Recipe proposal device, recipe proposal method, and program

Abstract

To provide a technique for proposing a recipe suited to a user's needs.SOLUTION: A recipe proposal device comprises: acquisition means for acquiring a purchasing history of a user and a cooking skill of the user associated with identification information on the user; food ingredient identification means for identifying a food ingredient frequently purchased by the user based on the purchasing history of the user; dish identification means for identifying a dish frequently cooked by the user based on the identified food ingredient; determination means for determining a recommended recipe that is a recipe corresponding to the cooking skill of the user and for cooking a dish other than the identified dish; and output means for outputting the recommended recipe.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a recipe suggestion device, a recipe suggestion method, and a program. [Background technology]

[0002] There is technology that presents recipes based on the user's cooking skill.

[0003] Patent Document 1 describes a technology that determines a user's cooking skill level based on the fuel consumption and usage time of a cooking appliance, and selects and provides a cooking recipe with an optimal level of difficulty to the user. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2002-260438 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, a user with low cooking skills may become bored with the same meals repeatedly by repeatedly using the same ingredients or cooking the same dishes. Therefore, when recipes are presented according to the cooking skill, it may not be possible to present a recipe that meets the user's needs.

[0006] One example of the objective of the present disclosure is to provide a technology that suggests recipes that meet the needs of a user. [Means for solving the problem]

[0007] A recipe suggestion device in one aspect of the present disclosure includes an acquisition means for acquiring a user's purchasing history and the user's cooking skills associated with the user's identification information, an ingredient identification means for identifying ingredients that the user frequently purchases based on the user's purchasing history, a dish identification means for identifying dishes that the user frequently cooks based on the identified ingredients, a determination means for determining a recommended recipe that is a recipe according to the cooking skill and is for cooking a dish other than the identified dish, and an output means for outputting the recommended recipe.

[0008] In one aspect of the recipe suggestion method of the present disclosure, a computer obtains a user's purchasing history and the user's cooking skills associated with the user's identification information, identifies ingredients that the user frequently purchases based on the user's purchasing history, identifies dishes that the user frequently cooks based on the identified ingredients, determines a recommended recipe that is a recipe according to the cooking skill and is for cooking a dish other than the identified dish, and outputs the recommended recipe.

[0009] A recipe suggestion program in one aspect of the present disclosure acquires a user's purchasing history and the user's cooking skills associated with the user's identification information, identifies ingredients that the user frequently purchases based on the user's purchasing history, identifies dishes that the user frequently cooks based on the identified ingredients, determines a recommended recipe that is a recipe according to the cooking skill and is for cooking dishes other than the identified dishes, and causes a computer to execute a process of outputting the recommended recipe.

[0010] Each program may be stored in a non-transitory computer-readable recording medium. Effect of the Invention

[0011] One example of the effect of the present disclosure is the ability to suggest recipes that meet the user's needs. [Brief description of the drawings]

[0012] [Figure 1]FIG. 1 is a diagram illustrating a configuration of a system including a recipe suggestion device. [Diagram 2] FIG. 2 is a block diagram showing an example of a functional configuration of the recipe suggestion device. [Diagram 3] FIG. 13 is a diagram illustrating an example of output. [Figure 4] 4 is a flowchart showing the operation of the recipe suggestion device. [Diagram 5] FIG. 13 is a block diagram showing another example of the functional configuration of the recipe suggestion device. [Figure 6] 4 is a flowchart showing the operation of the recipe suggestion device. [Figure 7] FIG. 13 is a block diagram showing another example of the functional configuration of the recipe suggestion device. [Figure 8] FIG. 13 is a block diagram showing another example of the functional configuration of the recipe suggestion device. [Figure 9] FIG. 13 is a block diagram showing another example of the functional configuration of the recipe suggestion device. [Figure 10] FIG. 1 is a diagram illustrating a hardware configuration for realizing a recipe suggestion device according to the present disclosure using a computer and its peripheral devices. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The present disclosure relates to techniques for suggesting recipes to a user.

[0014] The configuration of a recipe suggestion system including a recipe suggestion device will be described with reference to Fig. 1. The recipe suggestion system includes a recipe suggestion device 100, a user information database that stores information about users, a recipe information database that stores recipe information, and a user terminal device. Each database may be included in the recipe suggestion device 100.

[0015] The present disclosure discloses a technique for suggesting to a user recipes for cooking dishes other than the dishes the user frequently cooks. For example, the recipe suggestion device 100 identifies dishes the user frequently cooks and outputs recommended recipes for cooking dishes other than the identified dishes, which are recipes according to the user's cooking skills. The recipe suggestion device 100 can thus suggest recipes that meet the needs of a user who gets bored of cooking the same dishes over and over again, but whose cooking skills are low and who find it difficult or challenging to suddenly cook a difficult recipe. The functional configuration of the recipe suggestion device 100 will be described later.

[0016] The user information database stores user information, which is information about a user. The user information includes at least the user's purchase history. The user information may also include, for example, the user's cooking skill. The user's cooking skill is the degree of the user's cooking ability. The user's cooking skill is expressed, for example, in a plurality of stages. The user's cooking skill may be input by the user himself / herself. The user's cooking skill may also be estimated from the user information by the recipe suggestion device 100. The user's cooking skill may also be estimated by another device communicatively connected to the recipe suggestion device 100 and stored in the user information database. In this case, the user information may include information for estimating the user's cooking skill.

[0017] The user information may also include other information about the user, such as user attribute information and the user's behavior history.

[0018] The user's purchase history includes at least the products purchased, the number of products purchased, and the date the products were purchased. The purchase history includes, for example, the user's purchase history at various stores. The purchase history at various stores is collected, for example, using a system capable of collecting information on products purchased at various stores. For example, the system capable of collecting information on products purchased at various stores may be, but is not limited to, a point card system usable at multiple stores, a payment system, a purchase registration system, or a system combining these. The point card system usable at multiple stores is a point card system usable at stores operated by multiple companies. The payment system is, for example, a payment system using a credit card, a debit card, electronic money, code payment, or the like. The purchase registration system is, for example, a receipt registration system, a household account book recording system, or the like.

[0019] The user information database may be realized by two databases: a purchase history database that stores the user's purchase history, and a database that stores other user information. In this case, the two databases may be provided inside the recipe suggestion device 100, or may be provided outside the recipe suggestion device 100 in a communicable manner.

[0020] The user information database stores user information of multiple users in association with the identification information of each user. The user identification information is, for example, a membership number of the various systems mentioned above that collect user information. The user identification information is not limited to this example, and may be any information that can identify an individual user.

[0021] The recipe information database stores recipe information. The recipe information includes ingredients used in the dish, a method for making the dish, and a difficulty level of the recipe. The difficulty level of the recipe may be a recommended cooking skill level for a user making the recipe. The recipe information may also include an image of the dish. Furthermore, the recipe information may include information indicating cooking utensils used to prepare the recipe.

[0022] The terminal device is a terminal device used by a user. The terminal device displays recipes recommended to the user. For example, the recipe suggestion device 100 may cause the terminal device to display the recommended recipes via an application that suggests recipes or a website that suggests recipes. The terminal device may be, for example, a smartphone, a tablet, a personal computer, or the like. Also, for example, the terminal device may be a terminal device installed in a store that outputs a code or the like for displaying a recipe recommended to a user on a smartphone or the like used by the user. In this case, the terminal device may be a tablet or the like used by a user to register a product and output a code or the like on a screen, or may be a POS (Point Of Sales) terminal and output a code or the like on a receipt issued by the POS terminal.

[0023] Next, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0024] [First embodiment] The configuration of a recipe suggestion device 100 in the first embodiment will be described.

[0025] 2 is a block diagram showing the configuration of the recipe suggestion device 100. With reference to FIG. 2, the recipe suggestion device 100 includes an acquisition unit 101, an ingredient identification unit 102, a dish identification unit 103, a determination unit 104, and an output unit 105.

[0026] Next, the configuration of the recipe suggestion device 100 in the first embodiment will be described in detail.

[0027] In FIG. 2, the acquisition unit 101 is an example of an acquisition means for acquiring a purchase history and cooking skill of a user associated with the identification information of the user. The acquisition unit 101 acquires the purchase history and cooking skill of the user from a user information database using the identification information of the user to which recommended recipes are output. The cooking skill of the user may be estimated from the user information. The estimation of the cooking skill of the user will be described later in the second embodiment.

[0028] The ingredient identification unit 102 is an example of an ingredient identification means for identifying an ingredient that is frequently purchased by a user based on a purchase history. The ingredient identification unit 102 may identify one or more ingredients that are frequently purchased by a user. The ingredient identification unit 102 may identify a predetermined number of ingredients in descending order of the number of times purchased in a purchase history over a certain period of time as ingredients that are frequently purchased by a user. The predetermined number of ingredients may be, for example, 5, but is not limited to this example. The ingredient identification unit 102 may also identify ingredients that are purchased more frequently than a predetermined number of times in a purchase history over a certain period of time as ingredients that are frequently purchased by a user. For example, the ingredient identification unit 102 may identify an ingredient that is purchased more than once a week as a specific ingredient, or may identify an ingredient that is purchased more than once in two transactions as a specific ingredient. The ingredient identification unit 102 may also identify an ingredient that is purchased more frequently by a user than the average purchase frequency of each ingredient as an ingredient that is frequently purchased by a user. In this case, the ingredient identification unit 102 may refer to information associating ingredients with the average purchase frequency of each ingredient, and identify ingredients that are purchased by the user more frequently than the average purchase frequency of each ingredient as ingredients frequently purchased by the user. The information associating ingredients with the average purchase frequency of each ingredient may be stored in advance in a storage unit provided inside or outside the recipe suggestion device 100.

[0029] The purchase history period referred to by the ingredient identification unit 102 when identifying ingredients frequently purchased by the user may be a recent predetermined period. The predetermined period may be, for example, one month, one year, etc. The purchase history period referred to by the ingredient identification unit 102 is not limited to this example, and ingredients frequently purchased by the user may be identified.

[0030] Here, ingredients may include seasonings and cooked foods.

[0031] The dish identification unit 103 is an example of a dish identification means that identifies a dish that the user frequently cooks based on ingredients identified by the ingredient identification unit 102. For example, the dish identification unit 103 may identify a dish that the user frequently cooks based on information that associates ingredients with dishes using the ingredients, stored in a storage unit provided inside or outside the recipe suggestion device 100. Also, for example, the dish identification unit 103 may identify a dish that the user frequently cooks by inputting the identified ingredients into a trained model that has trained ingredients and dishes using the ingredients. Note that the dish identification unit 103 may identify a dish that the user frequently cooks based on a combination of identified ingredients.

[0032] The dish identification unit 103 may use information other than the identified ingredients to identify dishes that the user frequently cooks. For example, the dish identification unit 103 may use the user's website browsing history to identify dishes that are cooked using recipes that the user frequently viewed on websites as dishes that the user frequently cooks. In this case, the user's website browsing history may be stored in a storage unit or the like so as to be referable using the user's identification information.

[0033] The determination unit 104 is an example of a determination means for determining a recommended recipe that is a recipe according to the cooking skill of the user and is a recipe for cooking a dish other than the identified dish that the user frequently cooks. A recipe according to the cooking skill of the user is, for example, a recipe with a level of difficulty that can be cooked with the cooking skill of the user. Furthermore, recipes according to the cooking skill of the user may include recipes with a level of difficulty that can be cooked with a cooking skill one level higher than the cooking skill of the user. A recipe with a level of difficulty that can be cooked with a cooking skill one level higher than the cooking skill of the user is a recipe with a level of difficulty that the user can moderately challenge.

[0034] For example, the determination unit 104 excludes recipes for cooking the dishes identified by the dish identification unit 103 from the recipe information database. Then, the determination unit 104 extracts recipes with a difficulty level according to the cooking skill. The determination unit 104 may extract one or more recipes. That is, the determination unit 104 may determine the extracted recipe as the recommended recipe, or may determine a part of the extracted recipe as the recommended recipe. For example, the determination unit 104 may determine the recommended recipe using other information such as ingredients that the user intends to purchase, and examples of these will be described later in a modified example.

[0035] The determination unit 104 may determine, as the recommended recipe, both or either of a recipe with a difficulty level that can be cooked with the user's cooking skill and a recipe with a difficulty level that can be cooked with a cooking skill one level higher than the user's cooking skill. For example, the determination unit 104 may determine whether to determine, as the recommended recipe, a recipe with a difficulty level that can be cooked with the user's cooking skill and a recipe with a difficulty level that can be cooked with a cooking skill one level higher than the user's cooking skill, depending on how busy the user is, which will be described later in a modified example.

[0036] The output unit 105 is an example of an output means for outputting a recommended recipe. The output unit 105 outputs the recommended recipe to, for example, a user's terminal device. The form in which the output unit 105 outputs the recommended recipe is not particularly limited. For example, the output unit 105 may output a screen that allows the user to select whether to output the recommended recipe, and output the recommended recipe when the user selects to output the recommended recipe. The output unit 105 may also output the recommended recipe by at least one of the name and image of the recommended recipe and a button for proceeding to a screen displaying details of the recommended recipe, and when the button for proceeding to a screen displaying details of the recommended recipe is selected, the output unit 105 may output the details of the recommended recipe. Here, the details of the recommended recipe are the cooking procedure of the recommended recipe and the ingredients used in the recommended recipe.

[0037] Furthermore, when there are multiple recommended recipes, the output unit 105 may output the multiple recommended recipes in a predetermined order. The predetermined order may be, for example, ascending order of ingredient price or descending order of difficulty, but is not limited to these examples.

[0038] In addition, the output unit 105 may further output information for enabling the user to recognize the difficulty of the recommended recipe and the relationship between the difficulty of the recommended recipe and the cooking skill of the user together with the recommended recipe. For example, the output unit 105 may output the difficulty of the recommended recipe and the cooking skill of the user on the same screen in addition to the recommended recipe as information for enabling the user to recognize the relationship between the difficulty of the recommended recipe and the cooking skill of the user. In addition, the information for enabling the user to recognize the relationship between the difficulty of the recommended recipe and the cooking skill of the user may be, for example, a message or icon such as "This recipe suits your cooking skill" indicating that the difficulty of the recommended recipe matches the cooking skill of the user. In addition, for example, the information indicating the relationship between the difficulty of the recommended recipe and the cooking skill of the user may be a message or icon such as "Why not try a more difficult recipe than usual?" indicating that the difficulty of the recommended recipe is one level higher than the cooking skill of the user.

[0039] Fig. 3 is an example of an output screen on which the output unit 105 outputs recommended recipes. In Fig. 3, the output screen includes the user's cooking skill and three recommended recipes. Each recommended recipe is indicated by a button for displaying the name, image, difficulty level, and cooking procedure, and a button for displaying ingredients used in the cooking.

[0040] The timing at which the output unit 105 outputs the recommended recipe is not particularly limited. For example, the output unit 105 may output the recommended recipe when the user goes shopping. The timing at which the user goes shopping may be, for example, designated in advance by the user and stored as user information. The timing at which the user goes shopping may be estimated from the date and time of the user's purchase history. The output unit 105 may output the recommended recipe when the user accesses or logs into an application or website that provides recipe suggestions, or when the user's purchase history is updated. The output unit 105 may output the recommended recipe when the user is performing scan shopping. Scan shopping is a shopping method in which a user registers products by scanning barcodes or the like while wandering around a store.

[0041] The operation of the recipe suggestion device 100 configured as above will be described with reference to the flowchart of FIG.

[0042] First, the acquisition unit 101 acquires the purchase history and cooking skill of the user associated with the user's identification information (step S101).

[0043] Next, the ingredient identification unit 102 identifies ingredients that the user frequently purchases based on the user's purchase history acquired in step S101 (step S102).

[0044] Next, the dish identification unit 103 identifies dishes that the user frequently cooks based on the ingredients that the user frequently cooks, identified in step S102 (step S103).

[0045] Next, the determination unit 104 determines a recommended recipe, which is a recipe according to the cooking skill of the user acquired in step S101 and is for cooking a dish other than the dish identified in step S103 (step S104).

[0046] Then, the output unit 105 outputs the recommended recipe determined in step S104 (step S105).

[0047] This completes the series of operations of the recipe suggestion device 100. The processes of steps S101 to S104 may be performed when the recommended recipe is output. The processes of steps S101 to S104 may also be performed in advance when the user's purchase history is updated, or at a predetermined timing such as once a month or once every six months.

[0048] The recipe suggestion device in the above-described embodiment includes an acquisition unit, an ingredient identification unit, a dish identification unit, a determination unit, and an output unit. The acquisition unit acquires a user's purchase history and the user's cooking skill associated with the user's identification information. The ingredient identification unit identifies ingredients frequently purchased by the user based on the purchase history. The dish identification unit identifies dishes frequently cooked by the user based on the ingredients frequently purchased by the user. The determination unit determines a recommended recipe, which is a recipe according to the user's cooking skill and is for cooking a dish other than the dish frequently cooked by the user. Then, the output unit outputs the recommended recipe.

[0049] As a result, the recipe suggestion device in this embodiment can suggest recipes that meet the needs of the user. In the recipe suggestion device in this embodiment, the output unit outputs recommended recipes other than the specific recipes that the user frequently cooks. For example, this makes it possible to suggest appropriate recipes to a user who is dissatisfied with repeatedly eating the same or similar meals due to low cooking skills.

[0050] [Second embodiment] Next, a second embodiment of the present disclosure will be described in detail with reference to the drawings. In the following, the description of the second embodiment will be omitted unless the description of the present embodiment is unclear.

[0051] Fig. 5 is a block diagram showing the configuration of the recipe suggestion device 200. In Fig. 5, the recipe suggestion device 200 includes a skill estimation unit 206 in addition to the configuration of the first embodiment.

[0052] The skill estimation unit 206 is an example of a skill estimation means for estimating a cooking skill of a user based on dishes frequently cooked by the user identified by the dish identification unit 103. For example, the skill estimation unit 206 may estimate the skill based on at least one of the number of types of dishes frequently cooked by the user and the difficulty of each recipe for cooking the dishes frequently cooked by the user.

[0053] An example will be described in which the skill estimation unit 206 estimates the cooking skill of a user based on the number of types of dishes that the user frequently cooks. The more types of dishes that the user frequently cooks, the higher the cooking skill is assumed to be, since the user can cook a variety of dishes. Conversely, the fewer types of dishes that the user frequently cooks, the lower the cooking skill is assumed to be. Therefore, the skill estimation unit 206 may estimate the cooking skill of a user based on a preset relationship between the number of types of dishes that the user frequently cooks and the cooking skill.

[0054] An example will be described in which the skill estimation unit 206 estimates the cooking skill of a user based on the difficulty of a recipe for cooking a dish that the user frequently cooks. The difficulty of the recipe for the dish that the user frequently cooks is assumed to be a difficulty that can be cooked with the cooking skill of the user. Therefore, the skill estimation unit 206 identifies a recipe for cooking the dish that the user frequently cooks. Then, the skill estimation unit 206 may estimate the cooking skill of the user based on the difficulty of the recipe for cooking the dish that the user frequently cooks, by referring to the relationship between the difficulty of the recipe and the cooking skill that is set in advance. Note that, when the user frequently cooks multiple dishes, the difficulty of the recipe for cooking the dish that the user frequently cooks may be the median or average of the difficulty of the recipes for each of the multiple dishes.

[0055] The operation of the recipe suggestion device 200 configured as above will be described with reference to the flowchart of FIG.

[0056] The acquiring unit 101 acquires a purchase history of a user associated with the user's identification information (step S201). As in the first embodiment, the acquiring unit 101 may acquire the purchase history of a user associated with the user's identification information at the timing of outputting the above-mentioned recommended recipe.

[0057] Next, the ingredient identification unit 102 identifies ingredients that the user frequently purchases based on the user's purchase history acquired in step S201 (step S202).

[0058] Next, the dish identification unit 103 identifies dishes that the user frequently cooks based on the ingredients that the user frequently purchases, identified in step S202 (step S203).

[0059] Next, the skill estimation unit 206 estimates the cooking skill of the user based on the dishes frequently cooked by the user identified in step S203 (step S204).

[0060] Next, the determination unit 104 determines a recommended recipe, which is a recipe according to the cooking skill of the user estimated in step S204 and is for cooking a dish other than the dish identified in step S203 (step S205).

[0061] Then, the output unit 105 outputs the recommended recipe determined in step S205 (step S206).

[0062] This completes the series of operations of the recipe suggestion device 200. The processes of steps S201 to S205 may be performed when the recommended recipe is output. Furthermore, the processes of steps S201 to S205 may be performed when the user's purchase history is updated, or at a predetermined timing such as once a month or once every six months. Furthermore, step S204 may be performed in advance at a timing different from the timing when the recommended recipe is output.

[0063] The recipe suggestion device in the above-described embodiment includes an acquisition unit, an ingredient identification unit, a dish identification unit, a determination unit, an output unit, and a skill estimation unit. The acquisition unit acquires a purchase history of a user associated with the user's identification information. The ingredient identification unit identifies ingredients frequently purchased by the user based on the purchase history. The dish identification unit identifies dishes frequently cooked by the user based on ingredients frequently purchased by the user. The skill estimation unit estimates the user's cooking skill based on the dishes frequently cooked by the user. The determination unit determines a recommended recipe, which is a recipe according to the cooking skill and is for cooking a dish other than the dish frequently cooked by the user. Then, the output unit outputs the recommended recipe.

[0064] As a result, the recipe suggestion device in this embodiment can suggest recipes that meet the needs of the user. For example, in the recipe suggestion device in this embodiment, the skill estimation unit estimates the cooking skill of the user based on the user's purchase history. Therefore, the recipe suggestion device in this embodiment can output recommended recipes that match the cooking skill of the user even if the user does not know his / her own cooking skill or does not know the relationship between the difficulty of the recipe and his / her own cooking skill.

[0065] Next, modifications of each embodiment will be described.

[0066] [Variation 1] The determination unit 104 may determine the recommended recipes to be output using other information in addition to the cooking skills of the user and the dishes that the user frequently cooks. Various modified examples of the determination of the recommended recipes will be described.

[0067] (1) Outputting recommended recipes based on the user’s busy schedule First, the determination unit 104 may determine a recommended recipe according to how busy the user is. This is because it is considered that when the user is not busy, he / she may have time to try a difficult recipe. Conversely, when the user is busy, he / she may not have time to try a difficult recipe due to lack of time or fatigue. The user's busyness may be expressed in two stages, for example, "busy" or "not busy". Also, for example, the user's busyness may be expressed in three or more stages.

[0068] Fig. 7 is a block diagram showing the configuration of a recipe suggestion device 300. In Fig. 7, the recipe suggestion device 300 includes, in addition to the configuration of the first embodiment, a busyness estimation unit 307. Note that the busyness estimation unit 307 may be provided in addition to the configuration of the recipe suggestion device 200.

[0069] The busyness estimation unit 307 is an example of a busyness estimation means that estimates how busy a user is based on information for estimating how busy the user is, which is associated with the user's identification information. The information for estimating how busy the user is may be included in the user information. The busyness estimation unit 307 estimates how busy the user is by using the user information obtained from a terminal device carried by the user.

[0070] The information for estimating the user's busyness may be, for example, the wake-up time on the day the recommended recipe is output, physiological information on the day the user's recommended recipe is output, location information history on the day the user's recommended recipe is output, etc. The physiological information is, for example, information related to the user's physical condition, such as heart rate and body temperature. In this case, the acquisition unit 101 further acquires information for estimating the user's busyness.

[0071] For example, the busyness estimation unit 307 may estimate that the user is busy if the user wakes up earlier than a reference time based on the user's wake-up time on the day the recommended recipe is output. The reference time may be a predetermined time or an average wake-up time calculated from the user's wake-up time.

[0072] Also, for example, the busyness estimation unit 307 may estimate how busy the user is based on the physiological information of the user on the day the recommended recipe is output. For example, the busyness estimation unit 307 estimates how busy the user is from the physical condition based on the physiological information of the user. The physical condition is, for example, the exercise load, physical condition, etc. For example, the busyness estimation unit 307 may estimate that the exercise load is high and the user is busy when the average heart rate of the user is higher than a reference value or when the time during which the user's heart rate is higher than the reference value is longer than a certain period of time. Note that each reference value may be set in advance or may be the average value of the user, etc.

[0073] Also, for example, the busyness estimation unit 307 may estimate the busyness of the user based on the user's location information history on the day the recommended recipe is output. For example, the busyness estimation unit 307 may estimate the busyness according to the length of time that the user is estimated to have been away from home from the location information history. In this case, for example, the busyness estimation unit 307 may estimate that the user is busy when the time that the user has been away from home is longer than a reference value. Also, for example, the busyness estimation unit 307 may estimate that the user is busy from the location information history according to the amount of movement of the user. In this case, for example, the busyness estimation unit 307 may estimate that the user is busy when the amount of movement of the user is greater than a reference value. Note that each reference value may be set in advance or may be an average value of the user.

[0074] The method by which the busyness estimation unit 307 estimates busyness is not limited to these examples. The busyness estimation unit 307 may estimate the busyness of the user by combining a plurality of the above-mentioned busyness estimation methods. For example, the busyness estimation unit 307 may estimate the busyness of the user, which is expressed in a plurality of stages, by using the busyness estimated by each of the plurality of methods.

[0075] The determination unit 104 may determine the recommended recipe according to the busyness estimated as described above. For example, when the user is busy, the determination unit 104 may determine the recommended recipe to be a recipe with a difficulty level that can be cooked with the user's cooking skill, and when the user is not busy, the determination unit 104 may determine the recommended recipe to be a recipe with a difficulty level that can be cooked with a cooking skill one level higher than the user's cooking skill. Also, for example, when the user is busy, the determination unit 104 may determine the recommended recipe to be a recipe with a difficulty level that can be cooked with the user's cooking skill, and when the user is not busy, the determination unit 104 may determine the recommended recipe to be a recipe with a difficulty level that can be cooked with the user's cooking skill and a recipe with a difficulty level that can be cooked with a cooking skill one level higher than the user's cooking skill.

[0076] The busyness of the user may be specified by, for example, an input by the user himself / herself. For example, the output unit 105 may output a screen for allowing the user to select the busyness of the user before outputting the recommended recipe. In this case, the output unit 105 outputs the recommended recipe according to the busyness selected by the user.

[0077] By determining a recommended recipe according to busyness as described above, the determination unit 104 may be able to prevent the user from feeling annoyed when a difficult recipe is suggested to the user when the user is busy.

[0078] (2) Output of recommended recipes according to time of day The determination unit 104 may determine a recommended recipe according to a time period for outputting the recommended recipe. The time period for outputting the recommended recipe may be specified by the user or may be estimated from the date and time of the purchase history.

[0079] For example, when the time period for outputting the recommended recipe is late at night, the determination unit 104 may determine as the recommended recipe a recipe with a level of difficulty that can be cooked with the user's cooking skill, and when the time period for outputting the recommended recipe is not late at night, the determination unit 104 may determine as the recommended recipe a recipe with a level of difficulty that can be cooked with a cooking skill one level higher than the user's cooking skill. This is because it is considered that the user is less likely to attempt a recipe that is more difficult than usual during late hours.

[0080] Also, for example, when the time period for outputting the recommended recipes is late at night, the determination unit 104 may determine, as the recommended recipes, recipes for dishes suitable for eating late at night. Dishes suitable for eating late at night include dishes with calories lower than a reference value, dishes mainly containing vegetables, dishes that are easy to digest, etc. In this case, the recipe information may include information indicating whether each recipe is suitable for eating late at night.

[0081] For example, when the time period for outputting the recommended recipe is late at night, the determination unit 104 extracts recipes for cooking dishes suitable for eating late at night from the recipe information database. Then, the determination unit 104 outputs the recommended recipes that are the extracted recipes, that correspond to the cooking skill, and that are for cooking dishes other than the dishes that the user frequently cooks.

[0082] Here, the time period may include the day of the week. For example, the determination unit 104 may determine a recommended recipe depending on whether the time period for outputting the recommended recipe is late at night on a weekday or late at night on a holiday.

[0083] (3) Output of recommended recipes based on hobbies The determination unit 104 may determine the recommended recipes according to the user's hobbies. The user's hobbies may be included in the user information. For example, the recommended recipes according to the user's hobbies may be dishes that appear in the user's favorite dramas, movies, novels, manga, anime, videos, and the like. Also, for example, the recommended recipes according to the user's hobbies may be dishes related to the user's travel destinations, but are not limited to these examples. In this case, the recipe information may include information on related content.

[0084] For example, the acquiring unit 101 acquires information about the user's hobby from a user information database. The information about the user's hobby may be input in advance by the user, or may be estimated from a purchase history, a behavior history, or the like. The determining unit 104 then determines, as a recommended recipe, a recipe that is related to a content the user likes, corresponds to the cooking skill, and is for cooking a dish other than the dish the user frequently cooks.

[0085] By determining and outputting recipes that correspond to the user's preferences as recommended recipes, the user may be motivated to select recipes that are different from usual, or may be able to enjoy cooking.

[0086] (4) Output of recommended recipes based on cooking utensils The determination unit 104 may determine the recommended recipe according to the cooking utensils owned by the user. The cooking utensils owned by the user may be identified from the purchase history, or may be input in advance by the user. For example, the determination unit 104 may determine a recipe that can be cooked using cooking utensils included in the user's purchase history as the recommended recipe according to the cooking utensils. In this case, the recipe information may include information indicating the cooking utensils used in the recipe.

[0087] For example, the acquisition unit 101 acquires a purchase history. The determination unit 104 extracts recipes that use cooking utensils based on the cooking utensils included in the purchase history. The determination unit 104 then determines, as a recommended recipe, a recipe that uses cooking utensils owned by the user, corresponds to the cooking skill, and is for cooking a dish other than the dish that the user frequently cooks.

[0088] The cooking tools may include an oven range, an automatic cooking pot, a smoker, a barbecue set, etc. By outputting recommended recipes according to the cooking tools owned by the user, if the user is not familiar with the cooking tools, the user may be able to learn how to use the cooking tools, suitable ingredients, and suitable cooking methods.

[0089] (5) Output of recommended recipes based on ingredients The determination unit 104 may determine a recommended recipe according to ingredients that the user is going to purchase or ingredients that the user has purchased. In the following description, the ingredients that the user is going to purchase and the ingredients that the user has purchased are referred to as purchased ingredients.

[0090] The ingredients that the user wishes to purchase may be, for example, ingredients registered in a list of products to be purchased when the user performs scan shopping. Also, the ingredients that the user wishes to purchase may be ingredients registered in a list of products to be purchased on an EC (Electronic Commerce) site. In this case, the acquisition unit 101 acquires information on the ingredients that the user wishes to purchase as user information from a scan shopping system or the EC site. Then, the determination unit 104 may determine a recommended recipe according to the ingredients that the user wishes to purchase.

[0091] The ingredients purchased by the user may be, for example, ingredients newly added to the user's purchase history. The ingredients purchased by the user may be ingredients newly added to the user's purchase history and purchased within a predetermined time period. A recommended recipe for an ingredient purchased within a predetermined time period may be output before the user prepares a dish using the ingredient. Therefore, in this case, the predetermined time period may be set in advance to a time period such as one hour, or may be set for each user based on the distance from the store where the user purchased the ingredient to the user's home, for example. In this case, the determination unit 104 may determine a recommended recipe according to the ingredient that the user is going to purchase based on the purchase history.

[0092] The ingredients to be purchased may be input by the user.

[0093] The determination unit 104 may determine a recommended recipe depending on the type or amount of purchased ingredients.

[0094] For example, when the purchased ingredient is not included in the user's purchase history, the determination unit 104 may determine a recipe using the purchased ingredient as the recommended recipe. When the purchased ingredient is not included in the user's purchase history, it may be that the user has purchased the ingredient for the first time.

[0095] Also, for example, if the purchased ingredients are not included in the user's purchase history, the determination unit 104 may determine, as the recommended recipe, a recipe that matches the user's cooking skill and uses the purchased ingredients. This is because when a user purchases an ingredient that the user has never purchased before, it is assumed that the user has time to try cooking new ingredients and recipes.

[0096] Also, for example, when the amount of purchased ingredients is greater than the amount purchased in the user's purchase history, the determination unit 104 may determine a recipe using the purchased ingredients as a recommended recipe. The amount of purchased ingredients may be the amount of each purchased ingredient, or the total amount of ingredients purchased in one transaction. The purchase amount in the user's purchase history may be the average, median, maximum, or the like of the purchase amount. In this case, since the user has shopped in an amount different from usual, it is assumed that the user will cook for a larger number of people than usual. The determination unit 104 determines the purchase amount, which is the amount of ingredients purchased by the user, based on the ingredients that the user intends to purchase or the ingredients that the user has purchased. Alternatively, the determination unit 104 may determine the purchase amount in the user's purchase history based on the user's purchase history.

[0097] Also, for example, if the amount of purchased ingredients is greater than the amount purchased in the user's purchase history, the determination unit 104 may determine a recommended recipe according to the amount of purchased ingredients. The recommended recipe according to the amount of purchased ingredients may be a recipe that uses ingredients in an amount close to the amount of purchased ingredients. Also, the recommended recipe according to the amount of purchased ingredients may be a recipe for a menu for a party. A menu for a party may be, for example, a menu that is more luxurious or looks better than regular dishes. In this case, the recipe information may include information indicating whether the recipe is for a party or not.

[0098] Although various modified examples of the process of the determination unit 104 have been described so far, multiple modified examples of the determination unit 104 may be appropriately combined. For example, the determination unit 104 may determine a recommended recipe based on the busyness of the user, the time period for outputting the recommended recipe, and the amount of ingredients purchased by the user. In addition, when multiple modified examples are combined, a priority order may be set for multiple conditions in the determination by the determination unit 104. For example, in multiple conditions of the busyness of the user, the time period for outputting the recommended recipe, and the amount of ingredients purchased by the user, the priority order may be, in descending order, the busyness of the user, the time period for outputting the recommended recipe, and the amount of ingredients purchased by the user. For example, if the user is busy even if the amount of purchased ingredients is large, it is assumed that the amount of purchased ingredients is large not for an event such as a party, but for the purpose of stockpiling. In this case, the determination unit 104 may be able to determine a recipe that meets the needs of the user by determining a recipe for a busy user as the recommended recipe, rather than a recipe for a party.

[0099] [Variation 2] In the second modification, the determining unit 104 may determine a list of ingredients used in the recommended recipes, which allows the user to easily shop.

[0100] In this case, the recipe suggestion device 100, 200, or 300 may include an ingredient list specification unit. The ingredient list specification unit specifies a list of ingredients corresponding to ingredients included in the recommended recipe. The ingredients corresponding to the ingredients included in the recommended recipe are products with an amount equal to or greater than the amount of ingredients included in the recommended recipe, and are the smallest unit of products that the user can purchase. For example, if the ingredients included in the recommended recipe are 1 / 2 a carrot, the ingredients corresponding to the ingredients included in the recommended recipe may be 1 carrot.

[0101] The ingredient list specification unit may specify a list of ingredients to be additionally purchased according to ingredients remaining at the user's home. The ingredients remaining at the user's home may be input by the user, may be estimated from an image of the refrigerator or the like at the user's home, or may be estimated from the user's purchase history and recipes cooked by the user. For example, the ingredient list specification unit may specify a list of ingredients corresponding to ingredients included in a recommended recipe, excluding ingredients remaining at the user's home, as the list of ingredients to be additionally purchased.

[0102] [Variation 3] In a third modification, an example in which a method for preserving ingredients is also output will be described.

[0103] 8 is a block diagram showing the functional configuration of the recipe suggestion device 400. In addition to the configuration of the recipe suggestion device 100, the recipe suggestion device 400 further includes an extraction unit 408. For example, the extraction unit 408 is an example of an extraction means that extracts an ingredient storage method when the amount of purchased ingredients is greater than the purchase amount in the user's purchase history. Also, for example, the extraction unit 408 may extract an ingredient storage method when the amount of purchased ingredients is greater than the amount of ingredients used in the recommended recipe. The output unit 105 further outputs the extracted storage method.

[0104] For example, if a user has low cooking skills, the user may have little knowledge about ingredients. Also, for example, in retail stores such as supermarkets, ingredients are not sold in the exact amounts suggested in recipes, so the user may end up with leftover ingredients. In such cases, the user may have a need to know how to preserve the ingredients.

[0105] In this case, the preservation method of the ingredient may be associated with the type of ingredient in advance and stored in a storage unit, etc. The extraction unit 408 may output the preservation method by referring to the storage unit, etc., for at least one of the purchased ingredients that is more than the amount purchased in the user's purchase history and the amount of the purchased ingredient that is more than the amount of the ingredient used in the recommended recipe.

[0106] [Variation 4] In the fourth modification, the output unit 105 may notify the user that the cooking skill of the user estimated by the skill estimation unit 205 has improved. This may motivate the user to cook.

[0107] 9 is a block diagram showing the functional configuration of the recipe suggestion device 200 A. The recipe suggestion device 200 A includes a determination unit 209 in addition to the components of the recipe suggestion device 200 .

[0108] The determination unit 209 is an example of a determination means for determining whether cooking skills have improved by comparing past cooking skills with estimated cooking skills.

[0109] For example, the skill estimation unit 205 stores the estimated cooking skill as user information in association with the user's identification information. The determination unit 209 compares the cooking skill stored in association with the user's identification information with the cooking skill estimated by the skill estimation unit 205. If the cooking skill estimated by the skill estimation unit 205 is higher than the cooking skill stored in the user information, the determination unit 209 may determine that the cooking skill has improved, and the output unit 105 may notify the user that the user's cooking skill has improved.

[0110] In addition, when the cooking skill of the user is improved, a predetermined amount of points may be added to the points in the point system associated with the user, and the user may be notified that the points have been added. This may further motivate the user to cook. The predetermined amount of points added due to an improvement in cooking skill may be a fixed amount set in advance, or may be an amount according to a preset range of improvement in cooking skill.

[0111] Although the embodiments and modifications have been described above, the embodiments and modifications may be combined as appropriate.

[0112] [Hardware configuration] Some or all of the components of the devices or systems in the embodiments and modifications of the present disclosure described above are realized by any combination of an information processing device 1000 and a program as shown in Fig. 8. The information processing device 1000 includes, as an example, the following configuration.

[0113] ·CPU(Central Processing Unit)1001 ROM (Read Only Memory) 1002 ·RAM(Random Access Memory)1003 Program 1004 loaded into RAM 1003 A storage device 1005 for storing a program 1004 A drive device 1007 for reading and writing data from the recording medium 1006 A communication I / F 1008 that connects to a communication network 1009 Input / output I / F 1010 for inputting and outputting data A bus 1011 connecting each component Note that I / F is an abbreviation for Interface.

[0114] Each component of each device or system in each embodiment is realized by CPU 1001 acquiring and executing a program that realizes these functions. The program that realizes the function of each component of each device is stored in storage device 1005 or RAM 1003 in advance, for example, and is read by CPU 1001 as necessary. Program 1004 may be supplied to CPU 1001 via a communication network, or may be stored in recording medium 1006 in advance, and drive device 1007 may read the program and supply it to CPU 1001.

[0115] There are various variations in the method of implementing each device. For example, each device or system may be implemented by any combination of a separate information processing device 1000 and a program for each component. Also, multiple components included in each device may be implemented by any combination of a single information processing device 1000 and a program.

[0116] Further, a part or all of each component of each device or system is realized by a general-purpose or dedicated circuit including a processor or the like, or a combination of these. The circuit is, for example, a CPU, a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), or an LSI (Large Scale Integration) for AI (Artificial Intelligence) processing. These may be configured by a single chip, or may be configured by multiple chips connected via a bus. A part or all of each component of each device may be realized by a combination of the above-mentioned circuits and a program.

[0117] When a part or all of each component of each device or system is realized by a plurality of information processing devices, circuits, etc., the plurality of information processing devices, circuits, etc. may be centrally arranged or distributed. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, etc., in a form in which each is connected via a communication network.

[0118] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0119] In addition, although the operations are described in a sequence in the form of a flowchart, the sequence does not limit the order in which the operations are performed. Therefore, when implementing each embodiment, the sequence of the operations may be changed as long as it does not affect the content.

[0120] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.

[0121] [Appendix 1] An acquisition means for acquiring a purchase history of the user and a cooking skill of the user associated with the user's identification information; A food ingredient specification means for specifying food ingredients frequently purchased by the user based on the purchase history; A dish specifying means for specifying a dish that the user frequently cooks based on the ingredients; A determination means for determining a recommended recipe, which is a recipe for cooking a dish other than the dish, according to the cooking skill; an output means for outputting the recommended recipe; A recipe suggestion device comprising:

[0122] [Appendix 2] Further comprising a skill estimation means for estimating the cooking skill based on the dish. 2. The recipe suggestion device of claim 1.

[0123] [Appendix 3] The skill estimation means estimates the skill based on the number of types of dishes. 3. A recipe suggestion device as described in appendix 2.

[0124] [Appendix 4] The skill estimation means estimates the cooking skill of the user according to a difficulty level of a recipe for cooking the dish. 4. The recipe suggestion device according to claim 2 or 3.

[0125] [Appendix 5] The cooking skill estimation device further includes a means for judging whether the cooking skill has improved by comparing the cooking skill in the past with the estimated cooking skill, When the cooking skill has improved, the output means notifies the user that the cooking skill has improved. 5. A recipe suggestion device as described in appendix 4.

[0126] [Appendix 6] The system further includes a busyness estimation unit that estimates the busyness of the user based on information for estimating the busyness of the user, the information being associated with the identification information, The determining means determines a recommended recipe according to the busyness. 6. A recipe suggestion device according to any one of claims 1 to 5.

[0127] [Appendix 7] The information for estimating the busyness of the user is a wake-up time of the user, The busyness estimation means estimates the busyness based on the wake-up time. 7. The recipe suggestion device of claim 6.

[0128] [Appendix 8] The information for estimating the busyness of the user is physiological information of the user, The busyness estimation means estimates the busyness based on the physiological information. 8. The recipe suggestion device according to claim 6 or 7.

[0129] [Appendix 9] The information for estimating the user's busyness is a location information history of the user, The busyness estimation means estimates the busyness based on the location information history. 9. A recipe suggestion device according to any one of appendix 6 to 8.

[0130] [Appendix 10] the busyness estimation means estimates the busyness according to a length of time that the user is estimated to have been away from home based on the location information history; 10. The recipe suggestion device of claim 9.

[0131] [Appendix 11] The determination means determines the recommended recipe according to a time period for outputting the recommended recipe. 11. A recipe suggestion device according to any one of claims 1 to 10.

[0132] [Appendix 12] The acquiring means acquires information indicating a product to be purchased by the user, The determining means determines the recommended recipe according to the amount of the product to be purchased. 12. A recipe suggestion device according to any one of claims 1 to 11.

[0133] [Appendix 13] The determining means determines a recommended recipe related to the cooking utensil based on a cooking utensil included in a purchase history of the user. 13. A recipe suggestion device according to any one of claims 1 to 12.

[0134] [Appendix 14] The determination means determines the recommended recipe when the user purchases a product that is not included in the user's purchase history. 14. A recipe suggestion device according to any one of claims 1 to 13.

[0135] [Appendix 15] The determination means determines the recommended recipe according to a purchase amount, which is the amount of ingredients purchased by the user based on the purchase history. 15. A recipe suggestion device according to any one of claims 1 to 14.

[0136] [Appendix 16] When the purchase amount is large, the determination means determines the recommended recipe according to the purchase amount. 16. The recipe suggestion device of claim 15.

[0137] [Appendix 17] If the purchase amount is greater than the purchase amount based on the purchase history of the user, an extraction means is further provided for extracting a preservation method of the food material purchased by the user; The output means further outputs the storage method. 17. The recipe suggestion device according to claim 15 or 16.

[0138] [Appendix 18] The extraction means extracts the preservation method when the amount of ingredients purchased by the user is greater than the amount of ingredients used in the recipe selected by the user. 18. The recipe suggestion device of claim 17.

[0139] [Appendix 19] The computer Obtaining a purchase history of the user and a cooking skill of the user associated with the user's identification information; Identifying ingredients frequently purchased by the user based on the purchase history; Identifying a dish that the user frequently cooks based on the ingredients; determining a recommended recipe, the recommended recipe being a recipe for cooking a dish other than the dish, according to the cooking skill; Outputting the recommended recipe. How to suggest recipes.

[0140] [Appendix 20] The computer, Estimating the cooking skill based on the dish; 20. The recipe suggestion method of claim 19.

[0141] [Appendix 21] The computer, Estimating a skill based on the number of types of dishes; 21. The recipe suggestion method according to claim 20.

[0142] [Appendix 22] The computer, Estimating the cooking skill of the user according to the difficulty of a recipe for cooking the dish; 22. The recipe suggestion method according to claim 20 or 21.

[0143] [Appendix 23] The computer, By comparing the past cooking skill with the estimated cooking skill, it is determined that the cooking skill has improved; If the cooking skill has increased, a notification is sent that the cooking skill has increased. 23. The recipe suggestion method according to claim 22.

[0144] [Appendix 24] The computer, Estimating a busyness level of the user based on information for estimating a busyness level of the user, the information being associated with the identification information; Determining recommended recipes according to the busyness of the user; 24. The recipe suggestion method according to any one of appendix 19 to 23.

[0145] [Appendix 25] The information for estimating the busyness of the user is a wake-up time of the user, The computer, estimating the busyness based on the wake-up time; 25. The recipe suggestion method according to claim 24.

[0146] [Appendix 26] The information for estimating the busyness of the user is physiological information of the user, The computer, estimating the busyness based on the physiological information; 26. The recipe suggestion method according to claim 24 or 25.

[0147] [Appendix 27] The information for estimating the user's busyness is a location information history of the user, The computer, estimating the busyness based on the location information history; 27. A recipe suggestion method according to any one of appendix 24 to 26.

[0148] [Appendix 28] The computer, estimating the busyness according to a length of time that the user is estimated to have been away from home based on the location information history; 28. The recipe suggestion method according to claim 27.

[0149] [Appendix 29] The computer, determining the recommended recipe according to a date and time when the recommended recipe is to be output; 29. The recipe suggestion method of claim 19 or 28.

[0150] [Appendix 30] The computer, Acquire information indicating a product that the user intends to purchase; Determining a recommended recipe according to the amount of the product to be purchased; 30. The recipe suggestion method according to any one of appendix 19 to 29.

[0151] [Appendix 31] The computer, determining recommended recipes associated with the cooking utensils based on the cooking utensils included in the user's purchase history; 31. A recipe suggestion method according to any one of appendix 19 to 30.

[0152] [Appendix 32] The computer, determining the recommended recipe when the user purchases a product that is not included in the user's purchase history; 32. The recipe suggestion method according to any one of appendix 19 to 31.

[0153] [Appendix 33] The computer, determining the recommended recipe according to a purchase amount, which is the amount of ingredients purchased by the user based on the purchase history; 33. The recipe suggestion method according to any one of appendix 19 to 32.

[0154] [Appendix 34] The computer, When the purchase amount is large, the recommended recipe is determined according to the purchase amount. 34. The recipe suggestion method of claim 33.

[0155] [Appendix 35] The computer, If the purchase amount is greater than the purchase amount based on the purchase history of the user, extract a preservation method for the food material purchased by the user; and further outputting the storage method. 35. The recipe suggestion method according to claim 33 or 34.

[0156] [Appendix 36] The computer, extracting the preservation method when the amount of ingredients purchased by the user is greater than the amount of ingredients used in the recipe selected by the user; 36. The recipe suggestion method according to claim 35.

[0157] [Appendix 37] Obtaining a purchase history of the user and a cooking skill of the user associated with the user's identification information; Identifying ingredients frequently purchased by the user based on the purchase history; Identifying a dish that the user frequently cooks based on the ingredients; determining a recommended recipe, the recommended recipe being a recipe for cooking a dish other than the dish, according to the cooking skill; Outputting the recommended recipe. A program that causes a computer to carry out processing.

[0158] [Appendix 38] Estimating the cooking skill based on the dish; 38. The program according to claim 37, which causes the computer to execute the process.

[0159] [Appendix 39] Estimating a skill based on the number of types of dishes; 39. The program according to claim 38, which causes the computer to execute the process.

[0160] [Appendix 40] Estimating the cooking skill of the user according to the difficulty of a recipe for cooking the dish; 40. The program according to claim 38 or 39, which causes the computer to execute the process.

[0161] [Appendix 41] By comparing the past cooking skill with the estimated cooking skill, it is determined that the cooking skill has improved; If the cooking skill has increased, a notification is sent that the cooking skill has increased. 41. The program according to claim 40, which causes the computer to execute the process.

[0162] [Appendix 42] Estimating a busyness level of the user based on information for estimating a busyness level of the user, the information being associated with the identification information; Determining recommended recipes according to the busyness of the user; 42. A program for causing a computer to execute a process according to any one of appendices 37 to 41.

[0163] [Appendix 43] The information for estimating the busyness of the user is a wake-up time of the user, estimating the busyness based on the wake-up time; 43. The program according to claim 42, which causes the computer to execute the process.

[0164] [Appendix 44] The information for estimating the busyness of the user is physiological information of the user, estimating the busyness based on the physiological information; 44. The program according to claim 42 or 43, which causes the computer to execute the process.

[0165] [Appendix 45] The information for estimating the user's busyness is a location information history of the user, estimating the busyness based on the location information history; 45. A program for causing a computer to execute a process as described in any one of appendices 42 to 44.

[0166] [Appendix 46] estimating the busyness according to a length of time that the user is estimated to have been away from home based on the location information history; 46. ​​The program according to claim 45, which causes the computer to execute the process.

[0167] [Appendix 47] determining the recommended recipe according to a date and time when the recommended recipe is to be output; 47. A program for causing a computer to execute a process according to any one of claims 37 to 46.

[0168] [Appendix 48] Acquire information indicating a product that the user intends to purchase; Determining a recommended recipe according to the amount of the product to be purchased; 48. A program for causing a computer to execute a process according to any one of appendices 37 to 47.

[0169] [Appendix 49] determining recommended recipes associated with the cooking utensils based on the cooking utensils included in the user's purchase history; 49. A program for causing a computer to execute a process according to any one of appendices 37 to 48.

[0170] [Appendix 50] determining the recommended recipe when the user purchases a product that is not included in the user's purchase history; 50. A program for causing a computer to execute the process described in any one of appendices 37 to 49.

[0171] [Appendix 51] determining the recommended recipe according to a purchase amount, which is the amount of ingredients purchased by the user, based on the purchase history; 51. The program of claim 37, which causes the computer to execute the process.

[0172] [Appendix 52] When the purchase amount is large, the recommended recipe is determined according to the purchase amount. 52. The recording medium according to claim 51, for causing the computer to execute a process.

[0173] [Appendix 53] If the purchase amount is greater than the purchase amount based on the purchase history of the user, extract a preservation method for the food material purchased by the user; and further outputting the storage method. 53. The program according to claim 51 or 52, which causes the computer to execute the process.

[0174] [Appendix 54] extracting the preservation method when the amount of ingredients purchased by the user is greater than the amount of ingredients used in the recipe selected by the user; 54. A program according to any one of appendices 51 to 53, which causes the computer to execute a process. [Explanation of symbols]

[0175] 100 Recipe suggestion device 101 Acquisition Department 102 Food Identification Department 103 Food Specific Section 104 Decision Section 105 Output section 200 Recipe suggestion device 206 Skill Estimation Department 200A Recipe Proposal Device 209 Judgment Department 300 Recipe suggestion device 307 Busyness Estimation Department 400 Recipe suggestion device 408 Extraction part 1000 Information processing device 1001 CPU 1002 ROM 1003 RAM 1004 Program 1005 Storage device 1006 Recording media 1007 Drive device 1008 Communication I / F 1009 Communication Network 1010 Input / Output Interface 1011 Bus

Claims

1. An acquisition means for acquiring a purchase history of the user and a cooking skill of the user associated with the user's identification information; A food ingredient specification means for specifying food ingredients frequently purchased by the user based on the purchase history; A dish specifying means for specifying a dish that the user frequently cooks based on the ingredients; A determination means for determining a recommended recipe, which is a recipe for cooking a dish other than the dish, according to the cooking skill; an output means for outputting the recommended recipe; A recipe suggestion device comprising:

2. The cooking method further includes a skill estimation means for estimating the cooking skill based on the number of types of the dishes or the difficulty of a recipe for cooking the dishes. The recipe suggestion device according to claim 1 .

3. The cooking skill estimation device further includes a means for judging whether the cooking skill has improved by comparing the cooking skill in the past with the estimated cooking skill, When the cooking skill has improved, the output means notifies the user that the cooking skill has improved. The recipe suggestion device according to claim 2 .

4. The system further includes a busyness estimation unit that estimates the busyness of the user based on information for estimating the busyness of the user, the information being associated with the identification information, The determining means determines a recommended recipe according to the busyness. The recipe suggestion device according to claim 1 .

5. The determination means determines a recommended recipe according to a time period for outputting the recommended recipe. The recipe suggestion device according to claim 1 .

6. The determination means determines the recommended recipe using a product when the user purchases a product that is not included in the user's purchase history. The recipe suggestion device according to claim 1 .

7. When a purchase amount, which is the amount of ingredients purchased by the user, is greater than a purchase amount based on a purchase history of the user, the determination means determines the recommended recipe according to the purchase amount. The recipe suggestion device according to claim 1 .

8. The method further includes an extraction unit that extracts a preservation method for the ingredients purchased by the user when a purchase amount, which is the amount of ingredients purchased by the user, is greater than a purchase amount based on the purchase history of the user, The output means further outputs the storage method. The recipe suggestion device according to claim 7.

9. The computer Obtaining a purchase history of the user and a cooking skill of the user associated with the user's identification information; Identifying ingredients frequently purchased by the user based on the purchase history; Identifying a dish that the user frequently cooks based on the ingredients; determining a recommended recipe, the recommended recipe being a recipe for cooking a dish other than the dish, according to the cooking skill; outputting the recommended recipe; How to suggest recipes.

10. Obtaining a purchase history of the user and a cooking skill of the user associated with the user's identification information; Identifying ingredients frequently purchased by the user based on the purchase history; Identifying a dish that the user frequently cooks based on the ingredients; determining a recommended recipe, the recommended recipe being a recipe for cooking a dish other than the dish, according to the cooking skill; outputting the recommended recipe; A program that causes a computer to carry out processing.

Images