Information processing system, information processing method, and program

Abstract

To provide an information processing system capable of accurately predicting a waiting time at the store where a product ordered in store by a customer is collected for the customer and delivered to the customer.SOLUTION: An information processing system according to the present disclosure comprises: acquisition means which acquires workload information related to the load for preparing an ordered product ordered by a customer using a terminal operated in a store, the inventory of which is collected for the customer in the store, and delivered to customer; waiting time prediction means which predicts the waiting time until delivery of the ordered product to the customer based on the workload information; and output means which outputs the predicted waiting time.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an information processing system and the like. [Background technology]

[0002] In a typical store, products are displayed so that customers can freely pick them up. If multiple products of the same type are displayed, more display space is required. Store clerks are responsible for displaying the products.

[0003] According to a system in which a store clerk collects products ordered by a customer and the customer picks up the collected products at the store, the store's display space can be made more efficient. In addition, the work of the store clerk in displaying the products can be reduced. Patent Document 1 discloses a product provision system that obtains products ordered from a user's terminal from inventory and transports them to provide them to the user. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2022-090857 Summary of the Invention [Problem to be solved by the invention]

[0005] In stores that use a system in which customers order products at a store and have store staff collect the products and hand them over on the spot, customers have to wait while the products are collected. It can be difficult for customers to know an estimate of how long they will be waiting.

[0006] One of the objectives of the present disclosure is to provide an information processing system etc. that enables accurate prediction of waiting time in a store where products ordered by a customer at a store are collected for the customer and delivered. [Means for solving the problem]

[0007] The information processing system of the present disclosure includes an acquisition means for acquiring workload information related to the load of preparing an ordered item, which is ordered using a terminal operated by a customer at a store and for which the stock is collected and delivered at the store for the customer, a waiting time prediction means for predicting the waiting time until delivery of the ordered item to the customer based on the workload information, and an output means for outputting the predicted waiting time.

[0008] The information processing method disclosed herein obtains workload information related to the load of preparing an ordered item, which is ordered using a terminal operated by a customer at a store and for which the stock is collected and delivered at the store for the customer, predicts a waiting time until delivery of the ordered item to the customer based on the workload information, and outputs the predicted waiting time.

[0009] A program according to the present disclosure causes a computer to execute a process of acquiring workload information related to a load of preparing an ordered product, which is ordered by a customer using a terminal operated by the customer at a store and for which the ordered product is collected from stock at the store and delivered to the customer, predicting a waiting time until delivery of the ordered product to the customer based on the workload information, and outputting the predicted waiting time. The program may be stored in a computer-readable non-transitory recording medium. Effect of the Invention

[0010] According to the present disclosure, it is possible to accurately predict waiting times at a store where products ordered by a customer at a store are collected for the customer and delivered. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram illustrating an example of the configuration of an information processing system. [Diagram 2] 1 is a flowchart showing an example of the operation of the information processing system. [Diagram 3] FIG. 13 is a block diagram showing another configuration example of the information processing system. [Figure 4] FIG. 13 is a diagram showing an example of a product registration screen. [Diagram 5] FIG. 1 is a plan view showing an example of a store layout. [Figure 6] FIG. 13 is a diagram illustrating an example of a picking instruction screen. [Figure 7] FIG. 13 is a diagram showing an example of a display screen of a waiting time. [Figure 8] 10 is a flowchart showing a more detailed operation example of the information processing system. [Figure 9] FIG. 11 is a plan view showing another example of a store layout. [Figure 10] FIG. 13 is a block diagram showing another configuration example of the information processing system. [Figure 11] FIG. 13 is a diagram showing an example of a display screen of a waiting time. [Figure 12] FIG. 13 is a diagram showing an example of a notification screen for a customer. [Figure 13] 13 is a flowchart showing another operation example of the information processing system. [Figure 14] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The information processing system according to the present disclosure can be applied to a store where product samples or instructions are displayed to customers, and inventory to be handed over to customers is prepared in a location separate from the display location. The manner of display is not particularly limited, and actual products or empty boxes for display may be placed in the display location. Product samples and instructions may also be displayed electronically to customers at the display location. Product inventory is stored in a warehouse on the premises of the store, on a shelf behind the cash register, etc. The store may be, for example, a supermarket, a home appliance retailer, a drug store, or a general store. A customer who visits such a store requests the delivery of inventory using a terminal. In the following description, the product to be delivered is referred to as an ordered product. A store worker or a robot collects the inventory and delivers the product to the customer. While the ordered product is being prepared, the customer waits. Note that the above is an example of a store to which the information processing system according to the present disclosure can be applied, and the application of the present disclosure is not limited to the above example.

[0013] [First embodiment] The configuration of an information processing system 100 according to the first embodiment will be described with reference to Fig. 1. The information processing system 100 is connected to a display device 30 via a communication network so as to be able to communicate with the display device 30 in a wired or wireless manner. The display device 30 may be realized by a device separate from the hardware that realizes the information processing system 100, or may be realized by the hardware that realizes the information processing system 100.

[0014] The display device 30 outputs information to the customer by displaying on a screen or outputting audio. The display device 30 may be a terminal operated by the customer in the store to order the ordered item. The display device 30 operated by the customer is, for example, a customer terminal carried by the customer. However, the display device 30 is not limited to this. The display device 30 may be a store display installed in the store.

[0015] The information processing system 100 includes an acquisition unit 101, a waiting time prediction unit 102, and an output unit 103. Each functional block will be described below.

[0016] The acquisition unit 101 acquires workload information related to the load of preparing an ordered item, which is ordered by a customer using a terminal operated in a store and is stocked and delivered in a store for the customer. The load of preparing the ordered item is related to the length of time it takes a worker or a robot to prepare the ordered item. If the load increases, it takes more time to prepare the ordered item, and the time until delivery to the customer is extended. Preparation of the ordered item includes, for example, the work of a worker or a robot moving the storage location of the inventory, picking the item, moving it to a bagging location, checking it, bagging it, and making it available for delivery. Picking includes picking up the item placed in the storage location. The worker or the robot may check whether the collected item is correct, whether the number of items collected is as ordered, etc. In cases such as when a customer brings a bag, bagging may not be performed. The relationship between the content indicated by the workload information and the magnitude of the load may be determined in advance.

[0017] The workload information of the ordered items includes, for example, the number of items, the number of types, the storage location of the inventory, or the route around the storage location of the inventory. The load of preparing the ordered items varies depending on the number of items, the number of types, the storage location, or the route of the ordered items. The number of items is the total number of items calculated without distinguishing between the types of items. The method of classifying the types of the ordered items is determined appropriately. If two items of the same type are purchased, the number of items is "2" and the number of types of items is "1". Ordered items that are assigned the same product identifier may be classified as being of the same type. In addition, ordered items may be classified into types such as seasonings, beverages, dairy products, and frozen foods. If the number of items and types of items increases, more items will be prepared, and the load increases. Depending on the storage location, the amount of movement to pick up the items increases. Also, if multiple items are ordered, the route around the storage location becomes longer, and the amount of movement increases. If the amount of movement within the warehouse increases, the load of movement increases. Therefore, these pieces of information are related to the load of preparing the ordered items.

[0018] The workload information for the ordered items may also include product information related to the ease of carrying the ordered items, such as the size or weight of the ordered items. If the ordered items are large or heavy, they are difficult to carry and the workload of carrying and moving them increases. Thus, such information may be related to the workload of preparing the ordered items.

[0019] The customer's order is appropriately stored in a store's server that manages the orders received by the store. The server that manages the orders may be the information processing system 100 or a system separate from the information processing system 100. For example, the customer reads the code of the product using a terminal and orders the product. However, the method of receiving an order from a customer is arbitrary. An example of the method of receiving an order will be described in more detail in the second embodiment.

[0020] The acquisition unit 101 may acquire workload information such as the number of products and the number of types of products stored in a server that manages orders. The acquisition unit 101 may also acquire workload information such as storage location, size, weight, etc. for products stored in the server that manages orders by referring to a database (not shown). The acquisition unit 101 may acquire workload information such as a route generated in an external device (not shown).

[0021] For example, the acquisition unit 101 acquires workload information for each customer regarding ordered items ordered by the customer. The acquisition unit 101 may acquire workload information for multiple ordered items collectively after receiving from the customer's terminal a notification that addition of all ordered items to be ordered in this shopping has been completed. For example, when starting a checkout process, the customer's terminal transmits a notification that addition of all ordered items to be ordered in this shopping has been completed to the information processing system 100. Alternatively, the acquisition unit 101 may acquire workload information for an added ordered item each time the customer adds an ordered item, even before the order is confirmed.

[0022] The waiting time prediction unit 102 predicts the waiting time until delivery of the ordered items to the customer based on the workload information of the ordered items ordered by the customer. The waiting time prediction unit 102 predicts the waiting time for each customer.

[0023] For example, the waiting time prediction unit 102 predicts the waiting time based on information indicating the number of ordered items, which is the workload information. The more the number of items, the longer it may take to move them to a storage location, pick them up, and check them. Therefore, the waiting time prediction unit 102 may predict the waiting time by referring to information that predetermines the relationship between the number of items and the magnitude of the load. The waiting time prediction unit 102 may predict the waiting time by multiplying the number of items by a predetermined time. Furthermore, the waiting time prediction unit 102 may predict a time that is predetermined according to the number of items. For example, the standard waiting time for a customer whose number of items is below a standard may be predeterminable to be 5 minutes, and the waiting time for a customer whose number of items is above the standard may be predeterminable to be 10 minutes.

[0024] The waiting time prediction unit 102 may also predict the waiting time based on information indicating the number of types of ordered items, which is the workload information. In some cases, the storage locations of inventory are grouped by product type. Therefore, since ordered items of the same type are picked up from the storage location together, even if the number of ordered items of the same type increases, the time required for moving to the storage location does not increase. Therefore, the waiting time prediction unit 102 may predict a longer waiting time as the number of types of ordered items increases. The waiting time prediction unit 102 may predict the waiting time by referring to information that is predetermined regarding the relationship between the number of types and the magnitude of the load. The waiting time prediction unit 102 may predict the waiting time by multiplying the number of types by a predetermined time. The waiting time prediction unit 102 may also predict the waiting time to be a time that is predetermined according to the number of types.

[0025] The waiting time prediction unit 102 may also predict the waiting time based on information on the storage location of the inventory of the ordered items, which is the workload information. If the storage location of the inventory is far away, it is expected that it will take time to travel to and from the storage location. Therefore, the relationship between the storage location and the magnitude of the load may be determined in advance. The waiting time prediction unit 102 may predict a longer waiting time when an order includes an item stored in a distant location compared to when an order does not include an item stored in a distant location.

[0026] The waiting time prediction unit 102 may also predict the waiting time based on information on a route around the storage location of the ordered item, which is the workload information. The waiting time prediction unit 102 may calculate a route from the current location of the worker or robot through the storage location, bagging location, and delivery location of the ordered item, using the storage location of the ordered item, which is the workload information. Alternatively, the waiting time prediction unit 102 may obtain route information included in an instruction to prepare the ordered item, which is generated for the worker or robot in an external device. Then, for example, the waiting time prediction unit 102 may predict the waiting time according to the time required to move along the route. By using information on the route around the storage location of the inventory, the waiting time prediction unit 102 can predict the waiting time more accurately than when not using the information.

[0027] The waiting time prediction unit 102 may also predict the waiting time based on product information, such as the size or weight of the ordered products, which is the workload information. If the size of the ordered products is large or if the ordered products are heavy, it may take a long time to transport them. Thus, the waiting time prediction unit 102 may predict a longer waiting time when a large or heavy product is included compared to when a large or heavy product is not included. The waiting time prediction unit 102 may also calculate the total volume of the ordered products based on the size of the ordered products, and predict a longer waiting time as the volume increases. Similarly, the waiting time prediction unit 102 may predict a longer waiting time as the total weight increases.

[0028] The waiting time prediction unit 102 may combine a plurality of types of workload information and use it to predict the waiting time. In addition, the waiting time prediction unit 102 may separately predict the storage location movement time, picking time, checking time, bagging time, and the like, among the tasks of preparing the ordered items. For example, the waiting time prediction unit 102 may predict the storage location movement time based on the number of types of items, and may predict other times based on the number of items. The waiting time prediction unit 102 may predict that the larger the size of the ordered items being transported or the heavier the ordered items are, the longer the movement time from the storage location. The waiting time prediction unit 102 may then use the total time obtained by adding up the separately predicted times as the predicted waiting time. However, at least a part of the time for each task of preparing the ordered items may be predicted together. In addition, the picking time may be ignored if it is expected to be relatively short. There is no particular limitation on the specific method of calculating the waiting time, as long as the time from the start of collection to the handover can be calculated.

[0029] Moreover, the waiting time prediction unit 102 may predict a time longer than the time actually required for delivery. By the waiting time prediction unit 102 predicting a time with a margin of error compared to the actual time required, it is possible to prevent customer dissatisfaction due to delivery being delayed from the predicted time.

[0030] The waiting time prediction unit 102 may re-predict the waiting time of a customer each time workload information related to the ordered items of the customer is acquired. In this manner, the waiting time prediction unit 102 may repeat prediction of the waiting time until the ordered items of the customer are finalized. For example, the waiting time prediction unit 102 predicts a tentative waiting time of the customer each time the customer adds an ordered item. When predicting the tentative waiting time, the waiting time prediction unit 102 may predict the waiting time using a number greater than the number of registered ordered items, in anticipation of the addition of ordered items. Then, when the waiting time prediction unit 102 receives from the customer's terminal a notification that the addition of the ordered items has been completed or that the accounting process has started, the waiting time prediction unit 102 proceeds with the process assuming that the ordered items of the customer have been finalized. The waiting time prediction unit 102 may predict the waiting time for the finalized ordered items.

[0031] The output unit 103 outputs the predicted waiting time. There is no particular limitation on how the waiting time may be expressed. The waiting time may be expressed, for example, in minutes or in a scheduled delivery time. For example, the output unit 103 outputs the predicted waiting time to the display device 30. When the display device 30 is a terminal carried by a customer, the output unit 103 outputs the predicted waiting time to a terminal carried by the customer who ordered the ordered item. When the display device 30 is a store display, the output unit 103 outputs the predicted waiting time together with information identifying the order. The information identifying the order is, for example, an order number or customer identification information that identifies the customer.

[0032] The output unit 103 may output an updated waiting time for a customer every time the workload information for the customer is reacquired and an updated waiting time is predicted. The output unit 103 may output the latest predicted waiting time for the customer or a time longer than the predicted time. By the output unit 103 outputting a time with a margin, it is possible to prevent customer dissatisfaction due to delivery being delayed beyond the output time.

[0033] The output unit 103 may output content for passing the waiting time to the display device 30 based on the length of the predicted waiting time. For example, the output unit 103 outputs the content so that the waiting time and the content are displayed on the same screen. For example, the output unit 103 may output the content to the display device 30 on the condition that the predicted waiting time exceeds a predetermined standard, such as 10 minutes. The content to be output is not particularly limited. The output unit 103 may output content of a length corresponding to the length of the waiting time. The customer can pass the waiting time while viewing the output content. The output unit 103 may output advertisements for services provided in the store. This allows the customer to pass the waiting time while receiving services provided in the store. The services in the store include the provision of food and drink, the provision of an eating and drinking space, the issuance of gift certificates, and procedures related to membership cards. These services are provided in facilities in the store, such as a food court, an eating and drinking space, and a procedure counter in the store.

[0034] Next, an example of the operation of the information processing system 100 will be described with reference to Fig. 2. The information processing system 100 starts the process of Fig. 2 in response to, for example, a customer using a terminal to order an order item.

[0035] The acquisition unit 101 acquires workload information related to the load of preparing an ordered item, which is ordered by a customer using a terminal operated by the customer at the store and is collected from stock at the store and delivered to the customer (step S1).

[0036] The waiting time prediction unit 102 predicts the waiting time until the delivery of the ordered product to the customer based on the workload information of the ordered product acquired by the acquisition unit 101 (step S2).

[0037] The output unit 103 outputs the waiting time predicted by the waiting time prediction unit 102 (step S3). With the above, the information processing system 100 ends the process. The information processing system 100 may repeat the process from step S1 to step S3 every time the customer adds an ordered item.

[0038] According to the first embodiment, the acquisition unit 101 acquires workload information related to the load of preparing an ordered item, which is ordered by a customer using a terminal operated at the store and for which the stock is collected and delivered at the store for the customer. The waiting time prediction unit 102 predicts the waiting time until the delivery of the ordered item to the customer based on the workload information. Then, the output unit 103 outputs the predicted waiting time. Therefore, according to the first embodiment, it is possible for a customer to know the predicted waiting time at the store where the item ordered by the customer is collected and delivered to the customer. Also, according to the first embodiment, since the workload information of the ordered item is used to predict the waiting time, it is possible to predict the waiting time with high accuracy. And, compared to the case where the number of people waiting is simply presented or the customer's turn is presented, the customer can know the waiting time with high accuracy.

[0039] [Second embodiment] The configuration of the information processing system 110 according to the second embodiment will be described with reference to Fig. 3. Regarding the configuration of the information processing system 110 according to the second embodiment, the acquisition unit 101, the waiting time prediction unit 102, and the output unit 103, the description of the parts similar to the configuration of the information processing system 100 according to the first embodiment will be omitted.

[0040] Information processing system 110 differs from information processing system 100 in that it includes a product registration unit 104, an instruction unit 105, and an accounting processing unit 106. However, each of these components may be provided as necessary. Product registration unit 104, instruction unit 105, and accounting processing unit 106 may be provided in a system (not shown) separate from information processing system 110.

[0041] The information processing system 110 is also communicatively connected to, for example, a product information DB (Database) 21, a registered product storage unit 22, and a purchase history DB 23. However, the purchase history DB 23 may be provided as necessary. Furthermore, the information processing system 110 may be communicatively connected to a customer terminal 31, a store display 32, an employee terminal 33, and a cash register 34.

[0042] The product information DB21 manages product identifiers that identify products. The product identifiers are, for example, in-store codes, JAN (Japanese Article Number) codes, EAN (European Article Number) codes, or UPC (Universal Product Codes). The product identifiers may be used as PLU (Price Look Up) codes. The product information DB21 may further store product sizes, weights, or inventory storage locations.

[0043] The registered product storage unit 22 stores products that are registered as products that the customer will purchase in the current shopping trip. Storing products means storing information about the products. For example, the registered product storage unit 22 stores ordered products that the customer has ordered.

[0044] The purchase history DB 23 stores information about past purchases at the store for each customer. The purchase history DB 23 stores, for example, ordered products that the customer has purchased in the past.

[0045] The customer terminal 31 and the store display 32 are an embodiment of the display device 30 according to the first embodiment. The customer terminal 31 is a terminal that a customer operates in a store to order an order item. The customer terminal 31 may be portable within the store. The store display 32 is a display device that is installed in a store. The store display 32 may be provided as necessary.

[0046] The customer terminal 31 acquires product identification information through an operation by a customer. The product identification information includes a product identifier. There is no limitation on the method by which the customer terminal 31 acquires the product identification information. The customer terminal 31 also stores customer identification information that identifies the customer. Then, the customer terminal 31 transmits a registration request including the customer identification information and the product identification information of the ordered product to the information processing system 110.

[0047] For example, when a customer reads a product identifier code with a camera or scanner of the customer terminal 31, the customer terminal 31 acquires the product identifier. The code may be printed on a sample of the product for display, or may be placed on or near the product. The form of the code is not particularly limited, and may be, for example, a barcode or a two-dimensional code.

[0048] An example of a product registration screen displayed by the customer terminal 31 will be described with reference to Fig. 4. Area D1 of the screen displays an image of a barcode photographed by the customer terminal 31. The customer terminal 31 may receive the product name and price corresponding to the photographed barcode from the information processing system 110 and display them on the screen. The customer terminal 31 may send a registration request in response to pressing an add to shopping cart button D2.

[0049] The customer terminal 31 may acquire a product identifier input by the customer using the keyboard of the customer terminal 31. The product may also be searchable using the customer terminal 31. When a selection of products is displayed on the customer terminal 31, the customer terminal 31 may acquire a product identifier of a product selected by the customer. By being able to select a product from the screen, the customer can order the product even if he or she does not have the product identifier at hand and is far from where the products are displayed.

[0050] Furthermore, the customer terminal 31 may acquire a product image as the product identification information. For example, the customer terminal 31 transmits a product image photographed by the customer using the customer terminal 31 to the information processing system 110. The information processing system 110 may perform image recognition of the product from the product image and identify the product identifier of the recognized product.

[0051] The worker terminal 33 is a terminal used by a worker who collects inventory. The worker checks the information displayed on the worker terminal 33 and prepares the ordered items by picking, checking, bagging, etc. Note that the work of the worker described below may be performed by a robot. In this case, the worker terminal 33 may be replaced by the robot that performs the work.

[0052] The payment machine 34 is used to process payments for products purchased by customers. The payment machine 34 may have the function of registering ordered products and processing payments for registered products. The payment machine 34 may be operated by a store clerk or by the customer. Customers pay for products using cash, electronic money, credit cards, etc. Note that if payments can be processed using the customer terminal 31, the payment machine 34 may be provided as necessary.

[0053] An example of a store layout to which the information processing system 120 can be applied will be described with reference to FIG. 5. Products delivered to the store in FIG. 5 are not displayed on the sales floor, but are stored in the store's warehouse. Because the products are not displayed on the sales floor, the work of store clerks to display the products on the sales floor can be reduced. Each product and the storage location and inventory quantity of the product are managed. For example, the product inventory may be placed in a predetermined storage location. Alternatively, if the storage location is not predetermined and the delivered products are placed in an empty location, the product and the product storage location may be associated and stored each time using existing technology.

[0054] A customer enters the store through the entrance and wanders around the sales floor. The sales floor of the store in Figure 5 includes a sample display space. The customer refers to the product samples displayed in the sample display space and decides which products to order. The customer then uses a terminal to request collection and delivery of the ordered products.

[0055] Ordered items that a customer has requested to be delivered are collected, for example, at a store warehouse. At the warehouse, a worker moves between inventory storage locations and picks up the items. The items picked from the storage locations may be temporarily placed in a basket. The picked items are then transported to a bagging location and bagged. The picking and bagging of the ordered items may be performed by the same worker or by different workers.

[0056] The worker carries the ordered items to the item delivery port. An accounting machine 34 may be provided at the item delivery port. For example, the ordered items may be delivered by the store clerk to the customer after the payment is made at the accounting machine 34. If the customer terminal 31 is used for payment, the store clerk may check the payment completion screen and deliver the ordered items. The customer receives the ordered items at the item delivery port and leaves the store through the exit.

[0057] Although examples of store layouts and operations have been described above, the store layouts and operations are not limited to these. A bagging area does not need to be provided. A worker may bag the picked items as he picks them, without temporarily placing them in a basket. In addition, although an example has been described in which the store accepts requests for collection and delivery of all items, actual display items may be displayed in the store.

[0058] Next, each component of the information processing system 110 illustrated in FIG. 3 will be described in more detail.

[0059] The product registration unit 104 registers products purchased by customers. As a specific example, the product registration unit 104 may receive a registration request from a customer's terminal and register the ordered product. The registration request includes customer identification information and product identification information of the ordered product. When the product registration unit 104 registers the ordered product, a request for collection and delivery of the stock at the store is accepted.

[0060] The instruction unit 105 instructs preparation of the ordered items. The instruction unit 105 may instruct preparation after the customer has confirmed the order for all ordered items. Alternatively, the instruction unit 105 may instruct preparation of the ordered items each time the customer adds an item. The instruction unit 105 instructs preparation of the ordered items by outputting a picking instruction to the worker terminal 33. The instruction unit 105 may output a picking instruction including a product identifier, a product name, the number of items, and a storage location.

[0061] Furthermore, the instruction unit 105 may generate a route that the worker should take and output a picking instruction including the route. The worker collects the ordered items by sequentially tracing the storage locations of the ordered items along the route of the picking instruction, and then travels to a bagging location according to the instruction to bag the ordered items. The method of generating the route is not particularly limited. For example, the instruction unit 105 generates a route that passes through the storage locations of all requested ordered items. Alternatively, when the worker bags items during picking, the instruction unit 105 may generate a route taking into consideration the order of bagging. For example, the instruction unit 105 generates a route that passes through the storage locations of the ordered items in order of size or weight so that the worker can pick and bag large or heavy items first.

[0062] When multiple workers prepare products, the instruction unit 105 determines the worker who will collect the ordered products of the customer. For example, the instruction unit 105 determines the worker from among workers who are available. When receiving requests for ordered products from multiple customers, the worker may not be able to handle the orders of multiple customers at the same time. In this case, the instruction unit 105 may output picking instructions for the ordered products of the customers in order.

[0063] An example of a picking instruction screen displayed by the worker terminal 33 will be described with reference to FIG. 6. The picking instruction screen in FIG. 6 shows the product name, the number of products, the storage location of the products, and the route the worker should take. For example, the worker moves from a current location represented by a white star to a storage location represented by a black star. The picking instruction screen may further include information identifying the order, a target time until picking is completed or a target time until bagging is completed, and information regarding picking instructions for the next customer. When picking and bagging are completed, the worker may press a done button on the screen.

[0064] The speed at which each worker can move may differ. Therefore, the instruction unit 105 may calculate the time required for each worker to move to the storage location at a predetermined target speed, and calculate the target time for the worker to complete the work. The waiting time prediction unit 102 may calculate the time required for each worker to move to the storage location taking into account the speed of each worker, and predict the waiting time using the time required for movement.

[0065] For example, the instruction unit 105 acquires data on the determined stride length of the worker and calculates the number of steps required for the route around the storage location. The instruction unit 105 may output a picking instruction including the number of steps that the worker should take. Different workers may have different strides. Therefore, the instruction unit 105 may calculate the target number of steps by dividing the length of the route by a target stride length that is predetermined for each worker.

[0066] The instruction unit 105 may predict the target work status at each time point. Then, the instruction unit 105 may detect the actual work status based on the current location of the worker, etc. The waiting time prediction unit 102 may compare the target work status with the actual work status at a certain time point, and if there is a difference, re-predict the waiting time. This allows the output unit 103 to output a time that is earlier or later than the previously predicted waiting time. Therefore, the output unit 103 can notify the customer of the predicted waiting time with higher accuracy.

[0067] The accounting processing unit 106 processes the accounting for products purchased by the customer. The accounting processing unit 106 processes the accounting for, for example, ordered products. The accounting processing unit 106 sends accounting data to the accounting machine 34. Note that the customer terminal 31 may send the accounting data to the accounting processing unit 106 instead of the accounting machine 34. After the accounting is completed, the accounting processing unit 106 updates the purchase history DB 23. For example, the accounting processing unit 106 registers the ordered products purchased by the customer in the purchase history DB 23 based on the accounting data.

[0068] The output unit 103 may output the predicted waiting time to the customer terminal 31 of a customer who is in the middle of adding an ordered item. Alternatively, the output unit 103 may output the waiting time to the customer terminal 31 of a customer who has confirmed the order. In addition, the output unit 103 may output the waiting time to the store display 32.

[0069] An example of a waiting time display screen displayed by the customer terminal 31 will be described with reference to Fig. 7. The screen in Fig. 7 includes product information of the ordered product, the number of products, and the order number. In addition, the screen in Fig. 7 displays the scheduled delivery time of the ordered product as a predicted waiting time.

[0070] When the product is ready for delivery, the output unit 103 may output a notification that the product is ready for delivery. When the worker presses the completion button in FIG. 6, the output unit 103 may output a notification. For example, the output unit 103 outputs a notification to the customer terminal 31. Alternatively, the output unit 103 may output a notification that the product is ready for delivery to the store display 32.

[0071] The operation of the information processing system 110 is basically the same as the operation of the information processing system 100 according to the first embodiment. An example of the processing of the information processing system 110 will be described below with reference to FIG.

[0072] The information processing system 110 may start the process of Fig. 8 in response to the customer terminal 31 performing a store entry process. For example, the customer terminal 31 performs the store entry process by transmitting to the information processing system 110 location information of the customer terminal 31 or a read result of a two-dimensional code or the like provided in the store.

[0073] The product registration unit 104 registers the ordered product in the registered product storage unit 22 (step S11). For example, the product registration unit 104 registers the ordered product in response to a registration request from the customer terminal 31.

[0074] The instruction unit 105 outputs the picking instruction to the worker terminal 33 (step S12). For example, the instruction unit 105 references the ordered product stored in the registered product storage unit 22 to generate and output the picking instruction.

[0075] The acquisition unit 101 acquires workload information for the ordered items registered by the item registration unit 104 (step S13). The acquisition unit 101 may refer to the registered item storage unit 22 and acquire workload information that is the item number of the ordered items ordered by the customer. The acquisition unit 101 may also acquire, from the item information DB 21, the workload information associated with the item identifier stored in the registered item storage unit 22. Alternatively, the acquisition unit 101 may extract the workload information from the picking instruction generated by the instruction unit 105. For example, the acquisition unit 101 acquires a route around the storage location from the picking instruction. Note that step S12 may be executed after step S13.

[0076] The waiting time prediction unit 102 predicts the waiting time until delivery of the registered ordered items (step S14). The output unit 103 outputs the predicted waiting time (step S15). After the order is confirmed, the accounting processing unit 106 processes the accounting for the items purchased by the customer (step S16). For example, identification information such as a barcode or number is distributed electronically or on paper to the customer after the payment, and the customer receives the ordered items by presenting the identification information at the delivery counter. Note that the customer may make the payment after receiving the ordered items. In this case, the items delivered to the customer may be registered by the accounting machine 34. With the above, the information processing system 110 may end the processing of FIG. 8.

[0077] According to the second embodiment, the same effects as those of the first embodiment can be obtained.

[0078] [Third embodiment] The information processing system according to the third embodiment can be applied to a store in which samples or explanations of some products are displayed on the sales floor, and the actual products of other products are displayed on the sales floor. In the following description, the actual products displayed on the sales floor of the store are referred to as displayed products.

[0079] An example of a store layout to which the information processing system 120 can be applied will be described with reference to FIG. 9. The sales floor of the store in FIG. 9 includes a sample display space and a product display space. Some types of products delivered to the store in FIG. 9 are displayed in the sales floor, and the rest are stored in the store's warehouse. At least some types of products are not displayed in the sales floor, so the work of the sales staff to display products in the sales floor can be reduced. A customer acquires the actual product displayed in the product display space and purchases it. The payment for the displayed product may be made every time the customer acquires a displayed product, or may be made after the acquisition of the displayed product is completed. The payment for the displayed product may be made together with the payment for the ordered product. Note that the store layout in FIG. 9 is an example, and the display product samples and the actual displayed products acquired by the customer may be displayed on the same display shelf.

[0080] The configuration of the information processing system 120 according to the third embodiment will be described with reference to Fig. 10. Of the configuration of the information processing system 120 according to the third embodiment, the description of the parts similar to the configuration of the information processing system 110 according to the second embodiment will be omitted.

[0081] The information processing system 120 differs from the information processing system 110 in that it includes a shopping time prediction unit 107, a priority determination unit 108, and a product prediction unit 109. The product prediction unit 109 may be provided as necessary.

[0082] In the third embodiment, the product information DB21 may store the display location of the displayed products in the sales area of ​​the store. The registered product storage unit 22 may further store the displayed products acquired by the customer. The purchase history DB23 may store the ordered products and the displayed products separately. The purchase history DB23 may also store the time that the customer spends wandering around the store.

[0083] In the third embodiment, the customer terminal 31 communicating with the information processing system 120 may be further used to register a displayed product purchased by a customer. At this time, the customer terminal 31 transmits a registration request including customer identification information and product identification information of the ordered product or the displayed product to the information processing system 120.

[0084] In the third embodiment, the cash register 34 may have a function of registering displayed products and processing payments for the registered products.

[0085] The product registration unit 104 may further register displayed products acquired by a customer. Displayed products may be registered in response to a registration request from the customer terminal 31, similar to ordered products. Alternatively, displayed products may be registered in response to a registration request from a device different from that for the ordered products. For example, the product registration unit 104 may register displayed products in response to a registration request from the cash register 34. The product registration unit 104 registers products, for example, by storing product identifiers of the ordered products or displayed products in the registered product storage unit 22.

[0086] The instruction unit 105 outputs picking instructions for products ordered by customers in the order of decreasing customer priority determined by a priority determination unit 108 described below. A worker or a robot prepares for delivery of the ordered products in the order of decreasing priority.

[0087] The accounting processor 106 may further process the accounting for the displayed items. For example, the accounting processor 106 registers the ordered items or displayed items purchased by the customer in the purchase history DB 23 based on the accounting data.

[0088] The shopping time prediction unit 107 predicts the shopping time required for a customer to shop around the store based on information related to the customer's current or past purchasing behavior. The shopping time includes the time it takes for a customer to select an ordered product or a displayed product, and the time it takes to move around the store. The shopping time may further include the time it takes for a customer to register an ordered product or a displayed product, or to pay for a purchase. Purchasing behavior includes shopping around the store, registering a product as a product to be purchased, ordering a product to be delivered, etc.

[0089] As information related to the current purchasing behavior, the current location information of the customer and information on the displayed products that the customer is predicted to purchase are used. The case where the information on the displayed products that the customer is predicted to purchase is used will be described later.

[0090] An example of a method for predicting a shopping time based on information related to a customer's past purchasing behavior will be described. The shopping time prediction unit 107 may use information related to recent shopping with reference to the purchase history DB 23, or may use a customer's shopping tendency seen from information related to multiple purchases. For example, the shopping time prediction unit 107 may predict a shopping time based on a customer's past shopping time. The shopping time prediction unit 107 may predict, for example, a statistic of a customer's past shopping time as the shopping time of the customer during shopping with reference to the purchase history DB 23. The statistic of the past shopping time may be an average value, a median value, a mode value, a maximum value, a minimum value, or the like of the past shopping time.

[0091] The shopping time prediction unit 107 may predict the shopping time based on acquired load information related to the load of the customer acquiring the displayed products among the information related to the past purchasing behavior of the customer. The acquired load information related to the load of acquiring the displayed products is, for example, the number of displayed products, the number of types of the displayed products, or the display locations of the displayed products. The load of acquiring the displayed products varies depending on the number, number of types, or display locations of the displayed products to be purchased. It is assumed that a customer who has purchased many products in the past will also purchase many products next time and will take a long time to shop around the store. It is also assumed that a customer will purchase the same product or the same type of product next time as the product purchased in the past. The display locations of the store may be grouped by product type. Since a customer can acquire the same type of product in bulk, it is assumed that the shopping time will not increase when the same type of product is purchased. Therefore, the shopping time prediction unit 107 may predict that the shopping time will be longer the more displayed products a customer purchases or the more types of displayed products a customer purchases. Moreover, the shopping time prediction unit 107 may calculate a route passing through the display locations of the displayed merchandise to predict the shopping time.

[0092] The priority determination unit 108 determines the priority for collecting ordered items from each of a plurality of customers based on the predicted movement time. For example, the priority determination unit 108 determines the priority of a customer who is predicted to have a longer movement time to be lower than other customers. Since the collection work for ordered items of a customer with a higher priority starts earlier, the customer can receive the ordered items earlier. Therefore, the waiting time of customers with a shorter movement time can be reduced. Since the collection work for ordered items of a customer with a lower priority starts later, the time when preparation is completed may be delayed. Customers with a longer movement time can spend their waiting time wandering around the store.

[0093] The priority determination unit 108 may determine the priority by further using information other than the predicted shopping time. For example, the number of ordered items may be used as information other than the predicted shopping time. For example, the priority determination unit 108 may determine a higher priority for a customer who has ordered a smaller number of items.

[0094] The waiting time prediction unit 102 may predict the waiting time based on the priority determined by the priority determination unit 108 in addition to the workload information of the ordered items. For example, the waiting time prediction unit 102 predicts the waiting time of a customer with a lower priority by adding the time required to prepare the ordered items of a customer with a higher priority to the time required to prepare the ordered items ordered by the customer. The waiting time prediction unit 102 may also predict the waiting time by predicting the start time of the work to prepare the ordered items of a customer with a lower priority and adding the work time required for the ordered items of the customer to the start time.

[0095] The product prediction unit 109 predicts products that a customer will purchase during shopping based on information related to the customer's current or past purchasing behavior.

[0096] An example in which the product prediction unit 109 predicts an order product to be ordered by a customer will be described below. For example, the product prediction unit 109 predicts an order product to be ordered by a customer based on display products that are displayed in a store and registered as products to be purchased by a customer during shopping. As a specific example, a customer acquires an actual fresh food as a product to be purchased and registers the product. Thereafter, a grocery item that is likely to be purchased together with the registered fresh food may be predicted as an order product that is likely to be ordered by the customer. A combination of a display product and an order product that is likely to be purchased together with the display product may be stored in advance in a database or the like. The product prediction unit 109 may refer to information on the combinations stored in advance and predict an order product to be ordered by a customer. For example, a combination of a display product such as a specific vegetable or meat and an order product such as a seasoning that goes well with the vegetable or meat is determined by a store staff member or a manufacturer staff member. Alternatively, a combination of a display product and an order product may not be determined in advance. In this case, the product prediction unit 109 may refer to the purchase history DB 23 and predict the order product that is frequently purchased together with the displayed product as the order product that the customer will order. At this time, the purchase history of multiple customers may be referred to, or the purchase history of the customer to be predicted may be referred to.

[0097] The product prediction unit 109 may predict the ordered product regardless of the displayed product registered by the customer. For example, the product prediction unit 109 may predict that the customer will order another ordered product based on the ordered product ordered by the customer while shopping. Furthermore, the product prediction unit 109 may refer to the purchase history DB 23 and predict that the customer while shopping will re-order the ordered product that the customer previously purchased.

[0098] Then, the acquisition unit 101 acquires the workload information of the ordered products predicted by the product prediction unit 109. As a result, the waiting time prediction unit 102 predicts the waiting time based on the workload information of the predicted ordered products and the ordered products that have already been ordered.

[0099] When the product prediction unit 109 predicts the ordered products of a customer, the waiting time prediction unit 102 can predict the waiting time even if the customer has not yet ordered the ordered products. As a specific example, the product prediction unit 109 may predict three ordered products that the customer will order, based on the displayed products and purchase history registered by the customer. The acquisition unit 101 acquires, as the workload information, information that the number of predicted ordered products is three. The waiting time prediction unit 102 tentatively predicts the waiting time using the predicted number of ordered products. After the ordered products are added, the waiting time prediction unit 102 may predict the waiting time again.

[0100] The product prediction unit 109 may predict the displayed products that the customer will purchase based on information related to the customer's current or past purchasing behavior, similar to the prediction of the ordered products. For example, the product prediction unit 109 predicts the displayed products that the customer will purchase based on the customer's purchase history, the ordered products ordered by the customer during shopping, and the displayed products registered by the customer using the customer terminal 31 during shopping.

[0101] When the product prediction unit 109 predicts the displayed products to be purchased by the customer, the shopping time prediction unit 107 may predict the shopping time based on the workload information related to the acquisition of the predicted displayed products. For example, the shopping time prediction unit 107 predicts the shopping time taking into consideration the time required to move to the predicted displayed products and the time required to register the displayed products. In this way, the shopping time prediction unit 107 can predict the shopping time based on the current purchasing behavior of the customer for the current shopping trip.

[0102] The output unit 103 may output the predicted waiting time to the customer terminal 31 of a customer who is currently shopping. A customer who is currently shopping is a customer who is in the process of selecting an ordered product or a displayed product. For example, the output unit 103 may output the waiting time to a product registration screen or a confirmation screen for registered products. By outputting the waiting time to a customer who is currently shopping, the customer can decide to use the waiting time to wander around the store.

[0103] An example of a waiting time display screen displayed by the customer terminal 31 will be described with reference to FIG. 11. In the screen of FIG. 11, the scheduled delivery time of the ordered item is displayed as the waiting time. In the screen of FIG. 11, the customer can confirm the items registered as items to be purchased. In such a screen, the displayed items and the ordered items may be displayed in a distinguishable manner. Potatoes and a daily special lunch box are examples of the displayed items registered by the customer. Whiskey is an example of an ordered item ordered by the customer. The ordered items are collected by a worker and delivered to the customer. The customer can add the ordered item or the displayed item as an item to be purchased by pressing an add item button. When the customer terminal 31 receives a new waiting time, the displayed waiting time is updated in the screen of FIG. 11. The customer terminal 31 may start the checkout process for the registered items in response to the customer pressing the pay button. When the pay button is pressed, the customer terminal 31 may transmit to the information processing system 120 that the addition of all the ordered items to be ordered in this shopping has been completed. When the checkout process is completed, the customer finishes shopping.

[0104] An example of a notification screen displayed on the customer terminal 31 of a customer who is shopping will be described with reference to Fig. 12. As shown in Fig. 12, the customer terminal 31 may display a notification that the ordered product is ready for delivery on the confirmation screen of the registered product.

[0105] The output unit 103 may output content for passing the waiting time based on the predicted roaming time and the predicted waiting time. The type of content is not particularly limited. For example, the output unit 103 may output content when the predicted roaming time is shorter than the predicted waiting time. This allows the customer to pass the waiting time by viewing the content.

[0106] An example of content for customers to pass the time while waiting is an advertisement for products displayed in a store. The output unit 103 may output an advertisement for the products displayed in the store to the customer terminal 31 of a customer who is shopping. This allows the customer to pass the time while waiting by looking at the advertisement for the displayed products or going to the display location to pick up the displayed products. For example, the output unit 103 outputs an advertisement for a displayed product that is likely to be purchased together with a registered ordered product or a displayed product. The displayed products that are likely to be purchased together may be stored in advance in a database or the like.

[0107] The output unit 103 may output content based on the predicted waiting time. An example of the content will be described below using advertisements for displayed items. For example, the output unit 103 may output advertisements for displayed items on the condition that the predicted waiting time exceeds a predetermined standard, such as 5 minutes. The output unit 103 may also output advertisements for the number of displayed items according to the length of the predicted waiting time. If the waiting time is shorter than the standard, the output unit 103 outputs advertisements for one or two displayed items. If the waiting time is longer than the standard, the output unit 103 outputs advertisements for three or more displayed items. The output unit 103 may also output advertisements for the type of displayed items according to the length of the predicted waiting time. For example, if the waiting time is shorter than the standard, the output unit 103 outputs advertisements for the type of displayed items that the customer has purchased in the past. This is because the customer can quickly determine whether or not to purchase an item that has been purchased in the past. If the waiting time is longer than the standard, the output unit 103 outputs advertisements for the type of displayed items that the customer has not purchased. When customers have time, they can consider products they would not normally purchase.

[0108] The output unit 103 may further output advertisements for displayed items based on the customer's current location in the store. For example, if the waiting time is shorter than a reference, the output unit 103 may output advertisements for displayed items that are displayed near the current location. The output unit 103 may output advertisements for displayed items that require less time to travel from the current location to the display location than the predicted waiting time.

[0109] By displaying advertisements for displayed items to customers while they are shopping, customers can avoid forgetting to buy items. Furthermore, customers can use their waiting time to purchase displayed items. Furthermore, stores can increase sales of displayed items.

[0110] The output unit 103 may output content to the customer terminal 31 of a customer who has finished shopping. For example, the output unit 103 may output advertisements for services provided in the store to a customer who has finished shopping, rather than advertisements for displayed products.

[0111] The operation of the information processing system 120 is basically the same as the operation of the information processing system 110 according to the second embodiment. An example of the processing of the information processing system 120 will be described below with reference to FIG.

[0112] The product registration unit 104 registers the ordered product in the registered product storage unit 22 (step S21). The acquisition unit 101 acquires workload information about the ordered product registered by the product registration unit 104 (step S22).

[0113] If there are multiple customers who have placed orders for the ordered items and are waiting for delivery (step S23: YES), the movement time prediction unit 107 predicts the time required for the customers waiting for delivery to move around the store (step S24). The priority determination unit 108 determines the priority of the customers based on the predicted movement time (step S25). If there is only one customer waiting for delivery (step S23: NO), the prediction of the movement time and the determination of the priority may be omitted.

[0114] The instruction unit 105 outputs picking instructions to the worker terminal 33 in descending order of the determined priority (step S26). The waiting time prediction unit 102 predicts the waiting time until delivery of the ordered items instructed to be picked (step S27). The output unit 103 outputs the predicted waiting time (step S28). After the order is confirmed, the accounting processing unit 106 processes the accounting (step S29). With the above, the information processing system 120 can end the processing of FIG. 13.

[0115] The accounting processor 106 may start measuring the shopping time when the customer terminal 31 of the information processing system 110 performs a store entry process or starts product registration. The accounting processor 106 may stop measuring the shopping time after the order is confirmed or the shopping process is completed. The accounting processor 106 may then store the shopping time in the purchase history DB 23.

[0116] According to the third embodiment, the same effects as those of the first and second embodiments can be obtained. Moreover, according to the third embodiment, the output unit 103 outputs the predicted waiting time, which enables the customer to use the waiting time to wander around the store. Furthermore, by having the customer wander around the store, the possibility of purchasing other products increases, and the information processing system 110 can support the improvement of sales at the store.

[0117] By displaying only samples of some products, stores can secure space to display the actual products of other products. Stores can select products that will have an impact on attracting customers as the products to display in person. This allows stores to focus on creating an attractive store. Alternatively, stores may display samples of high-priced products. This is because if high-priced products are stolen, the loss will be greater.

[0118] When the information processing system according to the present disclosure is applied to a supermarket, the store may display samples of grocery items and daily necessities and accept requests for picking and delivery from customers' terminals. This is because such products do not vary from one another. Grocery items include, for example, foods such as seasonings, canned goods, bottled drinks, alcohol, and snacks, as well as daily necessities such as toilet paper and detergent. Daily necessities include items that arrive daily, such as eggs, bread, milk, and frozen foods. The store may also display actual fresh foods, fruits and vegetables, and prepared foods, allowing customers to select the products themselves. This is because displaying the actual products of such products can increase customers' desire to purchase them. Also, there may be individual differences in the products.

[0119] When the information processing system according to the present disclosure is applied to a drug store, the store may display samples of medicines that require explanations on their use and accept requests for collection and delivery from customers' terminals. Empty product boxes may be displayed as samples.

[0120] [Hardware configuration] In each of the above-described embodiments, each component of the information processing systems 100, 110, and 120 is represented as a functional block. A part or all of each component of the information processing systems 100, 110, and 120 may be realized by any combination of a computer 500 and a program.

[0121] Fig. 14 is a block diagram showing an example of a hardware configuration of a computer 500. Referring to Fig. 14, the computer 500 includes, for example, a processor 501, a read only memory (ROM) 502, a random access memory (RAM) 503, a program 504, a storage device 505, a drive device 507, a communication interface 508, an input device 509, an input / output interface 511, and a bus 512.

[0122] The processor 501 controls the entire computer 500. An example of the processor 501 is a CPU (Central Processing Unit). The number of processors 501 is not particularly limited, and the processor 501 may be one or more.

[0123] The program 504 includes instructions for implementing each function of the information processing systems 100, 110, and 120. The program 504 is stored in advance in the ROM 502, the RAM 503, and the storage device 505. The processor 501 implements each function of the information processing systems 100, 110, and 120 by executing the instructions included in the program 504. The RAM 503 may also store data to be processed in each function of the information processing systems 100, 110, and 120.

[0124] The drive device 507 reads and writes data from the recording medium 506. The communication interface 508 provides an interface with a communication network. The input device 509 is, for example, a mouse or a keyboard, and receives information input from a store manager or the like. The output device 510 is, for example, a display, and outputs (displays) information to the store manager or the like. The input / output interface 511 provides an interface with peripheral devices. The bus 512 connects these hardware components. The program 504 may be supplied to the processor 501 via a communication network, or may be stored in advance in the recording medium 506, read by the drive device 507, and supplied to the processor 501.

[0125] It should be noted that the hardware configuration shown in FIG. 14 is merely an example, and other components may be added, or some components may not be included.

[0126] There are various modified examples of the method of realizing the information processing systems 100, 110, and 120. For example, the information processing systems 100, 110, and 120 may be realized by any combination of a different computer and program for each component. Also, multiple components included in the information processing systems 100, 110, and 120 may be realized by any combination of a single computer and program.

[0127] At least a part of the information processing systems 100, 110, and 120 may be provided in a Software as a Service (SaaS) format. That is, at least a part of the functions for realizing the information processing systems 100, 110, and 120 may be executed by software executed via a network.

[0128] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, the configurations in each embodiment can be combined with each other without departing from the scope of the present disclosure.

[0129] A part or all of the above-described embodiments may be described as, but are not limited to, the following supplementary notes.

[0130] [Appendix 1] An acquisition means for acquiring workload information related to a workload for preparing an ordered product, the ordered product being ordered by a customer using a terminal operated by the customer at the store and being collected from stock at the store and delivered to the customer; a waiting time prediction means for predicting a waiting time until delivery of the ordered product to the customer based on the workload information; an output means for outputting the predicted waiting time; An information processing system comprising:

[0131] [Appendix 2] The workload information includes at least one of the number of items, the number of types, the storage location of the inventory, the route around the storage location of the inventory, the size, and the weight of the ordered items. 2. An information processing system according to claim 1.

[0132] [Appendix 3] The output means outputs an advertisement for a service provided at the store based on the predicted waiting time. 3. An information processing system according to claim 1 or 2.

[0133] [Appendix 4] The output means outputs an advertisement for the displayed item in the store based on the predicted waiting time. 4. An information processing system according to any one of claims 1 to 3.

[0134] [Appendix 5] the waiting time prediction means re-predicts the waiting time of the customer every time the workload information related to the ordered product of the customer is acquired; 5. An information processing system according to any one of claims 1 to 4.

[0135] [Appendix 6] a product prediction means for predicting a product to be ordered by the customer based on displayed products registered as products to be purchased by the customer during shopping among displayed products in the store, The acquisition means acquires predicted workload information of the ordered product, The waiting time prediction means predicts the waiting time based on the workload information of the predicted ordered product. 6. An information processing system according to any one of claims 1 to 5.

[0136] [Appendix 7] a shopping time prediction means for predicting a shopping time required for the customer to shop around the store based on information related to the customer's current or past purchasing behavior; and a priority determination means for determining a priority for collecting the ordered products from each of the plurality of customers based on the shopping time, The waiting time prediction means predicts the waiting time based on the workload information and the priority. 7. An information processing system according to any one of claims 1 to 6.

[0137] [Appendix 8] The information related to the past purchasing behavior of the customer includes acquired load information related to the load of the customer acquiring the products that are displayed in the store and that the customer has purchased in the past. 8. An information processing system according to claim 7.

[0138] [Appendix 9] Acquire workload information related to the load of preparing an ordered product, which is ordered by a customer using a terminal operated by the customer at the store and is stocked and delivered at the store for the customer; predicting a waiting time until delivery of the ordered goods to the customer based on the workload information; Output the predicted waiting time Information processing methods.

[0139] [Appendix 10] Acquire workload information related to the load of preparing an ordered product, which is ordered by a customer using a terminal operated by the customer at the store and is stocked and delivered at the store for the customer; predicting a waiting time until delivery of the ordered goods to the customer based on the workload information; Output the predicted waiting time A program that causes a computer to carry out processing. [Explanation of symbols]

[0140] 100, 110, 120 Information Processing Systems 101 Acquisition Department 102 Waiting Time Prediction Unit 103 Output section 104 Product Registration Department 105 Instruction section 106 Accounting Department 107 Migration Time Prediction Unit 108 Priority determination section 109 Product Forecasting Department 21 Product information DB 22 Registered Product Memory Section 23 Purchase history DB 30 Display device 31 Customer terminal 32 Store Display 33 Worker terminal 34 Accounting machine

Claims

1. An acquisition means for acquiring workload information related to a workload for preparing an ordered product, the ordered product being ordered by a customer using a terminal operated by the customer at the store and being collected from stock at the store and delivered to the customer; a waiting time prediction means for predicting a waiting time until delivery of the ordered product to the customer based on the workload information; an output means for outputting the predicted waiting time; An information processing system comprising:

2. The workload information includes at least one of the number of items, the number of types, the storage location of the inventory, the route around the storage location of the inventory, the size, and the weight of the ordered items. The information processing system according to claim 1 .

3. The output means outputs an advertisement for a service provided at the store based on the predicted waiting time. The information processing system according to claim 1 .

4. The output means outputs an advertisement for the displayed item in the store based on the predicted waiting time. The information processing system according to claim 1 .

5. the waiting time prediction means re-predicts the waiting time of the customer every time the workload information related to the ordered product of the customer is acquired; The information processing system according to claim 1 .

6. a product prediction means for predicting a product to be ordered by the customer based on displayed products registered as products to be purchased by the customer during shopping among displayed products in the store, The acquisition means acquires predicted workload information of the ordered product, The waiting time prediction means predicts the waiting time based on the workload information of the predicted ordered product. The information processing system according to claim 1 .

7. a shopping time prediction means for predicting a shopping time required for the customer to shop around the store based on information related to the customer's current or past purchasing behavior; and a priority determination means for determining a priority for collecting the ordered products from each of the plurality of customers based on the shopping time, The waiting time prediction means predicts the waiting time based on the workload information and the priority. The information processing system according to any one of claims 1 to 6.

8. The information related to the past purchasing behavior of the customer includes acquired load information related to the load of the customer acquiring the products that are displayed in the store and that the customer has purchased in the past. The information processing system according to claim 7.

9. Acquire workload information related to the load of preparing an ordered product, which is ordered by a customer using a terminal operated by the customer at the store and is stocked and delivered at the store for the customer; predicting a waiting time until delivery of the ordered goods to the customer based on the workload information; Output the predicted waiting time Information processing methods.

10. Acquire workload information related to the load of preparing an ordered product, which is ordered by a customer using a terminal operated by the customer at the store and is stocked and delivered at the store for the customer; predicting a waiting time until delivery of the ordered goods to the customer based on the workload information; Output the predicted waiting time A program that causes a computer to carry out processing.

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