition may be, for example, as shown in the example of the fourth embodiment, a condition that the time required to display an advertisement during the evaluation period is equal to or less than a reference value. Alternatively, the constraint condition may be, for example, as shown in the example of the fifth embodiment, a condition that the time required for travel during the evaluation period is equal to or less than a reference value. Alternatively, the constraint condition may be, for example, as shown in the example of the sixth embodiment, a condition that the total power consumption of the generator during the evaluation period is equal to or less than a reference value.

[0021] The control device 2 receives the first data and performs control in accordance with the received first data. The control device 2 controls a system that controls a control target such as a plurality of generators, as shown in the example of the sixth embodiment. The control device 2 performs control so that power is obtained from the generator represented by the received first data. The control target may be, for example, a device such as a robot, a manufacturing machine, an automatic guided vehicle, a truck, or heavy construction equipment.

[0022] The display device 3 may display the determined first data on a display. The display device 3 is, for example, a seat reservation system as shown in the example of the second embodiment. In this case, the display device 3 displays the determined first data on the display of the seat reservation system. The display device 3 may be, for example, a system that displays advertisements as shown in the example of the fourth embodiment. The display device 3 displays the determined first data, for example, on the right side of the browser.

[0023] In the processing example described above with reference to Fig. 2, the determination device 1 calculates the evaluation value for the evaluation period using the relationship model. The determination device 1 may further create a relationship model. The process of creating the relationship model will be described.

[0024] The creation unit 14 receives a data set in which the first data and the second data are associated with each other. The creation unit 14 creates the relational model that fits the data set. The creation unit 14 applies processing such as regression analysis or machine learning (e.g., neural network or support vector machine) to the input data set to create a relational model that represents the relationship between the first data and the second data. Then, the calculation unit 11 uses the relational model created by the creation unit 14 to perform the processing described above with reference to FIG. 2.

[0025] The updating unit 15 may acquire second data corresponding to the first data determined by the determining unit 13, and perform a process similar to that performed by the creating unit 14 on the first data and the acquired second data, thereby creating a relational model representing the relationship between the first data and the second data. The calculating unit 11 performs the process described above with reference to Fig. 2 using the relational model created by the updating unit 15. Therefore, it can also be said that the updating unit 15 acquires second data corresponding to the first data determined by the determining unit 13, and performs a process of updating the relational model using the acquired second data.

[0026] The relationship between the first data and the second data may be, for example, a relationship for a first period. In this case, the first period includes timings prior to each timing in the evaluation period. In the processing example described above with reference to FIG. 2, the determining device 1 calculates an evaluation value for the evaluation period using the relationship model.

[0027] A process in which a relational model creates a relationship between first data and second data based on the distribution of the second data (or the probability distribution of the second data) will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the flow of processing in the determining device 1 according to the first embodiment.

[0028] The calculation unit 11 uses a data set including a plurality of sets in which first data and second data are associated with each other, and calculates a relational model that fits the data set (step S201). In this case, the calculation unit 11 calculates the relational model based on the distribution (or probability distribution) of the second data. The data set is, for example, a data set in which prices are associated with the demand amounts at those prices, as will be described later in a second embodiment. The data set may include a set for each timing in a first period. Alternatively, as in the example of reserving seats on an airplane, as will be described later in a second embodiment, when a period between a start timing and an end timing occurs for each airplane, data sets may be created with the same length for multiple periods. In this case, the data set includes a set in which first data (e.g., price) and second data (e.g., demand amount) are associated for each timing in the period.

[0029] The evaluation unit 12 acquires first data and the likelihood of occurrence of the first data (step S202). This likelihood is determined in the processes of steps S202 to S205 so as to increase the evaluation value. The likelihood may represent a probability or may be a value calculated from the probability. The evaluation unit 12 may determine multiple first data and the likelihood of occurrence of each first data. The first data may be selected from a first dataset. The first dataset may be a given dataset or may be a dataset extracted from a relational model.

[0030] The evaluation unit 12 calculates second data for the first data using a first data set including a plurality of first data and the relational model (step S203).

[0031] Next, the evaluation unit 12 calculates an evaluation value for the evaluation period using an evaluation model including the second data as a parameter, the likelihood of occurrence of the first data, and the calculated second data for the evaluation period (step S204). The evaluation model is similar to the model described above and represents a process of calculating an evaluation value representing the degree of desirability (or preference). The evaluation model represents, for example, a process described below with reference to equation (3).

[0032] The determination unit 13 determines the first data and the likelihood of occurrence in the evaluation period when the calculated evaluation value increases (step S205).

[0033] The determination unit 13 may output the determined first data to an external device such as the control device 2 or the display device 3. The relationship model may be created by the creation unit 14 or updated by the update unit 15. The relationship model may also be, for example, second data for the first data for a first period. In this case, the first period includes timings before each timing in the evaluation period.

[0034] Next, the effects of the determination device 1 according to the first embodiment of the present invention will be described. The determination device 1 according to the first embodiment can improve efficiency such as control efficiency and cost performance, because an evaluation value is calculated using an evaluation model including second data as a parameter, and first data is determined when the evaluation value increases.

[0035] For example, the techniques disclosed in Patent Documents 1 and 2 predict demand, but are unable to evaluate an evaluation model that includes the demand as a parameter. However, the determination device 1 according to the first embodiment determines first data in the case where the evaluation value increases using an evaluation model that includes second data as a parameter, according to the process described above with reference to Figures 2 and 3. Therefore, the determination device 1 according to the first embodiment can improve efficiency such as control efficiency and cost performance.

[0036] <Second embodiment> Next, a second embodiment of the present invention based on the above-described first embodiment will be described.

[0037] With reference to Fig. 4, the processing in the determination device 1 according to the first embodiment will be described using an example in which it is applied to reserving a seat on an aircraft (hereinafter referred to as "seat reservation"). Fig. 4 is a block diagram showing the configuration of a seat reservation device 4 according to a second embodiment of the present invention. The seat reservation device 4 according to the second embodiment has a calculation unit 11, an evaluation unit 12, a determination unit 13, and a display unit 16. The seat reservation device 4 may also have a learning unit 17 and an update unit 15.

[0038] The calculation unit 11 has functions similar to those of the calculation unit 11 described above with reference to FIG. 1. The evaluation unit 12 has functions similar to those of the evaluation unit 12 described above with reference to FIG. 1. The determination unit 13 has functions similar to those of the determination unit 13 described above with reference to FIG. 1. The display unit 16 has functions similar to those of the display device 3 described above with reference to FIG. 1. The learning unit 17 has functions similar to those of the learning unit 17 described above with reference to FIG. 1. The update unit 15 has functions similar to those of the update unit 15 described above with reference to FIG. 1. Therefore, the seat reservation device 4 has functions similar to those of the determination device 1 described above with reference to FIG. 1.

[0039] Next, processing in the seat reservation device 4 according to the second embodiment of the present invention will be described in detail with reference to Fig. 5. Fig. 5 is a conceptual diagram showing an example in which the determination device 1 is applied to airline seat reservations. Reservations for aircraft seats can be made during the period from the time when reservations begin (hereinafter referred to as the "start timing") to the time when reservations end (hereinafter referred to as the "end timing T"). End timing T is, for example, the time when the number of reservations equals the number of seats, or the time immediately before the aircraft takes off. In the following explanation, for the sake of convenience, end timing T is assumed to be the time immediately before the aircraft takes off. The period from the start timing to the end timing is referred to as the "sales period".

[0040] The price of a seat reservation varies, for example, depending on the type of seat and the length of the period from the time the reservation is made (hereinafter referred to as "time t") to the end time T (hereinafter referred to as "remaining period"). In the following explanation, the price is set lower when the remaining period is 30 days or more than when the remaining period is less than 30 days. The period from the start time to 30 days before takeoff is referred to as the "discount period". The period from 29 days before takeoff to the end time T is referred to as the "regular period". The price during the discount period is referred to as the "discount price". The price during the regular period is referred to as the "regular price". The difference between the number of seats on the aircraft and the number of seats that have been reserved is referred to as the "remaining quantity". For convenience, the remaining quantity at time t is referred to as n(t).

[0041] Assume that the demand during the discount period is greater than the demand during the regular period. This indicates that there is a high demand to reserve a seat at a lower price before the seat reservation is completed. Assume that even during the regular period, the demand is greater just before the end time T (for example, two days and one day before the end time T) than at other times during the regular period. In this case, setting prices according to changes in demand is more likely to increase the total sales amount (hereinafter referred to as "total sales amount") than setting prices in two stages, discount price and regular price. Therefore, in order to increase total sales amount (or maximize total sales amount), it is necessary to set prices appropriately, for example, taking into account changes in demand amount. Hereinafter, in the example of airline seat reservations, the term total sales amount will be used, but terms such as profit or reward can also be used.

[0042] Demand quantity D(t, p(t)) is related to, for example, timing t and price p(t) at timing t. In other words, there is a relationship between timing t, price p(t), and demand quantity. The demand quantity when the price at timing t is price p(t) is expressed as "D(t, p(t))". A model expressing this relationship is expressed as "demand model λ". Therefore, in this case, demand quantity λ(t, p) is calculated by applying demand model λ to timing t and price p(t). It can also be said that price p(t) is a parameter related to demand quantity. It can also be said that demand model λ is a two-variable function of timing t and price p at timing t.

[0043] Information about the demand model λ may be stored in a storage unit (not shown). Alternatively, information about a processing procedure representing the demand model λ may be stored in a storage unit (not shown). In this case, the seat reservation device 4 may determine parameters in the demand model λ using training data as described below.

[0044] In this embodiment, the sales period is an example of the evaluation period described above in the first embodiment. The price for reserving an airplane seat is an example of the first data described above in the first embodiment. The demand quantity is an example of the second data described above in the first embodiment. The demand model is an example of the relational model described above in the first embodiment, and the process of calculating the total sales amount is an example of the evaluation model described above in the first embodiment.

[0045] In the seat reservation device 4, the determination unit 13 determines the price by finding a solution to a problem that maximizes an objective function that represents the total sales amount (or the expected value of the total sales amount) while satisfying the following constraint: Constraint: The total demand amount in the remaining period (i.e., the sum of λ(t', p) for each timing t' in the remaining period) is less than or equal to the remaining amount n(t). In other words, it is not possible to reserve more seats in the remaining period than the remaining amount at timing t.

[0046] In other words, the seat reservation device 4 calculates a solution to the problem of determining the price that increases the expected value of total sales while satisfying the above constraints. The solution may be an optimal solution that maximizes the objective function under the constraints, or it may be the output when a predetermined condition for terminating calculations in the solution-finding process is satisfied. Hereinafter, for convenience, such a problem will be referred to as an "optimization problem," and the solution to that problem will be referred to as an "optimal solution." Values ​​such as the mean and median will be collectively referred to as "average."

[0047] The process of finding an optimal solution to an optimization problem will be described in detail using an example of reserving an airplane seat. When the price of seat reservations and the demand for seat reservations fluctuate during a sales period, it is desirable to set the price p(t) appropriately according to the timing t in order to increase the expected value of the total sales amount. The determination unit 13 finds the price p(t) when the expected value of the total sales amount is maximized. The determination unit 13 may also find the price p(t) when the expected value of the total sales amount increases. In other words, the determination unit 13 finds a solution to the optimization problem described above while referring to the objective function. The process of the seat reservation device 4 will be described in detail below.

[0048] Even if the demand model λ that represents the relationship between the demand quantity λ(t,p), the timing t, and the price p is unknown, the learning unit 17 uses a first dataset (for example, a dataset in which prices are associated with the distribution of the demand quantities for the prices) to determine the demand model λ that calculates the demand quantity λ(t,p) so as to fit the first dataset. In this case, the demand model λ represents the relationship between the price p and the demand quantity for the price p (or the average, median, etc. of the demand quantity). The evaluation unit 12 calculates the total sales amount by executing the process shown in equation (3) etc. using the first data set exemplified in equation (1). The first data set P (hereinafter referred to as "price set") about prices at time t is, for example, a set such as equation (1).

[0049]

number

[0050] Here, K represents the number of prices. For convenience of explanation, it is assumed that the first data set (e.g., price set) is common at the timings described below with reference to Equation (2). However, for each timing, the number of elements in the first data set and the value p i (where 1≦i≦K) may vary. In other words, the price set may include multiple prices for each timing.

[0051] The timing set T (hereinafter referred to as "timing set") can be expressed as in equation (2).

[0052]

number

[0053] That is, a timing set includes a plurality of timings as elements, for example, each timing in a period from a start timing to an end timing.

[0054] The learning unit 17 estimates the demand model λ using training data including the demand amount at each timing (or the average of the demand amounts), the timing, and a set of prices at the timing. The demand amount may be actual data measured against the price, or may be data calculated by a process for estimating the demand amount.

[0055] The learning unit 17 may calculate the demand model λ by, for example, determining parameters of a surface (or a curve, plane, or line) that fits the training data (hereinafter referred to as "regression analysis"). The parameter may be a single fixed value or an ensemble of multiple values. Alternatively, the learning unit 17 may calculate the demand model λ that fits the training data using a machine learning algorithm such as a neural network or a support vector machine. Alternatively, the learning unit 17 may find the relationship between price and demand quantity at each timing, rather than explicitly using the demand model. When there are multiple prices at each timing, the learning unit 17 may calculate the demand quantity for each price.

[0056] Next, the processing in the seat reservation device 4 will be described with reference to Fig. 5. Fig. 5 is a flowchart showing the flow of processing in the seat reservation device 4 according to the second embodiment. The calculation unit 11 acquires the demand model λ. The demand model λ may be given or may be created by the learning unit 17.

[0057] Hereinafter, for convenience of explanation, it is assumed that the demand model λ represents the relationship between the price p and the demand quantity for the price p.

[0058] The calculation unit 11 calculates the price p and the likelihood that the price p will occur. The calculation unit 11 may select the price p from the price set exemplified in formula (1), for example. The price p and the likelihood that the price p will occur are updated so that the total sales amount in the remaining period increases, as will be described later with reference to formula (3). The calculation unit 11 calculates the demand quantity when the price is p at the timing t using the price p at the timing t within the evaluation period and the demand model λ. As described above, the evaluation period may be the first period or later, or may include a period that overlaps with the first period. Alternatively, the evaluation period may be the same period as the first period. In this case, it can also be said that the calculation unit 11 calculates the demand quantity for the evaluation period using the demand model λ for the first period. Alternatively, when the period from the start timing to the end timing occurs repeatedly, the first period and the evaluation period may be any two of the repeated periods.

[0059] For example, suppose that the timing set in the evaluation period has timing 1 and timing 2. The evaluation period is, for example, the remaining period. Then, suppose that the calculation unit 11 determines the price and the likelihood of the price occurring for each timing, as shown below. (1,p1(1),x1(1)), (1,p2(1),x2(1)), (2,p1(2),x1(2)), (2,p2(2),x2(2))

[0060] That is, there are two prices at timing 1: price p1(1) and price p2(1). Similarly, there are two prices at timing 2: price p1(2) and price p2(2). In this case, it is assumed that the price sets at the two timings are the same.

[0061] The likelihood of price p1(1) occurring is x1(1). The likelihood of price p2(1) occurring is x2(1). The likelihood of price p1(2) occurring is x1(2). The likelihood of price p2(2) occurring is x2(2).

[0062] The evaluation unit 12 uses the demand model λ to calculate the demand quantity for each price. For example, the evaluation unit 12 uses the demand model λ to calculate the demand quantity λ(1, p1(1)) for the price p1(1). The evaluation unit 12 uses the demand model λ to calculate the demand quantity λ(1, p2(1)) for the price p2(1). The evaluation unit 12 uses the demand model λ to calculate the demand quantity λ(2, p1(2)) for the price p1(2). The evaluation unit 12 uses the demand model λ to calculate the demand quantity λ(2, p2(2)) for the price p2(2).

[0063] The evaluation unit 12 calculates the expected value of the total sales amount in the remaining period, for example, according to the processing procedure exemplified in formula (3) (hereinafter referred to as the "remuneration model"). In this case, the processing procedure for calculating the expected value of the total sales amount is an example of an evaluation model. The processing procedure exemplified in formula (3) can also be said to be the processing procedure for calculating the expected value of the total sales amount in the remaining period.

[0064]

number

[0065] Σ represents the process of calculating the sum. In equation (3), p k represents an element of the price set P illustrated in equation (1). t' represents an element of the timing set P illustrated in equation (2). λ(t',p k ) is calculated using the demand model λ at timing t' and price p k The quantity demanded when x k (t) is the kth price p k This indicates the likelihood of occurrence.

[0066] In the process shown in equation (3), "p k λ(t',p k ) represents the process of calculating the total sales amount at timing t. Therefore, the left side of equation (3) represents the expected value of the total sales amount in the remaining period.

[0067] E P(t)[P(t')λ(t',P(t')] is the time t' and the price p k When expressing the expected value of the total sales amount in the case where , the processing shown on the left side of formula (3) can also be written as the processing shown on the right side of formula (3). Therefore, in the above example, the evaluation unit 12 calculates the expected value of the total sales amount, for example, according to the processing as follows. Σ ij p i ×λ(j,p i )×x i (j) However, Σ ij represents the process of calculating the sum for i and j.

[0068] The determination unit 13 calculates the price at which the sales amount increases and the likelihood of the price occurring from data that satisfies the constraints (described later with reference to equations (4) to (6)) on the total demand amount in the remaining period. That is, the determination unit 13 calculates the expected value of the total sales amount under the constraints in accordance with the process shown in equation (3), and determines the price at which the calculated expected value increases and the likelihood of the price occurring.

[0069] As described above, the constraint condition indicates that it is not possible to reserve more seats than the remaining capacity in the remaining period. In other words, the constraint condition indicates that the statistical value (for example, the average value) of the demand λ(t', P(t')) in the remaining period is equal to or less than the remaining capacity n(t) at timing t'. The determination unit 13 can execute the process of calculating the expected value of the demand (in this example, the number of reserved seats) under the constraint condition according to the processes shown in the following equations (4) to (6).

[0070]

number

[0071]

number

[0072]

number

[0073] The process shown on the left side of equation (4) can also be expressed as follows, for example. Σ_iΣ_j λ(j,p i )×x i (j)

[0074] The evaluation unit 12 calculates the expected value of the total sales amount according to the process shown in equation (3). Then, the determination unit 13 determines the price and the likelihood of the price occurring so as to increase the expected value of the total sales amount. For example, the determination unit 13 may determine the price and the likelihood of the price occurring when the expected value of the total sales amount is maximum.

[0075] Then, the determination unit 13 may output the calculated price to an external device such as the display device 3 or the control device 2. Alternatively, the determination unit 13 may output information representing the price to a system such as an electronic commerce system or an online auction system. The system receives the information representing the price and presents the price represented by the received information.

[0076] Next, a process for updating the demand model λ that the demand quantity follows will be described. It is assumed that a system such as an electronic commerce system or an online auction system has a measuring instrument (sensor) that measures the demand amount according to the price. In other words, it is assumed that the sensor measures the demand amount according to the price. In this case, the sensor measures the demand amount for the price at the timing t calculated by the determination unit 13, for example.

[0077] The update unit 15 acquires the demand amount d(t) measured by the sensor. In this case, the demand amount d(t) represents the demand amount for the price at the timing t. The update unit 15 updates the demand model λ using the set (t, P(t), d(t)) at the timing t.

[0078] For example, the update unit 15 presents a price p1 at time t1 to the system and acquires a demand d1 for that price p1 from a sensor. The update unit 15 uses the acquired demand to update, for example, the average demand for each price. When multiple demands for that price have been acquired, the update unit 15 updates the average demand by calculating the average of the multiple demands. This process can also be said to be a process in which the update unit 15 uses the demand acquired from the sensor to update the demand model λ so that it fits the demand.

[0079] According to the above-described process, the actual demand volume for a price is obtained, and the demand model λ is updated according to the obtained demand volume, so that it is possible to estimate the relationship between appropriate future data for multiple data that affect each other's fluctuations regarding the demand estimation target over time.

[0080] The processing of the determination device 1 has been described above with reference to an example of calculating the objective function and constraint conditions without using the demand model λ. However, when the calculation unit 11 acquires the demand model λ, it may perform processing similar to the above-described processing using the acquired demand model λ. In this case, the processing of calculating the expected value of the total sales amount as exemplified in equation (3) may be processing of performing the processing shown in the following steps A and B for each timing in the remaining period and calculating the sum of the calculated sales amounts (i.e., processing of finding the total sales amount).

[0081] (Step A) Apply the demand model λ to the timing in the remaining period and the price at that timing. That is, calculate the demand quantity for the price at that timing. (Step B) The sales amount is calculated by multiplying the calculated demand amount by the price.

[0082] Alternatively, as described above, the learning unit 17 may determine parameters in the process of calculating the demand model λ so as to fit the training data. In this case, the determination device 1 may use the obtained demand model λ to execute a process similar to the process described above while referring to step A and step B.

[0083] Next, the effects of the seat reservation device 4 according to the second embodiment of the present invention will be described. The seat reservation device 4 according to the second embodiment can improve efficiency such as control efficiency and cost performance. The reasons for this are the same as those explained in the first embodiment. Furthermore, the seat reservation device 4 according to the second embodiment can determine a price when the total sales amount increases during the evaluation period. The reason for this is that the total sales amount during the evaluation period can be calculated using the demand amount relative to the price.

[0084] <Third embodiment> Next, a third embodiment of the present invention based on the above-described first embodiment will be described. The example shown in the third embodiment represents an example in which products are delivered from a distribution center that manages product purchases to each business partner (e.g., a retailer, convenience store, sales agent, etc.), and the products are sold by the business partner.

[0085] Referring to Figure 6, the processing in the determination device 1 according to the first embodiment will be described using an example in which the processing is applied to the selection of a trading partner. Figure 6 is a block diagram showing the configuration of a transaction control device 5 according to a third embodiment of the present invention. The transaction control device 5 according to the third embodiment includes a calculation unit 11, an evaluation unit 12, a determination unit 13, and a control unit 18. The transaction control device 5 may also include a learning unit 17 and an update unit 15.

[0086] The calculation unit 11 has functions similar to those of the calculation unit 11 described above with reference to FIG. 1. The evaluation unit 12 has functions similar to those of the evaluation unit 12 described above with reference to FIG. 1. The determination unit 13 has functions similar to those of the determination unit 13 described above with reference to FIG. 1. The learning unit 17 has functions similar to those of the learning unit 17 described above with reference to FIG. 1. The control unit 18 has functions similar to those of the control device 2 described above with reference to FIG. 1. The update unit 15 has functions similar to those of the update unit 15 described above with reference to FIG. 1. Therefore, the transaction control device 5 has functions similar to those of the determination device 1 described above with reference to FIG. 1.

[0087] The evaluation model and the like used in the description of the processing in the transaction control device 5 according to the third embodiment will be described. N represents a trading partner set including multiple trading partners. K (K is a natural number), the customer set N is expressed as follows:

[0088] N={N1,N2,...,N K} ···(7)

[0089] In this embodiment, the information representing the trading partners is an example of the first data described above in the first embodiment. The demand model λ represents the average demand volume when the trading partners at timing t are N(t). The average demand volume is an example of the second data described above in the first embodiment. The demand model λ is an example of the relation model described above in the first embodiment.

[0090] The reward model represents a process of summing up, for each timing t in the evaluation period, rewards calculated according to a process such as that represented by equation (8).

[0091] r(t)×λ(t, N(t)) (8)

[0092] Here, r(t) represents the remuneration at timing t. In this example, for simplicity, it is assumed that r(t) is constant regardless of the trading partner. In this embodiment, the remuneration model is an example of the evaluation model in the first embodiment.

[0093] The constraint condition is that the total demand amount during the evaluation period must be less than or equal to the inventory amount of the product at the collection and delivery center. Therefore, the transaction control device 5 determines a trading partner for the evaluation period by executing a process similar to that described above with reference to Figure 2 or Figure 3. The transaction control device 5 may control the transaction to be conducted with the determined trading partner. This process will now be described in detail.

[0094] The calculation unit 11 calculates the demand amount from a trading partner during the evaluation period based on a demand model λ that represents the relationship between the trading partner and the demand amount from the trading partner. The evaluation unit 12 calculates an evaluation value for the evaluation period using a remuneration model that includes the demand amount as a parameter and the demand amount for the evaluation period. The determination unit 13 determines a trading partner for the evaluation period when the calculated evaluation value increases, and the control unit 18 controls the trading partner to be traded with the determined trading partner.

[0095] Next, effects of the transaction control device 5 according to the third embodiment of the present invention will be described. The transaction control device 5 according to the third embodiment can improve efficiency such as control efficiency and cost performance. The reasons for this are the same as those explained in the first embodiment. Furthermore, the transaction control device 5 according to the third embodiment can determine trading partners when the total demand volume increases during the evaluation period. This is because the total demand volume during the evaluation period can be calculated using a demand model λ that represents the relationship between trading partners and the demand volume from the trading partners.

[0096] <Fourth embodiment> Next, a fourth embodiment of the present invention based on the above-described first embodiment will be described. The example shown in the fourth embodiment is an example of efficiently selecting advertisements that are likely to be referenced (or that are likely to be accessed or viewed at the website indicated by the advertisement) when displaying advertisements on the Internet.

[0097] With reference to Fig. 7, the processing in the determination device 1 according to the first embodiment will be described using an example in which the processing is applied to advertisement selection. Fig. 7 is a block diagram showing the configuration of an advertisement control device 6 according to a fourth embodiment of the present invention. The advertisement control device 6 according to the fourth embodiment includes a calculation unit 11, an evaluation unit 12, a determination unit 13, and a display unit 16. The advertisement control device 6 may also include a learning unit 17 and an update unit 15.

[0098] The calculation unit 11 has functions similar to those of the calculation unit 11 described above with reference to FIG. 1. The evaluation unit 12 has functions similar to those of the evaluation unit 12 described above with reference to FIG. 1. The determination unit 13 has functions similar to those of the determination unit 13 described above with reference to FIG. 1. The display unit 16 has functions similar to those of the display device 3 described above with reference to FIG. 1. The learning unit 17 has functions similar to those of the learning unit 17 described above with reference to FIG. 1. The update unit 15 has functions similar to those of the update unit 15 described above with reference to FIG. 1. Therefore, the advertisement control device 6 has functions similar to those of the determination device 1 described above with reference to FIG. 1.

[0099] The following describes an evaluation model and the like used in the description of the processing in the advertisement control device 6 according to the fourth embodiment. Ad represents an advertisement set including multiple advertisements. K (K is a natural number), the advertisement set Ad is expressed as follows:

[0100] Ad={Ad1,Ad2,...,Ad K} ···(9)

[0101] In this embodiment, the information representing the advertisement is an example of the first data described above in the first embodiment. The rate model λ represents the number of accesses when the advertisement at timing t is Ad(t). In this embodiment, the number of accesses is an example of the second data described above in the first embodiment. The rate model λ is an example of the relationship model described above in the first embodiment.

[0102] The evaluation model represents a process of summing up the number of accesses λ(t, Ad(t)) for each timing t in the evaluation period for the evaluation period. The constraint condition is a condition that the total cost in the evaluation period is equal to or less than a predetermined limit. The cost may be, for example, the length of the period for displaying the advertisement, the monetary cost of displaying the advertisement, etc. The cost of an advertisement being Ad(t) at timing t can be expressed, for example, as in equation (10).

[0103] S(Ad(t)) (10)

[0104] The predetermined limit may represent, for example, an upper limit on the length of the period during which the advertisement can be displayed, or an upper limit on the monetary cost for displaying the advertisement. Therefore, the advertisement control device 6 determines the advertisement for the evaluation period by performing a process similar to the process described above with reference to FIG. 2 or FIG. 3. The advertisement control device 6 may perform control to display the determined advertisement. This process will be specifically described.

[0105] The calculation unit 11 calculates the rate for the advertisement during the evaluation period based on a rate model λ that represents the relationship between the advertisement and the rate at which the advertisement is viewed. The evaluation unit 12 calculates an evaluation value for the evaluation period using an evaluation model that includes the ratio as a parameter and the ratio for the evaluation period. The determination unit 13 determines an advertisement for the evaluation period when the calculated evaluation value increases. Then, the display unit 16 is controlled to display the determined advertisement.

[0106] Next, effects of the advertisement control device 6 according to the fourth embodiment of the present invention will be described. The advertisement control device 6 according to the fourth embodiment can improve efficiency such as control efficiency and cost performance. The reasons for this are the same as those explained in the first embodiment. Furthermore, the advertisement control device 6 according to the fourth embodiment can determine business partners when the total demand increases during the evaluation period. This is because the number of accesses during the evaluation period can be calculated using a rate model λ that represents the relationship between an advertisement and the number of accesses to the advertisement.

[0107] <Fifth embodiment> Next, a fifth embodiment of the present invention based on the above-described first embodiment will be described. The example shown in the fifth embodiment is an example of selecting a route to efficiently deliver an object such as a product to a delivery destination. In this example, the object is delivered to multiple specified locations, but the delivery route of the product from one location to another varies depending on the timing.

[0108] With reference to Fig. 8, the processing in the determination device 1 according to the first embodiment will be described while applying the processing to the above-mentioned example. Fig. 8 is a block diagram showing the configuration of a navigation device 7 according to a fifth embodiment of the present invention. The navigation device 7 according to the fifth embodiment has a calculation unit 11, an evaluation unit 12, a determination unit 13, and a display unit 16. The navigation device 7 may also have a learning unit 17 and an update unit 15.

[0109] The calculation unit 11 has functions similar to those of the calculation unit 11 described above with reference to FIG. 1. The evaluation unit 12 has functions similar to those of the evaluation unit 12 described above with reference to FIG. 1. The determination unit 13 has functions similar to those of the determination unit 13 described above with reference to FIG. 1. The display unit 16 has functions similar to those of the display device 3 described above with reference to FIG. 1. The learning unit 17 has functions similar to those of the learning unit 17 described above with reference to FIG. 1. The update unit 15 has functions similar to those of the update unit 15 described above with reference to FIG. 1. Therefore, the navigation device 7 has functions similar to those of the determination device 1 described above with reference to FIG. 1.

[0110] The evaluation model and the like used in the description of the processing in the advertisement control device 6 according to the fifth embodiment will be described. R represents a route set including multiple routes. K (K is a natural number), the path set R is expressed as follows:

[0111] R={r1,r2,...,r K} ···(11)

[0112] In this embodiment, the information representing the route is an example of the first data described above in the first embodiment. The required time model λ represents the time required to deliver to the next location when delivering an object via route r at timing t. In this embodiment, the information representing the required time is an example of the second data described above in the first embodiment. The required time model λ is an example of the relation model described above in the first embodiment.

[0113] The evaluation model represents a process of summing up, for each timing t in the evaluation period, values ​​calculated according to a process such as that expressed by equation (12).

[0114] G(r(t),t)×λ(t,r(t))...(12)

[0115] G(r(t), t) represents the reward etc. that can be obtained when delivering the target via route r(t) at timing t. The constraint condition is that the total time required during the evaluation period must be less than or equal to a predetermined time. Therefore, the navigation device 7 determines the route during the evaluation period by performing a process similar to that described above with reference to Figure 2 or Figure 3. The transaction control device 5 may perform control to display the determined route. This process will now be described in detail.

[0116] The calculation unit 11 calculates the travel time for a route when traveling during the evaluation period, based on a required time model λ that represents the relationship between the route and the travel time required for traveling using the route. The evaluation unit 12 calculates an evaluation value for the evaluation period using an evaluation model including the travel time as a parameter and the travel time during the evaluation period. The determination unit 13 determines a route in the evaluation period when the calculated evaluation value increases. Then, the control unit 18 controls the display of the determined route.

[0117] Next, effects of the navigation device 7 according to the fifth embodiment of the present invention will be described. The navigation device 7 according to the fifth embodiment can improve efficiency such as control efficiency and cost performance. The reason for this is the same as that explained in the first embodiment. Furthermore, the navigation device 7 according to the fifth embodiment can determine a route in which the remuneration increases during the evaluation period. The reason for this is that the total demand during the evaluation period can be calculated using a required time model λ that represents the relationship between the route and the required time for that route.

[0118] Sixth Embodiment Next, a sixth embodiment of the present invention based on the first embodiment described above will be described. The example shown in the sixth embodiment is an example of controlling a system having a plurality of generators so as to obtain power efficiently.

[0119] The processing in the control device 1 according to the sixth embodiment will be described while applying it to the above-mentioned example with reference to Fig. 9. Fig. 9 is a block diagram showing the configuration of the control device 2 according to the sixth embodiment of the present invention.

[0120] The control device 2 according to the sixth embodiment includes a calculation unit 11, an evaluation unit 12, a determination unit 13, and a control unit 18. The control device 2 may also include a learning unit 17 and an update unit 15. The calculation unit 11 has functions similar to those of the calculation unit 11 described above with reference to FIG. 1. The evaluation unit 12 has functions similar to those of the evaluation unit 12 described above with reference to FIG. 1. The determination unit 13 has functions similar to those of the determination unit 13 described above with reference to FIG. 1. The control unit 18 has functions similar to those of the control device 2 described above with reference to FIG. 1. The learning unit 17 has functions similar to those of the learning unit 17 described above with reference to FIG. 1. The update unit 15 has functions similar to those of the update unit 15 described above with reference to FIG. 1. Therefore, the control device 2 has functions similar to those of the determination device 1 described above with reference to FIG. 1.

[0121] An evaluation model and the like used in the description of the processing in the control device 2 according to the sixth embodiment will be described. I represents a generator set that contains multiple generators. K (K is a natural number), the path set I is expressed as follows:

[0122] I={I1,I2,...,I K} ···(13)

[0123] In this embodiment, the information representing the generator is an example of the first data described above in the first embodiment. The power model λ represents the power consumption when the generator I(t) is used at timing t. In this embodiment, the information representing the power consumption is an example of the second data described above in the first embodiment. The power model λ is an example of the relational model described above in the first embodiment.

[0124] The total power model represents a process of summing up, for each evaluation period, conversion coefficients calculated according to a process such as that represented by equation (14) for each timing t in the evaluation period.

[0125] R(I(t))×λ(t, I(t))...(14)

[0126] R(I(t)) represents the conversion coefficient between electric power and power for the generator I(t) at time t. The total power model is an example of the evaluation model described above in the first embodiment.

[0127] The constraint condition is that the total power consumption during the evaluation period must be equal to or less than the total power consumption that can be consumed during the evaluation period (i.e., the upper limit of the total power consumption).

[0128] The total power consumption during the evaluation period is calculated by adding up the power consumption λ(t, I(t)) for each timing during the evaluation period. Therefore, the control device 2 determines the business partner during the evaluation period by executing a process similar to the process described above with reference to FIG. 2 or FIG. 3. The control device 2 may perform control so that the determined generator is used to convert the power into motive power. This process will be described in detail.

[0129] The power consumption by the generator during the evaluation period is calculated based on a power model that represents the relationship between the generator and the power consumption of the generator. The evaluation unit 12 calculates an evaluation value for the evaluation period using a power model that represents the efficiency of converting power consumption into power and the power consumption during the evaluation period. The determination unit 13 determines the generator in the evaluation period when the calculated evaluation value increases. Then, the control unit 18 controls the determined generator to convert the energy into power.

[0130] Next, the effects of the control device 2 according to the sixth embodiment of the present invention will be described. According to the control device 2 of the sixth embodiment, it is possible to improve efficiency such as control efficiency and cost performance, for the same reasons as those explained in the first embodiment.

[0131] Furthermore, the control device 2 according to the sixth embodiment can efficiently obtain power from the system because it can determine the generator to be used during the evaluation period using a power model that represents the relationship between the generator and the power consumption of the generator.

[0132] (Hardware configuration) Figure 10 is a block diagram that schematically shows an example of the hardware configuration of a computing device that can realize the determination device 1, control device 2, seat reservation device 4, transaction control device 5, advertising control device 6, and navigation device 7 in each embodiment of the present invention.

[0133] An example of the configuration of hardware resources that realizes the determination device 1, the control device 2, the seat reservation device 4, the transaction control device 5, the advertisement control device 6, and the navigation device 7 using one calculation processing device (information processing device, computer) will be described. However, the determination device 1 may be realized physically or functionally using at least two calculation processing devices. Furthermore, the determination device 1 may be realized as a dedicated device.

[0134] The calculation processing device 20 has a central processing unit (hereinafter referred to as "CPU") 21, a volatile storage device 22, a disk 23, a non-volatile recording medium 24, and a communication interface (hereinafter referred to as "communication IF") 27. The calculation processing device 20 may be connectable to an input device 25 and an output device 26. The calculation processing device 20 can send and receive information to and from other calculation processing devices and communication devices via the communication IF 27.

[0135] The nonvolatile recording medium 24 is a computer-readable medium, such as a compact disc or a digital versatile disc. The nonvolatile recording medium 24 may also be a universal serial bus memory (USB memory), a solid state drive, or the like. The nonvolatile recording medium 24 stores the program and enables portability without requiring a power supply. The nonvolatile recording medium 24 is not limited to the above-mentioned media. Instead of the nonvolatile recording medium 24, the program may be transported via the communication IF 27 and a communication network.

[0136] The volatile storage device 22 is computer-readable and can temporarily store data. The volatile storage device 22 is a memory such as a dynamic random access memory (DRAM) or a static random access memory (SRAM).

[0137] That is, when executing a software program (computer program; hereinafter simply referred to as "program") stored on disk 23, CPU 21 copies the program to volatile storage device 22 and executes the arithmetic processing. CPU 21 reads data necessary for program execution from volatile storage device 22. When display is required, CPU 21 displays the output result on output device 26. When a program is input from outside, CPU 21 reads the program from input device 25. CPU 21 interprets and executes a program (FIG. 2 or 3) stored in volatile storage device 22 that corresponds to the functions (processing) represented by each unit shown in FIG. 1, FIG. 4, FIG. 6, FIG. 7, FIG. 8, or FIG. 9. CPU 21 executes the processing described in each embodiment of the present invention. That is, in such cases, each embodiment of the present invention can be understood to be realized by such a program. Furthermore, each embodiment of the present invention can be understood to be realized by a computer-readable non-volatile recording medium on which such a program is recorded.

[0138] The present invention has been described above using the above-described embodiment as an exemplary example. However, the present invention is not limited to the above-described embodiment. In other words, the present invention can be applied in various aspects that can be understood by a person skilled in the art within the scope of the present invention.

[0139] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0140] (Appendix 1) a calculation means for calculating second data in an evaluation period from first data in the evaluation period based on a relational model representing a relationship between the first data and the second data; evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period; a determination means for determining first data in the evaluation period when the calculated evaluation value increases; A determination device 1 comprising:

[0141] (Appendix 2) The determining means determines the first data in the evaluation period when a constraint condition including the second data in the evaluation period as a parameter is satisfied and the evaluation value increases. 10. The determination apparatus of claim 1.

[0142] (Appendix 3) a creation means for creating the relational model that is adapted to a data set by using the data set in which the first data and the second data are associated with each other; Furthermore, The calculation means calculates second data for the evaluation period using the created relational model. 10. The determination device of claim 1 or 2.

[0143] (Appendix 4) a creation means for creating the relational model that fits the data set based on a distribution related to the second data, using the data set in which the first data and the second data are associated; Furthermore, The calculation means calculates second data for the evaluation period using the created relational model. 10. The determination device of claim 1 or 2.

[0144] (Appendix 5) an update means for acquiring second data for the determined first data and updating the relational model using the acquired second data; Furthermore, The calculation means calculates second data for the evaluation period using the updated relational model. 5. The determination device according to any one of Supplementary Note 1 to Supplementary Note 4.

[0145] (Appendix 6) the relationship model represents a relationship between the first data in a first time period and the second data in the first time period; The first period includes timings before each timing in the evaluation period. 6. A determination device according to any one of Supplementary Note 1 to Supplementary Note 5.

[0146] (Appendix 7) A determination method in which a computer calculates second data for an evaluation period from first data for the evaluation period based on a relationship model that represents the relationship between first data and second data, calculates an evaluation value for the evaluation period using an evaluation model that includes the second data as a parameter and the calculated second data for the evaluation period, and determines first data for the evaluation period when the calculated evaluation value increases.

[0147] (Appendix 8) Based on a relational model that represents the relationship between first data and second data, second data for an evaluation period is calculated from the first data for the evaluation period, an evaluation value for the evaluation period is calculated using an evaluation model that includes the second data as a parameter and the calculated second data for the evaluation period, and first data for the evaluation period when the calculated evaluation value increases is determined. A recording medium that stores a program that enables a computer to implement a function.

[0148] (Appendix 9) a calculation means for calculating the demand quantity for an evaluation period from the price for the evaluation period based on the relationship between the price for reserving a seat and the demand quantity for the price; an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model representing revenue during the evaluation period, the price during the evaluation period, and the demand volume during the evaluation period; a determination means for determining a price during the evaluation period when the calculated evaluation value increases; a display means for displaying the determined price; A seat reservation device comprising:

[0149] (Appendix 10) a calculation means for calculating a demand amount from a trading partner during an evaluation period based on a relationship between the trading partner and the demand amount from the trading partner; an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including a demand amount as a parameter and the demand amount for the evaluation period; a determination means for determining a trading partner for the evaluation period when the calculated evaluation value increases; A control means for controlling transactions to be made with the determined business partners; A transaction control device comprising:

[0150] (Appendix 11) a calculation means for calculating the rate for an advertisement during an evaluation period based on a relationship between the advertisement and the rate at which the advertisement is viewed; evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including the ratio as a parameter and the ratio for the evaluation period; a determining means for determining an advertisement for the evaluation period when the calculated evaluation value increases; a display means for displaying the determined advertisement; An advertising control device comprising:

[0151] (Appendix 12) a calculation means for calculating a travel time for a route when traveling during an evaluation period based on a relationship between the route and the travel time required for traveling using the route; an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including the travel time as a parameter and the travel time in the evaluation period; a determining means for determining a route during the evaluation period when the calculated evaluation value increases; a display means for displaying the determined route; A navigation device comprising:

[0152] (Appendix 13) a calculation means for calculating the power consumption to be consumed by the generator during an evaluation period based on the relationship between the generator and the power consumption of the generator; an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model representing the efficiency of converting the power consumption into power and the power consumption during the evaluation period; a determination means for determining a generator for the evaluation period when the calculated evaluation value increases; a control means for controlling the power to be converted into the power using the determined generator; A control device comprising: [Explanation of symbols]

[0153] 1...Decision device 2. Control device 3...Display device 4. Seat reservation device 5. Transaction control device 6. Advertising control device 7. Navigation devices 11. Calculation section 12. Evaluation section 13. Decision section 14. Creation Department 15...Update section 16...Display section 17. Learning Department 18. Control unit

Claims

1. A creating means for creating a relational model that represents the relationship between the first data and the second data and that fits the dataset based on a distribution related to the second data, using a dataset in which first data and second data are associated; a calculation means for calculating second data in an evaluation period from first data in the evaluation period based on the relationship model representing the relationship between the first data and the second data; evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period; a determination means for determining first data in the evaluation period when the calculated evaluation value increases; A determination device comprising:

2. The determining means determines the first data in the evaluation period when a constraint condition including the second data in the evaluation period as a parameter is satisfied and the evaluation value increases. The determination device of claim 1 .

3. further comprising an update unit that acquires second data corresponding to the determined first data and updates the relational model using the acquired second data; The calculation means calculates second data for the evaluation period using the updated relational model.

3. A determination device according to claim 1 or claim 2.

4. the relationship model represents a relationship between the first data in a first time period and the second data in the first time period; The first period includes timings before each timing in the evaluation period. A determination device according to any one of claims 1 to 3.

5. a calculation means for calculating second data in an evaluation period from first data in the evaluation period based on a relational model representing a relationship between the first data and the second data; evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period; a determination means for determining first data in the evaluation period when the calculated evaluation value increases; an update means for acquiring second data corresponding to the determined first data and updating the relational model using the acquired second data; Equipped with The calculation means is a determination device that calculates second data for the evaluation period using the updated relational model.

6. The computer using a dataset in which first data and second data are associated, to create a relational model that represents the relationship between the first data and the second data and that fits the dataset based on a distribution for the second data; calculating second data for an evaluation period from first data for the evaluation period based on the relationship model representing the relationship between the first data and the second data; calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period; A determination method for determining first data in the evaluation period when the calculated evaluation value increases.

7. Using a data set in which first data and second data are associated, a relational model is created that represents the relationship between the first data and the second data and fits the data set based on a distribution related to the second data; calculating second data for an evaluation period from first data for the evaluation period based on the relationship model representing the relationship between the first data and the second data; calculating an evaluation value for the evaluation period using an evaluation model including the second data as a parameter and the calculated second data for the evaluation period; determining first data for the evaluation period when the calculated evaluation value increases; A program that enables a computer to perform a function.

8. Using a data set in which prices for reserving seats are associated with the demand quantities for those prices, a relational model is created that represents the relationship between the prices and the demand quantities and that fits the data set based on a distribution of the demand quantities; a calculation means for calculating the demand quantity for an evaluation period from the price for the evaluation period based on the relational model that represents the relationship between the price when reserving the seat and the demand quantity for the price; an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model representing revenue during the evaluation period, the price during the evaluation period, and the demand volume during the evaluation period; A determination means for determining a price during the evaluation period when the calculated evaluation value increases. and, a display means for displaying the determined price; A seat reservation device comprising:

9. a calculation means for calculating a demand amount from a trading partner during an evaluation period based on a relationship between the trading partner and the demand amount from the trading partner; an evaluation means for calculating an evaluation value for the evaluation period using an evaluation model including a demand amount as a parameter and the demand amount for the evaluation period; a determination means for determining a trading partner for the evaluation period when the calculated evaluation value increases; A control means for controlling transactions to be made with the determined business partners; A transaction control device comprising:

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