Shelf allocation proposal system, shelf allocation proposal method and program

Abstract

To improve the accuracy of shelf allocation proposals.SOLUTION: A learning model storage unit (201) of a shelf allocation proposal system (1) stores a trained learning model (M) that has learned a relation between training product information related to training products for training and training shelf allocation information related to a shelf allocation of the training products in a training store for training. An estimated product information acquisition unit (202) acquires estimated product information related to estimated products handled in an estimated store whose shelf allocation is estimated. A proposal unit (203) makes a proposal regarding the shelf allocation of the estimated products in the estimated store based on the learning model (M) and the estimated product information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a shelf layout proposal system, a shelf layout proposal method, and a program. [Background technology]

[0002] Conventionally, in stores where products are arranged on shelves, a task called planogramming is performed in which a person in charge of the store or a manufacturer or the like decides the arrangement of products. For example, Patent Document 1 describes a technology in which an ideal planogram pattern, which is an ideal planogram pattern, is generated based on the planogram patterns of each of a plurality of shelves in a store, the difference between the ideal planogram pattern and an actual planogram pattern, which is the planogram pattern applied to the shelf to be proposed, is calculated, and the ideal planogram pattern is corrected based on market trends and the difference to generate a final proposed planogram pattern.

[0003] For example, Patent Document 2 describes a technology for optimizing the layout of shelves at a logistics site based on the distance between parts arranged on the shelves. Patent Document 3 describes a technology for predicting product sales based on relationship information that represents the relationship between the arrangement of products on store shelves and product sales, and proposing a shelf allocation that is expected to produce the highest sales. Patent Document 3 also describes a technology for recognizing products using images of shelves photographed by a camera as learning data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-227272 [Patent Document 2] Japanese Patent Application Laid-Open No. 2015-079318 [Patent Document 3] Japanese Patent Publication No. 2023-030023 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology of Patent Document 1 generates an ideal shelf planogram within a range of predetermined shelf planogram patterns, so it cannot accommodate stores that do not have a shelf planogram pattern defined, and the accuracy of shelf planogram proposals cannot be sufficiently improved. The technology of Patent Document 2 is based on shelves at logistics sites, so it is difficult to apply it to shelf planograms in stores that sell products. The technology of Patent Document 3 proposes shelf planograms within a range of predetermined relationship information, so it cannot accommodate stores that do not have relationship information defined, and the accuracy of shelf planogram proposals cannot be sufficiently improved.

[0006] One of the objectives of the present disclosure is to improve the accuracy of shelf planogram proposals. [Means for solving the problem]

[0007] The shelf allocation proposal system according to the present disclosure includes a learning model storage unit that stores a trained learning model that has learned the relationship between training product information regarding training products for training and training shelf allocation information regarding the shelf allocation of the training products in a training store for training, an estimated product information acquisition unit that acquires estimated product information regarding estimated products handled in an estimated store whose shelf allocation is estimated, and a proposal unit that makes a proposal regarding the shelf allocation of the estimated products in the estimated store based on the learning model and the estimated product information. [Effects of the Invention]

[0008] The present disclosure can improve the accuracy of shelf allocation proposals. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a hardware configuration of a shelf layout proposal system. [Figure 2] FIG. 10 is a diagram illustrating an example of a shelving planogram proposal service provided to a person in charge by the shelving planogram proposal system. [Figure 3] FIG. 1 is a diagram illustrating an example of functions realized by the shelf layout proposal system. [Figure 4] FIG. 10 is a diagram illustrating an example of a training database. [Figure 5]FIG. 10 is a diagram illustrating an example of an estimation database. [Figure 6] FIG. 10 is a diagram illustrating an example of a shelf allocation proposal screen. [Figure 7] FIG. 10 is a diagram illustrating an example of processing executed in the shelf allocation proposal system. [Figure 8] FIG. 10 is a diagram illustrating an example of a function realized in a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0010] [1. Hardware configuration of shelf layout proposal system] An example of an embodiment of a shelf allocation proposal system, shelf allocation proposal method, and program according to the present disclosure will be described. FIG. 1 is a diagram illustrating an example of the hardware configuration of a shelf allocation proposal system. For example, the shelf allocation proposal system 1 includes a learning terminal 10, a server 20, and a staff member terminal 30. Each of the learning terminal 10, the server 20, and the staff member terminal 30 is connected to a network N such as the Internet or a LAN. While FIG. 1 illustrates one each of the learning terminal 10, the server 20, and the staff member terminal 30, there may be multiple units of at least one of the learning terminal 10, the server 20, and the staff member terminal 30.

[0011] The learning terminal 10 is a computer that performs learning of the learning model described below. For example, the learning terminal 10 is a personal computer, a server computer, a tablet, or a smartphone. For example, the learning terminal 10 includes a control unit 11, a memory unit 12, a communication unit 13, an operation unit 14, and a display unit 15. The control unit 11 includes at least one processor. The memory unit 12 includes at least one of volatile memory such as RAM and non-volatile memory such as flash memory. The communication unit 13 includes at least one of a communication interface for wired communication and a communication interface for wireless communication. The operation unit 14 is an input device such as a touch panel or a mouse. The display unit 15 is a display such as an LCD or organic EL.

[0012] The server 20 is a server computer of a service provider that provides a shelf layout proposal service. The shelf layout proposal service is a service that proposes shelf layouts to a shelf layout manager. The manager is a person in charge of shelf layout work. The manager may be any person. For example, the manager may be a store clerk or a manufacturer that produces products. For example, the server 20 includes a control unit 21, a memory unit 22, and a communication unit 23. The hardware configurations of the control unit 21, the memory unit 22, and the communication unit 23 may be the same as those of the control unit 11, the memory unit 12, and the communication unit 13, respectively.

[0013] The staff terminal 30 is the staff's computer. For example, the staff terminal 30 is a personal computer, a POS terminal, a tablet, or a smartphone. The staff terminal 30 includes a control unit 31, a memory unit 32, a communication unit 33, an operation unit 34, and a display unit 35. The hardware configurations of the control unit 31, the memory unit 32, the communication unit 33, the operation unit 34, and the display unit 35 may be similar to those of the control unit 11, the memory unit 12, the communication unit 13, the operation unit 14, and the display unit 15, respectively.

[0014] The programs stored in the storage units 12, 22, 32 may be supplied to the learning terminal 10, the server 20, or the person in charge's terminal 30 via the network N. Also, at least one of a reading unit (e.g., a memory card slot) that reads a computer-readable information storage medium and an input / output unit (e.g., a USB port) for inputting and outputting data to and from an external device may be included in the learning terminal 10, the server 20, or the person in charge's terminal 30. For example, a program stored in an information storage medium may be supplied to the learning terminal 10, the server 20, or the person in charge's terminal 30 via at least one of the reading unit and the input / output unit.

[0015] Furthermore, the shelving planogram proposal system 1 may include at least one computer. The computers included in the shelving planogram proposal system 1 are not limited to the example in FIG. 1. For example, the shelving planogram proposal system 1 may include only the learning terminal 10 and the server 20. In this case, the person in charge terminal 30 exists outside the shelving planogram proposal system 1. The shelving planogram proposal system 1 may include only the server 20. In this case, the learning terminal 10 and the person in charge terminal 30 exist outside the shelving planogram proposal system 1. For example, the shelving planogram proposal system 1 may include the server 20 and another computer not shown in FIG. 1.

[0016] [2. Overview of the shelf layout proposal system] In this embodiment, an example is given in which shelf allocation is performed by a staff member working for a manufacturer that produces products sold in a store. The staff member may perform shelf allocation only for products manufactured by the manufacturer for which he or she works, but in this embodiment, the staff member also performs shelf allocation for products manufactured by other manufacturers. The staff member performs optimal shelf allocation by comprehensively considering various factors such as the preferences of customers visiting the store, trends in the world, and the season. The shelf allocation proposal system 1 provides the staff member with a shelf allocation proposal service to reduce the staff member's burden.

[0017] FIG. 2 is a diagram showing an example of a shelving planogram proposal service provided to a staff member by the shelving planogram proposal system 1. As shown in FIG. 2, the staff member performs shelf allocation for at least a portion of the multiple shelves arranged in the store SP. In this embodiment, the shelving planogram proposal system 1 proposes an optimal shelving planogram to the staff member based on a trained learning model M. The learning model M is a model trained using a machine learning technique. The machine learning technique may be any of various known techniques. The technique for training the learning model M may also be any of various known techniques.

[0018] There are various definitions of machine learning, and the machine learning of this embodiment may be machine learning according to various known definitions. For example, the machine learning of this embodiment includes deep learning. The learning model M trained by the machine learning method of this embodiment includes AI. For example, the learning model M may be a generative AI. For example, the learning model M may be a neural network, a boosting model (decision tree model), a support vector machine, a large-scale language model, or other models.

[0019] For example, a learning model M has been trained with multiple pieces of training data. The training data includes an input portion that is input to the learning model M during learning, and an output portion that indicates the content that should be output from the learning model M during learning. The output portion can also be said to be the correct answer during learning. The format of the input portion of the training data is basically the same as the format of the estimation data that is input to the learning model M during estimation. The format of the output portion of the training data is basically the same as the format of the data that is output to the learning model M during estimation.

[0020] In this embodiment, the input portion of the training data includes training product information, training shelf information, and training purchase information. The input portion of the training data is not limited to the example of this embodiment. The input portion of the training data only needs to include training product information. For example, the input portion of the training data may include only training product information, without including training shelf information and training purchase information. The input portion of the training data may include only training product information and training shelf information, without including training purchase information. The input portion of the training data may include only training product information and training shelf information, without including training shelf information.

[0021] The training product information is information about training products, which are products used for training. The training products are not limited to products that exist in reality, and may be fictitious products. The training product information indicates the characteristics of the training products. For example, the training product information indicates product identification information such as the product name, content, type, price, size, design, taste, texture, JAN code, or other characteristics of the training products. The training product information may indicate multiple characteristics of the training products. In this embodiment, an example is given in which the training product information indicates the characteristics of each of multiple training products, but the training product information may also indicate the characteristics of only one training product. All training products may be placed, or some training products may not be placed. The training product information may indicate training products that have been decided to be placed on the shelves, or may indicate training products that have not been decided to be placed on the shelves (training products that are candidates for placement on the shelves).

[0022] Training shelf information is information about training shelves, which are shelves used for training. Training shelves are shelves arranged in a training store, which is a store used for training. The training store may be a real store or a fictitious store. Training shelf information indicates the characteristics of the training shelves. For example, the training shelf information indicates the size, capacity, number of shelves, type, design, material, location in the store, or other characteristics of the training shelves. The training shelf information may indicate multiple characteristics related to the training shelves. In this embodiment, an example is given in which the training shelf information indicates the characteristics of one training shelf, but the training shelf information may only indicate the characteristics of multiple training shelves. Furthermore, the training shelf information may only indicate the characteristics of some of the spaces in the training shelf.

[0023] The training purchase information is information about purchases at a training store. For example, the training purchase information may be information about purchases of training products placed on training shelves, or information about purchases of training products not placed on training shelves. The training purchase information indicates characteristics of purchases at a training store. For example, the training purchase information indicates the type, price, quantity, total amount, or other characteristics of training products purchased at the training store. The training purchase information may indicate changes over time in these. The training purchase information may indicate characteristics of training customers who purchased training products. For example, the training purchase information may indicate the gender, age group, preferences, behavior, or other characteristics of the training customers. In this embodiment, an example is given in which the training purchase information indicates characteristics of purchases at one training store, but the training purchase information may also indicate characteristics of purchases at each of multiple training stores. The training purchase information may include information obtained from a POS terminal at the training store.

[0024] In this embodiment, the output portion of the training data is training allocation information. For example, the training allocation information includes training promotion / demotion information and training substitute product information. The output portion of the training data is not limited to the example of this embodiment. For example, the output portion of the training data may be only either training promotion / demotion information or training substitute product information. The output portion of the training data may be information other than training promotion / demotion information and training substitute product information.

[0025] The training promotion / demotion information is information regarding the promotion and demotion of training products. Promotion refers to an increase in the importance of training products in the shelf planogram. For example, promotion refers to a change from a state in which training products are not placed on a training shelf to a state in which they are placed on the training shelf, a change in the position of training products from the top or bottom row to the middle row of a training shelf, a change in the position of training products from the left or right end of a training shelf to the center, an increase in the number of training products (e.g., the number of rows), or other changes. Demotion refers to a decrease in the importance of training products in the shelf planogram. For example, promotion refers to a change from a state in which training products are placed on a training shelf to a state in which they are not placed on the training shelf, a change in the position of training products from the middle row to the top or bottom row of a training shelf, a change in the position of training products from the center to the left or right end of a training shelf, a decrease in the number of training products (e.g., the number of rows), or other changes. In this embodiment, an example is given in which the training promotion / demotion information is information regarding the promotion and demotion of training products indicated by the training product information. For this reason, the training promotion / demotion information is taken as an example to be information on the promotion and demotion of each of a plurality of training products. The training promotion / demotion information may be information on the promotion and demotion of a training product that is not indicated in the training product information. The training promotion / demotion information may be information on the promotion and demotion of one training product.

[0026] Training substitute product information is information about training substitute products that substitute for training products. Training substitute products can also be said to be training products that are placed on training shelves in place of training products placed on training shelves. Training substitute product information may be information that can identify the training substitute product (e.g., product name or JAN code), or may be information that indicates the characteristics of the training substitute product. For example, the training substitute product information indicates product identification information such as the product name, content, type, price, size, design, taste, texture, JAN code, or other characteristics of the training substitute product. The training substitute product information may indicate multiple characteristics of the training substitute product. In this embodiment, an example is given in which the training substitute product information indicates the characteristics of each of multiple training substitute products, but the training substitute product information may only indicate the characteristics of one training substitute product.

[0027] It should be noted that the training planogram information is not limited to training promotion / demotion information and training substitute product information. The training planogram information may be any information related to the planogram that will be the correct answer during learning. For example, the training planogram information may indicate a list of training products that should be placed on the training shelf. Furthermore, the training planogram information may indicate a list of training products that should be placed in each space on the training shelf (for example, each space on the upper, middle, and lower shelves). The training planogram information may indicate a list of training products that should not be placed on the training shelf. For example, the training planogram information may indicate a score (probability) that indicates whether the training product should be placed on the training shelf.

[0028] In this embodiment, the above-described training data is learned by the learning model M. For example, the learning terminal 10 causes the learning model M to learn the training data. The learning terminal 10 may cause the learning model M to learn by including information on the arrangement of training products on the training shelves and the corresponding sales of those products in the training data. The learning terminal 10 may also train the learning model M based on training data in which the arrangement of training products is labeled according to whether the sales exceeded or fell short of some benchmark sales.

[0029] For example, the learning terminal 10 uploads the trained learning model M to the server 20. The server 20 receives the trained learning model M and records it in the memory unit 22. When a staff member considers the shelf allocation of the store that he or she is in charge of, he or she operates the staff member terminal 30 to log in to the shelf allocation proposal service and uses the trained learning model M recorded on the server 20.

[0030] For example, the person in charge operates the person in charge terminal 30 to input estimated data including estimated product information, estimated shelf information, and estimated purchase information into the trained learning model M. The estimated data is assumed to be pre-stored in the person in charge terminal 30. The format of the estimated data is basically the same as the format of the input portion of the training data. Therefore, the format of the estimated product information is basically the same as the format of the training product information. The format of the estimated shelf information is basically the same as the format of the training shelf information. The format of the estimated purchase information is basically the same as the format of the training purchase information.

[0031] The estimated product information is information about an estimated product that is a product to be estimated. The estimated product information indicates the characteristics of the estimated product. For example, the estimated product information indicates product identification information such as the product name, content, type, price, size, design, taste, texture, JAN code, or other characteristics of the estimated product. The estimated product information may indicate multiple characteristics of the estimated product. In this embodiment, an example is given in which the estimated product information indicates the characteristics of each of multiple estimated products, but the estimated product information may indicate only the characteristics of one estimated product. All estimated products may be placed, or some estimated products may not be placed. The estimated product information may indicate estimated products that have been decided to be placed on the shelf, or may indicate estimated products that have not been decided to be placed on the shelf (estimated products that are candidates to be placed on the shelf).

[0032] The estimated shelf information is information relating to an estimated shelf, which is an estimated shelf. The estimated shelf is a shelf arranged in an estimated store, which is an estimated store. The estimated store may be a real store or a fictitious store. The estimated shelf information indicates the characteristics of the estimated shelf. For example, the estimated shelf information indicates the size, capacity, number of shelves, type, design, material, location in the store, function such as refrigerated / non-refrigerated, or other characteristics of the estimated shelf. The estimated shelf information may indicate multiple characteristics related to the estimated shelf. In this embodiment, an example is given in which the estimated shelf information indicates the characteristics of one estimated shelf, but the estimated shelf information may only indicate the characteristics of multiple estimated shelves. Furthermore, the estimated shelf information may only indicate the characteristics of a portion of the space in the estimated shelf.

[0033] The estimated purchase information is information about purchases at an estimated store. For example, the estimated purchase information may be information about purchases of estimated products placed on estimated shelves, or information about purchases of estimated products not placed on estimated shelves. The estimated purchase information indicates characteristics of purchases at an estimated store. For example, the estimated purchase information indicates the type, price, quantity, total amount, or other characteristics of estimated products purchased at an estimated store. The estimated purchase information may also indicate changes over time in these information. The estimated purchase information may also indicate characteristics of an estimated customer who purchased an estimated product. For example, the estimated purchase information may indicate the gender, age group, or other characteristics of the estimated customer. In this embodiment, an example is given in which the estimated purchase information indicates characteristics of purchases at one estimated store. However, the estimated purchase information may also indicate characteristics of purchases at each of multiple estimated stores. The estimated purchase information may indicate characteristics of past purchases at the estimated store, or may indicate real-time purchase characteristics at the estimated store. The estimated purchase information may include information acquired from a POS terminal at the estimated store.

[0034] For example, when estimation data is input, the learning model M calculates an embedded representation (feature) of the estimation data based on parameters adjusted by learning. In this embodiment, the embedded representation is a multidimensional vector as an example. The embedded representation may be in a format other than a vector. For example, the embedded representation may be in an array format, a matrix format, multiple numerical values, a single numerical value, or other format. The learning model M outputs estimated shelf allocation information regarding shelf allocation that is estimated to be appropriate based on the embedded representation.

[0035] For example, the estimated shelf allocation information includes estimated promotion / demotion information and estimated substitute product information. The output from the learning model M is not limited to the example of this embodiment. For example, the output from the learning model M may be only either the estimated promotion / demotion information or the estimated substitute product information. The output from the learning model M may be information other than the estimated promotion / demotion information and the estimated substitute product information.

[0036] The estimated promotion / demotion information is information regarding the promotion and demotion of an estimated product. In this embodiment, an example is given in which the estimated promotion / demotion information is information regarding the promotion and demotion of an estimated product indicated by estimated product information. Therefore, an example is given in which the estimated promotion / demotion information is information regarding the promotion and demotion of each of multiple estimated products. The estimated promotion / demotion information may also be information regarding the promotion and demotion of an estimated product not indicated in the estimated product information. The estimated promotion / demotion information may also be information regarding the promotion and demotion of one estimated product.

[0037] The estimated substitute product information is information about an estimated substitute product that substitutes for the estimated product. The estimated substitute product can also be referred to as an estimated product that will be placed on an estimated shelf in place of the estimated product that is placed on an estimated shelf. The estimated substitute product information may be information that can identify the estimated substitute product (for example, product name or JAN code), or may be information that indicates the characteristics of the estimated substitute product. For example, the estimated substitute product information indicates product identification information such as the product name, contents, type, price, size, design, taste, texture, JAN code, etc. of the estimated substitute product, or other characteristics. The estimated substitute product information may indicate multiple characteristics of the estimated substitute product. In this embodiment, an example is given in which the estimated substitute product information indicates the characteristics of each of multiple estimated substitute products, but the estimated substitute product information may only indicate the characteristics of one estimated substitute product.

[0038] The estimated shelf allocation information is not limited to estimated promotion / demotion information and estimated substitute product information. The estimated shelf allocation information may be any information related to shelf allocation that the learning model M has estimated to be appropriate. For example, the estimated shelf allocation information may indicate a list of estimated products that should be placed on the estimated shelf. Furthermore, the estimated shelf allocation information may indicate a list of estimated products that should be placed in each space on the estimated shelf (for example, each space on the upper, middle, and lower shelves). The estimated shelf allocation information may indicate a list of estimated products that should not be placed on the estimated shelf. For example, the estimated shelf allocation information may indicate a score (probability) indicating whether the estimated product should be placed on the estimated shelf.

[0039] For example, the person in charge checks the estimated shelf allocation information output by the learning model M and considers the shelf allocation. The person in charge may complete the shelf allocation using the estimated shelf allocation information as is, or may complete the shelf allocation by modifying the estimated shelf allocation information. The person in charge may also perform shelf allocation themselves by referring to the estimated shelf allocation information. As described above, the shelf allocation proposal system 1 of this embodiment is capable of supporting the shelf allocation performed by the person in charge based on the learning model M that has learned training data such as that shown in FIG. 2. Details of the shelf allocation proposal system 1 will be explained below.

[0040] [3. Functions realized by the shelf layout proposal system] FIG. 3 is a diagram showing an example of functions realized by the shelf layout proposal system 1.

[0041] [3-1. Functions realized on the learning device] For example, the learning terminal 10 includes a data storage unit 100, a learning model storage unit 101, and a learning unit 102. The data storage unit 100 and the learning model storage unit 101 are each realized by a storage unit 12. The learning unit 102 is realized by a control unit 11.

[0042] [Data storage section] The data storage unit 100 stores data necessary for learning the learning model M. For example, the data storage unit 100 stores a training database DB1. Note that the data stored in the data storage unit 100 is not limited to the training database DB1. The data storage unit 100 only needs to store data necessary for learning the learning model M.

[0043] FIG. 4 is a diagram showing an example of the training database DB1. For example, a plurality of training data are stored in the training database DB1. Examples of individual training data are as described with reference to FIG. 2. In this embodiment, the service provider of the shelf layout proposal service prepares the training data. The training data may not be prepared manually, but may be automatically or semi-automatically created by a training data creation tool. The training data creation tool may be a known tool.

[0044] [Learning model memory section] The learning model storage unit 101 stores the learning model M. The learning model M includes a program indicating various processes required for estimating an appropriate shelf allocation and parameters referenced by the program. The program and parameters of the learning model M may be similar to programs and parameters used in known machine learning techniques. For example, the program of the learning model M includes program code indicating calculation of an embedded representation and program code indicating output processing according to the embedded representation. The parameters may be weights, biases, or other parameters.

[0045] For example, the learning model storage unit 101 stores a learning model M with parameters of initial values. When learning by the learning unit 102 is completed, the learning model storage unit 101 stores the trained learning model M. The learning model M with parameters of initial values ​​may be overwritten by the trained learning model M, or may be left in the learning model storage unit 101 separately from the trained learning model M. Instead of the learning model M with parameters of initial values, a learning model M that has undergone some pre-learning may be stored in the learning model storage unit 101. The pre-learned learning model M may be tuned by the learning unit 102.

[0046] [Study Department] The learning unit 102 learns the learning model M based on the training data stored in the training database DB1. Learning the learning model M involves adjusting the parameters of the learning model M. For example, the learning unit 102 learns the learning model M so that when an input portion of training data is input to the learning model M, an output portion of the training data is output. The learning algorithm may be the same as an algorithm adopted in a known machine learning method. For example, the learning unit 102 may cause the learning model M to learn the training data based on an algorithm such as gradient descent or backpropagation. Functions such as a loss function used by the learning unit 102 may also be functions adopted in a known machine learning method. When learning of the learning model M is completed, the learning unit 102 records the learned learning model M in the learning model storage unit 101. The learning unit 102 uploads the learned learning model M to the server 20.

[0047] In this embodiment, the input portion of the training data includes training product information, training shelf information, and training purchase information. The output portion of the training data includes training promotion / demotion information and training substitute product information as training shelf allocation information. A correlation exists between the input portion of the training data and the output portion of the training data. The learning model M learns the correlation between these by learning the training data. As a result, when the proposing unit 205 described below inputs estimated data to the learning model M, the learning model M can output estimated promotion / demotion information and estimated substitute product information as estimated shelf allocation information corresponding to the estimated data based on the correlation it has learned (i.e., by processing based on parameters adjusted by learning).

[0048] For example, training products with a certain product name may be better placed on the training shelf. On the other hand, training products with other product names may not be better placed on the training shelf. Furthermore, training products with a certain product name may be better placed near the center of the training shelf. On the other hand, training products with other product names may be better placed near the edge of the training shelf. In this case, there is a correlation between the product name indicated by the training product information and the content of the training planogram information (e.g., at least one of the promotion / demotion indicated by the training promotion / demotion information and the substitute product indicated by the training substitute product information). The learning model M learns the correlation between these by studying the training data. As a result, the learning model M can output estimated planogram information indicating content correlated with the product name indicated by the estimated product information included in the estimation data (e.g., at least one of the promotion / demotion indicated by the estimated promotion / demotion information indicating the promotion / demotion according to the product name and the estimated substitute product information indicating the substitute product according to the product name). The same applies when the training product information and the estimated product information each indicate content other than the product name. For example, the learning model M can learn the correlation between the contents, type, price, size, design, taste, texture, JAN code, and other product identification information or other features of the product, if there is a correlation between the contents of the training planogram information. The learning model M outputs according to the correlation it has learned.

[0049] For example, training products that are best placed on a training shelf of a certain size may differ from training products that are best placed on a training shelf of another size. Furthermore, training products that are best placed in a specific position (e.g., near the center or edge) of a training shelf of a certain size may differ from training products that are best placed in the same position on a training shelf of another size. In this case, there is a correlation between the size of the training shelf indicated by the training shelf information and the content of the training planogram information (e.g., at least one of the promotion / demotion indicated by the training promotion / demotion information and the substitute product indicated by the training substitute product information). The learning model M learns this correlation by studying the training data. As a result, the learning model M can output estimated planogram information that indicates content that is correlated with the estimated shelf size indicated by the estimated shelf information included in the estimation data (e.g., at least one of the promotion / demotion indicated by the estimated promotion / demotion information indicating the promotion / demotion according to the estimated shelf size and the estimated substitute product information indicating the substitute product according to the estimated shelf size). The same applies when the training shelf information and the estimated shelf information each indicate content other than size. For example, the learning model M can learn correlations between the contents of the training planogram information and the capacity, number of shelves, type, design, material, location in the store, or other features. The learning model M outputs data according to the correlations it has learned.

[0050] For example, training products that should be placed on training shelves in a training store where a certain type of training product is likely to be purchased may differ from training products that should be placed on training shelves in a training store where another type of training product is likely to be purchased. Furthermore, training products that should be placed in a specific position (e.g., near the center or edge) on training shelves in a training store where a certain type of training product is likely to be purchased may differ from training products that should be placed in the same position on training shelves in a training store where another type of training product is likely to be purchased. In this case, there is a correlation between the type of training product indicated by the training purchase information and the content of the training shelf allocation information (e.g., at least one of the promotion / demotion indicated by the training promotion / demotion information and the substitute product indicated by the training substitute product information). The learning model M learns the correlation between these by studying the training data. This allows the learning model M to output estimated shelf allocation information indicating content correlated with the type of estimated product indicated by the estimated purchase information included in the estimation data (for example, at least one of promotion / demotion indicated by estimated promotion / demotion information indicating promotion / demotion according to the type of training product that is likely to be purchased, and estimated substitute product information indicating substitute products according to the type of training product that is likely to be purchased). The same applies when the training purchase information and the estimated purchase information each indicate content other than type. For example, the learning model M can learn correlations between price, quantity, total amount, gender, age group, preferences, behavior, or other characteristics of the training customer that have a correlation with the content of the training purchase information. The learning model M outputs according to the correlations it has learned.

[0051] [3-2. Functions realized by the server] For example, the server 20 includes a data storage unit 200, a learning model storage unit 201, an estimated product information acquisition unit 202, an estimated shelf information acquisition unit 203, an estimated purchase information acquisition unit 204, and a proposal unit 205. Each of the data storage unit 200 and the learning model storage unit 201 is realized by the storage unit 22. Each of the estimated product information acquisition unit 202, the estimated shelf information acquisition unit 203, the estimated purchase information acquisition unit 204, and the proposal unit 205 is realized by the control unit 21.

[0052] [Data storage section] The data storage unit 200 stores data necessary for proposing shelving planograms. For example, the data storage unit 200 stores an estimation database DB2. Note that the data stored in the data storage unit 200 is not limited to the estimation database DB2. The data storage unit 200 only needs to store data necessary for proposing shelving planograms. For example, the data storage unit 200 may store a user database that stores various information about users (e.g., manufacturer personnel) who use the shelving planogram proposal service. The user database stores information such as a user ID and password for users to log in to the shelving planogram proposal service.

[0053] FIG. 5 is a diagram showing an example of the inference database DB2. For example, the inference database DB2 stores store identification information for identifying a store and estimated data for that store. The store identification information is information that can identify a store in the shelf allocation proposal service. For example, the store identification information is a store ID assigned to the store, a store name, an address, or other information. The inference data for the store includes estimated product information for products handled in the store, estimated shelf information for shelves located in the store, and estimated purchase information for purchases at the store.

[0054] In this embodiment, the person in charge of shelf allocation operates the person in charge terminal 30 to register estimated product information and estimated shelf information in the estimation database DB2. For example, when the person in charge operates the person in charge terminal 30 to log in to the shelf allocation proposal service, the person in charge inputs estimated product information of estimated products to be handled at the store for which the person in charge is responsible for shelf allocation. The person in charge inputs product identification information such as the product name, content, type, price, size, design, taste, texture, JAN code, or other characteristics of the estimated product. The person in charge may input information for each of multiple estimated products. The person in charge may input the store ID of the store for which the person in charge is responsible for shelf allocation. The person in charge terminal 30 uploads to the server 20 the store ID of the store for which the person in charge is responsible for shelf allocation and the estimated product information of the estimated products input by the person in charge.

[0055] For example, the server 20 stores in the inference database DB2 the store ID of the store for which the person in charge is in charge of shelf allocation and the estimated product information received from the person in charge terminal 30 in association with each other. The estimated product information may be input by a store clerk rather than by the person in charge. The estimated product information may also be stored in advance in the person in charge terminal 30, a store terminal, another terminal, or an information storage medium rather than being manually input. In this case, the store ID indicating which store the estimated product information corresponds to is also stored in advance. The server 20 may acquire the estimated product information stored in advance in these locations, and store the estimated product information in association with the store ID of the store that sells the estimated product in the inference database DB2.

[0056] For example, when a staff member operates staff member terminal 30 to log in to the shelf allocation proposal service, they input estimated shelf information for estimated shelves located in the store for which they are responsible for shelf allocation. The staff member inputs the size, capacity, number of shelves, type, design, material, location in the store, function such as refrigerated / non-refrigerated storage, or other features of the estimated shelves located in the store for which they are responsible for shelf allocation. Staff member terminal 30 uploads to server 20 the store ID of the store for which they are responsible for shelf allocation and the estimated shelf information for the estimated shelves input by the staff member.

[0057] For example, server 20 stores in estimation database DB2 the store ID of the store for which the person in charge is responsible for shelf allocation, in association with the estimated shelf information received from person in charge terminal 30. Note that the estimated shelf information may be entered by a store clerk rather than by the person in charge. Furthermore, the estimated shelf information may not be entered manually but may be stored in advance in person in charge terminal 30, a store terminal, another terminal, or an information storage medium. In this case, it is assumed that the store ID indicating which store the estimated shelf information corresponds to is also stored in advance. Server 20 may acquire the estimated shelf information stored in advance in these locations, and store the estimated shelf information in association with the store ID of the store that handles the estimated shelf in estimation database DB2.

[0058] For example, when a staff member operates staff member terminal 30 to log in to the shelf allocation proposal service, the staff member inputs estimated purchase information for the store for which the staff member is responsible for shelf allocation. The staff member inputs the type, price, quantity, total amount, or other characteristics of the estimated product purchased at the estimated store for which the staff member is responsible for shelf allocation. The staff member terminal 30 uploads to the server 20 the store ID of the store for which the staff member is responsible for shelf allocation and the estimated purchase information of the estimated store input by the staff member.

[0059] For example, the server 20 stores in the inference database DB2 the store ID of the store for which the person in charge is in charge of shelf allocation and the estimated purchase information received from the person in charge terminal 30, in association with each other. The estimated purchase information may be entered by a store clerk rather than by the person in charge. The estimated purchase information may also be stored in advance in the person in charge terminal 30, a store terminal, another terminal, or an information storage medium, rather than being entered manually. In this case, the store ID indicating which store the estimated purchase information corresponds to is also stored in advance. The server 20 may acquire the estimated purchase information stored in advance and store the estimated purchase information in the inference database DB2 in association with the store ID of the store that sells the estimated product.

[0060] [Learning model memory section] The learning model storage unit 201 stores a trained learning model M that has learned the relationship between training product information related to training products for training and training allocation information related to the shelf allocation of the training products in a training store for training. In this embodiment, since the learning model M is learned by the learning terminal 10, the learning model storage unit 201 stores the learning model M that the server 20 receives from the learning terminal 10. If the server 20 also trains the learning model M, the learning model storage unit 201 stores the learning model M that the server 20 has trained with training data. In this case, the server 20 has the same functions as the learning terminal 10. For example, the data storage unit 200 stores a training database DB1. The server 20 may include a learning unit 102.

[0061] [Estimated product information acquisition department] The estimated product information acquisition unit 202 acquires estimated product information related to estimated products handled in the estimated store for which shelf allocation is estimated. In this embodiment, the estimated product information is stored in the estimation database DB2, and therefore the estimated product information acquisition unit 202 acquires the estimated product information from the estimation database DB2. For example, when a person in charge operates the person in charge terminal 30 to request a shelf allocation proposal from the shelf allocation proposal service, the estimated product information acquisition unit 202 acquires, from the estimation database DB2, estimated product information associated with the store ID of the store for which the shelf allocation proposal is to be made. It is assumed that the store for which the shelf allocation proposal is to be made is specified by the person in charge. Note that the estimated product information acquisition unit 202 may acquire estimated product information from a database other than the estimation database DB2, the person in charge terminal 30, another terminal, or an information storage medium.

[0062] [Estimated shelf information acquisition section] The estimated shelf information acquisition unit 203 acquires estimated shelf information related to estimated shelves arranged in an estimated store. In this embodiment, estimated shelf information is stored in estimation database DB2, and so the estimated shelf information acquisition unit 203 acquires estimated shelf information from estimation database DB2. For example, when a person in charge operates the person in charge terminal 30 to request a shelf allocation proposal from the shelf allocation proposal service, the estimated shelf information acquisition unit 203 acquires, from estimation database DB2, estimated shelf information associated with the store ID of the store for which the shelf allocation proposal is to be made. Note that the estimated shelf information acquisition unit 203 may acquire estimated shelf information from a database other than estimation database DB2, the person in charge terminal 30, another terminal, or an information storage medium.

[0063] [Estimated purchase information acquisition department] The estimated purchase information acquisition unit 204 acquires estimated purchase information related to purchases at an estimated store. In this embodiment, the estimated purchase information is stored in the estimated database DB2, so the estimated purchase information acquisition unit 204 acquires the estimated purchase information from the estimated database DB2. For example, when a staff member operates the staff member terminal 30 to request a purchase discount proposal from the purchase discount proposal service, the estimated purchase information acquisition unit 204 acquires, from the estimated database DB2, estimated purchase information associated with the store ID of the store for which the purchase discount is proposed. Note that the estimated purchase information acquisition unit 204 may acquire estimated purchase information from a database other than the estimated database DB2, the staff member terminal 30, another terminal, or an information storage medium.

[0064] [Proposal Department] The suggestion unit 205 makes a suggestion regarding the shelf allocation of the estimated product in the estimated store based on the learning model M and the estimated product information. In this embodiment, the learning model M has also learned training shelf information regarding training shelves placed in the training store, so the suggestion unit 205 makes a suggestion further based on the estimated shelf information. Furthermore, in this embodiment, the learning model M has also learned training purchase information regarding purchases in the training store, so the suggestion unit 205 makes a suggestion further based on the training purchase information.

[0065] For example, the proposal unit 205 inputs estimated data including estimated product information, estimated shelf information, and estimated purchase information to the learning model M. When the estimated data is input, the learning model M calculates an embedded representation of the estimated data based on parameters adjusted by learning. The learning model M outputs estimated shelf allocation information according to the embedded representation. The proposal unit 205 makes a proposal regarding shelf allocation to the person in charge based on the estimated shelf allocation information output from the learning model M. For example, the proposal unit 205 makes a proposal regarding shelf allocation by displaying a shelf allocation proposal screen SC for proposing shelf allocation on the person in charge terminal 30 based on the estimated shelf allocation information.

[0066] 6 is a diagram showing an example of a shelf allocation proposal screen SC. In this embodiment, the learning model M has learned training promotion / demotion information regarding promotion and demotion related to shelf allocation in the training store as training shelf allocation information. Therefore, the proposal unit 205 acquires estimated promotion / demotion information regarding promotion and demotion related to shelf allocation in the estimated store based on the learning model and estimated product information, and makes a proposal based on the estimated promotion / demotion information. For example, as shown in FIG. 6, the proposal unit 205 displays a shelf allocation proposal screen SC showing estimated promotion / demotion information on the staff terminal 30.

[0067] In this embodiment, the learning model M has learned training substitute product information regarding training substitute products that are substituted for the training product in the shelf allocation at the training store as training planogram information. For example, the suggestion unit 205 acquires estimated substitute product information that is substituted for the estimated product in the shelf allocation at the estimated store based on the learning model and the estimated product information, and makes a proposal based on the estimated substitute product information. For example, as shown in FIG. 6, the suggestion unit 205 displays a shelf allocation proposal screen SC showing the estimated substitute product information on the staff terminal 30.

[0068] The suggestion unit 205 may make a suggestion by a method other than displaying the shelf allocation suggestion screen SC. For example, the suggestion unit 205 may make a suggestion using a communication means such as email. The suggestion unit 205 may also make a suggestion based on information other than the training promotion / demotion information and the training substitute product information. For example, the suggestion unit 205 may make a suggestion based on estimated shelf allocation information indicating a list of estimated products to be placed on the estimated shelf. Furthermore, the suggestion unit 205 may make a suggestion for each individual space on the estimated shelf (for example, each space on the upper, middle, and lower shelves) based on estimated shelf allocation information indicating a list of estimated products to be placed in that space. The suggestion unit 205 may also make a suggestion based on estimated shelf allocation information indicating a list of estimated products that should not be placed on the estimated shelf.

[0069] [3-3. Functions realized on the staff terminal] For example, the person in charge terminal 30 includes a data storage unit 300, an operation reception unit 301, and a display control unit 302. The data storage unit 300 is realized by the storage unit 32. The operation reception unit 301 and the display control unit 302 are realized by the control unit 31.

[0070] [Data storage section] The data storage unit 300 stores data necessary for the person in charge to use the shelf allocation proposal service. For example, the data storage unit 300 stores an application dedicated to the shelf allocation proposal service. When the person in charge uses the payment service from a browser instead of the application, the data storage unit 300 stores the browser.

[0071] [Operation reception section] The operation reception unit 301 receives various operations from the user. For example, the operation reception unit 301 receives operations for an application or a browser dedicated to the shelf layout proposal service. The operation reception unit 301 transmits data indicating the content of the user's operation to the server 20.

[0072] [Display control section] The display control unit 302 displays various screens on the display unit 35. For example, the display control unit 302 displays the shelving planogram proposal screen SC on the display unit 35. The display control unit 302 communicates with the server 20, receives data necessary for displaying the shelving planogram proposal screen SC, and displays the shelving planogram proposal screen SC on the display unit 35.

[0073] [4. Processing performed by the shelf layout proposal system] Fig. 7 is a diagram showing an example of processing executed in the shelf layout proposal system 1. The control units 11, 21, and 31 execute programs stored in the storage units 12, 22, and 32, respectively, to execute the processing in Fig. 7.

[0074] As shown in Figure 7, the learning terminal 10 learns the learning model M based on the training database DB1 (S1). The learning terminal 10 uploads the learned learning model M to the server 20 (S2). When the server 20 receives the learned learning model M from the learning terminal 10 (S3), it records the learned learning model M in the memory unit 22 (S4). The staff member terminal 30 executes a login process between the server 20 and the staff member based on the staff member's operation, allowing the staff member to log in to the shelf allocation proposal service (S5).

[0075] Based on the operation of the person in charge, the person in charge terminal 30 transmits estimated data including estimated product information, estimated shelf information, and estimated purchase information to the server 20 (S6). The server 20 receives the estimated data including estimated product information, estimated shelf information, and estimated purchase information from the person in charge terminal 30 (S7). The server 20 stores the estimated data in the estimation database DB2 (S8). Based on the operation of the person in charge, the person in charge terminal 30 requests the server 20 to propose a shelf allocation (S9). When the server 20 receives the request for a shelf allocation proposal from the person in charge terminal 30 (S10), it obtains the estimated data including estimated product information, estimated shelf information, and estimated purchase information from the estimation database DB2 (S11).

[0076] The server 20 inputs the estimated data acquired in S11 to the learning model M (S12). The server 20 acquires the estimated shelf allocation information output from the learning model M (S13). The server 20 generates display data for the shelf allocation proposal screen SC based on the estimated shelf allocation information and transmits it to the staff member terminal 30 (S14). When the staff member terminal 30 receives the display data for the shelf allocation proposal screen SC from the server 20 (S15), it displays the shelf allocation proposal screen SC on the display unit 35 (S16), and this process ends.

[0077] [5. Summary of embodiments] The shelf allocation proposal system 1 of this embodiment stores a trained learning model M that has learned the relationship between training product information and the training shelf allocation information. The shelf allocation proposal system 1 acquires estimated product information. The shelf allocation proposal system 1 proposes shelf allocations for estimated products in an estimated store based on the learning model M and the estimated product information. This allows the shelf allocation proposal system 1 to improve the accuracy of shelf allocation proposals. For example, the shelf allocation proposal system 1 can propose shelf allocations based on the learning model M, which can also accommodate unknown estimated product information, rather than proposing shelf allocations within a predetermined shelf allocation pattern or relationship information as described in prior art documents. When a salesperson considers shelf allocations for a new product, if the learning model M has learned training data for other products with similar characteristics to the new product, the learning model M can propose an appropriate shelf allocation for the new product based on parameters adjusted by the training data. Because the learning model M has learned training data from various training stores, the shelf allocation proposal system 1 can propose an appropriate shelf allocation that takes into account the trends of stores other than the estimated store where the salesperson is considering shelf allocations.

[0078] In this embodiment, the learning model M also learns training shelf information. The shelf planogram proposal system 1 acquires estimated shelf information. The shelf planogram proposal system 1 makes proposals further based on the estimated shelf information. This allows the shelf planogram proposal system 1 to propose an appropriate shelf plan based not only on estimated product information of the estimated product that is the target of the shelf planogram, but also on estimated shelf information of the estimated shelf that is the target of the shelf planogram, thereby further improving the accuracy of the shelf planogram proposal. For example, the shelf planogram proposal system 1 can propose an appropriate shelf planogram according to characteristics such as the size or capacity of the estimated shelf.

[0079] In this embodiment, the learning model M also learns training purchase information. The shelf planogram proposal system 1 acquires estimated purchase information. The shelf planogram proposal system 1 makes proposals further based on the estimated purchase information. This allows the shelf planogram proposal system 1 to propose an appropriate shelf planogram based not only on estimated product information of the estimated products that are the subject of shelf planogram proposal, but also on estimated purchase information of the estimated store that is the subject of shelf planogram proposal, thereby further improving the accuracy of shelf planogram proposals. For example, the shelf planogram proposal system 1 can propose an appropriate shelf planogram based on the characteristics of the customer base or best-selling products at the estimated store.

[0080] Furthermore, in this embodiment, the learning model M has learned training promotion / demotion information as training planogram information. The planogram proposal system 1 acquires estimated promotion / demotion information based on the learning model M and estimated product information. The planogram proposal system 1 makes a proposal based on the estimated promotion / demotion information. This allows the planogram proposal system 1 to propose promotion and demotion of estimated products. For example, the person in charge can easily grasp which estimated products should be promoted or demoted based on the current planogram, so the planogram proposal system 1 can reduce the workload of the person in charge.

[0081] Furthermore, in this embodiment, the learning model M has learned training substitute product information as training planogram information. The shelf allocation proposal system 1 acquires estimated substitute product information based on the learning model M and estimated product information. The shelf allocation proposal system 1 makes a proposal based on the estimated substitute product information. This allows the shelf allocation proposal system 1 to propose an estimated substitute product to substitute for the estimated product. For example, the shelf allocation proposal system 1 can reduce the workload of the person in charge because the person in charge can easily grasp the estimated product to replace the estimated product placed on the estimated shelf in the current shelf allocation.

[0082] [6. Modifications] The present disclosure is not limited to the above-described embodiments, and may be modified as appropriate without departing from the spirit of the present disclosure.

[0083] 8 is a diagram showing an example of functions realized in the modified example. For example, the server 20 includes an other estimated purchase information acquisition unit 206, another estimated shelf information acquisition unit 207, and an expected customer information acquisition unit 208. Each of the other estimated purchase information acquisition unit 206, the other estimated shelf information acquisition unit 207, and the expected customer information acquisition unit 208 is realized by the control unit 21.

[0084] [6-1. Variation 1] For example, the training purchase information may be information regarding purchases by training customers who visited the training store. A training customer who visited the training store is a customer who actually purchased training products at the training store. The training purchase information indicates information regarding purchases of at least one of training products placed on the training shelf for which the learning model M should output estimated shelf allocation information during learning and training products placed on other training shelves in the training store. For example, the training purchase information indicates the quantity of these training products purchased, the total amount, the number of training customers, characteristics of the training customers (e.g., gender or age group), or other characteristics. The training purchase information may also indicate changes in these over time.

[0085] The learning model M of variant 1 also learns other training purchase information related to purchases by other training customers related to the training customer who have not visited the training store. In variant 1, the other training purchase information is included in the input portion of the training data. The learning unit 102 trains the learning model M with training data whose input portion includes the other training purchase information. Although the input portion of the training data differs from that of the embodiment, the learning method of the learning model M may be the same as that of the embodiment. The learning unit 102 records the trained learning model M, which has learned the other training purchase information, in the data storage unit 200. The learning unit 102 uploads the trained learning model M to the server 20.

[0086] Other training customers are customers whose characteristics are similar to those of the training customer. For example, other training customers are customers whose gender, age group, preferences, behavior, or other characteristics are similar to those of the training customer. Furthermore, for example, other training customers may be customers whose attribute values ​​are common to the training customer for at least some of the attributes, including gender, age group, preferences, behavior, or other characteristics. The shelf allocation proposal system 1 may identify other training customers from information obtained in a physical store, or may identify other training customers from information obtained through services other than a physical store. Other training purchase information is training purchase information of other training customers. The content of the training purchase information is as described in the embodiment. The other training purchase information differs from the training purchase information in that it indicates the purchasing characteristics of other training customers, but may be similar to the training purchase information in other respects. For example, the other training purchase information may indicate the gender, age group, preferences, behavior, or other characteristics of other training customers.

[0087] For example, the shelf allocation proposal system 1 may identify other training customers with characteristics similar to those of the training customer based on a service database that accumulates usage status data indicating the usage status of users of e-commerce services, communication services, travel reservation services, electronic payment services, financial services, online flea market services, or other online services. For example, the shelf allocation proposal system 1 may analyze various data covering a large number of items, such as the attributes, purchasing trends, price preferences, and service usage trends of users who have a history of purchasing the target product, and extract users with similar characteristics from a user group with no purchase history as similar users. The training customer and other training customers may use these services, or may not particularly use these services. The shelf allocation proposal system 1 may identify what characteristics customers have that are similar to each other based on the service database.

[0088] For example, the shelf allocation proposal system 1 identifies users with similar characteristics by clustering the usage data stored in the service database. The clustering method may be a known method. For example, the clustering method may be a method such as DB-SCAN or k-nearest neighbor method. Alternatively, for example, the shelf allocation proposal system may use other methods such as collaborative filtering to identify other training customers with similar characteristics to the training customer. When clustering is performed, belonging to the same cluster corresponds to having similar characteristics.

[0089] For example, the shelf allocation proposal system 1 identifies, based on the service database, what characteristics customers have that are similar to each other, and records a similar customer database indicating the identification results in the data storage unit 100. The learning unit 102 identifies other training customers that have similar characteristics to the training customer based on the similar customer database. The learning unit 102 trains the learning model M not only with training data generated based on the training customer, but also with training data generated based on other training customers. This allows the learning model M to learn more training data, thereby improving the accuracy of the learning model M.

[0090] For example, there may be a correlation between the characteristics of not only the training customers who actually visited the training store, but also other training customers who have similar characteristics to the training customers, and the training planogram information that indicates the correct planogram for the training store. The learning model M learns the correlations between these by studying the training data. As a result, when the proposing unit 205 inputs estimated data that includes other estimated purchase information described below to the learning model M, the learning model M can output estimated promotion / demotion information and estimated substitute product information as estimated planogram information corresponding to the estimated data based on the correlations it has learned (i.e., by processing based on parameters adjusted by learning).

[0091] For example, information obtained from training customers who actually visited a training store may not be enough to accurately identify information such as the customer demographic that favors that training store. For this reason, the shelf allocation proposal system 1 of variant 1 can make the learning model M learn correlations that cannot be obtained from the training store alone by also using other training purchase information identified based on a large amount of usage status in services such as online services. For example, if the other training purchase information indicates a best-selling product in a service such as an online service, the learning model M can learn the correlation between that product and an appropriate shelf allocation. When estimated data including other estimated purchase information described below is input, the learning model M can output according to the correlations it has learned.

[0092] For example, the estimated purchase information may be information regarding purchases by estimated customers who visited the estimated store. An estimated customer who visited the estimated store is a customer who actually purchased an estimated product at the estimated store. The estimated purchase information indicates information regarding purchases of at least one of estimated products placed on an estimated shelf for which the learning model M should output estimated shelf allocation information during learning, and estimated products placed on other estimated shelves in the estimated store. For example, the estimated purchase information indicates the quantity of these estimated products purchased, the total amount, the number of estimated customers, characteristics of the estimated customers (e.g., gender or age group), or other characteristics. The estimated purchase information may also indicate changes in these items over time.

[0093] The shelf allocation proposal system 1 of the first modification includes an other estimated purchase information acquisition unit 206. The other estimated purchase information acquisition unit 206 acquires other estimated purchase information related to purchases by other estimated customers who are related to the estimated customer and who have not visited the estimated store. The other estimated customers are customers whose characteristics are similar to those of the estimated customer. For example, the other estimated customers are customers whose gender, age group, preferences, behavior, or other characteristics are similar to those of the estimated customer. The other estimated purchase information acquisition unit 206 may identify other estimated customers whose characteristics are similar to those of the estimated customer using a method similar to the method for identifying other training customers. This method is as described above.

[0094] For example, the data storage unit 200 may store the similar customer database described above. The other estimated purchase information acquisition unit 206 may identify other estimated customers with characteristics similar to those of the estimated customer based on the similar customer database and acquire other estimated purchase information of the other estimated customers. The other estimated purchase information is estimated purchase information of other estimated customers. The content of the estimated purchase information is as described in the embodiment. The other estimated purchase information differs from the estimated purchase information in that it indicates the purchasing characteristics of other estimated customers, but may be similar to the estimated purchase information in other respects. In Modification 1, the other estimated purchase information is stored in the estimated database DB2. The other estimated purchase information acquisition unit 206 acquires the other estimated purchase information from the estimated database DB2. Note that the other estimated purchase information acquisition unit 206 may acquire the other estimated purchase information from a database other than the estimated database DB2, the staff terminal 30, another terminal, or an information storage medium.

[0095] The other training customers may be customers whose embedded representations based on gender, age group, preferences, or other characteristics are similar to those of the training customer. The embedded representations of the other training customers satisfy predetermined conditions, such as Euclidean distance or cosine similarity, with respect to the embedded representation of the training customer. The shelf layout proposal system 1 may acquire the embedded representations by inputting attribute data, including the gender, age group, preferences, or other characteristics of the target customers, into a dedicated learning model, determine the similarity of the embedded representations between customers, and identify the other training customers. Here, the dedicated learning model may be a pre-trained model including a user model such as UserBERT, or a pre-trained model including a graph neural network in which relationships between customers are learned through representation learning.

[0096] The shelf allocation proposal system 1 may dynamically adjust the similarity determination conditions (similarity identification conditions) for determining the similarity of the training customer corresponding to the training product according to the sales performance of the training product indicated by the training purchase information. For example, when the purchase quantity of the training product exceeds or falls below a predetermined value, the shelf allocation proposal system 1 may identify, as other training customers, customers whose attribute values ​​for at least some of the attributes are common to the training customer. Furthermore, for example, when the purchase quantity of the training product exceeds or falls below a predetermined value, the shelf allocation proposal system 1 may adjust various parameters, such as the number of neighboring objects, to increase the number of other training customers similar to the training customer, and perform the clustering. Furthermore, for example, when the purchase quantity of the training product exceeds or falls below a predetermined value, the shelf allocation proposal system 1 may adjust thresholds for indices, such as Euclidean distance and cosine similarity, between embedded representations of customers to increase the number of other training customers similar to the training customer, and perform similarity determination for other training customers. In addition, when an estimated product is given in advance, the shelf allocation proposal system 1 may adjust the above-mentioned similarity judgment conditions according to a training product that is identical to the estimated product, generate or expand training data, and perform learning or additional learning of the learning model M.

[0097] The proposal unit 205 of Modification 1 makes a proposal further based on other estimated purchase information. The proposal unit 205 inputs estimated data including other estimated purchase information to the learning model M. When the example of the embodiment and Modification 1 are combined, the proposal unit 205 inputs estimated data including estimated product information, estimated shelf information, estimated purchase information, and other estimated purchase information. Although the estimated data input to the learning model M is different from that of the embodiment, the processing of the learning model M may be similar to that of the embodiment. The learning model M calculates an embedded expression of the estimated data and outputs estimated shelf allocation information according to the embedded expression. The proposal unit 205 makes a proposal regarding shelf allocation to the person in charge based on the estimated shelf allocation information output from the learning model M, taking into account the other estimated purchase information.

[0098] The learning model M of the first modification also learns other training purchase information related to purchases by other training customers who did not visit the training store. The shelf allocation proposal system 1 acquires other estimated purchase information related to purchases by other estimated customers who did not visit the estimated store. The shelf allocation proposal system 1 makes proposals based further on the other estimated purchase information. This allows the shelf allocation proposal system 1 to propose an appropriate shelf allocation based not only on the estimated customers who visited the estimated store that is the target of shelf allocation, but also on other estimated purchase information of other estimated customers, thereby further improving the accuracy of the shelf allocation proposal. For example, the shelf allocation proposal system 1 can propose an appropriate shelf allocation based on the characteristics of other estimated customers who have similar characteristics to the estimated customer who visited the estimated store. The shelf allocation proposal system 1 can also propose an appropriate shelf allocation that takes into account information obtained from services such as online services. For example, when the other training purchase information indicates a trend among other training customers similar to the training customer who visits the training store, and the trend cannot be identified from the training store alone, the shelf allocation proposal system 1 can propose an appropriate shelf allocation based on the trend.

[0099] [6-2. Variation 2] For example, in variant example 1, the learning model M may learn other training purchase information of other training customers who are in the same trade area as the training customer. A trade area is an area targeted by a business. Being in the same trade area as the training customer means being in the same trade area as the training store or being in a trade area within the training customer's usual range of activity. For example, the trade area may be identified based on the communication status of mobile communications. The trade area may be defined by the service provider of the shelf allocation proposal service, or a trade area defined by an external service may be used. Trade area definition data that defines the trade area is stored in the data storage unit 100 and the data storage unit 200. For example, the trade area definition data indicates areas across the country that are in the same trade area. The learning unit 102 may identify other training customers who are in the same trade area as a training customer who visited a certain training store based on the trade area definition data, and generate training data.

[0100] For example, the input portion of the training data in Modification Example 2 includes other training purchase information of other training customers who are in the same commercial area as a training customer who visited a certain training store. The learning unit 102 causes the learning model M to learn the training data whose input portion includes other training purchase information. This is different from Modification Example 1 in that the input portion of the training data does not include other training purchase information of other training customers who are in a different commercial area from the training customer who visited a certain training store, but the method of causing the learning model M to learn the other training purchase information may be the same as Modification Example 1.

[0101] The other estimated purchase information acquisition unit 206 of Modification 2 acquires other estimated purchase information of other estimated customers who are in the same commercial area as the estimated customer. For example, the other estimated purchase information acquisition unit 206 identifies other estimated customers who are in the same commercial area as the estimated customer who visited the estimated store in which the estimated shelf for which the planogram is proposed is located, based on the commercial area definition data. The other estimated purchase information acquisition unit 206 references the estimation database DB2 and acquires other estimated purchase information of the identified other estimated customers. This differs from Modification 1 in that other estimated purchase information of other estimated customers who are in a different commercial area from the estimated customer who visited the estimated store in which the estimated shelf for which the planogram is proposed is located is not acquired. However, the content of the other estimated purchase information itself may be the same as that of Modification 1.

[0102] The proposing unit 205 of the second modification makes a proposal based on other estimated purchase information of other estimated customers who are in the same trade area as the estimated customer. The proposing unit 205 inputs estimated data including other estimated purchase information of other estimated customers who are in the same trade area as the estimated customer to the learning model M. This modification differs from the first modification in that the estimated data does not include other estimated purchase information of other estimated customers who are in a trade area different from that of the estimated customer who visited the estimated store in which the estimated shelf for which the shelf allocation is proposed is located, but the processing of the learning model M may be the same as that of the first modification. The proposing unit 205 makes a proposal regarding shelf allocation to the person in charge based on the estimated shelf allocation information output from the learning model M, taking into consideration other estimated purchase information of other estimated customers who are in the same trade area.

[0103] The learning model M of the second modification has learned other training purchase information of other training customers who are in the same commercial area as the training customer. The shelf allocation proposal system 1 acquires other estimated purchase information of other estimated customers who are in the same commercial area as the estimated customer. The shelf allocation proposal system 1 makes a proposal based on other estimated purchase information of other estimated customers who are in the same commercial area as the estimated customer. This allows the shelf allocation proposal system 1 to propose an appropriate shelf allocation based on other estimated purchase information of other estimated customers who are in the same commercial area as the estimated customer who visited the estimated store that is the target of shelf allocation, thereby further improving the accuracy of the shelf allocation proposal. For example, the shelf allocation proposal system 1 can propose an appropriate shelf allocation based on the characteristics of other estimated customers who are more similar in characteristics to the estimated customer who visited the estimated store.

[0104] [6-3. Variation 3] For example, as explained somewhat in the embodiment, the training purchase information may be information about a training customer who purchased a training product from a training shelf located in a training store. The training purchase information of the third modification indicates the characteristics of the training customer himself, rather than the characteristics of the training product purchased by the training customer. For example, the training purchase information indicates the gender, age group, preferences, behavior, or other characteristics of the training customer. The other characteristics may be characteristics used as demographic information. For example, the training purchase information may indicate the residential area, range of activities, occupation, or annual income of the training customer. In the third modification, such training purchase information is assumed to be stored in the training database DB1. The learning unit 102 of the third modification learns the learning model M based on such training purchase information. The learning method of the learning model M may be the same as in the embodiment.

[0105] The estimated purchase information of the third modification is information about an estimated customer who purchased an estimated product on an estimated shelf placed in an estimated store. The estimated purchase information of the third modification indicates the characteristics of the estimated customer himself, rather than the characteristics of the estimated product purchased by the estimated customer. For example, the estimated purchase information indicates the estimated customer's gender, age group, preferences, behavior, or other characteristics. The other characteristics may be characteristics used as demographic information. For example, the estimated purchase information may indicate the estimated customer's residential area, range of activities, occupation, or annual income. In the third modification, such estimated purchase information is assumed to be stored in the estimation database DB2. The proposal unit 205 of the third modification proposes a shelf allocation based on such estimated purchase information. The processing of the learning model M during estimation may be the same as in the embodiment.

[0106] In the third modification, the training purchase information is information about training customers who purchased training products on training shelves placed in a training store. The estimated purchase information is information about estimated customers who purchased estimated products on estimated shelves placed in an estimated store. This allows the shelf allocation proposal system 1 to propose shelf allocations according to estimated customers, thereby further improving the accuracy of shelf allocation proposals. For example, the shelf allocation proposal system 1 can propose appropriate shelf allocations according to characteristics such as the gender or age group of estimated customers who visited the estimated store.

[0107] [6-4. Variation 4] For example, the learning model M may have learned the relationship between training product information and training shelf allocation information for each possible placement location where training products can be placed on a training shelf located in a training store. A possible placement location is an individual space on the training shelf. The training shelf is divided into multiple possible placement locations. For example, each level of the training shelf, such as the top, middle, and bottom levels, is a possible placement location. Within each level, horizontal positions such as the left, center, and right sides are also possible placement locations. The number of possible placement locations that a training shelf has may be any number.

[0108] The training data of Variation 4 includes a pair of an input part and an output part for each possible placement location on a training shelf. For example, if a training shelf is divided vertically into three sections, the top, middle, and bottom, and horizontally into three sections, the left, center, and right, the training shelf includes nine possible placement locations. The training data for the training shelf includes a pair of an input part and an output part for each of the nine possible placement locations. Therefore, one training data includes nine pairs. The learning unit 102 of Variation 4 trains such training data in a learning model M. In Variation 4, a boosting model (decision tree model) may be used as the trained learning model M. The learning model M may process a classification task to solve the classification of installation locations for each estimated product.

[0109] For example, the first input portion of the training data is training product information, etc., of a training product that is a candidate for placement in the available placement location on the left side of the top row. The output portion corresponding to this input portion is training planogram information that indicates an appropriate shelf allocation for the available placement location on the left side of the top row. The second input portion of the training data is training product information, etc., of a training product that is a candidate for placement in the available placement location in the center of the top row. The output portion corresponding to this input portion is training planogram information that indicates an appropriate shelf allocation for the available placement location in the center of the top row. Similarly, the third to ninth input and output portions of the training data respectively indicate training product information, etc., of training products that are candidates for placement in the available placement locations on the right side of the top row, the left side of the middle row, the center of the middle row, the right side of the middle row, the left side of the bottom row, the center of the bottom row, and the right side of the bottom row, and training planogram information that indicates an appropriate shelf allocation for these available placement locations.

[0110] The shelf layout proposal system 1 of Variation 4 includes a placement possibility information acquisition unit. The placement possibility information acquisition unit acquires placement possibility location information regarding placement possibility locations where estimated products can be placed on estimated shelves arranged in an estimated store. The meaning of the placement possibility locations on estimated shelves is the same as that of the placement possibility locations on training shelves. Here, like the placement possibility locations on training shelves, it is assumed that a certain estimated shelf is divided into three vertical sections, the top, middle, and bottom, and three horizontal sections, the left, center, and right. The estimated shelf has nine placement possibility locations. In Variation 4, a case is described in which the placement possibility locations on the estimated shelf and the placement possibility locations on the training shelf are the same, but these placement possibility locations may differ slightly. In Variation 4, it is assumed that such placement possibility information is stored in the estimation database DB2. The placement possibility information acquisition unit acquires the placement possibility information from the estimation database DB2. Note that the placement possibility information acquisition unit may acquire the placement possibility information from a database other than the estimation database DB2, the staff terminal 30, another terminal, or an information storage medium.

[0111] The proposing unit 205 of the fourth modification makes a proposal further based on the possible placement location information. The proposing unit 205 inputs estimated data including the possible placement location information to the learning model M. When the example of the embodiment and the fourth modification are combined, the proposing unit 205 inputs estimated data including estimated product information, estimated shelf information, estimated purchase information, and possible placement location information. Although the estimated data input to the learning model M is different from that of the embodiment, the processing of the learning model M may be the same as that of the embodiment. The learning model M calculates an embedded expression of the estimated data and outputs estimated shelf allocation information according to the embedded expression. The estimated shelf allocation information indicates an appropriate shelf allocation for each possible placement location of the estimated shelf. The proposing unit 205 makes a proposal regarding shelf allocation to the person in charge based on the estimated shelf allocation information output from the learning model M, taking into account the possible placement location information.

[0112] The learning model M of the fourth modification has learned the relationship between the training product information and the training shelf allocation information for each possible placement location. The shelf allocation proposal system 1 acquires the possible placement location information of the estimated store. The shelf allocation proposal system 1 makes a proposal further based on the possible placement location information. This allows the shelf allocation proposal system 1 to propose a shelf allocation for each possible placement location, thereby further improving the accuracy of the shelf allocation proposal. For example, the person in charge can grasp the optimal shelf allocation for each possible placement location, so the shelf allocation proposal system 1 can increase convenience for the person in charge.

[0113] [6-5. Variation 5] For example, the learning model M may also have learned other training shelf information regarding other training shelves located around a training shelf located in a training store. Other training shelves are other training shelves located in the same training store. Surrounding refers to being close in distance to the training shelf that is the target of shelf allocation in the training data. For example, other training shelves that are next to, in the same sales area, or on the same floor as the training shelf that is the target of shelf allocation in the training data are other surrounding training shelves. Surrounding training shelf data indicating other surrounding training shelves is assumed to be stored in the data storage unit 100 and the data storage unit 200. For example, the surrounding training shelf data indicates whether training shelves in a certain training store are located around each other. The learning unit 102 may identify other training shelves around a certain training shelf based on the surrounding training shelf data and generate training data.

[0114] For example, the input portion of the training data in Variation 5 includes other training shelf information for other training shelves surrounding a certain training shelf. The other training shelf information differs from the training shelf information in that it indicates the characteristics of the other training shelves, but may be similar to the training shelf information in other respects. For example, the other training shelf information may indicate the size, capacity, number of shelves, type, design, material, location in the store, functions such as refrigerated / non-refrigerated storage, or other characteristics of the other training shelves. The other training shelf information may also indicate training products arranged on other training shelves. For example, the other training shelf information may indicate product identification information such as the product name, content, type, price, size, design, flavor, texture, JAN code, or other characteristics of the training products arranged on other training shelves. The learning unit 102 trains the learning model M to learn the training data including the other training shelf information in the input portion. This differs from the embodiment in that the other training shelf information is not included in the input portion of the training data, but the method of training the learning model M to learn the training data may be similar to the embodiment.

[0115] For example, a shelf allocation appropriate for a certain training shelf may affect the shelf allocations of other training shelves. It may be better for a certain training shelf not to be equipped with training products or similar products placed on other surrounding training shelves. Conversely, depending on the type of training product, it may be better for a certain training shelf to be equipped with training products or similar products placed on other surrounding training shelves. For this reason, a correlation may exist between other training shelf information for other training shelves around a certain training shelf and training shelf allocation information that indicates an appropriate shelf allocation for a certain training shelf. By learning such correlations, the learning model M of variant 5 becomes able to output, during estimation, information that corresponds to other estimated shelves around a certain estimated shelf.

[0116] The shelf allocation proposal system 1 of Variation 5 includes an other estimated shelf information acquisition unit 207. The other estimated shelf information acquisition unit 207 acquires other estimated shelf information related to other estimated shelves around an estimated shelf located in the estimated store. The other estimated shelves are other estimated shelves located in the same estimated store. The meaning of "surrounding" may be the same as that of "other training shelves." For example, other estimated shelves located next to, in the same sales area, or on the same floor as the estimated shelf that is the target of shelf allocation in the estimation data are other estimated shelves in the surrounding area. The other estimated shelf information acquisition unit 207 may identify other estimated shelves around a certain estimated shelf based on the surrounding training shelf data and generate estimation data. In Variation 5, it is assumed that other estimated shelf information is stored in the estimation database DB2. The other estimated shelf information acquisition unit 207 acquires other estimated shelf information from the estimation database DB2. Note that the other estimated shelf information acquisition unit 207 may acquire other estimated shelf information from a database other than the estimation database DB2, the staff terminal 30, another terminal, or an information storage medium.

[0117] The proposal unit 205 of Modification 5 makes a proposal further based on other estimated shelf information. The proposal unit 205 inputs estimated data including other estimated shelf information to the learning model M. When the example of the embodiment and Modification 5 are combined, the proposal unit 205 inputs estimated data including estimated product information, estimated shelf information, estimated purchase information, and other estimated shelf information. Although the estimated data input to the learning model M is different from that of the embodiment, the processing of the learning model M may be similar to that of the embodiment. The learning model M calculates an embedded expression of the estimated data and outputs estimated shelf information according to the embedded expression. The proposal unit 205 makes a proposal regarding shelf allocation to the person in charge based on the estimated shelf information output from the learning model M, taking into account other estimated shelf information.

[0118] The learning model M of the fifth modification also learns other training shelf information related to other training shelves around the training shelf placed in the training store. The shelf planogram proposal system 1 acquires other estimated shelf information. The shelf planogram proposal system 1 makes a proposal further based on the other estimated shelf information. This allows the shelf planogram proposal system 1 to propose a shelf planogram that also takes into account the characteristics of other estimated shelves around the estimated shelf, thereby further improving the accuracy of the shelf planogram proposal. For example, the shelf planogram proposal system 1 can propose a shelf planogram that comprehensively considers the relationship between the estimated shelf and other estimated shelves around it.

[0119] [6-6. Variation 6] For example, the learning model M may also have learned training customer information about training customers who visited the training store. The training customer information may be the same as the training purchase information described in the embodiment and Modification Example 1. The training customer information may also be information about training customers who did not make a purchase at the training store (training customers who simply visited the training store but did not buy anything). The training customer information indicates the gender, age group, preferences, behavior, or other characteristics of these training customers. In Modification Example 6, training customer information is stored in the training database DB1. For example, the input portion of the training data includes training customer information. The learning unit 102 trains the learning model M with training data whose input portion includes training customer information. Although the input portion of the training data is different from that of the embodiment, the learning method of the learning model M may be the same as that of the embodiment. The learning unit 102 records the trained learning model M, which has learned the training customer information, in the data storage unit 200. The learning unit 102 uploads the trained learning model M to the server 20.

[0120] For example, there may be a correlation between the customer demographics of training customers who visited a training store and the shelf allocation appropriate for the training shelves placed in that training store. The shelf allocation appropriate for the training shelves of a training store with many young female customers may differ from the shelf allocation appropriate for the training shelves of a training store with many male office workers. By learning these correlations, the learning model M becomes able to output, at the time of estimation, according to the customer demographics of the estimated store. At the time of estimation, information on estimated customers who actually visited the estimated store may be used, but in variant example 6, information on expected customers who are expected to attract customers to the estimated store is used.

[0121] The shelf allocation proposal system 1 of the sixth modification includes a expected customer information acquisition unit 208. The expected customer information acquisition unit 208 acquires expected customer information related to expected customers that the estimated store is expected to attract. The expected customers are customers that the estimated store is expected to attract in the future. The expected customers may be designated by a store clerk or by the person in charge of shelf allocation. The expected customer information is information that indicates the characteristics of the expected customers. For example, the expected customer information indicates the gender, age group, preferences, behavior, or other characteristics of the expected customers. In the sixth modification, it is assumed that the expected customer information is stored in the estimated database DB2. The expected customer information acquisition unit 208 acquires the expected customer information from the estimated database DB2. Note that the expected customer information acquisition unit 208 may acquire the expected customer information from a database other than the estimated database DB2, the person in charge terminal 30, another terminal, or an information storage medium.

[0122] The proposal unit 205 of Modification 6 makes a proposal further based on expected customer information. The proposal unit 205 inputs estimated data including expected customer information to the learning model M. When the example of the embodiment and Modification 6 are combined, the proposal unit 205 inputs estimated data including estimated product information, estimated shelf information, estimated purchase information, and expected customer information. Although the estimated data input to the learning model M is different from that of the embodiment, the processing of the learning model M may be similar to that of the embodiment. The learning model M calculates an embedded expression of the estimated data and outputs estimated shelf allocation information according to the embedded expression. The proposal unit 205 makes a proposal regarding shelf allocation to the person in charge based on the estimated shelf allocation information output from the learning model M, taking into account the expected customer information as well.

[0123] The learning model M of the sixth modification also learns training customer information about training customers who visited the training store. The shelf allocation proposal system 1 acquires expected customer information. The shelf allocation proposal system 1 makes a proposal further based on the expected customer information. This allows the shelf allocation proposal system 1 to propose an appropriate shelf allocation according to the expected customers expected in the estimated store that is the target of the shelf allocation, thereby further improving the accuracy of the shelf allocation proposal. For example, the shelf allocation proposal system 1 can propose a shelf allocation that will increase the future customer attraction effect of the estimated store.

[0124] [6-7. Other variations] For example, the above modifications 1 to 6 may be combined.

[0125] For example, in the embodiment, an example has been given in which estimated shelf information and estimated purchase information are included in the estimated data, but a separate learning model M may be prepared for at least one of the estimated shelf information and the estimated purchase information. In this case, the proposing unit 205 may propose a shelf allocation based on a learning model M corresponding to the estimated shelf information out of the multiple learning models M. The proposing unit 205 may propose a shelf allocation based on a learning model M corresponding to the estimated purchase information out of the multiple learning models M.

[0126] For example, functions described as being realized by the learning terminal 10 may be realized by the server 20, the person in charge terminal 30, or another computer. Processing described as being realized by the learning terminal 10 may be shared among multiple computers. Processing described as being realized by the server 20 may be realized by the learning terminal 10, the person in charge terminal 30, or another computer. The main functions of the shelf allocation proposal system 1 may be shared among multiple computers.

[0127] [7. Notes] For example, the shelf layout proposal system can be configured as follows: (1) a learning model storage unit that stores a learned learning model in which a relationship between training product information on training products for training and training planogram information on the planogram of the training products in the training store for training is learned; an estimated product information acquisition unit that acquires estimated product information regarding estimated products handled in an estimated store whose shelf allocation is estimated; a proposal unit that makes a proposal regarding the shelf allocation of the estimated product in the estimated store based on the learning model and the estimated product information; A shelf layout proposal system including: (2) The learning model also learns training shelf information regarding training shelves arranged in the training store, The shelf allocation proposal system further includes an estimated shelf information acquisition unit that acquires estimated shelf information regarding estimated shelves arranged in the estimated store, the suggestion unit makes the suggestion further based on the estimated shelf information. The shelf layout proposal system according to (1). (3) The learning model also learns training purchase information regarding purchases at the training store, The shelf allocation proposal system further includes an estimated purchase information acquisition unit that acquires estimated purchase information regarding purchases in the estimated store, the suggestion unit makes the suggestion further based on the estimated purchase information. A shelf layout proposal system according to (1) or (2). (4) The training purchase information is information regarding purchases made by training customers who visited the training store, the estimated purchase information is information regarding purchases made by an estimated customer who visited the estimated store, The learning model also learns other training purchase information related to purchases by other training customers who are related to the training customer and who have not visited the training store, the shelf allocation proposal system further includes a different estimated purchase information acquisition unit that acquires different estimated purchase information regarding purchases by different estimated customers who are related to the estimated customer and who have not visited the estimated store; The suggestion unit makes the suggestion further based on the other estimated purchase information. (3) The shelf layout proposal system described above. (5) The learning model has learned the other training purchase information of the other training customers who are in the same trade area as the training customer, the other estimated purchase information acquisition unit acquires the other estimated purchase information of the other estimated customers who are in the same trade area as the estimated customer, the proposal unit makes the proposal based on the other estimated purchase information of the other estimated customers who are in the same trade area as the estimated customer. (4) The shelf layout proposal system described in (4). (6) The training purchase information is information about a training customer who purchased the training product on the training shelf located in the training store, The estimated purchase information is information about an estimated customer who purchased the estimated product on the estimated shelf arranged in the estimated store. A shelf layout proposal system according to any one of (3) to (5). (7) The learning model learns a relationship between the training product information and the training shelf allocation information for each possible location where the training product can be arranged on the training shelf arranged in the training store, The shelf allocation proposal system further includes an arrangement possibility information acquisition unit that acquires arrangement possibility location information regarding arrangement possibility locations where the estimated product can be arranged on the estimated shelf arranged in the estimated store, the proposing unit makes the proposal further based on the possible placement location information. A shelf layout proposal system according to any one of (1) to (6). (8) The learning model also learns other training shelf information regarding other training shelves around the training shelf arranged in the training store, The shelf allocation proposal system further includes an other estimated shelf information acquisition unit that acquires other estimated shelf information related to other estimated shelves around the estimated shelf arranged in the estimated store, The suggestion unit makes the suggestion further based on the other estimated shelf information. A shelf layout proposal system according to any one of (1) to (7). (9) The learning model also learns training customer information regarding training customers who visit the training store, The shelf allocation proposal system further includes an expected customer information acquisition unit that acquires expected customer information regarding expected customers that the estimated store is expected to attract, The proposal unit makes the proposal further based on the expected customer information. A shelf layout proposal system according to any one of (1) to (8). (10) The learning model has learned training promotion / demotion information regarding promotion and demotion related to shelf allocation in the training store as the training shelf allocation information, the suggestion unit acquires estimated promotion / demotion information regarding promotion and demotion of shelf allocation in the estimated store based on the learning model and the estimated product information, and makes the suggestion based on the estimated promotion / demotion information. A shelf layout proposal system according to any one of (1) to (9). (11) The learning model has learned, as the training planogram information, training substitute product information regarding a training substitute product that is substituted for the training product in the planogram at the training store, the suggestion unit acquires estimated substitute product information to be substituted for the estimated product in the shelf allocation in the estimated store based on the learning model and the estimated product information, and makes the suggestion based on the estimated substitute product information. A shelf layout proposal system according to any one of (1) to (10). [Explanation of symbols]

[0128] 1 Shelf allocation proposal system, 10 Learning terminal, 11, 21, 31 Control unit, 12, 22, 32 Memory unit, 13, 23, 33 Communication unit, 14, 34 Operation unit, 15, 35 Display unit, 20 Server, 30 Staff terminal, 100, 200, 300 Data memory unit, 101, 201 Learning model memory unit, 102 Learning unit, 202 Estimated product information acquisition unit, 203 Estimated shelf information acquisition unit, 204 Estimated purchase information acquisition unit, 205 Proposal unit, 206 Other estimated purchase information acquisition unit, 207 Other estimated shelf information acquisition unit, 208 Expected customer information acquisition unit, 301 Operation reception unit, 302 Display control unit, DB1 Training database, DB2 Estimation database, N network, M Learning model, SC Shelf allocation proposal screen.

Claims

1. a learning model storage unit that stores a learned learning model in which a relationship between training product information on training products for training and training shelf allocation information on shelf allocation of the training products in a training store for training is learned; an estimated product information acquisition unit that acquires estimated product information regarding estimated products handled in an estimated store whose shelf allocation is estimated; a proposal unit that makes a proposal regarding the shelf allocation of the estimated product in the estimated store based on the learning model and the estimated product information; A shelf layout proposal system including:

2. The learning model also learns training shelf information regarding training shelves arranged in the training store, The shelf allocation proposal system further includes an estimated shelf information acquisition unit that acquires estimated shelf information regarding estimated shelves arranged in the estimated store, the suggestion unit makes the suggestion further based on the estimated shelf information. The shelf layout proposal system according to claim 1 .

3. The learning model also learns training purchase information regarding purchases at the training store, The shelf allocation proposal system further includes an estimated purchase information acquisition unit that acquires estimated purchase information regarding purchases in the estimated store, the suggestion unit makes the suggestion further based on the estimated purchase information. The shelf layout proposal system according to claim 1 or 2.

4. The training purchase information is information regarding purchases made by training customers who visited the training store, the estimated purchase information is information regarding purchases made by an estimated customer who visited the estimated store, The learning model also learns other training purchase information regarding purchases by other training customers who are related to the training customer and who have not visited the training store, The shelf allocation proposal system further includes a different estimated purchase information acquisition unit that acquires different estimated purchase information regarding purchases by different estimated customers who are related to the different estimated customer and who have not visited the different estimated store, The suggestion unit makes the suggestion further based on the other estimated purchase information. The shelf layout proposal system according to claim 3 .

5. The learning model has learned the other training purchase information of the other training customers who are in the same trade area as the training customer, the other estimated purchase information acquisition unit acquires the other estimated purchase information of the other estimated customers who are in the same trade area as the estimated customer, the proposal unit makes the proposal based on the other estimated purchase information of the other estimated customers who are in the same trade area as the estimated customer. The shelf layout proposal system according to claim 4.

6. The training purchase information is information about a training customer who purchased the training product on the training shelf located in the training store, The estimated purchase information is information about an estimated customer who purchased the estimated product on the estimated shelf arranged in the estimated store. The shelf layout proposal system according to claim 3 .

7. The learning model learns a relationship between the training product information and the training shelf allocation information for each possible location where the training product can be arranged on the training shelf arranged in the training store, The shelf allocation proposal system further includes an arrangement possibility information acquisition unit that acquires arrangement possibility location information regarding arrangement possibility locations where the estimated product can be arranged on the estimated shelf arranged in the estimated store, the proposing unit makes the proposal further based on the possible placement location information. The shelf layout proposal system according to claim 1 or 2.

8. The learning model also learns other training shelf information regarding other training shelves around the training shelf arranged in the training store, The shelf allocation proposal system further includes an other estimated shelf information acquisition unit that acquires other estimated shelf information related to other estimated shelves around the estimated shelf arranged in the estimated store, The suggestion unit makes the suggestion further based on the other estimated shelf information. The shelf layout proposal system according to claim 1 or 2.

9. The learning model also learns training customer information regarding training customers who visit the training store, The shelf allocation proposal system further includes an expected customer information acquisition unit that acquires expected customer information regarding expected customers that the estimated store is expected to attract, The proposal unit makes the proposal further based on the expected customer information. The shelf layout proposal system according to claim 1 or 2.

10. The learning model has learned training promotion / demotion information regarding promotion and demotion related to shelf allocation in the training store as the training shelf allocation information, the suggestion unit acquires estimated promotion / demotion information regarding promotion and demotion of shelf allocation in the estimated store based on the learning model and the estimated product information, and makes the suggestion based on the estimated promotion / demotion information. The shelf layout proposal system according to claim 1 or 2.

11. The learning model has learned, as the training planogram information, training substitute product information regarding a training substitute product that is substituted for the training product in the planogram at the training store, the suggestion unit acquires estimated substitute product information to be substituted for the estimated product in the shelf allocation in the estimated store based on the learning model and the estimated product information, and makes the suggestion based on the estimated substitute product information. The shelf layout proposal system according to claim 1 or 2.

12. an estimated product information acquisition step of acquiring estimated product information regarding estimated products handled in an estimated store whose shelf allocation is estimated; a proposal step of making a proposal regarding the shelf allocation of the estimated product in the estimated store based on a trained learning model that has learned the relationship between training product information regarding training products for training and training shelf allocation information regarding the shelf allocation of the training products in the training store for training, and the estimated product information; A shelf layout proposal method including:

13. an estimated product information acquisition unit that acquires estimated product information regarding estimated products handled in an estimated store whose shelf allocation is estimated; a proposal unit that makes a proposal regarding the shelf allocation of the estimated product in the estimated store based on a trained learning model that has learned the relationship between training product information regarding training products for training and training shelf allocation information regarding the shelf allocation of the training products in the training store for training, and the estimated product information; A program that allows a computer to function as a

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