Shopping list creation system, shopping list creation method, and program

Abstract

A shopping list creation system (100) comprises: a user ingredient tag creation unit (130) that acquires information indicating conditions required by a user with respect to required ingredients, which are ingredients required by a user, and creates user ingredient tags on the basis of the information; a recommended product tag acquisition unit (140) that acquires recommended product tags that are linked to sold products, which are products sold by a retail business operator, and that indicate the reason why the retail business operator recommends the sold products; and a shopping list creation unit (150) that collates the user ingredient tags and the recommended product tags to create a shopping list including sold products that correspond to the required ingredients and that have a recommended product tag indicating a reason matching the conditions indicated by the user ingredient tags linked thereto.

Description

Shopping list generation system, shopping list generation method and program

[0001] The present disclosure relates to shopping list generation systems and the like.

[0002] Patent Literature 1 discloses a technology that proposes to users surplus inventory items that retailers want to dispose of or items that are approaching their expiration dates, along with recipes that use those items and that are likely to be preferred by consumers (users) based on their past purchase history. This technology allows users to purchase their preferred ingredients at the lowest price from multiple retailers.

[0003] Japanese Patent Application Laid-Open No. 2002-32645

[0004] However, as user needs for products are becoming more diverse and a wide variety of products are being sold to meet these diverse user needs, the technology of Patent Document 1, which simply starts with a product that a retailer wants to dispose of and suggests that product and a recipe that uses that product, makes it difficult to appropriately suggest products or recipes that meet the user's needs at each stage.

[0005] For example, when it comes to products needed for a recipe that a user is already planning to cook, or ingredients that the user has decided in advance to purchase (such as generic names like "onions" or "carrots"), it is difficult to select and suggest products that the user actually wants to purchase from the wide variety of products available (such as "5 Awaji Island onions for 300 yen," "1 bag of Hokkaido onions for 298 yen," "3 Hokkaido carrots for 198 yen," "1 pesticide-free carrot for 100 yen," etc.).

[0006] Therefore, the present disclosure provides a shopping list generation system that can generate a shopping list that is desirable for both users and retailers.

[0007] The shopping list generation system according to the present disclosure includes a user ingredient tag generation unit that generates user ingredient tags that indicate conditions desired by a user for necessary ingredients, which are ingredients needed by a user; a recommended product tag acquisition unit that acquires recommended product tags that are linked to sales products, which are products sold by a retailer, and indicate reasons why the retailer recommends the sales products; and a shopping list generation unit that generates a shopping list including the sales products that correspond to the necessary ingredients and are linked to the recommended product tags that indicate reasons why the sales products meet the conditions indicated by the user ingredient tags by comparing the user ingredient tags with the recommended product tags.

[0008] The shopping list generation method according to the present disclosure includes the steps of: generating user ingredient tags indicating the conditions desired by a user for necessary ingredients, which are ingredients needed by a user; obtaining recommended product tags linked to sales products, which are products sold by a retailer, indicating reasons why the retailer recommends the sales products; and generating a shopping list including the sales products corresponding to the necessary ingredients and linked to the recommended product tags indicating reasons why the sales products meet the conditions indicated by the user ingredient tags by matching the user ingredient tags with the recommended product tags.

[0009] A program according to the present disclosure is a program for causing a computer to execute the above-described shopping list generation method.

[0010] These comprehensive or specific aspects may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.

[0011] According to the shopping list generation system and the like of the present disclosure, it is possible to generate a shopping list that is desirable for both the user and the retailer.

[0012] 1 is a diagram showing an application example of the shopping list generating system according to embodiment 1. FIG. 2 is a block diagram showing an example of the shopping list generating system according to embodiment 1. FIG. 3 is a flowchart showing an example of the operation of the shopping list generating system according to embodiment 1. FIG. 4 is a diagram showing an example of a user ingredient tag according to embodiment 1. FIG. 5 is a diagram showing an example of a table in which user ingredient tags and recommended product tags are linked according to embodiment 1. FIG. 6 is a diagram showing an example of the degree of match between a sales product and a condition indicated by a user ingredient tag according to embodiment 1. FIG. 7 is a diagram showing an example of a shopping list according to embodiment 1. FIG. 8 is a diagram showing an example of a user ingredient tag according to a modified example of embodiment 1. FIG. 9 is a diagram showing an example of a table in which user ingredient tags and recommended product tags are linked according to a modified example of embodiment 1. FIG. 10 is a diagram showing an example of the degree of match between a sales product and a condition indicated by a user ingredient tag according to a modified example of embodiment 1. FIG. 11 is a diagram showing an example of a shopping list according to a modified example of embodiment 1. FIG. 12 is a flowchart showing an example of the operation of the shopping list generating system according to embodiment 2. FIG. 13 is a diagram showing an example of substitute ingredients linked to a sales product in embodiment 2. FIG. 14 is a diagram showing an example of a shopping list according to embodiment 2. FIG. 15 is a flowchart showing an example of the operation of the shopping list generating system according to embodiment 3. FIG. 16 is a diagram showing an example of accompanying ingredients linked to a sales product in embodiment 3. FIG. 17 is a diagram showing an example of a shopping list according to embodiment 3. FIG. 18 is a flowchart showing an example of the operation of the shopping list generating system according to embodiment 4. Fig. 10 is a diagram for explaining a method for suggesting products that combine a plurality of seasonings in embodiment 4. Fig. 11 is a flowchart showing an example of a shopping list generation method according to another embodiment.

[0013] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, more detailed description than necessary may be omitted. For example, detailed description of well-known matters or redundant description of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art.

[0014] The inventors have provided the accompanying drawings and the following description to enable those skilled in the art to fully understand the present disclosure, and do not intend for them to limit the subject matter described in the claims.

[0015] (First Embodiment) Hereinafter, a shopping list generation system according to a first embodiment will be described with reference to Figs. 1 to 4D.

[0016] FIG. 1 is a diagram showing an application example of a shopping list generation system 100 according to the first embodiment.

[0017] The shopping list generation system 100 is a system that automatically generates a shopping list that includes, for example, necessary ingredients that a user needs. For example, as shown in FIG. 1 , the shopping list generation system 100 can communicate with a user's communication terminal 200. As shown in FIG. 2 (described later), the user can view the generated shopping list via a UI (user interface) unit 210 included in the communication terminal 200. Also, as shown in FIG. 1 , the shopping list generation system 100 can communicate with an EC (Electronic Commerce) site 300. The user can purchase items included in the generated shopping list via the communication terminal 200 and the shopping list generation system 100. The EC site 300 is specifically a site operated by a retailer and stores information from a product database or input information from the retailer's administrator. The EC site 300 may also store information about products sold by the retailer and information about recommended product tags that indicate the reasons why the retailer recommends the products. The EC site 300 and the shopping list creation system 100 are separate applications or web pages using browsers, and are capable of two-way communication.

[0018] FIG. 2 is a block diagram showing an example of a shopping list generation system 100 according to the first embodiment.

[0019] The shopping list generation system 100 includes a shopping ingredient list proposal management unit 120, a user ingredient tag generation unit 130, a recommended product tag acquisition unit 140, a shopping list generation unit 150, an EC linkage unit 160, and a memory unit 110. The shopping ingredient list proposal management unit 120, the user ingredient tag generation unit 130, the recommended product tag acquisition unit 140, the shopping list generation unit 150, and the EC linkage unit 160 may be configured as dedicated hardware or may be implemented by executing software programs appropriate for each component. The shopping ingredient list proposal management unit 120, the user ingredient tag generation unit 130, the recommended product tag acquisition unit 140, the shopping list generation unit 150, and the EC linkage unit 160 may be implemented by a program execution unit such as a CPU (Central Processing Unit) or processor reading and executing software programs stored in storage such as a hard disk or semiconductor memory.

[0020] For example, each component of the shopping list generation system 100 is realized by a software program stored in a storage or memory on a server and executed on the server's processor. For example, the UI unit 210 is realized by a software program stored in a storage or memory (not shown) on a communication terminal 200, such as a smartphone or tablet, and executed on the processor. The components of the shopping list generation system 100 may be located on a single server or may be distributed across multiple servers. Furthermore, a portion of the configuration of the shopping list generation system 100 may be realized on the communication terminal 200, or a program for realizing the UI unit 210 may be stored in a storage on the server and downloaded to and executed on the communication terminal 200 via a communication interface.

[0021] For example, the UI unit 210 is configured from input devices such as a touch panel, a mouse, or a microphone, and output devices such as a liquid crystal display and a speaker.

[0022] The memory unit 110 stores, for example, an ingredient DB, a product DB, an inventory list, a recipe DB, an ingredient usage history DB, a product purchase history DB, a planned dish DB, and a cooking history DB. The memory unit 110 may be, for example, a main memory or a storage. Specifically, the main memory is a volatile storage area used to temporarily store data generated during processing by the shopping list generation system 100, used as a work area when the processor executes a program, and used to temporarily store data received by the EC linkage unit 160 and data accepted by the UI unit 210. The storage is a non-volatile storage device that holds various data such as programs. For example, the storage stores various pieces of information acquired or generated during a series of processes performed by the shopping list generation system 100, separated into databases.

[0023] The food ingredient DB is a database that contains information about food ingredients that are expressed as ingredients in recipes, etc. For example, the food ingredient DB contains information such as the ingredient ID, food ingredient name, classification information (hierarchically structured information such as meat → pork, vegetables → root vegetables, vegetables → herbs, processed foods → processed meat foods → canned foods), seasonal period, appropriate storage location and storage period (e.g., one week in the vegetable compartment, one month at room temperature), allergy information, and nutritional value.

[0024] The product DB is a database that contains information about products sold by retail businesses. For example, the product DB contains information such as product ID, product name, product description, content volume, place of origin, producer ID, producer name, arrival date, storage period, planned disposal date, number of units in stock, regular selling price, current selling price, pre-discount price, maximum discount rate, allergy information, nutritional value, etc. Note that the allergy information includes information on whether a product is an allergen for fresh foods, and includes information such as the names and IDs of ingredients that are allergenic for processed foods.

[0025] An inventory list is a list of ingredients that a user owns in a refrigerator, pantry, or the like. For example, the inventory list includes an inventory ingredient ID, an ingredient ID defined in an ingredient DB, remaining quantity, storage location, storage start date, product ID, product name, and the like. Note that ingredients in the inventory list are registered and updated using existing methods. For example, ingredients can be registered and updated in the inventory list using shopping history at a retail store or an EC site, or using a sensor or imaging device. Note that the registration and updating of ingredients in the inventory list is reflected by the results of an ingredient usage history DB and a product purchase history DB, and the results of these DBs may be reflected immediately or at regular intervals.

[0026] The recipe DB is a database that manages cooking recipe information. For example, the recipe DB contains information necessary for cooking and shopping for ingredients, such as a recipe ID, recipe name, recipe image, serving size, ingredient list (ingredient ID, ingredient name, and amount), and process information.

[0027] The ingredient use history DB is a database that manages ingredients that a user has used in the past. For example, the ingredient use history DB contains a usage history ID, ingredient ID, management start date, consumption completion date, consumption type (use up or discard), consumed amount or discarded amount, dish information (recipe ID, cooking history ID, dish name, etc.), and purchased product information (a list of purchase history IDs or a list of product IDs).

[0028] The product purchase history DB is a database that manages products that a user has previously purchased. For example, the product purchase history DB includes a purchase history ID, a product ID, a purchase date, and the number of purchases.

[0029] The planned dish DB is a database that manages recipes that the user plans to cook. Examples of recipes that the user plans to cook include recipes that the user selects when creating a dish, and recipes that are presented as menu suggestions. For example, the planned dish DB contains a planned dish ID, a recipe ID or dish name, a planned cooking date, and a planned portion size (information indicating the number of servings).

[0030] The cooking history DB is a database that manages dishes that the user has cooked in the past. For example, the cooking history DB includes a cooking history ID, a recipe ID, a cooking date, a portion size (information indicating how many servings were cooked), and information on ingredients used.

[0031] The shopping ingredient list proposal management unit 120 proposes and manages a shopping ingredient list, which is a list of ingredients that the user plans to purchase or that are determined to be necessary for a recipe that the user plans to cook. In other words, the shopping ingredient list proposal management unit 120 proposes and manages ingredients that the user needs to purchase. For example, the shopping ingredient list proposal management unit 120 estimates ingredients needed for a dish to be cooked from the planned-cooking dish DB and estimates which of these ingredients need to be purchased. For example, the shopping ingredient list proposal management unit 120 inputs an inventory list or ingredient usage history and estimates and suggests ingredients that are likely to run out soon (i.e., quantities below a predetermined value set in advance or by the user). For example, the shopping ingredient list proposal management unit 120 can estimate ingredients frequently purchased by the user from the ingredient usage history DB and further extract and suggest ingredients that are not on the inventory list or are likely to run out soon. Ingredients that the user needs to purchase, and ingredients frequently purchased by the user that are likely to run out or are out of stock, are examples of ingredients needed by the user (necessary ingredients). The shopping ingredient list suggestion management unit 120 compiles the suggested ingredients or ingredients directly input by the user via the UI unit 210 of the communication terminal 200 and manages them as a shopping ingredient list.

[0032] The user ingredient tag generation unit 130 generates user ingredient tags that indicate the user's desired conditions for the necessary ingredients that the user needs. For example, the user ingredient tag generation unit 130 generates user ingredient tags based on information input by the user, history information about ingredients used by the user in the past, information about the user's inventory of ingredients, information about the dishes the user plans to cook, information about the date and time the user will cook, or information about the user's attributes. Examples of information input by the user include information about the medical questionnaire answered by the user. Examples of information about the user's attributes include the user's family structure, gender, or age group.

[0033] For example, the user ingredient tag generation unit 130 generates tags representing the user's desired conditions for each ingredient on the shopping ingredient list. For example, the user ingredient tag generation unit 130 assigns user ingredient tags representing the user's desired conditions to products sold by retailers corresponding to each ingredient on the shopping ingredient list. The user's desired conditions are, for example, origin, price, nutritional value, shelf life, ease of processing, and excluded allergies, which are conditions the user requires when purchasing ingredients. The user's desired conditions may be determined based on past purchase history and medical interview results, or may be inferred. For example, in the case of "carrots," the user ingredient tag generation unit 130 can assign user ingredient tags such as "Origin: No preference," "Price: 80 yen or less per carrot," "Nutritional value: No preference," "Shelf life: Prioritize freshness (long shelf life)," and "Ease of processing: Prioritize cut vegetables." In addition, in the case of processed foods, the user ingredient tag generation unit 130 can assign user ingredient tags such as "Excluded allergy ingredients: [wheat, buckwheat]" and "Nutritional value: High protein." The user ingredient tag generator 130 can generate such user ingredient tags based on information previously entered by the user and static information from the ingredient database, or it can dynamically change the tags depending on the user's situation. For example, if cooking is expected to be done on a weekday, the user ingredient tag generator 130 may change the user ingredient tag to "Ease of processing: Prioritize cut vegetables" to shorten cooking time, and if cooking is expected to be done on a weekend, the user ingredient tag generator 130 may change the user ingredient tag to "Ease of processing: No preference." It is also possible to determine when an ingredient will be used from the planned dish database.

[0034] For example, even if a user normally prefers "Storage period: Prioritize freshness (long shelf life)," if the user is highly conscious of the issue of food waste or believes that a balance must also be struck between the storage period is necessary, the user ingredient tag generation unit 130 can change the user ingredient tag to "Storage period: Within one day" for ingredients that are planned to be used one day later.

[0035] The user ingredient tags described above are merely examples, and other user ingredient tags may be included. The user ingredient tags may also use LLM (Large Language Model) technology to learn the user's ingredient purchasing history and generate user ingredient tag results based on the user's desired conditions.

[0036] Furthermore, since it is time-consuming for the user to input the purchasing conditions for each ingredient in advance, the user ingredient tag generation unit 130 may generate user ingredient tags by estimating the user's purchasing trends from the product purchase history DB and ingredient usage history DB. For example, for carrots, if the user frequently purchases domestically produced carrots, the user ingredient tag generation unit 130 can change the user ingredient tag to "Origin: Domestic"; if the upper limit of the purchase price is 100 yen, the user ingredient tag can be changed to "Price condition: 100 yen / piece or less."

[0037] The user ingredient tag generation unit 130 may also assign a quantity condition such as "serves 4" or "serves 2" to the user ingredient tag depending on the amount needed by the user (e.g., the amount corresponding to the user's family structure, gender, or age group). If the user's desired amount is 4 servings, the user ingredient tag generation unit 130 may change the user ingredient tag to "Quantity condition: 4 servings." The user ingredient tag may also be "Quantity condition: 300g / pack," for example.

[0038] The user ingredient tag generation unit 130 may reflect the results estimated by the shopping ingredient list proposal management unit 120 in the user ingredient tags. For example, the shopping ingredient list proposal management unit 120 estimates the user's cooking tendencies from information in the cooking history database and inputs the estimated results to the user ingredient tag generation unit 130, which then generates tags such as "frequent use of leafy vegetables," "frequent use of spinach," "frequent cooking of salads," "frequent simmered dishes," and "frequent use of microwave ovens." The shopping ingredient list proposal management unit 120 may also estimate the cooking tendencies (frequent use and cooking) from the user's recipe search history. The cooking tendencies may be estimated based on, for example, specific cooking techniques, specific cooking appliances, recipe types, ingredients, and ingredient attributes.

[0039] Similarly to spinach, the shopping ingredient list suggestion management unit 120 may add komatsuna, which has tags such as "leafy vegetables" and "recommended for salads," to the shopping ingredient list. This allows ingredients that the user has little experience using but is likely to purchase to be added to the shopping ingredient list generated by the shopping ingredient list suggestion management unit 120.

[0040] In addition, the shopping list generation system 100 may have a table in which at least the names of ingredients are linked to keywords related to each ingredient, and the user ingredient tag generation unit 130 may generate keywords as tags.

[0041] The user ingredient tag generating unit 130 may also generate a user ingredient tag based on conditions other than those desired by the user.

[0042] The recommended product tag acquisition unit 140 acquires a recommended product tag that is linked to a product sold by a retailer and indicates the reason why the retailer recommends the product. For example, the recommended product tag acquisition unit 140 may acquire a recommended product tag from the EC site 300 via the EC linkage unit 160 based on food ingredients that can be purchased on the EC site 300. The recommended product tag acquisition unit 140 may acquire a recommended product tag by generating a recommended product tag based on information acquired from the EC site 300 via the EC linkage unit 160. In other words, the recommended product tag acquisition unit 140 may generate a recommended product tag that indicates a condition for recommending a product to a user for a product that the retailer wants to sell. For example, the recommended product tag acquisition unit 140 may acquire information indicating the reason why the retailer recommends a product sold by the retailer, generate a recommended product tag based on the information, and associate the recommended product tag with a product sold by the retailer.

[0043] For example, the recommended product tag acquiring unit 140 assigns conditions that the retailer wants to recommend to users to products on sale as recommended product tags based on information in a product DB stored on the EC site 300 or information input by an administrator of the retailer. The recommended product tags may be information about ordinary products, or may include additional conditions that the retailer assigns independently.

[0044] For example, in the case of "carrots," the recommended product tag acquisition unit 140 can generate and assign recommended product tags such as "Origin: Hokkaido," "Sales Price: 50 yen / piece," "Normal Sales Price: 70 yen / piece," "Discount Information: Advertised Item," "Storage Period: 2 weeks in the vegetable compartment (freshly arrived)," "Nutritional Value: Double Carotene," "Seasonal Information: Peak of Season," and "Appeal Words: [Advertised Item, Fresh (freshly arrived)]." For example, in the case of processed foods, the recommended product tag acquisition unit 140 can assign recommended product tags such as "Allergy Ingredients Included: [Wheat]," "Ease of Processing: Microwave 500W for 5 minutes," and "Nutritional Value: 25g or More of Protein" to the products. Such recommended product tags can be generated in advance as static information from information in the product database, or can be dynamically generated based on input information or sales rules from a retailer's administrator.

[0045] For example, if a sales rule is set to discount the price by 30% one day before the sell-by date and by 50% half a day before the sell-by date, for a product with a recommended product tag of "normal selling price: 100 yen," the recommended product tag may be changed to "selling price: 70 yen" and "discount information: 30% off" one day before the sell-by date, and to "selling price: 50 yen" and "discount information: 50% off" half a day before the sell-by date. Also, for example, the recommended product tag can be dynamically changed according to changes in the discount rate, such as "appeal words: [advertised item, fresh (just arrived)]," "appeal words: [advertised item, contribution to food waste]," or "appeal words: [advertised item, limited-time sale]."

[0046] The recommended product tag shown here is an example of a tag that can be assigned to a product, and does not necessarily have to be a recommended condition. The recommended product tag may also include elements that are not exemplified here.

[0047] The shopping list generation unit 150 compares the user ingredient tags with the recommended product tags to generate a shopping list that includes products that correspond to the necessary ingredients and are linked to recommended product tags that indicate why the products match the conditions indicated by the user ingredient tags. In other words, the shopping list generation unit 150 matches the ingredients on the shopping ingredient list with the user's desired conditions and the retailer's reasons for recommending the products, and generates a shopping list that includes products recommended by the retailer that correspond to the ingredients the user wants to purchase. For example, the shopping list generation unit 150 transmits the generated shopping list to the communication terminal 200.

[0048] For example, the shopping list generation unit 150 receives as input the user ingredient tags assigned by the user ingredient tag generation unit 130 to each ingredient on the shopping ingredient list and the recommended product tags assigned by the recommended product tag acquisition unit 140 to products currently on sale by retailers, extracts products that meet the conditions of the user ingredient tags or have similar recommended product tags, and generates a shopping list in which the products are associated with each ingredient on the shopping ingredient list.

[0049] For example, for the ingredient "carrot" whose user ingredient tag is "Origin: Domestic" and "Price condition: 80 yen / piece or less," there are three types of "carrot" products whose recommended product tags are "Origin: Hokkaido" and "Sales price: 70 yen / piece," "Origin: Foreign" and "Sales price: 50 yen / piece," and "Origin: Chiba Prefecture" and "Sales price: 90 yen / piece." The shopping list generation unit 150 selects the product with "Origin: Hokkaido" and "Sales price: 70 yen / piece" that meets the conditions of the user ingredient tag, and generates a shopping list that lists this product together with "carrot" from the shopping ingredient list. Note that the shopping list generation unit 150 may also generate a shopping list without listing "carrot" from the shopping ingredient list.

[0050] The shopping list generation unit 150 may select only one product that best meets the conditions, or may calculate the degree of match of the conditions and select multiple products in descending order of match. Furthermore, if weights are preset for each type of user ingredient tag (such as place of origin, price condition, and nutritional value), the shopping list generation unit 150 may select products based on the degree of match that takes into account not only the degree of match with the reason for the recommended product tag but also the weight. For example, the degree of match may be expressed by adding points for matching tags, with weights such as 2 points for a matching place of origin and 3 points for a matching price condition, or by using a weighted average of the weights. Points may be deducted if a specific tag does not match.

[0051] The shopping list generation unit 150 may also generate a shopping list by estimating product tags associated with user ingredient tags and comparing the product tags with recommended product tags. For example, the shopping list generation unit 150 may build and use a machine learning model that learns ingredient tags and conditions that the user often values ​​from data such as an ingredient usage history DB, cooking history DB, or product purchase history DB, and outputs the degree of importance of each tag or tag condition when they match or mismatch as a weight. The machine learning model may be, for example, LLM technology.

[0052] Furthermore, the shopping list generating unit 150 may add to the shopping list items selected by the user in addition to the items automatically selected by the shopping list generating unit 150.

[0053] The EC linking unit 160 links with the EC site 300 operated by a retailer. The EC linking unit 160 enables the user to operate the shopping list presented to the user on the UI unit 210 and send the products that the user has decided to purchase to the EC site 300, allowing the user to easily order products from the linked EC site 300.

[0054] The shopping list generation system 100 may include a communication terminal 200 and may further include a UI unit 210 that presents a shopping list to a user. For example, the UI unit 210 may provide the user with content data such as an inventory list, recipes, and shopping lists, and may also provide the user with an application that enables inventory management, recipe selection, shopping list management, or bulk purchasing of products. The shopping list generation system 100 may be implemented not only as an application, but also as a web page display screen via a browser. The shopping list generation system 100 may also be implemented as an application that includes functions such as an inventory management system or menu creation.

[0055] Next, the details of the operation of the shopping list creation system 100 according to the first embodiment will be described with reference to FIGS. 3 to 4D.

[0056] FIG. 3 is a flowchart showing an example of the operation of the shopping list generation system 100 according to the first embodiment.

[0057] First, the shopping ingredient list suggestion management unit 120 acquires user ingredient tags from the user ingredient tag generation unit 130 (step S301). The acquired user ingredient tags may be single or multiple. When there are multiple user ingredient tags, there are multiple search conditions for recommended product tags.

[0058] Next, the shopping ingredient list suggestion management unit 120 searches for recommended product tags based on the user ingredient tags (step S302). If the user needs multiple ingredients, the search can be performed by applying the same user ingredient tag to all the ingredients, or by applying a separate user ingredient tag to each ingredient.

[0059] Next, the shopping ingredient list suggestion management unit 120 uses the table to search for products that match the recommended product tag (step S303). Recommended product tags are tags set by retailers and refer to products that the retailer wants to sell. Either one or multiple recommended product tags can be set. Furthermore, recommended product tags may be single-layered or multi-layered. If there are no products that match the recommended product tag (NO in step S303), the generation of the shopping list ends. If there are no products that match the recommended product tag, the user may be notified that there are no matching products.

[0060] If a product matching the recommended product tag is found (YES in step S303), the shopping list generation unit 150 acquires matching product information indicating products that closely match the user ingredient tag and the recommended product tag (step S304). Matching product information includes the product name and may also include additional product information in addition to the product name. Specific examples of additional product information include the product's place of origin, price conditions, nutritional value, main recipe or cooking method, etc. Note that when a search is performed using multiple user ingredient tags, only the product that best matches the conditions may be selected, or multiple products may be selected in descending order of match by calculating the degree of match of the conditions.

[0061] Here, a specific example of the operation of the shopping list generation system 100 according to the first embodiment when the ingredients needed by the user are "egg" and "onion" will be described with reference to FIGS. 4A to 4D.

[0062] FIG. 4A is a diagram showing an example of a user ingredient tag according to the first embodiment.

[0063] FIG. 4B is a diagram showing an example of a table in which user ingredient tags and recommended product tags are linked together according to the first embodiment.

[0064] FIG. 4C is a diagram showing an example of the degree of match between a product for sale and a condition indicated by a user ingredient tag in the first embodiment.

[0065] FIG. 4D is a diagram showing an example of a shopping list according to the first embodiment.

[0066] As shown in Figure 4A, for example, assume that "fresh" and "particular" are acquired as user ingredient tags for the ingredient "egg," and "seasonal" and "particular" are acquired as user ingredient tags for the ingredient "onion." In this case, the user ingredient tags are matched with recommended product tags using the tables shown in Figures 4B and 4C. Note that the user ingredient tags may reflect the user's preferences or tendencies based on a medical interview or past purchasing history.

[0067] For the user ingredient tag "fresh" for the ingredient "egg," the table shown in FIG. 4B includes "expiration date" as the recommended product tag (major category), "new" as the recommended product tag (middle category), and "freshness, fresh" as the product tag keyword (approximation). Using the recommended product tag, the table shown in FIG. 4C is searched for products that have the same recommended product tag as the recommended product tag "expiration date" or "new" that corresponds to "fresh" shown in FIG. 4B, and the product "egg (ultra-high)" is extracted. Note that the product tag keyword in FIG. 4B is the keyword of the product tag for the ingredient that corresponds to the user ingredient tag.

[0068] For the user ingredient tag "Preferences" for the ingredient "Egg," the table shown in FIG. 4B includes "Other" as the recommended product tag (major category), "Other" as the recommended product tag (middle category), and "Origin, Brand" as the product tag keyword (approximate). Using the recommended product tag, the table shown in FIG. 4C is searched for products with the same recommended product tag as the recommended product tag "Other" corresponding to the "Preferences" shown in FIG. 4B. If no matching product is found, the search may be terminated as there is no corresponding user ingredient tag, or another similar user ingredient tag may be searched for. For example, as shown in FIG. 4B, the table shown in FIG. 4C is searched for products with the same recommended product tags as the recommended product tags "Cost" and "High" corresponding to the product tag keyword (approximate) "Brand" of the product tag keyword "Preferences," and the product "Egg (Extreme)" is extracted. The search for similar user ingredient tags may be performed multiple times, or the search may be terminated after a predetermined number of searches. Furthermore, the recommended product tag in FIG. 4C may be automatically updated if the ingredients' conditions change. Specifically, the product tag "Expiration Date" may be updated to "New" if the remaining time until the expiration date or best-before date is greater than a certain number of days, and to "Short" if the remaining time is less than a certain number of days. Furthermore, the expiration date may be determined based on the number of days since arrival, rather than the remaining time until the expiration date or best-before date. Recommended product tags may be obtained based on existing product information, or may be created and assigned by a retailer (assigned information). Recommended product tags or product tag keywords (approximate) may be generated by learning product information or assigned information using LLM technology. Recommended product tags may be assigned to some or all products.

[0069] The user ingredient tag "fresh" for the ingredient "egg" matches the recommended product tags "expiration date" and "new" for the product "egg (ultra-high)," and the recommended product tags "cost" and "expensive" for the product "egg (ultra-high)" match the "brand" that is similar to the user ingredient tag "particular" for the ingredient "egg." The degree of match between the user ingredient tag for the ingredient "egg" and the recommended product tag for the product "egg (ultra-high)" is, for example, "2." Note that the user ingredient tags "fresh" and "particular" for the ingredient "egg" do not match the recommended product tags "expiration date" and "short" for the product "egg (morning)," so the degree of match is, for example, "0." Therefore, the matching product for the necessary ingredient "egg" estimated from the user ingredient tag is "egg (ultra-high)."

[0070] Similarly, for the ingredient "onion," the user ingredient tag "seasonal" matches the recommended product tags "expiration date" and "season" for the product "new onion," so the product "new onion" that the user wants can be presented.

[0071] For example, as shown in Figure 4D, a shopping list is generated in which items with a high degree of matching are displayed at the top. For the ingredient "egg," the item "Egg (Extreme)" is displayed at the top, and for the ingredient "onion," the item "New Onion" is displayed at the top. Also, as shown in Figure 4D, user ingredient tags (i.e., the user's desired conditions) for the required ingredients may also be displayed.

[0072] As described above, a shopping list is generated that includes products recommended by the retailer (products tagged with a recommended product tag) and that meet the user's requirements. This allows for the creation of a shopping list that is desirable for both the user and the retailer. The user can obtain a personalized shopping list based on the user's requirements, such as the food item inventory status or past shopping history, which helps avoid unnecessary food purchases and contributes to reducing food waste. It also makes it easier for the user to find products that meet their preferences or needs, enabling a satisfying shopping experience. Meanwhile, retailers can appropriately manage their inventory by recommending surplus or near-expiration products, while also selling products that meet the user's needs. This increases sales and reduces waste risk. This reduces mismatches between users and retailers, providing a more satisfying shopping experience that is desirable for both parties.

[0073] (Modification of First Embodiment) Next, a shopping list creation system 100 according to a modification of the first embodiment will be described with reference to FIGS. 5A to 5D.

[0074] In the shopping list creation system 100 according to the variation of the first embodiment, the user ingredient tag creation unit 130 creates user ingredient tags that indicate the user's requirements for multiple necessary ingredients needed by the user, and links the multiple necessary ingredients to a common user ingredient tag, which is different from the system according to the first embodiment. Below, we will omit the same points and focus on the differences.

[0075] A specific example of the operation of the shopping list generation system 100 according to a variation of the first embodiment when the ingredients needed by the user are "egg" and "onion" will be described with reference to FIGS. 5A to 5D.

[0076] FIG. 5A is a diagram showing an example of a user ingredient tag according to a modification of the first embodiment.

[0077] FIG. 5B is a diagram showing an example of a table in which user ingredient tags and recommended product tags are linked together according to a modification of the first embodiment.

[0078] FIG. 5C is a diagram showing an example of the degree of match between a product for sale and a condition indicated by a user ingredient tag in a variation of the first embodiment.

[0079] FIG. 5D is a diagram showing an example of a shopping list according to a modification of the first embodiment.

[0080] As shown in Fig. 5A, for example, assume that "seasonal," "specialty," and "fresh" are acquired as common user ingredient tags for the ingredients "egg" and "onion." In this case, the user ingredient tags are matched with recommended product tags using the tables shown in Fig. 5B and Fig. 5C.

[0081] For the user ingredient tag "in season," the table shown in Fig. 5B includes "expiration date" as the recommended product tag (major category), "season" as the recommended product tag (middle category), and "spring, summer, fall, winter" as product tag keywords (approximation). Using the recommended product tag, a search is performed in the table shown in Fig. 5C to see if there are any products that have the same recommended product tag as the recommended product tag "expiration date" or "season" that corresponds to "in season" shown in Fig. 5B, and the product "new onion" is extracted.

[0082] For the user ingredient tag "Preferences," the table shown in FIG. 5B includes "Other" as the recommended product tag (major category), "Other" as the recommended product tag (middle category), and "Origin, Brand" as the product tag keyword (approximate). Using the recommended product tag, the table shown in FIG. 5C is searched for products with the same recommended product tag as the recommended product tag "Other" corresponding to the "Preferences" shown in FIG. 5B. If no matching product is found, the search can be terminated as there is no corresponding user ingredient tag, or another similar user ingredient tag can be searched for. For example, as shown in FIG. 5B, the table shown in FIG. 5C is searched for products with the same recommended product tag as the recommended product tags "Cost" and "Expensive" corresponding to the product tag keyword (approximate) "Brand" of the product tag keyword "Preferences," and the product "Egg (Extreme)" is extracted.

[0083] For the user ingredient tag "fresh," the table shown in Fig. 5B includes "expiration date" as a recommended product tag (major category), "new" as a recommended product tag (middle category), and "freshness, fresh" as a product tag keyword (approximation). Using the recommended product tag, the table shown in Fig. 5C is searched for products that have the same recommended product tag as the recommended product tag "expiration date" or "new" that corresponds to "fresh" shown in Fig. 5B, and the products "onion (regular)," "new onion," and "egg (extra large)" are extracted.

[0084] The user ingredient tag "seasonal" matches the recommended product tags "expiration date" and "new" for the products "new onion" and "egg (ultra-high)," while the product tag keyword (approximate) "brand," which is similar to the user ingredient tag "particular," matches the recommended product tags "cost" and "expensive" for the product "egg (ultra-high)," and the user ingredient tag "fresh" matches the recommended product tags "expiration date" and "new" for the products "onion (regular)," "new onion," and "egg (ultra-high)." Therefore, for the user ingredient tag common to the ingredients "egg" and "onion," for example, the match degree with the recommended product tag for the product "onion (regular)" is "1," the match degree with the recommended product tag for the product "new onion" is "2," and the match degree with the recommended product tag for the product "egg (ultra-high)" is "2." Therefore, the matching products for the necessary ingredients "egg" and "onion" estimated from the common user ingredient tags are "egg (ultra-high)" and "new onion."

[0085] For example, as shown in FIG. 5D , a shopping list is generated in which products with a high degree of matching are displayed at the top. It can be seen that for the ingredient "egg," the product "egg (ultra-high quality)" is displayed at the top, and for the ingredient "onion," the product "new onion" is displayed at the top. Furthermore, as shown in FIG. 5D , common user ingredient tags for the required ingredients (i.e., common requirements for ingredients that users have for ingredients) may also be displayed. Furthermore, a shopping list may be generated in which products with a high degree of matching are highlighted. Furthermore, products with a high degree of matching may be selected and ready for payment (e.g., added to cart).

[0086] Second Embodiment Next, a shopping list creation system 100 according to a second embodiment will be described with reference to FIGS. 6 to 7B.

[0087] The shopping list generation system 100 according to the second embodiment differs from that according to the first embodiment in that, if a sales item linked to a recommended product tag indicating the reason for meeting the conditions indicated by the user ingredient tag is out of stock, the shopping list generation unit 150 generates a shopping list that includes substitute products (substitute ingredients) that correspond to the necessary ingredients and are linked to the sales item linked to the recommended product tag indicating the reason for meeting the conditions indicated by the user ingredient tag. Below, a description of the same points will be omitted and the differences will be focused on.

[0088] 6 is a flowchart showing an example of the operation of the shopping list generation system 100 according to embodiment 2. The processes from step S301 to step S304 are the same as those in embodiment 1, and therefore a description thereof will be omitted.

[0089] If there is no product matching the recommended product tag (NO in step S303), the shopping ingredient list proposal management unit 120 uses a table to search for products matching the substitute product tag. A substitute product tag is a tag for a product that can be suggested as a substitute ingredient, set by a retailer. Furthermore, the substitute product tag may be single-layered or multi-layered. If there is no product matching the substitute product tag (NO in step S305), the generation of the shopping list ends. If there is no product matching the substitute product tag, the user may be notified that there are no applicable products.

[0090] If there is a product matching the substitute product tag (YES in step S305), the shopping list generation unit 150 acquires substitute matching product information indicating products that closely match the user ingredient tag and the substitute product tag (step S306). The substitute matching product information includes the name of the substitute product and may also include additional product information in addition to the name of the substitute product. Specific examples of the additional information for the substitute product include the product's place of origin, price conditions, nutritional value, main recipe or cooking method, etc. When a search is performed using multiple substitute ingredient tags, only the product that best meets the conditions may be selected, or multiple products may be selected in descending order of match by calculating the degree of match of the conditions.

[0091] 7A and 7B, a specific example of the operation of the shopping list generation system 100 according to the second embodiment when the ingredients needed by the user are "egg" and "onion" will be described. Note that Fig. 4A is also a diagram showing an example of a user ingredient tag according to the second embodiment, and Fig. 4B is also a diagram showing an example of a table in which the user ingredient tag and the recommended product tag according to the second embodiment are linked.

[0092] FIG. 7A is a diagram showing an example of substitute ingredients linked to a product for sale in the second embodiment.

[0093] FIG. 7B is a diagram showing an example of a shopping list according to the second embodiment.

[0094] As in the first embodiment, the recommended product tag is searched, and the matching product for the necessary ingredient "onion" is "new onion." However, when the inventory flag in the table shown in Figure 7A is looked up, the inventory flag for the product "new onion" is "none," and there is no product that meets the conditions of the user ingredient tag. In this case, a product with an inventory flag of "none" does not need to be selected as a matching product; for example, the product "onion (regular)" may be selected as a matching product.

[0095] If all results for the necessary ingredient "onion" in the table shown in FIG. 7A are displayed, all of the inventory flags for the ingredient "onion" are set to "none," indicating that the store does not have the ingredient "onion." In this case, the search may be terminated as there is no corresponding ingredient, or a search may be performed again using alternative ingredients. In other words, the alternative ingredients in the table shown in FIG. 7A may be used to match the user's necessary ingredients. If there are multiple alternative ingredients, a search may be performed again using all of the ingredients, or a search may be performed again limited to ingredients that the user has substituted in the past. Furthermore, among multiple alternative ingredients, a search with a higher degree of suitability may be prioritized, or the alternative ingredients to be used in the search may be determined based on information obtained from an external device.

[0096] The recommended product tags "cost" and "expensive" for the product "Green onion (extra long)" match the product tag keyword (approximate) "brand," which is similar to the user ingredient tag "particular" for the ingredient "onion," and the degree of match between the user ingredient tag for the ingredient "onion" and the recommended product tag for the product "Green onion (extra long)" is, for example, "1." Note that the recommended product tags "quantity" and "large" for the product "Green onion (bunch)" do not match the user ingredient tags "seasonal" and "particular" for the ingredient "onion," so the degree of match is, for example, "0." Therefore, the alternative matching product for the substitute ingredient "Green onion" for the necessary ingredient "onion" estimated from the user ingredient tag is the product "Green onion (extra long)."

[0097] For example, as shown in Figure 7B, a shopping list is generated that indicates that the ingredient "onion" is not available, and that substitute ingredients with high matching scores are displayed at the top. Specifically, it can be seen that the product "long onion (extreme)" is displayed at the top.

[0098] In this way, if there is no product in stock that meets the user's requirements, an alternative product can be suggested.

[0099] Third Embodiment Next, a shopping list creation system 100 according to a third embodiment will be described with reference to FIGS. 8 to 9B.

[0100] The shopping list generation system 100 according to the third embodiment differs from that according to the first embodiment in that the shopping list generation unit 150 generates a shopping list that includes associated products linked to sales products that correspond to necessary ingredients and that are linked to recommended product tags that indicate why the sales products match the conditions indicated by the user ingredient tags. Below, a description of the same points will be omitted and the differences will be mainly described.

[0101] 8 is a flowchart showing an example of the operation of the shopping list generation system 100 according to embodiment 3. The processes from step S301 to step S303 are the same as those in embodiment 1, and therefore a description thereof will be omitted.

[0102] If there is a product that matches the recommended product tag (YES in step S303), the shopping ingredient list proposal management unit 120 uses a table to search for products that match the associated product tag. An associated product tag is a tag for a product that is expected to be purchased together with a specific ingredient, set by a retailer. In addition, the associated product tag may be single-layered or multi-layered. If there is no product that matches the associated product tag (NO in step S307), the generation of the shopping list ends. If there is no product that matches the associated product tag, the user may be notified that there are no matching products.

[0103] If there is a product matching the associated product tag (YES in step S307), the shopping list generation unit 150 acquires matching product information that closely matches the user ingredient tag and the recommended product tag, and associated matching product information that closely matches the user ingredient tag and the associated product tag (step S308). The associated matching product information includes the name of the associated product and may also include additional product information in addition to the name of the associated product. Specific examples of additional information for the associated product include the product's origin, price conditions, nutritional value, main cooking recipe or cooking method, etc. Note that when a search is performed using multiple associated product tags, only one product that best meets the conditions may be selected, or multiple products may be selected in descending order of matching by calculating the degree of match of the conditions.

[0104] 9A and 9B, a specific example of the operation of the shopping list generation system 100 according to the third embodiment when the ingredients needed by the user are "egg" and "onion" will be described. Note that Fig. 4A is also a diagram showing an example of a user ingredient tag according to the third embodiment, and Fig. 4B is also a diagram showing an example of a table in which the user ingredient tag and the recommended product tag according to the third embodiment are linked.

[0105] FIG. 9A is a diagram showing an example of accompanying ingredients linked to a product for sale in the third embodiment.

[0106] FIG. 9B is a diagram showing an example of a shopping list according to the third embodiment.

[0107] As in the first embodiment, recommended product tags are searched, and the product matching the ingredient "egg" is the product "egg (ultra-high quality)." However, accompanying ingredients that are expected to be purchased together with the ingredient "egg" may also be presented. These accompanying ingredients correspond to "bread" and "ham," which are items stored in the table shown in FIG. 9A. In this case, accompanying ingredients may be searched for using the user ingredient tags used to search for matching products for the ingredient "egg."

[0108] The recommended product tags "cost" and "expensive" for the product "bread (luxury)" match the product tag keyword (approximate) "brand," which is similar to the user ingredient tag "particular" for the ingredient "egg," so the degree of match between the user ingredient tag for the ingredient "egg" and the recommended product tag for the product "bread (luxury)" is, for example, "1." Note that the recommended product tags "cost" and "cheap" for the product "bread (always)" do not match the user ingredient tags "seasonal" and "particular" for the ingredient "egg," so the degree of match is, for example, "0." Therefore, the associated matching product of the associated ingredient "bread" for the necessary ingredient "egg" estimated from the user ingredient tag is the product "bread (luxury)."

[0109] For example, as shown in FIG. 9B, a shopping list is generated in which items with a high degree of matching are displayed as auxiliary items.

[0110] In this way, it is possible to propose to the user additional products related to the sales products that meet the conditions desired by the user.

[0111] Fourth Embodiment Next, a shopping list creation system 100 according to a fourth embodiment will be described with reference to FIGS.

[0112] The shopping list generation system 100 according to the fourth embodiment differs from that according to the first embodiment in that the shopping ingredient list proposal management unit 120 further proposes products that combine multiple ingredients as necessary ingredients. The fourth embodiment also differs from that according to the first embodiment in that the storage unit 110 further stores a seasoning product DB. Below, we will omit a description of the same points and focus on the differences. The shopping ingredient list proposal management unit 120 is an example of a proposal unit.

[0113] The seasoning product DB includes product IDs, product names, recipe tags that express usable recipe features, representative basic seasonings or ingredients that make up the product, and corresponding food ingredient IDs. The seasoning product DB may be updated by the EC linkage unit 160.

[0114] The shopping ingredient list proposal management unit 120 estimates the condiments required for each recipe based on the planned dish DB and the recipe DB. The shopping ingredient list proposal management unit 120 generates a list of required condiments for each recipe and a corresponding condiment product list based on the ingredient DB. This generation is performed for each recipe, and the shopping list generation unit 150 integrates the condiment products for each listed recipe to generate a shopping list. Here, the correspondence between the required condiments and the condiment products does not have to be one-to-one. Instead, a combination of multiple condiments, such as a combination of condiments, may be associated with a required condiment, or a high degree of match may be found between the recipe tags managed in the recipe DB and the usable recipe tags for each condiment product managed in the condiment product DB. The shopping list generation unit 150 combines the generated shopping list of ingredients and the condiment product list and outputs the shopping list.

[0115] FIG. 10 is a flowchart showing an example of the operation of the shopping list generation system 100 according to the fourth embodiment.

[0116] FIG. 11 is a diagram for explaining a method for proposing a product that combines multiple seasonings in the fourth embodiment.

[0117] First, the shopping ingredient list suggestion manager 120 acquires a list of seasonings from the recipe ingredient list (step S401), and creates a combination of seasonings required for the recipe from the acquired list of seasonings (step S402). For example, as shown in Figure 11, a combination of "soy sauce," "mirin," and "dashi" is created for "oyakodon" (chicken and egg rice bowl). Also, "marinade" is acquired as a recipe tag for "marinated potatoes."

[0118] Next, the shopping ingredient list suggestion management unit 120 searches for products that match the above combination or recipe tag (step S403). For example, as shown in Figure 11, "Y Company Mentsuyu" is a combined seasoning containing "soy sauce," "mirin," and "dashi," and is a product that matches the combination of seasonings "soy sauce," "mirin," and "dashi" needed for "Oyakodon." Furthermore, the recipe tags for "X Company Vinegar" are "Teriyaki," "Sunomono," "Pickles," and "Marinade," making it a product that matches the recipe tag "Marinade" for "Potato Marinade."

[0119] If there is no product that matches the above combination or recipe tag (NO in step S403), the generation of the seasoning product list ends.

[0120] If there is a product that matches the combination or recipe tag (YES in step S403), the shopping list generator 150 acquires matching product information, including a list of condiment products (step S404). In this way, it is possible to propose not only products that are desirable for both the user and the retailer, but also products that combine multiple condiments, for example.

[0121] (Other Embodiments) As described above, the embodiments have been described as examples of the technology disclosed in this application. However, the technology in this disclosure is not limited to these, and can be applied to embodiments in which modifications, substitutions, additions, omissions, etc. are made as appropriate. Furthermore, it is also possible to combine the components described in the above embodiments to create new embodiments. Furthermore, the order of multiple processes in the operation of the system described in this disclosure may be changed, and multiple processes may be executed in parallel.

[0122] For example, a user may want to switch retailers where they shop. If the table definition is changed, the user's ingredient tag may become inconsistent with the previously set user ingredient tag. For example, the user ingredient tag "Preferences" for EC site A, an EC site 300 with average prices, may not match the user ingredient tag "Preferences" for EC site B, an upscale EC site 300. If "Preferences" is set as the user ingredient tag based on the user's shopping history on EC site A, selecting a product tag corresponding to "Preferences" on EC site B may result in matching with a product that is more expensive than the user expected.

[0123] Therefore, when a user switches retailers, the user ingredient tag generator 130 may generate a user ingredient tag based on information indicating the user's desired conditions and information indicating the new retailer. The information indicating the retailer may be, for example, information indicating the price range of the retailer's products.

[0124] For example, the user ingredient tag may be switched depending on the EC site 300. That is, a table may be generated for each EC site 300, and the table may be switched depending on the EC site 300 where the user is shopping.

[0125] Furthermore, for example, the user ingredient tag may be converted to match the EC site 300. For example, if the price of a "specialty" product on EC site A is equal to or higher than the price of a regular product on EC site B, the "specialty" tag is not set in the user ingredient tag on EC site B. Specifically, if a user has a purchase history of eggs tagged with a 128 yen value tag and eggs tagged with a 98 yen limited-time value tag on EC site A, the value of the eggs is defined as 128 yen or less based on the product type, tag information, and price information using AI such as a large-scale language model. Therefore, when eggs tagged with a value tag (eggs more expensive than 128 yen) on EC site B are presented to the user, the value tag is not set.

[0126] This allows you to set user ingredient tags for each retailer, allowing you to suggest products that are suitable for the new retailer.

[0127] The user ingredient tag may have three or more levels. Specifically, the user ingredient tags may be set in ascending order of price range, such as "Value," "Preference 1," and "Preference 2." For example, if the user ingredient tags "Value" and "Preference 1" are set on EC sites A and B, respectively, and the user switches from EC site A to EC site B, the "Value" tag on EC site B may be converted to "Preference 1," and the "Preference 1" tag on EC site B may be converted to "Preference 2."

[0128] For example, the present disclosure can be realized not only as the shopping list generation system 100 but also as a shopping list generation method including steps (processing) performed by the components that make up the shopping list generation system 100 .

[0129] FIG. 12 is a flowchart showing an example of a shopping list generation method according to another embodiment.

[0130] As shown in FIG. 12 , the shopping list generation method includes the steps of: generating user ingredient tags indicating the user's desired conditions for necessary ingredients, which are ingredients needed by the user (step S11); acquiring recommended product tags linked to sales products, which are products sold by the retailer, indicating the reason why the retailer recommends the sales products (step S12); and matching the user ingredient tags with the recommended product tags to generate a shopping list including sales products that correspond to the necessary ingredients and are linked to recommended product tags indicating the reason why the sales products meet the conditions indicated by the user ingredient tags (step S13).

[0131] For example, the present disclosure can be realized as a program for causing a computer (processor) to execute steps included in the shopping list generation method. Furthermore, the present disclosure can be realized as a non-transitory computer-readable recording medium, such as a CD-ROM, on which the program is recorded.

[0132] For example, when the present disclosure is realized as a program (software), each step is performed by running the program using hardware resources such as a computer's CPU, memory, input / output circuits, etc. In other words, each step is performed by the CPU acquiring data from memory or input / output circuits, etc., performing calculations, and outputting the calculation results to memory or input / output circuits, etc.

[0133] In the above embodiment, each component included in shopping list creation system 100 may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may also be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0134] Some or all of the functions of the shopping list generation system 100 according to the above embodiment are typically realized as an LSI, which is an integrated circuit. These functions may be individually integrated into a single chip, or some or all of them may be integrated into a single chip. Furthermore, the integrated circuit is not limited to an LSI, and may be realized using a dedicated circuit or a general-purpose processor. It is also possible to use an FPGA (Field Programmable Gate Array), which can be programmed after the LSI is manufactured, or a reconfigurable processor, which allows the connections and settings of circuit cells within the LSI to be reconfigured.

[0135] Furthermore, if an integrated circuit technology that can replace LSI emerges due to advances in semiconductor technology or other derivative technologies, that technology may naturally be used to integrate each component included in shopping list generation system 100 into an integrated circuit.

[0136] In addition, this disclosure also includes forms obtained by making various modifications to the embodiments that a person skilled in the art would think of, and forms realized by arbitrarily combining the components and functions in each embodiment within the scope of the present disclosure.

[0137] (Additional Notes) The above description of the embodiments discloses the following techniques.

[0138] (Technology 1) A shopping list generation system comprising: a user ingredient tag generation unit that generates user ingredient tags that indicate the conditions desired by a user for necessary ingredients, which are ingredients needed by a user; a recommended product tag acquisition unit that acquires recommended product tags that are linked to sales products, which are products sold by a retailer, and indicate the reasons why the retailer recommends the sales products; and a shopping list generation unit that generates a shopping list including the sales products that correspond to the necessary ingredients and are linked to the recommended product tags that indicate the reasons why the sales products meet the conditions indicated by the user ingredient tags by comparing the user ingredient tags with the recommended product tags.

[0139] This generates a shopping list that includes products recommended by the retailer and that meet the user's requirements. Therefore, a shopping list that is desirable for both the user and the retailer can be generated. The user can obtain a personalized shopping list based on the user's requirements, such as the food inventory status or past shopping history, which helps avoid unnecessary food purchases and contributes to reducing food waste. It also makes it easier for the user to find products that meet their preferences or needs, enabling a satisfying shopping experience. Meanwhile, retailers can appropriately manage inventory by recommending surplus or near-expiration products, while selling products that meet the user's needs. This increases sales and reduces waste risk. This reduces mismatches between users and retailers, providing a more satisfying shopping experience that is desirable for both parties.

[0140] (Technology 2) The user ingredient tag generation unit generates user ingredient tags that indicate the user's desired conditions for the multiple necessary ingredients needed by the user, and links the user ingredient tags that are common to the multiple necessary ingredients. The shopping list generation system described in Technology 1.

[0141] In this way, a common user ingredient tag may be linked to multiple necessary ingredients.

[0142] (Technology 3) The shopping list generation system described in Technology 1 or 2, wherein, when the sales product linked to the recommended product tag indicating the reason for meeting the conditions indicated by the user ingredient tag is out of stock, the shopping list generation unit generates the shopping list including an alternative product that is the sales product corresponding to the necessary ingredient and is linked to the sales product linked to the recommended product tag indicating the reason for meeting the conditions indicated by the user ingredient tag.

[0143] This allows the system to suggest alternative products if there are no products in stock that meet the user's requirements.

[0144] (Technology 4) A shopping list generation system described in any one of Technologies 1 to 3, wherein the shopping list generation unit generates the shopping list including ancillary products linked to the sales products that correspond to the necessary ingredients and are linked to the recommended product tags that indicate why the sales products meet the conditions indicated by the user ingredient tags.

[0145] This allows the user to be offered additional products related to the sales product that meets the user's desired conditions.

[0146] (Technology 5) A shopping list generation system described in any one of Technologies 1 to 4, in which, when the retailer where the user shops is switched, the user ingredient tag generation unit generates the user ingredient tag based on information indicating the conditions desired by the user and information indicating the retailer after the switch.

[0147] This allows a user to set ingredient tags for each retailer, making it possible to suggest products that suit the new retailer.

[0148] (Technology 6) A shopping list generation system described in any of Technologies 1 to 5, wherein the recommended product tag acquisition unit acquires information indicating the reason for recommending the product sold by the retailer, generates the recommended product tag based on the information, and links the recommended product tag to the product sold by the retailer.

[0149] In this way, the shopping list generation system itself may generate recommended product tags.

[0150] (Technology 7) A shopping list generation system described in any one of Technologies 1 to 6, wherein the user ingredient tag generation unit generates the user ingredient tag based on information input by the user, history information about ingredients used by the user in the past, information about the inventory of ingredients owned by the user, information about the dishes the user plans to cook, information about the date and time the user will cook, or information about the user's attributes.

[0151] This allows for the generation of user ingredient tags that reflect information entered by the user, history information, inventory information, information about the dish to be cooked, information about the date and time of cooking, or conditions related to the user's attributes.

[0152] (Technology 8) A shopping list generation system described in any one of Technologies 1 to 7, wherein the shopping list generation unit generates the shopping list by estimating product tags related to the user ingredient tags and matching the product tags with the recommended product tags.

[0153] In this way, a shopping list can be generated by matching the product tags estimated from the user's ingredient tags with the recommended product tags.

[0154] (Technology 9) The shopping list generation system according to any one of technologies 1 to 8, further comprising a user interface unit that presents the shopping list to the user.

[0155] Thus, the shopping list generation system may include a user interface unit where the shopping list is presented.

[0156] (Technology 10) The shopping list generation system according to any one of technologies 1 to 9, further comprising an EC site linking unit that links with an EC site operated by the retailer.

[0157] This allows you to easily order products from affiliated EC sites.

[0158] (Technology 11) The shopping list generation system according to any one of technologies 1 to 10, further comprising a suggestion unit that suggests products that combine multiple ingredients as the necessary ingredients.

[0159] For example, it is possible to suggest products that combine multiple seasonings.

[0160] (Technology 12) A shopping list generation method including: a step of generating user ingredient tags that indicate conditions desired by a user for necessary ingredients, which are ingredients needed by a user; a step of obtaining recommended product tags that are linked to sales products, which are products sold by a retailer, and that indicate reasons why the retailer recommends the sales products; and a step of generating a shopping list by matching the user ingredient tags with the recommended product tags, including sales products that correspond to the necessary ingredients and that are linked to the recommended product tags that indicate reasons why the sales products meet the conditions indicated by the user ingredient tags.

[0161] This makes it possible to provide a shopping list generation method that can generate a shopping list that is desirable for both the user and the retailer.

[0162] (Technology 13) A program for causing a computer to execute the shopping list generation method described in Technology 12.

[0163] This makes it possible to provide a program that can generate shopping lists that are desirable for both users and retailers.

[0164] The present disclosure is applicable to a system for generating a list of ingredients that a user should purchase.

[0165] REFERENCE SIGNS LIST 100 Shopping list generation system 110 Storage unit 120 Shopping ingredient list proposal management unit 130 User ingredient tag generation unit 140 Recommended product tag acquisition unit 150 Shopping list generation unit 160 EC linkage unit 200 Communication terminal 210 UI unit 300 EC site

Claims

1. a user ingredient tag generating unit that generates a user ingredient tag indicating the user's desired conditions for the necessary ingredients that are ingredients needed by the user; a recommended product tag acquisition unit that acquires a recommended product tag that is linked to a product sold by a retail business and indicates a reason why the retail business recommends the product; a shopping list generating unit that generates a shopping list including the sales products that correspond to the necessary ingredients and are linked to the recommended product tags that indicate why the sales products match the conditions indicated by the user ingredient tags by comparing the user ingredient tags with the recommended product tags; The user ingredient tag generation unit generates the user ingredient tags indicating the conditions required by the user for the plurality of necessary ingredients required by the user, and links the common user ingredient tags to the plurality of necessary ingredients. Shopping list generation system.

2. When the sales product linked to the recommended product tag indicating the reason for meeting the condition indicated by the user ingredient tag is out of stock, the shopping list generation unit generates the shopping list including the sales product corresponding to the necessary ingredient and an alternative product linked to the sales product linked to the recommended product tag indicating the reason for meeting the condition indicated by the user ingredient tag. The shopping list generation system of claim 1 .

3. the shopping list generation unit generates the shopping list including the sales product corresponding to the necessary ingredient, the sales product being linked to the recommended product tag indicating the reason why the sales product matches the condition indicated by the user ingredient tag, and ancillary products linked to the sales product; The shopping list generation system of claim 1 .

4. When the retailer where the user shops is switched, the user ingredient tag generation unit generates the user ingredient tag based on information indicating the conditions desired by the user and information indicating the retailer after the switch. The shopping list generation system of claim 1 .

5. the recommended product tag acquisition unit acquires information indicating a reason for recommending a product sold by the retailer, generates the recommended product tag based on the information, and associates the recommended product tag with the product sold by the retailer; The shopping list generation system according to any one of claims 1 to 4.

6. The user ingredient tag generation unit generates the user ingredient tag based on information input by the user, history information on ingredients used by the user in the past, information on the inventory of ingredients owned by the user, information on dishes the user plans to cook, information on the date and time the user will cook, or information on the user's attributes. The shopping list generation system according to any one of claims 1 to 4.

7. the shopping list generation unit estimates product tags related to the user ingredient tags and generates the shopping list by matching the product tags with the recommended product tags; The shopping list generation system according to any one of claims 1 to 4.

8. Further, a user interface unit is provided to present the shopping list to the user. The shopping list generation system according to any one of claims 1 to 4.

9. The electronic commerce (EC) site linking unit may further link with an EC (Electronic Commerce) site operated by the retailer. The shopping list generation system according to any one of claims 1 to 4.

10. Further, a suggestion unit is provided that suggests a product that combines a plurality of ingredients as the necessary ingredients. The shopping list generation system according to any one of claims 1 to 4.

11. generating a user ingredient tag indicating a condition required by the user for a necessary ingredient that is an ingredient required by the user; A step of acquiring a recommended product tag associated with a product sold by a retail business, the recommended product tag indicating a reason why the retail business recommends the product; and generating a shopping list including the sales products corresponding to the necessary ingredients and associated with the recommended product tags indicating why the sales products match the conditions indicated by the user ingredient tags by matching the user ingredient tags with the recommended product tags; In the step of generating the user ingredient tags, the user ingredient tags indicating the conditions required by the user are generated for the plurality of necessary ingredients required by the user, and the common user ingredient tags are linked to the plurality of necessary ingredients. How to generate a shopping list.

12. A program for causing a computer to execute the shopping list generating method according to claim 11.