Information processing program, information processing method, and information processing apparatus

Abstract

To easily evaluate how many commodities come into users' view.SOLUTION: An information processing program causes a computer to execute processing of acquiring first information including information on the arrangement of a plurality of commodities in a store, acquiring second information including information for specifying one or more commodities purchased by a plurality of customers, determining estimated areas where the plurality of customers are estimated to move in the store on the basis of the first information and the second information, and calculating an evaluation value for evaluating how many commodities come into the plurality of users' view on the basis of the first information and the estimated areas.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The embodiments of the present invention relate to an information processing program, an information processing method, and an information processing device. [Background technology]

[0002] If a product displayed in a store is easily visible to customers in the store, the customers may be more likely to purchase the product. In order to promote sales of products in stores, there is a demand to know how much customers look at each product displayed in the store. One technology to meet this demand is a technology that simulates the customer's line of sight and evaluates how much a product is looked at by the customer. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-47660 [Patent Document 2] International Publication No. 2015 / 033577 [Patent Document 3] US Patent Application Publication No. 2015 / 0310447 [Patent Document 4] US Patent Application Publication No. 2006 / 0200378 Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional technology requires equipment and technology to capture customer movement trajectories, making it difficult to simulate customer gaze directions. Therefore, there was a problem in that it was not easy to evaluate how much a product was in the customer's field of view.

[0005] According to one aspect, an object of the present invention is to provide a technology that can easily evaluate to what extent a product is in a customer's field of vision. [Means for solving the problem]

[0006] In one aspect, the information processing program causes a computer to execute the following processes: acquire first information including information regarding the placement of multiple products in a store; acquire second information including information identifying one or more of the products purchased by each of multiple customers; determine an estimated area within the store in which each of the multiple customers is estimated to have moved based on the first information and the second information; and calculate an evaluation value that evaluates how much the product is estimated to have been within the field of view of the multiple customers based on the first information and the estimated area. Effect of the Invention

[0007] According to one aspect, it is possible to easily assess how much of a product is in the customer's field of view. [Brief description of the drawings]

[0008] [Figure 1] 1 is a functional block diagram illustrating a configuration of an information processing device. [Diagram 2] FIG. 2 is a diagram illustrating an example of store information. [Diagram 3] FIG. 2 is a diagram illustrating an example of a product category master. [Figure 4] FIG. 13 is a diagram illustrating an example of a product placement master. [Diagram 5] FIG. 13 is a diagram illustrating an example of a product category arrangement master. [Figure 6] FIG. 2 is a diagram illustrating an example of a customer information master. [Figure 7] FIG. 13 is a diagram illustrating an example of an estimation area. [Figure 8] FIG. 1 is a diagram illustrating an example of a customer movement trajectory. [Figure 9] FIG. 13 is a diagram showing a first specific example of an estimation area. [Figure 10] FIG. 11 is a diagram showing a second specific example of an estimation area. [Figure 11] FIG. 13 is a diagram showing a third specific example of an estimation area. [Figure 12] FIG. 13 is a diagram showing a fourth specific example of an estimation area. [Figure 13] FIG. 11 is a diagram showing a first specific example of weighting. [Figure 14] FIG. 11 is a diagram illustrating an example of a method for calculating weights. [Figure 15] FIG. 11 is a diagram illustrating a second specific example of weighting. [Figure 16] FIG. 13 is a diagram showing a specific example of an evaluation value. [Figure 17] FIG. 11 is a flowchart illustrating an example of an information processing procedure. [Figure 18] FIG. 11 is a flowchart illustrating details of an information processing procedure. [Figure 19] FIG. 2 illustrates an example of a hardware configuration of an information processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an information processing program, an information processing method, and an information processing device according to the embodiments will be described with reference to the accompanying drawings. Note that the disclosed technology is not limited to these embodiments. Each embodiment can be appropriately combined as long as the processing contents are not contradictory.

[0010] 1 is a functional block diagram illustrating a configuration of an information processing device according to the present embodiment. As shown in FIG. 1, the information processing device 1 includes a communication unit 110, an input unit 120, an output unit 130, a storage unit 200, and a control unit 300.

[0011] The communication unit 110 is a processing unit that executes data communication with an external device (not shown) via a network. The communication unit 110 is an example of a communication device. The information processing device 1 may acquire store information and customer information, which will be described later, from the external device. For example, the information processing device 1 may receive POS data from a Point of Sales (POS) device.

[0012] The input unit 120 is an input device for inputting various types of information to the information processing device 1. A user may operate the input unit 120 to input store information and customer information, which will be described later.

[0013] The output unit 130 is an output device that displays information output from the control unit 300 .

[0014] The storage unit 200 is a functional unit that stores various information to be acquired, referenced, etc. in the information processing device 1, including an OS (Operating System) executed by the control unit 300. The storage unit 200 has a store information storage unit 210 and a customer information storage unit 220.

[0015] The store information storage unit 210 stores store information. The store information is information about the layout in the store. The store information includes information about the arrangement of multiple products displayed in the store. The information about the arrangement of products may be information about the arrangement of each product, or information about sections for each product category to which multiple products belong, or may include both. The store information may also include information about the arrangement of one or more entrances in the store, or information about the arrangement of one or more cash registers in the store. Examples of master data of the store information include a product category master 211, a product arrangement master 212, and a product category arrangement master 213, which will be described later. Hereinafter, products and product categories may also be called products.

[0016] Fig. 2 is a diagram for explaining an example of store information. Fig. 2 shows, as an example, a layout 21 of product categories of products arranged in a supermarket. Fig. 2 also shows, as an example, a layout of an entrance and a cash register arranged in the supermarket.

[0017] In Fig. 2, fresh fish, vegetables, fruits, meat, etc. are exemplified as product categories, but the type, number, arrangement, and range of types of products included in the product categories are not limited to these. In Fig. 2, the positions of first entrance 22a, second entrance 22b, third entrance 22c, and fourth entrance 22d are exemplified as store entrances. In Fig. 2, the positions of first register 23a, second register 23b, third register 23c, and fourth register 23d are exemplified as cash registers. The number and arrangement of the entrances and cash registers are not limited to the example in Fig. 2.

[0018] Fig. 3 is a diagram showing an example of a product category master. In the product category master 211, the correspondence between products in a store and product categories is defined. One or more products are included in one product category. Fig. 3 shows one product and four product categories corresponding to each product as an example only, but the products and product categories are not limited to the example shown in the figure.

[0019] In the example of the product category master 211 shown in Fig. 3, the product name "cabbage" is associated with the product category name "vegetables." In other words, the product name "cabbage" is included in the product category of the product category name "vegetables." The same applies to the other product names and product category names shown in Fig. 3, so their explanations are omitted.

[0020] FIG. 4 is a diagram showing an example of product placement master. In the product placement master 212, a correspondence relationship between products in a store and the shelf numbers on which the products are placed is defined. One or more product names are associated with one or more shelf numbers. FIG. 4 shows, as an example only, four selected products and shelf numbers for the shelves (not shown) on which each product is displayed, but the products and shelf numbers are not limited to the example shown. Also, any information related to product placement other than shelf numbers may be used.

[0021] In the example of the product placement master 212 shown in Fig. 4, the product name "cabbage" is associated with the shelf number "11." The other product names and shelf numbers shown in Fig. 4 are associated in the same manner, so their explanations are omitted.

[0022] Fig. 5 is a diagram showing an example of the product category arrangement master. In the product category arrangement master 213, the correspondence relationship between the product category and the arrangement of the product category is defined. In Fig. 5, the bottom left coordinate and the top right coordinate are extracted as the coordinate information of the rectangular area in which each of the four product categories exists, as an example only, but the coordinate information is not limited to the illustrated example. Note that the bottom left coordinate and the top right coordinate in Fig. 5 may correspond to the bottom left and the top right coordinate of the rectangular area of ​​each product category in Fig. 2.

[0023] In the example of the product category location master 213 shown in Fig. 5, the product category "vegetables" is associated with a rectangular area represented by the lower left coordinate (60, 170) and upper right coordinate (120, 210). The same applies to the other product category names and the lower left coordinates and upper right coordinates shown in Fig. 5, so a description thereof will be omitted.

[0024] However, the coordinate information of the product category is not limited to the example of the bottom left coordinate and the top right coordinate, and the expression of the coordinate information may be changed as appropriate as long as the area of ​​the product category can be specified.

[0025] Although not illustrated in the figure, the master data of the store information may include coordinate information regarding the location of the store entrance and cash register.

[0026] The store information storage unit 210 stores, as an example, a product category master 211 shown in FIG. 3, a product arrangement master 212 shown in FIG. 4, and a product category arrangement master 213 shown in FIG.

[0027] The customer information storage unit 220 stores customer information. The customer information is information about customers who have made purchases at a store. The customer information includes information that identifies one or more products purchased by each of the multiple customers. The customer information may also include information about the cash register used by each of the multiple customers, or information about an entrance that each of the multiple customers actually passed through or is presumed to have passed through. Furthermore, the customer information may include, for each of the multiple customers, the purchase order of multiple products purchased by the customer.

[0028] In addition, the purchase order information may be obtained by scanning the purchased items using a shopping cart integrated with a device equipped with a POS function, or a device such as a smartphone carried by the customer, thereby obtaining actual measurement data.

[0029] On the other hand, information on the purchase order may be estimated from information on the entrance through which the customer passed or the register where the customer made the payment, without acquiring actual measurement data. For example, it may be estimated that the customer purchased products in order from the closest to the entrance through which the customer passed. It may also be estimated that the customer purchased products in order from the furthest to the register where the customer made the payment.

[0030] FIG. 6 is a diagram showing an example of a customer information master, which is an example of customer information. In the customer information master 221, information on purchased items purchased by a customer, an entrance through which the customer actually passed or is presumed to have passed when entering the store, and a cash register through which the customer paid for the purchased items are associated with each customer ID that identifies the customer. Note that the entrance through which the customer is presumed to have passed does not have to be associated with the customer ID that identifies the customer. FIG. 6 shows four pieces of information related to purchased items, entrances, and cash registers associated with customer IDs, which are excerpts, but customer information is not limited to the example in FIG. 6.

[0031] 6, it is shown that a customer with a customer ID of "1" purchased "white bread," "500 ml of orange juice," and "lightly salted potato chips," passed through entrance "4," and paid at register "2." For example, information indicating the order of purchase, such as when a customer with a customer ID of "1" purchased "white bread," "500 ml of orange juice," and "lightly salted potato chips," in that order, may be included.

[0032] Information on the products purchased by the customer and the register where the customer paid for the purchased products can be obtained, for example, from POS information, which can be obtained by scanning the purchased products from a register equipped with a POS function, a shopping cart integrated with a device equipped with a POS function, or a device such as a smartphone carried by the customer.

[0033] For example, information on the entrance through which the customer passed may be acquired from a camera installed in the store. Alternatively, information on a security camera installed in the store may be used.

[0034] For example, the information on the entrance through which the customer is presumed to have passed may be randomly determined from a plurality of entrances in the store. When randomly determining the entrance, the entrance may be determined based on past information acquired from the store, etc., so that an entrance through which a large proportion of customers pass may be determined to have a high probability, and an entrance through which a small proportion of customers pass may be determined to have a low probability.

[0035] Information on the entrance through which the customer is presumed to have passed may be presumed from information on the purchased items purchased by the customer. For example, the entrance that is closest to the purchased item that the customer is presumed to have first purchased may be set as the entrance through which the customer is presumed to have passed.

[0036] It should be noted that the information on the entrance through which the customer is presumed to have passed does not necessarily have to match the entrance through which the customer actually passed.

[0037] The customer information storage unit 220 stores, as an example, a customer information master 221 shown in FIG.

[0038] Returning to the explanation of Fig. 1, the control unit 300 is a functional unit that performs overall control of the information processing device 1.

[0039] The control unit 300 includes an acquisition unit 310 , an estimation unit 320 , a specification unit 330 , and a calculation unit 340 .

[0040] The acquisition unit 310 is a processing unit that acquires store information and customer information from an external device (not shown) etc. The acquisition unit 310 stores the acquired store information in the store information storage unit 210, and stores the acquired customer information in the customer information storage unit 220.

[0041] The estimation unit 320 determines an estimated area in which each of a plurality of customers is estimated to have moved within the store, based on the store information and the customer information.

[0042] 7 is a diagram for explaining an example of an estimated area estimated from customer information. Note that a specific method for determining the estimated area will be described later (see FIGS. 9 to 12).

[0043] In addition to the store information shown in Fig. 2, Fig. 7 illustrates multiple purchased items purchased by a single customer and an estimated area through which the single customer is estimated to have moved. Fig. 7 illustrates an estimated area through which a single customer is estimated to have moved when the single customer enters the store from the fourth entrance 22d, purchases multiple purchased items, and pays for the multiple purchased items at the second register 23b.

[0044] The multiple purchased items include a first purchased item 24a, a second purchased item 24b, and a third purchased item 24c. In FIG. 7, as an example, a customer purchases the first purchased item 24a, the second purchased item 24b, and the third purchased item 24c in that order. Note that the purchase order in FIG. 7 may be an actually acquired purchase order, or may be an estimated purchase order based on store information and customer information. The estimated area includes a first estimated area 31a, a second estimated area 31b, a third estimated area 31c, and a fourth estimated area 31d.

[0045] The first estimated area 31a is an example of an estimated area to which a customer is estimated to have moved when the customer moves from the fourth entrance 22d to the position where the first purchased product 24a is located.

[0046] The second estimated area 31b is an example of an estimated area to which a customer is estimated to have moved when the customer moves from the location where the first purchased item 24a is located to the location where the second purchased item 24b is located.

[0047] The third estimated area 31c is an example of an estimated area to which a customer is estimated to have moved when the customer moves from the location where the second purchased product 24b is located to the location where the third purchased product 24c is located.

[0048] The fourth estimated area 31d is an example of an estimated area to which a customer is estimated to have moved when the customer moves from the position where the third purchased product 24c is located to the second register 23b.

[0049] In this way, the estimation unit 320 determines an estimated area in which a customer is estimated to move, including the first estimated area 31a, the second estimated area 31b, the third estimated area 31c, and the fourth estimated area 31d, based on the store information and the customer information.

[0050] The estimated area shown in Fig. 7 includes an estimated area based on information including the entrance where the customer entered and an estimated area based on information including the cash register where the customer paid, but does not have to include these. That is, the estimated area may include only an estimated area based on information of the products purchased by the customer. For example, taking Fig. 7 as an example, the estimated area estimated to have been moved by a certain customer may include only the second estimated area 31b and the third estimated area 31c.

[0051] In other words, the estimation unit 320 may determine the estimation area for each of a plurality of customers based on the total of (i-1) sets of information that identify the (i-1)th product purchased by the customer in the (i-1)th time and the (i)th product purchased in the (i)th time and the store information, where (i) is a natural number that is 2 or more and is equal to or less than the number of products purchased by the customer.

[0052] An example will be given of a portion of the estimated area in FIG. 7. The number of items purchased by a customer is three. The estimation unit 320 calculates an estimated estimated area (second estimated area 31b) from the first item (first purchased item 24a) and the second item (second purchased item 24b) when i is 2. The estimation unit 320 also calculates an estimated estimated area (third estimated area 31c) from the second item (second purchased item 24b) and the third item (third purchased item 24c) when i is 3. The estimation unit 320 determines the estimated area by combining the second estimated area 31b and the third estimated area 31c.

[0053] In addition, in FIG. 7, the customer information of the customer ID "1" in the customer information master shown in FIG. 6 is illustrated so as to correspond to the estimated area estimated from the customer information of the customer ID "1".

[0054] That is, as an example, inlet "4" shown in Figure 6 corresponds to the fourth inlet 22d shown in Figure 7. Register "2" shown in Figure 6 corresponds to the second register 23b shown in Figure 7.

[0055] Also, as an example, the purchased product "bread" shown in Fig. 6 corresponds to the first purchased product 24a shown in Fig. 7. The purchased product "orange juice 500ml" shown in Fig. 6 corresponds to the second purchased product 24b shown in Fig. 7. The purchased product "potato chips light salt flavor" shown in Fig. 6 corresponds to the third purchased product 24c shown in Fig. 7.

[0056] The product "Bread" is included in the product category "Bread". The product "Orange juice 500ml" is included in the product category "Beverage". The product "Potato chips light salt flavor" is included in the product category "Sweets".

[0057] The estimation unit 320 may determine the estimated area shown in FIG. 7 estimated from one piece of customer information for one customer. Note that, similar to the estimated area shown in FIG. 7, the estimation unit 320 may determine the estimated area for another customer estimated from another piece of customer information. For example, the estimation unit 320 may determine the estimated area for each piece of customer information with customer IDs "2," "3," and "4" in the customer information master 221 illustrated in FIG. 6 by estimating it from the customer information. The estimation unit 320 may also determine the estimated area by adding up the estimated areas for each of a plurality of customers.

[0058] Next, a method for determining an estimated area in which a customer is estimated to have moved within a store will be described, and a comparison will be made with the acquisition of a customer's movement trajectory within a store using conventional technology.

[0059] Fig. 8 is a diagram illustrating an example of a customer's movement trajectory within a store. Fig. 8 is an example of a schematic diagram 400 of a part of a store viewed from above, showing a plurality of product shelves 410 on which products are displayed and an aisle 420 between the plurality of product shelves 410. Note that in Fig. 8, the plurality of product shelves 410 are shown in a mesh pattern, and the aisle 420 is shown in white inside the schematic diagram 400. Fig. 8 also illustrates a first purchased product 431 and a second purchased product 432 purchased by one customer.

[0060] Assume that a customer moves through an aisle 420 when moving from a product shelf 410 on which a first purchased product 431 is displayed to a product shelf 410 on which a second purchased product 432 is displayed. In Fig. 8, three movement trajectories 441, 442, and 443 that can be considered as movement trajectories of a customer passing through the aisle 420 are illustrated as examples only.

[0061] Here, the problems of the conventional technology will be described. As described above, there is a problem that it is difficult to accurately acquire the movement trajectory of a customer. In addition, there is a problem that the equipment for acquiring the movement trajectory of a customer needs to be installed in the store, and therefore the equipment becomes large-scale.

[0062] Therefore, in the information processing device 1 of this embodiment, an estimated area in the store where a customer is estimated to move is determined from information on the products purchased by the customer, without acquiring a movement trajectory of the customer. In other words, the estimated area where a customer is estimated to have moved is an area where it can be estimated that there is a certain probability or more that the customer passed through when moving.

[0063] FIG. 9 is a diagram showing a first specific example of an estimated area where a customer is estimated to have moved. FIG. 9 is an example of a schematic diagram 500 of a part of a store seen from above, and is shown by a plurality of product shelves 510 on which products are displayed and an aisle 520 between the plurality of product shelves 510. In FIG. 9, the plurality of product shelves 510 are shown in a mesh pattern, and the aisle 520 is shown in white inside the schematic diagram 500. The product shelves 510 are shown as rectangles as an example. FIG. 9 also shows an example of the positions where a first purchased product 531 and a second purchased product 532 purchased by a customer were placed. As an example, information about the product shelves 510 is stored in the store information storage unit 210, and information about the positions where the first purchased product 531 and the second purchased product 532 were placed is stored in the customer information storage unit 220.

[0064] FIG. 9 also illustrates an estimated area 541 in which a customer is estimated to have moved when the customer moves from the product shelf 510 on which the first purchased product 531 is displayed to the product shelf 510 on which the second purchased product 532 is displayed, as an example, indicated by a diagonally shaded area. As an example, the estimated area 541 may be an area that includes a movement path that does not take a detour when moving from the product shelf 510 on which the first purchased product 531 is displayed to the product shelf 510 on which the second purchased product 532 is displayed. The estimated area 541 may also be rectangular. Furthermore, the rectangular estimated area 541 may be determined with the positions where the first purchased product 531 and the second purchased product 532 were placed as vertices. The estimated area in which the customer is estimated to have moved does not have to be limited to the estimated area 541 illustrated in FIG. 9.

[0065] Fig. 10 is a diagram showing a second specific example of an estimated area where a customer is estimated to have moved. Like Fig. 9, Fig. 10 shows a schematic diagram 500, multiple product shelves 510, multiple aisles 520, and the locations where a first purchased product 531 and a second purchased product 532 were located.

[0066] 10, when a customer moves from a product shelf 510 on which a first purchased product 531 is displayed to a product shelf 510 on which a second purchased product 532 is displayed, an estimated area 542 through which the customer is estimated to have moved is illustrated as an example by a shaded area. The estimated area 542 illustrated in FIG. 10 is different from the estimated area 541 illustrated in FIG. 9.

[0067] The estimated area 542 may extend from the estimated area 541. For example, the area may be determined to include a passageway adjacent to the first purchased item 531 in addition to the estimated area 541. The estimated area 542 may be an area that includes a circuitous travel path. The estimated area 542 may be a polygon. Furthermore, the polygonal estimated area 542 does not have to be determined with the first purchased item 531 and the second purchased item 532 as vertices.

[0068] Fig. 11 is a diagram showing a third specific example of an estimated area where a customer is estimated to have moved. Fig. 11 illustrates a schematic diagram 500, multiple product shelves 510, and multiple aisles 520, similar to Fig. 9. Fig. 11 also illustrates the positions where a first purchased product 533 and a second purchased product 534 purchased by the customer were located, which is different from Fig. 9.

[0069] FIG. 11(a) illustrates an estimated area 543 through which a customer is estimated to have moved when the customer moves from the product shelf 510 on which the first purchased product 533 is displayed to the product shelf 510 on which the second purchased product 534 is displayed, as shown by the shaded area.

[0070] At least one of the multiple sides of the estimated area 543 shown in Fig. 11(a) overlaps with the product shelf 510. In this case, the estimated area 543 may be changed so that one or more sides of the estimated area 543 do not overlap with the product shelf 510. Fig. 11(b) illustrates, as an example, an estimated area 544 after the estimated area 543 is expanded.

[0071] 11(b) does not overlap with the product shelf 510. Note that the estimated area 543 may be changed so that none of the multiple sides of the estimated area 544 overlap with the product shelf 510, or the estimated area 543 may be changed so that at least one or more sides do not overlap with the product shelf 510. The change of the estimated area 543 may include reducing the area.

[0072] FIG. 12 is a diagram showing a fourth specific example of an estimated area where a customer is estimated to have moved. FIG. 12 is an example of a schematic diagram 600 of a part of a store seen from above, and is shown by a plurality of product shelves 610 on which products are displayed and an aisle 620 between the plurality of product shelves 610. The product shelves 610 are shown as circles as an example. FIG. 12 also shows the positions where a first purchased product 631 and a second purchased product 632 purchased by a customer were placed. As an example, information about the product shelves 610 is stored in the store information storage unit 210, and information about the positions where the first purchased product 631 and the second purchased product 632 were placed is stored in the customer information storage unit 220.

[0073] 12, when a customer moves from a product shelf 610 on which a first purchased product 631 is displayed to a product shelf 610 on which a second purchased product 632 is displayed, an estimated area 641 through which the customer is estimated to have moved is illustrated as an example by a shaded area. Unlike the estimated area 541 illustrated in FIG. 9, the estimated area 641 illustrated in FIG. 12 does not have to be a polygon. For example, the estimated area 641 may be an ellipse, or may be any shape as long as it represents an estimated area through which it is estimated that the customer may pass.

[0074] Returning to the explanation of FIG. 1, the identification unit 330 identifies, from the store information, specific information included in the estimation area determined by the estimation unit 320. The specific information is information about products included in the estimation area. The information about products may be product information or product category information. Furthermore, the specific information may be product category information excluding the product category of the purchased product purchased by the customer. This makes it possible to evaluate whether products other than the product category of the purchased product have visibility to the customer.

[0075] An example of the specific information will be described with reference to Fig. 7. Here, the specific information will be described as product category information excluding the product category of the product purchased by the customer.

[0076] The product category included in the first estimated area 31a is "bread". The product categories included in the second estimated area 31b are "bread", "prepared dishes", "lunch boxes", "frozen foods" and "drinks". The product categories included in the third estimated area 31c are "drinks" and "sweets". The product categories included in the fourth estimated area 31d are "sweets", "alcoholic beverages" and "instant foods".

[0077] On the other hand, the product category of a first purchased product 24a purchased by one customer is "bread", the product category of a second purchased product 24b is "drinks", and the product category of a third purchased product 24c is "confectionery".

[0078] In this case, the identifying unit 330 identifies identifying information indicating the product categories excluding the product category of the purchased product, namely "prepared dishes," "lunch boxes," "frozen foods," "alcoholic beverages," and "instant foods."

[0079] Note that the specific information can be similarly specified not only for one customer but also for each of the other multiple customers. Furthermore, the specific information may include the specific information of the one customer and the specific information of the other multiple customers.

[0080] Returning to the explanation of FIG. 1, the calculation unit 340 calculates an evaluation value that evaluates how likely it is that a product has entered the field of view of multiple customers, based on the store information and the estimated area. To explain in more detail, the calculation unit 340 estimates that a product that has entered the estimated area for one customer is likely to have entered the field of view of the one customer. Similarly, the calculation unit 340 estimates that a product that has entered the estimated area for another customer is likely to have entered the field of view of the other customer. The calculation unit 340 similarly estimates for multiple customers and calculates the evaluation value.

[0081] The calculation unit 340 may calculate the evaluation value for all products placed in the estimated area of ​​one customer, assuming that the degree to which the products entered the field of view of the one customer is the same. The calculation unit 340 may also calculate the sum of the evaluation values ​​for all products placed in the estimated areas of each of multiple customers, assuming that the degree to which the products entered the field of view of each of multiple customers is the same. On the other hand, the calculation unit 340 may set weights in each estimated area and calculate the evaluation value so that the degree to which the products entered the field of view of the customers in the estimated area differs. For example, weights may be set in the estimated area according to the distance from the coordinates of the product purchased by the customer.

[0082] Fig. 13 is a diagram for explaining a first specific example of weighting. Fig. 13 illustrates the positions where a first purchased product 721 and a second purchased product 722 purchased by one customer were placed. Also, an example of weighting of an estimated area 711 that is estimated to have been moved by the one customer when the customer moves from the position where the first purchased product 721 was placed to the position where the second purchased product 722 was placed is shown. The estimated area 711 is shown in a darker color as the weight increases, and in a lighter color as the weight decreases.

[0083] As shown in Fig. 13, weighting can be set for the estimated area 711 such that the weight increases as the area approaches the coordinates of the product purchased by the customer. In Fig. 13, the estimated area 711 is divided into 8 vertically and 12 horizontally, and weights are set stepwise for each area, but weights may be set continuously without dividing the area. The calculation unit 340 calculates the evaluation value such that the greater the weight is set, the higher the degree to which the product is in the customer's field of view.

[0084] Fig. 14 is a diagram for explaining an example of a method for calculating weights. Fig. 14 illustrates the positions where a first purchased product 723 and a second purchased product 724 purchased by one customer were placed. Also illustrated is an estimated area 712 that is estimated to have been moved by the one customer when the customer moves from the position where the first purchased product 723 was placed to the position where the second purchased product 724 was placed. In Fig. 14, the estimated area 712 is illustrated as a rectangle.

[0085] As an example, the estimated area 712 is a rectangular area with the position where the first purchased product 723 was placed as the lower right coordinate and the position where the second purchased product 724 was placed as the upper left coordinate. As an example, the estimated area 712 is a rectangular area having as its sides a straight line extending upward by y from the position where the first purchased product 723 was placed and a straight line extending to the left by x. Here, the left side is the x direction and the upward side is the y direction. Note that, as an example, x is the value obtained by subtracting the x coordinate of the first purchased product 723 from the x coordinate of the second purchased product 724. Also, as an example, y is the value obtained by subtracting the y coordinate of the first purchased product 723 from the y coordinate of the second purchased product 724.

[0086] Here, when the position of the product 731 placed in the store is px in the x direction from the x coordinate of the first purchased product 723 and py in the y direction from the y coordinate of the first purchased product 723, the weighted evaluation value is calculated, for example, by equation (1). TIFF2025079027000002.tif15142

[0087] The weighted evaluation value is not limited to the formula (1). For example, any weight may be used as long as it can be set so that the weight increases as the coordinates of the product purchased by the customer approach.

[0088] Fig. 15 is a diagram for explaining a second specific example of weighting. Fig. 15 shows an example in which weighting is set for an estimated area 713 where a customer is estimated to have moved, using data related to the customer.

[0089] For example, the calculation unit 340 sets the weighting so that the weighting increases as the frequency of a customer passing through the estimated area 713 increases based on past data. The frequency of a customer passing through may be calculated based on image data captured by a camera installed in the store, for example. In addition, when setting the weighting, the calculation unit 340 does not need to detect the customer's passing motion, and may set the weighting so that the weighting increases as the time the customer was present in each certain section increases from the time the customer was present.

[0090] Fig. 16 is a diagram illustrating a specific example of an evaluation value that evaluates how often a product is considered to be in the field of view of multiple customers. Fig. 16 illustrates evaluation values ​​calculated by the calculation unit 340 for each product category. The evaluation value for the product category "meat" is the lowest at "120," and the evaluation value for the product category "bread" is the highest at "987." In other words, it is estimated that the product category "meat" is frequently in the field of view of customers, and the product category "bread" is infrequently in the field of view of customers.

[0091] By referring to the evaluation value shown in FIG. 16, the calculation unit 340 can evaluate to what extent a product is considered to be within the field of view of multiple customers, based on the store information and customer information acquired by the acquisition unit 310.

[0092] In addition, the acquisition unit 310 acquires new store information assuming a layout change, and the user can know the change in evaluation value calculated by the calculation unit 340 based on the same customer information as the customer information acquired before the layout change and the store information assuming the layout change.

[0093] FIG. 17 is a flowchart showing an example of a processing procedure of the information processing device 1 according to the present embodiment. As shown in FIG. 17, the acquisition unit 310 acquires store information (step S1). Next, the acquisition unit 310 acquires customer information (step S2). Then, the estimation unit 320, the identification unit 330, and the calculation unit 340 execute information processing (step S3). Details of the information processing will be described later (see FIG. 18). Finally, the output unit 130 outputs a calculation result of an evaluation value that evaluates how much a product is considered to be in the field of view of multiple customers (step S4).

[0094] Fig. 18 is a flowchart showing an example of details of information processing. As shown in Fig. 18, the estimation unit 320 sets the natural number n for identifying a customer to n=1. The estimation unit 320 also sets the natural number for identifying each product to i, and initializes the number of sightings Vi for each product i to 0 (step S11). Each product may be a product category. The number of sightings Vi may be an evaluation value that evaluates how often a product is considered to be in the field of view of multiple customers.

[0095] Next, the estimation unit 320 places customer n in the simulation at an initial position in the store where customer n was originally located or is estimated to have originally been located. The initial position is, for example, an entrance where the customer passed or is estimated to have passed. The initial position may be the position of the product that the customer first purchased or is estimated to have purchased. Furthermore, the estimation unit 320 sets m=1 for a natural number m that identifies the product purchased by the customer. In addition, the estimation unit 320 initializes the visual product set S=Φ (Φ is an empty set) (step S12). Here, the visual product set S is a set of products that are assumed to have come into the customer's field of vision.

[0096] Next, the estimation unit 320 moves the customer to the location of product m and adds product j within the estimated area where the customer is estimated to have moved to the visually observed products (S = S ∪ {j}) (step S13).

[0097] Then, the estimation unit 320 sets the number of products purchased by customer n as M, and if m < M (step S14 Yes), it sets m = m + 1. That is, the estimation unit 320 adds 1 to m (step S15) and executes the process of step S13.

[0098] On the other hand, if m < M is not satisfied (step S14 No), the estimation unit 320 moves the customer to the cashier and adds product j within the estimated area where the customer is estimated to have moved to the visually observed products (S = S ∪ {j}). Also, the estimation unit 320 adds to the visual count Vi (Vi = Vi + 1 for j ∈ S) (step S16). Here, 1 is added to the visual count Vi, but for example, when weighting is performed within the estimated area as shown in FIG. 13, the value added may be changed according to the position within the estimated area.

[0099] Then, the estimation unit 320 sets the total number of customers as N, and if n < N (step S17 Yes), it sets n = n + 1. That is, the estimation unit 320 adds 1 to n (step S18) and executes the process of step S13.

[0100] On the other hand, if n < N is not satisfied (step S17 No), the estimation unit 320 ends the process.

[0101] Here, the effects of the information processing described above will be explained.

[0102] In this embodiment, the information processing device 1 determines an estimated area in the store where each of the multiple customers is estimated to have moved, based on information about the arrangement of multiple products in the store and information identifying one or more products purchased by each of the multiple customers. In other words, the information processing device 1 determines an estimated area in which each of the multiple customers is estimated to have moved, without requiring any equipment or technology for acquiring the movement trajectory of the customers. This allows, for example, a user to easily evaluate how much a product was in the customer's field of view using the information processing device 1.

[0103] Additionally, the user can easily consider changing the layout within the store.

[0104] For example, the information processing device 1 acquires store information related to the current store layout and customer information in the current store layout. After that, the information processing device 1 determines an estimated area in which the customer is estimated to have moved, and calculates a first evaluation value that evaluates how much the product is in the customer's field of view.

[0105] Next, the information processing device 1 acquires store information related to a tentative store layout to be changed and customer information in the current store layout. After that, the information processing device 1 determines an estimated area in which the customer is estimated to move, and calculates a second evaluation value that evaluates how much the product is in the customer's field of view.

[0106] The user compares the first evaluation value with the second evaluation value, and if, for example, the second evaluation value is higher for a product that the user wants to have more in the customer's field of vision, the user can easily determine that the provisional store layout they are considering changing is better.

[0107] In this way, the user can easily grasp the difference in evaluation of the visibility of products for each store layout using the information processing device 1 without having to change the layout in the actual store.

[0108] In this embodiment, the information processing device 1 can also consider the purchase order of multiple products purchased by each of multiple customers. The purchase order may be estimated based on at least one of the store entrances or cash registers used by the customer. This allows the information processing device 1 to improve the accuracy of determining the estimated area in which each customer is estimated to have moved.

[0109] In this embodiment, the information processing device 1 may determine an estimated area for each of a plurality of customers based on the total of i-1 sets of information that identify the i-1th product purchased by the customer at the i-1st time and the i-th product purchased at the i-th time and on the store information. Here, i is a natural number that is 2 or more and is less than or equal to the number of products purchased by the customer. This allows the information processing device 1 to improve the accuracy of determining the estimated area in which each customer is estimated to have moved.

[0110] In this embodiment, the information processing device 1 can easily determine the estimated area by determining, as the estimated area, a rectangular area having at least one vertex that is the coordinates of the product purchased by the customer.

[0111] In this embodiment, the information processing device 1 can determine an estimated area that includes at least one route that can be traveled from the coordinates of one product purchased by a customer to the coordinates of another product purchased by the customer. This allows the information processing device 1 to improve the accuracy of determining the estimated area in which each customer is estimated to have moved.

[0112] In this embodiment, the information processing device 1 can set a weighting for the estimated area, and can evaluate how much of the product is in the customer's field of view based on the weighted estimated area. For example, the information processing device 1 can set the weighting so that the closer it is to the coordinates of the product purchased by the customer within the estimated area, the larger the weighting becomes. This allows the information processing device 1 to improve the accuracy of the evaluation.

[0113] 19 is a diagram showing an example of a hardware configuration of an information processing device 1 according to this embodiment. The information processing device 1 is an information processing device including, for example, a CPU (Central Processing Unit) 82, a memory 83, a storage device 84, a communication device 85, a medium reading device 86, an input device 87, and an output device 88, which are interconnected by a bus 81.

[0114] The CPU 82 performs various operational controls in the information processing device 1. The CPU 82 may read out a program stored in the memory 83 or the storage device 84, and execute processing and control to realize the control unit 300 and each functional unit included in the control unit 300 shown in FIG. 1. The control unit 300 may be implemented by a hardware processor such as an MPU (Micro Processing Unit) in addition to the CPU. Here, a CPU or an MPU is exemplified as an example of a processor, but the control unit 300 may be implemented by any processor, whether general-purpose or specialized. In addition, the control unit 300 may be realized by hardwired logic such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0115] The memory 83 and the storage device 84 store programs for executing the various processes described in this embodiment and various data used in the various processes. The memory 83 is, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory. The storage device 84 is, for example, a storage medium such as a hard disk drive (HDD) or a solid state drive (SSD). Each of the memory 83 and the storage device 84 can function as the storage unit 200 described in FIG. 1.

[0116] The communication device 85 is hardware used for transmitting and receiving data via a wired or wireless network. The communication device 85 is, for example, a network interface card (NIC). The communication device 85 can function as the communication unit 110 shown in FIG. 1 under the control of the CPU 82.

[0117] The medium reading device 86 is a device for reading data from a recording medium. The medium reading device 86 is, for example, a disk drive that reads data stored in a disk medium such as a CD-ROM (Compact Disc Read Only Memory) or a DVD-ROM (Digital Versatile Disc Read Only Memory), or a card slot that reads data stored in a memory card. A part or all of the data stored in the storage unit 200 described above may be stored in a recording medium that can be read using the medium reading device 86.

[0118] The input device 87 is a device that accepts inputs and specifications from a user of the information processing device 1. The input device 87 is, for example, a keyboard, a mouse, a touch pad, etc. The input device can function as the input unit 120 shown in FIG.

[0119] The output device 88 is a device that displays information output from the control unit 300 under the control of the CPU 82. The output device 88 is, for example, a touch panel, a liquid crystal display, an organic EL (Electro-Luminescence) display, etc. The output device can function as the output unit 130 shown in FIG.

[0120] Although the disclosed embodiments and their advantages have been described in detail, it will be appreciated that those skilled in the art may make various modifications, additions and omissions therein without departing from the scope of the invention as clearly set forth in the following claims.

[0121] The following supplementary notes are further disclosed regarding the embodiment described with reference to FIGS. (Appendix 1) On the computer, Acquire first information including information regarding the arrangement of a plurality of products in a store; acquiring second information including information identifying one or more of the products purchased by each of a plurality of customers; determining an estimated area in which each of the plurality of customers is estimated to have moved within the store based on the first information and the second information; calculating an evaluation value for evaluating how much the product is considered to be within the field of view of the plurality of customers based on the first information and the estimated area; An information processing program that causes a process to be executed. (Appendix 2) The first information further includes at least one of information regarding the arrangement of one or more cash registers in the store and information regarding the arrangement of one or more entrances in the store; The second information further includes at least one of information regarding a cash register used by each of the plurality of customers among the cash registers and information regarding an entrance through which each of the plurality of customers has passed or is presumed to have passed among the entrances; The computer includes: estimating, for each of the plurality of customers, a purchase order of the plurality of products purchased by the customer based on the first information and the second information; determining the estimated region based on the first information, the second information, and the estimated purchase order; 2. The information processing program according to claim 1, which causes a process to be executed. (Appendix 3) The computer includes: Acquire a purchase order of the plurality of products purchased by each of the plurality of customers; determining the estimated area based on the first information, the second information, and the acquired purchase order; 2. The information processing program according to claim 1, which causes a process to be executed. (Appendix 4) The computer includes: For each of the multiple customers, for a set of information identifying an (i-1)th product (i is a natural number that is 2 or more and is equal to or less than the number of the products purchased by the customer) purchased by the customer at the (i-1)th time and information identifying an (i)th product purchased at the (i)th time, among the multiple products purchased by the customer, the set of information identifying a total of (i-1) products and the first information is used to determine the estimated area; 4. The information processing program according to claim 2 or 3, which causes a process to be executed. (Appendix 5) The estimated area is a rectangular area having at least one vertex corresponding to the coordinates of the product purchased by the customer. 2. The information processing program according to claim 1, (Appendix 6) The estimated area is an area including at least one route that can be traveled from the coordinates of one of the products purchased by the customer to the coordinates of another of the products purchased by the customer. 2. The information processing program according to claim 1, (Appendix 7) The computer includes: A weight is set for the estimated area according to a distance from the coordinates of the product purchased by the customer; calculating the evaluation value based on the estimated region to which the weighting is set; 4. The information processing program according to any one of claims 1 to 3, which causes a process to be executed. (Appendix 8) The computer includes: A weight is set so that the closer the estimated area is to the coordinates of the product purchased by the customer, the greater the weight is. calculating the evaluation value based on the estimated region to which the weighting is set; 4. The information processing program according to any one of claims 1 to 3, which causes a process to be executed. (Appendix 9) On the computer, Acquire first information including information regarding the arrangement of a plurality of products in a store; acquiring second information including information identifying one or more of the products purchased by each of a plurality of customers; determining an estimated area in which each of the plurality of customers is estimated to have moved within the store based on the first information and the second information; calculating an evaluation value for evaluating how much the product is considered to be within the field of view of the plurality of customers based on the first information and the estimated area; 2. An information processing method comprising: (Appendix 10) The first information further includes at least one of information regarding the arrangement of one or more cash registers in the store and information regarding the arrangement of one or more entrances in the store; The second information further includes at least one of information regarding a cash register used by each of the plurality of customers among the cash registers and information regarding an entrance through which each of the plurality of customers has passed or is presumed to have passed among the entrances; The computer includes: estimating, for each of the plurality of customers, a purchase order of the plurality of products purchased by the customer based on the first information and the second information; determining the estimated region based on the first information, the second information, and the estimated purchase order; 10. The information processing method according to claim 9, further comprising executing a process. (Appendix 11) The computer includes: Acquire a purchase order of the plurality of products purchased by each of the plurality of customers; determining the estimated area based on the first information, the second information, and the acquired purchase order; 10. The information processing method according to claim 9, further comprising executing a process. (Appendix 12) The computer includes: For each of the multiple customers, for a set of information identifying an (i-1)th product (i is a natural number that is 2 or more and is equal to or less than the number of the products purchased by the customer) purchased by the customer at the (i-1)th time and information identifying an (i)th product purchased at the (i)th time, among the multiple products purchased by the customer, the set of information identifying a total of (i-1) products and the first information is used to determine the estimated area; 12. The information processing method according to claim 10 or 11, further comprising executing a process. (Appendix 13) The estimated area is a rectangular area having at least one vertex corresponding to the coordinates of the product purchased by the customer. 10. The information processing method according to claim 9, (Appendix 14) The estimated area is an area including at least one route that can be traveled from the coordinates of one of the products purchased by the customer to the coordinates of another of the products purchased by the customer. 10. The information processing method according to claim 9, (Appendix 15) The computer includes: A weight is set for the estimated area according to a distance from the coordinates of the product purchased by the customer; calculating the evaluation value based on the estimated region to which the weighting is set; 12. The information processing method according to any one of claims 9 to 11, further comprising executing a process. (Appendix 16) The computer includes: A weight is set so that the closer the estimated area is to the coordinates of the product purchased by the customer, the greater the weight is. calculating the evaluation value based on the estimated region to which the weighting is set; 12. The information processing method according to any one of claims 9 to 11, further comprising executing a process. (Appendix 17) an acquisition unit that acquires first information including information regarding the placement of a plurality of products in a store and second information including information identifying one or more of the products purchased by each of a plurality of customers; an estimation unit that determines an estimated area in the store in which each of the plurality of customers is estimated to have moved based on the first information and the second information; a calculation unit that calculates an evaluation value that evaluates how much the product is considered to be within the field of view of the multiple customers based on the first information and the estimated area; An information processing device comprising: (Appendix 18) The first information further includes at least one of information regarding the arrangement of one or more cash registers in the store and information regarding the arrangement of one or more entrances in the store; The second information further includes at least one of information regarding a cash register used by each of the plurality of customers among the cash registers and information regarding an entrance through which each of the plurality of customers has passed or is presumed to have passed among the entrances; the estimation unit estimates, for each of the plurality of customers, a purchase order of the plurality of products purchased by the customer based on the first information and the second information; The estimation unit determines the estimated area based on the first information, the second information, and the estimated purchase order. 18. The information processing device according to claim 17. (Appendix 19) The acquisition unit acquires, for each of the plurality of customers, a purchase order of the plurality of products purchased by the customer; The estimation unit determines the estimation area based on the first information, the second information, and the acquired purchase order. 18. The information processing device according to claim 17. (Appendix 20) The estimation unit determines the estimation area for each of the multiple customers based on a set of information specifying an i-1th product purchased by the customer at the i-1th order (i is a natural number that is 2 or more and is equal to or less than the number of the products purchased by the customer) and information specifying an i-th product purchased at the i-th order, based on information of a total of i-1 sets of information and the first information. 20. The information processing device according to claim 18 or 19. (Appendix 21) The estimated area is a rectangular area having at least one vertex corresponding to the coordinates of the product purchased by the customer. 18. The information processing device according to claim 17. (Appendix 22) The estimated area is an area including at least one route that can be traveled from the coordinates of one of the products purchased by the customer to the coordinates of another of the products purchased by the customer. 18. The information processing device according to claim 17. (Appendix 23) the calculation unit sets a weight for the estimated area in accordance with a distance from a coordinate of the product purchased by the customer, and calculates the evaluation value based on the estimated area to which the weight is set. 20. The information processing device according to any one of claims 17 to 19. (Appendix 24) the calculation unit sets a weighting for the estimated area so that the weighting increases as the estimated area approaches a coordinate of the product purchased by the customer, and calculates the evaluation value based on the estimated area to which the weighting is set. 20. The information processing device according to any one of claims 17 to 19. [Explanation of symbols]

[0122] 1. Information processing device 110 Communications Department 120 Input section 130 Output section 200 Storage section 210 Store information storage unit 220 Customer information storage unit 300 Control section 310 Acquisition Department 320 Estimation Department 330 Specific section 340 Calculation Department

Claims

1. On the computer, Acquire first information including information regarding the arrangement of a plurality of products in a store; acquiring second information including information identifying one or more of the products purchased by each of the plurality of customers; determining an estimated area in which each of the plurality of customers is estimated to have moved within the store based on the first information and the second information; calculating an evaluation value for evaluating how much the product is considered to be within the field of view of the plurality of customers based on the first information and the estimated area; An information processing program that causes a process to be executed.

2. The first information further includes at least one of information regarding the arrangement of one or more cash registers in the store and information regarding the arrangement of one or more entrances in the store; The second information further includes at least one of information regarding a cash register used by each of the plurality of customers among the cash registers and information regarding an entrance through which each of the plurality of customers has passed or is presumed to have passed among the entrances; The computer includes: estimating, for each of the plurality of customers, a purchase order of the plurality of products purchased by the customer based on the first information and the second information; determining the estimated region based on the first information, the second information, and the estimated purchase order; 2. The information processing program according to claim 1, further comprising: a program for executing a process.

3. The computer includes: Acquire a purchase order of the plurality of products purchased by each of the plurality of customers; determining the estimated area based on the first information, the second information, and the acquired purchase order; 2. The information processing program according to claim 1, further comprising: a program for executing a process.

4. The computer includes: For each of the plurality of customers, a set of information specifying an i-1th product purchased by the customer in the i-1th order (i is a natural number that is 2 or more and is equal to or less than the number of the products purchased by the customer) and information specifying an i-th product purchased in the i-th order among the plurality of products purchased by the customer is determined based on the information of a total of i-1 sets of information and the first information; 4. The information processing program according to claim 2, further comprising: a program for executing a process.

5. The estimated area is a rectangular area having at least one vertex corresponding to the coordinates of the product purchased by the customer.

2. The information processing program according to claim 1,

6. The estimated area is an area including at least one route along which the customer can move from the coordinates of one of the products purchased by the customer to the coordinates of another of the products purchased by the customer.

2. The information processing program according to claim 1,

7. The computer includes: A weight is set for the estimated area according to a distance from the coordinates of the product purchased by the customer; calculating the evaluation value based on the estimated region to which the weighting is set; 4. The information processing program according to claim 1, which causes a process to be executed.

8. The computer includes: A weight is set so that the closer the estimated area is to the coordinates of the product purchased by the customer, the greater the weight is. calculating the evaluation value based on the estimated region to which the weighting is set; 4. The information processing program according to claim 1, which causes a process to be executed.

9. On the computer, Acquire first information including information regarding the arrangement of a plurality of products in a store; acquiring second information including information identifying one or more of the products purchased by each of the plurality of customers; determining an estimated area in which each of the plurality of customers is estimated to have moved within the store based on the first information and the second information; calculating an evaluation value for evaluating how much the product is considered to be within the field of view of the plurality of customers based on the first information and the estimated area; 2. An information processing method comprising:

10. an acquisition unit that acquires first information including information regarding the placement of a plurality of products in a store and second information including information identifying one or more of the products purchased by each of a plurality of customers; an estimation unit that determines an estimated area in the store in which each of the plurality of customers is estimated to have moved based on the first information and the second information; a calculation unit that calculates an evaluation value that evaluates how much the product is considered to be within the field of view of the multiple customers based on the first information and the estimated area; An information processing device comprising:

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