Recommended equipment, methods, and programs for the product.

Abstract

To provide a merchandise recommendation device that allows a customer to easily find merchandise suitable for the customer from among the merchandise sold in a store.SOLUTION: A merchandise recommendation device comprises an identification unit, an acquisition unit, a specification unit, and an output unit. The identification unit identifies a customer who visits a store. The acquisition unit acquires information of owned merchandise owned by the identified customer. The specification unit specifies, from among selling merchandise sold in the store, selling merchandise in which a combination of wearing feeling when the selling merchandise and the owned merchandise are worn in combination matches a combination condition of the wearing feeling favored by the customer. The output unit outputs information of the identified selling merchandise as recommendation merchandise information.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a product recommendation device and the like.

Background Art

[0002] In shopping for clothing and accessories, there is a technology for delivering content and the like to customers in order to induce them to purchase products. For example, in the technology disclosed in Patent Document 1, the time when a consumer uses an accessory or clothing taken in hand by a consumer located in front of a product display shelf is displayed. Also, for example, in the technology disclosed in Patent Document 1, combination or coordination information of a product taken in hand by a consumer and other products is displayed. Also, for example, in the technology disclosed in Patent Document 2, personal color is used to recommend products to customers.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in actual shopping for clothing and accessories in a store, customers may not be able to easily find products suitable for themselves. For example, in Patent Document 1, although the state of using a product or coordination information and the like are displayed to customers, it is difficult for customers to determine which of the multiple products is actually suitable for themselves.

[0005] An example of the object of the present disclosure is to provide a product recommendation device and the like that facilitate customers to find products suitable for them from the products sold in the store.

Means for Solving the Problems

[0006] A product recommendation device in one aspect of the present disclosure includes: identification means for identifying a customer who has come to a store; acquisition means for acquiring information on owned products owned by the identified customer; identification means for identifying products sold in the store that, based on the acquired information on owned products, are in combination with the owned products and meet predetermined combination conditions; and output means for outputting information on the identified products as recommended product information for the customer to wear, wherein the predetermined combination conditions include combination conditions for the customer's preferred wearing comfort, and the identification means identifies products sold in the store that, when worn together with the owned products, result in a combination of wearing comfort that matches the customer's preferred wearing comfort conditions.

[0007] A method in one aspect of the present disclosure involves a computer performing a process in which it identifies a customer who has come to a store, obtains information about the goods owned by the identified customer, identifies a product sold in the store that matches a predetermined combination condition when combined with the owned goods based on the obtained information about the owned goods, and outputs the information about the identified product as recommended product information for the customer to wear, wherein the predetermined combination condition includes a combination condition for the customer's preferred fit, and in the identifying process, the computer identifies a product sold in the store whose combination of the product and the owned goods, when worn together, matches the combination condition for the customer's preferred fit.

[0008] A program in one aspect of this disclosure causes a computer to perform the following processes: identify a customer who has come to a store; obtain information about the goods owned by the identified customer; identify a product sold in the store that, based on the information about the owned goods, matches a predetermined combination condition with the owned goods; and output the information about the identified product as recommended product information for the customer to wear. The predetermined combination condition includes a combination condition for the customer's preferred fit, and in the identifying process, the program identifies a product sold in the store that, when worn together with the owned goods, matches a combination condition for the customer's preferred fit. [Effects of the Invention]

[0009] According to this disclosure, it is possible to make it easier for customers to find products that are suitable for them from among the products sold in stores. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a block diagram showing an example configuration of a product recommendation device according to Embodiment 1. [Figure 2] Figure 2 is a flowchart showing an example of operation of the product recommendation device according to Embodiment 1. [Figure 3] Figure 3 is an explanatory diagram showing an example configuration of the system according to Embodiment 2. [Figure 4] Figure 4 is a block diagram showing an example configuration of the system according to Embodiment 2. [Figure 5] Figure 5 is an explanatory diagram showing an example of a customer identification database. [Figure 6] Figure 6 is an explanatory diagram showing an example of a database of owned products. [Figure 7] Figure 7 is an explanatory diagram showing an example of a sales product database. [Figure 8] Figure 8 is an explanatory diagram showing an example of a combination condition. [Figure 9]FIG. 9 is an explanatory diagram showing an example from combination to output. [Figure 10] FIG. 10 is an explanatory diagram showing an output example in ascending order of total amount. [Figure 11] FIG. 11 is a flowchart showing an operation example of the product recommendation device according to Embodiment 2. [Figure 12] FIG. 12 is a flowchart showing an example of the specific process in FIG. 11. [Figure 13] FIG. 13 is a flowchart showing an example of the output process in FIG. 11. [Figure 14] FIG. 14 is an explanatory diagram showing an example of the hardware configuration of the system.

Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments of the product recommendation device, information processing method, and program according to the present disclosure will be described in detail with reference to the drawings. This embodiment does not limit the disclosed technology.

[0012] (Embodiment 1) FIG. 1 is a block diagram showing a configuration example of the product recommendation device according to Embodiment 1. The product recommendation device 10 recommends sales products sold in the store to customers who visit the store. Here, the store is not particularly limited. For example, the store is a store that sells clothing. Also, the store may be a commercial facility that is, for example, a collection of a plurality of stores.

[0013] The product recommendation device 10 recommends sales products to customers who visit the store. In FIG. 1, the product recommendation device 10 includes an identification unit 101, an acquisition unit 102, a specification unit 103, and an output unit 104.

[0014] The identification unit 101 identifies customers who visit the store. The identification method is not particularly limited. The identification unit 101 may identify a customer by performing biometric authentication when the customer visits the store. The types of biometric authentication are not particularly limited, such as face, fingerprint, vein, iris, voiceprint, etc. Specifically, for example, the identification unit 101 may perform biometric authentication near the entrance of the store for a customer who visits the store. Or, for example, when the store interior can be constantly imaged by an imaging device, the imaging device images a customer who visits the store. Then, the identification unit 101 may extract the face data of the customer from the image. The face data is, for example, the image data of the customer's face part or the feature amount of the customer's face part. And the identification unit 101 may match the extracted face data with the face data stored in the storage unit in advance. Thereby, the identification unit 101 can identify the customer without the customer performing a new operation or the like. Also, the identification unit 101 may identify a customer by reading a point card with a predetermined device when the customer visits the store. Or, the identification unit 101 may identify a customer by reading, with a predetermined device, information unique to each customer displayed on the screen of the display device of the customer's terminal device when the customer visits the store. Also, the identification unit 101 may identify a customer based on information from the terminal device by wirelessly communicating with the customer's terminal device using a beacon device or the like.

[0015] The acquisition unit 102 acquires information about owned goods owned by the identified customer. Owned goods are goods that the customer has purchased in the past or goods that the customer has received as a gift in the past. There are no particular limitations on where the customer owns the owned goods. Hereafter, the information about owned goods will be referred to as owned goods information. The owned goods information is stored in advance in a storage unit accessible by the product recommendation device 10, for example. The storage unit may be a storage unit connected to the internal bus of the product recommendation device 10, or a storage unit of a device accessible by the product recommendation device 10 via a network. The owned goods information may be registered in advance by the customer in a storage unit or on a terminal device owned by the customer, for example. The owned goods information may also be information about owned goods that have been sold within a predetermined period of time. Furthermore, the owned goods information may also be information about goods similar to those owned by the customer.

[0016] The identification unit 103 identifies products sold in the store that match predetermined combination conditions when combined with the owned products, based on the owned product information. The predetermined combination conditions are not particularly limited. For example, details of the predetermined combination conditions will be described in Embodiment 2.

[0017] The output unit 104 outputs information about products for sale identified based on predetermined combination conditions as recommended product information. Hereafter, this information will be referred to as product information. The output method is not particularly limited. For example, the output unit 104 may output recommended product information to the customer's terminal device. More specifically, the output unit 104 may output an email or electronic message presenting recommended product information to the customer's terminal device. Alternatively, the output unit 104 may output recommended product information to a store display device, for example, and the display device may display the recommended product information, thereby providing the customer with the information. The output unit 104 may also output recommended product information by, for example, printing the recommended product information to be provided to the customer.

[0018] Figure 2 is a flowchart illustrating an example of the operation of the product recommendation device 10 according to Embodiment 1. Here, the processing results of each step by the product recommendation device 10 in Figure 2 are stored in a storage unit accessible by the product recommendation device 10, such as a storage unit. The identification unit 101 identifies the customer (step S101). Next, the acquisition unit 102 acquires product information of the products owned by the customer (step S102).

[0019] The identification unit 103 then identifies products sold in the store that match predetermined combination conditions when combined with the customer's owned products (step S103). The output unit 104 outputs the product information of the identified products as product recommendation information to a customer terminal or a store display device (step S104). After step S104, the product recommendation device 10 terminates the operation of the flow.

[0020] Next, the effects of Embodiment 1 will be described. The product recommendation device 10 recommends products to customers whose combination of owned items and products for sale matches predetermined combination conditions. As a result, the product recommendation device 10 can recommend products that are suitable for the owned items of customers who visit the store. Therefore, the product recommendation device 10 can make it easier for customers who visit the actual store to find the optimal product.

[0021] (Embodiment 2) Next, Embodiment 2 will be described in detail with reference to the drawings. To the extent that the description of Embodiment 2 remains clear, any content that overlaps with the previous description will be omitted. Furthermore, in Embodiment 2, the method of customer identification is not particularly limited, as described in Embodiment 1. In Embodiment 2, a method using images will be used as an example of customer identification.

[0022] Figure 3 is an explanatory diagram showing an example configuration of a system according to Embodiment 2. In Figure 3, the system includes a product recommendation device 20, a terminal device 21, and an imaging device 22. The product recommendation device 20, the imaging device 22, and the terminal device 21 can be connected, for example, via a communication network. The imaging device 22 is installed, for example, in a store. Multiple imaging devices 22 may be provided, such as imaging device 22-1 to imaging device 22-2. In Figure 3, the terminal device 21 is a device owned by a customer. The terminal device 21 may be a device installed in the store. Alternatively, the terminal device 21 may be a device owned by a store employee. The imaging device 22 captures images, for example, to identify customers who have come to the store. The product recommendation device 20 outputs recommended products to customers who have come to the store. The terminal device 21 also displays the recommended products output from the product recommendation device 20 to the customer on its display unit. Alternatively, the terminal device 21 notifies the customer of the recommended products output from the product recommendation device 20 via email, electronic message, or voice. In Figure 3, when outputting recommended products to a customer, the product recommendation device 20 may output to an output device instead of the customer's terminal device 21, for example, by displaying it on a display device in the store or printing it on a printing device.

[0023] Figure 4 is a block diagram showing an example configuration of the system according to Embodiment 2. In Figure 4, System 2 includes a product recommendation device 20, a terminal device 21, and an imaging device 22.

[0024] The imaging device 22 captures images or videos. The imaging device 22 may capture images in accordance with the instructions of the product recommendation device 20. Alternatively, the imaging device 22 may continuously capture images of the store interior. The imaging device 22 may also transmit the images to the product recommendation device 20 or the storage device in accordance with the instructions of the product recommendation device 20. When the imaging device 22 captures images of the store interior, it also captures images of customers who have come into the store. As shown in Figure 4, there may be multiple imaging devices 22, such as imaging devices 22-1 to 22-n, where n is a positive integer of 1 or more.

[0025] The terminal device 21 is, for example, a device owned by a customer (example in Figure 3), a device owned by a store employee, or a device installed in the store. In Embodiment 2, as an example, the product recommendation device 20 may output recommended product information to the terminal device 21. The product recommendation device 20 may output sales recommendation information when instructed by the customer via the terminal device 21. Alternatively, the product recommendation device 20 may output sales recommendation products to the terminal device 21 in the form of a push notification. Although Figures 3 and 4 show an example with one customer, if there are multiple customers in the store, there may be as many terminal devices 21 as there are customers. Also, the terminal device 21 and the product recommendation device 20 may be the same.

[0026] The product recommendation device 20 has the functions of the product recommendation device 10 described in Embodiment 1. The product recommendation device 20 includes an image acquisition unit 205, an identification unit 201, an owned product acquisition unit 202 (acquisition unit), a behavior analysis unit 206, a specification unit 203, an output unit 204, and a storage unit 210. The image acquisition unit 205, the behavior analysis unit 206, and the storage unit 210 are newly added to the product recommendation device 20 according to Embodiment 1. The identification unit 201, the owned product acquisition unit 202, the specification unit 203, and the output unit 204 have the functions of the identification unit 101, the acquisition unit 102, the specification unit 103, and the output unit 104, respectively, as described in Embodiment 1.

[0027] First, the storage unit 210 stores the processing results of each function of the product recommendation device 20. The storage unit 210 also stores the data used for processing each function. In Figure 4, the storage unit 210 stores, for example, a customer identification database 211, a owned product database 212, a sold product database 213, and combination conditions 214 as stored contents. The stored contents of the storage unit 210 in Figure 4 are just an example. For example, this various information may also be stored in the storage unit 210 of another device (for example, a storage device) connected to the product recommendation device 20 via a communication network or the like.

[0028] As will be explained in detail in Figure 14 below, the recommended product device 20 loads a program containing various instructions for realizing each functional unit into the storage unit 210 and executes it. The type of storage unit 210 is not limited. For example, the storage unit 210 can be ROM (Read Only Memory), RAM (Random Access Memory), semiconductor memory, HDD (Hard Disk Drive), or SSD (Solid State Drive). The storage unit 210 may also be a combination of these.

[0029] Next, we will explain an example of customer identification DB211, owned product DB212, sold product DB213, and combination condition 214.

[0030] Figure 5 is an explanatory diagram showing an example of the customer identification DB 211. The customer identification DB 211 associates, for example, customer ID (Identification) information, customer name information, facial feature information, contact information, and other customer-related information for each customer. The customer ID is an identifier used to identify a customer. The customer name is the customer's name. The facial feature is the facial feature extracted from the customer's facial image. Instead of feature, the facial image data may be associated with the customer ID information. The notification destination is, for example, the number of the customer's terminal device 21 or the email address to which electronic messages are sent when notifications are sent to the customer.

[0031] Here, to illustrate the use of facial features as a method of customer identification, we have described an example where the customer's facial feature information is included in the customer identification DB211. Therefore, the contents of the customer identification DB211 can be modified in various ways depending on the method of customer identification.

[0032] Furthermore, the customer identification DB 211 does not necessarily have to include some of the information shown in the diagram. The customer identification DB 211 may also include other information not shown in the diagram. For example, the customer identification DB 211 may include information about the customer's physical characteristics, such as the customer's height and weight. The customer identification DB 211 may also include information about the customer's general clothing size and the clothing size the customer purchased from a particular manufacturer. This customer information may also be included in the owned goods DB 212.

[0033] Figure 6 is an explanatory diagram showing an example of the owned products DB212. The owned products DB212 contains owned product information for each customer. The owned product information includes attribute values ​​for multiple attributes of the owned products. In the owned products DB212, for example, the customer ID, product name, product ID, and attributes are associated with each customer. In Figure 6, the attributes of the owned products are type, size, dimensions, image, pattern, style, fit, and material. In addition, the owned products DB212 may also associate the name or identifier of the product manufacturer and the name or identifier of the distributor with the owned products. The owned products DB212 may register various data based on each customer's sales history, for example. Alternatively, the owned products DB212 may have various data pre-registered by the customer. The owned product information is each piece of information associated with the customer ID. That is, the owned product information is a row-by-row record in the owned products DB212.

[0034] The customer ID is an identifier used to identify a customer. The customer ID here is the same as the customer ID shown in Figure 5. The owned goods database 212 and the customer identification database 211 are linked by the customer ID. Note that the customer identification database 211 and the owned goods database 212 may be the same database.

[0035] Furthermore, the product name is the name of the product owned by the customer. The product ID is an identifier for the product owned by the customer. The type is, for example, the type of product owned. The type here is not particularly limited. For example, clothing can be broadly categorized as tops and bottoms. Alternatively, the type here can be more specifically classified by tops or bottoms. For example, for tops, the types could be T-shirts, dress shirts, blouses, tank tops, cardigans, knitwear, and hoodies. For bottoms, the types could be jeans (pants), wide-leg pants, tapered pants, chinos, flared skirts, tight skirts, pencil skirts, and wrap skirts. Other types of clothing could include various types of dresses, jackets, and coats. Additionally, types of jewelry such as rings and necklaces, shoes, bags, and hats could also be included.

[0036] Color refers to the color of the owned item. The method of indicating the color is not particularly limited. Size refers to the dimensions of the owned item. The method of indicating the size is not particularly limited. Sizes may be expressed by sizes such as S (Small), M (Medium), L (Large), F (Free), or sizes such as 7, 9, and 11. Dimensions refer to the dimensions of the owned item. The method of indicating the dimensions is not particularly limited. Dimensions may include numerical values ​​such as the length of the garment or the waist measurement of the garment, provided by the manufacturer or distributor. Alternatively, dimensions may be numerical values ​​such as the length actually measured by the customer. Image refers to image data of the owned item, for example. The image here may be data linked to a storage device where the image is stored. The image may be obtained from, for example, the manufacturer of the owned item, a store where it is sold, or the internet. Alternatively, the image may be an image taken by the customer. Pattern refers to the pattern (design) of the owned item, for example. The pattern may be a picture applied to the surface of clothing, jewelry, etc. The type of pattern is not particularly limited. For example, patterns include point patterns with symbols such as hearts and stars, animal prints, polka dots, and stripes.

[0037] Style refers to, for example, the style of the owned product. Style is not particularly limited. For example, style could refer to the place, situation, or atmosphere of the outfit in which the owned product is expected to be used, such as casual, formal, office, or wedding. Fit could refer to, for example, the feeling when wearing the owned product. Fit could refer to, for example, tight, loose, fitted, long length, short length, loose waist, loose neck, etc. Fit may be specified by the customer, or it may be provided in advance by the manufacturer or distributor. Fit may be determined by the product recommendation device 20 from various information. For example, fit may be determined based on the material of the owned product. Fit may be determined based on the length of the owned product. Fit may be determined based on the shape of the owned product extracted from an image of the owned product. Fit may be determined based on an image of the customer or store clerk wearing the owned product, and the customer's or store clerk's body type. Material refers to, for example, the material of the owned product. For example, materials include cotton, polyester, and nylon. Alternatively, the material composition could be expressed as 50% cotton and 50% polyester.

[0038] The owned product database 212 may include other information not shown in the illustration. For example, the owned product database 212 may also include information provided by the manufacturer or distributor, or the store, such as washing instructions. Furthermore, the owned product database 212 may not include some of the information shown in the illustration.

[0039] Figure 7 is an explanatory diagram showing an example of the Sales Product DB213. The Sales Product DB213 associates product ID information, product name information, and attribute values ​​of the product for each product sold in the store. In Figure 7, the attributes of the products sold include color, size, dimensions, stock quantity, image, price, pattern, style, fit, and material.

[0040] The Product ID is an identifier for the product being sold. The Product Name is the name of the product being sold. The Color is the color of the product being sold. The Size is the size of the product being sold. The method of expressing the size is not particularly limited. For example, sizes may be expressed as S, M, L, F, or sizes 7, 9, 11. The Dimensions are the dimensions of the owned product. For example, dimensions may be numerical values ​​such as the length of the garment or the waist measurement of the garment, provided by the manufacturer, retailer, or store. The Stock Quantity is the number of items in stock at the store. The Image is, for example, data of an image of the owned product. As shown in the diagram, the size, dimensions, and stock quantity information may be associated with different colors.

[0041] The price is the price of the product being sold. Multiple prices may be registered, such as a price including consumption tax and a price excluding consumption tax. The pattern is the pattern of the product being sold. The pattern may be the same as the pattern of owned product DB212. The style is the style of the product being sold. The style may be the same as the style of owned product DB212. The fit may be, for example, the feeling when wearing the product being sold. As mentioned above, fit may include tight, loose, and fitted. The fit may be specified by the customer or store clerk who purchased the product. Alternatively, the fit may be provided in advance by the manufacturer or distributor. Alternatively, the fit may be determined based on the material of the product being sold. The fit may be determined based on the length of the product being sold. The fit may be determined based on the shape of the product being sold extracted from the image of the product being sold. The material is, for example, the material of the product being sold. The material may be the same as the material of owned product DB212. Others are not particularly limited. Others may be, for example, other features of the product being sold or examples of how the product is used. Furthermore, the sales product database 213 does not need to include some of the information shown in the diagram. Also, the sales product database 213 may include other information not shown in the diagram.

[0042] Figure 8 is an explanatory diagram showing an example of a combination condition 214. In Figure 8, for ease of understanding, the combination condition 214 is represented as a database. The method of defining the combination condition 214 is not particularly limited. The combination condition 214 may be, for example, a conditional expression.

[0043] The combination condition 214 may be determined, for example, by the attribute values ​​of the product's attributes. Examples of attributes for the combination condition 214 include color, color combinations, patterns, pattern combinations, style, feel, and material, as shown in Figure 8. In Figure 8, the attribute values ​​are shown below each attribute. The combination condition 214 shown in Figure 8 may include only some of the multiple attributes. Furthermore, the combination condition 214 may include other attributes not shown. The attribute values ​​of each attribute in the combination condition 214 may be preferred. Also, the attribute values ​​of each attribute in the combination condition 214 may be undesirable. Furthermore, for example, the customer, store clerk, user of the product recommendation device 20, or administrator of the product recommendation device 20 may be able to set which attribute values ​​are used as the combination condition 214.

[0044] If each attribute relates to preferences, then combination condition 214 is, for example, that the combination of goods for sale and goods for ownership includes a preference. Alternatively, combination condition 214 is, for example, that the combination of goods for sale and goods for ownership is a preference combination.

[0045] Furthermore, if each attribute relates to something that is disliked, the combination condition 214 may be, for example, that the combination of items for sale and items for possession does not include anything that is disliked. Alternatively, the combination condition 214 may be, for example, that the combination of items for sale and items for possession is not a combination that is disliked.

[0046] Taking color as an example, the color can be a preferred color. For example, a preferred color could be a customer's preferred color, or a color recommended by a store employee. Alternatively, a preferred color could be a trendy color. In the case of a preferred color, for example, combination condition 214 would be that the combination of the product sold and the product owned includes the preferred color. Alternatively, the color can be a disliked color. A disliked color could be, for example, a color that the customer does not like or a color that a store employee does not recommend. In the case of a disliked color, for example, combination condition 214 would be that the combination of the product sold and the product owned does not include the disliked color.

[0047] The color combination may be a preferred color combination. For example, a preferred color combination may be defined as a combination of colors for tops, bottoms, hats, and shoes. For example, a preferred color combination may include a trendy color in one of the items such as tops, bottoms, hats, or shoes. For example, in the case of a preferred color combination, combination condition 214 may be that the combination of the product for sale and the product owned is a preferred color combination. For example, if the combination of black and white is preferred, then a combination of the product for sale and the product owned in which one of the items is black and the other is white will satisfy combination condition 214. The color combination may also be a color combination that is not preferred. In the case of a color combination that is not preferred, combination condition 214 may be that the combination of the product for sale and the product owned in which the items owned are not a preferred color combination. For example, if the combination of white and yellow is not preferred, then a combination of the product for sale and the product owned in which one of the items is white and the other is yellow will not satisfy combination condition 214.

[0048] Returning to the explanation of Figure 4, let's describe each functional part. As explained in Embodiment 1, the identification method by the identification unit 201 is not particularly limited. In Embodiment 2, we will explain the method of customer identification by the identification unit 201 using an image as an example.

[0049] The image acquisition unit 205 acquires, for example, an image captured by the imaging device 22. The image here may be a moving image.

[0050] The identification unit 201 identifies customers by identifying people's faces from captured images. Specifically, the identification unit 201 extracts facial features from the image, for example. The identification unit 201 may then identify customers by matching the extracted facial feature information with the facial feature information stored in the customer identification DB 211. For example, the identification unit 201 obtains customer ID information associated with the matched facial feature information.

[0051] Next, the owned product acquisition unit 202 acquires the owned product information of the identified customer. Specifically, the owned product acquisition unit 202 acquires the customer's owned product information from the owned product DB 212, for example, based on the customer ID information. The customer's owned product information is each piece of information (a row-wise record) associated with the customer ID information in the owned product DB 212.

[0052] The identification unit 203 identifies sales products that match the combination condition 214 when combined with the acquired owned product information. The identified sales products become recommended sales products. The identification unit 203 identifies recommended sales products from the sales products sold in the store. For example, the combination condition 214 may be as shown in Figure 8. Also, the combination condition 214 may be specified by the customer. Therefore, the combination condition 214 can be changed in various ways. Furthermore, the sales product information is obtained from the sales product DB 213 described above.

[0053] Furthermore, the identification unit 203 may identify products for sale whose combination of the attribute value of the product for sale and the attribute value of the owned product matches the combination condition 214. As shown in Figure 8, the attributes used in the combination condition 214 are, for example, as shown in Figure 8. Let's take a favorite color combination as an example. Let's take the case where the favorite color combination is a white top and red bottoms. In this case, the identification unit 203 identifies products for sale whose combination of the product for sale and owned product is a white top and red bottoms.

[0054] Let's take the example where the combination condition 214 is that it does not include combinations of patterns that the customer dislikes. If the attribute value of a combination of patterns that the customer dislikes is "same pattern", the identification unit 203 identifies products from the list of products for sale in which the combination of the product for sale and the owned product is not the same pattern. For example, the combination of a polka-dot skirt for sale and a plain dress shirt owned is not the same pattern, so the identification unit 203 identifies the polka-dot skirt.

[0055] Let's take the example where combination condition 214 is a combination of preferred fit. If the preferred fit is "tight top and loose bottoms," the identification unit 203 identifies products from the products for sale where the combination of the fit of the product for sale and the fit of the owned product is "tight top and loose bottoms." For example, if the type of product for sale is a shirt and its fit is loose, and the type of owned product is a skirt and its fit is loose, the top is not tight, so this combination of product for sale and owned product does not match combination condition 214. On the other hand, for example, if the type of product for sale is a shirt and its fit is tight, and the type of owned product is a skirt and its fit is tight, this combination of product for sale and owned product matches combination condition 214. Therefore, the identification unit 203 identifies a product for sale that is a tight shirt.

[0056] Although not shown in the diagram, let us also consider the case where the combination condition 214 is whether or not the product is featured in a magazine. If the product is featured in a magazine, the identification unit 203 may identify the product for sale whose combination with the owned product is currently featured in the magazine. In such cases, information on combinations including the product for sale that has been featured in the magazine during a predetermined period is stored in the storage unit 210 in advance as coordination information. Alternatively, the magazine feature information may be obtained via a communication network. The identification unit 203 may also identify, for example, the product for sale whose combination with the owned product is featured in a specified magazine. The magazine may be specified by the customer, a store clerk, a user of the product recommendation device 20, or the administrator of the product recommendation device 20.

[0057] Furthermore, combination condition 214 may be determined by the attributes of the products shown in Figure 8 and whether or not they are featured in a magazine. For example, combination condition 214 may be a combination of preferred fit and the product being featured in a magazine.

[0058] Furthermore, the combination condition 214 may be determined, for example, by whether the number of combinations is equal to or greater than a second predetermined number. Specifically, the identification unit 203 identifies, for example, sales products for which the number of combinations with owned products is equal to or greater than a second predetermined number. This allows the product recommendation device 20 to recommend sales products to customers that have many patterns of being worn with owned products. The second predetermined number can be set, for example, by the customer, store staff, users of the product recommendation device 20, or the administrator of the product recommendation device 20. The second predetermined number can be changed in various ways depending on the customer or the customer's age group.

[0059] Furthermore, for example, the identification unit 203 may identify recommended products from among the products sold in response to customer behavior in the store. Examples of products sold in response to customer behavior in the store include products picked up by the customer, products that the customer has looked at for a predetermined amount of time or longer, or products similar to these. Specifically, for example, the behavior analysis unit 206 may analyze the behavior of the identified customer. The behavior analysis unit 206 may identify the products picked up by the customer from the images acquired by the image acquisition unit 205. The behavior analysis unit 206 may also identify products that the customer has been looking at for a predetermined amount of time from the images acquired by the image acquisition unit 205. The predetermined amount of time may be specified by the customer, the user of the product recommendation device 20, or the store staff. The identification unit 203 may then identify recommended products from among the products identified by the behavior analysis unit 206. As a result, the product recommendation device 20 can recommend products that are suitable for the customer from among the products that the customer was interested in when they visited the store, or products similar to those products.

[0060] Next, the processing by the output unit 204 will be described. The output unit 204 outputs the sales product information of the identified sales product to the customer as recommended product information. The sales product information is obtained from the sales product DB 213 described above. As described above, the output method is not particularly limited. The output unit 204 may, for example, output the recommended product information to the customer's terminal device 21 using email or electronic message. Alternatively, the output unit 204 may, for example, display the recommended product information on the display device of the customer's terminal device 21. The output unit 204 may also output the recommended product information to an output device installed in the store. The timing of the output is not particularly limited. The output unit 204 may output the recommended product information at the time the recommended product information is identified from the product recommendation device 20, such as via push notification. Alternatively, the output unit 204 may output the recommended product information at a timing specified by the customer.

[0061] The specific output content is not particularly limited. For example, the output unit 204 may output images of combinations that match the combination condition 214. The output unit 204 may also output, for example, the product name of the product being sold. In addition to the product name and product image, the output unit 204 may also output, for example, the price of the product and the number of items in stock at the store.

[0062] Here, an example of processing between the identification unit 203 and the output unit 204 will be explained using Figure 9. Figure 9 is an explanatory diagram showing an example from combination to output. In Figure 9, for example, the products for sale are products "A001", "A002", "A003", and "A010". Here, for example, product "A001" refers to a product for sale identified by product ID "A001". Products "A001", "A002", and "A003" are skirts with different patterns and different lengths. Product "A010" is a hat with a polka dot pattern. In Figure 9, for example, the owned products are owned products "B001" and "B002". Here, for example, owned product "B001" refers to an owned product identified by product ID "B001". It is a shirt with a different pattern from owned product "B001" and "B002". Therefore, there are six possible combinations of products for sale and products owned. The combination condition 214 is, for example, a coordinated outfit featured in magazine X. For example, let's assume that only the combination of product for sale "A001" and product owned "B001" is a coordinated outfit featured in magazine X. The identification unit 203 then identifies product for sale "A001". The output unit 204 then outputs information about product for sale "A001" as product recommendation information. In Figure 9, the output unit 204 displays the name and price of product for sale "A001" and the fact that it is featured in magazine X on the customer's terminal device 21.

[0063] Returning to the explanation of Figure 4, the output unit 204 may output recommended product information to the customer in a predetermined output order. Alternatively, the output unit 204 may narrow down the recommended product information to be output.

[0064] For example, the output unit 204 outputs recommended product information in an order corresponding to the number of combinations that match the combination condition 214. The order according to the number of combinations can be, for example, in descending order of the number of combinations, descending order of the number of combinations, or in an order in which the number of combinations is close to a specified number. Let's explain with an example where sales product "A001" and sales product "A002" are identified by the identification unit 203. Suppose that for sales product "A001", two combinations match the combination condition 214: the combination of sales product "A001" and owned product "B001", and the combination of sales product "A001" and owned product "B002". On the other hand, suppose that for sales product "A002", one combination matches the combination condition 214: the combination of sales product "A002" and owned product "B001". In such a case, the output unit 204 outputs recommended product information in the order of sales product "A001" to sales product "A002". For example, the order of sorting by the number of combinations (most common), the order of sorting by the number of combinations (least common), and the order of sorting by the number of combinations (closest to a specified number) may be switchable on the screen of the terminal device 21 by the customer or store clerk.

[0065] Furthermore, the output unit 204 outputs product information for sales products for which the number of combinations matching the combination condition 214 is greater than or equal to a predetermined number, as recommended product information. The predetermined number can be set by the customer, store staff, users of the product recommendation device 20, or the administrator of the product recommendation device 20. Specifically, for example, the predetermined number may be input on the screen of the terminal device 21. The predetermined number can be changed in various ways depending on the customer or the customer's age group. In the example above, there are two combinations for sales product "A001" and one combination for sales product "A002". If the predetermined number is 2, the output unit 204 outputs product information for sales product "A001" as recommended product information. On the other hand, if the predetermined number is 2, the output unit 204 does not output product information for sales product "A002" as recommended product information. The predetermined number may also be set to be less than or equal to the second predetermined number mentioned above.

[0066] Furthermore, the output unit 204 outputs recommended product information in order according to the number of products sold in combinations that match the combination condition 214. The order according to the number of products sold can be in descending order of the number of products sold, descending order of the number of products sold, or in order of the number of products sold being closest to a specified number. In the case of descending order of the number of products sold, the product recommendation device 20 can recommend products in descending order of the number of products that customers will newly purchase. The order according to the number of products sold may also be fixed, and can be specified by the customer, store staff, or users of the product recommendation device 20. Specifically, for example, these orders may be switchable on the screen of the terminal device 21. Here, the combination condition 214 is a combination published in magazine X. For example, the combination of product A001 and owned product B001 is published in magazine X. In this case, the number of products sold in the combination is 1. For example, the combination of product "A002" for sale, product "B001" owned, and product "A010" for sale is currently featured in magazine X. In this case, the number of products for sale in this combination is 2. If the products are ordered by the number of products for sale, the output unit 204 will output the recommended product information in the order of product "A002" and then product "A001".

[0067] Furthermore, the output unit 204 outputs recommended product information in order of the total sales amount for combinations that match the combination condition 214. The order of the total sales amount is from highest to lowest total amount, or from lowest to highest total amount. In addition, the customer, store staff, or user of the product recommendation device 20 may specify whether to order from highest to lowest or lowest to highest. Specifically, for example, whether to order from highest to lowest or lowest to highest may be input or switched on the screen of the terminal device 21. Here, the combination condition 214 is a combination published in magazine X. For example, the combination of a skirt, product "A001," and a shirt, product "B001," is published in magazine X, but in magazine X, this combination does not include any products other than product "A001." In this case, the total sales amount for the combination is the amount of product "A001." For example, the combination of a skirt, product "A002," and a shirt, product "B001," is published in magazine X. Furthermore, in magazine X, this combination includes a hat, product "A010." In this case, the total value of the products sold in the combination is the sum of the value of product "A001" and product "A010". If the products are ordered in descending order of total value, the output unit 204 outputs the recommended product information in the order of product "A001" and then product "A002".

[0068] Figure 10 is an explanatory diagram showing an example of output sorted by total amount from lowest to highest. The total amount of the sales product in the combination of sales product "A001" and owned product "B001" is 3,900 yen, which is the amount of sales product "A001". The total amount of the sales product in the combination of sales product "A002", sales product "A010", and owned product "B001" is 8,000 yen, which is the sum of the amounts of sales product "A010" and sales product "A002". In Figure 10, the output is sorted from lowest to highest, and the customer's terminal device 21 displays the combination of sales product "A001" and owned product "B001" first. Then, the customer's terminal device 21 displays the combination of sales product "A002", sales product "A010", and owned product "B001" second. For example, it may be possible to switch between sorting by total amount from highest to lowest on the screen of the terminal device 21.

[0069] Returning to the explanation of Figure 4, the output unit 204 may output, as recommended product information, sales products whose total sales amount in combinations that meet the combination condition 214 is within the budget range. Let's take a budget range of 3,000 yen to 7,000 yen as an example. In Figure 10, the total sales amount in the combination of sales product "A002", sales product "A010", and owned product "B001" is 8,000 yen, which is outside the budget range. Therefore, the output unit 204 does not output the information for sales product "A002" and sales product "A010" as recommended product information. Alternatively, the output unit 204 may output recommended product information in order of the total sales amount in combinations whose total sales amount is within the budget range.

[0070] Furthermore, the output unit 204 may output recommended product information in order of the number of owned products that match the combination condition 214. The order based on the number of owned products could be, for example, from most to least owned products that match the combination condition 214. This allows the product recommendation device 20 to recommend products that can be coordinated using the owned products. Alternatively, the order based on the number of owned products could be, for example, from least owned products that match the combination condition 214. The customer, store staff, or users of the product recommendation device 20 may also choose between most to least or least owned products as appropriate.

[0071] Furthermore, the output unit 204 may output recommended product information in an order corresponding to the attribute values ​​of the sales products in combinations that match the combination conditions 214. For example, the output unit 204 may prioritize outputting sales products of a predetermined color. This allows the product recommendation device 20 to recommend sales products that are coordinated to make use of the customer's existing products and better suit their preferences.

[0072] Furthermore, the output unit 204 may combine the output order and output filtering method described above. For example, the output unit 204 may output recommended product information in order of the number of combinations and the lowest total sales amount of the products in each combination. For example, the output unit 204 may output recommended product information in order of the number of combinations and the total sales amount of the products in each combination being within the budget range.

[0073] Figure 11 is a flowchart illustrating an example of the operation of the product recommendation device 20 according to Embodiment 2. Here, for example, the processing results of each step by the product recommendation device 20 in Figure 2 are stored in the storage unit 210 or a storage unit accessible by the product recommendation device 20. The image acquisition unit 205 acquires a customer image (step S201). Next, the identification unit 201 identifies the customer from the acquired customer image (step S202). Then, the owned product acquisition unit 202 acquires owned product information (step S203). The identification unit 203 and the behavior analysis unit 206 perform identification processing (step S204). In detail of step S204, the behavior analysis unit 206 and the output unit 204 perform output processing (step S205). After step S205, the product recommendation device 20 terminates the operation of the flow.

[0074] Figure 12 is a flowchart illustrating an example of the identification process (step S204) in Figure 11. The behavior analysis unit 206 tracks the behavior of the identified customer for a certain period of time (step S211). The behavior analysis unit 206 then extracts sales products corresponding to the customer's behavior (step S212). Sales products corresponding to the customer's behavior include, for example, sales products that the customer picked up, sales products that the customer looked at for a predetermined amount of time or longer, and sales products similar to the products the customer picked up.

[0075] Next, the identification unit 203 identifies sales products from the extracted sales products that match the combination condition 214 when combined with owned products (step S213). Then, after step S213, the product recommendation device 20 returns to the original flow. That is, after step S213, the product recommendation device 20 moves to step S205 in Figure 11.

[0076] Figure 13 is a flowchart showing an example of the output processing (step S205) in Figure 11. The product recommendation device 20 determines the order in which the identified sales products will be output as sales recommendation products (step S221). The output order can be changed in various ways. For example, as mentioned above, the output order can be based on the total amount of the combination, the number of combinations, or the number of sales products in the combination. Next, the product recommendation device 20 outputs the sales recommendation product information to the terminal device 21, etc., in the determined order (step S222). After step S222, the product recommendation device 20 terminates the operation of the flow.

[0077] Next, the effects of Embodiment 2 will be described. The product recommendation device 20 outputs information on products for sale that match predetermined combination conditions when a customer's owned products and the products for sale meet those conditions, as recommended product information. As a result, the product recommendation device 20 can recommend products for sale that are suitable for the customer's owned products. Therefore, the product recommendation device 20 can make it easier for customers to find products that are suitable for them from the products for sale in the store.

[0078] Furthermore, the product recommendation device 20 outputs recommended product information in order of the number of combinations that meet predetermined combination conditions. This allows the product recommendation device 20 to recommend products for sale that have more mix-and-match patterns with the customer's owned items. Therefore, the product recommendation device 20 can recommend products for sale that are more suitable for the customer.

[0079] Furthermore, the product recommendation device 20 outputs product information for sales products where the number of combinations that meet predetermined combination conditions is greater than or equal to a predetermined number, as recommended product information. This allows the product recommendation device 20 to recommend sales products to customers that have many possible outfit combinations with their existing products. Therefore, the product recommendation device 20 can recommend sales products that are more suitable for the customer.

[0080] Furthermore, the product recommendation device 20 outputs recommended product information in order of the number of products sold in combinations that meet predetermined combination conditions. This allows the product recommendation device 20 to recommend, for example, outfits that include many products for sale. Therefore, the product recommendation device 20 can output products for sale in a way that encourages customers to purchase more products.

[0081] Furthermore, the product recommendation device 20 outputs recommended product information in order of the total price of the products sold in combinations that meet predetermined combination conditions. This allows the product recommendation device 20 to help customers choose products according to their budget. Also, when the product recommendation device 20 outputs recommended product information in order of lowest total price, it can output a combination that is suitable for the customer and keeps the total price down.

[0082] Furthermore, the product recommendation device 20 outputs product information as recommended product information for products whose total price in combinations that meet predetermined combination conditions 214 is within the budget range. This allows the product recommendation device 20 to recommend products that can be purchased within the budget range, as well as coordinated outfits that include those products.

[0083] Furthermore, the product recommendation device 20 identifies products from the available merchandise that match predetermined combination conditions when combined with items the customer already owns, based on the customer's behavior in the store. This allows the product recommendation device 20 to recommend products, for example, from the available merchandise that matches the interests of the customer who has visited the store. Therefore, the product recommendation device 20 can make it easier for customers to find products that are suitable for them from the merchandise available in the store.

[0084] Furthermore, the product recommendation device 20 identifies products from among the products sold in the store whose combination of attribute values ​​for the product's attributes and attribute values ​​for the customer's owned products matches predetermined combination conditions. As mentioned above, attributes include the product's color, pattern, fit, fashion style, or material. This allows the product recommendation device 20 to facilitate, for example, customers finding products that are suitable for them based on combinations of product attributes.

[0085] The predetermined combination conditions may, for example, be specified by the customer. This allows the product recommendation device 20 to recommend products that can be coordinated according to the customer's preferences based on the products the customer owns. Therefore, the product recommendation device 20 can make it easier for customers to find products that are suitable for them, for example, in a store.

[0086] Figure 14 is an explanatory diagram showing an example of the system's hardware configuration. System 3 includes, for example, a product recommendation device 30, a terminal device 31, and an imaging device 32. First, we will explain the case where the product recommendation devices 10 and 20 according to Embodiments 1 and 2 are implemented using a computer. Product recommendation device 30 is an example where the product recommendation devices 10 and 20 described in Embodiments 1 and 2 are implemented using a computer.

[0087] The recommended product device 30 includes a CPU (Central Processing Unit) 301, a ROM 302, a RAM 303, a storage device 304, and a communication interface 305. Each component is connected via a bus 306.

[0088] The CPU 301 controls the entire product recommendation device 30. For example, the CPU 301 may control the entire product recommendation device 30 by running an OS (Operating System). The CPU 301 may have multiple cores.

[0089] The product recommendation device 30 includes, for example, a ROM 302, a RAM 303, and a storage device 304 as storage units. The storage device 304 may be a semiconductor memory such as flash memory, an HDD, or an SSD. For example, the storage device 304 stores various programs such as OS programs, application programs, and programs according to these embodiments 1 and 2. Alternatively, the ROM 302 stores application programs. The ROM 302 may also store programs according to these embodiments 1 and 2. The RAM 303 is used as a work area for the CPU 301.

[0090] The CPU 301 also loads a program stored in the storage device 304 or ROM 302. Then, the CPU 301 executes each process coded in the program. The CPU 301 may also download various programs via the communication network 310. Furthermore, the CPU 301 functions as part or all of the product recommendation device 30. The CPU 301 may also execute processes or instructions in the illustrated flowchart based on the program.

[0091] The communication interface 305 is connected to the LAN (Local Area Network) or WAN (Wide Area Network) communication network 310 via a wireless or wired communication line. This allows the product recommendation device 30 to connect to external devices and external computers via the communication network 310. The communication interface 305 manages the interface between the communication network 310 and the internal workings of the product recommendation device 30. Furthermore, the communication interface 305 controls the input and output of data from external devices and external computers.

[0092] However, the hardware configuration of the product recommendation device 30 shown in Figure 14 is just an example, and other components may be added, or some components may be omitted. For example, the product recommendation device 30 may have a drive device. The CPU 301 may read programs and data from the recording medium installed in the drive device into the RAM 303. Examples of recording media include optical discs, flexible discs, magneto-optical discs, and USB (Universal Serial Bus) memory. Also, for example, the product recommendation device 30 may have input devices such as a keyboard and a mouse. The product recommendation device 30 may also have output devices such as a printer and a display.

[0093] Next, the hardware configuration of the terminal device 31 described in each embodiment will be explained. The terminal device 31 is an example in which the terminal device 21 described in Embodiment 2 is implemented using a computer. The terminal device 31 includes, for example, a CPU 311, a ROM 312, a RAM 313, a storage device 314, a communication interface 315, and an input / output device 317. Each component is connected via a bus 316.

[0094] The CPU 311 controls the entire terminal device 31. The terminal device 31 has a ROM 312, RAM 313, and a storage device 314 as its memory units. The storage device 314 may be a semiconductor memory such as flash memory, an HDD, or an SSD. For example, the storage device 314 stores the OS program and application programs. Alternatively, the ROM 312 stores application programs. The RAM 313 is used as the work area for the CPU 311.

[0095] The CPU 311 also loads a program stored in the storage device 314 or ROM 312. Then, the CPU 311 executes each process coded in the program. The CPU 311 may also download various programs via the communication network 310. Furthermore, the CPU 311 functions as part or all of the terminal device 31. The CPU 311 may also execute processes or instructions in the illustrated flowchart based on the program.

[0096] The communication interface 315 is connected to a communication network 310, such as a LAN or WAN, via a wireless or wired communication line. This allows the terminal device 31 to connect to external devices and external computers via the communication network 310. The communication interface 315 manages the interface between the communication network 310 and the internal workings of the terminal device 31. The communication interface 315 controls the input and output of data from external devices and external computers. The input / output device 317 accepts input from customer operations and outputs data. The input / output device 317 is, for example, a touch panel display.

[0097] The hardware configuration of the terminal device 31 shown in Figure 14 is an example. Other components may be added, or some components may be omitted. For example, the terminal device 31 may have a drive device. The CPU 311 may read programs and data from the recording medium installed in the drive device into the RAM 313. Examples of recording media include optical discs, flexible discs, magneto-optical discs, and USB (Universal Serial Bus) memory. Also, for example, the terminal device 31 may have input devices such as a keyboard or mouse. The terminal device 31 may also have output devices such as a printer. The terminal device 31 is not particularly limited and can be, for example, a smartphone, mobile phone, tablet device, or PC (Personal Computer).

[0098] Next, an example of the hardware configuration of the imaging device 32 will be described. The imaging device 32 corresponds to the imaging device 22 described in Embodiment 2. The imaging device 32 includes a camera 321 and a communication interface 322. Each component is connected by a bus 323. The communication interface 322 is connected to a communication network 310 such as a LAN or WAN via, for example, a wireless or wired communication line. The camera 321 has the function of capturing images. The camera 321 may also be capable of capturing video. Furthermore, as mentioned above, multiple imaging devices 32 may be provided. Also, the hardware configuration of the imaging device 32 is just one example. Components other than those shown in Figure 14 may be added.

[0099] This concludes the description of the hardware configuration of System 3. Furthermore, there are various modifications to the implementation methods of the product recommendation devices 10 and 20 described in Embodiments 1 and 2. For example, the product recommendation devices 10 and 20 may be implemented by any combination of different computers and programs for each component. Alternatively, the multiple components of each device may be implemented by any combination of a single computer and program.

[0100] Furthermore, some or all of the components of the recommended product devices 10 and 20 may be implemented using application-specific circuits. Also, some or all of the recommended product devices 10 and 20 may be implemented using general-purpose circuits, including a processor such as an FPGA (Field Programmable Gate Array). Furthermore, some or all of the recommended product devices 10 and 20 may be implemented using a combination of application-specific circuits, a combination of general-purpose circuits, or a combination of application-specific and general-purpose circuits. These circuits may also be a single integrated circuit, or they may be divided into multiple integrated circuits. These multiple integrated circuits may be connected via a bus or the like.

[0101] Furthermore, if some or all of the components of each device are implemented by multiple computers or circuits, these multiple computers or circuits may be centrally located or distributed.

[0102] The information processing methods described in each embodiment are implemented by the product recommendation devices 10 and 20. Alternatively, the information processing methods are implemented by a computer, such as a product recommendation device, executing a pre-prepared program. The programs described in each embodiment are recorded on a computer-readable recording medium such as an HDD, SSD, flexible disk, optical disk, magneto-optical disk, or USB memory. The program is then executed by a computer after being read from the recording medium. The program may also be distributed via a communication network 310.

[0103] Each component of the product recommendation devices 10 and 20 in the embodiments described above may be implemented in hardware, such as the computer product recommendation device 30 shown in Figure 14. Alternatively, each component may be implemented in a computer device or firmware based on program control.

[0104] The present disclosure has been described above with reference to the embodiments described herein, but the present disclosure is not limited to the embodiments described above. The structure and details of each present disclosure may include embodiments that apply various modifications that can be grasped by those skilled in the art within the scope of the present disclosure. The present disclosure may include embodiments that combine or substitute the matters described herein as appropriate. For example, matters described using a particular embodiment may be applied to other embodiments to the extent that they do not cause a contradiction. For example, although several operations are described sequentially in the form of a flowchart, the order in which they are described does not limit the order in which the operations are performed. Therefore, when implementing each embodiment, the order of the operations can be changed to the extent that it does not impair the content.

[0105] Some or all of the above embodiments may also be described as follows. However, some or all of the above embodiments are not limited to the following.

[0106] (Note 1) An identification means for identifying customers who visit a store, An acquisition means for acquiring information on the goods owned by the identified customer, Based on the acquired information on the owned goods, a means for identifying products sold at the store that, in combination with the owned goods, meet predetermined combination conditions; An output means for outputting information on the identified sales product as recommended product information, A product recommendation device equipped with the following features.

[0107] (Note 2) The output means outputs the recommended product information in an order corresponding to the number of combinations that meet the predetermined combination conditions. Recommended equipment as described in Appendix 1.

[0108] (Note 3) The output means outputs information about the products for sale in which the number of combinations that meet the predetermined combination conditions is greater than or equal to a predetermined number, as the recommended product information. Recommended equipment for the products listed in Appendix 1 or 2.

[0109] (Note 4) The output means outputs the recommended product information in order corresponding to the number of products sold in the combination that matches the predetermined combination conditions. Recommended device for the product as described in any one of the items 1 to 3 of the appendix.

[0110] (Note 5) The output means outputs the recommended product information in order corresponding to the total amount of the products sold in the combination that matches the predetermined combination conditions. Recommended equipment for products as described in any one of the items in Appendix 1 to 4.

[0111] (Note 6) The output means outputs information on the sales products among the identified sales products in which the total amount of sales products in the combination that meets the predetermined combination conditions is within the budget, as the recommended product information. Recommended device for the product as described in any one of the items 1 to 5 of the appendix.

[0112] (Note 7) The aforementioned identification means identifies, from among the products sold in the store according to the customer's actions, a product whose combination with the owned product matches the predetermined combination conditions. Recommended equipment for products as described in any one of the items 1 to 6 of the appendix.

[0113] (Note 8) The information of the owned goods includes the attribute values ​​of the attributes of the owned goods. The identification means identifies, from the products sold at the store, products whose combination of the attribute value of the attribute of the product and the attribute value of the attribute of the owned product matches the predetermined combination condition. Recommended equipment for products as described in any one of the items 1 through 7 of the appendix.

[0114] (Note 9) The aforementioned predetermined combination conditions are specified by the customer. Recommended equipment for products as described in any one of the items 1 through 8 of the appendix.

[0115] (Note 10) Identify customers who visit the store, Obtain information on the goods owned by the identified customer, Based on the acquired information about the owned goods, the store identifies products sold at the store that, when combined with the owned goods, meet predetermined combination conditions. The information on the identified sales products is output as recommended product information. method.

[0116] (Note 11) On the computer, Identify customers who visit the store, Obtain information on the goods owned by the identified customer, Based on the acquired information about the owned goods, the store identifies products sold at the store that, when combined with the owned goods, meet predetermined combination conditions. The information on the identified sales products is output as recommended product information. A program that executes a process.

[0117] This application claims priority based on Japanese Patent Application No. 2020-180992, filed on 29 October 2020, and incorporates all of its disclosures herein. [Explanation of symbols]

[0118] 2,3 Systems 10, 20, 30 Recommended Products 101,201 Identification Unit 102 Acquisition Department 103,203 Specific part 104,204 Output section 202 Owned Product Acquisition Department 205 Image acquisition unit 206 Behavior Analysis Department 210 Storage section 211 Customer identification DB 212 Owned product DB 213 Sales Product Database

Claims

1. A storage means for storing predetermined combination conditions specified by a customer, An identification means for identifying customers who visit a store, An acquisition means for acquiring information on the goods owned by the identified customer, Based on the acquired information about the owned goods, a selection means identifies a product sold at the store whose combination with the owned goods matches the predetermined combination conditions stored in the storage means, by referring to the predetermined combination conditions stored in the storage means. An output means that outputs the information of the identified sales product as recommended product information for the customer to wear, Equipped with, The aforementioned predetermined combination conditions include the customer's preferred combination of wearing conditions, The aforementioned identification means identifies, from among the products sold in the store, a product whose combination of wearing comfort when worn in combination with the customer's owned product matches the customer's preferred combination of wearing comfort. Recommended product device.

2. The output means outputs the recommended product information in an order corresponding to the number of combinations that meet the predetermined combination conditions. Product recommendation device according to claim 1.

3. The output means outputs information about the products for sale in which the number of combinations that meet the predetermined combination conditions is greater than or equal to a predetermined number, as the recommended product information. Product recommendation device according to claim 1 or 2.

4. The output means outputs the recommended product information in order corresponding to the number of products sold in the combination that matches the predetermined combination conditions. A product recommendation device according to any one of claims 1 to 3.

5. The output means outputs the recommended product information in order corresponding to the total amount of the products sold in the combination that matches the predetermined combination conditions. A product recommendation device according to any one of claims 1 to 4.

6. The output means outputs information on the sales products among the identified sales products in which the total amount of sales products in the combination that meets the predetermined combination conditions is within the budget, as the recommended product information. A product recommendation device according to any one of claims 1 to 5.

7. Computers Identify customers who visit the store, Obtain information on the goods owned by the identified customer, Based on the acquired information about the owned goods, the system refers to a storage means that stores predetermined combination conditions specified by the customer, and identifies from the products sold in the store whose combination with the owned goods matches the predetermined combination conditions stored in the storage means. The information of the identified sales product is output as recommended product information for the customer to wear. Execute the process, The aforementioned predetermined combination conditions include the customer's preferred combination of wearing conditions, In the aforementioned identification process, from among the products sold at the store, products are identified in which the combination of the product and the owned product, when worn together, matches the customer's preferred combination of wearing conditions. method.

8. On the computer, Identify customers who visit the store, Obtain information on the goods owned by the identified customer, Based on the acquired information about the owned goods, the system refers to a storage means that stores predetermined combination conditions specified by the customer, and identifies from the products sold in the store whose combination with the owned goods matches the predetermined combination conditions stored in the storage means. The information of the identified sales product is output as recommended product information for the customer to wear. Execute the process, The aforementioned predetermined combination conditions include the customer's preferred combination of wearing conditions, In the aforementioned identification process, from among the products sold at the store, products are identified in which the combination of the product and the owned product, when worn together, matches the customer's preferred combination of wearing conditions. program.

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