Processing apparatus, processing method, and processing system

Abstract

To provide a processing device, a processing program, a processing method, and a processing system capable of more appropriately calculating a recommended display number of commodities.SOLUTION: A process of generating demand prediction information related to a demand quantity of a product at an arbitrary time by a demand prediction model generated on the basis of demand information of the product displayed in a store, and generating recommendation information indicating a display quantity of the product recommended at the arbitrary time on the basis of the generated demand prediction information and display-related information related to display of the product is executed.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to a processing device, a processing program, a processing method, and a processing system capable of executing processing related to products displayed in a store. [Background technology]

[0002] A processing system that checks the current display status of products in a store and requests the display of new products has been known. For example, Patent Document 1 describes a "shipment management system comprising an image recognition camera unit arranged in a store, a weight sensor mat unit, a terminal device of a producer, and a management server device, wherein the management server device includes a reception unit that receives image data from the image recognition camera unit and weight data from the weight sensor mat unit, an image analysis unit that analyzes the image data and identifies products and display quantities, a storage unit that stores at least a shipping request threshold value, a production item weight, an approximate range, the image data, and the weight data, an acquisition unit that acquires the shipping request threshold value, the production item weight, the approximate range, the display quantity identified by the image analysis, etc., and a processing ... The document describes a shipping management system that includes a display quantity calculation unit that divides the weight related to the weight data received by the attachment unit by the weight of the produced item, determines whether the divided value is within the approximate range, and if it is within the approximate range, updates the display quantity to the display quantity identified by the image analysis unit, and if it is outside the approximate range, updates it to the display quantity calculated by the division; a shipping determination unit that compares the display quantity calculated by the display quantity calculation unit with a shipping threshold value to determine whether or not to ship; and a shipping request unit that makes a shipping request based on the determination result of the shipping determination unit, and the producer's terminal device has a receiving unit that receives a notification related to the shipping request and a display unit that displays the notification. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-189989 Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, in light of the above-described technology, the present disclosure aims to provide a processing device, processing program, processing method, and processing system that can more appropriately calculate the recommended number of products to display through various embodiments. [Means for solving the problem]

[0005] According to one aspect of the present disclosure, there is provided "a processing device having at least one processor, wherein the at least one processor is configured to execute processing to generate demand forecast information relating to the demand quantity of a product at any time using a demand forecast model generated based on demand information for the product displayed in a store, and to generate recommendation information indicating the recommended number of the product to be displayed at the any time based on the generated demand forecast information and display-related information related to the display of the product."

[0006] According to one aspect of the present disclosure, there is provided a processing program that, when executed by at least one processor, causes the at least one processor to function to generate demand forecast information relating to the demand quantity of a product at any time using a demand forecast model generated based on demand information for the product displayed in a store, and to generate recommendation information indicating the recommended number of the product to be displayed at the any time based on the generated demand forecast information and display-related information related to the display of the product.

[0007] According to one aspect of the present disclosure, there is provided a "processing method executed by at least one processor, the processing method including: generating demand forecast information relating to the demand quantity of a product at any time using a demand forecast model generated based on demand information for the product displayed in a store; and generating recommendation information indicating the recommended number of the product to be displayed at the any time based on the generated demand forecast information and display-related information related to the display of the product."

[0008] According to one aspect of the present disclosure, the processing system includes "the processing device described above; and a terminal device that is arranged in a store where products are displayed so as to be able to communicate with the processing device, receives recommendation information indicating the recommended number of products to be displayed from the processing device, and is configured to output the received recommendation information." [Effects of the Invention]

[0009] According to the present disclosure, it is possible to provide a processing device, a processing program, a processing method, and a processing system that are capable of more appropriately calculating the recommended number of products to display.

[0010] It should be noted that the above effects are merely illustrative for the sake of convenience and are not limiting. In addition to or instead of the above effects, any effect described in this disclosure or an effect obvious to a person skilled in the art may be achieved. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. [Figure 2A] FIG. 2A is a block diagram showing a configuration of a processing device 100 according to an embodiment of the present disclosure. [Figure 2B] FIG. 2B is a block diagram showing a configuration of a terminal device 200 according to an embodiment of the present disclosure. [Figure 3A] FIG. 3A is a diagram conceptually illustrating a product management table stored in the processing device 100 according to an embodiment of the present disclosure. [Figure 3B] FIG. 3B is a diagram conceptually illustrating demand forecast information stored in the processing device 100 according to an embodiment of the present disclosure. [Figure 3C] FIG. 3C is a diagram conceptually illustrating a recommended information management table stored in the processing device 100 according to an embodiment of the present disclosure. [Figure 3D] FIG. 3D is a diagram conceptually showing a work information management table stored in a processing device 100 according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 5] FIG. 5 is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a diagram conceptually illustrating a process in which recommendation information at time t′ is generated by the processing device 100 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] Various embodiments of the present invention will be described below with reference to the accompanying drawings. Note that common components in the drawings are designated by the same reference numerals. It should also be noted that components depicted in one drawing may be omitted in another drawing for the sake of clarity. It should also be noted that the accompanying drawings are not necessarily drawn to scale.

[0013] The various systems, methods, and devices described in this disclosure should not be construed as limiting in any way. Indeed, the present disclosure is directed to all novel features and aspects of each of the various disclosed embodiments, combinations of these various embodiments with each other, and combinations of portions of these various embodiments with each other. The various systems, methods, and devices described in this disclosure are not limited to specific aspects, specific features, or combinations of such aspects with specific features, nor do the products and methods described in this disclosure require that one or more particular advantages be present or problems be solved. Furthermore, various features or aspects of the various embodiments described in this disclosure, or portions of such features or aspects, may be used in combination with each other.

[0014] Although the operations of some of the various methods disclosed in this disclosure are described in a particular order for convenience, it should be understood that description in this manner encompasses rearranging the order of the operations unless a particular order is required by specific text below. For example, operations described in a sequence may, in some cases, be rearranged or performed simultaneously. Furthermore, for purposes of simplicity, the accompanying drawings do not show the various ways in which the various items and methods described in this disclosure can be used in conjunction with other items and methods.

[0015] Any theories of operation, scientific principles, or other theoretical descriptions presented in this disclosure related to the devices or methods of the present disclosure are provided for the purpose of better understanding and are not intended to limit the scope of the technology, and the devices and methods in the appended claims are not limited to devices and methods that operate in a manner described by such theories of operation.

[0016] Any of the various methods disclosed in this disclosure may be implemented using computer-executable instructions stored on one or more computer-readable media and executed on a computer. The one or more media may be non-transitory computer-readable storage media, such as at least one optical media disk, volatile memory components, or non-volatile memory components. Here, the volatile memory components may include, for example, DRAM or SRAM. Also, the non-volatile memory components may include, for example, hard drives and solid-state drives (SSDs). Furthermore, the computer may include any computer available on the market, including, for example, smartphones and other mobile devices with computing hardware.

[0017] Any such computer-executable instructions for implementing the techniques disclosed in this disclosure, along with any data generated or used during the implementation of various embodiments disclosed in this disclosure, may be stored on one or more computer-readable media (e.g., non-transitory computer-readable storage media). Such computer-executable instructions may, for example, be part of a separate software application, or may be part of a software application accessed or downloaded via a web browser or other software application (such as a remote computing application). Such software may, for example, be executed on a single local computer (e.g., as a process running on any suitable commercially available computer) or in a networked environment (e.g., the Internet, a wide area network, a local area network, a client-server network (such as a cloud computing network), or other such network) using one or more networked computers.

[0018] For clarity, only certain selected aspects of various software-based implementations are described. Other details that are well known in the art are omitted. For example, the techniques disclosed in this disclosure are not limited to a particular computer language or program. For example, the techniques disclosed in this disclosure may be implemented by software written in C, C++, Java, or any other suitable programming language. Similarly, the techniques disclosed in this disclosure are not limited to a particular computer or a particular type of hardware. Specific details of suitable computers and hardware are well known and need not be described in detail in this disclosure.

[0019] Moreover, any of the various such software-based embodiments (e.g., including computer-executable instructions for causing a computer to perform any of the various methods disclosed in this disclosure) may be uploaded, downloaded, or remotely accessed by suitable communications means, including, for example, the Internet, the World Wide Web, an intranet, a software application, cable (including fiber optic cable), magnetic communication, electromagnetic communication (including RF, microwave, and infrared communication), electronic communication, or other such communications means.

[0020] 1. Overview of Processing System 1 A store displays one or more products. As consumers visit the store and purchase these products, the number of products on display decreases. However, each product generally has a shelf life, such as a use-by date or best-before date, set as a shelf life to maintain freshness. Since it is difficult to predict when and how much a product will be purchased by consumers, unplanned display arrangements result in products that have passed their expiration date being removed from the display and discarded without being purchased by consumers. This disposal not only increases the environmental burden but also results in economic losses. Therefore, maintaining an appropriate number of products on display in a store is extremely important in preventing an increase in the environmental burden and economic losses.

[0021] The processing system 1 according to the present disclosure is used to generate recommendation information indicating the recommended number of products to display at any given time. That is, by generating the recommendation information, the processing system 1 makes it possible to maintain an appropriate number of products displayed in a store, and further makes it possible to prevent an increase in environmental load and economic loss.

[0022] Specifically, the processing system 1 generates demand forecast information related to the demand quantity of a product at any time using a demand forecast model generated based on demand information for the product displayed in the store. Then, the processing system 1 generates recommendation information indicating the recommended number of products to display at any time based on the generated demand forecast information and display-related information related to the display of the products.

[0023] The processing system 1 also generates the recommendation information for each product. The processing system 1 also generates recommendation information for each set time interval, such as the time set next to the arbitrary time. Therefore, the processing system 1 can generate recommendation information indicating the recommended number of products to be displayed at each time interval for each product.

[0024] In this disclosure, a store may be any store capable of displaying merchandise. Examples of such stores include department stores (large department stores and other department stores), general merchandise stores (large general merchandise stores and medium-sized general merchandise stores), specialty supermarkets (clothing specialty stores, grocery specialty stores, home-related specialty stores, etc.), convenience stores (all-day stores and other stores), drug stores, other supermarkets, specialty stores (clothing specialty stores, grocery specialty stores, home-related specialty stores, etc.), and main stores (clothing-focused stores, grocery-focused stores, home-related stores, etc.). Stores are not limited to the above, and can also include restaurants, traditional Japanese restaurants, inns, hotels, etc., as long as they have space capable of displaying merchandise. In the following, convenience stores will be mainly used as an example of stores, but stores are not limited to these.

[0025] In addition, in the present disclosure, a "product" may be any product that can be displayed in a store. Textiles, clothing, and personal items such as kimonos, clothing fabrics, bedding, men's clothing, women's clothing, children's clothing, shoes, footwear (excluding shoes), bags, pouches, underwear, Western goods, small articles, other clothing and personal items Vegetables, fruits, meat, eggs, poultry, fresh fish, alcohol, confectionery (manufactured), confectionery (non-manufactured), bread (manufactured), bread (non-manufactured), milk, beverages (excluding milk, including tea beverages), tea, cooked food, rice, processed foods such as tofu and kamaboko, dried foods, other food and beverages · Automobiles (new cars), used cars, auto parts and accessories, motorcycles, bicycles and other automobiles and bicycles Electrical machinery and equipment (excluding second-hand items), electrical office machinery and equipment (excluding second-hand items), second-hand electrical products, other machinery and equipment, etc. Furniture, building materials, tatami mats, religious implements, metalwork, hardware, ceramics and glassware, other carpeting, over-the-counter medicines, prescription medicines, cosmetics, agricultural machinery and equipment, seedlings and seeds, fertilizer and feed, fuel (gas stations), fuel (excluding gas stations), books and magazines (excluding second-hand books), used books, newspapers, paper and stationery, sporting goods, toys and recreational items, musical instruments, cameras and photographic materials, watches, glasses and optical equipment, tobacco and smoking accessories, flowers and plants, building materials, jewelry, pets and pet supplies, antiques, second-hand goods (excluding antiques), etc. Among these, the product is preferably a food product, more preferably a food product prepared by a staff member at a store. Note that the following description will be given taking as an example a food product prepared by a staff member at a store (for example, so-called FF products such as fried chicken), but the product is not limited to this.

[0026] In the present disclosure, the "display-related information" may be any information related to the display of each product. For example, such display-related information preferably includes cooking count information indicating the number of times each product can be cooked at each time, cooking cost information indicating the cooking cost of the displayed products, the location of the store, and the cooking time required to cook the products, with cooking count information and cooking cost information being more preferred.

[0027] 1 is a block diagram showing a configuration of a processing system 1 according to an embodiment of the present disclosure. According to Fig. 1, the processing system 1 includes a processing device 100 and a terminal device 200, and each device is connected to each other so as to be able to communicate with each other via a wired or wireless network.

[0028] In the present disclosure, the processing device may be any device capable of performing the processing executed by the processing device 100. That is, various devices such as an on-premise server device, a cloud server device, a smartphone, a tablet device, a laptop PC, and a desktop PC can be used as the processing device. The terminal device 200 can also function as the processing device. Furthermore, in the present disclosure, the storage and processing performed by the processing device may be distributed to other processing devices, a server device, a database device, and the like. That is, the processing device is not limited to a device configured from a single housing, but also includes a combination of the various devices exemplified above.

[0029] Furthermore, in the present disclosure, the terminal device may be any device capable of performing the processing executed by the terminal device 200. Various devices such as a smartphone, tablet device, laptop PC, desktop PC, cash register, and POS device can be used as the terminal device 200. Furthermore, in the present disclosure, the processing performed by the terminal device may be distributed to other terminal devices, processing devices, server devices, database devices, and the like. In other words, the terminal device is not limited to devices configured from a single housing, but also includes combinations of the various devices exemplified above.

[0030] 1 shows only one terminal device, the processing system 1 can include multiple terminal devices corresponding to the number of stores or staff members, for example. For example, multiple stores or multiple staff members can share a single terminal device, or one store or one staff member can use multiple terminal devices.

[0031] FIG. 2A is a block diagram showing the configuration of a processing device 100 according to an embodiment of the present disclosure. According to FIG. 2A, the processing device 100 includes a processor 111, a memory 112, and a communication interface 113. These components are electrically connected to one another via control lines and data lines. The processing device 100 does not need to include all of the components shown in FIG. 2A; some components may be omitted, or other components may be added. For example, an external memory, a database device, a server device, or the like connected in a communicable manner as a memory may be used. Furthermore, some processing may be distributed and executed by other processing devices, including other server devices. In other words, the processing device 100 is not limited to a single device, but may be distributed across multiple devices depending on the information handling and processing load.

[0032] The processor 111 functions as a control unit that controls other components of the processing system 1 based on a processing program stored in the memory 112. The processor 111 is mainly composed of one or more CPUs, but may also be combined with a GPU, an FPGA, or the like as appropriate. The processor 111 generates demand forecast information for products based on the processing program stored in the memory 112, and generates recommendation information indicating the recommended number of products to be displayed. Specifically, the processor 111 executes, based on the processing program stored in the memory 112, "processing to generate demand forecast information related to the demand amount of products at any time using a demand forecast model generated based on demand information for products displayed in a store" and "processing to generate recommendation information indicating the recommended number of products to be displayed at any time based on the generated demand forecast information and display-related information related to the display of products."

[0033] The memory 112 is composed of RAM, ROM, non-volatile memory, HDD, SSD, etc., and functions as a storage unit. The memory 112 stores instructions and commands for various controls of the processing system 1 according to this embodiment as processing programs. Specifically, the memory 112 stores programs that the processor 111 executes, such as "a process of generating demand forecast information related to the demand quantity of a product at any time using a demand forecast model generated based on demand information for products displayed in a store" and "a process of generating recommendation information indicating the recommended number of products to display at any time based on the generated demand forecast information and display-related information related to the display of the products." In addition to these programs, the memory 112 also stores various information stored in a product management table, a recommendation information management table, a work information management table, etc. Note that this information does not necessarily need to be constantly stored in the memory 112 within the processing device 100, but may be stored in a remotely installed database device or server device. In such cases, the database device or server device is also included in the memory 112.

[0034] The communication interface 113 functions as a notification unit for transmitting and receiving various information to and from the terminal device 200 or other processing devices connected via a wired or wireless network. Examples of the communication interface 113 include a connector for wired communication such as USB or SCSI, a transmitting and receiving device for wireless communication such as broadband wireless communication such as wireless LAN, Bluetooth (registered trademark) or LTE, or infrared, and various connection terminals for printed circuit boards or flexible circuit boards.

[0035] 2B is a block diagram showing a configuration of a terminal device 200 according to an embodiment of the present disclosure. According to FIG. 2B, the terminal device 200 includes a processor 211, a memory 212, an input interface 213, an output interface 214, and a communication interface 215. These components are electrically connected to each other via control lines and data lines. Note that the terminal device 200 does not need to include all of the components shown in FIG. 2B; it is possible to omit some of the components or add other components.

[0036] The processor 211 functions as a control unit that controls other components of the terminal device 200 based on a program stored in the memory 212. The processor 211 is mainly composed of one or more CPUs, but may also be combined with a GPU, an FPGA, or the like as appropriate. The processor 211 executes processes for outputting recommended information received from the processing device 100 based on the processing program stored in the memory 212. Specifically, the processor 211 executes processes such as "receiving recommended information from the processing device 100 via the communication interface 215" and "outputting recommended information received via the output interface 214" based on the program stored in the memory 212.

[0037] The memory 212 is composed of RAM, ROM, non-volatile memory, HDD, etc., and functions as a storage unit. The memory 212 stores instructions and commands for various controls of the processing system 1 according to this embodiment as programs. Specifically, the memory 212 stores programs to be executed by the processor 211, such as "processing for receiving recommended information from the processing device 100 via the communication interface 215" and "processing for outputting recommended information received via the output interface 214."

[0038] The input interface 213 functions as an input unit that accepts user operation inputs to the terminal device 200. Examples of the input interface 213 include physical key buttons and a touch panel having an input coordinate system corresponding to the display coordinate system of the display. In the case of a touch panel, icons are displayed on the display, and the operator selects each icon by inputting operations via the touch panel. The method for detecting the user's operation input using the touch panel may be any method, such as a capacitive method or a resistive method. The input interface 213-2 does not always need to be physically provided on the terminal device 200, and may be connected as needed via a wired or wireless network. Therefore, in addition to the above, a mouse, a keyboard, etc. can also be used as the input interface 213-2.

[0039] The output interface 214 functions as an output unit for outputting various information. An example of the output interface 214 is a display, but the output interface 214 is not limited to this and may be composed of other liquid crystal panels, organic EL displays, plasma displays, printers, etc. Also, a display does not have to be provided. For example, an interface for connecting to a display or the like connectable to the processing device 100 via a wired or wireless network can function as the output interface 214 for outputting display data to the display or the like.

[0040] The communication interface 215 functions as a communication unit for transmitting and receiving information to and from the processing device 100, other terminal devices 200, and other processing devices. Examples of the communication interface 215 include a connector for wired communication such as USB or SCSI, a transmitting and receiving device for wireless communication such as broadband wireless communication such as wireless LAN, Bluetooth (registered trademark), or LTE, or infrared, and various connection terminals for printed circuit boards and flexible circuit boards.

[0041] 3. Various information used in processing in the processing system 1 3A, 3C, and 3D show various tables that are stored in the processing device 100 and contain information that is sent to and received from the processing device 100 as the processing progresses. This information is updated and stored as needed as the processing progresses. Note that the information shown in FIGS. 3A, 3C, and 3D may be stored in the memory 112 of the processing device 100, or may be stored in a remotely installed database device or server device and read out as needed as the processing progresses.

[0042] 3A is a diagram conceptually illustrating a product management table stored in the processing device 100 according to an embodiment of the present disclosure. According to Fig. 3A, the product management table stores information such as cooking quantity information, disposal quantity information, sales quantity information, display quantity information, demand forecast information, recommendation information, and cooking cost information in association with product ID information.

[0043] "Product ID information" is information specific to products displayed in a store and is used to identify each product. For example, product ID information is generated by a store employee or product manager each time a new product type is registered. Note that any product ID information can be used as long as it can identify the product, and various information such as the product name can be used.

[0044] The "cooking quantity information" is information indicating the number of products displayed in a store that are actually cooked at each time interval. This information is used as one piece of demand information for products displayed in a store and identified by each product ID information. This information is newly stored each time the actual number for each time is acquired. This information may be deleted after a certain period of time has passed, or may be constantly added and stored. Specifically, the cooking quantity information is stored in association with each time and the number of products cooked, such as "2 units at 0:00 on June 1, 2024," "8 units at 5:00 on June 1, 2024," "10 units at 8:00 on June 1, 2024," "10 units at 11:00 on June 1, 2024," "5 units at 14:00 on June 1, 2024," "10 units at 17:00 on June 1, 2024," and "2 units at 20:00 on June 1, 2024."

[0045] "Discard number information" is information indicating the number of products actually discarded at each time interval. This information is used as one piece of demand information for products displayed in a store and identified by each product ID information. This information is newly stored each time the actual number for each time is acquired. This information may be deleted after a certain period of time has passed, or may be constantly added and stored. Specifically, the discard number information associates each time with the number of discarded products, such as "1 unit at 0:00 on June 1, 2024," "2 units at 5:00 on June 1, 2024," "3 units at 8:00 on June 1, 2024," "1 unit at 11:00 on June 1, 2024," "4 units at 14:00 on June 1, 2024," "3 units at 17:00 on June 1, 2024," and "0 units at 20:00 on June 1, 2024."

[0046] "Sales quantity information" is information indicating the number of products actually sold in each time period separated by an arbitrary interval. This information is used as one piece of demand information for products displayed in a store and identified by each product ID information. This information is newly stored each time the actual number for each time period is acquired. This information may be deleted after a certain period of time has passed, or may be constantly added and stored. Specifically, the sales quantity information is stored in such a way that each time period and the number of products sold correspond to each other, such as "2 units at 0:00 on June 1, 2024," "4 units at 5:00 on June 1, 2024," "8 units at 8:00 on June 1, 2024," "5 units at 11:00 on June 1, 2024," "6 units at 14:00 on June 1, 2024," "3 units at 17:00 on June 1, 2024," and "6 units at 20:00 on June 1, 2024."

[0047] "Display quantity information" is information indicating the number of products actually displayed at each time interval. This information is used as one piece of demand information for products displayed in a store and identified by each product ID information. This information is newly stored each time the actual number for each time interval is acquired. This information may be deleted after a certain period of time has passed, or may be constantly added and stored. Specifically, the display quantity information associates each time interval with the number of products displayed, such as "2 units at 0:00 on June 1, 2024," "4 units at 5:00 on June 1, 2024," "2 units at 8:00 on June 1, 2024," "6 units at 11:00 on June 1, 2024," "1 unit at 14:00 on June 1, 2024," "5 units at 17:00 on June 1, 2024," and "1 unit at 20:00 on June 1, 2024."

[0048] "Demand forecast information" is information related to the demand volume of each product predicted at each future time interval. This information is obtained as output by, for example, the processing device 100 inputting each piece of demand information into a demand forecast model. Here, FIG. 3B is a diagram conceptually illustrating demand forecast information stored in the processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 3B is a diagram illustrating demand forecast information including the predicted demand volume and its accuracy for a product with product ID information "A1" at a time specified by time information "J1." Specifically, according to Figure 3B, for a product with product ID information "A1", demand forecast information and its accuracy are stored in correspondence with each other, such as demand forecast information F1-1 (e.g., 0 units) and its accuracy I1 (e.g., 5%) at the time specified by time information "J1", demand forecast information F1-2 (e.g., 1 unit) and its accuracy I2 (e.g., 7.5%), demand forecast information F1-3 (e.g., 2 units) and its accuracy I3 (e.g., 8.3%), demand forecast information F1-4 (e.g., 3 units) and its accuracy I4 (e.g., 23%), demand forecast information F1-5 (e.g., 4 units) and its accuracy I5 (e.g., 34%), and demand forecast information F1-n (e.g., n units) and its accuracy I1 (e.g., n%).

[0049] Although not specifically shown in Fig. 3B, similar demand time information is generated for the product with product ID information "A1" at other future times other than the time information "J1". Also, similar demand time information is generated for each time period for other products other than the product ID information "A1".

[0050] Furthermore, the demand forecast information may be any information that indicates the demand amount (sales volume) at each time. Here, an example is given in which the demand forecast information is calculated from a demand forecast model in the processing device 100, but it may simply be information that indicates any classification and its accuracy, such as "demand is expected to increase," "demand is expected to decrease," "demand is expected to increase significantly," or "demand is expected to decrease significantly."

[0051] Returning to FIG. 3A again, "recommended information" is information indicating the recommended number of displays for each product at each future time interval separated by an arbitrary interval. Here, FIG. 3C is a conceptual diagram of a recommended information management table stored in the processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 3C is a diagram showing a table for managing recommended information stored in association with product ID information "A1" for a product. In addition to the table associated with product ID information "A1" in FIG. 3C, similar tables are generated for each product ID information. According to FIG. 3C, recommended information indicating the recommended number of displays for the product with product ID information "A1" is stored in association with each future time interval (time J1 to time Jn) separated by an arbitrary interval. For example, recommendation information indicating the recommended number of displays for each time is stored in association with each other, such as "4 items" recommended for the time information "June X, 2024, 0:00," "8 items" recommended for the time information "June X, 2024, 5:00," "3 items" recommended for the time information "June X, 2024, 8:00," "8 items" recommended for the time information "June X, 2024, 11:00," "10 items" recommended for the time information "June X, 2024, 14:00," "10 items" recommended for the time information "June X, 2024, 17:00," and "3 items" recommended for the time information "June X, 2024, 20:00."

[0052] The recommendation information may be any information indicating the recommended number of products to display at each time. While the example in Fig. 3C shows the recommended display number determined by the processing device 100, it is also possible to use the number that needs to be replenished, calculated by subtracting the number of products currently on display from the recommended display number, as the recommendation information. Furthermore, information that simply notifies the need for replenishment, such as "replenishment required," can also be used as recommendation information.

[0053] Returning to FIG. 3A again, "cooking cost information" is information indicating the cost required to cook each product identified by the product ID information. This information is used as part of the display-related information related to the display of the products identified by each product ID information. This information may be registered in advance by a store staff member or product manager when a new product is registered, or may be updated each time the actual cost is calculated. As described above, cooking cost information may be any information indicating the cost required for cooking, and may include various information such as the cost of purchasing cooking equipment, the cost of operating the cooking equipment, labor costs, or the cost of ingredients. As such, the more times a product is cooked, the higher the cooking cost, so lower cooking costs are desirable. However, using cooking cost information enables efficient store operation by reducing the number of cooking times.

[0054] Fig. 3D is a diagram conceptually illustrating a work information management table stored in a processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 3D is a diagram showing a table for managing work information in which the staff member ID information of staff working at a store with store ID information "Z1" and their working hours are associated with each other; in addition to the table associated with store ID information "Z1" in Fig. 3D, similar tables will be generated for each store ID information of each store. Note that the work information managed in this work information management table is information used as one piece of display-related information related to the display of products identified by each product ID information.

[0055] "Time information" is information indicating each time period separated by an arbitrary interval. It is preferable that the time information is the same as or corresponds to each time period separated by an arbitrary interval used in the cooking number information, waste number information, sales number information, display number information, and recommendation information shown in FIG. 3A. Specifically, the time information includes past times such as "June 1, 2024, 0:00," "June 1, 2024, 5:00," "June 1, 2024, 8:00," "June 1, 2024, 11:00," "June 1, 2024, 14:00," "June 1, 2024, 17:00," and "June 1, 2024, 20:00," as well as future times such as "June X, 2024, 0:00," "June X, 2024, 5:00," "June X, 2024, 8:00," "June X, 2024, 11:00," "June X, 2024, 14:00," "June X, 2024, 17:00," and "June X, 2024, 20:00." As one example of this information, the shift change times of the person in charge are set.

[0056] "Staff member ID information" is information specific to each store staff member and is used to identify each staff member. For example, staff member ID information is generated by the store staff member or product manager each time a new staff member is registered. Note that any staff member ID information can be used as long as it can identify the staff member, and various information such as the name of the staff member can be used. In other words, staff member ID information is stored in association with each piece of time information, and staff member ID information for one or more staff members working at each time indicated by each piece of time information is stored. This makes it possible to manage who is working at each time indicated by each piece of time information. "Cooking count information" is information that indicates the number of times each product can be cooked in each time period specified by the time information. For example, the cooking count is limited to a certain number of times, such as once or twice, in each time period, and cooking count information is used to determine how many cooking times remaining are possible in comparison with this limit.

[0057] 4. Processing flow executed by the processing device 100 4 and 5 are diagrams showing processing flows executed in the processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 4 is a diagram showing a processing flow for processing related to generation of a demand prediction model and a recommendation information generation model. Also, FIG. 5 is a diagram showing a processing flow for generating recommendation information indicating the number of products recommended to be displayed at any given time. Each processing flow is mainly performed by the processor 111 of the processing device 100 reading and executing a program stored in the memory 112.

[0058] (A) Processing flow for generating a demand forecasting model and a recommendation information generation model 4, the processor 111 reads out various types of demand information used to generate a demand forecasting model from the product management table (S111). Examples of the demand information to be read out include sales number information indicating the number of products actually sold at each time, and display number information indicating the number of products actually displayed in the store at each time.

[0059] The processor 111 generates a demand forecasting model based on the various types of read demand information (S112). As an example, the demand forecasting model performs machine learning using the read sales volume information and display quantity information as feature quantities, and predicts the demand quantity (sales volume) of the product at any time in the future and its accuracy as demand forecast information. The demand forecasting model is preferably generated for each product. Therefore, the demand quantity and accuracy are generated using the demand forecasting model generated for each product. As will be described in detail below, such a demand forecasting model is preferably generated based on a Poisson regression model following the Poisson distribution, but of course it may also be a model generated by other methods.

[0060] The processor 111 stores the generated demand forecasting model in the memory 112 (S113).

[0061] Next, the processor 111 reads various types of demand information used to generate a recommendation information generation model from the product management table (S114). Examples of the demand information to be read include sales volume information, cost rate information, and display quantity information. Note that cost rate information indicating the cost rate of a product is acquired by reading an arbitrary value set in advance by a user or administrator, regardless of the product. However, for example, in the product management table shown in FIG. 3A, the cost rate of each product may be stored in association with each product ID information, and read from the product management table in the same way as sales volume information, etc.

[0062] Based on the various types of read demand information, processor 111 generates a recommendation information generation model for outputting recommendation information indicating the recommended number of products to display for each time period divided at arbitrary intervals (S115). As an example, the recommendation information generation model performs machine learning using the various types of read demand information as features and the recommendation information as an objective function, and predicts the recommended number of products to display for each time period divided at arbitrary intervals as recommendation information. The recommendation information generation model is preferably generated for each product. Therefore, the recommendation information generation model generated for each product is used to generate recommendation information indicating the recommended number of products to display for each time period divided at arbitrary intervals. As described in detail below, such a recommendation information generation model is generated using a method that utilizes reinforcement learning, but it may naturally be generated by other methods.

[0063] The processor 111 stores the generated recommendation information generation model in the memory 112 (S116). This completes the processing flow.

[0064] 4, the demand forecasting model or the recommendation information generation model is generated by the processor 111 of the processing device 100, but these processes can also be performed by other processing devices or server devices other than the processing device 100. Also, in FIG. 4, the generated demand forecasting model or recommendation information generation model is stored in the memory 112, but it is also possible to store the demand forecasting model in other processing devices or server devices.

[0065] (A1-1) Example of a demand forecast model based on the Poisson distribution An example of a demand forecasting model for predicting the demand volume at any time for each product displayed in a store based on demand information (sales volume information and display volume information) is described below. This demand forecasting model takes into account the probabilistic behavior of sales volumes indicated by sales volume information, and therefore takes an approach in which the demand volume is predicted not as a single estimated value but as a probability distribution. Therefore, this demand forecasting model is generated based on a Poisson regression model. Specifically, it is assumed that the sales volume at any time for each product follows a Poisson distribution, and the parameter μ of this Poisson distribution is estimated. In this case, it is desirable that the time granularity used to calculate the predicted value in the demand forecasting model be the same as the time granularity used to generate the recommendation information.

[0066] Furthermore, in demand forecasting, it is important to predict sales figures assuming that there are sufficient display units for each product. In a situation where there is insufficient display units, even if users have demand, their purchasing behavior is restricted. As a result, true demand may exceed the actual sales figures, and true demand and actual sales figures may not necessarily match. Therefore, in this demand forecasting model, it is possible to estimate true demand more precisely by taking into account the display units specified by the display unit information, rather than simply using the sales figures specified by the sales unit information. Therefore, as shown in the demand forecasting model in equation (1), a demand forecasting model is constructed for each case where there is a high possibility that the sales figures and true demand will match, and a case where there is a high possibility that they will not. Specifically, for each time period i separated by an arbitrary interval, the display unit number m specified by the display unit information is used. iUsing the actual data of i m i In this case, the number of sales is m i The likelihood function l(μ; k, m) shown in the lower part of equation (1) is generated and learning is performed. i The number of displays is m i If it is smaller than , the likelihood function l(μ;k,m) shown in the upper part of equation (1) is generated and learning is performed. In this way, by using a demand forecasting model that is trained separately according to the number of items on display and the number of items sold, it becomes possible to predict demand more accurately.

[0067]

number

[0068] Among the parameters used in the above formula (1), the parameter μ i is calculated by the following formula (2). Specifically, the feature quantity of the training data at time i is x i ∈R M , the parameter μ of the Poisson distribution at time i i A model for predicting is generated as shown in the following formula (2). As shown in the following formula (2), the formula (2) includes day of the week information, which is one of the demand information, as an explanatory variable. In other words, since the number of products sold is also affected by the day of the week, by using the day of the week information, it is possible to predict demand more precisely. Note that s represents the effect common to all days of the week, β represents the effect for each day of the week, and x i indicates the day of the week for the i-th data.

[0069]

number

[0070] In the above formula (2), θ is a parameter used in the model for predicting μ, and θ = {s, β}(s∈R, β∈R M) Therefore, the likelihood function L(θ) is expressed as in the following formula (3), which calculates the parameters θ and β in the demand forecasting model based on the Poisson distribution of formula (1).

[0071]

number

[0072] In addition, x in the above formula (2) i However, instead of or in addition to this, it is also possible to use event information indicating events (such as sales promotion events) held during product display, or period information specifying the season or time when the product is displayed.

[0073] In this way, by using the parameters obtained in equations (2) and (3) and utilizing the demand forecasting model of equation (1), it is possible to obtain the demand quantity (sales volume) and its accuracy.

[0074] (A1-2) Examples of other demand forecasting models In addition to the demand forecasting model exemplified in (A1-1) above, the processing device 100 can also generate demand forecasting models using other machine learning techniques. For example, the processor 111 of the processing device 100 acquires, as learning time information, information on each time period separated by an arbitrary interval specified by the sales number information shown in FIG. 3A. Next, the processor 111 assigns the sales number information for each time period specified by the sales number information as correct label information to each piece of learning time information. The processor 111 executes a step of performing machine learning to predict a demand quantity (sales number) for each future time period using the learning time information and the sales number information for that time period. As an example, the machine learning is performed by providing a neural network consisting of a combination of neurons with a set of learning time information and information on the sales number for that time period (correct label), and repeating learning while adjusting the parameters of each neuron so that the output from the neural network is the same as the correct label information. After completing the adjustment, the processor 111 acquires the adjusted model as a demand forecasting model.

[0075] Here, the demand forecasting model can also be generated using machine learning techniques such as neural networks, convolutional neural networks, multi-layer neural networks (MLP), long short-term memory (LSTM), gated recurrent units (GRU), graph neural networks (GNN), and transformers; gradient boosting decision trees (GBDT) such as light gradient boosting machines (LightGBM), XGBoost, and CatBoost; ridge regression, logistic regression, support vector regression (SVR), nearest neighbor methods, decision trees, regression trees, and random forests.

[0076] (A1-3) Examples of other demand forecasting models Although the above (A1-1) and (A1-2) show examples of methods using machine learning, it is also possible to generate a model using a logic circuit approach based on past performance data such as sales volume information stored in the product management table shown in Fig. 3. As an example, it is also possible to identify, from the information stored in the product management table, a past time that meets predetermined conditions, such as the same day of the week as the arbitrary time to be predicted, or the same day as the event being held at the arbitrary time to be predicted, and to construct a demand forecasting model that uses the sales volume information for that time as demand forecasting information.

[0077] (A2) Example of a recommendation generation model An example of a recommendation information generation model that utilizes the demand forecasting model generated in S112 of Fig. 4 to generate recommendation information indicating the recommended number of products to display at each time t (t=0, 1, 2, ..., T) divided into arbitrary intervals for each product is described below. The recommendation information generation model uses a reinforcement learning model that uses information on the number of products discarded and the number of products sold, which are types of demand information, as feature quantities, and therefore can output the optimal recommended number of products to display, which can reduce food waste and increase profits.

[0078] To generate a recommendation information generation model, we first formulate the recommended number of displays as an objective function J. Specifically, we define the optimal number of additional displays b at any given time interval t. t , the number of products sold is n t , the sales price is γ, and the cost rate is KThe objective function J is expressed as shown in equation (4). The processor 111 reads and uses, as the display-related information, display number information indicating the number of additional displays, sales number information indicating the number of products sold, cost rate information indicating the cost rate, and sales price information indicating the sales price. The display number information and sales number information are information read from the product management table of FIG. 3A. Although not specifically shown in FIG. 3A, sales price information is information stored in the product management table in association with the product ID information of each product. The cost rate information is information acquired by reading an arbitrary value set in advance by a user or administrator, regardless of the product. However, for example, the cost rate of each product may be stored in association with each product ID information in the product management table shown in FIG. 3A, and read from the product management table in the same way as sales number information, etc.

[0079]

number

[0080] Here, the first term in equation (4) represents the total sales amount for each time period separated by an arbitrary interval, and the second term represents the total display costs for each of those times. The total sales amount for each time period is calculated from the number of items sold and the sales price. The total display costs for each time period is calculated from the number of items displayed, the sales price, and the cost rate. Furthermore, E[·] represents the expected value for the true demand n' (i.e., the amount of sales profit). Furthermore, K can be interpreted as a hyperparameter that controls the trade-off between food waste and profit. In other words, the objective function J is the amount of sales profit obtained from displaying the product in question.

[0081] Next, the recommendation information, which is the recommended number of displays at time t separated by any interval, can be shown according to a Markov decision process, and the objective function J in the above formula (4) is transformed as shown in formula (5). Specifically, if the value (i.e., the expected profit amount) when the display state at time t is s and the display plan at time t is π(t) items is r(s, t, π(t)), and the probability that the display state at time t+1 will be s' when the display state at time t is s and the display plan at time t is π(t) items is p(s'|s, t, π(t)), the objective function J in the above formula (4) is transformed as shown in formula (5).

[0082]

number

[0083] The display state s refers to the state of the products displayed at time t. As an example, it is information indicating when the products displayed at time t will be discarded, such as three products to be discarded at time t+1 and two products to be discarded at time t+2. This information is obtained by reading out information such as discard count information as display-related information from the product management table shown in FIG. 3A. The display plan π(t) is information indicating the recommended number of products to be displayed at time t.

[0084] Here, E π [·|t] represents the value (i.e., the expected profit amount) associated with the display state s at any time interval t when display plan π is followed. In other words, by finding a display plan π that maximizes this objective function, it is possible to optimize food waste reduction and profit improvement.

[0085] Next, we will formulate r(s, t, π(t)) and p(s'|s, t, π(t)) used in the above formula (5). Specifically, first, let n' be the number of sales at time t divided by an arbitrary interval. t , and its probability distribution is f t , display status s t , the recommended number of displays is at where s t indicates the value that holds the time until disposal of each displayed product in the form of an array. Based on this setting, (s t ,a t ) to s t+1 The probabilistic transition to (t=0,1,2,...,T-1) is expressed as follows: 1:b t =max(a t -|s t |,0) ·2:n' t ~f t 3:n t =min(n' t ,|s t |) 4:s t+1 =g(s t ,n t ,b t )

[0086] Furthermore, |s| is the total number of exhibits in s, and the function g(s t ,n t ,b t ) by s t The following operations are performed on Step 1: n descending orders t Eliminate individual elements Step 2: Decrement the value of all elements by 1 Step 3: Eliminate elements with a value of 0 Step 4: Set the elements with value h to b t Add

[0087] Here, h indicates the time from when a product is put on display until it is discarded. Step 1 indicates that products are sold in order, starting with the oldest products. Step 2 indicates that the deadline for disposal is shortened. Step 3 indicates that products that have reached their deadline are discarded. Step 4 indicates that new products are added and put on display.

[0088] From the above, r(s, t, π(t)) and p(s'|s, t, π(t)) can be formulated as shown in equations (6) and (7), respectively. Here, f t(n) is the probability that the sales volume at time t, separated by an arbitrary interval, will be n, and uses the demand amount (sales volume n) and its accuracy (probability of occurrence), which are the demand forecast information output from the demand forecasting model. I(·) is a function that takes the value of 1 when the equation in parentheses holds, and 0 when it does not. In other words, r(s,t,π(t)) in equation (6) is expressed as a reward function in which the expected value of sales amount at time t is used as the loss function, and the display cost at time t is used as the penalty term.

[0089]

number

number

[0090] Next, the objective function J formulated as above is optimized with respect to the display plan π. If we take the approach of working backwards from time T, which is the end of a period of time (t = 0, 1, 2, ..., T) separated by an arbitrary interval, we can calculate the recommended number of displays for each (s, t) = (display state, time), but we cannot calculate the recommended number of displays for each time t. On the other hand, when considering the recommended number of displays for each time t, it is possible to calculate the recommended number of displays at a specific time by fixing the recommended display plans for other times. Specifically, by fixing the display plans for t = 0, 1, 2, ..., t'-1, t'+1, t'+2, ..., T, the recommended number of displays for t = t' can be calculated as follows:

[0091] <Process 1> By fixing the display plan for t=t'+1, t'+2,...,T, the value of the display plan at t=t'+1 (i.e., the amount of sales profit from time t'+1 onwards) is calculated using a backward recurrence formula. <Process 2> By fixing the display plan for t=0, 1, 2,..., t'-1, the probability of occurrence of the display state at t=t' is calculated using a forward recurrence formula.

[0092] Here, the recommended number of displays at time t=t' can be determined by combining the value of the display plan from time t'+1 onwards (i.e., the amount of sales profit from time t'+1 onwards) calculated by processes 1 and 2 above, the probability of occurrence of the display state at t=t', and demand forecast information from a demand forecasting model at time t=t'. Then, by repeating processes 1 and 2 through reinforcement learning until the value of the display plan π at times t separated by any interval converges, the number of displays π at time t is calculated. Furthermore, by repeating reinforcement learning at times other than time t in the same way until the value of the display plan π converges, the optimal display plan π for all times, i.e., the recommended number of products to display at each time, can be calculated.

[0093] Therefore, the processes shown in the above processes 1 and 2 are formulated as shown in equations (8) to (10) and used as a recommendation information generation model.

[0094] <Process 1A> In the order of t=T, T-1,..., 1, 0, V is generated based on the recommendation information generation model shown as a backward recurrence formula in Equation (8). π Calculate. <Process 2A> In the order of t=0,1,...,T-1, we follow the recommendation information generation model shown as the forward recursion formula of Equations (9) and (10), and p π Calculate (s|t) and π(t).

[0095]

number

number

number

[0096] In addition, V π(s,t) indicates the value of the display plan (i.e., the amount of sales profit after time t'+1) when the display state is s at time t and the display plan π is followed from time t onwards. π (s|t) indicates the probability of the display state when display plan π is followed up to time t.

[0097] Specifically, in process 1A, a random value is set for the display plan π at time t, and V, which indicates the value of the display plan at time t'+1 (i.e., the amount of sales profit after time t'+1) when the display state is s at time t, is calculated using equation (8). π At this time, as shown in formula (6) and formula (7), the demand quantity (sales quantity n) and its accuracy (probability of occurrence), which are the demand forecast information output from the demand forecast model, are used in calculating the values ​​of r(s, t, π(t)) and p(s'|s, t, π(t)).

[0098] Next, in Process 2A, the same arbitrary value as in Process 1A is set for the display plan π at time t, and p, which indicates the probability that the display state will be s at time t, is calculated using equation (9). π (s|t) is calculated. Then, based on the value of the display plan (sales profit amount) calculated using equation (8) and the probability calculated using equation (9), the number of displays a that maximizes the value of the display plan (sales profit amount) is calculated using equation (10). This series of processes is repeated using reinforcement learning until the display plan π converges. Similarly, reinforcement learning is also performed for other times other than time t. This allows the recommended number of displays a to be calculated for each time period separated by any time interval.

[0099] (B) Processing flow for generating recommendation information 5, the processor 211 of the terminal device 200 accepts an operational input from a person in charge or a manager via the input interface 213 to select a product and a time (e.g., date) for which recommendation information is to be output. The processor 211 of the terminal device 200 transmits a request for generating recommendation information to the processing device 100 via the communication interface 215, together with product ID information indicating the selected product and information indicating the time. Upon receiving the generation request, etc. via the communication interface 113, the processor 111 of the processing device 100 inputs information indicating the desired time into the demand forecasting model generated in S112 of FIG. 4 corresponding to the received product ID information (S211). As an example, the information is input into a likelihood function L(θ), which is a demand forecasting model shown in equation (3). Note that the demand forecasting model used here is merely an example, and it is naturally possible to use other demand forecasting models described above in (A1-2) and (A1-3), etc.

[0100] Next, the processor 111 acquires, from the demand forecast model, the demand amount and its accuracy for each time period obtained by dividing the input time period into arbitrary intervals, as demand forecast information (S212). In S212, it is sufficient to acquire the demand forecast information for each time period, and the demand amount and its accuracy calculated by the person in charge or the manager may be input via the input interface 213 of the terminal device 200 to acquire the demand forecast information. The processor 111 stores the acquired demand forecast information in the product management table in association with the product ID information.

[0101] Next, processor 111 estimates the value of the display plan (i.e., the amount of sales profit) at each time separated by an arbitrary interval (S213). As an example, as explained in the above process 1A, the demand forecast information etc. acquired in S212 is input into the recommendation information generation model shown as a backward recurrence formula of equation (8), thereby acquiring the value of the display plan at time t (i.e., the amount of sales profit) as an output. Similarly, the value of the display plan at times other than time t (i.e., the amount of sales profit) is also acquired as an output from the recommendation information generation model.

[0102] Next, processor 111 estimates the probability of occurrence of each display state at each time interval (S214). As an example, as explained in the above process 1B, by inputting time t among the time intervals and the demand forecast information acquired in S212 into the recommendation information generation model shown as a forward recurrence formula of formula (9), the probability p π (s|t) is obtained as the output. Similarly, the occurrence probability of the display state (e.g., s-1) at time other than t (e.g., p π (s|t-1)) is also obtained as an output from the recommendation information generation model.

[0103] Next, processor 111 determines the number of products to be displayed at each time based on the value of the display plan (i.e., the amount of sales profit) at each time segmented by an arbitrary interval acquired in S213 and the occurrence probability of the display state at each time segmented by an arbitrary interval acquired in S214. As an example, as described above as process 1B, the value of the display plan at time t acquired in S213 (i.e., the amount of sales profit) and the occurrence probability p of the display state s at time t are added to the recommendation information generation model shown as equation (10). πFrom (s|t), the recommended display number a of products at time t is calculated. Processor 111 then repeats the processes of S213 to S215 until the display plan π converges, and determines the finally obtained display number a as the recommended display number a of products. Similarly, the recommended display number a of products at times other than time t is determined.

[0104] FIG. 6 is a diagram conceptually illustrating a process for generating recommendation information at time t by the processing device 100 according to an embodiment of the present disclosure. Specifically, the diagram conceptually illustrates the value of the display plan π (i.e., the amount of sales profit) calculated using the recurrence formula shown in step 1 above, and the occurrence probability calculated using the recurrence formula shown in step 2. According to FIG. 6, as shown in S213 of FIG. 5, the value of the display plan at time t (i.e., the total amount of sales profit from time t+1 onward) is calculated by fixing the display plan π at each of times t+1, t+2, ..., T. That is, based on the recurrence formula of equation (8), the display number that should be at the time immediately preceding time T is calculated from the display number that should be there at time T, which is the final time, in order to maximize the amount of sales profit. This calculation is then repeated, and the display number that should be at time t is calculated from the display number that should be there at time t+1.

[0105] Furthermore, as shown in S214 of Fig. 5, the occurrence probability of display plan s at time t is calculated by fixing display plan π at each time t = 0, 1, 2, ..., t-1. That is, based on the recurrence formula of equation (9), the occurrence probability is calculated from display state s0 at time 0, which is the starting point, to display state s1 at the next time, time 1 (i.e., the state of the products displayed at time 1). Then, by repeating this calculation, the display state S at time t-1 is calculated. t-1 The probability p of occurrence of display state s occurring at the next time t is π (s|t) is calculated.

[0106] Then, based on the value of the display plan (sales profit amount) calculated using equation (8) and the probability calculated using equation (9), the number of displays a that maximizes the value of the display plan (sales profit amount) is calculated using equation (10).

[0107] 5, processor 111 stores the calculated number of recommended products to be displayed at each time as recommendation information in association with the product ID information in the product management table. Processor 111 then transmits the stored recommendation information via communication interface 113 to terminal device 200 that has transmitted the request to generate recommendation information (S216). This completes the processing flow.

[0108] 5, the terminal device 200 can display the recommended information received via the output interface 214 on a display or the like. This allows the person in charge or the manager to accurately grasp how many items should be displayed at what time, and further, how many items should be cooked based on the current display quantity information.

[0109] Furthermore, although not shown in detail in Figure 5, for products other than those for which recommendation information has been generated by the processing flow of Figure 5, it is possible to generate recommendation information for those other products by processing similar to the processing flow of Figure 5 using each demand forecasting model and recommendation information generation model generated corresponding to each of the other products.

[0110] In this way, according to this processing flow, the recommended display quantity for each product is calculated using not only demand forecasts but also display-related information such as display quantity information, sales quantity information, cost rate information, and sales price information. Therefore, not only can the recommended display quantity be calculated more accurately or efficiently, but by operating the store in accordance with the calculated display quantity, it is possible to reduce food waste and improve profits.

[0111] (C) Example of using display-related information As described above, the process shown in Figures 4 and 5 calculates the recommended number of products to display using not only demand forecasts but also display-related information such as display number information, sales number information, cost rate information, and sales price information. This not only makes it possible to calculate the recommended number of products to display more accurately and efficiently, but also makes it possible to reduce food waste and improve profits by operating the store in accordance with the calculated number of products to display. However, by calculating the recommended number of products to display at each time based on other display-related information in this process, it becomes possible to make the calculation even more accurate and efficient, thereby further reducing food waste and improving profits.

[0112] 4 and 5, examples of the display-related information include cooking cost information indicating the cooking cost of the displayed products, cooking count information indicating the number of times each product can be cooked in each time period, the location of the store, the cooking time required to cook the product, or a combination of these. Below, we will explain the case where cooking cost information or cooking count information is used as display-related information.

[0113] Here, the cooking cost specified by the cooking cost information increases as the number of times a product is cooked increases, and is therefore an important factor, particularly for improving profits. Therefore, by providing the cooking cost information as a penalty term in the recommendation information generation model, it becomes possible to generate recommendation information more accurately and efficiently.

[0114] In actual stores, cooking of products is often carried out according to the timing of the change in the working hours of the staff in charge, known as shift times. Furthermore, for store staff with a variety of tasks, cooking multiple times during their shifts not only disrupts other tasks but also increases cooking costs incurred as labor costs. Therefore, it is desirable to optimize cooking so that each product is cooked only once within a time period divided into arbitrary intervals managed in the staff work information management table. Therefore, by using cooking count information, which indicates the number of times each product can be cooked within each time period, as a variable in the recommendation information generation model, it is possible to generate recommendation information more accurately and efficiently.

[0115] The cooking cost and cooking count information are obtained from the cooking cost information shown in FIG. 3A or the work information management table shown in FIG. 3D, respectively.

[0116] (C-1) Example of using cooking cost information as display-related information First, an example in which cooking cost information is used as display-related information will be described. The processor 111 of the processing device 100 assigns the cooking cost identified by the cooking cost information as a penalty term in the recommendation information generation model generated in S115 of FIG. 4. Here, as shown in the above equations (6) and (7), the recommendation information generation model is generated by formulating r(s, t, π(t)), which is the value (i.e., the expected profit amount) when the number of displays at time t is π(t), and p(s'|s, t, π(t)), which is the probability that the display state at time t+1 will be s' when the display state at time t is s and the display plan at that time is π(t). In this case, r(s, t, π(t)) in equation (6) is expressed as a regression model in which the sum of sales amounts at time t is used as a loss function and the sum of display costs at time t is used as a penalty term, and the cooking cost of the product at time t is further added as a penalty term.

[0117] Therefore, the value of the display plan (i.e., the amount of sales profit from time t'+1 onwards) is calculated using the backward recurrence formula shown in formula (8), the probability that the display state will be s is calculated using the forward recurrence formula shown in formula (9), and the number of displays a that maximizes the value of the display plan (amount of sales profit) is calculated using formula (10), where each recurrence formula, which is the recommendation information generation model, is shown as formula (8a), formula (9a) and formula (10a). Note that the cooking cost is shown as φ.

[0118]

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[0119] In this way, first, in process 1A, a random value is set for the display plan π at time t, and φ is set as the cooking cost. Then, using equation (8a), V is calculated, which indicates the value of the display plan at time t'+1 (i.e., the amount of sales profit after time t'+1) when the display state is s at time t. π At this time, as in equation (8), as shown in equations (6) and (7), the demand forecast information output from the demand forecast model, that is, the demand quantity (quantity sold n) and its accuracy (probability of occurrence), are used in calculating the values ​​of r(s, t, π(t)) and p(s'|s, t, π(t)).

[0120] Next, in Process 2A, the display plan π at time t is set to the same arbitrary value as in Process 1A, and p, which indicates the probability that the display state at time t will be s, is calculated using equation (9a). π(s|t) is calculated. Then, based on the value of the display plan (sales profit amount) calculated using formula (8a) and the probability calculated using formula (9a), the number of displays a that maximizes the value of the display plan (sales profit amount) is calculated using formula (10a). This series of processes is repeated using reinforcement learning until the display plan π converges. Similarly, reinforcement learning is also performed for other times other than time t. This allows the recommended number of displays a to be calculated for each time period separated by any time interval.

[0121] As described above, the generated recommendation information generation model can be used in the same way to calculate the value of the display plan (i.e., the amount of sales profit from time t'+1 onwards) as described in S213 and S214 of Figure 5, and to calculate the probability that the display state will be s.

[0122] Therefore, by providing cooking cost information as a penalty term in the recommendation information generation model, it becomes possible to generate recommendation information more accurately and efficiently.

[0123] (C-2) Example of using cooking frequency information as a variable in the recommendation information generation model Next, an example will be described in which cooking count information is used as a variable of the recommendation information generation model. The processor 111 of the processing device 100 refers to the work information table and calculates the remaining cooking count for each time period specified by the time information based on the cooking count information. Here, as shown in the above equations (6) and (7), the recommendation information generation model is generated by formulating r(s, t, π(t)), which is the value (i.e., the expected profit amount) when the number of display items at time t is π(t), and p(s'|s, t, π(t)), which is the probability that the display state at time t+1 will be s' when the display state at time t is s and the number of display items at that time is π(t). In this case, p(s'|s, t, π(t)) in equation (7) is expressed as the sum of the probabilities of sales occurring such that the display state will be s' at time t+1. An additional variable is added: the probability that when the remaining cooking count at time t is c, the remaining cooking count at time t+1 will transition to c'.

[0124] Therefore, the value of the display plan (i.e., the amount of sales profit from time t'+1 onwards) is calculated using the backward recurrence formula shown in formula (8), the probability that the display state will be s is calculated using the forward recurrence formula shown in formula (9), and the number of displays a that maximizes the value of the display plan (amount of sales profit) is calculated using formula (10), where each recurrence formula, which is the recommendation information generation model, is shown as formulas (8b), (9b), and (10b). Note that the remaining number of times that cooking can be done at time t is shown as c, and the remaining number of times that cooking can be done at time t+1 is shown as c'.

[0125]

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[0126] In this way, first, in process 1A, a random value is set for the display plan π at time t, and then, using equation (8b), when the display state at time t is s and the number of times that cooking is possible is c, V , which indicates the value of the display plan at time t'+1 (i.e., the amount of sales profit after time t'+1), is calculated. π In this case, r(s,c,t,π(c,t)), which is the value (i.e., the expected profit amount) when the number of items on display at time t is π(t) and the number of times cooking is possible is c, is calculated using equation (6) based on the demand (volume sales number n) and its accuracy (probability of occurrence), which are the demand forecast information output from the demand forecasting model. Also, when the display state at time t is s, the display plan at time t is π(t), and the number of remaining cooking times is c, the probability that the display state at time t+1 will be s' is calculated using equation (6) based on the demand (volume sales number n) and its accuracy (probability of occurrence), which are the demand forecast information output from the demand forecasting model.

[0127] Next, in Process 2A, the same arbitrary value as in Process 1A is set for the display plan π at time t, and p is calculated using equation (9b), which indicates the probability that the display state at time t will be s and the number of times that cooking is possible will be c. π (s,c|t) is calculated. Then, based on the value of the display plan calculated using equation (8b) (sales profit amount) and the probability calculated using equation (9b), equation (10b) is used to calculate the number of displays a that will maximize the value of the display plan (sales profit amount) when the number of times cooking can be done at time t is c. This series of processes is repeated using reinforcement learning until the display plan π converges. Similarly, reinforcement learning is also performed for other times other than time t. This allows the recommended number of displays a to be calculated for each time period separated by any time interval.

[0128] In the above (C-1) and (C-2), the cooking cost information and the cooking count information are used separately in the recommendation information generation model, but it is also possible to use both in combination. By using both in combination, it becomes possible to calculate the recommended display quantity particularly accurately.

[0129] As described above, in this embodiment, it is possible to provide a processing device, a processing program, a processing method, and a processing system that can more appropriately calculate the recommended number of products to display. [Explanation of symbols]

[0130] 1 Processing System 100 Processing equipment 200 Terminal Device

Claims

1. A processing device comprising at least one processor, the at least one processor: generating demand forecast information relating to the demand quantity of the product at any time using a demand forecast model generated based on demand information of the product displayed in the store; generating recommendation information indicating the number of products to be displayed at the given time based on the generated demand forecast information and display-related information related to the display of the products; a processing unit configured to perform processing for:

2. The processing device according to claim 1 , wherein the product is a food or beverage product prepared by a staff member at the store.

3. The processing device according to claim 2 , wherein the display-related information includes cooking count information indicating the number of times the person in charge can cook the product at the given time.

4. The processing device according to claim 2 , wherein the display-related information includes cooking cost information indicating the cost of cooking the product for displaying the product.

5. The processing device according to claim 2 , wherein the display-related information includes sales volume information, cost rate information, and sales price information of the merchandise.

6. The processing device according to claim 1 , wherein the demand forecast information includes the demand amount and a probability of achieving the demand amount.

7. The processing device according to claim 1 , wherein the demand information includes information indicating the number of sales of the product by time.

8. The processing device according to claim 1 , wherein the demand forecasting model is generated based on a Poisson regression model.

9. The processing device according to claim 1 , wherein the arbitrary time is determined based on work information of a person who prepares the product.

10. When executed by at least one processor, generating demand forecast information relating to the demand quantity of the product at any time using a demand forecast model generated based on demand information of the product displayed in the store; generating recommendation information indicating the number of products to be displayed at the given time based on the generated demand forecast information and display-related information related to the display of the products; A processing program that causes the at least one processor to function in such a manner.

11. A processing method executed by at least one processor, comprising: generating demand forecast information relating to the demand quantity of the product at any time using a demand forecast model generated based on demand information of the product displayed in the store; generating recommendation information indicating a recommended number of products to be displayed at the given time based on the generated demand forecast information and display-related information related to the display of the products; A processing method comprising:

12. A processing device according to any one of claims 1 to 9; a terminal device that is arranged in a store where the products are displayed so as to be able to communicate with the processing device, and that is configured to receive recommendation information indicating a recommended number of the products to be displayed from the processing device, and to output the received recommendation information; A processing system comprising:

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