JP2014013535A - Commodity purchase support device, virtual trial fitting apparatus, and commodity purchase support program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To inform a customer that the customer has already purchased commodities similar to a commodity to be purchased.SOLUTION: A commodity purchase support device has customer identification input means, commodity identification input means, similarity determination means, and output means. The customer identification input means receives an input of customer identification information allowing the customer to be identified. The commodity identification input means receives an input of commodity identification information allowing the commodity which the customer plans to purchase to be identified. The similarity determination means determines a similarity of the commodity which the customer plans to purchase, identified by the commodity identification information input via the commodity identification input means, with respect to commodities already purchased by the customer identified by the customer identification information input via the customer identification input means for each of the commodities already purchased by the customer. The output means outputs the results of determination by the similarity determination means.

Description

  Embodiments of the present invention provide a product purchase support device that supports the purchase of clothing such as clothing and ornaments, a virtual try-on device that virtually attempts to try on clothing, and a computer that functions as a product purchase support device. It relates to a product purchase support program.

  2. Description of the Related Art In recent years, there has been known a technique for forming a composite image in which image data obtained by photographing clothing such as clothing and ornaments is superimposed on image data obtained by photographing a person. By using this technology, it is possible to capture an image of a customer who is considering purchasing clothing items wearing the clothing items without actually wearing them.

  However, since the image of wearing clothing was good, it was purchased, but if a similar item was purchased in the past, the customer would own two or more similar clothing items. For this reason, when purchasing clothing items, not only the image of try-on, but also information on whether similar items have been purchased in the past is an important criterion.

JP 2002-032640 A

  A problem to be solved by the present invention is a product purchase support device that can notify when a customer has purchased a product similar to a product planned for purchase in the past and can effectively support the customer's product purchase, and the like. It is intended to provide a virtual try-on device equipped with a simple product purchase support function.

  In one embodiment, the product purchase support apparatus includes customer identification input means, product identification input means, similarity determination means, and output means. The customer identification input means accepts input of customer identification information that can identify the customer. The product identification input means accepts input of product identification information that allows the customer to identify the product scheduled for purchase. Similarity determination means is a method in which a customer identified by customer identification information input via customer identification input means of a product to be purchased identified by product identification information input via product identification input means has been purchased in the past. The similarity to the purchased product is determined for each purchased product. The output means outputs the determination result by the similarity determination means.

  In addition to the customer identification input unit, the product identification input unit, and the similarity determination unit, the virtual try-on device includes a photographing unit, an output unit, and a purchase planned product try-on unit. The photographing means photographs the customer. The output unit outputs the purchased product when the similarity determination unit determines that there is a purchased product having a similarity equal to or greater than a predetermined first threshold. When it is determined by the similarity determination unit that there is no purchased product having a similarity equal to or higher than the first threshold, the purchase planned product try-on unit adds the purchase planned product to the customer image data captured by the imaging unit. Display images superimposed.

The system block diagram of this embodiment. The block diagram of the goods data record preserve | saved at a goods database. The block diagram of the image data record preserve | saved at an image database. The block diagram of the recommended product data record preserve | saved in a recommended product database. The block diagram of the customer data record preserve | saved at a customer database. The perspective view which shows the fitting room provided with the fitting room terminal. The block diagram which shows the principal part structure of a fitting room terminal. The flowchart which shows the procedure of the information processing which CPU of a fitting room terminal performs according to a goods purchase assistance program. FIG. 9 is a flowchart specifically illustrating a procedure of a forward merchandise continuous operation process in FIG. 8.

  Hereinafter, embodiments will be described with reference to the drawings. In this embodiment, the fitting room terminal 12 provided in the fitting room 13 of a store that sells clothing as merchandise has a product purchase support function and a virtual fitting function.

FIG. 1 is a system configuration diagram of a store provided with a fitting room terminal 12. The system includes a virtual try-on system 10 and a POS system 20.
The virtual fitting system 10 includes a fitting support server 11, a plurality of fitting room terminals 12 installed for each fitting room 13, and a network 14 such as the Internet. The fitting support server 11 is connected to each fitting room terminal 12 via the network 14 so as to be capable of two-way communication. A POS server 21 of the POS system 20 is also connected to the network 14. The POS system 20 includes a POS server 21, a plurality of POS terminals 22, and a network 23 such as a LAN (Local Area Network). The POS server 21 is connected to each POS terminal 22 via the network 23 so as to be capable of bidirectional communication. The POS terminal 22 registers the sales data of the product purchased by the customer. The POS server 21 collects and aggregates sales data of each product registered at each POS terminal 22 via the network 23 and manages sales, inventory, and the like of the entire store.

  The try-on support server 11 manages a product database 15, an image database 16, a recommended product database 17, and a customer database 18. The product database 15 stores data related to products (clothing) sold at the store. The image database 16 stores image data of each product (clothing). The recommended product database 17 stores data related to recommended products for each category of clothing. The customer database 18 stores data regarding customers who have become members.

  FIG. 2 is a configuration diagram of a product data record 15 </ b> R stored in the product database 15. As shown in FIG. 2, the product data record 15R includes items such as a product ID, a classification type, a type type, a product name, a price, the number of inventory, pattern information, and color information. The product ID is a unique code (product identification information) assigned to identify the product individually. The classification type is a code that specifies the clothing classification (suit, dress, jacket, shirt, tie, etc.) of the product identified by the corresponding product ID. The type classification indicates the type of the product identified by the corresponding product ID (for example, if the clothing classification is “suit”, single 2 buttons, single 3 buttons, double 2 buttons, double 3 buttons, etc.) This is the code to identify.

  The product name, price, quantity in stock, pattern information, and color information are data of the product identified by the corresponding product ID. The number of stocks is set when the product identified by the corresponding product ID is received, and then the registration score is subtracted every time the product is registered for sale in the POS system 20. The pattern information and the color information are obtained by quantifying the pattern and color of the product identified by the corresponding product ID, and take closer values as they are similar.

  In the present embodiment, the pattern information and the color information are digitized in the range of “1” to “40”, respectively. In other words, in the case of a product with pattern information “1”, other products with pattern information “2” have the most similar pattern information, and other products with pattern information “40” have the most similar pattern information. It can be said that it is not. In the case of a product with the pattern information “20”, the other products with the pattern information “19” or “21” have the most similar pattern information, and the other products with the pattern information “40” have the highest pattern. It can be said that the information is not similar. The same applies to the color information.

  FIG. 3 is a configuration diagram of the image data record 16R stored in the image database 16. As shown in FIG. 3, the image data record 16R includes a product ID and product image data. The product image data is data of a two-dimensional image obtained by photographing a product (clothing) identified by a corresponding product ID.

  FIG. 4 is a configuration diagram of the recommended product data record 17 </ b> R stored in the recommended product database 17. As shown in FIG. 4, the recommended product data record 17 </ b> R includes classification identification, type identification, the recommended number of products, and product IDs corresponding to the number of products. Classification identification and type identification are as described above. The recommended number of products is the number of products recommended among products in the same category as the category of the product (clothing) specified by the corresponding classification identification and type identification. In the recommended product data record 17R, the product IDs of the recommended products are collectively set.

  FIG. 5 is a configuration diagram of the customer data record 18R stored in the customer database 18. As shown in FIG. 5, the customer data record 18R includes a customer ID, the gender, name, and purchased product information of the customer specified by the customer ID. Purchased product information is information on clothing previously purchased by the corresponding customer. This information includes classification identification, type identification, product name, pattern information, color information, product image data, purchase date, etc. It is.

  Purchased product information is added based on product sales data registered in the POS system 20. That is, the POS terminal 22 is provided with a reader for reading the customer ID. When the customer ID is read by the reader and sales data of the product purchased by the customer is input to the POS terminal 22, the POS server 21 associates the product ID of the product with the customer ID and tries on through the network 14. It transmits to the support server 11. The try-on support server 11 searches the product database 15 and the image database 16 to obtain the classification identification, type identification, product name, design information, color information, product image data, etc. of the product specified by the product ID. Create purchased product information. Then, the try-on support server 11 searches the customer database 18 and additionally registers the purchased product information in the customer data record 18R including the customer ID.

  Note that the purchased product information is not limited to information obtained from the POS system 20 of the store where the virtual try-on system 10 is constructed. For example, by connecting the POS server of the POS system of another store to the network 14, it is also possible to additionally register information on clothing purchased by the same customer at the other store in the customer database 18.

  FIG. 6 is a perspective view showing a fitting room 13 provided with the fitting room terminal 12. As shown in FIG. 6, the fitting room 13 has a rectangular parallelepiped shape in which an upper surface and a part of the front face are opened. An opening portion in front of the fitting room 13 serves as an entrance 31 for customers to enter and exit. A cloth curtain 32 is attached to the doorway 31 so as to be freely opened and closed by a curtain rail (not shown). The entrance / exit 31 of the fitting room 3 may be provided with a door that can be slidably opened and closed, for example, instead of the curtain 32.

  In FIG. 6, since the fitting room 13 with the curtain 32 opened is shown, the inside of the fitting room 13 can be visually recognized. As shown in FIG. 6, a fitting room terminal 12 that displays an image of a customer who tries on clothes as a product is attached to the back wall in the fitting room 13. And the illumination 33 which is a fluorescent lamp is attached to the upper position of this fitting room terminal 12. FIG.

  The fitting room terminal 12 includes a thick flat plate-shaped housing 34. A display unit 35 made of, for example, a liquid crystal display and a camera 36 as a photographing unit for photographing a person who enters the fitting room 13 and tries on a product are attached to the housing 34. On the display surface of the display unit 35, a touch panel sensor 37, which is an operation means, is arranged to overlap. A scanner 38 for reading a barcode and a card reader 39 for reading a customer card are attached in the vicinity of the housing 34.

  The scanner 38 and the card reader 39 are wired to the fitting room terminal 12 so that data communication is possible. The barcode is printed on a product tag attached to the clothing item, and represents the product ID of the clothing item to which the product tag is attached. In the customer card, the customer ID of the customer who owns the card is recorded. Here, the scanner 38 constitutes input means for product identification information (product ID) that can identify the product that the customer plans to purchase. The card reader 39 constitutes input means for customer identification information (customer ID) that can identify a customer.

  On the upper surface of the fitting room 13, an elongated cylindrical pole 41 is erected. For example, a light emitting unit 42 capable of emitting red light is provided at the tip of the pole 41. The light emitting unit 42 is connected to the fitting room terminal 12 through a communication line (not shown) passing through the pole 41, and is subjected to operation control by a CPU (Central Processing Unit) 51 (see FIG. 7) of the fitting room terminal 12. In addition, the light emission part 42 is not performing light emission operation in the state which has not received operation control by CPU51.

  FIG. 7 is a block diagram showing a main configuration of the fitting room terminal 12. The fitting room terminal 12 includes a CPU 51 as a control unit main body. Further, a ROM (Read Only Memory) 52 and a RAM (Random Access Memory) 53 which are main storage units, and a flash memory 54 which is a nonvolatile memory are provided. The ROM 52, RAM 53, and flash memory 54 are connected to the CPU 51 via a bus line 55 such as an address bus or a data bus.

  The fitting room terminal 12 connects the CPU 51 with a communication interface 56, a scanner interface 57, a camera controller 58, a display controller 59, a touch panel controller 60, a card reader controller 61, and an output port 62 via a bus line 55.

  The communication interface 56 is connected to the network 14 and manages data communication performed with the fitting support server 11 via the network 14. The scanner interface 57 takes in the barcode data read by the scanner 38. The camera controller 58 controls the shooting operation of the camera 36. The display controller 59 controls the screen display of the display unit 35. The touch panel controller 60 detects the coordinate data of the touch position based on the output signal from the touch panel sensor 37. The card reader controller 61 controls the card reader 39 to capture data (customer ID) read from the customer card. The output port 62 outputs a light emission on / off signal to the light emitting unit 42.

  The ROM 52 stores fixed data such as a product purchase support program and a try-on support program. The RAM 53 stores variable data such as image data captured by the camera 36 and product data specified by the barcode read by the scanner 38.

  The CPU 51 of the fitting room terminal 12 having such a configuration displays a start button on the display unit 35 as an initial state. When detecting that the start button is touched by a signal from the touch panel sensor 37, the product purchase support program is started. By starting this program, the CPU 51 starts processing of the procedure shown in the flowchart of FIG.

  First, the CPU 51 receives an input of a customer ID of a customer who uses the try-on system (ST1: customer identification input means). At this time, the CPU 51 causes the display unit 35 to display a message indicating that the customer card data is read by the card reader 39, and prompts the customer to input the customer ID. When the customer ID recorded on the customer card is read by card reader 39 (YES in ST1), CPU 51 transmits a search command for customer database 18 including the customer ID to try-on support server 11.

  Upon receiving this command, the fitting support server 11 searches the customer database 18 to read the customer data record 18R including the customer ID in the command and transmits it to the fitting room terminal 12 that is the command transmission source. The CPU 51 of the fitting room terminal 12 that has received this customer data record 18R acquires all the purchased product information from the customer data record 18R and stores it in the purchased product information area of the RAM 53 (ST2).

  Next, the CPU 51 receives an input of a product ID of a product to be purchased (clothing) (ST3: product identification input means). At this time, the CPU 51 causes the display unit 35 to display a message that causes the scanner 38 to read the barcode printed on the product tag of the product to be purchased, and prompts the customer to input the product ID. When the barcode of the product tag attached to the product to be purchased is read by scanner 38 (YES in ST3), CPU 51 transmits a search command for product database 15 including the product ID to try-on support server 11.

  Upon receiving this command, the fitting support server 11 searches the product database 15, reads the product data record 15R including the product ID in the command, and transmits it to the fitting room terminal 12 that is the command transmission source. The CPU 51 of the fitting room terminal 12 that has received the product data record 15R acquires product information such as classification identification, type identification, product name, price, quantity in stock, pattern information, color information, etc. from the product data record 15R. Store in the planned purchase product area (ST4).

  Next, the CPU 51 calculates the degree of similarity X with the product to be purchased for each purchased product that the customer has purchased in the past (ST5). Specifically, the degree of similarity is calculated by comparing the classification type, the type type, the pattern information, and the color information for the planned purchase product and each purchased product. First, both classification types are compared. If the classification types are different, the similarity X is set to “0”. If the classification types match, both types are compared. If the type types are different, the similarity X is set to “10”. If the type types also match, a numerical value difference ΔA indicating both pieces of pattern information is taken. Further, a numerical difference ΔB indicating both color information is also taken. Then, the similarity X is calculated by the following equation (1).

X = 100− (ΔA + ΔB) (1)
As described above, in the present embodiment, since the pattern information and the color information are quantified in the range of “1” to “40”, the similarity X when both the classification type and the type type match is It changes in the range of “20” to “100”. And as the degree of similarity X is larger, it can be said that the planned purchase product and the purchased product are more similar.

  In this way, if the similarity X with the purchase planned product is calculated for each purchased product, the CPU 51 purchases the similarity X equal to or greater than the first threshold value S1 that is an arbitrary value equal to or greater than “10”. It is determined whether or not a finished product exists (ST6: similarity determination means).

  When the first threshold value S1 is set to “10”, for example, if there is at least one purchased product having the same classification type as the planned purchase product, “YES” is determined in step ST6. Further, when the first threshold value S2 is set to “60”, for example, it is a purchased product having the same classification type and type type as the planned purchase product, and the pattern information difference ΔA and the color information If there is a purchased product whose total with the difference ΔB is “40” or less, “YES” is determined in step ST6.

  If there is no purchased product with similarity X equal to or greater than first threshold value S1 (NO in ST6), CPU 51 proceeds to the process of step ST9. On the other hand, when there is a purchased product with similarity X equal to or higher than first threshold value S1 (YES in ST6), CPU 51 purchases with similarity score X equal to or higher than first threshold value S1. Information on the finished product is read from the purchased product information area and stored in the similar product area. Then, the CPU 51 creates a list of purchased products whose information is stored in the similar product area, and displays the list on the display unit 35 (ST7: output means).

  In the list, it is assumed that images of purchased products are arranged in descending order of similarity X. Alternatively, it is assumed that images of purchased products are arranged in order of purchase date from oldest to newest. In the list, a button image for declaring whether or not to perform “recommended search” and a message prompting the customer to declare are displayed together.

  The CPU 51 waits for a declaration input as to whether or not to perform “recommended search” (ST8). When the signal from touch panel sensor 37 declares that “recommended search” is not to be performed (NO in ST8), CPU 51 proceeds to the process of step ST9. On the other hand, when it is declared that “recommended search” is to be performed (YES in ST8), CPU 51 proceeds to the process of step ST10.

  In step ST9, the CPU 51 executes a try-on process with a purchase-scheduled product (purchased product try-on means). This process follows the fitting support program. That is, the CPU 51 displays a “shoot” button on the display unit 35 and waits for a touch. When the “shoot” button is touched, the CPU 51 operates the camera 36. Thus, since the inside of the fitting room 13 is photographed, the CPU 51 cuts out person image data from the image photographed by the camera 36 and stores it in the customer image area of the RAM 53. Further, the CPU 51 displays a human image on the display unit 35 from the image data stored in the customer image area. Further, the CPU 51 transmits a search command for the image database 16 including the product ID of the product information stored in the planned purchase product area of the RAM 53 to the try-on support server 11.

  The fitting support server 11 that has received this command searches the image database 16 to read the image data record 16R including the product ID in the command, and transmits it to the fitting room terminal 12 that is the command transmission source. The CPU 51 of the fitting room terminal 12 that has received the image data record 16R reads the product image data from the image data record 16R, and displays the product image data image superimposed on the person image of the display unit 35. Thus, the process according to the try-on support program and the product purchase support program is completed.

  In step ST10, the CPU 51 executes recommended product search processing according to the procedure specifically shown in FIG. First, the CPU 51 detects the classification type and the type type from the information of the purchase planned product stored in the purchase planned product area. Then, the CPU 51 transmits a search command for the recommended product database 17 including the classification type and the type type to the try-on support server 11 (ST21).

  Upon receiving this command, the try-on support server 11 searches the recommended product database 17, reads the recommended product data record 17R including the classification type and type type in the command, and transmits it to the fitting room terminal 12 that is the command transmission source. To do. If the recommended product data record 17R including the classification type and the type type is not registered in the recommended product database 17, the try-on support server 11 sends the reply data indicating no registration to the fitting room terminal of the command transmission source. 12 to send.

  The CPU 51 of the fitting room terminal 12 that has transmitted the search command for the recommended product database 17 waits for reception of the recommended product data record 17R (ST22). If the answer data indicating no registration is received (NO in ST22), the CPU 51 proceeds to the process of step ST37. In step ST <b> 37, the CPU 51 executes a try-on process with a product scheduled for purchase. Since the procedure of this fitting process is the same as the procedure of step ST9, the description thereof is omitted here.

  When the recommended product data record 17R is received (YES in ST22), the CPU 51 acquires the recommended product number and the product IDs for the recommended product number from the recommended product data record 17R and stores them in the recommended product area of the RAM 53. Then, the CPU 51 sets the recommended number of products in the register N (ST23). Further, the CPU 51 resets the first counter m and the second counter n to “0”, respectively (ST24).

  Next, the CPU 51 counts up the second counter n by “1” (ST25). Then, the CPU 51 checks whether or not the count value of the second counter n has exceeded the value of the register N (ST26). When the count value of second counter n does not exceed the value of register N (NO in ST26), CPU 51 determines nth product IDn (n is the count value of second counter n) from the recommended product area of RAM 53. Read and transmit a search command of the product database 15 including the product IDn to the try-on support server 11.

  Upon receiving this command, the fitting support server 11 searches the product database 15, reads the product data record 15R including the product IDn in the command, and transmits it to the fitting room terminal 12 that is the command transmission source. The CPU 51 of the fitting room terminal 12 that has received the product data record 15R acquires product information such as classification identification, type identification, pattern information, and color information from the product data record 15R, and stores it in the similarity determination area of the RAM 53 ( ST27).

  Next, the CPU 51 calculates the similarity X with the recommended product having the product information stored in the similarity determination area for each purchased product that the customer has purchased in the past (ST28). Since the procedure of this similarity calculation process is the same as the process procedure of step ST5, description thereof is omitted here.

  When the similarity X with the recommended product of the product IDn is calculated for each purchased product, the CPU 51 has already purchased the similarity X equal to or greater than a second threshold value S2 that is an arbitrary value greater than “20”. It is determined whether or not there is any product (ST29: recommended candidate selection means).

  When the second threshold value S2 is set to “60”, for example, the purchased product has the same classification type and type type as the recommended product of the product IDn, and the pattern information difference ΔA and the color information difference ΔB If there is even one purchased product with a total of “40” or less, “NO” is determined in step ST29. Similarly, when the second threshold value S2 is set to “80”, for example, the purchased product has the same classification type and type type as the recommended product of the product IDn, and the pattern information difference ΔA and the color information If there is at least one purchased product whose total with the difference ΔB is “20” or less, “NO” is determined in step ST29.

  If there is even one purchased product with similarity X equal to or greater than second threshold value S2 (NO in ST29), CPU 51 returns to the process in step ST25. On the other hand, if there is no purchased product with similarity X equal to or greater than second threshold value S2 (YES in ST29), CPU 51 executes the processes of steps ST30 and ST31, The process returns to step ST25.

  In step ST30, the CPU 51 acquires the maximum value Xmax in the similarity X calculated in the similarity calculation process in step ST28. Then, the CPU 51 stores the product information of the recommended product with the product IDn and the similarity maximum value Xmax in a pair in the recommended product buffer of the RAM 53. In step ST31, the CPU 51 counts up the first counter m by “1”.

  The CPU 51 repeatedly executes the processes of steps ST26 to ST31 every time the second counter n is incremented by “1”. If the count value of second counter n exceeds the value of register N (YES in ST26), CPU 51 checks whether or not first counter m is greater than “0” (ST32). When the first counter m is “0” (NO in ST32), a recommended product in which there is no purchased product having a similarity X equal to or higher than the second threshold value S2 is set in the recommended product database 17. Absent. In this case, the CPU 51 proceeds to the process of step ST37 described above.

  On the other hand, when first counter m is greater than “0” (YES in ST32), CPU 51 creates a recommended product list based on the product information stored in the recommended product buffer, and displays the display unit. 35 (ST33: recommended product guidance means).

  In the list, it is assumed that images of recommended products are arranged in order of increasing or decreasing similarity maximum value Xmax. The image data of the recommended product is acquired by inquiring the try-on support server 11 with the product ID of the product information stored in the recommended product buffer. Recommended products on the list can be selected by touching an image. In the list, a button image for declaring whether or not to select a recommended product and a message prompting the customer to declare are displayed together.

  The CPU 51 waits for a declaration input as to whether or not to select a recommended product (ST34). If a signal from touch panel sensor 37 declares that the recommended product is not to be selected (NO in ST34), CPU 51 proceeds to the process of step ST37 described above.

  On the other hand, when any recommended product on the list is selected (YES in ST34), CPU 51 executes a try-on process using the selected recommended product (ST35: recommended product try-on means). . This process follows the fitting support program. That is, the CPU 51 displays a “shoot” button on the display unit 35 and waits for a touch. When the “shoot” button is touched, the CPU 51 operates the camera 36. Thus, since the inside of the fitting room 13 is photographed, the CPU 51 cuts out person image data from the image photographed by the camera 36 and stores it in the customer image area of the RAM 53. Further, the CPU 51 displays a human image on the display unit 35 from the image data stored in the customer image area. Further, the CPU 51 displays the selected recommended product image superimposed on the person image of the display unit 35.

  The CPU 51 waits for a declaration input to end the fitting process (ST36). If there is no declaration to end the try-on process (NO in ST36) and another product is selected from the recommended product list (YES in ST34), CPU 51 determines the selected recommended product. A fitting process is executed (ST35). If there is no declaration to end the try-on process (NO in ST36) and it is declared that the recommended product is not selected (NO in ST34), CPU 51 proceeds to the process of step ST37 described above. .

  If the signal from touch panel sensor 37 is declared to end the fitting process (YES in ST36), the process according to the fitting support program and the product purchase support program is ended.

  A customer who uses the fitting room 13 provided with the fitting room terminal 12 having such a configuration first causes the card reader 39 to read the data of his / her customer card. Next, the barcode of the clothing item scheduled for purchase is read by the scanner 38. Then, the fitting room terminal 12 calculates the similarity X between the clothing purchased by the customer in the past and the clothing planned to be purchased this time. Then, in the fitting room terminal 12, it is determined whether or not there is a purchased product having a similarity X equal to or higher than a first threshold value S1 set in advance.

  Here, when there is no purchased clothing having a similarity degree X equal to or higher than the first threshold value S1, the fitting room terminal 12 performs a fitting process on the clothing to be purchased. That is, the image of the clothing to be purchased is superimposed on the person image of the customer, and an image of trying on the clothing is displayed on the display unit 35.

  On the other hand, when there is even one piece of purchased clothing having a similarity X equal to or higher than the first threshold S1, the fitting room terminal 12 has a similarity X equal to or higher than the first threshold S1. A list of purchased clothing items is displayed on the display unit 35.

  Therefore, the customer can know that he / she has purchased clothing that is relatively similar to the clothing scheduled to be purchased this time. Further, since the image of the similar clothing is displayed, the customer can accurately confirm even the pattern and color of the similar clothing.

  Now, the customer who has confirmed the list of purchased clothing items decides whether to purchase clothing items scheduled to be purchased or to use “recommended search”. When purchasing, the display unit 35 is touched to declare that “recommended search” is not used. Then, the fitting room terminal 12 executes a fitting process with the clothing to be purchased.

  On the other hand, when using “recommended search”, the customer touches the display unit 35 and declares that “recommended search” is to be used. Then, among the recommended products registered in the recommended product database 17, the degree of similarity X with the clothing purchased in the past by the customer in the same classification and the same type as the clothing planned to be purchased. Are selected that are all less than the second threshold value S2. That is, a recommended product that is of the same classification and type as the clothing that was planned to be purchased but is not similar to the clothing that the customer has purchased so far is selected. Then, a list image of the recommended products is displayed on the display unit 35.

  The customer then touches the desired clothing image from the list and selects the clothing to be tried on. Then, the image of the clothing scheduled to be purchased is superimposed on the person image of the customer, and an image of trying on the clothing is displayed on the display unit 35. The same applies when another clothing image in the list is touched. In addition, when a declaration that the recommended product is not selected is made, the try-on process is executed with the original clothing that was planned to be purchased.

  As described above, according to the present embodiment, it is possible to prevent a problem that a customer mistakenly purchases clothing similar to clothing that the customer has purchased in the past. In addition, since the same type of clothing that is not similar to clothing that has been purchased in the past can be recommended to the customer, a sales promotion effect can be expected.

The present invention is not limited to the above embodiment.
For example, in the above-described embodiment, the output unit that outputs the determination result by the similarity determination unit is a unit that displays and outputs an image of a purchased product having a similarity X with respect to a purchase-scheduled product of a predetermined first threshold value S1 or more. However, the output means is not limited to this. For example, the customer may simply be notified of the purchase of a similar product in the past by display or audio output. In addition, the recommended product guide means may display the recommended product name in a selectable manner instead of displaying the recommended product image in a selectable manner.

  Further, the method of calculating the similarity degree X is not limited to the method of the above embodiment, and it goes without saying that various modifications can be made. Further, the first or second threshold values S1 and S2 to be compared with the similarity degree X are not particularly limited as long as the user can set a desired value.

  In the above embodiment, the customer ID, which is customer identification information, is input by the card reader 39. However, the means for inputting the customer ID is not limited to the card reader 39. Similarly, the input means for the product ID, which is product identification information, may be limited to the scanner 38. In short, customer identification information that can identify a customer and product identification information that can identify a product may be input to the computer.

  In addition, an embodiment may be an information processing apparatus that does not have a virtual try-on function, that is, a so-called product purchase support apparatus. Moreover, it is also possible to construct an embodiment using a cloud computing system. In this case, the fitting support server 11 may be arranged in the cloud so that each fitting room terminal 12 can access the fitting support server 11 via the Internet.

  In the above embodiment, a product purchase support program for realizing the functions of the invention is recorded in advance in a ROM which is a program storage unit in the apparatus. However, the present invention is not limited to this, and a similar program may be downloaded from the network to the apparatus. Alternatively, a similar program recorded on the recording medium may be installed in the apparatus. The recording medium may be in any form as long as it can store a program such as a CD-ROM or a memory card and can be read by the apparatus. Further, the function obtained by installing or downloading the program may be realized in cooperation with an OS (operating system) in the apparatus.

  In addition, although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

  DESCRIPTION OF SYMBOLS 10 ... Virtual try-on system, 11 ... Try-on support server, 12 ... Try-on room terminal, 13 ... Try-on room, 14 ... Network, 15 ... Product database, 16 ... Image database, 17 ... Recommended product database, 18 ... Customer database, 20 ... POS system, 35 ... display unit, 36 ... camera, 37 ... touch panel sensor, 38 ... scanner, 39 ... card reader, 51 ... CPU.

Claims (6)

Customer identification input means for accepting input of customer identification information capable of identifying the customer;
Product identification input means for receiving input of product identification information capable of identifying the product that the customer plans to purchase;
Purchased products that have been purchased in the past by the customer identified by the customer identification information input via the customer identification input means of the product scheduled for purchase identified by the product identification information input via the commodity identification input means Similarity determination means for determining the similarity to a product for each purchased product;
Output means for outputting the determination result by the similarity determination means;
A product purchase support apparatus comprising:   2. The product purchase support apparatus according to claim 1, wherein the output unit displays and outputs a purchased product having a similarity to the purchase planned product of a predetermined first threshold value or more. The customer identified by the customer identification information input via the customer identification input means from among a plurality of recommended products for the purchase planned product identified by the product identification information input via the commodity identification input means. A recommended candidate selection means for selecting recommended products whose similarity to previously purchased products is less than a predetermined second threshold;
Recommended product guidance means for guiding the recommended products selected by the recommended candidate selection means to be selectable,
The product purchase support apparatus according to claim 1, further comprising: Photographing means for photographing customers;
Customer identification input means for receiving input of customer identification information capable of identifying the customer;
Product identification input means for receiving input of product identification information capable of identifying the product that the customer plans to purchase;
Purchased products that have been purchased in the past by the customer identified by the customer identification information input via the customer identification input means of the product scheduled for purchase identified by the product identification information input via the commodity identification input means Similarity determination means for determining the similarity to a product for each purchased product;
If it is determined by the similarity determination means that there is a purchased product having the similarity equal to or higher than the predetermined first threshold, the purchased product whose similarity is equal to or higher than the predetermined first threshold is output. Output means for
When it is determined by the similarity determination means that there is no purchased product with the similarity equal to or higher than the first threshold value, an image of the product to be purchased is added to the image data of the customer photographed by the photographing means. Means to try on the product to be purchased,
A virtual try-on device characterized by comprising: The customer identified by the customer identification information input via the customer identification input means from among a plurality of recommended products for the purchase planned product identified by the product identification information input via the commodity identification input means. A recommended candidate selection means for selecting recommended products whose similarity to previously purchased products is less than a predetermined second threshold;
Recommended product guidance means for guiding the recommended products selected by the recommended candidate selection means to be selectable,
When any one product is selected from the recommended products guided by the recommended product guidance means, the image of the selected recommended product is superimposed on the image data of the customer photographed by the photographing means. Recommended product try-on means,
The virtual try-on device according to claim 4, further comprising: On the computer,
Customer identification input function that accepts input of customer identification information that can identify customers,
A product identification input function for accepting input of product identification information capable of identifying a product that the customer plans to purchase;
Purchased products that have been purchased in the past by the customer identified by the customer identification information input through the customer identification input function of the product scheduled for purchase identified by the product identification information input through the product identification input function A similarity determination function for determining the similarity to a product for each purchased product, and
An output function for outputting the determination result by the similarity determination function,
Product purchase support program to realize

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