JP7298644B2 - Processing device, processing method and program - Google Patents

Description

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

Patent Literature 1 discloses a device that creates cashier waiting time information based on the average checkout processing time per customer and displays it so that customers can see it. The average checkout processing time per customer is calculated as the sum of the average product registration time and the average checkout processing time.

In Patent Document 2, the times (e.g., start time and end time) that define individual checkouts are recorded for each clerk, and based on the recorded times, a reduction limit value for the time required for at least part of the checkout is determined. A device is disclosed that calculates for each store clerk and notifies a support request based on the current value of the reduction limit value and the required time.

Patent Literature 3 discloses a device that calculates a predicted waiting time for a POS device based on the amount of merchandise and the number of people lined up at the POS device.

JP-A-11-161849 JP 2016-110228 A JP 2017-68717 A

In the technique described in Patent Document 3, the time required for payment is not taken into consideration when calculating the cash register waiting time. Payment is made by cash, credit card, IC card, mobile terminal (smartphone, etc.), etc., and the time required for payment varies depending on which one is selected. Also, the time required for payment varies depending on the characteristics of the customer (whether they are accustomed to payment, etc.). In the case of the technique described in Patent Document 3, which does not consider the time required for payment in calculating the cashier waiting time, the accuracy of the calculated cashier waiting time is not sufficient.

In the technique described in Patent Document 1, the time required for payment is taken into consideration when calculating the cash register waiting time. However, with the technique described in Patent Document 1, which creates cashier waiting time information based on the average checkout processing time per customer, the calculation results do not reflect the characteristics of each customer who is actually waiting for cashiers. Therefore, the accuracy of the calculated registration waiting time is not sufficient.

Patent document 2 is not a technique for calculating the cash register waiting time.

An object of the present invention is to accurately calculate a cash register waiting time.

According to the invention,
estimating means for estimating attributes of customers waiting in line for a checkout;
calculation means for calculating the checkout waiting time of the column based on the estimated attribute and the correspondence information that associates the payment time information related to the time required for payment with the attribute;
A processing apparatus is provided comprising:

Moreover, according to the present invention,
the computer
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
A processing method is provided for performing

Moreover, according to the present invention,
to the computer,
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
A program is provided that causes the

According to the present invention, it is possible to accurately calculate the cash register waiting time.

The above objectives, as well as other objectives, features and advantages, will become further apparent from the preferred embodiments described below and the accompanying drawings below.

It is a figure which shows an example of the hardware constitutions of the processing apparatus of this embodiment. It is a figure which shows an example of the functional block diagram of the processing apparatus of this embodiment. It is a figure which shows typically an example of the information processed with the processing apparatus of this embodiment. It is a flowchart which shows an example of the flow of a process by the processing apparatus of this embodiment. It is a figure which shows typically an example of the information processed with the processing apparatus of this embodiment. It is a figure which shows typically an example of the information processed with the processing apparatus of this embodiment. It is a figure which shows typically an example of the information processed with the processing apparatus of this embodiment. It is a figure which shows typically an example of the information processed with the processing apparatus of this embodiment. It is a figure which shows typically an example of the information processed with the processing apparatus of this embodiment. It is a figure which shows an example of the functional block diagram of the processing apparatus of this embodiment. It is a figure which shows an example of the apparatus which displays estimated cash register waiting time of this embodiment. It is a flowchart which shows an example of the flow of a process by the processing apparatus of this embodiment.

<First embodiment>
First, the outline of the processing apparatus of this embodiment will be described. The processing apparatus of this embodiment is used in shops. The processing device identifies customers waiting in line for checkout, for example, by face recognition processing. Then, the processing device calculates an estimated cashier waiting time when lined up at the end of the line, based on the payment time information regarding the time required for each identified customer to pay.

According to the processing device of the present embodiment, it is possible to calculate the estimated cash register waiting time of each line, taking into consideration "who is in line for the cash register waiting line". As a result, according to the processing device of the present embodiment, it is possible to improve the accuracy of estimating the cash register waiting time.

The configuration of the processing apparatus of this embodiment will be described in detail below. Note that in the present embodiment, processing for registering product information (eg, identification information) of a product to be purchased in a cash register (eg, POS (point of sales) register) is referred to as "registration processing". The time required for the registration process is called "registration time". In the registration process, the clerk reads, for example, a code attached to the product (e.g., bar code) with a reader, presses a physical button provided at the cash register, and touches a button displayed on the touch panel display of the cash register. Product information is registered in the cash register by work.

In addition, in the present embodiment, the process of paying the total purchase amount with cash, credit card, IC card, mobile terminal (smartphone, etc.) is referred to as "payment process". The time required for payment processing is called "payment time". In the payment process, for example, the customer takes out cash, credit card, etc. and hands it to the clerk. The clerk deposits the received cash into the cash register, inputs the total amount of money received into the cash register, and causes the reader to read the credit card information. As a result, payment is completed. In addition, the customer takes out an IC card, a mobile terminal (such as a smart phone), etc., and communicates with the reader. As a result, payment is completed.

In this embodiment, registration processing and payment processing are collectively referred to as “accounting processing”. The sum of the registration time and the payment time is called "accounting time". These premises are the same for all the following embodiments.

Next, the configuration of the processing apparatus of this embodiment will be described in detail. First, an example of the hardware configuration of the processing device will be described. The functions provided by the processing device include the CPU (Central Processing Unit) of any computer, memory, programs loaded into the memory, storage units such as hard disks that store the programs (programs stored in advance from the stage of shipping the device). In addition, it can store programs downloaded from a storage medium such as a CD (Compact Disc) or a server on the Internet), and is realized by any combination of hardware and software centering on a network connection interface. It should be understood by those skilled in the art that there are various modifications to the implementation method and apparatus.

FIG. 1 is a block diagram illustrating the hardware configuration of a processing device. As shown in FIG. 1, the processing device has a processor 1A, a memory 2A, an input/output interface 3A, a peripheral circuit 4A and a bus 5A. The peripheral circuit 4A includes various modules. Note that the peripheral circuit 4A may not be provided.

The bus 5A is a data transmission path for mutually transmitting and receiving data between the processor 1A, memory 2A, peripheral circuit 4A and input/output interface 3A. The processor 1A is an arithmetic processing device such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The memory 2A is, for example, a RAM (Random Access Memory) or a ROM (Read Only Memory). The input/output interface 3A is an interface for acquiring information from input devices (eg, keyboard, mouse, microphone, etc.), external devices, external servers, external sensors, etc., and output devices (eg, displays, speakers, printers, mailers, etc.). etc.), an interface for outputting information to an external device, an external server, and the like. The processor 1A can issue commands to each module and perform calculations based on the calculation results thereof.

Next, the functional configuration of the processing device 10 will be described. FIG. 2 shows an example of a functional block diagram of the processing device 10. As shown in FIG. As illustrated, the processing device 10 has a calculation material acquisition unit 11 and a calculation unit 12 .

The calculation material acquisition unit 11 acquires materials for calculating an estimated cashier waiting time when lined up at the end of the line. The calculation material acquisition unit 11 of the present embodiment identifies customers lined up in a line waiting for a cash register. The calculation material acquisition unit 11 identifies each customer by face authentication processing.

In this embodiment, a camera is installed near the cash register for photographing customers waiting in line for the cash register. Moreover, face image data of each of a plurality of customers is registered in advance in association with the member ID (identifier) of each customer.

A member ID is information for identifying a member of the store. A customer who wishes to become a member of a store presents predetermined information (eg, name, address, date of birth, gender, etc.) and makes a membership request to the store. As a result, a member ID is issued to the customer requesting membership. The member operates a terminal device (eg, smart phone, mobile phone, PC (personal computer)) or a dedicated terminal installed in the store to input his or her member ID and register his or her face image. The means for realizing the registration is not particularly limited.

Then, the calculation material acquisition unit 11 identifies the customer by face authentication processing using image data generated by a camera installed near the register and face image data of members registered in advance. That is, the calculation material acquisition unit 11 identifies the member ID of each customer in line.

The calculation unit 12 calculates an estimated cash register waiting time based on the identified payment time information of each customer. The payment time information is information related to the time required for payment.

FIG. 3 schematically shows an example of payment time information for a certain customer (member ID: A00001). The illustrated payment time information indicates the customer's past payment record. The payment time information associates the date and time of payment in the past, the payment time required for payment at that time, and the payment method at that time (eg, cash, credit card, electronic money, etc.). Note that the payment time information may not have at least one of the items shown. Also, the payment time information may have other items not shown.

The payment time is the time required for payment processing. For example, (1) take out cash, credit card, IC card, mobile terminal (smartphone, etc.), etc., (2) hand cash or credit card to the clerk, and (3) This is the time required for tasks such as communicating an IC card or mobile terminal with a reader, (4) signing for credit card payment, and (5) entering a password for credit card payment.

The start timing of the payment process used to calculate the payment time may be, for example, the timing at which the product information of the last product among one or more products purchased by a certain customer is registered at the cash register. In this case, the cashier may record the date and time when the product information for each product was registered.

Alternatively, the timing at which the clerk performs another predetermined operation on the cash register may be set as the timing for starting payment processing. In this case, the cashier may record the date and time when the other predetermined operation was performed.

It should be noted that the illustration here is merely an example, and other timing may be used as the start timing of the payment process.

The end timing of the payment process used to calculate the payment time may be the timing when the customer leaves the cash register. In this case, the timing (date and time) when the customer leaves the cash register can be identified by analyzing the image data generated by the camera that captures the area in front of the cash register.

In addition, the timing when cash is input to the cash register, the timing when the deposit amount is input at the cash register, the timing when the payment is completed by communication between the IC card or mobile terminal and the reader, and the communication between the system of the credit card company and the cash register. The timing at which the payment is completed through the payment processing may be set as the timing at which the payment process ends. In this case, the cashier may record the date and time of these timings.

It should be noted that the illustration here is merely an example, and other timing may be used as the end timing of the payment process.

Here, a method for calculating the estimated cash register waiting time based on the payment time information as described above will be described.

"Estimated cash register waiting time calculation example 1"
The calculation unit 12 predicts the current payment time for each customer in line based on the past payment time of each customer. For example, the calculation unit 12 may use statistical values of past payment times (eg, average value, median value, mode value, maximum value, minimum value, etc.) as predictions of the current payment time.

In addition, the calculation unit 12 identifies the most frequent payment method among the past payments of each customer, and the statistical values of the past payment times at the time of that payment method (e.g., average value, median value, mode, maximum value, minimum value, etc.) may be used as the prediction of the current payment time.

In addition, the calculation unit 12 identifies the most frequent payment method among the most recent predetermined number of payments, and the statistical values of past payment times for that payment method (e.g., average, median, mode, maximum value, minimum value, etc.) may be an estimate of the current payment time.

The calculation unit 12 also predicts the registration time required for registering product information for each customer in line. A registration time prediction method will be described in detail in the following embodiments, but the registration time prediction method is not limited in this embodiment. For example, the calculation unit 12 may predict the registration time for each customer by using the average registration time per customer of the salesclerk in charge of the cash register in that row.

The calculation unit 12 calculates the estimated accounting time by adding the estimated payment time and the estimated registration time for each customer in line. Then, the calculation unit 12 calculates the estimated checkout waiting time by adding up the predicted checkout times of all the customers in line.

"Estimated cash register waiting time calculation example 2"
The calculation unit 12 predicts the current payment method for each customer in line based on the past payment method of each customer. For example, the calculation unit 12 may predict that the current payment method is the payment method that is the most frequently used among the past payments of each customer, or that the payment method that is the most frequent payment method among the recent predetermined number of payments is the current payment method. can be predicted. In addition, the calculation unit 12 holds in advance information indicating a guideline (general value) of payment time required for each payment method. Then, the calculation unit 12 may use the estimated payment time of the “predicted payment method” as the prediction of the current payment time.

The calculation unit 12 also predicts the registration time required for registering product information for each customer in line. A registration time prediction method will be described in detail in the following embodiments, but the registration time prediction method is not limited in this embodiment. For example, the calculation unit 12 may predict the registration time for each customer by using the average registration time per customer of the salesclerk in charge of the cash register in that row.

The calculation unit 12 calculates the estimated accounting time by adding the estimated payment time and the estimated registration time for each customer in line. Then, the calculation unit 12 calculates the estimated checkout waiting time by adding up the predicted checkout times of all the customers in line.

"Estimated cashier waiting time calculation example 3"
In the estimated cash register waiting time calculation example 3, the calculation unit 12 estimates the time obtained by adding the estimated cash register waiting time calculated in the estimated cash register waiting time calculation example 1 or 2 to the remaining checkout time of the customer currently undergoing checkout processing. Calculated as cash register waiting time. The remaining checkout time of a customer currently undergoing checkout processing can be, for example, a value obtained by subtracting "elapsed time from the start of checkout processing for that customer" from "estimated checkout time of that customer".

The timing to start the checkout process may be the timing when the first product information about the customer is registered at the cash register, or other timing corresponding thereto.

Next, an example of the processing flow of the processing device 10 will be described with reference to the flowchart of FIG. In S10, the calculation ingredient acquisition unit 11 acquires ingredients for calculating the estimated cash register waiting time. In the present embodiment, the calculation material acquisition unit 11 identifies customers lined up in a queue waiting for a checkout by face authentication processing. In S11, the calculation unit 12 predicts the payment time and registration time for each of the identified customers. In S12, the calculation unit 12 calculates an estimated cash register waiting time.

According to the processing device 10 of the present embodiment described above, it is possible to calculate the estimated cash register waiting time of each queue, taking into consideration "who is in line for the cash register waiting line". As a result, it is possible to improve the accuracy of estimating the cash register waiting time.

Further, according to the processing device 10 of the present embodiment, it is possible to predict the current payment time based on the past payment results of each customer. The prediction can then be used to calculate an estimated checkout wait time for each row. As a result, it is possible to improve the accuracy of estimating the cash register waiting time.

<Second embodiment>
The processing device 10 of the present embodiment predicts the payment time based on the customer's attributes (gender, age, nationality, etc.) when there are customers waiting in line waiting for the cashier who do not have payment time information. This embodiment differs from the first embodiment in that the estimated cashier waiting time is calculated based on the result of . A customer who does not have payment time information is, for example, a customer who visits the store for the first time, a customer who does not have a member ID, or the like. Differences from the first embodiment will be described below.

An example of the hardware configuration of the processing device 10 is the same as in the first embodiment.

An example of a functional block diagram of the processing device 10 is shown in FIG. 2 as in the first embodiment. As illustrated, the processing device 10 has a calculation material acquisition unit 11 and a calculation unit 12 .

The calculation material acquisition unit 11 analyzes the image data of the customer's face and estimates the attribute of the customer. The inferred attributes are those that influence the time to pay and may include, for example, at least one of gender, age and nationality. Note that the attributes to be estimated may include other items.

The calculation material acquisition unit 11 may estimate the attributes of all the customers lined up in line, or may estimate the attributes of only those customers who do not have payment time information among the customers lined up in line. .

It should be noted that the calculation material acquisition unit 11 can identify customers lined up in a line waiting for a cash register, as in the first embodiment.

The calculation unit 12 estimates the payment time of the customer based on the attribute of the customer when there is a customer who does not have the payment time information in the queue waiting for the cash register.

A calculation unit 12 obtains the trend of the payment time of each attributed customer based on the payment time information of the customer who has the payment time information, and uses this to estimate the payment time of the customer who does not have the payment time information. can be done.

For example, as shown in FIG. 5, attributes such as sex, age, and nationality may be registered in advance for each member. This may be registered by the member's self-reporting, or may be registered by estimating the attribute by analyzing the face image of the registered member with a computer.

Then, based on the registration information of FIG. 5, the calculation unit 12 creates a group of members having the same or similar attributes, and calculates the trend of payment time and/or payment method for each group. FIG. 6 shows an example of calculated information. In the illustrated information, group attributes and tendencies are associated with each other. The tendency can be calculated, for example, by statistical processing. Statistical values of payment time (eg, average, median, mode, maximum, minimum, etc.) may be calculated for each group and used as the trend of payment time for each group. Alternatively, the most common payment method may be calculated for each group and used as the tendency of the payment method for each group. The statistical processing may be performed by a computer or by a person.

The calculation unit 12 extracts the tendency corresponding to the attributes of customers who do not have payment time information from the information shown in FIG. 6, and predicts the customer's payment time based on the information. When the extracted tendency indicates the payment time, the calculation unit 12 can estimate the extracted tendency of the payment time as the customer's payment time. Further, when the extracted tendency indicates a payment method, the calculation unit 12 calculates the payment time of the payment method indicated by a pre-stored guideline (general value) of the payment time required for each payment method. can be estimated as the customer's payment time.

The method of calculating the payment time of customers having payment time information, the method of calculating the registration time of each customer, the method of calculating the checkout time of each customer, and the method of calculating the estimated cash register waiting time using these methods are described in the first method. is similar to the embodiment of An example of the processing flow of the processing device 10 is the same as in the first embodiment.

According to the processing apparatus 10 of this embodiment described above, it is possible to achieve the same effects as those of the first embodiment. In addition, according to the processing device 10 of the present embodiment, when a customer who does not have payment time information for reasons such as a first visit to the store or a customer who does not have a member ID is lined up, based on the customer's attribute , the payment time can be estimated. As a result, it is possible to improve the accuracy of estimating the cash register waiting time.

<Third Embodiment>
The processing device 10 of the present embodiment predicts the payment time of each customer by further considering who the cashier is in charge of the checkout line in the queue, and estimates based on the result of the prediction. This differs from the first and second embodiments in that the registration waiting time is calculated. Differences from the first and second embodiments will be described below.

An example of the hardware configuration of the processing device 10 is the same as in the first and second embodiments.

An example of a functional block diagram of the processing device 10 is shown in FIG. 2, as in the first and second embodiments. As illustrated, the processing device 10 has a calculation material acquisition unit 11 and a calculation unit 12 .

The calculation material acquisition unit 11 identifies the store clerk in charge of the cash register for each row. That is, the calculation material acquiring unit 11 acquires the clerk ID of the clerk in charge of the cash register of each row. For example, a store clerk may enter his or her ID into a cash register before beginning each cash register operation. Then, the calculation material acquiring unit 11 may identify the clerk in charge of the cash register of each line based on the information input to the cash register. In addition, the calculation material acquisition unit 11 performs face authentication processing using the face image of the cashier operating the cash register captured by the camera installed at the cash register, and identifies the cashier in charge of each row. good too.

It should be noted that the calculation material acquisition unit 11 can identify customers lined up in a line waiting for a cash register, as in the first embodiment. Moreover, the calculation material acquisition part 11 may estimate a customer's attribute like 2nd Embodiment.

The calculation unit 12 calculates an estimated cashier waiting time based on the payment time information regarding the time required for payment for each pair of each identified customer and each identified clerk.

FIG. 7 schematically shows an example of payment time information for a certain customer (member ID: A00001). The illustrated payment time information indicates the customer's past payment record. The payment time information includes the date and time when the payment was made in the past, the ID of the clerk who was in charge of the cash register at that time, the payment time required for the payment at that time, and the payment method at that time (e.g. cash, credit card, electronic money, etc.). Note that the payment time information may not have at least one of the items shown. Also, the payment time information may have other items not shown.

Here, a method for calculating the estimated cash register waiting time based on the payment time information shown in FIG. 7 will be described.

"Estimated cashier waiting time calculation example 4"
A calculation unit 12 predicts the current payment time for each customer lined up in a line based on the past payment time processed by the clerk in charge of the cash register of the line (the line in which the line is lined up). . For example, the calculation unit 12 calculates statistical values (eg, average value, median value, mode value, maximum value, minimum value, etc.) of past payment times processed by the clerk in charge of the cash register in that column. may be used as the prediction of the current payment time.

In addition, the calculation unit 12 identifies the payment method that is the most common when the clerk in charge of the cash register in the row processes it, and when the clerk processes it and pays with that payment method Statistical values of past payment times (eg, average, median, mode, maximum, minimum, etc.) may be used as predictions of current payment times.

In addition, the calculation unit 12 identifies the most frequent payment method among the most recent predetermined number of payments processed by the clerk in charge of the cash register in that row, and causes the clerk to process the payment. Statistical values of past payment times (eg, average, median, mode, maximum, minimum, etc.) when paying by payment method may be used as predictions of current payment times.

The calculation unit 12 also predicts the registration time required for registering product information for each customer in line. A registration time prediction method will be described in detail in the following embodiments, but the registration time prediction method is not limited in this embodiment. For example, the calculation unit 12 may predict the registration time for each customer by using the average registration time per customer of the salesclerk in charge of the cash register in that row.

The calculation unit 12 calculates the estimated accounting time by adding the estimated payment time and the estimated registration time for each customer in line. Then, the calculation unit 12 calculates the estimated checkout waiting time by adding up the predicted checkout times of all the customers in line.

"Estimated cash register waiting time calculation example 5"
The calculation unit 12 predicts the current payment method for each customer in line based on the past payment method of each customer. For example, the calculation unit 12 may predict the current payment method to be the payment method that is the most frequently used when the cashier in charge of the row (the row in line) performs the processing. The current payment method may be predicted to be the payment method that has the highest number of payments among the most recent predetermined number of payments processed by the clerk in charge of the cash register. Further, the calculation unit 12 holds in advance information indicating an estimate of payment time required for each payment method. In addition, the said information may show the said guideline for every salesclerk. Then, the calculation unit 12 may use the estimated payment time of the “predicted payment method” as the prediction of the current payment time.

The calculation unit 12 also predicts the registration time required for registering product information for each customer in line. A registration time prediction method will be described in detail in the following embodiments, but the registration time prediction method is not limited in this embodiment. For example, the calculation unit 12 may predict the registration time for each customer by using the average registration time per customer of the salesclerk in charge of the cash register in that row.

The calculation unit 12 calculates the estimated accounting time by adding the estimated payment time and the estimated registration time for each customer in line. Then, the calculation unit 12 calculates the estimated checkout waiting time by adding up the predicted checkout times of all the customers in line.

"Estimated cash register waiting time calculation example 6"
In Estimated cash register waiting time calculation example 6, the estimated cash register waiting time calculated in Estimated cash register waiting time calculation example 5 or 6 is added to the remaining checkout time of the customer who is currently processing the checkout as the estimated cash register waiting time. do. The remaining checkout time of a customer currently undergoing checkout processing can be, for example, a value obtained by subtracting "elapsed time from the start of checkout processing for that customer" from "estimated checkout time of that customer".

The checkout process start timing may be the timing at which the first product information is registered at the cash register, or may be other timing according to this.

"Estimated cashier waiting time calculation example 7"
When a customer who does not have payment time information is lined up in a queue waiting for a cash register, the calculation unit 12 can predict the customer's payment time based on the attribute of the customer. In this embodiment, as shown in FIG. 6, the calculation unit 12 creates a group of members who have the same or similar attributes, and for each group, the payment time and / Or calculate the tendency of the payment method and the like. In the illustrated information, the attribute of the group, the store clerk in charge, and the tendency are associated and stored. The tendency can be calculated by statistical processing, for example, as in the second embodiment.

The calculation unit 12 extracts from the information shown in FIG. 8 the attributes of customers who do not have payment time information and the tendencies corresponding to pairs of store clerks who are in charge of each column, and calculates the payment of the customer based on the information. predict time. When the extracted tendency indicates the payment time, the calculation unit 12 can estimate the extracted tendency of the payment time as the customer's payment time. Further, when the extracted tendency indicates the payment method, the calculation unit 12 calculates the estimated payment time for the payment method, which is indicated by the estimated payment time required for each payment method held in advance, for the customer. payment time can be estimated.

The method of calculating the payment time of customers who have payment time information, the method of calculating the registration time of each customer, the method of calculating the accounting time of each customer, and the method of calculating the estimated cashier waiting time using these This is the same as waiting time calculation examples 4 to 6.

An example of the processing flow of the processing apparatus 10 of this embodiment is the same as in the first and second embodiments.

According to the processing apparatus 10 of this embodiment described above, it is possible to achieve the same effects as those of the first and second embodiments. Moreover, according to the processing device 10 of the present embodiment, it is possible to estimate the payment time in consideration of the characteristics of each pair of the clerk and each customer. For example, in the case of a pair of a regular customer who has visited the store many times and a veteran store clerk, the payment processing may proceed quickly as a result of being able to predict each other's movements. The payment time can be estimated by considering the characteristics of each pair of clerk and each customer. According to this embodiment, it is possible to improve the accuracy of estimating the cash register waiting time.

<Fourth Embodiment>
The processing device 10 of the present embodiment identifies the clerk in charge of the cash register in each row, estimates the registration time of each customer based on the skill information indicating the skill of the clerk in the registration process, and uses the estimation result to This differs from the first to third embodiments in that an estimated cash register waiting time is calculated. Differences from the first to third embodiments will be described below.

An example of the hardware configuration of the processing device 10 is the same as in the first to third embodiments.

An example of a functional block diagram of the processing device 10 is shown in FIG. 2, as in the first to third embodiments. As illustrated, the processing device 10 has a calculation material acquisition unit 11 and a calculation unit 12 .

The calculation material acquisition unit 11 identifies the store clerk in charge of the cash register for each row. The calculation material acquisition unit 11 can identify the clerk who is in charge of the checkout of each line by the same method as the method described in the third embodiment.

It should be noted that the calculation material acquisition unit 11 can identify customers lined up in a line waiting for a cash register, as in the first embodiment. Moreover, the calculation material acquisition part 11 may estimate a customer's attribute like 2nd Embodiment.

The calculation unit 12 estimates the registration time of each customer based on the skill information indicating the salesclerk's skill in the product information registration process. Then, the calculation unit 12 calculates the predicted accounting time by adding the predicted payment time and the predicted registration time for each customer in line. Then, the calculation unit 12 calculates the estimated checkout waiting time by adding up the predicted checkout times of all the customers in line. For the prediction of the payment time of each customer, those described in the first to third embodiments can be adopted.

Processing for estimating the registration time of each customer based on the skill information of each salesclerk will be described below. First, information as shown in FIG. 9 is collected for each store clerk. The information indicates the record of past registration processing of each store clerk.

In FIG. 9, the date and time when the registration process was performed, the customer ID (member ID) at that time, the number of products registered at that time (product number), the product ID (product ID) registered at that time, and the product ID at that time is stored in association with the registration time of .

The calculation unit 12 analyzes the information and generates skill information for each salesclerk. For example, the calculation unit 12 may calculate statistical values (eg, average value, median value, mode value, maximum value, minimum value, etc.) of the collected registration times. In addition, the calculation unit 12 may calculate a statistical value of the registration time in the latest predetermined number of registration processes. In addition, the calculation unit 12 may calculate a statistical value of registration time for each member ID. In addition, the calculation unit 12 may calculate the statistical value of the registration time for each item number. In addition, the calculation unit 12 may calculate the statistical value of the registration time for each pair of member ID and number of products. In addition, the calculation unit 12 may calculate a statistical value of registration time in registration processing including a predetermined product. In addition, the calculation unit 12 may calculate a statistical value of the registration time for each number of products when a predetermined product is included in the registration target. Predetermined commodities are commodities that greatly affect the length of registration time. Examples include heavy commodities such as rice, a case of 6 PET bottles, and commodities that are fragile or crumble and require careful handling. .

The calculation unit 12 estimates the registration time of each customer based on the skill information as described above. An example of calculating the registration time will be described below.

"Registration time calculation example 1"
The calculation unit 12 can use the statistic value of the registration time of the store clerk in charge of the checkout line (the collected statistic value of the registration time) as the prediction of the registration time of each customer. In this case, the predicted registration times of all the customers in the same row will be the same value.

"Registration time calculation example 2"
The calculation unit 12 calculates the statistical value of the registration time in the most recent predetermined number of registration processes of the store clerk who is in charge of the register in the queue (the statistical value of the registration time in the most recent predetermined number of registration processes) to each customer. registration time prediction. In this case, the predicted registration times of all the customers in the same row will be the same value.

"Registration time calculation example 3"
The calculation unit 12 calculates the statistical value of the registration time (the statistical value of the registration time for each member ID) when the clerk in charge of the cash register in the row performs the registration process for each customer, and calculates the registration time of each customer. can be predicted.

"Registration time calculation example 4"
The calculation unit 12 calculates the statistical value of the registration time (the statistical value of the registration time for each product number) when the store clerk in charge of the cash register of the row performs the registration process of the predetermined number of products. It can be a prediction of time. The predetermined number of items is a predicted value of the number of items held by each customer waiting for a checkout. For example, if the predicted value of the number of items held by the first customer waiting in a certain line is 5, the calculation unit 12 calculates that the clerk in charge of the cash register in that line has five items in the past. The statistical value of the registration time when the registration process of 1 is performed is used as the prediction of the registration time of the first customer. In this case, the calculation material acquisition unit 11 predicts the number of products held by each customer waiting for the cash register by image analysis. Then, the calculation unit 12 uses the predicted value obtained by the calculation material acquisition unit 11 to predict the registration time of each customer.

"Registration time calculation example 5"
The calculation unit 12 calculates the statistical value of the registration time when the store clerk in charge of the cash register in the queue performs the registration process of the predetermined number of products for each customer (registration for each pair of the member ID and the number of products). time statistics) can be an estimate of each customer's registration time. For example, if the predicted value of the number of products held by a first customer waiting in a certain line is 5, the calculation unit 12 determines that the clerk in charge of the cash register in that line has been the first customer in the past. The statistical value of the registration time when the registration processing of 5 products is performed for , is assumed to be the prediction of the registration time of the first customer. In this case, the calculation material acquisition unit 11 predicts the number of products held by each customer waiting for the cash register by image analysis. Then, the calculation unit 12 uses the predicted value obtained by the calculation material acquisition unit 11 to predict the registration time of each customer.

"Registration time calculation example 6"
The calculation unit 12 calculates the statistical value of the registration time (the statistical value of the registration time in the registration process including the predetermined product) when the store clerk in charge of the cash register in the row performs the registration process including the predetermined product. It can be a prediction of each customer's registration time. For example, if the first customer waiting in a certain line has rice, the calculation unit 12 calculates the number of times the clerk in charge of the register in that line has performed the registration process including rice in the past. Let the registration time statistic be the prediction of the first customer's registration time. In this case, the calculation material acquiring unit 11 predicts the type of product held by each customer waiting for the cash register by image analysis. Then, the calculation unit 12 uses the prediction result obtained by the calculation material acquisition unit 11 to predict the registration time of each customer.

"Registration time calculation example 7"
The calculation unit 12 calculates the statistical value of the registration time when the store clerk in charge of the cash register in the queue includes a predetermined product and performs registration processing for a predetermined number of products (the above registration target includes a predetermined product Statistical value of registration time by item count in each case) can be used as an estimate of registration time for each customer. The predetermined number of items is a predicted value of the number of items held by each customer waiting for a checkout. For example, if a first customer waiting in a certain line holds rice and the predicted value of the number of products held by the first customer is 5, the calculation unit 12 selects the register in that line. The statistical value of the registration time when the store clerk in charge performed the registration processing of five products including rice in the past is assumed to be the prediction of the registration time of the first customer. In this case, the calculation material acquisition unit 11 predicts the number of products and the type of products held by each customer waiting for the cash register by image analysis. Then, the calculation unit 12 uses the prediction result obtained by the calculation material acquisition unit 11 to predict the registration time of each customer.

Note that, when the calculation unit 12 can estimate the types and/or number of products held by the customer waiting in line for the checkout, the calculation unit 12 calculates the type and/or number of products held by the customer, The registration time may be calculated based on the skill information and the estimated cashier waiting time may be calculated based on the registration time.

On the other hand, if the calculation unit 12 cannot estimate the type and/or number of products held by the customer waiting in line for the checkout, Alternatively, the registration time may be calculated based on the salesclerk's skill information, and the estimated cash register waiting time may be calculated based on the registration time.

That is, in principle, the calculation unit 12 predicts the registration time of each customer according to any of the registration time calculation examples 4 to 7, and if the type and/or number of products cannot be estimated by image analysis, the registration time calculation example 1 The registration time of each customer may be predicted by any one of 3. "When the type and/or number of products cannot be estimated by image analysis," for example, when other objects (e.g. customers, shelves, etc.) overlap and the product held by the customer cannot be photographed, or when the product behind the line There may be a case where the number and type of products cannot be estimated with sufficient accuracy because the products included in the image are too small because they are lined up in a row.

An example of the processing flow of the processing apparatus 10 of this embodiment is the same as in the first to third embodiments.

According to the processing apparatus 10 of this embodiment described above, the same effects as those of the first to third embodiments can be achieved. Further, according to the processing device 10 of the present embodiment, it is possible to predict the registration time of each customer in consideration of the characteristics of the store clerk, the number of products held by the customer, the product type, and the like. As a result, it is possible to improve the accuracy of estimating the cash register waiting time.

<Fifth Embodiment>
The processing device 10 of the present embodiment outputs the calculated estimated cash register waiting time to customers, and performs processing according to the difference between the estimated cash register waiting time when each customer is lined up in line and the actual cash register waiting time. It differs from the first to fourth embodiments in terms of execution. Differences from the first to fourth embodiments will be described below.

An example of the hardware configuration of the processing device 10 is the same as in the first to fourth embodiments.

An example of a functional block diagram of the processing device 10 is shown in FIG. As illustrated, the processing device 10 includes a calculation material acquisition unit 11, a calculation unit 12, an output unit 13, a detection unit 14, a first identification unit 15, a second identification unit 16, and an execution unit. 17. The configurations of the calculation material acquisition unit 11 and the calculation unit 12 are the same as those of the first to fourth embodiments.

The output unit 13 outputs the estimated registration wait time calculated by the calculation unit 12 . For example, a display device (eg, display) may be installed corresponding to each cash register. Then, the output unit 13 may display the estimated cash register waiting time at that time on the display device. FIG. 11 shows an example of a display device.

The detection unit 14 detects that a new customer has lined up in line. The detection unit 14 detects that a new customer has lined up in line by image analysis.

The first specifying unit 15 specifies the estimated cash register waiting time output by the output unit 13 at the timing when a new customer is lined up.

The second specifying unit 16 specifies the cash register waiting time from the time the customer lines up in line to the time the checkout process starts for each customer. The timing to start the checkout process may be the timing when the first product information about the customer is registered at the cash register, or other timing corresponding thereto.

The execution unit 17 executes processing according to the difference between the estimated cash register waiting time identified by the first identifying unit 15 and the cash register waiting time identified by the second identifying unit 16 .

For example, when the difference obtained by subtracting the estimated cash register waiting time from the cash register waiting time is greater than a predetermined value, the execution unit 17 gives the customer a benefit corresponding to the difference. For example, the execution unit 17 may update the points registered in the server in association with the member ID, or print privilege information (eg, discount coupon, product exchange coupon, etc.) on the customer's receipt. Alternatively, instructions directed to the clerk may be displayed on the cash register display during the checkout process for the customer. The instruction may indicate, for example, to hand over a predetermined present.

In addition, when the difference obtained by subtracting the estimated cash register waiting time from the cash register waiting time is greater than a predetermined value, the executing unit 17 may notify that effect. Note that, when a predetermined number of customers continue to have a difference obtained by subtracting the estimated cash register waiting time from the cash register waiting time and the difference is greater than a predetermined value, the execution unit 17 may notify that effect.

The person to be notified may be the clerk in charge of the cash register. In this case, the execution unit 17 may display information to that effect on the cash register display, for example. Alternatively, the person to be notified may be an administrator (eg, store manager, cash register manager, etc.). In this case, the execution unit 17 may, for example, send an e-mail to a predetermined address to indicate the fact, or may transmit predetermined information (eg, "1st cash register, confirmation please”) may be output.

An example of the processing flow of the processing apparatus 10 of this embodiment is the same as that of the first to fourth embodiments.

FIG. 12 shows an example of the flow executed in response to a new customer (hereafter referred to as "first customer") lining up in line when the detection unit 14 detects that the customer is standing in line. Description will be made using a flow chart.

In S20, the first specifying unit 15 specifies the estimated cash register waiting time output by the output unit 13 at the timing when the first customer was lined up. In S21, the second specifying unit 16 starts counting the cashier waiting time from when the first customer is lined up to when checkout processing is started. Note that the sequential relationship of S20 and S21 is not limited to the illustrated one.

After starting the counting of the cash register waiting time, the second specifying unit 16 determines whether or not the first customer has disappeared from the line (S22), and whether or not the first customer's checkout process has started. is determined (S23). Determination of whether the first customer has disappeared from the line is realized by image analysis. Determination as to whether or not the checkout process for the first customer has started is realized by input contents to the cash register, image analysis, or the like.

When the first customer disappears from the line (Yes in S22), the second specifying unit 16 finishes counting the cash register waiting time. Then, the flow corresponding to the first customer ends.

When the checkout process for the first customer has started (Yes in S23), the second identifying unit 16 ends counting the cash register waiting time and identifies the cash register waiting time (S24).

After that, the execution unit 17 calculates the difference between the estimated cash register waiting time specified in S20 and the cash register waiting time specified in S24 (S25), and executes a process according to the difference (S26).

According to the processing apparatus 10 of this embodiment described above, it is possible to achieve the same effects as those of the first to fourth embodiments. Further, according to the processing device 10 of the present embodiment, the estimated waiting time of each line can be output to the customer. In this case, it is expected that the customers will be evenly divided into a plurality of rows.

Further, according to the processing device 10 of the present embodiment, it is possible to execute processing according to the difference between the estimated cash register waiting time and the actual cash register waiting time.

For example, when the difference obtained by subtracting the estimated cash register waiting time from the cash register waiting time is greater than a predetermined value, the processing device 10 can provide the customer with a benefit corresponding to the difference. This is expected to reduce customer dissatisfaction with waiting for cashiers.

In addition, if the difference obtained by subtracting the estimated cash register waiting time from the cash register waiting time is greater than a predetermined value, the clerk or manager in charge of the cash register can be notified to that effect. In this case, it is possible to notify the store clerk or manager in charge of the cash register that the difference between the estimated cash register waiting time and the estimated cash register waiting time is larger than a predetermined value. As a result, it is possible to take measures to improve the inconvenience. When a predetermined number of customers continue to have a difference obtained by subtracting the estimated cash register waiting time from the cash register waiting time, the store clerk or manager in charge of the cash register can be notified of this fact. Even in such a case, similar effects can be achieved.

Examples of reference forms are added below.
1. estimating means for estimating attributes of customers waiting in line for a checkout;
calculation means for calculating the checkout waiting time of the column based on the estimated attribute and the correspondence information that associates the payment time information related to the time required for payment with the attribute;
A processing device comprising:
2. 1. In the processing device according to 1,
The processing device, wherein the attributes include at least one of gender, age and nationality.
3. In the processing device according to 1 or 2,
The processing device, wherein the payment time information includes at least one of the time actually required for payment in past payment processing and the payment method used.
4. 4. In the processing device according to any one of 1 to 3,
The estimation means is a processing device that estimates the attribute of the customer using an image including the customer.
5. In the processing device according to any one of 1 to 4,
The payment time information is stored in a storage means for each of the plurality of customers,
The estimation means estimates the attribute for the first customer whose payment time information is not stored in the storage means,
The calculation means is
Estimate the payment time of the first customer using the attribute of the first customer and the matching information,
A processing device for estimating the payment time of the second customer whose payment time information is stored in the storage means, using the payment time information stored in the storage means.
6. 6. In the processing device according to any one of 1 to 5,
A processing device that generates the association information using the attribute of the customer who made the payment and the payment time information of the customer.
7. In the processing device according to any one of 1 to 6,
an acquisition means for acquiring store clerk information indicating the store clerk in charge of the cash register in that row;
The payment time information is information on payment for each pair of attributes of a clerk and a customer,
The calculating means is a processing device that calculates a checkout waiting time of the line based on the estimated attribute, the store clerk information, and the association information.
8. 8. In the processing device according to any one of 1 to 7,
an acquisition means for acquiring store clerk information indicating the store clerk in charge of the cash register in that row;
The calculation means is a processing device for calculating a checkout waiting time of the line based on skill information indicating the skill of the salesclerk in product registration processing, the estimated attribute, and the salesclerk information.
9. the computer
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
The processing method to do.
10. In the processing method according to 9,
The processing method, wherein the attribute includes at least one of gender, age and nationality.
11. In the processing method according to 9 or 10,
The payment time information includes at least one of the time actually required for payment in past payment processing and the payment method used.
12. In the processing method according to any one of 9 to 11,
The processing method, wherein the computer estimates the attribute of the customer using an image including the customer in the estimation step.
13. In the processing method according to any one of 9 to 12,
The payment time information is stored for each of the plurality of customers,
The computer is
In the estimating step, estimating the attribute for the first customer for whom the payment time information is not stored,
In the calculation step,
Estimate the payment time of the first customer using the attribute of the first customer and the matching information,
A second processing method for estimating the payment time of the customer whose payment time information is stored using the payment time information stored in the storage means.
14. In the processing method according to any one of 9 to 13,
A processing method in which the computer generates the matching information using the attribute of the customer who made the payment and the payment time information of the customer.
15. In the processing method according to any one of 9 to 14,
The computer is
performing an acquisition step of acquiring clerk information indicating the clerk in charge of the cash register in that row;
The payment time information is information on payment for each pair of attributes of a clerk and a customer,
A processing method for calculating, in the calculating step, the checkout waiting time of the row based on the estimated attribute, the store clerk information, and the association information.
16. In the processing method according to any one of 9 to 15,
The computer is
performing an acquisition step of acquiring clerk information indicating the clerk in charge of the cash register in that row;
In the calculating step, a processing method for calculating the checkout waiting time of the column based on skill information indicating the skill of the salesclerk in the product registration process, the estimated attribute, and the salesclerk information.
17. to the computer,
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
program to run.
18. 18. The program according to 17,
A program in which the attributes include at least one of gender, age and nationality.
19. 17 or 18, wherein the program is
A program in which the payment time information includes at least one of the time actually required for payment in past payment processing and the payment method used.
20. 19. The program according to any one of 17 to 19,
A program that causes the computer to estimate the attribute of the customer using an image including the customer in the estimation step.
21. In the program according to any one of 17 to 20,
The payment time information is stored for each of the plurality of customers,
to the computer;
causing the attribute to be estimated for the first customer for whom the payment time information is not stored in the estimation step;
In the calculation step,
Estimate the payment time of the first customer using the attribute of the first customer and the matching information,
A program for estimating the payment time of the second customer whose payment time information is stored using the payment time information stored in the storage means.
22. 22. In the program according to any one of 17 to 21,
A program that causes the computer to generate the matching information using the attribute of the customer who made the payment and the payment time information of the customer.
23. 23. In the program according to any one of 17 to 22,
to the computer;
perform an acquisition step of acquiring clerk information indicating the clerk in charge of the cash register in that row;
The payment time information is information on payment for each pair of attributes of a clerk and a customer,
A program for calculating, in the calculating step, the checkout waiting time of the line based on the estimated attribute, the store clerk information, and the association information.
24. 24. In the program according to any one of 17 to 23,
to the computer;
perform an acquisition step of acquiring clerk information indicating the clerk in charge of the cash register in that row;
A program for calculating, in the calculating step, the checkout waiting time of the row based on skill information indicating the skill of the salesclerk in product registration processing, the estimated attribute, and the salesclerk information.

This application claims priority based on Japanese Patent Application No. 2017-243478 filed on December 20, 2017, and the entire disclosure thereof is incorporated herein.

Claims (13)

estimating means for estimating attributes of customers waiting in line for a checkout;
calculation means for calculating the checkout waiting time of the column based on the estimated attribute and the correspondence information that associates the payment time information related to the time required for payment with the attribute;
with
The payment time information includes the payment method used,
The calculating means is a processing device that identifies the most frequent payment method associated with the estimated attribute, and calculates a checkout waiting time based on the payment time held corresponding to the most frequent payment method. estimating means for estimating attributes of customers waiting in line for a checkout;
identification means for identifying the customer;
calculation means for calculating the checkout waiting time of the column based on the estimated attribute and the correspondence information that associates the payment time information related to the time required for payment with the attribute;
with
The payment time information is stored in a storage means for each of the plurality of customers,
The identification means identifies a customer whose payment time information is not stored in the storage means as a first customer, and identifies a customer whose payment time information is stored in the storage means as a second customer. identify,
The estimation means estimates the attribute for the first customer,
The calculation means is
Estimate the payment time of the first customer using the attribute of the first customer and the matching information,
A processing device for estimating the payment time of the second customer using the payment time information stored in the storage means. estimating means for estimating attributes of customers waiting in line for a checkout;
calculation means for calculating the checkout waiting time of the column based on the estimated attribute and the correspondence information that associates the payment time information related to the time required for payment with the attribute;
an acquisition means for acquiring store clerk information indicating the store clerk in charge of the cash register in that row;
The payment time information is information set for each pair of store clerk and customer attributes and includes information on trends in payment time and/or payment method,
The calculation means extracts information on the tendency of the payment time and/or the payment method corresponding to the pair of the salesclerk corresponding to the acquired salesclerk information and the estimated attribute, and calculates the checkout waiting time of the column. A processing unit that calculates. In the processing apparatus according to any one of claims 1 to 3,
The processing device, wherein the attributes include at least one of gender, age and nationality. In the processing apparatus according to any one of claims 1 to 4,
The estimation means is a processing device that estimates the attribute of the customer using an image including the customer. In the processing apparatus according to any one of claims 1 to 5,
A processing device that generates the association information using the attribute of the customer who made the payment and the payment time information of the customer. In the processing apparatus according to any one of claims 1 to 6,
an acquisition means for acquiring store clerk information indicating the store clerk in charge of the cash register in that row;
The calculation means is a processing device for calculating a checkout waiting time of the line based on skill information indicating the skill of the salesclerk in product registration processing, the estimated attribute, and the salesclerk information. the computer
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
and
The payment time information includes the payment method used,
In the calculating step, the computer identifies the most common payment method associated with the estimated attribute, and calculates the checkout waiting time based on the payment time held in correspondence with the most common payment method. processing method. the computer
an estimation step of estimating attributes of customers waiting in line for a checkout;
an identification step of identifying the customer;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
and
The payment time information is stored in a storage means for each of the plurality of customers,
The computer is
In the identifying step, a customer whose payment time information is not stored in the storage means is identified as a first customer, and a customer whose payment time information is stored in the storage means is identified as a second customer. identify,
In the estimation step, the attribute is estimated for the first customer,
In the calculation step,
Estimate the payment time of the first customer using the attribute of the first customer and the matching information,
A processing method for estimating the payment time of the second customer using the payment time information stored in the storage means. the computer
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
an acquisition step of acquiring clerk information indicating the clerk in charge of the cash register in that row;
and
The payment time information is information set for each pair of store clerk and customer attributes and includes information on trends in payment time and/or payment method,
The computer is
In the calculating step, information on the tendency of the payment time and/or the payment method corresponding to the pair of the store clerk corresponding to the acquired store clerk information and the estimated attribute is extracted, and the checkout waiting time of the column is extracted. The processing method to calculate. to the computer,
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
and
The payment time information includes the payment method used,
A program in which the calculating step identifies the most common payment method associated with the estimated attribute, and calculates the checkout waiting time based on the payment time held in correspondence with the most common payment method. to the computer,
an estimation step of estimating attributes of customers waiting in line for a checkout;
an identification step of identifying the customer;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
and
The payment time information is stored in a storage means for each of the plurality of customers,
In the identifying step, a customer whose payment time information is not stored in the storage means is identified as a first customer, and a customer whose payment time information is stored in the storage means is identified as a second customer. identify,
The estimation step estimates the attributes of the first customer,
The calculation step includes:
Estimate the payment time of the first customer using the attribute of the first customer and the matching information,
A program for estimating the payment time of the second customer using the payment time information stored in the storage means. to the computer,
an estimation step of estimating attributes of customers waiting in line for a checkout;
a calculation step of calculating the checkout waiting time of the column based on the estimated attribute and correspondence information that associates the payment time information regarding the time required for payment with the attribute;
an acquisition step of acquiring clerk information indicating the clerk in charge of the cash register in that row;
and
The payment time information is information set for each pair of store clerk and customer attributes and includes information on trends in payment time and/or payment method,
The calculating step extracts information on the tendency of the payment time and/or the payment method corresponding to the pair of the salesclerk corresponding to the acquired salesclerk information and the estimated attribute, and calculates the checkout waiting time of the column. Program to calculate.

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