JP7548392B2 - Recommended information identification device, recommended information identification system, recommended information identification method, and program - Google Patents

Description

The present invention relates to a recommended information identification device, a recommended information identification system, a recommended information identification method, and a program that identify recommended information that matches the preferences of a user who uses a store.

In electronic commerce and advertising on the Internet, a method is known in which so-called recommended information tailored to a user's preferences is extracted from a vast amount of information and displayed on a terminal device used by the user. The recommended information is also called recommendation information, and corresponds to, for example, product information including recommended products that are likely to match the user's preferences and should be recommended. The recommended information is identified based on, for example, a user's behavioral history, such as history information on products purchased on a website (product purchase history) and history information on viewing product introduction pages (product viewing history). A function that analyzes the behavioral history and identifies and presents the recommended information tailored to the user's preferences based on a specified algorithm is called a recommendation function, and has been used frequently in recent years.

Conventionally, a recommendation device equipped with the recommendation function has been proposed (see Patent Document 1). The recommendation device displays recommended information tailored to the preferences of customers (visitors) on tablets used by employees in stores such as retail stores.

In recent years, a payment system has been proposed that uses information codes such as barcodes and QR codes (registered trademarks) that indicate the contents and price of a product (see Patent Document 2). The mobile terminals (e.g., smartphones) used by customers who visit a store are equipped with a code reading function that reads the information codes attached to products. With this payment system, customers use their mobile terminals to read the information codes attached to the product packages, place the products in their shopping carts, and then make payment for the product price included in the read information code. Payment for the product price can be made, for example, at a cash register that can communicate with the mobile terminal, or payment can be made using electronic money registered in the mobile terminal, or online credit card payment can be used.

A shopping support device is also known that searches for cooking recipes using a user's product purchase history and presents them to the user (see Patent Document 3). The shopping support device displays a list of ingredients recently purchased by the user and a list of ingredients that caught the user's eye in the store on the user's terminal device, and when the user selects ingredients they would like to use from the list, the device compares these ingredients with ingredients for dishes in a database that stores cooking recipes, searches for cooking recipes suitable for the ingredients they would like to use, and presents them to the user.

JP 2017-215667 A JP 2015-185089 A JP 2014-49039 A

However, conventional recommendation functions identify recommended information that matches a user's preferences from past product purchase history and product browsing history, making it difficult to present recommended information that takes into account the user's current preferences and wishes while, for example, shopping at a retail store or other store (shopping around). Furthermore, conventional shopping support devices cannot provide cooking recipes that match the user's current preferences and wishes unless the user selects ingredients and obtains that information.

The object of the present invention is to provide a recommended information identification device, a recommended information identification system, a recommended information identification method, and a program that are capable of identifying recommended information including product information that matches the real-time preferences of a user who is shopping in a store.

A recommended information identification device according to one aspect of the present invention includes a purchase target information acquisition unit that acquires purchase target information related to each product selected as a purchase target by the user at a store from a terminal device used by the user at the store, and a recommended information identification unit that identifies recommended information for the user from multiple product information related to multiple products provided at the store based on ranking information related to the selection ranking of the selected products and the purchase target information.

In addition, a recommended information identification system according to another aspect of the present invention includes a purchase target information acquisition unit that acquires purchase target information for each product selected as a purchase target by the user at the store from a terminal device used by the user at the store, a recommended information identification unit that identifies recommended information for the user from multiple product information for multiple products provided at the store based on ranking information on the selection ranking of the selected products and the purchase target information, and a recommended information output unit that outputs the recommended information identified by the recommended information identification unit to the terminal device.

In addition, a recommended information identification method according to another aspect of the present invention includes a purchase target information acquisition step of acquiring purchase target information related to each product selected as a purchase target by the user at the store from a terminal device used by the user at the store, and a recommended information identification step of identifying recommended information for the user from multiple product information related to multiple products provided at the store based on the purchase target information and ranking information related to the selection ranking of the selected products.

The present invention can also be understood as a program for causing a computer to execute each step of the method for identifying recommended information, or as a computer-readable recording medium on which such a program is non-temporarily recorded.

According to the present invention, it is possible to identify recommended information including product information that matches the real-time preferences of a user who is shopping in a store. By presenting such recommended information to a user, the user can quickly obtain product information that matches his or her current preferences.

According to the present invention, by further comprising a recipe acquisition unit and a shortage product determination unit, it is possible to extract shortage product information related to a shortage product that is lacking in a cooking recipe from multiple product information, and identify the cooking recipe acquired by the recipe acquisition unit and the shortage product information as the recommended information. By presenting such recommended information to the user, the user can obtain a cooking recipe that matches his/her current preferences, and can easily decide on a menu that he/she was undecided about. In addition, by obtaining product information that is lacking in the cooking recipe, it is possible to prevent forgetting to buy products such as ingredients, seasonings, and kitchen items that are necessary for the decided menu. In addition, the store (seller) can promote sales of the shortage products, which can increase the purchase rate of the products.

FIG. 1 is a diagram showing the configuration of an information processing system according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of a screen displayed on a mobile terminal constituting the information processing system. FIG. 3 is a block diagram showing the configuration of each device constituting the information processing system. FIG. 4 is a block diagram showing the configuration of a ranking determination unit of a server device constituting the information processing system. FIG. 5 is a diagram showing the ranking of each product handled by a store. FIG. 6 is a flowchart illustrating a first processing example of a recommendation process executed in the information processing system. FIG. 7 is a flowchart showing a second processing example of the recommendation process executed in the information processing system. FIG. 8 is a flowchart illustrating a third processing example of the recommendation process executed in the information processing system. FIG. 9 is a flowchart showing a fourth process example of the recommendation process executed in the information processing system. FIG. 10 is a flowchart showing an example of a learning operation executed by the learning unit of the information processing system.

Below, an embodiment of the present invention will be described with reference to the drawings as appropriate. Note that the embodiment described below is merely an example of the present invention and does not limit the technical scope of the present invention.

[Information processing system 100]
As shown in FIG. 1, an information processing system 100 according to an embodiment of the present invention (recommended information identification system of the present invention) includes a server device 11 (an example of a recommended information identification device of the present invention), a store entrance gate device 12, a store exit gate device 13, a self-service register 14, a user terminal 15 (an example of a terminal device of the present invention), an employee terminal 16 used by employees of a store 200, and a recipe database 17 (an example of a recipe storage unit of the present invention) that stores product information used in cooking in association with recipes. The gate device 12, the gate device 13, and the register 14 may each be one or more. In this embodiment, an example will be described in which the information processing system 100 is introduced in a store 200 such as a supermarket that handles a wide variety of products. Of course, the store 200 in which the information processing system 100 is introduced is not limited to a supermarket, and may be any real retail store that handles multiple products, or may be a virtual store on the Internet.

Gate device 12 is installed near the entrance of store 200, and gate device 13 is installed near the exit of store 200. User terminal 15 is a terminal device carried and used by a user who enters store 200 when purchasing an item displayed in store 200, and is, for example, a mobile terminal such as a smartphone, mobile phone, or tablet terminal. Employee terminal 16 is a terminal device used by an employee of store 200, and is, for example, a laptop computer or a mobile terminal such as a smartphone or tablet terminal.

Below, with reference to Figures 1 and 2, we will explain an example of the sequence of events from when a user visits store 200 to when he or she purchases a product and leaves the store.

First, a user who has come to the store 200 approaches the gate device 12 for entering the store from the entrance while holding a user terminal 15. When the user approaches the gate device 12, the user terminal 15 communicates with the gate device 12 by short-range wireless communication such as Bluetooth or FeliCa (registered trademark). At this time, the user identification information (ID number, name, gender, age, etc.) stored in the user terminal 15 is transmitted to the gate device 12. The user identification information is information for identifying the user of the store 200. The user identification information is transferred from the gate device 12 to the server device 11 through a communication network N1 such as a wireless LAN (in-store LAN) installed in the store 200. As a result, the server device 11 recognizes that a user who has been registered in advance has come to the store. Note that if the user terminal 15 contains purchase history information of the user at the store 200, the purchase history information is also transmitted to the gate device 12 and transferred to the server device 11 through the communication network N1. Here, the purchase history information is historical information about products that the user previously purchased at the store 200.

A shopping application used in a system in which shopping is performed while scanning information codes (such as barcodes or QR codes) attached to products is pre-installed on the user terminal 15. When the user terminal 15 communicates with the gate device 12, the user terminal 15 automatically starts the shopping application. At this time, the operation display unit 22 (see FIG. 3) of the user terminal 15 displays, for example, a screen as shown in FIG. 2 (A). This enables the user to use the shopping application to purchase products within the store 200.

While shopping around the store 200, the user picks up a product he or she wishes to purchase (a product to be purchased) from the display shelf and captures (scans) the information code attached to the product using the imaging unit 24 (see FIG. 3) of the user terminal 15. The shopping application of the user terminal 15 reads the price information of the product (information including the product name, amount, and product ID) contained in the information code and displays it on the operation display unit 22 (see FIG. 3) of the user terminal 15. For example, if the user scans three products a, b, and c, the user terminal 15 displays a screen shown in FIG. 2 (B) that includes the price information of the three products a, b, and c selected as products to be purchased. The read price information is stored in the user terminal 15 and is also sent from the user terminal 15 to the server device 11 via the communication network N1 and is also stored in the server device 11.

To remove a product selected as a purchase target from the list of purchase targets, i.e., to cancel a planned purchase, the user selects the product to be canceled from the screen shown in FIG. 2 (B) and then selects "Cancel." This causes the price information of the product to be canceled to be deleted from the screen, and also transmits cancellation information indicating that the product has been canceled from the user terminal 15 to the server device 11 via the communication network N1.

The server device 11 reads out the purchase history information (an example of the behavioral history of the present invention) corresponding to the user identification information transferred from the gate device 12 from the memory unit 32 (customer information storage unit 321) and executes the recommendation process (see FIG. 7) described below. This recommendation process is a process of identifying recommendation information (an example of the recommended information of the present invention) corresponding to the user from multiple product information related to all products (products that can be sold at the store 200) managed in the server device 11. Specifically, product information that is likely to match the user's preferences is extracted based on a predetermined algorithm, and the extracted product information is identified as the recommendation information. The identified recommendation information is then transmitted to the user terminal 15 via the communication network N1. In the user terminal 15, the recommendation information is displayed in a pop-up format or the like on the operation display unit 22 (see FIG. 3) of the user terminal 15 so that the user can easily notice it while shopping around.

When making payment (settlement) for the scanned product, the user goes to the cash register 14 and selects "Payment" (see FIG. 2(B)) while holding the user terminal 15 over the communication unit provided on the cash register 14. This causes the user terminal 15 to communicate with the cash register 14 via the short-range wireless communication, and the user identification information is sent from the user terminal 15 to the cash register 14. The user identification information sent to the cash register 14 is also transferred from the cash register 14 to the server device 11 via the communication network N1. The server device 11 then extracts the price information of the payment object corresponding to the user identification information and transfers it to the cash register 14. The cash register 14 then executes a payment process for the amount indicated by the price information based on the payment method specified by the user. When the payment process is completed, a screen indicating that the payment has been completed is displayed on the user terminal 15.

It should be noted that the settlement process may be performed online between the user terminal 15 and the server device 11 without using the cash register 14. The settlement process is performed as follows. For example, when the user selects "Payment" on the screen of FIG. 2 (B), the settlement process is started. For example, the user terminal 15 transmits the price information of the products a, b, and c to be settled to the server device 11 together with the user identification information. Based on the price information, the server device 11 transmits a payment request to a settlement server (not shown) connected via the Internet. The settlement server executes the settlement process using a settlement system corresponding to the settlement business operator. After that, a message that the settlement process has been executed is transmitted to the user terminal 15 via the server device 11, and a message indicating this is displayed on the operation display unit 22 (see FIG. 3).

When the payment process is complete, the register 14 sends information about the purchased items for which payment has been completed to the server device 11. The server device 11 updates the inventory quantity of items managed by the server device 11 based on the information received from the register 14. The server device 11 also updates the purchase history information stored and managed for each user based on the actual purchase information received from the register 14.

When a user wants to leave the store 200, the user approaches the exit gate device 13. When the user approaches the gate device 13, the user terminal 15 communicates with the gate device 13 by short-range wireless communication. At this time, flag information indicating that shopping is complete is sent to the gate device 13 together with the user's user identification information, and the identification information and flag information are transferred from the gate device 13 to the server device 11 via the communication network N1. As a result, the server device 11 recognizes that shopping has been completed without any fraudulent activity and that the user has left the store.

In this manner, the user can shop at store 200 using the shopping application.

As described above, in the information processing system 100, the recommendation process is executed to identify the recommendation information according to the user, and the identified recommendation information is displayed on the user terminal 15.

Below, the specific configuration of the information processing system 100 for implementing the recommendation processing will be described with reference to Figure 3.

[User terminal 15]
3, the user terminal 15 includes a control unit 21, an operation display unit 22, a storage unit 23, an imaging unit 24, a communication I/F 25, etc. The user terminal 15 is, for example, a mobile terminal owned by a user of the store 200, such as a smartphone, a mobile phone, or a tablet terminal. The user terminal 15 may be a mobile terminal loaned by the store 200, or may be a cart terminal installed in a shopping cart.

The imaging unit 24 is a digital camera that captures an image of a subject and outputs it as digital image data. A user shopping in the store 200 captures the information code attached to the product to be purchased with the imaging unit 24. The captured image of the information code is read by the reading processing unit 211 described below, thereby obtaining price information (product name, amount, product ID, etc.) of the product to be purchased.

The communication I/F 25 is a communication interface that connects the user terminal 15 to the communication network N1 by wire or wirelessly and executes data communication in accordance with a predetermined communication protocol with an external device such as the server device 11 via the communication network N1. The communication I/F 25 also enables data communication between the user terminal 15 and devices such as the gate devices 12, 13 and the cash register 14 wirelessly (for example, short-range wireless such as Bluetooth).

The operation display unit 22 is a user interface that includes a display unit such as a liquid crystal display or an organic EL display that displays various information, and an operation unit such as a mouse, keyboard, or touch panel that accepts user operations. The recommendation information according to the user is displayed on the display unit of the operation display unit 22.

The storage unit 23 is a non-volatile storage medium such as a flash memory that stores various types of information. The storage unit 23 stores control programs that cause the control unit 21 to execute various processes.

The memory unit 23 also stores user identification information for identifying a user. The user identification information is information for identifying a user, such as an ID number, name, gender, and age. The user identification information is preregistered in the server device 11 installed in the store 200, and is stored and managed together with purchase history information in the memory unit 32 (customer information storage unit 321) within the server device 11.

The memory unit 23 also stores the price information of the product to be purchased that is read by the reading processing unit 211 described below.

The control unit 21 has control devices such as a CPU, a ROM, and a RAM. The CPU is a processor that executes various arithmetic operations. The ROM is a non-volatile storage unit in which control programs such as a BIOS and an OS for causing the CPU to execute various processes are stored in advance. The RAM is a volatile or non-volatile storage unit that stores various information, and is used as a temporary storage memory (work area) for the various processes executed by the CPU. The control unit 21 controls the user terminal 15 by having the CPU execute various control programs that are stored in advance in the ROM or the storage unit 23.

Specifically, as shown in FIG. 3, the control unit 21 includes various processing units such as a reading processing unit 211, a display processing unit 212, a notification processing unit 213 (an example of a transmission processing unit of the present invention), and an action result transmission unit 214. The control unit 21 functions as the various processing units by executing various processes according to the control program with the CPU. Some or all of the processing units included in the control unit 21 may be configured with electronic circuits. The control program may be a program for causing multiple processors to function as the various processing units.

The reading processing unit 211 reads the price information (product name, amount, product ID, etc.) of the product contained in the information code based on the image data of the information code captured by the imaging unit 24. For ease of explanation, the price information read for a product to be purchased will be referred to as purchase target information below. This purchase target information is information about a product that a user of the store 200 has selected as a purchase target from multiple products in the store 200. The read purchase target information is stored in the memory unit 23 until it is canceled or payment for the product to be purchased is completed.

The display processing unit 212 displays the recommendation information transmitted from the server device 11 on the display unit of the operation display unit 22. For example, the display processing unit 212 displays the recommendation information on the display unit of the operation display unit 22 in a pop-up format. When the recommendation information includes a product image, a product name, etc., the display processing unit 212 displays this information on the display unit of the operation display unit 22 in a pop-up format. The product image and product name are linked to an address that stores more detailed information about the recommended product (e.g., product description, product price, product display location in the store 200), and when a user touches the display screen of the product image or product name, the display processing unit 212 reads out the detailed information stored in the address and displays it on the display unit. In addition, when the recommendation information includes a URL of a WEB site including a photo image or description of the product, the display processing unit 212 displays the URL on the display unit of the operation display unit 22. In this case, when the user touches the URL, the display processing unit 212 accesses the URL and displays the website on the display unit.

The notification processing unit 213 transmits various information to the server device 11, the gate devices 12 and 13, and the register 14. For example, the notification processing unit 213 transmits the user identification information, etc. to the gate device 12 when the user enters the store 200, and transmits the user identification information, etc. to the gate device 13 when the user leaves the store 200.

The notification processing unit 213 also transmits the purchase target information read during the shopping trip to the server device 11. This transmission process is performed each time the purchase target information is read. In other words, when the user uses the imaging unit 24 to capture an image of the information code of the product to be purchased and the reading processing unit 211 performs a reading process to read the purchase target information, the purchase target information is immediately transmitted to the server device 11 by the notification processing unit 213. The notification processing unit 213 also associates ranking information indicating the scan order (selection order) in which the purchase target information was read with the purchase target information and transmits it to the server device 11 together with the purchase target information. The scan order is the order in which the user scanned the product in the store 200 and the order in which the user selected the product as a purchase target (selection order). The scan order is counted by the control unit 21 each time the reading process is performed, and the ranking information indicating the counted order is added to the header section of the purchase target information. The server device 11 that receives the purchase target information stores the purchase target information in the purchase target product storage unit 323.

In addition, if the purchase target information is canceled during shopping, the notification processing unit 213 transmits cancellation information (cancellation information) indicating that the purchase target information has been canceled to the server device 11. In addition, upon receiving the cancellation information, the server device 11 deletes the purchase target information corresponding to the cancellation information from the purchase candidate storage unit 323.

The action result sending unit 214 sends an action result indicating whether or not the user has taken an action in response to the recommendation information displayed on the display unit of the operation display unit 22 to the ranking determination unit 33 of the server device 11 (see FIG. 3). Examples of the action include an action of viewing the recommendation information displayed on the display unit of the operation display unit 22 (viewing action), an action of reading product information of an information code of a product indicated by the recommendation information by the reading processing unit 211 (reading action), or an action of purchasing a product indicated by the recommendation information displayed on the display unit (purchasing action). The action result includes a positive result indicating that each of the actions has occurred, or a negative result indicating that each of the actions has not occurred.

The control unit 21 performs a process of determining whether or not the user has taken action in response to the recommendation information displayed on the display unit of the operation display unit 22. The control unit 21 that performs such a determination process can be considered as a behavior determination unit that determines whether or not an action has been taken in response to the recommendation information.

For example, the control unit 21 performs a process of determining whether or not the product image or the product name has been touched, or whether or not the URL has been touched, during the period from when the recommendation information is displayed on the display unit of the operation display unit 22 until a certain period of time (time-up time) has elapsed. In this case, if it is determined that a touch operation has been performed within the certain period of time, the behavior result sending unit 214 sends information on a positive result (behavior result) indicating that the user has viewed the recommendation information on the user terminal 15 to the ranking determination unit 33 of the server device 11.

The control unit 21 also performs a process of determining whether or not the reading processing unit 211 has read product information of the information code of the product indicated by the recommendation information during the period until the certain time has elapsed or the payment process is performed. In this case, when it is determined that the product information of the information code of the product indicated by the recommendation information has been read, the behavior result sending unit 214 sends information of a positive result (behavior result) indicating that the user of the user terminal 15 plans to purchase the product indicated by the recommendation information to the ranking determination unit 33 of the server device 11.

The control unit 21 also performs a process of determining whether or not the product indicated by the recommendation information has been purchased. In this case, if the product indicated by the recommendation information is included in the post-purchase products for which the payment has been processed, the behavior result sending unit 214 sends information of a positive result (behavior result) indicating that the product indicated by the recommendation information has been purchased to the ranking determination unit 33 of the server device 11.

The behavior result sending unit 214 sends information on a negative result (behavior result) indicating that the behavior did not occur to the ranking determination unit 33 of the server device 11 if the browsing behavior or reading behavior was not performed within the certain period of time, if the reading behavior was not performed before the user performed the payment process at the register 14, or if the purchasing behavior was not performed during the payment process.

[Cooking recipe database 17]
The cooking recipe database 17 is a storage unit that stores cooking recipes in association with product information used in cooking. The cooking recipe database 17 stores a large amount of cooking recipe data using various ingredients, seasonings, and other products. The cooking recipe database 17 is configured as an external device such as another server device or storage device that can communicate data with the server device 11 via the communication network N1, the Internet, or the like. The server device 11 reads out cooking recipes from the cooking recipe database 17 as necessary. The cooking recipe database 17 may be a storage unit such as a HDD (Hard Disk Drive) or SSD (Solid State Drive) provided in the server device 11.

[Server device 11]
The server device 11 is a server computer including a control unit 31, a memory unit 32, a ranking unit 33, a recipe selection unit 36 (an example of a recipe selection unit of the present invention), a communication I/F 35, etc. The server device 11 is not limited to a single computer, but may be a computer system in which multiple computers work together. Various processes executed by the server device 11 may be executed in a distributed manner by one or multiple processors.

The communication I/F 35 is a communication interface that connects the server device 11 to the communication network N1 by wire or wirelessly, and performs data communication in accordance with a specified communication protocol with external devices such as the user terminal 15, gate devices 12 and 13, and cash register 14 via the communication network N1.

The storage unit 32 is a non-volatile storage unit including a semiconductor memory, a hard disk drive (HDD), or a solid state drive (SSD) that stores various types of information. In the present embodiment, the storage unit 32 is provided in the server device 11, but some or all of the various types of information in the storage unit 32 may be stored in an external device such as another server device or storage device that can communicate with the server device 11 via the communication network N1 or the Internet. In this case, the server device 11 reads out necessary information from the external device or stores information in the external device as necessary.

The storage unit 32 stores control programs for causing the control unit 31 to execute various control processes and recommendation processes (recommendation extraction process, recommendation selection process) shown in FIG. 7 and described below. For example, the control programs are non-temporarily recorded on a computer-readable recording medium such as a CD or DVD, and are read by a reading device (not shown) such as a CD drive or DVD drive provided in the server device 11 and stored in the storage unit 32.

The memory unit 32 also stores information such as the number of settings and the setting time used in the determination process in the recommendation process described below.

The memory unit 32 is divided into multiple memory areas. Specifically, the memory unit 32 includes a customer information storage unit 321 as a behavior history storage unit, a product information storage unit 322, a product to be purchased storage unit 323, and a priority order storage unit 324 as a priority order storage unit.

The customer information storage unit 321 stores information for each user (hereinafter referred to as customer information) that has been registered in advance. The customer information includes the user identification information, which includes the user's ID number, name, gender, age, etc., as well as purchase history information regarding products that the user has previously purchased at the store 200. In other words, the purchase history information is information that indicates the user's behavioral history regarding products sold at the store 200.

The customer information is stored (registered) as the user identification information in the customer information storage unit 321 of the memory unit 32, for example, when the user registers as a member of the store 200. Thereafter, when the user purchases a product in the store 200, purchase history information including product information and the purchase date for the purchased product is overwritten and stored in the customer information storage unit 321 while being linked to the user identification information of the purchasing user. In other words, the customer information is updated. The purchase history information is used in the recommendation extraction process by the recommendation extraction processing unit 311, which will be described later.

The product information storage unit 322 stores product information on all products that can be sold at the store 200. The product information is information that can identify the product, and includes, for example, price information including the product name, the price of the product, and the product ID (JAN code, etc.). The product information also includes information such as the type of product, the product's lifespan (best-before date, expiry date, use-by date, etc.), whether or not there is a discount and the discount rate, the display location of the product, and the URL of a website that includes a photo and description of the product, and includes information that improves the convenience of users when shopping at the store 200. In addition, if the product is a cooked food (processed food) such as a side dish, the product information may include information indicating the cooking time (including the cooking date and the scheduled cooking time) that is the processing date and time of the cooked food, and the state after cooking (processing state). In addition, if the product is, for example, the target of a sales promotion campaign, the product information may include information for determining whether or not the product is the target of the sales promotion campaign. Furthermore, the product information may include information that only the seller at store 200 should know, such as the product's profit margin, the degree of risk of disposal, and the number of items in stock.

The product information storage unit 322 also stores past sales records and past disposal history for each product. The past sales records include the date and time when the product was sold in the past, the price, the number of units sold, whether or not it was discounted, the discount rate, etc. The past disposal history includes whether or not unsold products were disposed of, the date and time of past disposals, the amount disposed of, etc.

The product information stored in the product information storage unit 322 is updated whenever a change occurs. For example, the number of products in stock is updated immediately by the control unit 31 of the server device 11 when a user makes a payment for the product. In addition, the product price, number in stock, whether or not there is a discount, the discount rate, cooking time, whether or not there is a promotional campaign, exhaust risk, and increase in stock due to new arrivals are updated by the control unit 31 based on information entered by employees from the employee terminal 16.

The purchase target information transmitted sequentially from the user terminal 15 each time the reading processing unit 211 performs a reading process is temporarily stored in the purchase target product storage unit 323. This purchase target product storage unit 323 functions as a virtual shopping basket. The purchase target information is stored in the purchase target product storage unit 323 until the purchase of the product is canceled or the payment for the purchase target product is completed. In addition, the purchase target product storage unit 323 stores the scan order (selection order) transmitted together with the purchase target information in a state associated with the purchase target information.

The priority order storage unit 324 stores multiple product information items as well as sales priorities for the multiple products. FIG. 5 shows a ranking list TB1 indicating the priority order for each of the multiple products. Ranking list TB1 includes multiple products (hereinafter referred to as priority products) that the store 200 (seller) wishes to prioritize selling among all the products stored in the product information storage unit 322, and a sales priority order is set for each priority product. The priority order indicates the order of products to be promoted, and the product ranked first is the product that the store 200 wishes to most highly promote. The priority order may be set for each of all the products stored in the product information storage unit 322.

The priority of each priority product in the ranking list TB1 is determined based on variable information related to variable factors that cause fluctuations in the priority. The variable information is, for example, the price, type, lifespan, whether or not there is a discount, the discount rate, the cooking time of the priority product if the product is a cooked food such as a prepared dish, whether or not it is subject to the promotional campaign, the profit margin, the degree of waste risk, the number of items in stock, past sales performance, and past waste history. In addition, in the case where the priority product is a cooked food such as a prepared dish, the variable information includes the cooking date, cooking time, and state after cooking. In addition, the variable information includes the temperature, humidity, air pressure, and weather at the location of the store 200. In addition, the variable information includes the current time, the number of customers at the store 200 on the day, and the customer history. The customer history is statistical information indicating the number of customers by month or day in the past. In addition, the number of customers includes the expected number of customers predicted from the customer history. Such variable information can be said to be information that causes changes in the circumstances surrounding the product (product circumstances), including the condition of the product, the user's willingness to purchase, etc. In determining the priority order of the priority products, the variable information used in the determination process may include information on one or more of the specific variable factors described above.

The priority order varies depending on the product situation and various factors on the store 200 side (seller). For example, products with a large stock are assigned a higher priority, while products with a small stock are assigned a lower priority. This is because the greater the stock, the more likely it is that unsold products will occur. Also, products with a short product lifespan are assigned a higher priority, while products with a long product lifespan are assigned a lower priority. Products with a high profit margin are assigned a higher priority, while products with a low profit margin are assigned a lower priority. Products with a high risk of waste are assigned a higher priority, while products with a low profit margin are assigned a lower priority. The priority order also varies depending on the type of product; for example, ingredients and food products (including seasonings), foods (including cooked foods), and beverages are assigned a higher priority, while detergents, stationery, and kitchen supplies are assigned a lower priority. In addition, even for cooked foods such as side dishes, the priority order varies depending on the cooking time or the condition after cooking. If the time that has passed since cooking is long, if the waiting time until the cooking time is long, or if the condition after cooking is poor, the food is assigned a higher priority; if the time that has passed since cooking is short, if the waiting time is short, or if the condition after cooking is good, the food is assigned a lower priority.

Factors that change the priority order include the price of the product, whether or not it is discounted, the discount rate, past sales, past disposal history, the temperature, humidity, air pressure, and weather at the location of the store 200, the number of customers, and the customer history. When multiple pieces of variable information exist, the above-mentioned multiple pieces of variable information may affect each other and change the priority order. For example, since many customers are expected to visit the store on a sunny day, in this case, the order of each priority product is determined by placing more importance on the effect of profit margins than on the effect of inventory. When the weather is good but the temperature is high, the order is determined by placing more importance on the type of product. For example, in the case of cooked foods, heated foods and cooked foods kept warm that are estimated to be difficult to sell based on past sales are placed at the top, and cold dishes that are estimated to be popular are placed at the bottom.

In this embodiment, the priority order is determined or updated by the ranking determination unit 33, which will be described later, based on the multiple pieces of change information described above. The ranking determination unit 33 will be described later.

[Control unit 31]
The control unit 31 has control devices such as a CPU, a ROM, and a RAM. The CPU is a processor that executes various arithmetic processes. The ROM is a non-volatile storage unit in which control programs such as a BIOS and an OS for causing the CPU to execute various arithmetic processes are stored in advance. The RAM is a volatile or non-volatile storage unit that stores various information, and is used as a temporary storage memory (work area) for various processes executed by the CPU. The control unit 31 controls the server device 11 by executing various control programs stored in advance in the ROM or the storage unit 32 by the CPU.

Specifically, as shown in FIG. 3, the control unit 31 includes various processing units such as a recommendation extraction processing unit 311, a recommendation selection processing unit 312 (an example of a recommended information identification unit of the present invention), a recommendation information transmission unit 313 (an example of a recommended information output unit of the present invention), an information acquisition unit 314 (an example of a purchase target information acquisition unit and a cancellation information acquisition unit of the present invention), a recipe selection processing unit 315 (an example of a cooking recipe acquisition unit of the present invention), and a shortage product determination unit 316 (an example of a shortage product determination unit of the present invention). The control unit 31 functions as the various processing units by executing various processes according to the control program with the CPU. In addition, some or all of the processing units included in the control unit 31 may be composed of electronic circuits. In addition, the control program may be a program for causing multiple processors to function as the various processing units.

The information acquisition unit 314 acquires the purchase target information transmitted from the user terminal 15. The purchase target information read during shopping is transmitted to the server device 11 by the notification processing unit 213 of the user terminal 15 each time the purchase target information is read. At this time, ranking information indicating the scanning order (selection order) in which the purchase target information was read is also added to the purchase target information and transmitted. In the server device 11, the purchase target information is acquired by the information acquisition unit 314.

The information acquisition unit 314 also acquires the cancellation information transmitted from the user terminal 15. If the purchase target information is canceled during shopping, the notification processing unit 213 of the user terminal 15 transmits the cancellation information to the server device 11. In the server device 11, the cancellation information is acquired by the information acquisition unit 314. Having acquired the cancellation information, the server device 11 deletes the purchase target information corresponding to the cancellation information from the purchase target product storage unit 323.

In addition, the information acquisition unit 314 acquires the priority order regarding the sales of multiple products provided at the store 200 from the priority order storage unit 324. Specifically, the information acquisition unit 314 acquires the priority order of the required product from the priority order storage unit 324. More specifically, when the recommendation extraction process by the recommendation extraction processing unit 311 selects product information of multiple recommended product candidates (recommended product candidates) that are estimated to match the user's preferences, the information acquisition unit 314 acquires the priority order of the selected multiple product information from the priority order storage unit 324.

In addition, the information acquisition unit 314 acquires the user's purchase history information for products provided at the store 200 from the customer information storage unit 321. Specifically, when the user identification information of the user is sent from the gate device 12 when the user visits the store, the information acquisition unit 314 extracts the customer information corresponding to the user identification information from the customer information storage unit 321, and extracts the purchase history information included in the customer information.

The recommendation extraction processing unit 311 executes a recommendation extraction process that extracts recommendation information that matches the user's preferences from multiple product information based on the user's purchase history information stored in the customer information storage unit 321. As a control program for executing the recommendation extraction process, the memory unit 32 stores a recommendation engine (recommendation program) that extracts the recommendation information using a predetermined, conventionally known recommendation algorithm that uses the purchase history information. The recommendation extraction processing unit 311 uses the recommendation engine to extract a preset number of two or more recommended product candidates from multiple product information. In other words, the recommendation extraction processing unit 311 executes the recommendation extraction process based on a predetermined algorithm using the recommendation engine stored in the memory unit 32.

The recommendation selection processing unit 312 executes a recommendation selection process to select a specific recommended product candidate. A control program for executing the recommendation selection process is stored in the storage unit 32. The recommendation selection processing unit 312 executes a process to select information on a recommended product according to a user using the user terminal 15 that transmitted the purchase target information from a plurality of product information stored in the product information storage unit 322, based on the purchase target information acquired by the information acquisition unit 314 and ranking information indicating the scan order (selection order) in which the purchase target information was read. In this embodiment, the recommendation selection processing unit 312 selects information on the recommended product from a plurality of product information in the product information storage unit 322, based on the purchase target information whose scan order is up to a predetermined set order. In addition, the recommendation selection processing unit 312 selects information on the recommended product using the priority order of the priority product stored in the priority order storage unit 324 in addition to the purchase target information and the ranking information.

When the information acquisition unit 314 acquires the cancellation information transmitted from the user terminal 15, the recommendation selection processing unit 312 performs a process of selecting information on the recommended product from the multiple product information based on the purchase target information and the ranking information from which the canceled product indicated by the cancellation information has been excluded.

The recommendation information transmission unit 313 outputs recommendation information including product information of the specific recommended product candidates selected by the recommendation selection processing unit 312 to the user terminal 15 via the communication network N1. When the user terminal 15 receives the transmitted recommendation information, it displays the recommendation information on the operation display unit 22 of the user terminal 15. The recommendation information transmission unit 313 also transmits the recommendation information to the employee terminal 16.

The recipe selection processing unit 315 selects and acquires from the recipe database 17 cooking recipes that use the top products corresponding to the top rankings up to the set ranking acquired by the information acquisition unit 314. The recipe selection processing unit 315 may select one or more cooking recipes. The cooking recipes selected by the recipe selection processing unit 315 use the products selected by the user as purchase targets, and therefore can be said to be cooking recipes that match the products selected by the user.

The shortage product determination unit 316 compares the products used in the recipe selected by the recipe selection processing unit 315 with the top products to determine the products that are in short supply for the dish indicated by the selected recipe. In addition to the recipe, the recipe database 17 associates product information such as ingredients used in the recipe. Therefore, the shortage product determination unit 316 reads out product information corresponding to the selected recipe from the recipe database 17, and determines the products that are in short supply from the read product information, excluding the top products.

[Ranking determination unit 33]
The ranking determination unit 33 will be described below with reference to Fig. 4. The ranking determination unit 33 is a learning device configured to determine the priority order by machine learning, and has a learning model for determining the priority order. Fig. 4 is a block diagram showing the configuration of the ranking determination unit 33, and shows an example in which reinforcement learning is applied as a machine learning algorithm.

As shown in FIG. 4, the ranking determination unit 33 includes a fluctuation information observation unit 331 (fluctuation information acquisition unit), an action result acquisition unit 332 (action result acquisition unit), a learning unit 40, and a data update processing unit 333. The ranking determination unit 33 can use a general-purpose CPU, but in order to enable faster calculation processing, it is desirable to use, for example, a GPGPU (General-Purpose computing on Graphics Processing Units) or a large-scale PC cluster.

The variable information observation unit 331 acquires the variable information used to determine the priority order of each priority product shown in the ranking list TB1 in FIG. 5. If the variable information is the product price, the product type, the product lifespan (best before date, expiry date, use by date, etc.), whether or not there is a discount and the discount rate, the cooking time or scheduled cooking time for cooked food, information on whether or not it is subject to a sales promotion campaign, the product's profit margin, the degree of waste risk, the number of items in stock, past sales performance, and past waste history, the variable information observation unit 331 monitors the product information in the product information storage unit 322 and constantly acquires real-time information from the product information storage unit 322.

If the fluctuation information is the temperature, humidity, and air pressure at the location of the store 200, the fluctuation information observation unit 331 receives sensor output values output from temperature sensors, humidity sensors, and air pressure sensors installed indoors or outdoors in the store 200, and obtains the real-time temperature, humidity, and air pressure indicated by the sensor output values. If the fluctuation information is the weather around the store 200, the control unit 31 downloads weather information from a weather information database connected to the server device 11 via the Internet, and the fluctuation information observation unit 331 receives the weather information from the control unit 31.

The acquired variation information is used for learning by the learning unit 40 and is stored in a memory unit (not shown) in the ranking determination unit 33. The variation information is input from the memory unit to the learning unit 40.

The behavior result acquisition unit 332 acquires the behavior result information transmitted from the behavior result transmission unit 214 of the user terminal 15. In other words, the behavior result acquisition unit 332 acquires a behavior result (positive result, negative result) indicating whether or not the user viewed or purchased the recommended product indicated by the recommendation information. The acquired behavior result information is information used for learning by the learning unit 40, and is stored in a memory unit (not shown) in the ranking determination unit 33. The behavior result is input from the memory unit to the learning unit 40.

The learning unit 40 learns the priority order based on the fluctuation information acquired by the fluctuation information observation unit 331. In this embodiment, the learning unit 40 uses not only the acquired fluctuation information but also the behavioral results acquired by the behavioral result acquisition unit 332 to machine-learn the priority order of products that the store 200 wants to sell with priority. The learning unit 40 will be described later.

The data update processing unit 333 refers to the priority order learned by the learning unit 40 and updates the priority order stored in the priority order storage unit 324, specifically, the priority order of each of the priority products shown in the ranking list TB1 in FIG. 5, to the ranking order after learning.

The learning unit 40 has the function of analyzing and extracting useful rules, knowledge expressions, judgment criteria, etc. from the set of inputted variable information, outputting the judgment results, and learning knowledge. Machine learning includes algorithms such as supervised learning, unsupervised learning, and reinforcement learning, and furthermore, to realize these methods, a method called "deep learning" is used, which learns to extract the features themselves.

Here, supervised learning is an algorithm that learns the "relationship between input and output" from data that has been input in advance. The input data is given the correct answer for that data along with the input value, and by providing a large amount of such data to the learning unit 40, the learning unit 40 learns the characteristics of the data and estimates the output (result) from the input. If the relationship between the given input and output data can be learned, it becomes possible to predict the output by applying that relationship to unknown data.

Unsupervised learning is an algorithm that learns features such as the structure, characteristics, and new knowledge of data from a large amount of input data, without being given correct output data. It differs from supervised learning in that the original data from which learning is done is not given a correct answer.

Furthermore, reinforcement learning is not based on fixed and clear data, as in supervised and unsupervised learning, but is an algorithm in which the program itself observes a given environment (current state), evaluates a series of consecutive actions, and learns appropriate actions based on the interactions between the actions and the environment, that is, actions that will maximize future rewards. Typical methods include TD learning and Q-learning.

In reinforcement learning, the results caused by actions are unknown. Therefore, when reinforcement learning is adopted as the machine learning of the learning unit 40, learning starts from a state in which the priority of each product to be learned is completely unknown, and in the initial stage, the priority obtained by learning may not be the priority desired by the store 200. Therefore, it is desirable to perform pre-learning using supervised learning, set the pre-learned priority as the initial state, and then perform reinforcement learning.

For example, when the reinforcement learning in the learning unit 40 is Q-learning, a general update formula (action value table) for the action value function Q(s, a) is expressed by the following formula (1):

In addition, in formula (1), s _t represents the environment at time t, and a _t represents the action at time t. The environment changes to s _t+1 due to the action a _t . r _t+1 is the reward obtained due to the change in the environment. The term with max is the Q value multiplied by γ when the action a with the highest Q value known at that time is selected under the environment s _t+1 . γ is also called the discount rate and is a parameter of 0<γ≦1. α is a learning coefficient in the range of 0<α≦1. When Q-learning is applied, the actions of browsing the recommendation information displayed on the user terminal 15, reading the information code of the product indicated by the recommendation information, and purchasing the product by the user (viewing action, reading action, purchasing action) are the action a _t .

As shown in FIG. 4, the learning unit 40 has a reward calculation unit 401 (an example of a reward output unit of the present invention) and a value function update unit 402 (an example of a learning model update unit of the present invention). The reward calculation unit 401 calculates and outputs a reward based on the fluctuation information acquired by the fluctuation information observation unit 331. Furthermore, the value function update unit 402 updates a value function related to the priority order based on the fluctuation information and the reward calculated by the reward calculation unit 401. This value function corresponds to the learning model.

As described above, the variable information causes the priority to change, and the variable information itself changes from moment to moment. Therefore, when the variable information changes, the priority is also updated. Whether the changed priority is appropriate or not can be evaluated based on whether the behavioral results of each of the browsing behavior, reading behavior, and purchasing behavior are positive or negative. Therefore, for example, when Q-learning is applied, the behavioral results (positive result, negative result) are essentially the behavior a _t in formula (1), and the behavior value function Q(s, a) expressed by formula (1) is used as a value function for changing the behavior a _t .

In this embodiment, the reward calculation unit 401 outputs a positive reward when the behavior result is a positive result indicating that the recommended product indicated by the recommendation information was viewed or purchased, and outputs a negative reward when the behavior result is a negative result indicating that the recommended product was not viewed or purchased. The absolute value of the reward that is output may vary depending on the content of the behavior result. For example, the reward for the behavior result of browsing behavior may be set to the lowest, and the reward corresponding to the behavior result of purchasing behavior may be set to the highest. In addition, a weighting coefficient for the reward may be set for each user. For example, when placing importance on the reward for the behavior result of a user who frequently uses the store 200, the weighting coefficient to be multiplied by the reward may be set to a value proportional to the frequency of use.

The recommended information identification method of the present invention will be described below with reference to the flowchart in FIG. 6, along with a first processing example of the recommendation processing executed by the control unit 31 of the server device 11. In the following description, it is assumed that the recommendation processing is performed between the time when the user enters the store 200 and passes through the gate device 12 and the time when the user leaves the store and passes through the gate device 13 of the store 200. In other words, the recommendation processing is performed while the user is shopping around the store 200. In FIG. 6, S11, S12, ... indicate the numbers (step numbers) of the processing procedure.

First, when a user enters the store 200, the control unit 31 acquires user identification information transmitted from the user terminal 15 of the user who has entered the store. This allows the control unit 31 to identify the user who has entered the store.

In step S11, the control unit 31 acquires the purchase target information transmitted from the user terminal 15. The purchase target information is transmitted and acquired each time the user reads the information code of a product he or she has picked up using the user terminal 15. This process is performed by the information acquisition unit 314 of the control unit 31. Step S11 corresponds to the purchase target information acquisition step of the present invention.

In the next step S12, the purchase target information acquired by the information acquisition unit 314 is stored in the purchase candidate product storage unit 323 of the memory unit 32. As described above, the purchase target information is associated with ranking information indicating the scanning order (selection order) in which the purchase target information was read. Therefore, the control unit 31 can obtain the ranking information by checking the header section of the purchase target information. Note that if ranking information is not associated with the purchase target information, the control unit 31 may count the number of times the purchase target information is received each time it is received. The counted number of times the information code is read by the user, and indicates the order in which the purchase target information was sent, i.e., the scanning order in which the information code was read.

Next, in step S13, the control unit 31 determines whether the number of items the user plans to purchase has reached a set number. The set number is an arbitrary threshold used in the determination in step S13. This set number corresponds to the set ranking. The control unit 31 determines the current number of items based on the ranking information of the purchase target information last stored in the purchase target item storage unit 323, and compares this number of items with the set number. In step S13, if it is determined that the number of items has reached the set number (Yes in S13), the process proceeds to step S14. On the other hand, if it is determined in step S13 that the number of items is less than the set number (No in S13), the process proceeds to step S15.

In step S14, the control unit 31 performs a recipe selection process to select and acquire from the recipe database 17 recipes that use the products (hereinafter referred to as top products) indicated by the set number of purchase target information items. This process is performed by the recipe selection processing unit 315 of the control unit 31. In this embodiment, the control unit 31 selects from the recipe database 17 recipes that use the top products as the main ingredients.

The top products are the products that the user selected as purchase candidates shortly after entering the store. Therefore, it can be said that these top products are products that the user had decided to purchase from the beginning of the store and that the user decided to purchase without any hesitation when purchasing the product. On the other hand, products that exceed the set number are products that the user decided to purchase after a considerable amount of time had passed since entering the store, and it can be presumed that the purchase was not planned from the beginning of the store and was decided on while shopping around. Therefore, the cooking recipes that use the top products selected in step S14 can be said to be cooking recipes that match the products selected by the user and the user's preferences.

In step S15, the control unit 31 judges whether the time interval (corresponding to the selection time of the present invention) when the user sequentially reads the information codes of multiple products is equal to or longer than a preset time. The time interval is the time taken from the selection of the previous product to the selection of the next product. The set time is an arbitrary threshold value used in the judgment of step S15. The time interval indicates the time taken from the reading of the information code of the previous product to the decision to purchase the next product, and corresponds to the time taken to select the next product to be purchased. If the time interval is less than the set time, it means that the user decided on the next product without much hesitation, which can be inferred to be a situation in which the user is not agonizing over the menu for a meal, for example. In this way, if the time interval is less than the set time, there is a possibility that the next product to be purchased will also be selected without hesitation, so the processing from step S11 onwards is repeated until the number of products reaches the set number.

On the other hand, if the time interval is equal to or longer than the set time, it means that the user was agonizing over which product to purchase next, which can be assumed to mean, for example, that the user is agonizing over what meal to make. In this way, if the time interval is equal to or longer than the set time, it can be assumed that the product purchased thereafter is likely to be unrelated to the meal plan. In this case, without waiting for the number of products to reach the set number, the recipe selection process of step S14 is performed based on the currently acquired purchase target information that is less than the set number, excluding the most recently acquired purchase target information. In other words, a process is performed to select a cooking recipe that uses a product that is ranked higher than the ranking information of the most recently acquired purchase target information.

In the next step S16, the control unit 31 compares the product used in the recipe selected in step S14 with the top product to determine the product that is missing for the dish indicated by the recipe. If it is determined that there is no product that is missing, the process proceeds to step S17, where the control unit 31 performs the same recipe selection process (recipe reselection process) as in step S14, and selects and acquires another recipe from the recipe database 17.

When it is determined in step S16 that the missing product exists, the control unit 31 identifies the missing product as a recommended product that matches the user's preferences, and extracts product information related to the missing product (hereinafter referred to as missing product information) from the product information storage unit 322 (S18). The missing product information is product information that includes price information of the missing product, etc. The process of step S16 is performed by the recommendation selection processing unit 312 of the control unit 31. Step S16 corresponds to the recommended information identification step of the present invention.

Then, the recommendation information including the recipe selected in step S14 and the missing product information is sent to the user terminal 15 of the user by the recommendation information sending unit 313. In the user terminal 15, the received recommendation information is displayed on the display unit of the operation display unit 22. The recommendation information is also sent to the employee terminal 16.

As described above, in this embodiment, recipes that match the user's preferences and missing product information are displayed on the user terminal 15 as recommendation information. Therefore, the user can obtain recipes that match their current preferences, and can easily decide on a menu that they have been undecided about. In addition, they can obtain information on products that are missing from the recipe, and can prevent forgetting to buy products such as ingredients, seasonings, and kitchen items that are necessary for the decided menu. In addition, the store 200 (seller) can promote sales of products that are in short supply, which can increase the purchase rate of the products.

The recommendation information is also sent to the employee terminal 16, so that the employee can check the recommendation information for each user that he or she has received.

For example, if a scanned product that was to be purchased is canceled and the information acquisition unit 314 acquires the cancellation information transmitted from the user terminal 15, in the above-mentioned step S14, a process is performed in which, based on the purchase target information from which the canceled product indicated by the cancellation information has been excluded, a cooking recipe that uses the product indicated by the purchase target information is selected and acquired from the cooking recipe database 17.

The recommended information identification method of the present invention will be described below with reference to the flowchart in FIG. 7, along with a second processing example of the recommendation process executed by the control unit 31 of the server device 11. The second processing example differs from the first processing example described above in that step S161 is added, and step S181 is performed instead of step S18. Therefore, only the differences will be described below.

If it is determined in step S16 that there is a missing item, the control unit 31 performs the process of step S161. In step S161, the control unit 31 determines whether the missing items include an item with a higher priority in the ranking list TB1 of FIG. 5. For example, the control unit 31 determines whether the missing item is an item with a higher priority within a predetermined reference ranking (10th in this embodiment). The reference ranking is a threshold value that determines whether the priority is high or low, and can be determined arbitrarily.

In step S161, if it is determined that the shortage product is within the top 10 in terms of priority (Yes in S161), the control unit 31 identifies the shortage product with the top 10 in terms of priority as a recommended product that matches the user's preferences, and extracts product information related to the product from the product information storage unit 322 (S181).

On the other hand, if it is determined in step S161 that the missing product is not one of the top 10 products in terms of priority (No in S161), a recipe reselection process is performed in step S17, and the processes from step S16 onwards are repeated.

The recommended information identification method of the present invention will be described below with reference to the flowchart in FIG. 8, along with a third processing example of the recommendation process executed by the control unit 31 of the server device 11. The third processing example differs from the first processing example described above in that steps S131 to S133 have been added. Therefore, only the differences will be described below.

When the process proceeds from step S13 or S15 to the next step S131, the control unit 31 acquires purchase history information of the user who entered the store from the customer information storage unit 321. This process is performed by the information acquisition unit 314 of the control unit 31. In addition, in the next step S132, the control unit 31 extracts the user's behavioral details from the purchase item information acquired in step S11 and the purchase history information acquired in step S131.

Next, in step S133, the control unit 31 uses the recommendation engine to perform a process of extracting multiple candidates for recommended products (recommended product candidates) that are likely to match the user's preferences based on the behavioral content. This process is performed by the recommendation extraction processing unit 311 of the control unit 31. In this embodiment, the recommendation extraction processing unit 311 determines, for example, five candidates.

Next, in step S14, the control unit 31 performs a recipe selection process in which, based on the multiple recommended product candidates extracted in step S133, the control unit 31 selects and acquires from the recipe database 17 cooking recipes that use the products indicated by these recommended product candidates.

The recommended information identification method of the present invention will be described below with reference to the flowchart in FIG. 9, along with a fourth processing example of the recommendation process executed by the control unit 31 of the server device 11. The fourth processing example differs from the first processing example described above in that steps S14 to S18 are replaced with steps S131 to S191. Therefore, only the differences will be described below.

When processing proceeds from step S13 or S15 to the next step S131, the control unit 31 performs processing of steps S131 to S133 of the third processing example described above.

Then, in the next step S162, the control unit 31 determines whether the multiple recommended product candidates extracted in step S133 include a product with a high priority that belongs to the ranking list TB1 in FIG. 5. For example, the control unit 31 determines whether the multiple recommended product candidates are products with a priority ranking within the top 10.

If it is determined in step S162 that a product with a higher priority is included in the multiple recommended product candidates (Yes side of S162), in the next step S182, a product with a higher priority is selected from the multiple recommended product candidates, and the selected product is identified as a recommended product. This process is performed by the recommendation selection processing unit 312 of the control unit 31. Step S182 corresponds to the recommended information identification step of the present invention. Thereafter, the recommendation information including the recommended product is transmitted to the user terminal 15 of the user by the recommendation information transmission unit 313 (S191). Then, in the user terminal 15, the received recommendation information is displayed on the display unit of the operation display unit 22. The recommendation information is also transmitted to the employee terminal 16.

On the other hand, if it is determined in step S162 that the multiple recommended product candidates do not include a product with a higher priority (No in S162), the control unit 31 again uses the recommendation engine to perform a candidate re-extraction process to extract multiple recommended product candidates different from the candidates extracted in step S134 based on the behavioral content (step S163). Thereafter, the process of step S162 is executed, and the processes of steps S162 and S163 are repeated until it is determined that the multiple recommended product candidates include a product with a higher priority.

Below, an example of the learning operation flow of the learning unit 40 of the ranking determination unit 33 using reinforcement learning will be described with reference to the flowchart in FIG. 10. In FIG. 10, S21, S22, ... indicate the numbers (step numbers) of the processing procedure.

First, in step S21, the behavior result acquisition unit 332 of the ranking determination unit 33 acquires the behavior results (behavior results of browsing behavior, reading behavior, and purchasing behavior) transmitted from the user terminal 15. In this step S21, the behavior results of all users who use the store 200 are transmitted from each of the user terminals 15.

In step S22, the reward calculation unit 401 of the learning unit 40 calculates a judgment evaluation value for judging whether the current priority order is appropriate based on the behavior results acquired in step S21. The judgment evaluation value is calculated by adding up the evaluation points set for each content of the behavior result. For example, positive evaluation points are set for the positive results of browsing behavior, reading behavior, and purchasing behavior, and negative evaluation points are set for the negative results. The judgment evaluation value is calculated by adding up the evaluation points for all behavior results. Note that if a weighting coefficient is set for the evaluation points for each user, for example, if a weighting coefficient proportional to the frequency of use is set for the evaluation points for the behavior results of users who frequently use the store 200, the judgment evaluation value may be calculated by taking that weighting coefficient into account.

In the next step S23, it is determined whether the judgment evaluation value is within a predetermined allowable range. If it is determined that the judgment evaluation value is within the allowable range, the current priority is deemed appropriate, and the process proceeds to step S24, where a positive reward is set. On the other hand, if it is determined that the judgment evaluation value is outside the allowable range, the current priority is deemed inappropriate, and the process proceeds to step S25, where a negative reward is set.

Then, in step S26, the value function update unit 402 of the learning unit 40 updates the value function related to the priority order, i.e., the action value function Q(s, a), and the process returns to step S21 to repeat the same process.

In the above embodiment, an information processing system 100 applied to a real store 200 is exemplified, but the present invention can also be applied to a virtual store on the Internet where users shop via a user terminal 15.

The above-described embodiment includes the following additional features.
(1) a purchase target information acquisition unit that acquires purchase target information regarding each of products selected as purchase targets by the user at the store from a terminal device used by the user at the store;
and a recommended information identification unit that identifies recommended information including recommended products tailored to the user from a plurality of pieces of product information related to a plurality of products offered in the store based on ranking information related to a selection ranking of the selected products and the purchase target information.
(2) The recommended information identification device according to (1), wherein the recommended information identification unit identifies the recommended information from the plurality of product information based on the purchase target information relating to top-ranking products up to a predetermined set ranking.
(3) a recipe selection unit that selects and acquires a recipe that uses a top-ranked product corresponding to a top ranking up to the set ranking from a recipe storage unit that stores information on a product used in cooking in association with a recipe; and
A shortage product determination unit that compares the products used in the recipes acquired by the recipe selection unit with the top products to determine the shortage products that are not used in the recipes,
The recommended information identification unit extracts shortage product information related to the shortage product from the multiple product information, and identifies the cooking recipe acquired by the cooking recipe selection unit and the shortage product information as the recommended information.
(4) The recommended information identification device described in (3), wherein the recipe selection unit selects and acquires a recipe that uses a product that is ranked higher than the selection rank of the next product when the selection time from when the previous product is selected to when the next product is selected is longer than a predetermined set time.
(5) The recommended information identification device according to (3) or (4), wherein the recipe selection unit selects a recipe that uses the top-ranked product as a main ingredient.
(6) A cancellation information acquisition unit that acquires cancellation information for canceling a selection of a product selected as a purchase target by the user from the terminal device,
The recommended information identification device described in any one of (1) to (5), wherein the recommended information identification unit identifies the recommended information from the plurality of product information based on the purchase target information and the ranking information from which the canceled product indicated by the cancellation information is excluded.
(7) A recommended information identification device described in any one of (1) to (6), wherein the recommended information identification unit identifies the recommended information from the multiple product information by using, in addition to the purchase item information and the ranking information, a predetermined priority order regarding the sales of multiple products offered at the store.
(8) The recommended information identification device described in (3), wherein the recommended information identification unit extracts shortage product information related to a shortage product that has a higher predetermined priority among the shortage products determined by the shortage product determination unit with respect to the sales of multiple products offered at the store, and identifies the cooking recipe acquired by the cooking recipe selection unit and the shortage product information as the recommended information.
(9) The recommended information identifying device according to any one of (1) to (8), further comprising a recommended information output unit that outputs the recommended information identified by the recommended information identifying unit to the terminal device.
(10) a purchase target information acquisition unit that acquires purchase target information regarding each of the products selected as purchase targets by the user at the store from a terminal device used by the user at the store;
a recommended information specifying unit that specifies recommended information corresponding to the user from a plurality of pieces of product information related to a plurality of products provided in the store based on ranking information related to a selection ranking of the selected products and the purchase target information;
a recommended information output unit that outputs the recommended information identified by the recommended information identifying unit to the terminal device.
(11) a purchase object information acquisition step of acquiring purchase object information regarding each of the products selected as purchase objects by the user at the store from a terminal device used by the user at the store;
and a recommended information identifying step of identifying recommended information tailored to the user from multiple product information related to multiple products offered in the store based on the purchase target information and ranking information related to a selection ranking of the selected products.
(12) a purchase object information acquisition step of acquiring purchase object information regarding each of the products selected as purchase objects by the user at the store from a terminal device used by the user at the store;
and a recommended information identification step of identifying recommended information suited to the user from multiple product information related to multiple products offered in the store based on the purchase target information and ranking information related to the selection ranking of the selected products.

11: Server device 12: Gate device 13: Gate device 14: Cash register 15: User terminal 16: Employee terminal 17: Cooking recipe database 21: Control unit 22: Operation display unit 23: Storage unit 24: Imaging unit 25: Communication I/F
31: Control unit 32: Storage unit 33: Ranking determination unit 35: Communication I/F
36: recipe selection unit 40: learning unit 100: information processing system 200: store 211: reading processing unit 212: display processing unit 213: notification processing unit 214: behavior result transmission unit 311: recommendation extraction processing unit 312: recommendation selection processing unit 313: recommendation information transmission unit 314: information acquisition unit 315: recipe selection processing unit 316: shortage product determination unit 321: customer information storage unit 322: product information storage unit 323: planned purchase product storage unit 324: priority order storage unit 331: change information observation unit 332: behavior result acquisition unit 333: data update processing unit 401: reward calculation unit 402: value function update unit

Claims (10)

a purchase target information acquisition unit that acquires a plurality of pieces of purchase target information relating to a plurality of purchase target products selected as purchase targets by the user at the store from a terminal device used by the user at the store;
a determination unit that determines whether or not at least one of the following conditions is satisfied: a time interval from when the previous piece of purchase target information is acquired until when the next piece of purchase target information is acquired is equal to or longer than a predetermined set time, based on a timing of acquisition of the purchase target information by the purchase target information acquisition unit; or, based on the number of pieces of the purchase target information acquired by the purchase target information acquisition unit, a number of items of the purchase target product has reached a predetermined set number;
a recommendation information identification unit that, when it is determined by the determination unit that the condition is satisfied, extracts behavioral details of the user using the plurality of pieces of purchase target information acquired by the purchase target information acquisition unit until the condition is satisfied, and identifies recommendation information including recommended products that match the preferences of the user from a product information storage unit that stores a plurality of pieces of product information related to a plurality of products provided in the store, based on the behavioral details;
A recommended information identifying device comprising: A recommended product candidate extraction unit extracts recommended product candidates that match the user's preferences from the product information storage unit based on the behavior content,
The recommended information identification unit identifies the recommended product candidates extracted by the recommended product candidate extraction unit as the recommended information.
The recommended information identifying device according to claim 1 . a recommended product candidate extraction unit that extracts recommended product candidates that match the user's preferences from the product information storage unit based on the behavior content;
a top product determination unit that determines whether the recommended product candidates extracted by the recommended product candidate extraction unit include a top product having a predetermined top priority in terms of sales of the plurality of products provided at the store,
when the higher-ranked product determination unit determines that the higher-ranked product is included in the recommended product candidates, the recommended information identification unit selects a product corresponding to the higher-ranked product from the recommended product candidates, and identifies product information of the selected product as the recommended information.
The recommended information identifying device according to claim 1 . a ranking determination unit that acquires a result of an action of the user with respect to the recommendation information and determines the priority order based on the acquired result of the action,
The recommended information identifying device according to claim 3 . The ranking determination unit is
A behavior result acquisition unit that acquires the behavior result;
a rank learning unit that learns the priority order based on the behavior result acquired by the behavior result acquisition unit,
The recommended information identifying device according to claim 4 . The recommended information identification device according to any one of claims 1 to 5, wherein the purchase target information acquisition unit acquires the purchase target information from the terminal device each time the user reads the purchase target information from the purchase target product by the terminal device. a recommended information output unit that outputs the recommended information identified by the recommended information identification unit to the terminal device.
The recommended information identifying device according to claim 1 . a purchase target information acquisition unit that acquires a plurality of pieces of purchase target information relating to a plurality of purchase target products selected as purchase targets by the user at the store from a terminal device used by the user at the store;
a determination unit that determines whether or not at least one of the following conditions is satisfied: a time interval from when the previous piece of purchase target information is acquired until when the next piece of purchase target information is acquired is equal to or longer than a predetermined set time, based on a timing of acquisition of the purchase target information by the purchase target information acquisition unit; or, based on the number of pieces of the purchase target information acquired by the purchase target information acquisition unit, a number of items of the purchase target product has reached a predetermined set number;
a recommendation information identification unit that, when it is determined by the determination unit that the condition is satisfied, extracts behavioral details of the user using the plurality of pieces of purchase target information acquired by the purchase target information acquisition unit until the condition is satisfied, and identifies recommendation information including recommended products that match the preferences of the user from a product information storage unit that stores a plurality of pieces of product information regarding a plurality of products provided in the store, based on the behavioral details;
A recommendation information identification system comprising: a purchase target information acquisition step of acquiring, from a terminal device used by a user at a store, a plurality of pieces of purchase target information relating to each of a plurality of purchase target products selected as purchase targets by the user at the store;
a determining step of determining whether or not at least one of the following conditions is satisfied: a time interval from when the previous piece of purchase target information is acquired until when the next piece of purchase target information is acquired is equal to or longer than a predetermined set time, based on a timing of acquisition of the purchase target information by the purchase target information acquisition step; or, based on the number of pieces of the purchase target information acquired by the purchase target information acquisition step, a number of items of the purchase target product has reached a predetermined set number;
a recommendation information specifying step of extracting action details of the user using the plurality of pieces of purchase target information acquired by the purchase target information acquiring step until the condition is satisfied, when it is determined by the determining step that the condition is satisfied, and specifying recommendation information including recommended products matching the preferences of the user from a product information storage unit in which a plurality of pieces of product information relating to a plurality of products provided in the store is stored, based on the action details;
The method for identifying recommended information, wherein each of the steps is performed by one or more processors. A program for causing one or more processors to execute each step of the method for identifying recommended information described in claim 9.

Images