Product presentation support system, and product presentation support method

Abstract

To provide a product presentation support system capable of supporting product presentation according to values.SOLUTION: The product presentation support system includes: an ideal behavior search unit that searches for ideal behavior from values using the relationship between values and ideal behavior; an action requirement associating unit that associates the elemental behaviors that constitute the searched ideal behavior with functional requirements using the relationship between element behavior and functional requirements; a state requirement associating unit that associates the element behaviors constituting the searched ideal behavior and functional requirements resulting from the state at the time of the element behavior using the relationship between the state during the element behavior and the functional requirements resulting from that state; a functional group generation unit that groups functional requirements that can be associated with a behavior element using the association of the element behavior with functional requirements and the distance between functional requirements; a product function connection unit that associates functional requirements grouped by the functional group generation unit and product functions; and a presentation policy generation unit that generates product information related to the entered values using the functionality information in the results of the associated product features.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a product presentation support system that supports the planning of a promotion method for handling products to customers in marketing and the like. [Background technology]

[0002] An increasing number of companies are formulating management policies with an eye toward improving customer experience and differentiating themselves from other companies. This trend is not limited to the tourism and service industries; even in "product selling" businesses such as retail, an increasing number of companies are launching marketing strategies that focus on the customer's experiences and emotions in various situations, from the experience leading up to the product purchase to the experience when using the purchased product.

[0003] One of the underlying factors is the spread of purchasing styles using e-commerce (EC). An example in the retail industry is online supermarkets. Online supermarkets are a service in which customers select products online, make payments, and have the products delivered. Online supermarkets have become widespread in society due to the convenience they provide, allowing customers to shop without visiting a store. Furthermore, from the perspective of businesses, the barriers to entry are low, as no capital investment is required for stores, etc., and market competition in the online supermarket industry is becoming increasingly fierce.

[0004] The following prior art exists as background technology in this technical field: Patent Document 1 (JP 2020-74228 A) describes a shelf layout information creation device that includes an image feature calculation unit that calculates image features including color information of a plurality of products from images of the plurality of products, and a shelf layout calculation unit that calculates the size of a predetermined space to be allocated to each of the plurality of products based on information about the sizes of the plurality of products and information about the size of the predetermined space in which the plurality of products are arranged, and calculates the shelf layout of the plurality of products so that the distribution of the image features of the plurality of products arranged in the calculated predetermined space appears in a predetermined manner.

[0005] Furthermore, Patent Document 2 (JP 2016-164754 A) describes an automatic shelf allocation pattern creation method executed by an automatic shelf allocation pattern creation device that creates shelf allocation patterns for a group of stores consisting of multiple stores with different sales floor sizes for the product, the automatic shelf allocation pattern creation method including a standard shelf allocation pattern management step that accepts registration and update of standard shelf allocation patterns that include a maximum shelf allocation pattern for the store in the group of stores with the largest sales floor size for the product and a minimum shelf allocation pattern for the store in the group of stores with the smallest sales floor size for the product, and a new shelf allocation pattern creation step that creates, based on the standard shelf allocation pattern, a new shelf allocation pattern for stores in the group of stores excluding the store with the largest and smallest sales floor sizes for the product. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2020-74228 [Patent Document 2] Japanese Patent Application Laid-Open No. 2016-164754 Summary of the Invention [Problem to be solved by the invention]

[0007] For retailers, improving customer loyalty is crucial to ensure that customers choose them over many competitors. Therefore, it is important for them to be recognized as a partner that helps customers realize the lifestyle they aspire to, that is, to make customers realize that "by using this store, I can realize the lifestyle I want to achieve and obtain the products necessary to realize that lifestyle," thereby making them realize the improved experience value in their lives and building lasting relationships with customers.

[0008] An important element in considering experiential value is the design of customer contact points (touchpoints). Experiential value depends on a design that captures the various situations in which customers come into contact, and the most important touchpoint in the retail industry is the sales floor. In other words, designing how to effectively display products and other merchandise is important when considering experiential value.

[0009] For the reasons stated above, in businesses that provide customers with merchandise such as goods, including retail businesses, it is important to design sales floors that effectively display merchandise in a way that appeals to customers, while taking into consideration the experiential value of the product in their daily lives.

[0010] Various innovations have been made in sales floor design. Patent Document 1 shows that by evaluating differences in the color of products and arranging them so that a change in color occurs when viewed at a glance, it is possible to generate a shelf plan that enhances cognitive effects. This is expected to contribute to a smooth purchasing experience by increasing customer visibility in the store and making desired items immediately catch the customer's eye, but it does not take into account the experiential value in lifestyle.

[0011] Furthermore, Patent Document 2 describes a method for expanding manually created shelf layout patterns to fit the shelf size of a store. In this case, it is conceivable that a veteran shelf layout designer could consider the customer experience value based on their experience, but since the customer demographics differ depending on the store location, the lifestyles they are trying to achieve and the methods for promoting products also differ, making it difficult to provide a uniform response. Furthermore, the products handled by each store differ, and it is necessary to determine for each store how to promote the products they handle to customers, which also makes it difficult to provide a uniform response in this regard.

[0012] For this reason, it is necessary to design sales areas that take into account the experiential value based on the characteristics of the customer and the store, but this has traditionally been difficult to achieve.

[0013] The purpose of the present invention is to support the selection of expressions and display methods that appeal to customers, based on the values ​​(lifestyles they are trying to achieve) of the store's customer base and the products sold in the store. [Means for solving the problem]

[0014] A representative example of the invention disclosed in the present application is as follows: That is, a product presentation support system that generates product information from customer values ​​is configured by a computer having an arithmetic unit that executes predetermined processing and a storage device connected to the arithmetic unit, wherein the arithmetic unit has an ideal behavior search unit that searches for an ideal behavior from input values ​​using the relationship between the value and an ideal behavior that is a means for realizing the value, an action requirement linking unit that associates elemental behaviors that constitute the ideal behavior with functional requirements that are a means for realizing the value, and a behavior requirement linking unit that associates the elemental behaviors that constitute the searched ideal behavior with functional requirements using the relationship between the state at the time of the elemental behavior and the functional requirements that result from the state. the computing device comprises a state requirement combination unit that associates the element actions that make up the ideal action with functional requirements resulting from the state at the time of the element actions; a functional group generation unit that groups the functional requirements associated with the element actions using the association between the element actions and functional requirements by the state requirement combination unit and the distance between the functional requirements; a product function combination unit that associates the functional requirements grouped by the functional group generation unit with product functions; and a performance policy generation unit that generates product information related to the inputted values ​​using function information resulting from the association by the product function combination unit. [Effects of the Invention]

[0015] According to one aspect of the present invention, it is possible to generate product information for display according to the values ​​of customer segments and the product lineup of a store. Objects, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiment of the present invention. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a block diagram showing a logical configuration of a product presentation support system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a data configuration of a customer value graph according to the first embodiment. [Figure 3]FIG. 10 is a diagram illustrating an example of a data configuration of an ideal behavior search result according to the first embodiment. [Figure 4] FIG. 10 is a diagram illustrating an example of the data configuration of a behavior requirement graph according to the first embodiment. [Figure 5] FIG. 10 is a diagram illustrating an example of the data configuration of a behavioral requirement combination result according to the first embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of the data configuration of a state requirement graph according to the first embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of a data configuration of a state requirement combination result according to the first embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of a data configuration of a product function graph according to the first embodiment. [Figure 9] FIG. 10 is a diagram illustrating an example of the data configuration of the inter-function distance evaluation result according to the first embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a data configuration of a result of generating functional groups according to the first embodiment. [Figure 11] FIG. 10 is a diagram showing an example of a data configuration of a product function combination result in the first embodiment. [Figure 12] FIG. 10 is a diagram illustrating an example of the data configuration of a final output in the first embodiment. [Figure 13] 1 is a flowchart of the overall process executed by the product presentation support system of the first embodiment. [Figure 14] FIG. 10 is a diagram showing an example of an input screen displayed by an ideal behavior search unit according to the first embodiment. [Figure 15] 10 is a flowchart of a process executed by a behavioral requirement combination unit according to the first embodiment. [Figure 16] 10 is a flowchart of a process executed by a state requirement combination unit according to the first embodiment. [Figure 17] 10 is a flowchart of a process executed by a functional group generation unit according to the first embodiment. [Figure 18] 10 is a flowchart of a process executed by a product function combining unit according to the first embodiment. [Figure 19] 10 is a flowchart of a process executed by a rendering policy generation unit of the first embodiment. [Figure 20] FIG. 10 is a diagram showing an example of an output result screen according to the first embodiment. [Figure 21]FIG. 10 is a diagram illustrating an example of the data configuration of a product function graph according to the second embodiment. [Figure 22] 10 is a flowchart of a process executed by a product function combining unit according to the second embodiment. [Figure 23] FIG. 10 is a diagram showing an example of the data configuration of a product function combination result in the second embodiment. [Figure 24] 10 is a flowchart of a process executed by a direction policy generating unit according to a second embodiment. [Figure 25] FIG. 10 is a diagram showing an example of an output result screen according to the second embodiment. [Figure 26] 1 is a block diagram showing the physical configuration of a computer that constitutes the product presentation support system of the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0018] Generally, sales floor design consists of a step of grouping products according to set criteria, a step of determining the zoning that determines the rough layout of each group (in the case of a store, which section or shelf the products will be displayed on), and a step of facing that determines the layout at a detailed level based on factors such as how the products appear in the customer's field of vision. In the product presentation support system 100 of the embodiment of the present invention, the product grouping that is the basis of sales floor design is determined based on the values ​​and lifestyles of customer segments.

[0019] Example 1 The first embodiment is an embodiment of the product presentation support system 100 that groups products that appeal to a customer segment from among a group of products based on the values ​​of the customer segment. In particular, the first embodiment illustrates a case where a sales floor design policy is formulated for an exhibition space that attracts customers who visit a store, such as a promotion space or a prominent space.

[0020] FIG. 1 is a block diagram showing the logical configuration of a product presentation support system 100 according to the first embodiment.

[0021] The product presentation support system 100 of Example 1 has an ideal behavior search unit 101, a behavior requirement combination unit 102, a state requirement combination unit 103, a functional group generation unit 104, a product function distance calculation unit 105, a product function combination unit 106, and a presentation policy generation unit 107. In addition, the information that is generated, updated, or used during these processing steps includes a customer value graph 109 (see Figure 2), request data 108 (see Figure 14), ideal behavior search results 110 (see Figure 3), behavioral requirement graph 111 (see Figure 4), behavioral requirement combination results 112 (see Figure 5), state requirement graph 113 (see Figure 6), state requirement combination results 114 (see Figure 7), product function graph 115 (see Figure 8), inter-function distance evaluation results 116 (see Figure 9), function group generation results 117 (see Figure 10), product function combination results 118 (see Figure 11), and final output 119 (see Figure 12).

[0022] The ideal behavior search unit 101 generates an ideal behavior search result 110 based on the request data 108 and with reference to a customer value graph 109. The process executed by the ideal behavior search unit 101 will be described later with reference to FIG.

[0023] The behavioral requirement combining unit 102 generates a behavioral requirement combining result 112 based on the ideal behavior search result 110 and the behavioral requirement graph 111. The processing executed by the behavioral requirement combining unit 102 will be described later with reference to FIG.

[0024] The state requirement combining unit 103 generates a state requirement combining result 114 based on the state requirement graph 113 and the action requirement combining result 112. The process executed by the state requirement combining unit 103 will be described later with reference to FIG.

[0025] The functional group generation unit 104 generates a functional group generation result 117 based on the state requirement combination result 114 and the inter-function distance evaluation result 116. The processing executed by the functional group generation unit 104 will be described later with reference to FIG.

[0026] The product inter-function distance calculation unit 105 generates an inter-function distance evaluation result 116 based on the product function graph 115.

[0027] The product function combination unit 106 generates a product function graph 115 and a product function combination result 118 based on the function group generation result 117. The processing executed by the product function combination unit 106 will be described later with reference to FIG.

[0028] The rendering policy generating unit 107 generates a final output 119 based on the product function combination result 118. The processing executed by the rendering policy generating unit 107 will be described later with reference to FIG.

[0029] The product presentation support system 100 can be classified into two types of processing: processing performed when a request for generating sales floor design support information is received, and preparatory processing. The preparatory processing is processing performed by the product inter-function distance calculation unit 105, which generates an inter-function distance evaluation result 116 based on the product function graph 115. The preparatory processing can be omitted by providing the inter-function distance evaluation result 116 to the product presentation support system 100 in advance. In other words, the product inter-function distance calculation unit 105 and its input product function graph 115 are optional components of the product presentation support system 100. In this embodiment, the product inter-function distance calculation unit 105 performs the preparatory processing during a series of processing steps. However, once the product function graph 115 is prepared, the product inter-function distance calculation unit 105 may generate an inter-function distance evaluation result 116 based on the product function graph 115, and thereafter refer to the inter-function distance evaluation result 116.

[0030] FIG. 26 is a block diagram showing the physical configuration of the computer that constitutes the product presentation support system 100 of the first embodiment.

[0031] The product presentation support system 100 of this embodiment is configured by a computer having a processor (CPU) 1, a memory 2, an auxiliary storage device 3, and a communication interface 4. The product presentation support system 100 may also have an input interface 5 and an output interface 6.

[0032] The processor 1 is a computing device that executes programs stored in the memory 2. The processor 1 executes various programs to realize the various functional units of the product presentation support system 100 (e.g., an ideal behavior search unit 101, a behavior requirement combination unit 102, a state requirement combination unit 103, a functional group generation unit 104, a product function distance calculation unit 105, a product function combination unit 106, a presentation policy generation unit 107, etc.). Note that some of the processing performed by the processor 1 by executing the programs may be executed by another computing device (e.g., hardware such as a GPU, ASIC, or FPGA).

[0033] The memory 2 includes a ROM, which is a non-volatile storage element, and a RAM, which is a volatile storage element. The ROM stores unchanging programs (e.g., BIOS), etc. The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the processor 1 and data used when the programs are executed.

[0034] The auxiliary storage device 3 is a large-capacity, non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD). The auxiliary storage device 3 also stores data used by the processor 1 when executing a program and the program executed by the processor 1. That is, the program is read from the auxiliary storage device 3, loaded into the memory 2, and executed by the processor 1 to realize each function of the product presentation support system 100.

[0035] The communication interface 4 is a network interface device that controls communication with other devices in accordance with a predetermined protocol.

[0036] The input interface 5 is an interface to which input devices such as a keyboard 7 and a mouse 8 are connected and which receives input from an operator. The output interface 6 is an interface to which output devices such as a display device 9 and a printer (not shown) are connected and which outputs the results of program execution in a format that can be viewed by an operator.

[0037] The program executed by the processor 1 is provided to the product presentation support system 100 via removable media (CD-ROM, flash memory, etc.) or a network, and is stored in a non-volatile auxiliary storage device 3, which is a non-transitory storage medium. For this reason, the product presentation support system 100 preferably has an interface for reading data from removable media.

[0038] The product presentation support system 100 may be a computer system configured on a single physical computer or on multiple logically or physically configured computers, and may operate on a virtual computer built on multiple physical computer resources. For example, each functional unit may operate on a separate physical or logical computer, or multiple functional units may be combined to operate on a single physical or logical computer. The product presentation support system 100 may be connected via a network to a distribution system that manages distribution and retail data in an integrated manner. In this case, the interface 5 may input information from the distribution system, and the output interface may output information to the distribution system.

[0039] FIG. 2 is a diagram showing an example of the data configuration of the customer value graph 109 according to the first embodiment.

[0040] The customer value graph 109 is data with a tree-like graph structure, including a hierarchy of value nodes 201 shown in the upper part of FIG. 2 and a hierarchy of ideal behavior nodes 202 shown in the lower part. Values ​​are important factors in building a lifestyle, such as being outdoorsy or child-rearing-oriented. Ideal behaviors are typical behaviors that are directly linked to the realization of values ​​and contribute to the realization of that lifestyle. Values ​​and ideal behaviors have a goal-means relationship, and edges are generated between ideal behaviors that contribute to the realization of values. Weighting may also be applied based on the strength of contribution to the realization of values. FIG. 2 shows an example in which a contribution level 203 ranging from 0 to 100 is assigned to edges. The contribution level 203 may be calculated from the strength of the relationship between values ​​and ideal behaviors, obtained, for example, through a customer survey, by acquiring personal attribute information that can identify values ​​and behavioral attribute information that can identify ideal behaviors, such as activities of interest. Note that the contribution level 203 may also be calculated using other methods.

[0041] Additionally, a group of keywords describing the values ​​is stored as property information in association with the node 201 of values. Elemental action information indicating detailed action steps constituting the ideal action, a group of keywords describing the ideal action, the state of the ideal action, and attribute information (e.g., seasonality) used for determining conditions specified by the user when making a request are stored as property information in association with the node 202. The elemental actions included in the elemental action information are described according to the order in which the actions are performed. The state of the ideal action is information to be evaluated as a state requirement, and includes, for example, elemental information about the agent of action whose state changes in the ideal action occur, and about the peripheral objects of the agent of action.

[0042] FIG. 3 is a diagram showing an example of the data configuration of the ideal behavior search result 110 according to the first embodiment.

[0043] The ideal behavior search result 110 is information on ideal behaviors extracted by the ideal behavior search unit 101 by searching the customer value graph 109 based on the request data 108 input by the user. For example, FIG. 3 shows an example of ideal behaviors extracted when customers with values ​​of "outdoorsy" and "child-rearing" are selected as targets in the request data 108. In the ideal behavior search result 110 shown in FIG. 3, points are assigned to each value, representing the evaluation result of its relevance to the ideal behavior. The points for each value may be calculated by multiplying the weighting value input by the user (see FIG. 14) by the contribution 203. The points for the ideal behavior are the evaluation result based on the values ​​input by the user, and may be calculated by adding up the points assigned to each value associated with the ideal behavior. The keywords, states, attributes, and elemental behaviors are property information associated with the ideal behavior.

[0044] FIG. 4 is a diagram showing an example of the data configuration of the behavior requirement graph 111 according to the first embodiment.

[0045] The behavioral requirements graph 111 is data with a tree-like graph structure, and includes a hierarchy of elemental behavior nodes 401 shown in the upper part of FIG. 4 and a hierarchy of functional requirement nodes 402 shown in the lower part. The elemental behaviors are described in the property information of the ideal behavior (node ​​202) in the customer value graph 109, and are preferably defined at the same granularity as the elemental behaviors described as detailed behavioral steps that make up the ideal behavior. In addition, the properties of the elemental behaviors (node ​​401) include information on state changes that occur when the behavior is performed. Functional requirements are functional requirements for realizing the elemental behaviors. The elemental behaviors and functional requirements are in a purpose-means relationship, and edges are generated between functional requirements that contribute to the realization of the elemental behaviors. The edge between the elemental behaviors and functional requirements is assigned a contribution 403 to the realization of the elemental behavior.

[0046] FIG. 5 is a diagram showing an example of the data configuration of the behavioral requirement combination result 112 in the first embodiment.

[0047] The behavioral requirement combination result 112 is obtained by adding detailed information (e.g., state changes, functional requirements) to the elemental behaviors of the ideal behavior search result 110 using the behavioral requirement graph 111. The state changes can be obtained from the properties of the elemental behaviors in the behavioral requirement graph 111, and the functional requirements can be obtained from the functional requirement nodes 402 in the behavioral requirement graph 111. The functional requirements are annotated with a contribution level 403 determined in relation to the elemental behaviors. Figure 5 shows, as an example, the result of combining information from behavioral requirement results for the elemental behaviors that make up playing in the river.

[0048] FIG. 6 is a diagram illustrating an example of the data configuration of the state requirement graph 113 according to the first embodiment.

[0049] The state requirement graph 113 is data with a tree-like graph structure, and includes a hierarchy of state type nodes 601 shown in the upper part of FIG. 6 and a hierarchy of functional requirement nodes 602 shown in the lower part. State types are types of states that can be taken by elements (e.g., physical strength, surroundings, position) related to the agent that causes a change in state, which are stored as property information of the ideal behavior. Functional requirements are functional requirements required for the corresponding state. Edges are generated between functional requirements required from state types. Note that weighting may be assigned to the edges between state types and functional requirements depending on the strength of the relationship between the state type and functional requirement.

[0050] FIG. 7 is a diagram showing an example of the data configuration of the state requirement combination result 114 in the first embodiment.

[0051] The state requirement combination result 114 indicates the result of updating the functional requirements of each elemental action after evaluating the state changes of the elements related to the agent of action when undergoing the elemental actions that make up the ideal action. For example, the state requirement combination unit 103 refers to the state requirement graph 113, obtains the results of the evaluation by simulation from the properties of the customer value graph 109, and describes the functional requirements for each elemental action.

[0052] FIG. 8 is a diagram showing an example of the data configuration of the product function graph 115 according to the first embodiment.

[0053] The product function graph 115 is data with a tree-like graph structure, and includes a hierarchy of product nodes 801 shown in the upper part of FIG. 8 and a hierarchy of function nodes 802 shown in the lower part. Products are basically classified into SKUs (Stock Keeping Units), which are the minimum unit of item number for inventory management. However, the item unit may be coarser depending on the purpose, for example, by grouping together product types that do not differ functionally due to differences in size or color. Functions are effects created by using a product (e.g., the effect of hydration caused by ingestion) or functions that the product possesses (e.g., high portability). Edges are generated between products and functions. Note that weighting may be assigned to the edges between products and functions depending on the strength of the relationship between the product and function.

[0054] FIG. 9 is a diagram showing an example of the data configuration of the inter-function distance evaluation result 116 in the first embodiment.

[0055] The inter-function distance evaluation result 116 defines the distance between pairs of functions according to the similarity of the functions. Since there is no order to the functions that make up the pair, there is no need to have data for pairs in which function 1 and function 2 are swapped. The more co-occurring functions there are, the closer the inter-function distance will be.

[0056] FIG. 10 is a diagram showing an example of the data configuration of the functional group generation result 117 according to the first embodiment.

[0057] The functional group generation result 117 generates an arbitrary number of functional groups by clustering close functions using the inter-function distance for each elemental behavior of the ideal behavior.

[0058] FIG. 11 is a diagram showing an example of the data configuration of the product function combination result 118 in the first embodiment.

[0059] The product function combination result 118 describes product information corresponding to each of the clustered function groups in the function group generation result 117. In the example shown in FIG. 11 , function group 0 described in the first line is {Status: [Portable], Action: [Hydration, Salt Replenishment, ...]}, and is associated with product group 0 {0: [ITM000001, ITM000009, ...]}. Similarly, function group 1 described in the second line is {Status: [Portable], Action: [Water Wipe, ...]}, and is associated with product group 1 {1: [ITM000002, ITM000008, ...]}. Elements within the product group (such as ITM000001) are product codes, and may be defined for each product group in a separately provided product master, or the product group may be defined in the product properties of the product function graph 115.

[0060] FIG. 12 is a diagram showing an example of the data configuration of the final output 119 in the first embodiment.

[0061] The final output 119 is a description of information useful for designing a sales area, such as catchy slogans generated by organizing the information of the product function combination result 118 and using keyword information.

[0062] FIG. 13 is a flowchart of the overall process executed by the product presentation support system 100 according to the first embodiment.

[0063] First, the ideal behavior search unit 101 displays the input screen shown in FIG. 14 to prompt the user to input request data and receives the request data 108 (S1001). The input screen shown in FIG. 14 is configured with a GUI (Graphical User Interface), and a user (e.g., a sales floor designer) uses this input screen to input request data to the product presentation support system 100. For example, multiple pieces of value information can be input, and weights can be entered for each piece of value information. For example, the weights can be determined by the user based on the importance of the target customers or on statistical information of local residents. Furthermore, conditions to be considered in designing the sales floor can be set separately. For example, input fields for setting the season (spring, summer, fall, winter), product categories and manufacturers to be considered for the sales floor design, and the number of display sections can be provided. Information about the number of display sections is useful when there are multiple promotion spaces and different themes are desired for each display space.

[0064] Next, the ideal behavior search unit 101 searches for ideal behavior information (S1002). The ideal behavior search unit 101 evaluates ideal behaviors based on the value information included in the acquired request data 108, with reference to the customer value graph 109. For example, for each value included in the request data 108, the ideal behaviors associated with the value are referenced via edges, and ideal behaviors whose property attribute information matches a condition (e.g., the seasonal attribute is "summer") are extracted. Then, points for the ideal behavior are calculated based on the weight information of the value registered in the request data 108 and the contribution of the ideal behavior associated with the value (e.g., multiplying the contribution by the weight of the value). The points for the ideal behaviors for all values ​​included in the request data 108 are added up to arrive at a final score. Next, the ideal behavior search unit 101 extracts ideal behaviors in descending order of ideal behavior points, the number of which is equal to the number of display sections included in the request data 108. As a result of processing by the ideal behavior search unit 101, an ideal behavior search result 110 (FIG. 3) is output.

[0065] Next, the behavioral requirement combining unit 102 combines the ideal behavior search result 110 and the behavioral requirement graph 111 (S1003). Details will be described with reference to FIG. 15. First, the behavioral requirement combining unit 102 acquires the ideal behavior search result 110 and the behavioral requirement graph 111 (S1011, S1012). Next, the behavioral requirement combining unit 102 selects one ideal behavior from the acquired ideal behavior search result 110 (S1013), and selects component behaviors that constitute the selected ideal behavior (S1014). Next, the behavioral requirement combining unit 102 searches the behavioral requirement graph 111 for functional requirements associated with nodes of the selected component behavior, and extracts the functional requirements (S1015). Steps S1014 to S1015 are repeatedly executed for the component behaviors associated with the ideal behavior (S1016). Furthermore, steps S1013 to S1016 are repeatedly executed for the acquired ideal behaviors (S1017). As a result of the processing by the behavioral requirement combining unit 102, a behavioral requirement combining result 112 (FIG. 5) is output.

[0066] Next, the state requirement combining unit 103 evaluates the state change in the ideal action (S1004). Details will be described with reference to FIG. 16. First, the state requirement combining unit 103 acquires the action requirement combining result 112 and the state requirement graph 113 (S1021, S1022). Next, the state requirement combining unit 103 selects one ideal action from the acquired action requirement combining result 112 (S1023) and selects one component action constituting the selected ideal action (S1024). Next, the state requirement combining unit 103 refers to the state requirement graph 113 based on the possible states of the elements related to the actor at the start of the component action, and acquires the functional requirements associated with that state (S1025). For example, if the location of the elements related to the actor is "away from home," the unit acquires information such as "portable" and "lightweight" from the state requirement graph 113 and registers them as functional requirements related to the state of the component action. Note that initial setting values ​​may be referenced as the states of the elements related to the actor in the first component action. For example, physical strength may be "good," personal belongings may be "dressed at home," and location may be "home." Other than the above, factors related to the agent may include physical strength and fatigue level, the agent's emotions and concentration level, the agent's body surface characteristics, and the agent's frequency of interactions with people around the agent, and changes in these states may be evaluated. Next, the state requirement combination unit 103 updates the state so that the state is changed by achieving the elemental behavior based on the state change in the acquired behavior requirement combination result 112 (S1026). For example, as a result of the elemental behavior of "rest," the state of "physical strength" is updated to "good." Steps S1024 to S1026 are repeatedly executed for the elemental behaviors associated with the ideal behavior (S1027). Steps S1023 to S1027 are repeatedly executed for the acquired ideal behavior (S1028). As a result of the processing by the state requirement combination unit 103, the state requirement combination result 114 (FIG. 7) is output.

[0067] Next, the product function distance calculation unit 105 calculates the inter-function distance (S1005). Based on the product function graph 115, the product function distance calculation unit 105 calculates the frequency with which a combination of two functions co-occurs in the same product as the distance. For example, the Jaccard coefficient may be used for each combination of functions. That is, for each product, it is evaluated whether a certain function appears when another certain function appears, and the inter-function distance is calculated by dividing the number of co-occurring functions by the total number of functions and subtracting the value from 1 according to the following formula:

[0068] Distance between functions A and B = 1-(A∩B) / (A∪B)

[0069] The results of calculating the distances between all functions are written in the function distance evaluation result 116 (FIG. 9).

[0070] As described above, the processing executed by the product function distance calculation unit 105 is a preparatory processing, and therefore may be executed at any timing other than between steps S1004 and S1006.

[0071] Next, the functional group generation unit 104 generates functional groups (S1006). For example, according to the state requirement combination result 114, when the ideal action is "playing in the river" and the element action is "resting," the element actions have useful functional requirements of hydration, salt replenishment, and wiping off with water. However, it is difficult to think of a product that satisfies both the hydration and water wiping functional requirements. In order to find an appropriate product combination, the functional requirements are divided into groups. Furthermore, among the functional requirement types, some functional requirements related to the state are common to all products (for example, the functional requirement "portability," which is necessary for use on the go, is not assigned to any one group but is common to all groups). Therefore, the functional group generation unit 104 performs processing taking this into consideration. Furthermore, the functional group generation unit 104 evaluates the functional requirements of the element actions separately into functional requirements related to the state of the element action and functional requirements related to the execution of the element action, and groups the functional requirements.

[0072] Details will be described with reference to FIG. 17. First, the functional group generation unit 104 acquires the state requirement combination result 114 and the inter-function distance evaluation result 116 (S1031, S1032). Next, the functional group generation unit 104 selects one ideal action from the acquired state requirement combination result 114 (S1033), and selects the component actions that make up the selected ideal action (S1034). Next, the functional group generation unit 104 acquires only the action-related functional requirements from the functional requirements described in association with the component action, excluding those related to states (S1035). Next, the functional group generation unit 104 performs clustering within this functional group to generate functional groups (S1036). For example, if hierarchical clustering is used, the functions are grouped into clusters in descending order of inter-function distance, and clusters are generated until a predetermined termination condition is met. Note that other clustering methods may also be used. Next, the functional group generation unit 104 acquires functional requirements related to the state from the state requirement combination result 114, and adds the functional requirements related to the state to the functional group generated in S1036 (S1037). Steps S1033 to S1037 are repeatedly executed for the number of element actions associated with the ideal action (S1038). Furthermore, steps S1034 to S1038 are repeatedly executed for the number of acquired ideal actions (S1039). A functional group is generated by the processing by the state requirement combination unit 103, and the functional group generation result 117 (FIG. 10) is output.

[0073] Next, the product function combining unit 106 combines the product functions (S1007). Details will be described with reference to FIG. 18. First, the product function combining unit 106 acquires the functional group generation result 117 and the product function graph 115 (S1041). Next, the product function combining unit 106 selects one ideal behavior from the acquired functional group generation result 117 (S1042), selects elemental behaviors constituting the selected ideal behavior (S1043), and selects function groups constituting the selected elemental behavior (S1044). Next, the product function combining unit 106 refers to the product function graph 115 and determines whether or not the product is associated with the functional requirements of the functional group (S1045). For example, a product having all the functional requirements of one functional group (e.g., "portable," "hydration," and "salt supplementation" in the first functional group) is determined as a relevant product, and the rest are determined as non-relevant products. Based on this determination result, the relevant product is associated with the functional group (a collection of products corresponding to each functional group is referred to as a product group). Steps S1044 to S1045 are repeatedly executed for the number of function groups associated with the element behavior (S1046). Steps S1043 to S1046 are repeatedly executed for the number of element behaviors associated with the ideal behavior (S1047). Next, after the product function association unit 106 has completed associating the function groups with the products for each element behavior of the ideal behavior, it selects element behaviors to be used in the display based on the results of the association between the function groups and the products (S1048). For example, when prioritizing store inventory or sales strategy, element behaviors associated with function groups that contain many products that are targeted for promotion in the sales strategy (or products with large inventory volumes in the store) may be selected. When prioritizing interest, element behaviors may be evaluated in advance based on a subjective evaluation, such as how easily they attract interest, against a predetermined criterion (e.g., the number of products associated with a functional group is N or more), and the element behavior that first satisfies the criterion may be selected. Steps S1042 to S1048 are repeatedly executed for the number of ideal behaviors acquired (S1049). Product groups are generated by the processing performed by the product function combination unit 106, and product function combination results 118 (FIG. 11) are output.

[0074] Next, the rendering policy generation unit 107 generates a rendering policy (S1008). Details will be described with reference to FIG. 19. First, the rendering policy generation unit 107 acquires the product function combination result 118 (S1051). Next, the rendering policy generation unit 107 selects one product group from the acquired product function combination result 118 (S1052). Next, the rendering policy generation unit 107 acquires keywords associated with the selected product group from the acquired product function combination result 118 (S1053). For example, it is preferable to acquire keywords that are previously assigned as properties to values, ideal behaviors, elemental behaviors, states, functions, etc. associated with the product group. Alternatively, keywords of functional requirements may be acquired. Next, the rendering policy generation unit 107 generates a prompt for generating media information (text, image) using the acquired keywords (S1054). Next, the production policy generation unit 107 inputs the generated prompt into a machine learning model to generate promotional information (media information such as text, such as a catchphrase, and images, such as a promotional video) for promoting the product, and formats the generated media information into a format for presenting to the user (S1055). For example, when generating text, the following might be used: "Please come up with a catchphrase that appeals to customers with certain values ​​by promoting the function of △△ in a certain action scene. Please use the following keywords: ◇◇." This is passed as a query to the text generation application, and the generated text is received from the machine learning model. The machine learning model used in step S1055 can be, for example, a generation AI, which has trained on pairs of keywords and promotional information. When a prompt containing the keywords is input, the machine learning model generates promotional information (media information, such as text and images). This machine learning model may be provided inside or outside the product production support system 100. The presentation policy generation unit 107 executes the processing from steps S1052 to S1055 for each product group (S1056), and after processing for all product groups is completed, outputs the final output 119 (FIG. 12) (S1057), and the output result screen is displayed.

[0075] FIG. 20 is a diagram illustrating an example of an output result screen in the first embodiment.

[0076] The output result screen presents the information described in the final output 119, i.e., information on the values ​​of the customers targeted by the product group, information on the behavioral scenes to be presented to the customers, information on the functions to be promoted in the behavioral scenes, the generated catchphrases and image images, and a list of applicable products.

[0077] Using the results above, the user of the product presentation support system 100 can design sales areas such as promotional spaces and prominent spaces.

[0078] <Example 2> Like Example 1, Example 2 is an example of the product presentation support system 100 that groups products that appeal to a customer segment from among a group of products based on the values ​​of the customer segment. In particular, an example is given of a case where a sales floor design policy is formulated taking into consideration the zoning of the entire store. While Example 1 focused on the design of promotional spaces and prominent spaces, Example 2 focuses on the placement of products throughout the entire store, and also takes into consideration the customer experience resulting from in-store wandering behavior. In this case, excluding some products featured in promotions, etc., it is easier to find products if products are grouped and placed by category (a category refers to a classification of products generally defined in the retail industry, such as classifying various drink products as "beverages"), and therefore ideal behaviors, element behaviors, and functional groups are associated and processed by category.

[0079] FIG. 21 is a diagram showing an example of the data configuration of the product function graph 115 according to the second embodiment.

[0080] In the second embodiment, product category information is taken into consideration, and therefore the information in the product function graph 115 differs from that in the first embodiment. The product function graph 115 in the second embodiment is data with a tree-like graph structure, and includes a hierarchy of product nodes 2101 shown in the upper part of Fig. 8 and a hierarchy of function nodes 2102 shown in the lower part. Products are basically classified into stock-keeping units (SKUs), which are units of the minimum number of items for inventory management, but the item unit may be coarser depending on the application, for example, by grouping together product types that have no functional difference due to differences in size or color. The second embodiment differs from the first embodiment in that product category information is set as property information of the product node 2101. The function is an effect created by using the product (for example, the effect of hydration caused by ingestion) or a function possessed by the product (for example, high portability). An edge is generated between the product and the function. Note that a weight may be assigned to the edge between the product and the function depending on the strength of the relationship between the product and the function.

[0081] The system configuration of the product presentation support system 100 is the same as that of the first embodiment (FIG. 1), but the processes executed by the product function combining unit 106 and the presentation policy generating unit 107 are different from those of the first embodiment.

[0082] In step S1007, the product function combination unit 106 combines product functions on a product category basis. Details will be described with reference to FIG. 22. First, the product function combination unit 106 acquires the functional group generation result 117 and the product's function graph 115 (S1061). Next, the product function combination unit 106 selects one ideal behavior from the acquired functional group generation result 117 (S1062), selects elemental behaviors constituting the selected ideal behavior (S1063), and selects function groups constituting the selected elemental behavior (S1064). Next, the product function combination unit 106 refers to the product's function graph 115 and determines whether or not the product is associated with the functional requirements of the functional group (S1065). For example, a product having all the functional requirements of one functional group (e.g., "portable," "hydration," and "salt supplementation" in the first functional group) is determined as a relevant product, and the rest are determined as non-relevant products. Based on this determination result, the relevant product is associated with the functional group. Steps S1064 to S1065 are repeatedly executed for the number of functional groups associated with the element behavior (S1066). Steps S1063 to S1066 are repeatedly executed for the number of element behaviors associated with the ideal behavior (S1067). Steps S1062 to S1067 are repeatedly executed for the number of ideal behaviors obtained (S1068). The processing up to this point allows the same product to be associated with multiple functional groups. Next, the product function combination unit 106 assigns product categories (S1069). For each product category, the functional group that contains the most products of that category is identified. This assigns the product category to one of the functional groups. A group of product categories assigned to each functional group is called a product group. Product groups are generated by the processing by the product function combination unit 106, and a product function combination result 118 (FIG. 23) is output.

[0083] FIG. 23 is a diagram showing an example of the data configuration of the product function combination result 118 in the second embodiment.

[0084] In the product function combination result 118 of the second embodiment, product information corresponding to each of the clustered function groups in the function group generation result 117 is described. In the product function combination result 118 of the first embodiment, product groups are associated with function groups, but in the product function combination result 118 of the second embodiment, product categories are associated with function groups. In the example shown in Fig. 23 , the function group 0 described in the first line is {Status: [Portable], Action: [Hydration, Salt Replenishment, ...]}, and is associated with the product category 0 {0: [CAT001, ...]}. Similarly, the function group 1 described in the second line is {Status: [Portable], Action: [Water Wipe, ...]}, and is associated with the product category 1 {1: [CAT003, ...]}.

[0085] In step S1007, the rendering policy generation unit 107 generates a rendering policy. Details will be described with reference to FIG. 24. First, the rendering policy generation unit 107 acquires the product function combination result 118 (S1071). Next, the rendering policy generation unit 107 evaluates the relationship between product categories based on the association between each product category and functional group information, with reference to the acquired product function combination result 118 (S1072). For example, since ideal behaviors, elemental behaviors, and functional groups form a three-level hierarchy of ideal behavior-elemental behavior-functional group, the product categories can be mapped in a tree diagram to express the relationship between each product category. That is, a tree diagram is created with ideal behaviors at the top level, elemental behaviors at the second level, functional groups at the third level, and product categories at the bottom level. Next, the rendering policy generation unit 107 determines the group hierarchy for creating a sales floor design policy (S1073). For example, the hierarchical structure of the tree diagram created in step S1072 is referenced to determine the hierarchy for creating media information in a later step. For example, if product category A corresponds to the functional group for the elemental action "resting" of the ideal action "playing in the river," and product category B corresponds to the functional group for a different elemental action "swimming" within the same ideal action "playing in the river," designing a sales floor around the common ideal action of "playing in the river" would evoke the multifaceted behavioral scenes of playing in the river and enable effective display. Therefore, it would be effective to design a large area with the theme of "playing in the river" and then create subareas within it with the themes of "swimming" and "resting." To achieve this type of sales floor design, it is recommended to derive sales floor design policies at the functional group level and at the ideal action level that encompasses these functional groups. Therefore, in the group hierarchy determination process (S1073), the structure of the tree diagram is evaluated. If there are branches below the ideal action and multiple elemental actions associated with product categories, the ideal action level is determined to be the group hierarchy for which sales floor design policies should be created. Similarly, if a branch occurs below an elemental behavior and there are multiple function groups associated with a product category, the elemental behavior hierarchy is determined to be the group hierarchy for creating a sales floor design policy.Next, the presentation policy generating unit 107 determines that the functional group associated with the relevant product category is a group hierarchy for creating a sales floor design policy.

[0086] Next, the directing policy generation unit 107 selects one group hierarchy from the acquired determined group hierarchy (S1074). Next, the directing policy generation unit 107 acquires a group of keywords associated with the selected group hierarchy from the acquired product function combination result 118 (S1075). For example, keywords previously assigned as properties to values, ideal behaviors, elemental behaviors, etc. associated with the product group may be acquired. Alternatively, keywords of functional requirements may be acquired. Next, the directing policy generation unit 107 generates a prompt for generating media information (text, images) based on this information (S1076). Next, the directing policy generation unit 107 inputs the generated prompt into a machine learning model to generate media information (text, images), and formats the generated media information into a format for presentation to the user (S1077). For example, when generating text, the following might be used: "Please come up with a catchphrase that appeals to customers with values ​​of XX in an XX behavioral scenario. In doing so, please use the following keywords: ◇◇." This is passed as a query to the text generation application, and the generated text is received from the machine learning model. The machine learning model used in step S1055 can be, for example, a generation AI, which has learned from pairs of keywords and catchphrase text, and generates catchphrase text when a prompt including the keyword is entered. The production policy generation unit 107 executes the processes of steps S1074 to S1077 for each group hierarchy (S1078), and after processing for all groups is completed, outputs the final output 119 (S1079), and the output result screen (FIG. 25) is displayed.

[0087] It is also possible to generate prompts starting from the highest hierarchical group (in the order of ideal behavior, element behavior, and functional group), generate media information, and then generate prompts for the lower hierarchical group associated with the higher hierarchical group that also include keywords that newly appeared in the media information of the higher hierarchical group. By processing the groups starting from the highest hierarchical group, it is possible to generate media information on topics that are consistent with the media information of the higher hierarchical group.

[0088] FIG. 25 is a diagram illustrating an example of an output result screen according to the second embodiment.

[0089] The output result screen outputs the information described in the final output 119. The final output 119 contains information on the ideal behavior, elemental behavior, and functional group to which each product category belongs. Based on this information, a relationship diagram between product categories is expressed as a tree diagram with ideal behavior at the top level, elemental behavior at the second level, functional groups at the third level, and product categories at the bottom level. This relationship diagram makes it possible to understand the relative positions of product categories (such as the connections between product categories at higher levels such as ideal behavior and elemental behavior). The final output 119 also stores information on sales floor design policies for each group level determined by the presentation policy generation unit 107. The sales floor design policies include value information for target customers in each functional group, behavioral scenes to be presented to those customers, functions to be appealed to in those behavioral scenes, catchy slogans, and image images. This information is used to display the sales floor design policy information. The screen displays only information on the sales floor design policy of the object (functional group, etc.) selected by the user on the tree diagram that shows the relationships between product categories, as well as information on the sales floor design policy of its higher level (sales floor design policy in the hierarchy of ideal behavior positioned at the higher level). In addition, in conjunction with the above object selection, a list of product categories belonging to the selected object is displayed.

[0090] Using the above results and referring to the tree diagram information, the layout relationship of each product category can be considered, and the sales floor can be designed by setting hierarchical themes for each area, such as the entire area and sub-areas.

[0091] The present invention is not limited to the above-described embodiments, but includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to configurations including all of the described configurations. Furthermore, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Furthermore, the configuration of another embodiment may be added to the configuration of one embodiment. Furthermore, part of the configuration of each embodiment may be added, deleted, or replaced with other configurations.

[0092] Furthermore, the aforementioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by having a processor interpret and execute a program that realizes each function.

[0093] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or in a recording medium such as an IC card, an SD card, or a DVD.

[0094] In addition, the control lines and information lines shown are those that are considered necessary for explanation, and do not necessarily represent all the control lines and information lines that are necessary for implementation. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0095] 1 processor 2. Memory 3 Auxiliary storage 4. Communication Interface 5 Input Interface 6 Output Interface 7 Keyboard 8. Mouse 9 Display Devices 100 Product Presentation Support System 101 Ideal behavior search department 102 Behavior Requirement Connection Section 103 State Requirement Joint 104 Functional Group Generation Unit 105 Product function distance calculation section 106 Product function connection section 107 Production Policy Generation Unit 108 Request Data 109 Customer Values ​​Graph 110 Ideal behavior search results 111 Requirement Graph 112 Action requirement combination result 113 Requirement Graph 114 State Requirement Combination Results 115 Function Graph 116 Functional Distance Evaluation Results 117 Functional group generation results 118 Product function combination results 119 Final Output

Claims

1. A product presentation support system that generates product information based on customer values, The computer is configured by an arithmetic unit that executes predetermined processing and a storage device connected to the arithmetic unit, an ideal behavior search unit that searches for ideal behavior from input values ​​using a relationship between values ​​and ideal behaviors that are means for realizing the values; a behavioral requirement combining unit in the computing device that associates the elemental behaviors constituting the ideal behavior with functional requirements, which are means for realizing the elemental behaviors, using the relationship between the elemental behaviors constituting the ideal behavior and the functional requirements; a state requirement combining unit in the computing device that associates the element actions constituting the searched ideal action with the functional requirements resulting from the state at the time of the element action, using a relationship between the state at the time of the element action and the functional requirements resulting from the state; a functional group generation unit configured to group functional requirements associated with the elemental behaviors using the association between the elemental behaviors and functional requirements by the state requirement combination unit and the distances between the functional requirements; the computing device has a product function linking unit that links the functional requirements grouped by the functional group generating unit with product functions; A product presentation support system characterized in that the calculation device is provided with a presentation policy generation unit that generates product information related to the input values ​​using functional information resulting from the association by the product function combination unit.

2. The product presentation support system according to claim 1, The state requirement combination unit evaluates the location of the subject performing the elemental behavior, the subject's physical strength and level of fatigue, the subject's emotions and level of concentration, the subject's body surface characteristics, the subject's frequency of interactions with people around them, the state and its changes, and based on the results of this evaluation, associates the state of the elemental behavior with the functional requirements resulting from the state.

3. The product presentation support system according to claim 1, The product presentation support system is characterized in that the functional group generation unit divides functional requirements into those related to the state of the element behavior and those related to the execution of the element behavior, generates clusters based on the functional requirements related to the execution of the element behavior, and groups the functional requirements by adding functional requirements related to the state of the element behavior to each cluster.

4. The product presentation support system according to claim 1, The product presentation support system is characterized in that the functional group generation unit groups the functional requirements based on a distance between functions that indicates a degree of co-occurrence of functions associated with the product.

5. The product presentation support system according to claim 1, The direction policy generation unit Prompts are created using keywords that are pre-assigned to registered values, ideal behaviors, elemental behaviors, states, functions, etc. Inputting the created prompt into a machine learning model; A product presentation support system characterized by acquiring promotion information from the machine learning model.

6. The product presentation support system according to claim 5, The product presentation support system is characterized in that the presentation policy generation unit identifies keywords common to the product category using the relationship between hierarchically associated ideal behaviors, elemental behaviors, functions, and product categories, and creates prompts using the identified keywords.

7. A product presentation support method executed by a product presentation support system, the product presentation support system is configured by a computer having an arithmetic unit that executes predetermined processing and a storage device connected to the arithmetic unit, The product presentation support method includes: an ideal behavior search procedure in which the computing device searches for the customer's ideal behavior from the input values ​​using a relationship between the values ​​and ideal behaviors that are means for realizing the values; a behavioral requirement combining step in which the computing device associates the elemental behaviors constituting the ideal behavior with the functional requirements that are means for realizing the elemental behaviors; a state requirement combining step in which the computing device associates the element actions constituting the searched ideal action with the functional requirements resulting from the state at the time of the element action, using the relationship between the state at the time of the element action and the functional requirements resulting from the state; a functional group generation step in which the calculation device groups the functional requirements associated with the element behaviors using the association between the element behaviors and the functional requirements in the state requirement combination step and the distances between the functional requirements; a product function combining step in which the computing device associates the functional requirements grouped in the function group generating step with functional information of the product; A product presentation support method characterized in that the calculation device comprises a presentation policy generation procedure that generates product information related to the input values ​​using functional information in the association results of the product function combination procedure.

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