Product packaging suggestion device

Abstract

It is possible to create product packaging with high precision that will result in the intended sales trends. [Solution] The system includes: a model generation means that generates a machine learning model that outputs a product package by machine learning a dataset of product types, characteristics related to product sales, and product packaging, and inputting the product type and the characteristics related to the sales target of the product; a model storage means that stores parameters that define the input and output characteristics of the machine learning model; and a product manufacturing means that processes inputting a dataset of one of the product types and the characteristics related to the sales target of the product into the machine learning model and outputting one of the product packages.

Description

Technical Field

[0001] It relates to a technology for automatically creating product packages.

Background Art

[0002] Product packages are important factors that influence the sales performance of products, such as becoming long-term bestsellers, having high initial sales, and driving sales by promoting new ingredients.

[0003] Under such circumstances, attempts have been made to analyze product packages using computers. For example, in Patent Document 1, a technology for evaluating the prominence of changes in product package design has been proposed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, the above prior art has a problem in that it is unable to accurately create a product package that exhibits the characteristics related to the sales performance intended by the business operator.

[0006] Therefore, in view of the above problems, an object of the present invention is to provide a product package proposal device that accurately creates a product package that conforms to the intended sales trend.

Means for Solving the Problems

[0007] <000003One form of the disclosed product package proposal device is characterized by comprising: a model generation means that generates a machine learning model that outputs a product package by machine learning a dataset of product types, characteristics related to product sales, and product packages, and inputting the product types and characteristics related to the sales target of the product; a model storage means that stores parameters that define the input and output characteristics of the machine learning model; and a product manufacturing means that processes inputting a dataset of one of the product types and characteristics related to the sales target of the product into the machine learning model and outputting one of the product packages. [Effects of the Invention]

[0008] The disclosed product package proposal device can accurately create product packages that reflect the intended sales trends. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an overview of the product package suggestion device according to this embodiment. [Figure 2] This is a functional block diagram of the product package suggestion device according to this embodiment. [Figure 3] This figure shows an example of a training dataset for the machine learning model according to this embodiment. [Figure 4] This figure shows an example of the hardware configuration of the product package suggestion device according to this embodiment. [Figure 5] This flowchart shows an example of the processing flow by the product package suggestion device according to this embodiment. [Modes for carrying out the invention]

[0010] The embodiments for carrying out the present invention will be described with reference to the drawings. (Operating principle of the product package suggestion device according to this embodiment)

[0011] The operating principle of the product package suggestion device (hereinafter simply referred to as "this device") 100 according to this embodiment will be explained using Figures 1 and 2. Figure 1 is a diagram showing the connection relationship between this device 100 and other devices, and Figure 2 is a functional block diagram of this device 100.

[0012] As shown in Figure 1, the device 100 is connected to the store terminal 270 via a communication network 280. The communication network 280 may be either wired or wireless. The store terminal 270 is a device that informs the device 100 of the sales status and inventory status of products sold in stores (including virtual stores), and may be, for example, a POS (Point of Sales) system.

[0013] As shown in Figure 2, the device 100 includes a learning data storage means 110, a model storage means 120, a model generation means 130, a product manufacturing means 140, a verification information calculation means 150, a goal achievement determination means 160, an additional learning means 170, and a product remanufacturing means 180. Note that the storage means 110 and 120 do not necessarily have to be provided by the device 100; the device 100 may utilize storage means 110 and 120 provided by an external device.

[0014] The learning data storage means 110 stores a learning dataset 240 containing product types 210, product sales characteristics 220, and product packaging (including images) 230. The learning dataset 240 is added to and updated over time.

[0015] Fig. 3 shows an example of the learning data storage means 110. As shown in Fig. 3, the learning data storage means 110 stores, for example, by associating, product type 210: laundry detergent, feature 220: long seller, new ingredient promoted, product package 230: package of product A. Also, the learning data storage means 110 stores, for example, by associating, product type 210: household detergent, feature 220: high initial sales, high new acquisition rate, product package 230: package of product D. Further, the feature 220 regarding the sales performance of the product is, for example, information such as long seller, new ingredient promoted, high new acquisition rate, high initial sales, etc., but is not limited thereto. The model storage means 120 stores parameters that define the input / output characteristics of the machine learning model 250 described later.

[0016] The model generation means 130 generates a machine learning model 250 that outputs a product package 230 by subjecting the learning data set 240 to machine learning with the machine learning model 250 and inputting the product type 210 and the feature 220 regarding the sales performance of the product to be sold. Note that the learning algorithm is not particularly limited.

[0017] The product manufacturing means 140 performs a process of manufacturing a product 260 including one product package 230 by inputting a data set of one product type 210 and the feature 220 regarding the sales performance of the product to be sold to the machine learning model 250. Then, the product 260 is provided for sale at a store.

[0018] The verification information calculation means 150 calculates the sales or sales volume regarding the product 260 including one product package. The verification information calculation means 150 acquires and aggregates information regarding the sales or sales volume regarding the product 260 from the store terminal 270.

[0019] The target achievement determination means 160 determines whether the sales or sales volume calculated by the verification information calculation means 150 is below a predetermined target value. The target value can be set as appropriate.

[0020] The additional learning means 170 causes the machine learning model 250 to perform additional learning using the additional / updated learning dataset 240. Generally, the input / output characteristics of the machine learning model 250 change before and after the additional learning. Also, when the processing by the additional learning means 170 is performed, the information stored in the model storage means 120 is updated.

[0021] When the product remanufacturing means 180 is determined to be "below" by the target achievement determination means 160, it inputs a dataset of the characteristics 220 regarding the sales of a certain product type 210 and the product to be used as the sales target into the machine learning model 250 after the additional learning, and performs a process of manufacturing a product 260 equipped with the output other product package 230. Based on the above operating principle, the present apparatus 100 can accurately create a product package 230 that conforms to the intended sales trend. (Hardware configuration of the product package proposal apparatus according to the present embodiment)

[0022] Using FIG. 4, an example of the hardware configuration of the present apparatus 100 will be described. FIG. 4 is a diagram showing an example of the hardware configuration of the present apparatus 100. As shown in FIG. 4, the present apparatus 100 includes a CPU (Central Processing Unit) 510, a ROM (Read-Only Memory) 520, a RAM (Random Access Memory) 530, an auxiliary storage device 540, a communication I / F 550, an input device 560, a display device 570, and a storage medium I / F 580.

[0023] The CPU 510 is a device that executes the program stored in the ROM 520, performs arithmetic processing on the data developed (loaded) into the RAM 530 according to the instructions of the program, and controls the entire present apparatus 100. The ROM 520 stores the programs and data executed by the CPU 510. The RAM 530 develops (loads) the programs and data to be executed when the CPU 510 executes the program stored in the ROM 520, and temporarily holds the arithmetic data during the arithmetic operation.

[0024] The auxiliary storage device 540 is a device that stores the basic software, such as the OS (Operating System) and the application program according to this embodiment, along with related data. For example, it may include a learning data storage means 110 and a model storage means 120. The auxiliary storage device 540 is, for example, an HDD (Hard Disk Drive) or flash memory.

[0025] The communication interface 550 is an interface for exchanging data with other devices (such as POS systems) 270 that provide communication functions, by connecting to a communication network 280 such as a wired or wireless LAN (Local Area Network) or the Internet.

[0026] The input device 560 is a device for inputting data into the main device 100, such as a keyboard. The display device (output device) 570 is a device consisting of an LCD (Liquid Crystal Display) or the like, and functions as a user interface for the user to use the functions of the main device 100 and to make various settings. The storage medium I / F 580 is an interface for sending and receiving data with storage media 590 such as CD-ROMs, DVD-ROMs, and USB memory.

[0027] Each of the means of this device 100 may be realized by the CPU 510 executing a program corresponding to each means stored in the ROM 520 or auxiliary storage device 540. Alternatively, each of the means of this device 100 may be realized by the processing related to each means being implemented as hardware. Furthermore, the program according to the present invention may be read from an external server device via a communication I / F 550, or read from a storage medium 590 via a storage medium I / F 580, and the device 100 may execute the program. (Example of processing by the product package suggestion device according to this embodiment) An example of processing performed by the device 100 will be explained using Figure 5. Figure 5 is a flowchart showing the flow of processing performed by the device 100.

[0028] In S10, the model generation means 130 trains the machine learning model 250 on the training dataset 240, and generates a machine learning model 250 that outputs a product package 230 by inputting the product type 210 and the sales characteristics 220 of the target product. The learning algorithm is not particularly limited. Furthermore, the parameters that define the input / output characteristics of the machine learning model 250 generated by the processing in S10 are stored in the model storage means 120.

[0029] In S20, the product manufacturing means 140 inputs a dataset of one product type 210 and sales characteristics 220 of the target product into the machine learning model 250 generated in S10, and manufactures a product 260 comprising one product package 230 that is output. The product 260 is then put up for sale in stores.

[0030] Furthermore, after processing in S20, the additional learning means 170 performs additional learning on the machine learning model 250 using the additional and updated training dataset 240. Generally, the input / output characteristics of the machine learning model 250 change before and after the additional learning. Also, when processing is performed by the additional learning means 170, the information stored in the model storage means 120 is updated.

[0031] In S30, the verification information calculation means 150 calculates the sales or number of units sold for a product 260 comprising a single product package. The verification information calculation means 150 obtains and aggregates information regarding sales or the number of units sold from the store terminal 270.

[0032] Then, in S30, the target achievement determination means 160 determines whether the sales or sales figures calculated by the verification information calculation means 150 are below a predetermined target value. The target value can be set as appropriate.

[0033] If it is determined in S30 that the value is "below a predetermined target value", in S40 the product remanufacturing means 180 inputs a dataset of one product type 210 and features 220 related to the sales performance of the target product into the machine learning model 250 after additional training, and manufactures a product 260 that includes the output product package 230. The product 260 with the product package 230 is then put up for sale in the store. By performing the above-described process, the device 100 can accurately create product packages 230 that reflect the intended sales trends.

[0034] Although embodiments of the present invention have been described in detail above, the present invention is not limited to these specific embodiments, and various modifications and changes are possible within the scope of the gist of the present invention as described in the claims. [Explanation of Symbols]

[0035] 100 Product Package Proposal Device 110 Learning data storage means 120 Model Storage Methods 130 Model generation means 140 Means of manufacturing goods 150 Verification Information Calculation Method 160 Goal achievement determination means 170 Additional learning methods 180 Means of remanufacturing goods 210 Product types Characteristics of sales performance of 220 products 230 product packages 240 training datasets 250 Machine Learning Models 260 Products featuring product packaging generated by machine learning models 270 store terminals (POS systems) 280 Communication Networks 510 CPU 520 ROM 530 RAM 540 Auxiliary storage 550 Communication Interfaces 560 Input Device 570 Output device 580 Storage Media Interface 590 Storage medium

Claims

1. A model generation means that generates a machine learning model that outputs the product package by using machine learning on a dataset of product types, characteristics related to product sales, and product packaging, and inputting the product types and the characteristics related to product sales targets. A model storage means for storing parameters that define the input / output characteristics of the machine learning model, A product package proposal device characterized by having a product manufacturing means that processes input into a machine learning model a dataset of one product type and characteristics related to the sales performance of the product for which sales targets are to be met, and outputs one product package.

2. A computer equipped with a model storage means for storing parameters that define the input / output characteristics of a machine learning model that outputs a product package by inputting the product type and the sales characteristics of the product that are the sales target, after machine learning a dataset of product types, characteristics related to product sales, and product packages, The model generation means performs machine learning on a dataset of product types, characteristics related to product sales, and product packaging to generate a machine learning model that outputs the product packaging by inputting the product types and characteristics related to product sales targets. A product package proposal method comprising the steps of: inputting a dataset of one product type and characteristics relating to the sales performance of the product to be sold as a sales target into the machine learning model and outputting one product package.

3. A product package suggestion program for causing a computer to perform the method described in claim 2.

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