WO2025041265A1 - Dispositif de recommandation de repas et procédé de recommandation de repas - Google Patents

Dispositif de recommandation de repas et procédé de recommandation de repas Download PDF

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Publication number
WO2025041265A1
WO2025041265A1 PCT/JP2023/030158 JP2023030158W WO2025041265A1 WO 2025041265 A1 WO2025041265 A1 WO 2025041265A1 JP 2023030158 W JP2023030158 W JP 2023030158W WO 2025041265 A1 WO2025041265 A1 WO 2025041265A1
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Prior art keywords
dishes
dish
menu
user
category
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English (en)
Japanese (ja)
Inventor
悠 安達
渚 関口
実奈 片桐
なぎさ 塩見
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NTT Inc
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Nippon Telegraph and Telephone Corp
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Priority to PCT/JP2023/030158 priority Critical patent/WO2025041265A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9035—Filtering based on additional data, e.g. user or group profiles

Definitions

  • menu recommendations that use dietary history aim to provide nutritional intake that takes into account the preferences for individual dishes.
  • the menu recommendation may end up recommending a menu that combines dishes that do not go well together for the user. In this case, it is difficult to recommend a menu that takes into account nutritional balance while maintaining the user's food preferences.
  • the first aspect of the present disclosure is a menu recommendation device that includes a creation unit that extracts dishes for each cuisine category based on the degree of co-occurrence, which indicates the compatibility between dishes, and the frequency of intake, which indicates the preference for individual dishes, obtained from the user's meal history, and creates menu candidates to be recommended to the user by combining the extracted dishes for each cuisine category, and a replacement unit that replaces similar dishes within the same cuisine category of a specific cuisine category with the menu candidates based on the similarity between dishes calculated based on the ingredient composition of each dish.
  • the second aspect of the present disclosure is a menu recommendation method in which a menu recommendation device extracts dishes for each cuisine category based on the degree of co-occurrence, which indicates the compatibility between dishes, and the frequency of intake, which indicates the preference for individual dishes, obtained from the user's meal history, combines the extracted dishes for each cuisine category to create menu candidates to be recommended to the user, and replaces similar dishes within the same cuisine category of a specific cuisine category with the menu candidates based on the similarity between dishes calculated based on the ingredient composition of each dish.
  • the disclosed technology has the effect of being able to recommend menus that take into account nutritional balance while maintaining the user's food preferences as much as possible.
  • FIG. 2 is a block diagram showing an example of a hardware configuration of the menu recommendation device according to the embodiment.
  • FIG. 11 is a diagram for explaining a menu recommendation process according to a comparative example.
  • FIG. 2 is a block diagram showing an example of a functional configuration of a menu recommendation device according to an embodiment in a learning phase.
  • FIG. FIG. 2 is a diagram illustrating an example of a model structure of Word2vec.
  • 1 is a block diagram showing an example of a functional configuration in an estimation phase of a menu recommendation device according to an embodiment.
  • FIG. FIG. 13 is a diagram showing an example of a menu recommended to a target user.
  • FIG. 13 is a diagram for explaining a menu recommendation process according to an embodiment;
  • FIG. 11 is a diagram for explaining a menu recommendation process according to a comparative example.
  • FIG. 2 is a block diagram showing an example of a functional configuration of a menu recommendation device according to an embodiment in a learning phase.
  • FIG. FIG. 2 is
  • FIG. 13 is a diagram for explaining a menu recommendation process according to an embodiment
  • 11 is a diagram illustrating a menu recommendation process according to an embodiment.
  • FIG. 11 is a flowchart showing an example of a flow of a menu recommendation process by the menu recommendation program according to the embodiment.
  • the menu recommendation device provides certain improvements over conventional methods of recommending menus and represents an advancement in the technical field of menu recommendations.
  • FIG. 1 is a block diagram showing an example of the hardware configuration of a menu recommendation device 10 according to this embodiment.
  • the menu recommendation device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and a communication interface (I/F) 17.
  • a CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • storage 14 an input unit
  • I/F communication interface
  • Each component is connected to each other so that they can communicate with each other via a bus 18.
  • the CPU 11 is a central processing unit that executes various programs and controls each part. That is, the CPU 11 reads out a program from the ROM 12 or storage 14, and executes the program using the RAM 13 as a working area. The CPU 11 controls each of the above components and performs various calculation processes according to the program stored in the ROM 12 or storage 14.
  • the ROM 12 or storage 14 stores a menu recommendation program for recommending a menu according to this embodiment. Note that instead of the CPU, for example, a GPU (Graphics Processing Unit) may be used.
  • ROM 12 stores various programs and data.
  • RAM 13 temporarily stores programs or data as a working area.
  • Storage 14 is composed of a HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various programs including the operating system and various data.
  • the input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs to the device itself.
  • the display unit 16 is, for example, a liquid crystal display, and displays various information.
  • the display unit 16 may also function as the input unit 15 by adopting a touch panel system.
  • the communication interface 17 is, for example, an interface for communicating with other external devices.
  • a wired communication standard such as Ethernet (registered trademark) or FDDI (Fiber Distributed Data Interface)
  • a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark) is used.
  • the menu recommendation device 10 is implemented as a general-purpose computer device such as a server computer or a personal computer (PC).
  • a general-purpose computer device such as a server computer or a personal computer (PC).
  • FIG. 2 is a diagram used to explain the menu recommendation process according to the comparative example.
  • the deviation from the dietary intake standards e.g., energy intake standards, carbohydrate intake standards
  • the dietary intake standards e.g., energy intake standards, carbohydrate intake standards
  • the food combination that gives the highest score obtained by multiplying the preference score obtained in (S11) by the nutritional scores obtained in (S12) and (S13) is then recommended to the user.
  • the menu recommendation device 10 quantifies the co-occurrence and intake frequency from the user's meal history as index values representing food preferences, and creates menu candidates to be recommended to the user based on these index values. Then, to ensure nutritional balance, the similarity of dishes in the created menu candidates is quantified based on the ingredient composition of the dishes, and similar dishes are replaced with dishes from the same food category of a specific food classification based on the similarity. This makes it possible to recommend to the user a menu that ensures nutritional balance while maintaining the compatibility between dishes and food preferences as much as possible.
  • “co-occurrence” is an index value that indicates the compatibility between dishes.
  • “Frequency of intake” is also called frequency of appearance or frequency of use, and is an index value that indicates the preference for a single dish.
  • “Menu” refers to a meal menu and includes one or more dishes.
  • “Dish” refers to ingredients that are prepared as is or combined with other ingredients to make them easier to eat, or that are cooked and flavored according to the ingredients. “Dish” also includes dishes that combine multiple ingredients and are cooked, as well as ingredients such as vegetables or fruits.
  • “Dish category” refers to categories that make up a menu, such as staple foods, main dishes, side dishes, dairy products, fruits, etc.
  • “Dish category” refers to categories such as meat dishes, fish dishes, egg dishes, soup dishes, etc.
  • FIG. 3 is a block diagram showing an example of the functional configuration of the menu recommendation device 10 in the learning phase according to this embodiment.
  • the menu recommendation device 10 has, as its functional components, a first learning unit 101, a second learning unit 102, a similar user determination unit 103, and a third learning unit 104.
  • Each functional component is realized by the CPU 11 reading out the menu recommendation program stored in the ROM 12 or storage 14, expanding it in the RAM 13, and executing it.
  • the input data set includes, for example, a target user meal history DB (Database) 201, other users meal history DB 202, and general dish combination list DB 203.
  • the target user meal history DB 201, other users meal history DB 202, and general dish combination list DB 203 may be stored in the storage 14, or may be obtained from an external storage device.
  • the target user meal history DB201 is a database that registers the meal history of the target user, who is the target user for menu recommendations, over a specified period of time in the past.
  • the target user's meal history is registered by food category (staple food, main dish, side dish, dairy product, fruit, etc.).
  • Other users' meal history DB202 is a database that registers the meal history of multiple users other than the target user over a specified period of time in the past.
  • the meal history of other users is registered by food category (staple food, main dish, side dish, dairy product, fruit, etc.).
  • the common food combination list DB203 is a database that lists and registers common food combinations by genre, such as Japanese food, Western food, Chinese food, etc.
  • the first learning unit 101 inputs the target user's meal history from the target user meal history DB 201, and generates a target user meal preference model 204, which is a meal preference model for the target user, by machine learning the target user's meal history.
  • a Word2vec model is used as the target user meal preference model 204.
  • Figure 4 shows an example of a Word2vec model structure.
  • Word2vec uses one of two model structures: the CBOW (Continuous Bag of Words) model and the Skip-gram model.
  • Word2vec is a model that expresses the associations between words, and by using this Word2vec model, it is possible to derive the co-occurrence relationships between words, that is, combinations of words that appear frequently.
  • this Word2vec model is applied to meal history, and by replacing words with "dishes included in the meal history,” it is possible to derive the compatibility between dishes, that is, the degree of co-occurrence.
  • each dish included in the target user's meal history is scored based on the degree of co-occurrence.
  • the "score” may be a value indicating the degree of co-occurrence itself, or a value corresponding to the degree of co-occurrence.
  • the target user food preference model 204 outputs dishes having a score close to the score of the dish in question, that is, dishes that go well together.
  • the degree of co-occurrence is an index value that indicates the compatibility between dishes. According to Word2vec, dishes with high co-occurrence tend to have scores that are close to each other.
  • “Scores that are close” here means, for example, that the scores are determined to be close when the difference in scores is below a threshold.
  • the second learning unit 102 inputs the meal history of the similar user and performs machine learning on the meal history of the similar user to generate a similar user meal preference model 205, which is a meal preference model for the similar user.
  • a "similar user” is a user who has similar preferences to the target user.
  • a Word2vec model is used for the similar user meal preference model 205, as with the target user meal preference model 204.
  • the similar user determination unit 103 inputs the meal history of the target user and the meal history of other users, and determines and extracts the meal history of the similar user from the meal history of the other users. For example, other users who have a similar frequency of intake of a specific dish in the meal history of the other users as the target user may be determined to be similar users.
  • the similar user determination unit 103 outputs the extracted meal history of the similar user to the second learning unit 102.
  • the similar user food preference model 205 When a dish related to a similar user is input, the similar user food preference model 205 outputs dishes that have a score close to the score of the dish in question, that is, dishes that go well together.
  • “scores are close” means, for example, that the scores are determined to be close when the difference between the scores is equal to or less than a threshold value.
  • the third learning unit 104 inputs combinations of general dishes by genre from the general dish combination list DB 203, and generates a general food preference model 206, which is a food preference model for general food combinations by genre, by machine learning the general dish combinations.
  • a general food preference model 206 is a food preference model for general food combinations by genre, by machine learning the general dish combinations.
  • a Word2vec model is used for the general food preference model 206.
  • the general food preference model 206 determines whether the dish combination is appropriate relative to general dish combinations by genre, and outputs the determination result. For example, if the score of the input dish combination is close to the score of a general dish combination in Chinese cuisine, that is, if the degree of co-occurrence is high, it is determined to be appropriate.
  • FIG. 5 is a block diagram showing an example of the functional configuration of the menu recommendation device 10 in the estimation phase according to this embodiment.
  • the menu recommendation device 10 includes, as functional components, a staple food prediction unit 110, a creation unit 111, a replacement unit 112, a determination unit 113, a calculation unit 114, and a selection unit 115.
  • Each functional component is realized by the CPU 11 reading out the menu recommendation program stored in the ROM 12 or storage 14, expanding it in the RAM 13, and executing it.
  • the storage 14 stores a cooking DB 208, a target user dietary history DB 209, a target user dietary preference model 204, a similar user dietary preference model 205, a general dietary preference model 206, and a dietary intake reference DB 210.
  • the cooking DB 208, the target user dietary history DB 209, the target user dietary preference model 204, the similar user dietary preference model 205, the general dietary preference model 206, and the dietary intake reference DB 210 may be stored in an external storage device.
  • the target user dietary preference model 204, the similar user dietary preference model 205, and the general dietary preference model 206 are learned models obtained by prior learning in the learning phase of the menu recommendation device 10.
  • the target user meal history DB209 is a database that registers the meal history of the target user for the past N days by dish category.
  • the data group in the target user meal history DB209 is newer than the data group in the target user meal history DB201 used in the learning phase, but some of the data group may overlap.
  • the dietary intake reference DB210 registers information on dishes such as calories and nutrients.
  • the staple food prediction unit 110 inputs the meal history for the past N days from the target user's meal history DB 209, weights the "staple food” dishes by their “ingestion frequency (occurrence frequency),” and randomly selects one "staple food” dish.
  • the creation unit 111 extracts dishes for each cuisine category based on the co-occurrence and intake frequency obtained from the target user's meal history, and creates menu candidates to be recommended to the target user by combining the extracted dishes for each cuisine category. Specifically, the creation unit 111 inputs the staple dish selected by the staple food prediction unit 110 to the target user food preference model 204.
  • the target user food preference model 204 outputs dishes with co-occurrence scores close to those of the staple dish, for example, in the order of "main dish,” “side dish,” “dairy product,” and "fruit.”
  • the creation unit 111 obtains the frequency of intake and the number of days since intake for each cuisine category from the meal history for the past N days, and corrects the co-occurrence score for each cuisine output from the target user meal preference model 204.
  • the following formula (1) is used to correct the score.
  • N indicates the number of days in the meal history
  • G indicates the frequency of intake (number of times consumed) of a certain cuisine
  • D indicates the number of days since intake of a certain cuisine.
  • the creation unit 111 selectively narrows down the dishes output from the target user's dietary preference model 204 to those with high corrected scores.
  • the creation unit 111 extracts dishes that are frequently consumed for each cuisine category and have been consumed for a long time, and combines the extracted dishes for each cuisine category to create menu candidates to be recommended to the target user.
  • the creation unit 111 may use the meal history of a similar user with similar tastes to the target user to extract dishes of the similar user for the specific cuisine category, and use the extracted dishes of the similar user as the dishes of the specific cuisine category.
  • a specific cuisine category e.g., main dish, side dish
  • the creation unit 111 may use the meal history of a similar user with similar tastes to the target user to extract dishes of the similar user for the specific cuisine category, and use the extracted dishes of the similar user as the dishes of the specific cuisine category.
  • main dishes and side dishes cannot be extracted from the target user, there is a possibility that the dietary intake standards cannot be met in the first place.
  • Similar users can be integrated into one model as collective intelligence. Similar users can also be grouped using user-based collaborative filtering.
  • the relevance between dish A and dish B is expressed by the following formula (2).
  • the determination unit 113 determines whether the dish combination of a menu candidate in which dishes have been replaced by the replacement unit 112 is appropriate with respect to the dish combinations of menus predefined for each food genre.
  • the general food preference model 206 is used to determine the appropriateness of the dish combination of a menu candidate in which dishes have been replaced, so as not to disrupt the dish combination.
  • the general food preference model 206 determines whether the dish combination is appropriate with respect to general dish combinations and outputs the determination result. In other words, if the degree of co-occurrence between dishes of each menu candidate is higher than a threshold value, it is determined to be appropriate with respect to general dish combinations, and is left as a menu candidate.
  • the general food preference model 206 can be used to check whether the combination of menu candidates with replaced dishes is close to a combination of general dishes.
  • the calculation unit 114 obtains calorie information and nutrient information about dishes from the dietary reference intake DB 210, calculates the calories and nutrients that the target user should consume, and calculates the dietary reference intakes for the target user.
  • the selection unit 115 selects, from among the multiple menu candidates obtained above, the menu candidate with the smallest deviation from the dietary reference intakes calculated by the calculation unit 114 as the menu to be recommended to the target user. Specifically, for each menu candidate, the deviation from the dietary reference intakes is expressed as, for example, the mean absolute percentage error (MAPE). As shown in Figure 6, the selection unit 115 selects the menu candidate with the smallest MAPE.
  • FIG 6 is a diagram showing an example of a menu to be recommended to the target user.
  • MAPE is calculated by the following formula (3): where M represents MAPE, A t represents the ideal total of calories and nutrients, F t represents the total of calories and nutrients of each dish in the menu candidates, and n represents the number of dishes (number of items).
  • FIGS. 7, 8A, and 8B are diagrams used to explain the menu recommendation process according to this embodiment.
  • the staple food prediction unit 110 randomly selects a staple dish from the target user's meal history, and the creation unit 111 utilizes the property of dishes with high co-occurrence scores being close to each other in Word2vec of the target user's food preference model 204 to extract dishes with high co-occurrence from the staple food in the order of "main dish,” “side dish,” “dairy product,” and “fruit.”
  • the extracted results are shown as a co-occurrence map.
  • the creation unit 111 selectively narrows down the dishes extracted using the target user's dietary preference model 204 to those with high corrected scores.
  • dishes that are frequently consumed and have been consumed many days ago are extracted, and the extracted dishes for each cuisine category are combined to create menu candidates to be recommended to the target user.
  • Fig. 8A shows a table in which main dishes are vectorized by their ingredient composition
  • Fig. 8B shows a table in which side dishes are vectorized by their ingredient composition.
  • the creation unit 111 creates multiple menu candidates that include staple foods, main dishes, side dishes, and fruit dishes as examples of food categories.
  • the selection unit 115 selects, from among the multiple menu candidates obtained above (e.g., combinations A to C), the menu candidate that has the smallest deviation from the dietary reference intakes calculated by the calculation unit 114, as the menu to be recommended to the target user.
  • FIG. 9 is a flowchart showing an example of the flow of a menu recommendation process by the menu recommendation program according to this embodiment.
  • the menu recommendation process by the menu recommendation program is realized by the CPU 11 of the menu recommendation device 10 writing the menu recommendation program stored in the ROM 12 or storage 14 to the RAM 13 and executing it.
  • step S101 of FIG. 9 the CPU 11 retrieves the target user's meal history for the past N days from the target user meal history DB 209.
  • step S102 the CPU 11 weights the "staple food” dishes from the meal history for the past N days acquired in step S101 by "ingestion frequency (frequency of occurrence)" and randomly selects one "staple food” dish.
  • step S103 the CPU 11 inputs the staple dish selected in step S102 into the target user food preference model 204.
  • the target user food preference model 204 outputs dishes with co-occurrence scores close to those of the staple dish, for example in the order of "main dish,” “side dish,” “dairy products,” and “fruit.”
  • the CPU 11 obtains the frequency of intake and the number of days since intake for each food category from the meal history for the past N days, corrects the co-occurrence scores of each dish output from the target user food preference model 204, and narrows down the dishes with high co-occurrence.
  • step S104 the CPU 11 extracts dishes for each cuisine category that are frequently consumed and have been consumed a long time ago based on the scores corrected in step S103, and creates menu candidates to be recommended to the target user by combining the extracted dishes for each cuisine category.
  • step S105 the CPU 11 determines whether or not there are any main dishes or side dishes in the menu candidates created in step S104. If it is determined that there are main dishes and side dishes (if the determination is negative), the process proceeds to step S106, and if it is determined that there are no main dishes and side dishes (if the determination is positive), the process proceeds to step S107.
  • step S106 the CPU 11 replaces similar dishes in the same cuisine category of a specific cuisine division with those in the menu candidates created in step S104, based on the similarity between the dishes calculated based on the ingredient composition of each dish.
  • step S107 the CPU 11, for example, uses the similar user food preference model 205 to extract dishes with similar scores in the order of "main dish” and "side dish” from the same staple food of the similar user, and complements the extracted main dish and side dish dishes of the similar user as the main dish and side dish dishes of the target user, and proceeds to step S106.
  • step S108 the CPU 11 determines whether the combination of dishes in the menu candidate in which dishes have been replaced in step S106 is appropriate, relative to the combination of dishes in the menu that has been predefined for each food genre. Specifically, to ensure that the combination of dishes is not disrupted, the general food preference model 206 is used, for example, to determine the appropriateness of the combination of dishes in the menu candidate in which dishes have been replaced.
  • step S109 the CPU 11 determines whether or not all staple foods have been completed. If it is determined that all staple foods have been completed (if the determination is positive), the process proceeds to step S110, and if it is determined that all staple foods have not been completed (if the determination is negative), the process returns to step S102 and repeats the process.
  • step S110 the CPU 11 selects, from among the multiple menu candidates obtained above, the menu candidate that has the smallest deviation from the dietary reference intakes calculated for the target user as the menu to be recommended to the target user, and ends the series of processes performed by this menu recommendation program.
  • this embodiment takes into account not only the frequency of intake, which indicates the preference for a single dish, but also the degree of co-occurrence, which indicates the compatibility between dishes, so it is possible to recommend a menu that takes into account nutritional balance while maintaining the user's food preferences as much as possible.
  • the menu recommendation process that is executed by loading the program by the CPU may be executed by various processors other than the CPU.
  • processors in this case include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacture, and dedicated electrical circuits such as ASICs (Application Specific Integrated Circuits), which are processors with circuit configurations designed specifically to execute specific processes.
  • the menu recommendation process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, and a combination of a CPU and an FPGA).
  • the hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor elements.
  • the menu recommendation program is described as being pre-stored (also called “installed") in ROM or storage, but this is not limiting.
  • the menu recommendation program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory.
  • the menu recommendation program may also be downloaded from an external device via a network.
  • Memory at least one processor coupled to the memory; Including, The processor, extracting dishes for each cuisine category based on the degree of co-occurrence, which indicates the compatibility between dishes, and the frequency of intake, which indicates the preference for each cuisine, obtained from the user's meal history, and creating menu candidates to be recommended to the user by combining the extracted dishes for each cuisine category; For the menu candidates, replacing similar dishes within the same dish category of a specific dish division based on the similarity between the dishes calculated based on the ingredient composition of each dish. Menu recommendation device.
  • a non-transitory storage medium storing a program executable by a computer to execute a menu recommendation process
  • the menu recommendation process includes: extracting dishes for each cuisine category based on the degree of co-occurrence, which indicates the compatibility between dishes, and the frequency of intake, which indicates the preference for each cuisine, obtained from the user's meal history, and creating menu candidates to be recommended to the user by combining the extracted dishes for each cuisine category; For the menu candidates, replacing similar dishes within the same dish category of a specific dish division based on the similarity between the dishes calculated based on the ingredient composition of each dish.
  • Non-transitory storage media Non-transitory storage media.

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Abstract

Ce dispositif de recommandation de repas comprend : une unité de création qui extrait un plat de chaque groupe de plats parmi de multiples groupes de plats sur la base d'un degré de cooccurrence représentant la compatibilité entre plats et de la fréquence de consommation représentant des préférences concernant des plats individuels, le degré de cooccurrence et la fréquence de consommation étant obtenus à partir de l'historique de repas d'un utilisateur, et crée un repas candidat à recommander à l'utilisateur en combinant les plats extraits des groupes de plats ; et une unité de remplacement qui, sur la base d'un degré de similarité entre plats calculé sur la base de l'ensemble d'ingrédients de chaque plat, effectue la substitution, dans le repas candidat, d'un plat similaire tiré du même groupe de plats.
PCT/JP2023/030158 2023-08-22 2023-08-22 Dispositif de recommandation de repas et procédé de recommandation de repas Pending WO2025041265A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016012247A (ja) * 2014-06-30 2016-01-21 富士機械製造株式会社 献立作成システム
JP2020064666A (ja) * 2016-04-07 2020-04-23 楽天株式会社 情報処理装置、情報処理方法、プログラム
CN115080832A (zh) * 2021-03-10 2022-09-20 松下电器研究开发(苏州)有限公司 食谱推荐方法、食谱推荐装置及冰箱

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016012247A (ja) * 2014-06-30 2016-01-21 富士機械製造株式会社 献立作成システム
JP2020064666A (ja) * 2016-04-07 2020-04-23 楽天株式会社 情報処理装置、情報処理方法、プログラム
CN115080832A (zh) * 2021-03-10 2022-09-20 松下电器研究开发(苏州)有限公司 食谱推荐方法、食谱推荐装置及冰箱

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