Purchasing support device, purchasing support method and program

Abstract

Customers may find it difficult to check the coupons they are offered and to use them at stores. [Solution] The purchasing support device disclosed herein comprises an acquisition means for acquiring facial images of customers in a store, a determination means for determining a recommended coupon to recommend to the customer based on information about product purchases acquired from the customer's facial image, an output means for outputting information about the recommended coupon including information about products eligible for the recommended coupon to a display device in the store, and an execution means for executing a facial recognition payment by comparing the customer's facial image with the customer's facial image acquired for facial recognition payment, in which the recommended coupon recommended to the matched customer is applied to the eligible product.

Description

[Technical Field]

[0001] The present disclosure relates to a purchasing support device, a purchasing support method, and a program. [Background technology]

[0002] A store may provide customers with coupon information related to products sold at the store.

[0003] Patent Document 1 describes a service terminal that takes a picture of a user who is about to purchase a product in a store and displays recommended information such as coupons. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2022 / 009414 Summary of the Invention [Problem to be solved by the invention]

[0005] Customers may find it difficult to check the coupons they are offered and to use them at stores.

[0006] One object of the present disclosure is to provide a purchasing support device or the like that allows customers to easily use coupons. [Means for solving the problem]

[0007] A purchasing support device in one aspect of the present disclosure includes an acquisition means for acquiring a facial image of a customer in a store, a determination means for determining a recommended coupon to recommend to the customer based on information regarding product purchases acquired from the facial image of the customer, an output means for outputting information about the recommended coupon including information about products eligible for the recommended coupon to a display device in the store, and an execution means for performing a facial recognition payment by comparing the facial image of the customer with the facial image of the customer acquired for facial recognition payment, in which the recommended coupon recommended to the matched customer is applied to the product.

[0008] A purchasing support method in one aspect of the present disclosure acquires a facial image of a customer in a store, determines a recommended coupon to recommend to the customer based on information about product purchases acquired from the customer's facial image, outputs information about the recommended coupon including information about products that are eligible for the recommended coupon to a display device in the store, and performs a facial recognition payment by matching the customer's facial image with the customer's facial image acquired for facial recognition payment, in which the recommended coupon recommended to the matched customer is applied to the eligible product.

[0009] A program in one aspect of the present disclosure acquires a facial image of a customer in a store, determines a recommended coupon to recommend to the customer based on information about product purchases acquired from the customer's facial image, outputs information about the recommended coupon including information about products that are eligible for the recommended coupon to a display device in the store, and compares the customer's facial image with the customer's facial image acquired for facial recognition payment, causing a computer to execute a process of performing facial recognition payment in which the recommended coupon recommended to the matched customer is applied to the eligible product.

[0010] Each program may be stored in a non-transitory computer-readable recording medium. [Effects of the Invention]

[0011] One example of the effect of the present disclosure is that customers can easily use coupons. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of a system including a purchasing support device. [Figure 2] FIG. 1 is an image diagram showing an example of a store to which a purchase support device is applied. [Figure 3] FIG. 2 is a block diagram showing an example of the configuration of a purchasing support device. [Figure 4] 10 is an example of a display screen on which product information of recommended products is displayed. [Figure 5] 10 is an example of a display screen on which product information of recommended products is displayed. [Figure 6] 10 is a flowchart showing the operation of the purchasing support device. [Figure 7] 10 is a flowchart showing the operation of the purchasing support device. [Figure 8] 10 is a flowchart showing the operation of the purchasing support device. [Figure 9] FIG. 2 is a block diagram showing an example of the configuration of a purchasing support device. [Figure 10] 10 is a flowchart showing the operation of the purchasing support device. [Figure 11] FIG. 2 is a diagram illustrating an example of a hardware configuration of a purchasing support device. DETAILED DESCRIPTION OF THE INVENTION

[0013] Embodiments of the present disclosure will be described in detail with reference to the drawings.

[0014] [Embodiment] An example of the configuration of a system including a purchasing support device 10 will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration of a system including a purchasing support device 10. Referring to Fig. 1, the purchasing support device 10 is connected to a camera 90, a display device 91, and a database 92 via a wired or wireless network.

[0015] The camera 90 is an imaging device capable of capturing images including moving images. The camera 90 is installed in a position where it can capture images of customers. The camera 90 is installed, for example, inside a store. The camera 90 installed inside the store can capture images of customers who visit the store. In other words, the camera 90 installed inside the store can capture images of customers staying in the store.

[0016] Referring to FIG. 1, one camera 90 is connected to the purchasing support device 10, but the number of cameras 90 is not limited. In other words, there may be one or more cameras 90. The required number of cameras 90 may be installed in the required locations depending on the layout of the store and the way products are displayed. The cameras 90 installed in the store may include, for example, security cameras.

[0017] The display device 91 is a device capable of displaying at least one of product information and coupon information. The display device 91 is installed inside the store. Customers entering the store can view the display screen of the display device 91 installed inside the store.

[0018] Referring to FIG. 1, one display device 91 is connected to the purchasing support device 10, but the number of display devices 91 is not limited. That is, there may be one or more display devices 91. For example, a display device 91 may be installed on each product display shelf. The locations where the display devices 91 can be placed within a store are not limited to these. The required number of display devices 91 may be installed in the required locations depending on the layout of the store and the way products are displayed.

[0019] The display device 91 is, for example, a display. A digital signage display can also be used as the display device 91. However, the display device 91 is not limited to these.

[0020] The database 92 stores information about customers. The information about customers includes, for example, purchase history, coupon usage history by the customer, and information about coupons provided to the customer. The purchase history is information about products purchased by the customer at the store. The coupon usage history by the customer is information about coupons used by the customer when purchasing products at the store. The coupon usage history may be included in the purchase history, for example. The information about coupons provided to the customer is information about coupons provided to the customer before the customer visits the store. For example, if the customer is a registered member, the store may provide coupons to the member. Also, the customer may have received a coupon when previously purchasing products at the store. The information about coupons provided to the customer is, for example, information about these coupons. The coupon information may include, for example, the discount amount, discount rate, information about the applicable product, and expiration date. In other words, the coupon information is information about the content of the coupon. However, the coupon information is not limited to these. Here, the coupon provided to the customer may be provided electronically or in print. In other words, the form of the coupon provided to the customer does not matter.

[0021] The database 92 may also store information related to facial recognition payments. Information related to facial recognition payments includes, for example, information related to the customer's facial image. Information related to the customer's facial image may be registered in advance by the customer. Information related to facial recognition payments may also include payment information registered by the customer. Payment information is information necessary to execute a payment. A specific example of payment information is credit card information used by the customer for payment. Payment information is not limited to these.

[0022] The database 92 may store customer attribute information. The customer attribute information may include, for example, at least one of the customer's gender, age, occupation, and family structure. The customer attribute information is not limited to these. The customer attribute information is registered by the customer, for example.

[0023] Here, the database 92 is realized by storing the above-mentioned information in a database server.

[0024] Here, an application example of a purchasing support system including purchasing support device 10 will be described with reference to Fig. 2. Fig. 2 is an image diagram showing an example of a store to which a purchasing support system including purchasing support device 10 is applied. With reference to Fig. 2, the flow of a customer using a store to which a purchasing support system including purchasing support device 10 is applied will be described.

[0025] A customer who visits a store looks around at the products displayed in the store. The customer can also select a product to purchase. At this time, the purchase support device 10 can capture a facial image of the customer in the store using a camera 90 installed in the store. The facial image may include body parts other than the face. In other words, the facial image must include at least the customer's face.

[0026] At this time, the emotion of the customer may be estimated from the facial image of the customer captured by the camera 90. A publicly known technique is used for the emotion estimation method. Facial authentication may be performed using the facial image of the customer captured by the camera 90. The customer can register a facial image in advance. Facial authentication is performed using the facial image registered by the customer in advance and the facial image captured by the camera 90. By performing facial authentication, it is possible to identify the customer. A publicly known technique is used for the facial authentication method.

[0027] Purchasing support device 10 then determines information about recommended coupons based on information about product purchases acquired from the customer's facial image. Purchasing support device 10 then outputs the information about the recommended coupons. The information about the recommended coupons is output, for example, to a display device 91 installed in the store. The customer can check the coupon information displayed on the screen of display device 91. Details of purchasing support device 10 will be explained later. A method for determining recommended coupons will also be explained later.

[0028] Once a customer has selected the products they wish to purchase, they make a facial recognition payment at the cash register. First, the customer registers the products they wish to purchase. Publicly known technology is used to register the products. The customer then pays for the registered products using facial recognition payment. The customer has registered information regarding facial recognition payment in advance. Then, payment for the purchased products is made using the facial recognition payment information registered by the customer in advance. Facial recognition payment is made using publicly known technology.

[0029] At this time, purchasing support device 10 compares the facial image of the customer making the purchase with the facial image of the customer acquired for face authentication payment. Then, purchasing support device 10 applies the recommended coupon recommended to the matched customer to the product covered by the recommended coupon. In other words, the customer can receive a discount on the product covered by the recommended coupon.

[0030] Information on purchased products for which a customer has paid is saved as a purchase history. The purchase history is saved in association with identification information that identifies the customer. The purchase history and identification information are saved, for example, in database 92. However, the storage location of the purchase history and identification information is not limited to this.

[0031] Then, the customer who has purchased the product leaves the store. Application examples of a purchasing support system including purchasing support device 10 are not limited to these.

[0032] The configuration of purchasing support device 10 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the configuration of purchasing support device 10. Purchasing support device 10 includes, as basic functions, an acquisition unit 101, a determination unit 102, an output unit 103, and an execution unit 104.

[0033] The acquisition unit 101 is one aspect of acquisition means for acquiring facial images of customers in the store. The acquisition unit 101 acquires facial images of customers captured by a camera 90 installed in the store. The facial image is an image that includes the customer's face. For example, the facial image of the customer may include other objects besides the customer's face. The camera 90 captures facial images of customers in the store at any timing. For example, the camera 90 can capture a facial image of the customer when the customer's face enters a range that the camera 90 can capture. If multiple cameras 90 are installed, each of the multiple cameras 90 may capture a facial image of the customer. In other words, the camera 90 may capture a facial image of one customer once or multiple times.

[0034] The acquisition unit 101 then acquires the facial image of the customer captured by the camera 90. The acquisition unit 101 may acquire the facial image of one customer once or multiple times. The facial image acquired by the acquisition unit 101 is used by the determination unit 102, which will be described next, to determine a recommended coupon to be recommended to the customer.

[0035] The determination unit 102 is one aspect of a determination means for determining a recommended coupon to be recommended to a customer based on information about the customer's situation in the store, obtained from a facial image of the customer. The determination unit 102 determines the content of the coupon to be recommended to the customer. An example of the content of the coupon is the target product to which the coupon applies a discount. The content of the coupon may also be the discount amount or discount rate. Furthermore, the content of the coupon may include an expiration date. The coupon may also be a coupon that provides another target product for free when a predetermined number of target products are purchased. The content of the coupon is not limited to these. There may be one or more recommended coupons.

[0036] Next, a method for determining the contents of the coupon will be described.

[0037] An example of information related to product purchases is information about the situation of the customer in the store obtained from a facial image of the customer. The situation of the customer in the store includes the customer's emotions in the store. The customer's emotions are the customer's psychological state. For example, examples of customer emotions are enjoyment, joy, a sense of accomplishment, and relaxation. Other examples of customer emotions are anxiety, depression, sadness, anger, and impatience. In other words, the customer's emotions represent the customer's mood. The customer's emotions are not limited to these.

[0038] A specific example of customer emotions in a store is the emotions a customer feels when shopping in the store. In other words, customer emotions in a store represent the mood in which the customer feels while shopping.

[0039] The customer's situation in the store may include attributes of the customer, such as, but not limited to, at least one of the customer's gender, age, occupation, and family structure.

[0040] The customer's situation in the store may include information about the customer's visit pattern. The customer's visit pattern indicates how many people the customer visits the store with. In other words, the visit pattern is a classification of customers based on the number of people who visit the store together. Examples of customer visit patterns include a solo visit, where a customer visits the store alone, and a group visit, where multiple customers visit the store together. In the case of a group visit, all customers in the group may enter the store together, or each customer may enter at different times and shop together in the store.

[0041] Furthermore, for example, in the case of group visits, the visit type may be further categorized. The visit type may include, for example, a family visit when a customer visits with their family, a friend group visit when a customer visits with multiple friends, and a couple visit when a customer visits as a couple. Examples of visit types are not limited to these.

[0042] The situation of the customer in the store is estimated using a facial image of the customer making a purchase. That is, the estimation unit 105 estimates the situation of the customer in the store using the facial image of the customer making a purchase. A publicly known technique is used to estimate emotions using the facial image. Also, a publicly known technique is used to estimate attributes using the facial image.

[0043] Furthermore, the estimation unit 105 can estimate the customer's visit pattern using an image captured inside the store. The camera 90 captures an image of the inside of the store. At this time, the captured image includes customers. The estimation unit 105 then detects the customers included in the image. Furthermore, the estimation unit 105 recognizes a group of customers included in the image. That is, the estimation unit 105 can recognize whether a customer is visiting the store alone or in a group of multiple customers. As a result, the estimation unit 105 can estimate the customer's visit pattern. Publicly known techniques are used to detect customers included in the image and recognize customer groups. Customer groups may be recognized by, for example, calculating the distance between customers. Customer groups may also be recognized by, for example, analyzing the orientation of each customer's face. The method for recognizing customer groups is not limited to these.

[0044] Furthermore, when customers visit a group, the estimation unit 105 may estimate the customer's visit mode in more detail by estimating the attributes of each customer using facial images of each customer. For example, if the estimation unit 105 estimates that the attributes of three customers are a man in his 40s, a woman in his 40s, and a teenage boy, respectively, it can estimate that the customers are a family. Furthermore, if the attributes of all five customers are men in their 20s, it can estimate that the customers are a group of friends. The method of estimating the visit mode by the estimation unit 105 is not limited to these. Furthermore, the visit mode estimated by the estimation unit 105 is not limited to these.

[0045] The determination unit 102 can determine recommended coupons according to the situation of the customer in the store. The determination unit 102 can determine recommended coupons, for example, based on a table in which recommended coupons are associated with the situation of the customer in the store. First, the estimation unit 105 estimates the situation of the customer in the store. Then, the determination unit 102 refers to the table and determines recommended coupons associated with the estimated situation of the customer.

[0046] A table in which customer situations are associated with recommended coupons is stored, for example, in the database 92. The contents of the recommended coupons associated with the customer situations are predetermined, for example, by the store. Here, an example will be described in which the estimation unit 105 estimates the emotions of customers in the store. The determination unit 102 determines recommended coupons according to the customer emotions. In this case, the table in which recommended coupons are associated with customer situations associates recommended coupons with customer emotions. For example, a happy emotion is associated with a coupon offering a 20 yen discount on cream puffs. In this example, the target product is cream puffs and the discount rate is 20 yen. As another example, an impatient emotion is associated with a recommended coupon offering a 10% discount on protein bars. In this example, the target product is a protein bar and the discount rate is 10%. Examples of recommended coupons associated with customer emotions are not limited to these. For example, a happy emotion is associated with a recommended coupon offering a 20% discount on chocolate until February 14th. In this example, the target product is chocolate and the discount rate is 20%. The expiration date is February 14th.

[0047] In a table in which customer situations are associated with recommended coupons, emotions may be categorized. An example will be described in which emotions are categorized into positive emotions and negative emotions. In this case, a recommended coupon for a 10% discount on sweets is associated with positive emotions. Furthermore, a recommended coupon for a 100 yen discount on energy drinks is associated with negative emotions. Examples of emotion categorization are not limited to these. Furthermore, examples of recommended coupons associated with emotion categories are not limited to these. The determination unit 102 can determine recommended coupons associated with the customer emotions estimated by the estimation unit 105.

[0048] When the estimation unit 105 estimates the attributes of a customer, the determination unit 102 determines recommended coupons according to the attributes of the customer. At this time, the determination unit 102 can determine the recommended coupons by referring to a table in which recommended coupons are associated with customer attributes. For example, if the customer's attributes are a male office worker in his 40s, a coupon that offers 10% off coffee is associated with the attribute. Also, a recommended coupon that offers 50 yen off chocolate is associated with a teenage student customer. Examples of associations between attributes and recommended coupons are not limited to these. Furthermore, emotions may be categorized. The determination unit 102 can determine recommended coupons that are associated with the customer attributes estimated by the estimation unit 105.

[0049] When the estimation unit 105 estimates the customer's visit mode, the determination unit 102 determines a recommended coupon according to the customer's visit mode. At this time, the determination unit 102 can determine the recommended coupon by referring to a table in which recommended coupons are associated with the customer's visit mode. For example, when the customer's visit mode is a group of friends, the determination unit 102 can determine a recommended coupon that offers a 20% discount on beer. Furthermore, when the customer's visit mode is a family visit, the determination unit 102 can determine a recommended coupon that offers a 10% discount on ice cream. Emotions may be categorized. The determination unit 102 can determine a recommended coupon associated with the customer's attribute estimated by the estimation unit 105.

[0050] The method for determining recommended coupons according to the customer's situation in the store is not limited to these. For example, if the estimation unit 105 estimates the customer's vital data based on a facial image of the customer, the determination unit 102 may determine recommended coupons according to the customer's vital data. Publicly known techniques may be used to estimate the vital data.

[0051] Another example of a method for determining the contents of recommended coupons will be described.

[0052] The determination unit 102 may determine recommended coupons using information related to product purchases by customers identified based on facial images. The identification unit 106 identifies customers based on facial images of the customers making purchases. The determination unit 102 then determines recommended coupons using information related to product purchases by the identified customers. Customer identification may be performed by an information processing device (not shown) different from the purchasing support device 10. In this case, the determination unit 102 acquires the customer identification result obtained by the information processing device. The determination unit 102 may then determine recommended coupons using information related to product purchases by the identified customers.

[0053] The identification unit 106 identifies a customer by facial recognition, for example, from a facial image of the customer making a purchase. The customer can register a facial image in advance. Facial recognition is performed using the facial image registered by the customer in advance and the facial image of the customer making a purchase. A publicly known technology is used as the facial recognition method.

[0054] The determining unit 102 acquires information about the product purchases of the identified customer. The customer and the information about the product purchases of the customer are associated and stored. In other words, identification information that identifies the customer is associated with the information about the product purchases of the customer. Thus, the determining unit 102 can acquire the information about the product purchases of the identified customer. The acquisition unit 101 may acquire the information about the product purchases of the identified customer.

[0055] An example of the information related to product purchases is at least one of a customer's coupon usage history and a customer's purchase history. That is, the determination unit 102 can determine recommended coupons based on at least one of the coupons included in the usage history and the products included in the purchase history. The customer's coupon usage history and customer purchase history are stored in, for example, the database 92.

[0056] For example, the determination unit 102 may determine, as a recommended coupon, the same coupon as one that the customer has used in the past. The determination unit 102 may also determine, as a target product for the recommended coupon, the same product as one that the customer has used in the past for the coupon. In this case, the determination unit 102 may set a discount rate or discount amount for the recommended coupon that is different from that of the coupon included in the usage history.

[0057] Furthermore, the determination unit 102 may determine a recommended coupon, for example, with a product included in the purchase history as the target product. For example, if a coupon is provided for a product that the customer has previously purchased, the customer may purchase the product again. In this case, the determination unit 102 may determine the discount rate or discount amount of the recommended coupon based on, for example, the purchase frequency or number of purchases of the target product. The purchase frequency or number of purchases is calculated from the purchase history. The discount rate or discount amount of the recommended coupon may be determined by the store. Alternatively, for example, the determination unit 102 may determine the discount rate or discount amount of the recommended coupon based on the tendency of coupons included in the usage history. For example, if the customer tends to use coupons with discount rates equal to or greater than a predetermined percentage, the determination unit 102 may determine the predetermined percentage as the discount rate of the recommended coupon.

[0058] Examples of recommended coupons determined based on coupons included in usage history or products included in purchase history are not limited to these. Furthermore, methods for determining recommended coupons based on coupons included in usage history or products included in purchase history are not limited to these.

[0059] Another example of information related to product purchases is coupon information provided to a customer in advance. That is, the determination unit 102 can determine a recommended coupon based on the coupon information provided to a customer in advance. A coupon may be provided to a customer before the customer visits the store. In this case, the customer and the coupon information provided to the customer are linked. That is, the information identifying the customer are linked to the coupon information. Therefore, the determination unit 102 may determine a coupon linked to a customer identified by the identification unit 106 as a recommended coupon. A coupon provided to a customer in advance may be provided electronically or in printed form. That is, the form of the coupon provided to the customer does not matter. Even if the coupon is printed, the customer and the coupon information provided to the customer are linked.

[0060] Examples of recommended coupons determined based on coupon information provided to customers in advance are not limited to these. Furthermore, methods for determining recommended coupons based on coupon information provided to customers in advance are not limited to these.

[0061] Another example of information related to product purchases is customer attribute information registered in advance by the customer. That is, the determination unit 102 can determine recommended coupons based on the customer attribute information registered in advance by the customer. The method for determining recommended coupons based on customer attribute information registered in advance by the customer is similar to the method for determining recommended coupons based on customer attribute information estimated by the estimation unit 105.

[0062] The determination unit 102 may determine recommended coupons by combining information related to product purchases. For example, the determination unit 102 may determine recommended coupons according to customer attributes based on the analysis results of the relationship between coupons included in the customer's coupon usage history and the customer's attributes. The analysis results of the relationship between coupons included in the customer's coupon usage history and the customer's attributes are the analysis results of analyzing what kind of customers have what kind of coupons they have used. Then, the determination unit 102 determines recommended coupons according to the attributes of customers who visit the store. The customer attributes used to determine recommended coupons may be attribute information registered by the customer or may be attribute information estimated by the estimation unit 105.

[0063] The analysis result of the relationship between the coupons included in the customer's coupon usage history and the customer's attributes may be, for example, a trained model that has been trained on the relationship between the coupons included in the customer's coupon usage history and the customer's attributes. The determination unit 102 inputs the customer's attributes into the trained model. The trained model then outputs recommended coupons according to the customer's attributes. Therefore, the determination unit 102 obtains recommended coupons as an output from the trained model.

[0064] The trained model, which has been trained on the relationship between coupons included in a customer's coupon usage history and the attributes of the customer, may be generated by an analysis unit (not shown). The analysis unit trains the model on the relationship between coupons included in a customer's coupon usage history and the attributes of the customer. For example, customers may use different coupons depending on their attributes. Therefore, the analysis unit can train the model on customer attributes and the coupon usage trends of customers with those attributes. Here, the analysis unit uses attribute information and coupon usage histories of multiple customers. The analysis unit can acquire attribute information and coupon usage histories of multiple customers stored in, for example, the database 92. Here, the customer attribute information may be attribute information registered by the customer or attribute information estimated by the estimation unit 105. The determination unit 102 trains the model on the relationship between coupons included in a customer's coupon usage history and the attributes of the customer who used the coupon.

[0065] The analysis results of the relationship between coupons included in a customer's coupon usage history and customer attributes are not limited to these. In other words, the analysis method of the relationship between coupons included in a customer's coupon usage history and customer attributes is not limited to these.

[0066] The determination unit 102 may further determine the recommended coupon based on a list of discountable products at the store. For example, a store may have some products that may be discounted and some products that may not be discounted. Therefore, the store may set discountable products in advance. The discountable product list may be a list of products eligible for the recommended coupon, or a list of recommended coupons. That is, the discountable product list may include percentages, discount amounts, etc. in addition to the products eligible for the recommended coupon. The determination unit 102 may determine the recommended coupon based on discountable products preset by the store as the target products. The determination unit 102 may also determine the target products of the recommended coupon based on the discountable product list and the purchase history. For example, the determination unit 102 may determine, as the target products of the recommended coupon, products included in the discountable product list that the customer has previously purchased. Alternatively, the determination unit 102 may determine, as the target products of the recommended coupon, products included in the discountable product list that the customer has not previously purchased.

[0067] The determination unit 102 may further determine recommended coupons according to trending products. Trending products are, for example, products that are trending on social networking services (SNS) or products that have been featured on information programs. For example, in a list of products sold in a store, trending products are flagged. The flagging is performed, for example, by the store. Then, the determination unit 102 can determine products eligible for recommended coupons from the flagged products among the products sold in the store.

[0068] The determination unit 102 may determine the content of the coupons using different methods. For example, the determination unit 102 may determine the target product of the recommended coupon and the discount rate of the recommended coupon using different methods. The determination unit 102 may determine the target product based on the customer's sentiment, and the discount rate based on the customer's coupon usage history. Examples of determining the content of the coupons using different methods are not limited to these.

[0069] The recommended coupon determined by the determination unit 102 is stored in association with the customer's facial image. In other words, the information on which customer a similar recommended coupon has been determined is stored. Then, when the customer performs a facial recognition payment that includes a target product, the recommended coupon is applied to the target product. In other words, the customer can receive a discount on the target product.

[0070] The determination unit 102 may determine recommended products to be recommended to a customer. The determination unit 102 may determine the recommended products from products sold in the store. The recommended products are determined in the same manner as the method used by the determination unit 102 to determine products eligible for recommended coupons.

[0071] The output unit 103 is one form of output means that outputs information about recommended coupons, including information about products eligible for the recommended coupons, to the display device 91 in the store. The information about recommended coupons includes information about products eligible for the coupons. The information about recommended coupons may also include, for example, the discount amount, discount rate, and coupon expiration date. The coupon information is not limited to these.

[0072] The output destination to which the output unit 103 outputs the information on the recommended coupons is, for example, a display device 91 in the store. The output unit 103 outputs the information on the recommended coupons to the display device 91. The display device 91 can then display the information on the recommended coupons received from the output unit 103 on a display screen. The customer can then view the display device 91 and recognize the information on the recommended coupons.

[0073] The output unit 103 may output information about recommended coupons to one display device 91. Alternatively, the output unit 103 may output information about recommended coupons to multiple display devices 91. Here, the display device 91 to which the output unit 103 outputs information about recommended coupons may be determined by the determination unit 102. In other words, the determination unit 102 may determine the display device 91 to which the information about recommended coupons is to be output. The determination unit 102 can determine where to output information about recommended coupons.

[0074] The output unit 103 may output information about the recommended coupon to a display device 91 that corresponds to the camera 90 that captured the face image of the customer. For example, a camera 90 is associated with a display device 91 that is close to the camera 90. In this case, the determination unit 102 can determine to output information about the recommended coupon to the display device 91 that is associated with the camera 90 that captured the face image of the customer. In other words, the determination unit 102 can determine to output information about the recommended coupon to the display device 91 that is close to the camera 90 that captured the face image of the customer.

[0075] Furthermore, the output unit 103 may output information about the recommended coupon to a display device 91 that corresponds to the location of the customer and the location of the target product. That is, the determination unit 102 may determine the display device 91 to which to output information about the recommended coupon depending on the location of the customer and the location of the target product. The determination unit 102 may determine, as the output destination, a display device 91 that is installed within a predetermined range from the location of the customer identified by the customer's facial image in the store and within a predetermined range from the display position of the target product of the recommended coupon. For example, when the customer is near the display position of the target product, the output unit 103 may output information about the recommended coupon to a display device 91 installed on a display shelf of the target product. Here, the predetermined range from the location of the customer and the predetermined range from the display position of the target product of the recommended coupon may be the same or different.

[0076] Here, an example of a method for identifying a customer's location will be described. First, the acquisition unit 101 acquires a facial image of the customer captured by a certain camera 90. Then, the determination unit 102 determines a recommended coupon based on the facial image. The acquisition unit 101 may also acquire a new facial image of the same customer that is different from the facial image used to determine the recommended coupon. In this case, the determination unit 102 can identify the location of the matched customer by comparing the facial image used to determine the recommended coupon with the newly acquired facial image. In other words, the customer's location can be identified based on the position of the camera 90 that captured the newly acquired facial image. The comparison between the facial image used to determine the recommended coupon and the newly acquired facial image may be performed by the comparison unit 107, which will be described later.

[0077] The method of determining the display device 91 to which the output unit 103 outputs the product information is not limited to these. Also, examples of the display device 91 to which the output unit 103 outputs the product information are not limited to these.

[0078] The output unit 103 may output a display screen that includes information about the recommended coupons and that can accept a request to use the recommended coupons. The recommended coupons for which a request to use them has been accepted may then be applied to eligible products in a face authentication payment, which will be described later.

[0079] Here, an example of the display screen of the display device 91 will be described with reference to the drawings. The output unit 103 outputs information about recommended coupons to the display device 91. The display device 91 then displays the received information about the recommended coupons on the display screen. Fig. 4 shows an example of the display screen of the display device 91.

[0080] FIG. 4 is an example of a display screen displaying information about a recommended coupon. FIG. 4 is an example of a display screen when a refresh drink is determined as the product covered by the recommended coupon. In the example shown in FIG. 4, the discount amount is 30 yen. Referring to FIG. 4, the display screen displays an image, name, price, and discount amount of the product covered as information about the recommended coupon. Customers can obtain information about the recommended coupon by checking a display screen such as the example shown in FIG. 4.

[0081] In the example of Figure 4, a "Use" button is displayed on the display screen. When a customer wants to use a recommended coupon, they press the "Use" button. By pressing the "Use" button, the customer can use the recommended coupon. When the customer presses the "Use" button, the customer's facial image is linked to the recommended coupon. Then, when the customer makes a facial recognition payment that includes a target product, the recommended coupon is applied to the target product. In other words, the customer can receive a discount on the target product.

[0082] Next, a case where the determination unit 102 determines multiple recommended coupons will be described. In this case, the recommended coupon information output by the output unit 103 includes information on multiple recommended coupons. The output unit 103 may also output a display screen that can accept a usage request for a recommended coupon that the customer wants to use from the multiple recommended coupons. For example, the customer inputs a usage request via the screen for a recommended coupon that the customer wants to use from the multiple recommended coupons displayed on the screen. The output unit 103 accepts the usage request for the recommended coupon for which the customer has input a usage request. Then, the recommended coupon for which the usage request has been accepted is applied to the target product in the facial recognition payment described below. The usage request for the recommended coupon may be one or multiple. In other words, the customer may request to use one or multiple recommended coupons from the multiple displayed recommended coupons.

[0083] Here, other examples of the display screen of the display device 91 will be described with reference to the drawings. FIG. 5 shows an example of the display screen of the display device 91. FIG. 5 is an example of a display screen on which information on a plurality of recommended coupons is displayed. Referring to FIG. 5, four recommended coupons are displayed on the screen. The information on each recommended coupon includes an image of the target product, its name, price, and discount amount. The method of displaying each recommended coupon is not limited to this example.

[0084] When the output unit 103 outputs information about multiple recommended coupons, the display order of the multiple recommended coupons may be determined, for example, based on the position of the display device 91. For example, the multiple recommended coupons may be displayed in order of proximity to the display position of each of the products that the multiple recommended coupons are displayed on and the location where the display device 91 is installed.

[0085] The display order of the multiple recommended coupons may be determined based on, for example, the customer's purchase history. The multiple coupons may be displayed, for example, in descending order of the number of purchases of each of the products covered by the multiple recommended coupons. By offering a coupon for a product that a customer frequently purchases, the customer may be encouraged to purchase that product. Furthermore, the multiple coupons may be displayed, for example, in descending order of the number of purchases of each of the products covered by the multiple recommended coupons. By offering a coupon for a product that a customer has never purchased before or has purchased infrequently, a customer may purchase such a product.

[0086] The method for determining the display order of the multiple recommended coupons is not limited to the above. The display order of the multiple recommended coupons may be determined based on, for example, the coupon usage history of the customer.

[0087] Referring again to FIG. 5, an explanation will be given. In the example of FIG. 5, a "Use" button is displayed for each recommended coupon. When a customer wants to use a recommended coupon, they press the "Use" button. By pressing the "Use" button, the customer can use the coupon. The customer may press the "Use" button for multiple recommended coupons. When the customer presses the "Use" button, the customer's facial image and the recommended coupon are linked and saved. Then, when the customer performs a facial recognition payment that includes a target product, the recommended coupon is applied to the target product. In other words, the customer can receive a discount on the target product.

[0088] The above-mentioned "Use" button is one example of a method for accepting a request to use a recommended coupon. The method for accepting a request to use a recommended coupon does not have to be by pressing a button. For example, the request to use a recommended coupon may be accepted by voice recognition. When information about the recommended coupon is displayed on the display device 91, the output unit 103 may ask the customer by voice output whether they intend to use the recommended coupon. If the customer replies that they intend to use the recommended coupon, the output unit 103 may determine that they have accepted the request to use the recommended coupon. If the customer does not replies that they intend to use the recommended coupon, the output unit 103 may determine that they have not accepted the request to use the recommended coupon.

[0089] The display screens shown in FIGS. 4 and 5, for example, can be terminated when a customer moves away from the display device 91. Whether or not a customer has moved away from the display device 91 is determined based on an image captured by the camera 90. For example, if a camera 90 close to the display device 91 that displays information about recommended coupons cannot capture an image of the customer, it may be determined that the customer has moved away from the display device 91. Furthermore, the display screens shown in FIGS. 4 and 5, for example, may be terminated after a predetermined time has elapsed since the display. The predetermined time is, for example, a time period determined in advance by the store. The predetermined time is, for example, a time period during which a customer can check information about recommended coupons displayed on the display screen. The timing for terminating the display is not limited to these.

[0090] A case where the determination unit 102 determines a recommended product will also be described. At this time, the output unit 103 may output information about the recommended product to a display device 91 in the store. An example of information about the recommended product is an advertisement for the recommended product. That is, information about the recommended product includes the name and price of the recommended product. Information about the recommended product may include an image of the recommended product and a catchphrase for the recommended product. Product information about the recommended product is not limited to these. Product information about the recommended product may be any information that the store wants to provide to customers regarding the recommended product. Furthermore, the output destination of the information about the recommended product may be one display device 91 or multiple display devices 91.

[0091] The execution unit 104 is one aspect of an execution means for executing a facial recognition payment in which a facial image of a customer making a purchase is compared with a facial image of the customer acquired for facial recognition payment, and a recommended coupon recommended to the matched customer is applied to the target product. The customer pays for the product to be purchased using facial recognition payment. At this time, a facial image of the customer is acquired for facial recognition payment. The execution unit 104 then compares the facial image of the customer acquired for facial recognition with a facial image of the customer making a purchase captured in the store.

[0092] The execution unit 104 checks whether or not there is a customer whose face image is captured in the store that is the same as the customer whose face image is captured in the face image acquired for face recognition payment. If there is a customer whose face image is captured in the store that is the same as the customer whose face image is captured for face recognition payment, matching is successful. If the customer whose face image is captured in the store matches the customer whose face image is captured in the face image acquired for face recognition payment, matching is successful.

[0093] If the matching is successful, the execution unit 104 executes a facial recognition payment in which the coupon recommended to the customer who matches the matching is applied to the target product. In other words, the execution unit 104 executes a facial recognition payment in which the recommended coupon linked to the face image of the same customer as the customer captured in the face image acquired for the facial recognition payment is applied to the target product. The facial recognition payment is executed in a state in which the coupon whose information is output to the display device 91 is applied to the target product of the coupon. For example, if the coupon is for a 20% discount on the target product, the facial recognition payment is executed for the amount of the target product that is 20% off. Examples of applying a coupon to a target product are not limited to this.

[0094] Even when a customer's request to use a coupon is accepted, the execution unit 104 executes facial recognition payment in which a recommended coupon linked to the facial image of the same customer as the customer captured in the facial image acquired for facial recognition payment is applied to the target product. When a customer's request to use a recommended coupon is accepted, the recommended coupon linked to the facial image of the customer shopping in the store is the recommended coupon that the customer's request to use has been accepted.

[0095] If none of the customers whose face images are captured in the store are the same as the customer captured in the face image acquired for facial recognition payment, matching fails. If the customer whose face image is captured in the store does not match the customer captured in the face image acquired for facial recognition payment, matching fails. If matching fails, the execution unit 104 executes facial recognition payment for the product purchased by the customer. A case in which matching fails would be, for example, if a customer making a facial recognition payment is not provided with a coupon via the display device 91 in the store. Therefore, no coupon is applied to the payment of a customer making a facial recognition payment. If the customer brings or registers coupons when making a facial recognition payment, those coupons may be applied.

[0096] Here, the facial image matching is performed using known technology. For example, the facial image matching is performed by comparing facial features contained in each facial image. Furthermore, if the purchasing support device 10 is equipped with a matching unit 107, the matching unit 107 may match the facial image of the customer making a purchase with the facial image of the customer acquired for facial recognition payment.

[0097] If the products eligible for the recommended coupon are not included in the purchased items, the recommended coupon will not be applied to facial recognition payment.If the products eligible for the recommended coupon that you have requested to use are not included in the purchased items, the recommended coupon will not be applied to facial recognition payment.

[0098] 6 to 8, the operation of the purchasing support device 10, which includes an acquisition unit 101, an estimation unit 105, a determination unit 102, an execution unit 104, an estimation unit 105, an identification unit 106, and a matching unit 107, will be described. FIGS. 6 to 8 are flowcharts showing the operation of the purchasing support device 10. FIGS. 6 and 7 are flowcharts showing an example of the operation of the purchasing support device 10 when it outputs information about recommended coupons.

[0099] FIG. 6 is a flowchart showing an example of the operation of purchasing support device 10 when estimating a customer's situation in a store and determining recommended coupons. First, in step S101, acquisition unit 101 acquires a facial image of a customer shopping. In step S102, estimation unit 105 estimates the customer's situation in the store using the facial image acquired by acquisition unit 101. In step S103, determination unit 102 determines recommended coupons according to the estimated customer situation. In step S104, output unit 103 outputs information about the recommended coupons to display device 91 in the store. Then, purchasing support device 10 ends its operation.

[0100] FIG. 7 is a flowchart showing an example of the operation of purchasing support device 10 when identifying a customer and determining recommended coupons using information related to the identified customer's product purchases. First, in step S201, acquisition unit 101 acquires a facial image of the customer making a purchase. In step S202, identification unit 106 identifies the customer based on the facial image acquired by acquisition unit 101. In step S203, determination unit 102 determines recommended coupons using information related to the identified customer's product purchases. In step S204, output unit 103 outputs information about the recommended coupons to display device 91 in the store. Then, purchasing support device 10 ends its operation.

[0101] FIG. 8 is a flowchart showing an example of the operation of purchasing support device 10 when performing facial recognition payment. In other words, a recommended coupon determined by the operation of FIG. 6 or FIG. 7 may be applied to a target product in the facial recognition payment of FIG. 8. Referring to FIG. 8, in step S301, acquisition unit 101 acquires a facial image of a customer for facial recognition payment. In step S302, matching unit 107 matches the facial image of a customer who visited the store with the facial image of the customer acquired for the facial recognition payment. Then, in step S303, execution unit 104 executes facial recognition payment in which the recommended coupon recommended to the matched customer is applied to the target product. Then, purchasing support device 10 ends its operation.

[0102] In this embodiment, the determining unit 102 of the purchasing support device 10 determines recommended coupons to recommend to customers based on information about product purchases acquired from facial images of customers making purchases. The output unit 103 then outputs recommended coupon information, including information about products eligible for the recommended coupons, to the display device 91 in the store. The execution unit 104 then compares the facial image of the shopping customer with the facial image of the customer acquired for facial recognition payment, thereby executing facial recognition payment in which the recommended coupon recommended to the matched customer is applied to the eligible products. This configuration of the purchasing support device 10 allows customers to easily use coupons.

[0103] Coupon information provided by a store may be sent to a terminal used by a customer. In this case, the customer may need to operate the terminal to check the coupon information. Furthermore, the customer may need to operate the terminal to use the coupon. Even if the coupon is printed, the customer must carry or present the coupon. However, in the purchasing support device 10, the output unit 103 outputs recommended coupon information to a display device 91 in the store. Therefore, while shopping, the customer can obtain coupon information by looking at the screen of the display device 91. In other words, the customer can reduce the effort required to check the coupon. Furthermore, the execution unit 104 executes a facial recognition payment in which the recommended coupon recommended to the customer is applied to the target product. Therefore, the customer can reduce the effort required to present the coupon. As a result, the customer can easily use the coupon.

[0104] Furthermore, the execution unit 104 compares the facial image of the customer making a purchase with the facial image of the customer acquired for the facial recognition payment, thereby executing a facial recognition payment in which a recommended coupon recommended to the matched customer is applied to the target product. Because the execution unit 104 compares the facial image of the customer making a purchase with the facial image of the customer acquired for the facial recognition payment, the execution unit 104 can execute the facial recognition payment in which the recommended coupon is applied even if the customer has not registered as a member with the store. For example, even if the customer visits the store for the first time, the execution unit 104 can execute the facial recognition payment in which the recommended coupon is applied. In other words, the execution unit 104 compares the facial image of the customer making a purchase with the facial image of the customer acquired for the facial recognition payment, thereby executing a facial recognition payment in which a recommended coupon recommended to the matched customer is applied to the target product, thereby improving customer convenience.

[0105] In addition, for example, when a customer needs to present a coupon at checkout, it may take the customer time to present the coupon. In other words, the time required for checkout may be extended because the customer has to present the coupon. Furthermore, as a result of the extended checkout time, there may be waiting times at checkout. However, by executing a facial recognition payment in which a coupon recommended to the customer is applied to the target product, the execution unit 104 can reduce the time required for the customer to present the coupon. As a result, the customer can checkout smoothly, which can improve customer satisfaction. Furthermore, it is possible to prevent waiting times at checkout and alleviate congestion at the cash register.

[0106] Furthermore, customers may purchase products that they would not purchase if the coupon could not be used. In other words, making it easier for customers to use coupons may increase the likelihood that they will purchase products covered by the coupon. As a result, stores can increase their sales.

[0107] In this embodiment, the output unit 103 of the purchasing support device 10 outputs a display screen including information about the recommended coupon and capable of accepting a request to use the recommended coupon. The execution unit 104 executes a facial recognition payment in which the recommended coupon, for which the request to use the coupon has been accepted, is applied to the target product. For a store, offering discounts using coupons can sometimes hurt sales. Therefore, it may be desirable for the store to apply discounts to coupons that the customer intends to use. By configuring the purchasing support device 10 to accept a request to use a recommended coupon through customer operation, the store can confirm the customer's intention to use the recommended coupon. When the purchasing support device 10 accepts a request to use the recommended coupon, the customer is looking at the display screen, which may have a promotional effect on the target product of the recommended coupon. For example, a customer may purchase a product that they would not purchase if the coupon were not available, but the fact that the coupon can be used. Therefore, the store can advertise the target product of the recommended coupon to customers and then offer a discount.

[0108] Furthermore, the information on the recommended coupon may include information on multiple recommended coupons. The output unit 103 of the purchasing support device 10 outputs a display screen that can accept a request to use a recommended coupon to be used by the customer from among the multiple recommended coupons. When the output unit 103 outputs information on multiple recommended coupons, it may not be desirable for the store to offer discounts on products covered by all of the recommended coupons. Therefore, the execution unit 104 executes a facial recognition payment in which a recommended coupon for which a request to use has been accepted is applied to the target product, thereby advertising the target product to the customer and then offering a discount. Even for products covered by recommended coupons for which a request to use has not been accepted from the customer, the customer can check the information on the target product via the display screen, which may have a promotional effect.

[0109] In this embodiment, the output unit 103 of the purchasing support device 10 outputs a display screen displaying information about multiple recommended coupons in an order changed according to the position of the display device 91. A customer may purchase a product covered by a recommended coupon after viewing the information about the recommended coupon. In this case, if the product the customer intends to purchase is displayed far from the customer's location, the customer may find it inconvenient to go and retrieve the product. Therefore, for example, the multiple recommended coupons may be displayed in order of proximity to the display location of each product covered by the multiple recommended coupons and the location where the display device 91 is installed, thereby increasing the likelihood that the customer will purchase the product covered by the recommended coupon. This can increase store sales.

[0110] In the purchasing support device 10 according to this embodiment, the information related to product purchases is at least one of the customer's coupon usage history or the customer's purchase history. The determination unit 102 determines recommended coupons based on coupons included in the usage history or products included in the purchase history. When recommended coupons are determined based on coupons used by the customer in the past, the customer is more likely to use the recommended coupons. For example, the determination unit 102 may determine the same coupons as coupons used by the customer in the past as recommended coupons. When recommended coupons are determined based on the customer's purchase history, the customer is more likely to use the recommended coupons. Increasing the likelihood that customers will use recommended coupons can improve store sales.

[0111] In the purchasing support device 10 according to this embodiment, information related to product purchases is information related to the customer's situation in the store. The determination unit 102 determines recommended coupons according to the customer's situation in the store. By determining recommended coupons according to the customer's situation, the customer is more likely to use the recommended coupons. Increasing the likelihood that customers will use the recommended coupons can improve store sales.

[0112] The information about the situation in the store includes information about the customer's emotions, estimated based on facial images of the customers shopping. The determination unit 102 then determines recommended coupons based on the customer's emotions. A customer may purchase different products depending on their emotions. Therefore, by determining products eligible for recommended coupons based on the customer's emotions, the likelihood that the customer will purchase the products increases. As a result, the store's sales can be improved.

[0113] The information about the situation in the store also includes information about the customer's visit mode, which is estimated based on a facial image of the customer shopping. The determination unit 102 then determines recommended coupons according to the customer's visit mode. The products a customer purchases may change depending on the customer's visit mode. In other words, the products a customer purchases will change depending on who they visit the store with. Therefore, by determining products eligible for recommended coupons according to the customer's visit mode, the likelihood that the customer will purchase the products increases. As a result, it is possible to improve store sales.

[0114] [Second embodiment] The second embodiment will be described in detail with reference to the drawings. Below, the description of the second embodiment will be omitted to the extent that it does not make the description of the present embodiment unclear.

[0115] The configuration of purchasing support device 20 will be described with reference to Fig. 9. Fig. 9 is a block diagram showing an example of the configuration of purchasing support device 20. Purchasing support device 20 includes an acquisition unit 201, a determination unit 202, an output unit 203, and an execution unit 204.

[0116] The acquisition unit 201 is one aspect of acquisition means for acquiring facial images of customers in the store. The acquisition unit 201 acquires facial images of customers captured by a camera 90 installed in the store. The acquisition unit 201 may acquire a facial image of one customer once or multiple times. The facial image acquired by the acquisition unit 101 is used by the determination unit 202, which will be described next, to determine recommended coupons to be recommended to the customer.

[0117] The determination unit 202 is one aspect of a determination means that determines recommended coupons to be recommended to a customer based on information about the customer's situation in the store, obtained from a facial image of the customer. The determination unit 202 determines the content of the coupons to be recommended to the customer. The content of the recommended coupons has been described above. The method for determining recommended coupons by the determination unit 202 is the same as the method for determining recommended coupons by the determination unit 202.

[0118] The output unit 203 is one aspect of output means that outputs information about recommended coupons, including information about products eligible for the recommended coupons, to the display device 91 in the store. As described above, the information about recommended coupons includes information about products eligible for the coupons. The information about recommended coupons may also include, for example, the discount amount, discount rate, and coupon expiration date. Coupon information is not limited to these. The output destination to which the output unit 203 outputs the information about recommended coupons is, for example, the display device 91 in the store.

[0119] The execution unit 204 is one aspect of an execution means for executing a facial recognition payment by matching a facial image of a shopping customer with a facial image of the customer acquired for facial recognition payment, in which a recommended coupon recommended to the matched customer is applied to the target product. The customer pays for the product to be purchased using facial recognition payment. At this time, a facial image of the customer is acquired for facial recognition payment. The execution unit 204 then matches the facial image of the customer acquired for facial recognition with a facial image of the shopping customer captured in the store. The execution unit 204 checks whether the customers whose facial images are captured in the store are the same as the customer captured in the facial image acquired for facial recognition payment. If the customers whose facial images are captured in the store are the same as the customer captured in the facial image acquired for facial recognition payment, the matching is successful. If the matching is successful, the execution unit 204 executes a facial recognition payment in which a recommended coupon recommended to the customer who matched the matching is applied to the target product. That is, the execution unit 204 executes the facial recognition payment in which the recommended coupon linked to the facial image of the same customer captured in the facial image acquired for the facial recognition payment is applied to the target product. If there is no customer whose facial image is captured in the store who is the same customer captured in the facial image acquired for the facial recognition payment, matching fails. If matching fails, the execution unit 204 executes the facial recognition payment for the product purchased by the customer.

[0120] 10, the operation of purchasing support device 20 including acquisition unit 201, determination unit 202, output unit 203, and execution unit 204 will be described. FIG. 10 is a flowchart showing the operation of purchasing support device 20.

[0121] In step S401, the acquisition unit 201 acquires a facial image of a customer in the store. In step S402, the determination unit 202 determines a recommended coupon to recommend to the customer based on information about the customer's situation in the store acquired from the customer's facial image. In step S403, the output unit 203 outputs recommended coupon information, including information about the products eligible for the recommended coupon, to the display device 91 in the store. In step S404, the execution unit 204 compares the facial image of the shopping customer with the facial image of the customer acquired for facial recognition payment, thereby executing facial recognition payment in which the recommended coupon recommended to the matched customer is applied to the eligible products. Then, the purchasing support device 10 ends operation.

[0122] In this embodiment, the determining unit 102 of the purchasing support device 10 determines recommended coupons to recommend to customers based on information about product purchases acquired from facial images of customers making purchases. The output unit 103 then outputs recommended coupon information, including information about products eligible for the recommended coupons, to the display device 91 in the store. The execution unit 104 then compares the facial image of the shopping customer with the facial image of the customer acquired for facial recognition payment, thereby executing facial recognition payment in which the recommended coupon recommended to the matched customer is applied to the eligible products. This configuration of the purchasing support device 10 allows customers to easily use coupons.

[0123] [Hardware configuration example] 11 is a diagram illustrating an example of the hardware configuration of a purchasing support device 30 according to the present disclosure. The purchasing support device 30 is realized by a computer. The purchasing support device 30 is an example in which the purchasing support device 10 or the purchasing support device 20 is realized by a computer.

[0124] Purchasing support device 30 includes processor 301, ROM (Read Only Memory) 302, RAM (Random Access Memory) 303, storage device 304 such as a hard disk for storing programs, input / output interface 305 for inputting and outputting data, and communication interface 306 for network connection. Each component is connected via bus 307.

[0125] The processor 301 runs an operating system to control the entire computer. Examples of the processor 301 include a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and a GPU (Graphics Processing Unit). The processor 301 loads a program stored in, for example, a ROM 302 or a storage device 304. The processor 301 then executes each process coded in the program. The processor 301 may execute the processes or instructions in the illustrated flowchart based on the program.

[0126] The ROM 302 stores application programs, programs according to the embodiments, etc. The RAM 303 is used as a work area for the processor 301.

[0127] Examples of the storage device 304 include a semiconductor memory such as a flash memory, a hard disk drive (HDD), etc. The storage device 304 stores, for example, an operating system (OS) program, application programs, and programs according to each embodiment.

[0128] The input / output interface 305 is connected to peripheral devices (not shown) via a wired network or a wireless network.

[0129] The communication interface 306 is connected to a communication network (not shown), such as a LAN (Local Network) or WAN (Wide Area Network), via a wireless or wired network. The communication network may be configured with multiple communication networks. This allows the computer to be connected to external devices via the communication network. The purchasing support device 30 may have components other than those shown in FIG. 11. For example, the purchasing support device 30 may have a drive device or the like. For example, the processor 301 may be attached to a drive device or the like and read programs and data stored in a non-transitory tangible recording medium into the RAM 303.

[0130] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, the configurations in the respective embodiments can be combined with each other without departing from the scope of the present disclosure.

[0131] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0132] (Appendix 1) an acquisition means for acquiring a facial image of a customer shopping at a store; a determining means for determining a recommended coupon to be recommended to the customer based on information regarding the purchase of a product obtained from a facial image of the customer making a purchase; an output means for outputting information about the recommended coupon, including information about products eligible for the recommended coupon, to a display device in the store; an execution means for executing the facial recognition payment by comparing a facial image of the shopping customer with a facial image of the customer acquired for the facial recognition payment, and applying the recommended coupon recommended to the matched customer to the target product; A purchasing support device comprising:

[0133] (Appendix 2) the output means outputs a display screen including information about the recommended coupons and capable of accepting a request to use the recommended coupons; The execution means executes the face authentication payment in which the recommended coupon for which the use request has been received is applied to the target product. 2. A purchasing assistance device as described in appendix 1.

[0134] (Appendix 3) the information on the recommended coupons includes information on a plurality of the recommended coupons; The output means outputs a display screen capable of accepting the request to use the recommended coupon to be used by the customer from among the plurality of recommended coupons. 2. A purchasing assistance device as described in appendix 2.

[0135] (Appendix 4) The output means outputs a display screen on which information on the plurality of recommended coupons is displayed in an order changed according to the position of the display device. 4. A purchasing assistance device as described in appendix 3.

[0136] (Appendix 5) The information regarding the product purchase is a coupon usage history of the customer or a purchase history of the customer, The determining means determines the recommended coupon based on the coupon included in the usage history or the product included in the purchase history. 5. A purchasing support device according to any one of appendices 1 to 4.

[0137] (Appendix 6) the information regarding the product purchase is information regarding the situation of the customer in the store, The determining means determines the recommended coupons according to the situation of the customer in the store. 6. A purchasing support device according to any one of appendices 1 to 5.

[0138] (Appendix 7) the information about the situation in the store includes information about the emotion of the customer estimated based on a facial image of the customer; The determining means determines the recommended coupons according to the customer's feelings when visiting the store. 6. A purchasing assistance device as described in Appendix 6.

[0139] (Appendix 8) the information about the situation in the store includes information about the customer's visit pattern estimated based on a facial image of the customer making a purchase; The determining means determines the recommended coupons according to the type of visit of the customer to the store. 8. A purchasing support device according to claim 6 or 7.

[0140] (Appendix 9) Acquire a facial image of a customer shopping at the store, determining a recommended coupon to be recommended to the customer based on information regarding the product purchase obtained from the facial image of the customer making a purchase; outputting information about the recommended coupon, including information about the product eligible for the recommended coupon, to a display device in the store; By comparing a facial image of the shopping customer with a facial image of the customer acquired for facial recognition payment, the recommended coupon recommended to the matched customer is applied to the target product, and the facial recognition payment is performed. Purchasing support method.

[0141] (Appendix 10) Acquire a facial image of a customer shopping at the store, determining a recommended coupon to be recommended to the customer based on information regarding the product purchase obtained from the facial image of the customer making a purchase; outputting information about the recommended coupon, including information about the product eligible for the recommended coupon, to a display device in the store; By comparing a facial image of the shopping customer with a facial image of the customer acquired for facial recognition payment, the recommended coupon recommended to the matched customer is applied to the target product, and the facial recognition payment is performed. A program that causes a computer to perform a process.

[0142] (Appendix 11) Acquire a facial image of a customer shopping at the store, determining a recommended coupon to be recommended to the customer based on information regarding the product purchase obtained from the facial image of the customer making a purchase; outputting information about the recommended coupon, including information about the product eligible for the recommended coupon, to a display device in the store; By comparing a facial image of the shopping customer with a facial image of the customer acquired for facial recognition payment, the recommended coupon recommended to the matched customer is applied to the target product, and the facial recognition payment is performed. A recording medium that stores a program that causes a computer to execute a process.

[0143] Some or all of the configurations described in Supplementary Notes 2-8, which are dependent on Supplementary Note 1, may also be dependent on Supplementary Notes 9-11 in the same dependent relationship as Supplementary Note 2-8. Not limited to Supplementary Notes 1 and 9-11, some or all of the configurations described as Supplements may also be dependent on various hardware, software, various recording devices for recording software, or systems, within the scope of each of the above-mentioned embodiments. [Explanation of symbols]

[0144] 10, 20, 30 Purchasing support device 101, 201 Acquisition Department 102, 202 Decision Section 103, 203 Output section 104, 204 Executive Department 105 Estimation part 106 Specific part 107 Matching Unit 301 processor 302 ROM 303 RAM 304 Storage device 305 Input / Output Interface 306 Communication Interface 307 Bus 90 Camera 91 Display device 92 databases

Claims

1. an acquisition means for acquiring a facial image of a customer shopping at a store; a determining means for determining a recommended coupon to be recommended to the customer based on information regarding the purchase of a product obtained from a facial image of the customer making a purchase; an output means for outputting information about the recommended coupon, including information about products eligible for the recommended coupon, to a display device in the store; an execution means for executing the facial recognition payment by comparing a facial image of the shopping customer with a facial image of the customer acquired for the facial recognition payment, and applying the recommended coupon recommended to the matched customer to the target product; A purchasing support device comprising:

2. the output means outputs a display screen including information about the recommended coupons and capable of accepting a request to use the recommended coupons; The execution means executes the face authentication payment in which the recommended coupon for which the use request has been received is applied to the target product. The purchasing support device according to claim 1 .

3. the information on the recommended coupons includes information on a plurality of the recommended coupons; The output means outputs a display screen capable of accepting the request to use the recommended coupon to be used by the customer from among the plurality of recommended coupons. The purchasing support device according to claim 2 .

4. The output means outputs a display screen on which information on the plurality of recommended coupons is displayed in an order changed according to the position of the display device. The purchasing support device according to claim 3 .

5. The information regarding the product purchase is at least one of a coupon usage history of the customer and a purchase history of the customer, The determining means determines the recommended coupon based on the coupon included in the usage history or the product included in the purchase history. The purchasing support device according to claim 1 .

6. the information regarding the product purchase is information regarding the situation of the customer in the store, The determining means determines the recommended coupons according to the situation of the customer in the store. The purchasing support device according to claim 1 .

7. the information about the situation in the store includes information about the emotion of the customer estimated based on a facial image of the customer making a purchase; The determining means determines the recommended coupons according to the customer's feelings. The purchasing support device according to claim 6.

8. the information about the situation in the store includes information about the customer's visit pattern estimated based on a facial image of the customer making a purchase; The determining means determines the recommended coupons according to the type of visit of the customer to the store. The purchasing support device according to claim 6.

9. Acquire a facial image of a customer shopping at the store, determining a recommended coupon to be recommended to the customer based on information regarding the product purchase obtained from the facial image of the customer making a purchase; outputting information about the recommended coupon, including information about the product eligible for the recommended coupon, to a display device in the store; By comparing a facial image of the shopping customer with a facial image of the customer acquired for facial recognition payment, the recommended coupon recommended to the matched customer is applied to the target product, and the facial recognition payment is performed. Purchasing support method.

10. Acquire a facial image of a customer shopping at the store, determining a recommended coupon to be recommended to the customer based on information regarding the product purchase obtained from the facial image of the customer making a purchase; outputting information about the recommended coupon, including information about the product eligible for the recommended coupon, to a display device in the store; By comparing a facial image of the shopping customer with a facial image of the customer acquired for facial recognition payment, the recommended coupon recommended to the matched customer is applied to the target product, and the facial recognition payment is performed. A program that causes a computer to perform a process.

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