System

Abstract

An object of a system according to an embodiment is to quickly propose a recipe based on a dish name and ingredients.SOLUTION: A system includes an input unit, a generation unit, and a display unit. The input part inputs a dish name or an ingredient. The generation unit generates a recipe based on the dish name or the ingredient input by the input unit. The display unit displays the recipe generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to quickly suggest appropriate recipes based on specific dish names and ingredients.

[0005] The system according to the embodiment aims to quickly suggest recipes based on the name of a dish and ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs a dish name or ingredients. The generation unit generates a recipe based on the dish name or ingredients input by the input unit. The display unit displays the recipe generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly suggest recipes based on the name of a dish and ingredients. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The cooking recipe suggestion service according to an embodiment of the present invention is a system in which a generation AI suggests a recipe when the user inputs the name of a dish and ingredients. As a result, the cooking recipe suggestion service allows the generation AI to suggest a recipe based on the name of a dish and ingredients input by the user, enabling the user to recreate the taste of school lunches at home.

[0029] A cooking recipe suggestion service according to an embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs a dish name or ingredients. For example, a user can input information such as "curry rice" or "chicken, potatoes, and carrots." The generation unit generates a recipe based on the dish name or ingredients input by the input unit. For example, the generation AI generates a recipe based on a school lunch menu based on the input information. The generation AI can generate a recipe using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, in the case of "curry rice," the generation AI suggests a recipe that can be recreated at home based on the curry rice recipe provided in school lunches. The display unit displays the recipe generated by the generation unit. For example, the recipe generated by the generation AI is displayed together with detailed steps and a list of necessary ingredients. For example, it might say something like, "Cut the chicken into bite-sized pieces, and cut the potatoes and carrots to bite sizes. Heat oil in a pot and fry the chicken. Next, add the potatoes and carrots and fry some more. Add water and simmer, then add the curry roux and it's done." This allows the generative AI to suggest and display recipes based on the dish name and ingredients entered by the user.

[0030] The input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe for the dish name or ingredients entered by the user. For example, when a user enters "curry rice," the input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable curry rice recipe. For example, the input unit may adapt a curry rice recipe served at a specific school so that it can be recreated at home. When a user enters "chicken, potatoes, and carrots," the input unit allows the generation AI to search a database of past school lunch menus and suggest the most suitable recipe using these ingredients. For example, it may suggest recipes such as simmered chicken and vegetables or curry rice. When a user enters "spaghetti," the input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable spaghetti recipe. For example, it may enable recipes such as spaghetti with meat sauce or spaghetti Neapolitan to be recreated at home. This allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe based on the dish name and ingredients entered by the user.

[0031] The input unit can automatically calculate the nutritional value of the input ingredients and provide health advice. For example, when a user inputs "chicken, potatoes, and carrots," the generation AI automatically calculates the nutritional value of these ingredients and provides health advice. For example, it displays the calorie, protein, and vitamin content and suggests a balanced meal. Also, when a user inputs "curry rice," the generation AI automatically calculates the nutritional value of the curry rice ingredients and provides health advice. For example, it displays the amount of calories and fat and provides advice for a healthy diet. Also, when a user inputs "spaghetti," the generation AI automatically calculates the nutritional value of the spaghetti ingredients and provides health advice. For example, it displays the amount of carbohydrates and dietary fiber content and suggests a healthy meal. In this way, the nutritional value of the input ingredients can be automatically calculated and health advice can be provided.

[0032] The input unit can be equipped with a voice input function, allowing the user to input the name of a dish and ingredients simply by speaking. For example, when the user simply speaks "curry rice," the generation AI recognizes the voice and inputs the name of the dish. This makes it easy to input the name of a dish even when the user's hands are full. Alternatively, when the user simply speaks "chicken, potatoes, carrots," the generation AI recognizes the voice and inputs the ingredients. This makes it easy to input ingredients even when the user's hands are dirty. Alternatively, when the user simply speaks "spaghetti," the generation AI recognizes the voice and inputs the name of the dish. This makes it easy to input the name of a dish even while cooking. This makes it easy to input the name of a dish and ingredients simply by speaking.

[0033] The input unit allows the generative AI to suggest multiple recipes using ingredients entered by the user. For example, if a user enters "chicken, potatoes, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as curry rice, stewed chicken and vegetables, and stew. Alternatively, if a user enters "tomatoes, basil, and mozzarella cheese," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as caprese salad, tomato pasta, and pizza. Alternatively, if a user enters "pork, cabbage, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as stir-fried pork and cabbage, pork soup, and cabbage rolls. This allows the generative AI to suggest multiple recipes using the ingredients entered by the user.

[0034] The generation unit can add history or background information of past school lunch menus to recipes proposed by the generation AI and provide them to users. For example, when the generation AI proposes a recipe for "curry rice," the generation unit adds history and background information of past school lunch menus. For example, it provides information such as, "This curry rice was a popular menu item in school lunches during the Showa era." Furthermore, when the generation AI proposes a recipe for "fried bread," the generation unit adds history and background information of past school lunch menus. For example, it provides information such as, "fried bread was a standard dessert in school lunches during the Showa era." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," the generation unit adds history and background information of past school lunch menus. For example, it provides information such as, "spaghetti with meat sauce was a popular menu item in school lunches during the Heisei era." In this way, the generation AI can add history and background information of past school lunch menus to recipes proposed by the generation AI and provide them to users.

[0035] The generation unit can customize recipe suggestions by taking into account the user's past preferences or allergy information. For example, when the generation AI suggests a recipe for "curry rice," the generation unit customizes the recipe by taking into account the user's past preferences and allergy information. For example, if the user does not like spicy food, the generation unit proposes a less spicy recipe. Furthermore, when the generation AI suggests a recipe for "chicken and vegetable stew," the generation unit customizes the recipe by taking into account the user's past preferences and allergy information. For example, if the user is allergic to carrots, the generation unit proposes a recipe that does not use carrots. Furthermore, when the generation AI suggests a recipe for "spaghetti," the generation unit customizes the recipe by taking into account the user's past preferences and allergy information. For example, if the user prefers gluten-free meals, the generation unit proposes a recipe that uses gluten-free spaghetti. In this way, recipes can be customized by taking into account the user's past preferences and allergy information.

[0036] The generation unit can add videos or images to recipes proposed by the generation AI to make them visually easier to understand. For example, when the generation AI proposes a recipe for "curry rice," the generation unit adds videos and images showing the cooking steps. For example, videos show scenes of frying chicken and adding curry roux. Furthermore, when the generation AI proposes a recipe for "chicken and vegetable stew," the generation unit adds videos and images showing the cooking steps. For example, images show scenes of cutting vegetables and simmering them. Furthermore, when the generation AI proposes a recipe for "spaghetti," the generation unit adds videos and images showing the cooking steps. For example, videos show scenes of boiling spaghetti and adding sauce. In this way, videos and images can be added to recipes proposed by the generation AI to make them visually easier to understand.

[0037] When suggesting a recipe, the generation unit can provide variations based on school lunch menus from different regions or countries. For example, when the generation AI suggests a recipe for "curry rice," the generation unit provides variations based on school lunch menus from different regions or countries. For example, the generation unit suggests not only Japanese curry rice, but also Indian curry and Thai curry. When the generation AI suggests a recipe for "chicken and vegetable stew," the generation unit provides variations based on school lunch menus from different regions or countries. For example, the generation unit suggests not only Japanese stews, but also Chinese stews and French ratatouille. When the generation AI suggests a recipe for "spaghetti," the generation unit provides variations based on school lunch menus from different regions or countries. For example, the generation unit suggests not only Italian spaghetti with meat sauce, but also American macaroni and cheese and Japanese Neapolitan spaghetti. This makes it possible to provide variations based on school lunch menus from different regions or countries.

[0038] The display unit can add cooking tips or points to the detailed display of a recipe, allowing the user to make it even more delicious. For example, when the generation AI displays the details of a "curry rice" recipe, the display unit adds cooking tips and points. For example, it displays advice such as, "When frying chicken, brown it thoroughly to enhance the flavor." In addition, when the generation AI displays the details of a "chicken and vegetable stew," the display unit adds cooking tips and points. For example, it displays advice such as, "When stewing vegetables, simmering them slowly over low heat will bring out their sweetness." In addition, when the generation AI displays the details of a "spaghetti" recipe, the display unit adds cooking tips and points. For example, it displays advice such as, "When boiling spaghetti, adding a lot of salt will enhance the flavor." In this way, cooking tips and points can be added to the detailed display of a recipe, allowing the user to make it even more delicious.

[0039] The display unit can add nutritional value or calorie information to the detailed display of a recipe and provide health advice. For example, when the generation AI displays the details of a "curry rice" recipe, the display unit adds nutritional value and calorie information. For example, it displays information such as "This curry rice has 500 kcal per serving." The display unit also adds nutritional value and calorie information when the generation AI displays the details of a "chicken and vegetable stew." For example, it displays information such as "This stew has 300 kcal per serving." The display unit also adds nutritional value and calorie information when the generation AI displays the details of a "spaghetti" recipe. For example, it displays information such as "This spaghetti has 600 kcal per serving." This allows nutritional value and calorie information to be added to the detailed display of a recipe and health advice to be provided.

[0040] The display unit adds a function that allows users to post photos of dishes they have actually made, thereby enabling the formation of a community. For example, when the generation AI displays details of a recipe for "curry rice," the display unit adds a function that allows users to post photos of the curry rice they have actually made. This allows users to share the results of their cooking with other users. Furthermore, when the generation AI displays details of a recipe for "stewed chicken and vegetables," the display unit adds a function that allows users to post photos of the stew they have actually made. This allows users to share the results of their cooking with other users. Furthermore, when the generation AI displays details of a recipe for "spaghetti," the display unit adds a function that allows users to post photos of the spaghetti they have actually made. This allows users to share the results of their cooking with other users. This allows the formation of a community by adding a function that allows users to post photos of dishes they have actually made.

[0041] The display unit can display reviews or ratings from other users for reference. For example, when the generation AI displays details of a recipe for "curry rice," the display unit displays reviews and ratings from other users. For example, it displays a review such as "This recipe was easy and delicious!". Furthermore, when the generation AI displays details of a recipe for "stewed chicken and vegetables," the display unit displays reviews and ratings from other users. For example, it displays a review such as "This stew was very nutritious and my family loved it!". Furthermore, when the generation AI displays details of a recipe for "spaghetti," the display unit displays reviews and ratings from other users. For example, it displays a review such as "This spaghetti was easy to make and delicious!". This allows reviews and ratings from other users to be displayed for reference.

[0042] The generation unit can add a function to compare the differences between past and present school lunch menus, allowing users to enjoy the differences. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds a function to compare the differences between past and present school lunch menus. For example, it provides information such as, "Curry rice in the Showa era had few ingredients, but now it uses a lot of vegetables." Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit adds a function to compare the differences between past and present school lunch menus. For example, it provides information such as, "In the Showa era, fried bread was sprinkled with sugar, but now it uses cinnamon sugar." Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds a function to compare the differences between past and present school lunch menus. For example, it provides information such as, "In the Heisei era, tomato sauce was the norm for spaghetti with meat sauce, but cream sauce is also popular these days." This adds a function to compare the differences between past and present school lunch menus, allowing users to enjoy the differences.

[0043] The generation unit can add a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, the generation unit posts a story such as, "This curry rice is a taste I remember eating when I was in elementary school." Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit adds a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, the generation unit posts a story such as, "This fried bread is my favorite dessert when I was in elementary school." Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, the generation unit posts a story such as, "This spaghetti with meat sauce is a Western dish I first tried when I was in elementary school." This will allow parents and children to share their school lunch memories by adding a feature that allows them to post stories and photos about past school lunch menus.

[0044] The generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, when the generation AI suggests a recipe for "curry rice," the generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, a "forum for sharing memories of curry rice from the Showa era" can be provided. Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, a "forum for sharing memories of fried bread from the Showa era" can be provided. Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, a "forum for sharing memories of spaghetti with meat sauce from the Heisei era" can be provided. In this way, a forum can be provided where users of different generations can interact with each other, providing a place to share memories of school lunches.

[0045] The generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, when the generation AI suggests a recipe for "curry rice," the generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, an "event that recreates curry rice from the Showa era" can be held. Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, an "event that recreates fried bread from the Showa era" can be held. Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, an "event that recreates spaghetti with meat sauce from the Heisei era" can be held. In this way, events that recreate past school lunch menus can be held, providing an opportunity for users to actually experience it.

[0046] The generation unit can provide a story or episode based on the recipe proposed by the generation AI, allowing the user to enjoy it. For example, when the generation AI proposes a recipe for "curry rice," the generation unit provides a story or episode based on the recipe. For example, it provides information such as, "This curry rice was a popular menu item in school lunches during the Showa era, and was a dish that children looked forward to." Furthermore, when the generation AI proposes a recipe for "fried bread," the generation unit provides a story or episode based on the recipe. For example, it provides information such as, "This fried bread was a standard dessert in school lunches during the Showa era, and was a dish that children loved." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," the generation unit provides a story or episode based on the recipe. For example, it provides information such as, "This spaghetti with meat sauce was a popular menu item in school lunches during the Heisei era, and was a dish that children looked forward to." In this way, it is possible to provide a story or episode based on the recipe proposed by the generation AI, allowing the user to enjoy it.

[0047] The generation unit can add historical or background information about the dishes suggested by the generation AI, allowing users to enjoy them more deeply. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds historical and background information about the dish. For example, it provides information such as, "Curry rice is a long-loved menu item in Japanese school lunches and is a traditional dish that has been around since the Showa era." Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit adds historical and background information about the dish. For example, it provides information such as, "fried bread was a standard dessert in school lunches during the Showa era and was a dish that children loved." Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds historical and background information about the dish. For example, it provides information such as, "spaghetti with meat sauce was a popular menu item in school lunches during the Heisei era and was a dish that children looked forward to." This allows the generation AI to add historical and background information about the dishes it suggests, allowing users to enjoy them more deeply.

[0048] The generation unit can provide quizzes or games based on the recipes proposed by the generation AI, allowing users to enjoy them. For example, when the generation AI proposes a recipe for "curry rice," the generation unit provides quizzes or games based on that recipe. For example, it provides a "quiz to guess the ingredients in curry rice" or a "game to learn the history of curry rice." Furthermore, when the generation AI proposes a recipe for "fried bread," the generation unit provides quizzes or games based on that recipe. For example, it provides a "quiz to learn how to make fried bread" or a "game to learn the history of fried bread." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," the generation unit provides quizzes or games based on that recipe. For example, it provides a "quiz to guess the ingredients in spaghetti with meat sauce" or a "game to learn the history of spaghetti with meat sauce." In this way, it is possible to provide quizzes or games based on the recipes proposed by the generation AI, allowing users to enjoy them.

[0049] The generation unit can add music or video for the dish suggested by the generation AI, allowing the user to enjoy it visually and aurally as well. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds music and video related to the dish. For example, it provides "background music for making curry rice" and "video showing the cooking steps for curry rice." When the generation AI suggests a recipe for "fried bread," the generation unit adds music and video related to the dish. For example, it provides "background music for making fried bread" and "video showing the cooking steps for fried bread." When the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds music and video related to the dish. For example, it provides "background music for making spaghetti with meat sauce" and "video showing the cooking steps for spaghetti with meat sauce." In this way, the generation AI can add music and video for the dish suggested by the generation AI, allowing the user to enjoy it visually and aurally as well.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe for the dish name or ingredients entered by the user. For example, if a user enters "curry rice," the generation AI will refer to a database of past school lunch menus and suggest the most suitable curry rice recipe. For example, the system could adapt a curry rice recipe served at a specific school so that it can be recreated at home. Similarly, if a user enters "chicken, potatoes, and carrots," the generation AI will search a database of past school lunch menus and suggest the most suitable recipe using these ingredients. For example, it could suggest recipes such as simmered chicken and vegetables or curry rice. Similarly, if a user enters "spaghetti," the generation AI will refer to a database of past school lunch menus and suggest the most suitable spaghetti recipe. For example, it could create recipes such as spaghetti with meat sauce or spaghetti Neapolitan so that they can be recreated at home. This allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe based on the dish name and ingredients entered by the user.

[0052] The input unit allows the generative AI to suggest multiple recipes using ingredients entered by the user. For example, if a user enters "chicken, potatoes, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as curry rice, chicken and vegetable stew, and stew. Alternatively, if a user enters "tomatoes, basil, and mozzarella cheese," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as caprese salad, tomato pasta, and pizza. Alternatively, if a user enters "pork, cabbage, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as stir-fried pork and cabbage, pork soup, and cabbage rolls. This allows the generative AI to suggest multiple recipes using the ingredients entered by the user.

[0053] The generation unit can add history or background information of past school lunch menus to recipes proposed by the generation AI and provide them to users. For example, when the generation AI proposes a recipe for "curry rice," it adds history and background information of past school lunch menus. For example, it provides information such as, "This curry rice was a popular school lunch menu item during the Showa era." Furthermore, when the generation AI proposes a recipe for "fried bread," it adds history and background information of past school lunch menus. For example, it provides information such as, "fried bread was a standard dessert in school lunches during the Showa era." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," it adds history and background information of past school lunch menus. For example, it provides information such as, "spaghetti with meat sauce was a popular school lunch menu item during the Heisei era." In this way, it is possible to add history and background information of past school lunch menus to recipes proposed by the generation AI and provide them to users.

[0054] When suggesting a recipe, the generation unit can customize it by taking into account the user's past preferences or allergy information. For example, when the generation AI suggests a recipe for "curry rice," it customizes it by taking into account the user's past preferences and allergy information. For example, if the user does not like spicy food, it will suggest a recipe with a milder spiciness. When the generation AI suggests a recipe for "chicken and vegetable stew," it customizes it by taking into account the user's past preferences and allergy information. For example, if the user is allergic to carrots, it will suggest a recipe that does not use carrots. When the generation AI suggests a recipe for "spaghetti," it customizes it by taking into account the user's past preferences and allergy information. For example, if the user prefers gluten-free meals, it will suggest a recipe that uses gluten-free spaghetti. This makes it possible to customize recipes by taking into account the user's past preferences and allergy information.

[0055] The generation unit can provide variations based on school lunch menus from different regions or countries. For example, when the generation AI suggests a recipe for "curry rice," it provides variations based on school lunch menus from different regions or countries. For example, it may suggest not only Japanese curry rice, but also Indian curry and Thai curry. Furthermore, when the generation AI suggests a recipe for "chicken and vegetable stew," it provides variations based on school lunch menus from different regions or countries. For example, it may suggest not only Japanese stews, but also Chinese stews and French ratatouille. Furthermore, when the generation AI suggests a recipe for "spaghetti," it provides variations based on school lunch menus from different regions or countries. For example, it may suggest not only Italian spaghetti with meat sauce, but also American macaroni and cheese and Japanese Neapolitan spaghetti. This makes it possible to provide variations based on school lunch menus from different regions or countries.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The input unit inputs the name of the dish or ingredients. For example, the user can input information such as "curry rice" or "chicken, potatoes, carrots." Step 2: The generation unit generates a recipe based on the dish name or ingredients entered by the input unit. For example, the generation AI generates a recipe based on the school lunch menu based on the input information. The generation AI can generate a recipe using text generation AI (e.g., LLM) or multimodal generation AI. For example, in the case of "curry rice," the generation AI will suggest a recipe that can be recreated at home based on the curry rice recipe provided in school lunches. Step 3: The display unit displays the recipe generated by the generator. For example, a recipe generated by the generation AI may be displayed with detailed steps and a list of required ingredients. For example, it may be displayed in the following format: "Cut the chicken into bite-sized pieces, and cut the potatoes and carrots to bite size pieces. Heat oil in a pot and fry the chicken. Next, add the potatoes and carrots and fry some more. Add water and simmer, then add the curry roux and it's done."

[0058] (Example 2) The cooking recipe suggestion service according to an embodiment of the present invention is a system in which a generation AI suggests a recipe when the user inputs the name of a dish and ingredients. As a result, the cooking recipe suggestion service allows the generation AI to suggest a recipe based on the name of a dish and ingredients input by the user, enabling the user to recreate the taste of school lunches at home.

[0059] A cooking recipe suggestion service according to an embodiment includes an input unit, a generation unit, and a display unit. The input unit inputs a dish name or ingredients. For example, a user can input information such as "curry rice" or "chicken, potatoes, and carrots." The generation unit generates a recipe based on the dish name or ingredients input by the input unit. For example, the generation AI generates a recipe based on a school lunch menu based on the input information. The generation AI can generate a recipe using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, in the case of "curry rice," the generation AI suggests a recipe that can be recreated at home based on the curry rice recipe provided in school lunches. The display unit displays the recipe generated by the generation unit. For example, the recipe generated by the generation AI is displayed together with detailed steps and a list of necessary ingredients. For example, it might say something like, "Cut the chicken into bite-sized pieces, and cut the potatoes and carrots to bite sizes. Heat oil in a pot and fry the chicken. Next, add the potatoes and carrots and fry some more. Add water and simmer, then add the curry roux and it's done." This allows the generative AI to suggest and display recipes based on the dish name and ingredients entered by the user.

[0060] The input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe for the dish name or ingredients entered by the user. For example, when a user enters "curry rice," the input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable curry rice recipe. For example, the input unit may adapt a curry rice recipe served at a specific school so that it can be recreated at home. When a user enters "chicken, potatoes, and carrots," the input unit allows the generation AI to search a database of past school lunch menus and suggest the most suitable recipe using these ingredients. For example, it may suggest recipes such as simmered chicken and vegetables or curry rice. When a user enters "spaghetti," the input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable spaghetti recipe. For example, it may enable recipes such as spaghetti with meat sauce or spaghetti Neapolitan to be recreated at home. This allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe based on the dish name and ingredients entered by the user.

[0061] The input unit can automatically calculate the nutritional value of the input ingredients and provide health advice. For example, when a user inputs "chicken, potatoes, and carrots," the generation AI automatically calculates the nutritional value of these ingredients and provides health advice. For example, it displays the calorie, protein, and vitamin content and suggests a balanced meal. Also, when a user inputs "curry rice," the generation AI automatically calculates the nutritional value of the curry rice ingredients and provides health advice. For example, it displays the amount of calories and fat and provides advice for a healthy diet. Also, when a user inputs "spaghetti," the generation AI automatically calculates the nutritional value of the spaghetti ingredients and provides health advice. For example, it displays the amount of carbohydrates and dietary fiber content and suggests a healthy meal. In this way, the nutritional value of the input ingredients can be automatically calculated and health advice can be provided.

[0062] The input unit uses the emotion estimation function to analyze the emotion of the user when inputting information and can suggest ingredients and dish names that will elicit positive emotions. For example, the input unit uses the emotion estimation function to analyze the emotion of the user when inputting "curry rice" and suggests ingredients and dish names that will elicit positive emotions. For example, if the user is inputting with a happy expression, the input unit also suggests salads and desserts that go well with curry rice. The input unit also uses the emotion estimation function to analyze the emotion of the user when inputting "chicken, potatoes, and carrots" and suggests ingredients and dish names that will elicit positive emotions. For example, if the user is slightly tired, the input unit suggests easy recipes. The input unit also uses the emotion estimation function to analyze the emotion of the user when inputting "spaghetti" and suggests ingredients and dish names that will elicit positive emotions. For example, if the user is inputting with a happy expression, the input unit also suggests appetizers and desserts that go well with spaghetti. In this way, the input unit can analyze the emotion of the user and suggest ingredients and dish names that will elicit positive emotions.

[0063] The input unit can be equipped with a voice input function, allowing the user to input the name of a dish and ingredients simply by speaking. For example, when the user simply speaks "curry rice," the generation AI recognizes the voice and inputs the name of the dish. This makes it easy to input the name of a dish even when the user's hands are full. Alternatively, when the user simply speaks "chicken, potatoes, carrots," the generation AI recognizes the voice and inputs the ingredients. This makes it easy to input ingredients even when the user's hands are dirty. Alternatively, when the user simply speaks "spaghetti," the generation AI recognizes the voice and inputs the name of the dish. This makes it easy to input the name of a dish even while cooking. This makes it easy to input the name of a dish and ingredients simply by speaking.

[0064] The input unit allows the generative AI to suggest multiple recipes using ingredients entered by the user. For example, if a user enters "chicken, potatoes, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as curry rice, stewed chicken and vegetables, and stew. Alternatively, if a user enters "tomatoes, basil, and mozzarella cheese," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as caprese salad, tomato pasta, and pizza. Alternatively, if a user enters "pork, cabbage, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as stir-fried pork and cabbage, pork soup, and cabbage rolls. This allows the generative AI to suggest multiple recipes using the ingredients entered by the user.

[0065] The input unit uses the emotion estimation function to analyze the emotion of the user when inputting information in real time, and can provide encouragement and advice according to the input content. For example, the input unit analyzes the emotion of the user when inputting "curry rice" in real time, and provides encouragement and advice to elicit positive emotions. For example, a message such as "You can make delicious curry rice!" is displayed. The input unit also analyzes the emotion of the user when inputting "chicken, potatoes, and carrots" in real time, and provides encouragement and advice to elicit positive emotions. For example, a message such as "You can make a great dish with these ingredients!" is displayed. The input unit also analyzes the emotion of the user when inputting "spaghetti" in real time, and provides encouragement and advice to elicit positive emotions. For example, a message such as "You can make delicious spaghetti!" is displayed. In this way, the input unit can analyze the emotion of the user in real time, and provide encouragement and advice according to the input content.

[0066] The generation unit can add history or background information of past school lunch menus to recipes proposed by the generation AI and provide them to users. For example, when the generation AI proposes a recipe for "curry rice," the generation unit adds history and background information of past school lunch menus. For example, it provides information such as, "This curry rice was a popular menu item in school lunches during the Showa era." Furthermore, when the generation AI proposes a recipe for "fried bread," the generation unit adds history and background information of past school lunch menus. For example, it provides information such as, "fried bread was a standard dessert in school lunches during the Showa era." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," the generation unit adds history and background information of past school lunch menus. For example, it provides information such as, "spaghetti with meat sauce was a popular menu item in school lunches during the Heisei era." In this way, the generation AI can add history and background information of past school lunch menus to recipes proposed by the generation AI and provide them to users.

[0067] The generation unit can customize recipe suggestions by taking into account the user's past preferences or allergy information. For example, when the generation AI suggests a recipe for "curry rice," the generation unit customizes the recipe by taking into account the user's past preferences and allergy information. For example, if the user does not like spicy food, the generation unit proposes a less spicy recipe. Furthermore, when the generation AI suggests a recipe for "chicken and vegetable stew," the generation unit customizes the recipe by taking into account the user's past preferences and allergy information. For example, if the user is allergic to carrots, the generation unit proposes a recipe that does not use carrots. Furthermore, when the generation AI suggests a recipe for "spaghetti," the generation unit customizes the recipe by taking into account the user's past preferences and allergy information. For example, if the user prefers gluten-free meals, the generation unit proposes a recipe that uses gluten-free spaghetti. In this way, recipes can be customized by taking into account the user's past preferences and allergy information.

[0068] The generation unit can use the emotion estimation function to adjust the difficulty or cooking time of a recipe based on the user's emotions. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adjusts the difficulty or cooking time of the recipe based on the user's emotions. For example, if the user is tired, the generation unit suggests a recipe that is easy and can be made in a short time. Furthermore, when the generation AI suggests a recipe for "chicken and vegetable stew," the generation unit adjusts the difficulty or cooking time of the recipe based on the user's emotions. For example, if the user is relaxed, the generation unit suggests a recipe that requires a little more effort. Furthermore, when the generation AI suggests a recipe for "spaghetti," the generation unit adjusts the difficulty or cooking time of the recipe based on the user's emotions. For example, if the user is busy, the generation unit suggests a recipe that can be made in a short time. In this way, the difficulty or cooking time of a recipe can be adjusted based on the user's emotions.

[0069] The generation unit can add videos or images to recipes proposed by the generation AI to make them visually easier to understand. For example, when the generation AI proposes a recipe for "curry rice," the generation unit adds videos and images showing the cooking steps. For example, videos show scenes of frying chicken and adding curry roux. Furthermore, when the generation AI proposes a recipe for "chicken and vegetable stew," the generation unit adds videos and images showing the cooking steps. For example, images show scenes of cutting vegetables and simmering them. Furthermore, when the generation AI proposes a recipe for "spaghetti," the generation unit adds videos and images showing the cooking steps. For example, videos show scenes of boiling spaghetti and adding sauce. In this way, videos and images can be added to recipes proposed by the generation AI to make them visually easier to understand.

[0070] When suggesting a recipe, the generation unit can provide variations based on school lunch menus from different regions or countries. For example, when the generation AI suggests a recipe for "curry rice," the generation unit provides variations based on school lunch menus from different regions or countries. For example, the generation unit suggests not only Japanese curry rice, but also Indian curry and Thai curry. When the generation AI suggests a recipe for "chicken and vegetable stew," the generation unit provides variations based on school lunch menus from different regions or countries. For example, the generation unit suggests not only Japanese stews, but also Chinese stews and French ratatouille. When the generation AI suggests a recipe for "spaghetti," the generation unit provides variations based on school lunch menus from different regions or countries. For example, the generation unit suggests not only Italian spaghetti with meat sauce, but also American macaroni and cheese and Japanese Neapolitan spaghetti. This makes it possible to provide variations based on school lunch menus from different regions or countries.

[0071] The generation unit can use the emotion estimation function to adjust the order of recipe suggestions based on the user's emotions, displaying the most interesting recipe first. For example, when the generation AI suggests a "curry rice" recipe, the generation unit adjusts the order of recipe suggestions based on the user's emotions. For example, if the user is excited, the most interesting recipe is displayed first. Furthermore, when the generation AI suggests a "chicken and vegetable stew" recipe, the generation unit adjusts the order of recipe suggestions based on the user's emotions. For example, if the user is relaxed, the most interesting recipe is displayed first. Furthermore, when the generation AI suggests a "spaghetti" recipe, the generation unit adjusts the order of recipe suggestions based on the user's emotions. For example, if the user is busy, the easiest and quickest recipe to make is displayed first. In this way, the order of recipe suggestions can be adjusted based on the user's emotions, displaying the most interesting recipe first.

[0072] The display unit can add cooking tips or points to the detailed display of a recipe, allowing the user to make it even more delicious. For example, when the generation AI displays the details of a "curry rice" recipe, the display unit adds cooking tips and points. For example, it displays advice such as, "When frying chicken, brown it thoroughly to enhance the flavor." In addition, when the generation AI displays the details of a "chicken and vegetable stew," the display unit adds cooking tips and points. For example, it displays advice such as, "When stewing vegetables, simmering them slowly over low heat will bring out their sweetness." In addition, when the generation AI displays the details of a "spaghetti" recipe, the display unit adds cooking tips and points. For example, it displays advice such as, "When boiling spaghetti, adding a lot of salt will enhance the flavor." In this way, cooking tips and points can be added to the detailed display of a recipe, allowing the user to make it even more delicious.

[0073] The display unit can add nutritional value or calorie information to the detailed display of a recipe and provide health advice. For example, when the generation AI displays the details of a "curry rice" recipe, the display unit adds nutritional value and calorie information. For example, it displays information such as "This curry rice has 500 kcal per serving." The display unit also adds nutritional value and calorie information when the generation AI displays the details of a "chicken and vegetable stew." For example, it displays information such as "This stew has 300 kcal per serving." The display unit also adds nutritional value and calorie information when the generation AI displays the details of a "spaghetti" recipe. For example, it displays information such as "This spaghetti has 600 kcal per serving." This allows nutritional value and calorie information to be added to the detailed display of a recipe and health advice to be provided.

[0074] The display unit can use the emotion estimation function to analyze the user's emotions when viewing a recipe and display comments or advice to elicit positive emotions. For example, when the generation AI displays the details of a "curry rice" recipe, the display unit analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This curry rice is a flavor the whole family will enjoy!". Furthermore, when the generation AI displays the details of a "chicken and vegetable stew" recipe, the display unit analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This stew is packed with nutrients and is healthy!". Furthermore, when the generation AI displays the details of a "spaghetti" recipe, the display unit analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This spaghetti is easy to make and delicious!". In this way, the display unit can analyze the user's emotions when viewing a recipe and display comments and advice to elicit positive emotions.

[0075] The display unit adds a function that allows users to post photos of dishes they have actually made, thereby enabling the formation of a community. For example, when the generation AI displays details of a recipe for "curry rice," the display unit adds a function that allows users to post photos of the curry rice they have actually made. This allows users to share the results of their cooking with other users. Furthermore, when the generation AI displays details of a recipe for "stewed chicken and vegetables," the display unit adds a function that allows users to post photos of the stew they have actually made. This allows users to share the results of their cooking with other users. Furthermore, when the generation AI displays details of a recipe for "spaghetti," the display unit adds a function that allows users to post photos of the spaghetti they have actually made. This allows users to share the results of their cooking with other users. This allows the formation of a community by adding a function that allows users to post photos of dishes they have actually made.

[0076] The display unit can display reviews or ratings from other users for reference. For example, when the generation AI displays details of a recipe for "curry rice," the display unit displays reviews and ratings from other users. For example, it displays a review such as "This recipe was easy and delicious!". Furthermore, when the generation AI displays details of a recipe for "stewed chicken and vegetables," the display unit displays reviews and ratings from other users. For example, it displays a review such as "This stew was very nutritious and my family loved it!". Furthermore, when the generation AI displays details of a recipe for "spaghetti," the display unit displays reviews and ratings from other users. For example, it displays a review such as "This spaghetti was easy to make and delicious!". This allows reviews and ratings from other users to be displayed for reference.

[0077] The display unit uses the emotion estimation function to analyze the user's emotions in real time when viewing a recipe and provide encouragement and advice according to the emotions. For example, when the generation AI displays the details of a "curry rice" recipe, the display unit analyzes the user's emotions in real time and provides encouragement and advice according to the emotions. For example, if the user looks anxious, the display unit displays a message such as "Don't worry, this recipe is easy!". When the generation AI displays the details of a "chicken and vegetable stew" recipe, the display unit analyzes the user's emotions in real time and provides encouragement and advice according to the emotions. For example, if the user looks tired, the display unit displays a message such as "This recipe can be made in a short time!". When the generation AI displays the details of a "spaghetti" recipe, the display unit analyzes the user's emotions in real time and provides encouragement and advice according to the emotions. For example, if the user is excited, the display unit displays a message such as "This spaghetti will delight the whole family!". This allows the display unit to analyze the user's emotions in real time when viewing a recipe and provide encouragement and advice according to the emotions.

[0078] The generation unit can add a function to compare the differences between past and present school lunch menus, allowing users to enjoy the differences. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds a function to compare the differences between past and present school lunch menus. For example, it provides information such as, "Curry rice in the Showa era had few ingredients, but now it uses a lot of vegetables." Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit adds a function to compare the differences between past and present school lunch menus. For example, it provides information such as, "In the Showa era, fried bread was sprinkled with sugar, but now it uses cinnamon sugar." Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds a function to compare the differences between past and present school lunch menus. For example, it provides information such as, "In the Heisei era, tomato sauce was the norm for spaghetti with meat sauce, but cream sauce is also popular these days." This adds a function to compare the differences between past and present school lunch menus, allowing users to enjoy the differences.

[0079] The generation unit can add a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, the generation unit posts a story such as, "This curry rice is a taste I remember eating when I was in elementary school." Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit adds a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, the generation unit posts a story such as, "This fried bread is my favorite dessert when I was in elementary school." Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds a function that allows parents and children to post stories or photos about past school lunch menus so that they can share their school lunch memories. For example, the generation unit posts a story such as, "This spaghetti with meat sauce is a Western dish I first tried when I was in elementary school." This will allow parents and children to share their school lunch memories by adding a feature that allows them to post stories and photos about past school lunch menus.

[0080] The generation unit can use the emotion estimation function to analyze the emotions a user felt when viewing past school lunch menus and display comments or advice designed to elicit positive emotions. For example, when the generation AI suggests a recipe for "curry rice," the generation unit analyzes the user's emotions and displays comments and advice designed to elicit positive emotions. For example, it displays a message such as, "This curry rice has a nostalgic flavor, and the whole family will love it!". Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit analyzes the user's emotions and displays comments and advice designed to elicit positive emotions. For example, it displays a message such as, "This fried bread has a nostalgic flavor, and kids will love it!". Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit analyzes the user's emotions and displays comments and advice designed to elicit positive emotions. For example, it displays a message such as, "This spaghetti with meat sauce has a nostalgic flavor, and the whole family will enjoy it!". In this way, it is possible to analyze the emotions a user felt when viewing past school lunch menus and display comments and advice designed to elicit positive emotions.

[0081] The generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, when the generation AI suggests a recipe for "curry rice," the generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, a "forum for sharing memories of curry rice from the Showa era" can be provided. Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, a "forum for sharing memories of fried bread from the Showa era" can be provided. Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit can provide a forum where users of different generations can interact with each other, providing a place to share memories of school lunches. For example, a "forum for sharing memories of spaghetti with meat sauce from the Heisei era" can be provided. In this way, a forum can be provided where users of different generations can interact with each other, providing a place to share memories of school lunches.

[0082] The generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, when the generation AI suggests a recipe for "curry rice," the generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, an "event that recreates curry rice from the Showa era" can be held. Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, an "event that recreates fried bread from the Showa era" can be held. Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit can hold an event that recreates a past school lunch menu, providing an opportunity for users to actually experience it. For example, an "event that recreates spaghetti with meat sauce from the Heisei era" can be held. In this way, events that recreate past school lunch menus can be held, providing an opportunity for users to actually experience it.

[0083] The generation unit uses the emotion estimation function to analyze the user's emotions in real time when viewing past school lunch menus and provide encouragement or advice according to the emotions. For example, when the generation AI suggests a recipe for "curry rice," the generation unit analyzes the user's emotions in real time and provides encouragement or advice according to the emotions. For example, if the user is feeling nostalgic, the generation unit displays a message such as, "This curry rice has a nostalgic taste, and the whole family will enjoy it!". Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit analyzes the user's emotions in real time and provides encouragement or advice according to the emotions. For example, if the user is feeling nostalgic, the generation unit displays a message such as, "This fried bread has a nostalgic taste, and your kids will love it!". Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit analyzes the user's emotions in real time and provides encouragement or advice according to the emotions. For example, if the user is feeling nostalgic, the generation unit displays a message such as, "This spaghetti with meat sauce has a nostalgic taste, and the whole family will enjoy it!". This allows the system to analyze in real time the emotions users feel when viewing past school lunch menus, and provide encouragement and advice based on those emotions.

[0084] The generation unit can provide a story or episode based on the recipe proposed by the generation AI, allowing the user to enjoy it. For example, when the generation AI proposes a recipe for "curry rice," the generation unit provides a story or episode based on the recipe. For example, it provides information such as, "This curry rice was a popular menu item in school lunches during the Showa era, and was a dish that children looked forward to." Furthermore, when the generation AI proposes a recipe for "fried bread," the generation unit provides a story or episode based on the recipe. For example, it provides information such as, "This fried bread was a standard dessert in school lunches during the Showa era, and was a dish that children loved." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," the generation unit provides a story or episode based on the recipe. For example, it provides information such as, "This spaghetti with meat sauce was a popular menu item in school lunches during the Heisei era, and was a dish that children looked forward to." In this way, it is possible to provide a story or episode based on the recipe proposed by the generation AI, allowing the user to enjoy it.

[0085] The generation unit can add historical or background information about the dishes suggested by the generation AI, allowing users to enjoy them more deeply. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds historical and background information about the dish. For example, it provides information such as, "Curry rice is a long-loved menu item in Japanese school lunches and is a traditional dish that has been around since the Showa era." Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit adds historical and background information about the dish. For example, it provides information such as, "fried bread was a standard dessert in school lunches during the Showa era and was a dish that children loved." Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds historical and background information about the dish. For example, it provides information such as, "spaghetti with meat sauce was a popular menu item in school lunches during the Heisei era and was a dish that children looked forward to." This allows the generation AI to add historical and background information about the dishes it suggests, allowing users to enjoy them more deeply.

[0086] The generation unit can use the emotion estimation function to analyze the emotions a user feels when reading a recipe and display comments or advice to elicit positive emotions. For example, when the generation AI suggests a recipe for "curry rice," the generation unit analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This curry rice has a nostalgic taste, and the whole family will enjoy it!". Furthermore, when the generation AI suggests a recipe for "fried bread," the generation unit analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This fried bread has a nostalgic taste, and kids will love it!". Furthermore, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This spaghetti with meat sauce has a nostalgic taste, and the whole family will enjoy it!". In this way, it is possible to analyze the emotions a user feels when reading a recipe and display comments and advice to elicit positive emotions.

[0087] The generation unit can provide quizzes or games based on the recipes proposed by the generation AI, allowing users to enjoy them. For example, when the generation AI proposes a recipe for "curry rice," the generation unit provides quizzes or games based on that recipe. For example, it provides a "quiz to guess the ingredients in curry rice" or a "game to learn the history of curry rice." Furthermore, when the generation AI proposes a recipe for "fried bread," the generation unit provides quizzes or games based on that recipe. For example, it provides a "quiz to learn how to make fried bread" or a "game to learn the history of fried bread." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," the generation unit provides quizzes or games based on that recipe. For example, it provides a "quiz to guess the ingredients in spaghetti with meat sauce" or a "game to learn the history of spaghetti with meat sauce." In this way, it is possible to provide quizzes or games based on the recipes proposed by the generation AI, allowing users to enjoy them.

[0088] The generation unit can add music or video for the dish suggested by the generation AI, allowing the user to enjoy it visually and aurally as well. For example, when the generation AI suggests a recipe for "curry rice," the generation unit adds music and video related to the dish. For example, it provides "background music for making curry rice" and "video showing the cooking steps for curry rice." When the generation AI suggests a recipe for "fried bread," the generation unit adds music and video related to the dish. For example, it provides "background music for making fried bread" and "video showing the cooking steps for fried bread." When the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit adds music and video related to the dish. For example, it provides "background music for making spaghetti with meat sauce" and "video showing the cooking steps for spaghetti with meat sauce." In this way, the generation AI can add music and video for the dish suggested by the generation AI, allowing the user to enjoy it visually and aurally as well.

[0089] The generation unit uses the emotion estimation function to analyze the user's emotions in real time when reading a recipe and provide encouragement or advice according to the emotions. For example, when the generation AI suggests a recipe for "curry rice," the generation unit analyzes the user's emotions in real time and provides encouragement or advice according to the emotions. For example, if the user looks anxious, the generation unit displays a message such as, "Don't worry, this recipe is easy!". Also, when the generation AI suggests a recipe for "fried bread," the generation unit analyzes the user's emotions in real time and provides encouragement or advice according to the emotions. For example, if the user looks anxious, the generation unit displays a message such as, "Don't worry, this recipe is easy!". Also, when the generation AI suggests a recipe for "spaghetti with meat sauce," the generation unit analyzes the user's emotions in real time and provides encouragement or advice according to the emotions. For example, if the user is excited, the generation unit displays a message such as, "This spaghetti with meat sauce is a flavor the whole family will enjoy!". This allows the generation AI to analyze the user's emotions in real time when reading a recipe and provide encouragement or advice according to the emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The input unit allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe for the dish name or ingredients entered by the user. For example, if a user enters "curry rice," the generation AI will refer to a database of past school lunch menus and suggest the most suitable curry rice recipe. For example, the system could adapt a curry rice recipe served at a specific school so that it can be recreated at home. Similarly, if a user enters "chicken, potatoes, and carrots," the generation AI will search a database of past school lunch menus and suggest the most suitable recipe using these ingredients. For example, it could suggest recipes such as simmered chicken and vegetables or curry rice. Similarly, if a user enters "spaghetti," the generation AI will refer to a database of past school lunch menus and suggest the most suitable spaghetti recipe. For example, it could create recipes such as spaghetti with meat sauce or spaghetti Neapolitan so that they can be recreated at home. This allows the generation AI to refer to a database of past school lunch menus and suggest the most suitable recipe based on the dish name and ingredients entered by the user.

[0092] The input unit allows the generative AI to suggest multiple recipes using ingredients entered by the user. For example, if a user enters "chicken, potatoes, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as curry rice, chicken and vegetable stew, and stew. Alternatively, if a user enters "tomatoes, basil, and mozzarella cheese," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as caprese salad, tomato pasta, and pizza. Alternatively, if a user enters "pork, cabbage, and carrots," the generative AI will suggest multiple recipes using these ingredients. For example, it will present recipes such as stir-fried pork and cabbage, pork soup, and cabbage rolls. This allows the generative AI to suggest multiple recipes using the ingredients entered by the user.

[0093] The generation unit can add history or background information of past school lunch menus to recipes proposed by the generation AI and provide them to users. For example, when the generation AI proposes a recipe for "curry rice," it adds history and background information of past school lunch menus. For example, it provides information such as, "This curry rice was a popular school lunch menu item during the Showa era." Furthermore, when the generation AI proposes a recipe for "fried bread," it adds history and background information of past school lunch menus. For example, it provides information such as, "fried bread was a standard dessert in school lunches during the Showa era." Furthermore, when the generation AI proposes a recipe for "spaghetti with meat sauce," it adds history and background information of past school lunch menus. For example, it provides information such as, "spaghetti with meat sauce was a popular school lunch menu item during the Heisei era." In this way, it is possible to add history and background information of past school lunch menus to recipes proposed by the generation AI and provide them to users.

[0094] When suggesting a recipe, the generation unit can customize it by taking into account the user's past preferences or allergy information. For example, when the generation AI suggests a recipe for "curry rice," it customizes it by taking into account the user's past preferences and allergy information. For example, if the user does not like spicy food, it will suggest a recipe with a milder spiciness. When the generation AI suggests a recipe for "chicken and vegetable stew," it customizes it by taking into account the user's past preferences and allergy information. For example, if the user is allergic to carrots, it will suggest a recipe that does not use carrots. When the generation AI suggests a recipe for "spaghetti," it customizes it by taking into account the user's past preferences and allergy information. For example, if the user prefers gluten-free meals, it will suggest a recipe that uses gluten-free spaghetti. This makes it possible to customize recipes by taking into account the user's past preferences and allergy information.

[0095] The generation unit can provide variations based on school lunch menus from different regions or countries. For example, when the generation AI suggests a recipe for "curry rice," it provides variations based on school lunch menus from different regions or countries. For example, it may suggest not only Japanese curry rice, but also Indian curry and Thai curry. Furthermore, when the generation AI suggests a recipe for "chicken and vegetable stew," it provides variations based on school lunch menus from different regions or countries. For example, it may suggest not only Japanese stews, but also Chinese stews and French ratatouille. Furthermore, when the generation AI suggests a recipe for "spaghetti," it provides variations based on school lunch menus from different regions or countries. For example, it may suggest not only Italian spaghetti with meat sauce, but also American macaroni and cheese and Japanese Neapolitan spaghetti. This makes it possible to provide variations based on school lunch menus from different regions or countries.

[0096] The input unit uses the emotion estimation function to analyze the emotion of the user when inputting information and suggest ingredients and dish names that will elicit positive emotions. For example, the emotion estimation function analyzes the emotion of the user when inputting "curry rice" and suggests ingredients and dish names that will elicit positive emotions. For example, if the user is inputting with a happy expression, the input unit also suggests salads and desserts that go well with curry rice. The input unit also uses the emotion estimation function to analyze the emotion of the user when inputting "chicken, potatoes, carrots" and suggests ingredients and dish names that will elicit positive emotions. For example, if the user is slightly tired, the input unit suggests easy recipes. The input unit also uses the emotion estimation function to analyze the emotion of the user when inputting "spaghetti" and suggests ingredients and dish names that will elicit positive emotions. For example, if the user is inputting with a happy expression, the input unit also suggests appetizers and desserts that go well with spaghetti. In this way, the user's emotions can be analyzed and ingredients and dish names that will elicit positive emotions can be suggested.

[0097] The input unit uses the emotion estimation function to analyze the emotion of the user when inputting information in real time, and can provide encouragement and advice according to the input content. For example, the input unit can analyze the emotion of the user when inputting "curry rice" in real time, and provide encouragement and advice to elicit positive emotions. For example, a message such as "You can make delicious curry rice!" is displayed. The input unit can also analyze the emotion of the user when inputting "chicken, potatoes, and carrots" in real time, and provide encouragement and advice to elicit positive emotions. For example, a message such as "You can make a great dish with these ingredients!" is displayed. The input unit can also analyze the emotion of the user when inputting "spaghetti" in real time, and provide encouragement and advice to elicit positive emotions. For example, a message such as "You can make delicious spaghetti!" is displayed. This makes it possible to analyze the user's emotion in real time and provide encouragement and advice according to the input content.

[0098] The generation unit can use the emotion estimation function to adjust the difficulty or cooking time of a recipe based on the user's emotions. For example, when the generation AI suggests a recipe for "curry rice," it adjusts the difficulty and cooking time of the recipe based on the user's emotions. For example, if the user is tired, it will suggest a recipe that is easy and can be made in a short time. Furthermore, when the generation AI suggests a recipe for "chicken and vegetable stew," it adjusts the difficulty and cooking time of the recipe based on the user's emotions. For example, if the user is relaxed, it will suggest a recipe that requires a little more effort. Furthermore, when the generation AI suggests a recipe for "spaghetti," it adjusts the difficulty and cooking time of the recipe based on the user's emotions. For example, if the user is busy, it will suggest a recipe that can be made in a short time. This makes it possible to adjust the difficulty and cooking time of a recipe based on the user's emotions.

[0099] The generation unit uses the emotion estimation function to adjust the order of recipe suggestions based on the user's emotions, displaying the most interesting recipe first. For example, when the generation AI suggests a "curry rice" recipe, it adjusts the order of recipe suggestions based on the user's emotions. For example, if the user is excited, it displays the most interesting recipe first. Furthermore, when the generation AI suggests a "chicken and vegetable stew" recipe, it adjusts the order of recipe suggestions based on the user's emotions. For example, if the user is relaxed, it displays the most interesting recipe first. Furthermore, when the generation AI suggests a "spaghetti" recipe, it adjusts the order of recipe suggestions based on the user's emotions. For example, if the user is busy, it displays the easiest and quickest recipe first. This allows the generation AI to adjust the order of recipe suggestions based on the user's emotions, displaying the most interesting recipe first.

[0100] The display unit can use the emotion estimation function to analyze the user's emotions when viewing a recipe and display comments or advice to elicit positive emotions. For example, when the generation AI displays the details of a "curry rice" recipe, it analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This curry rice is a flavor the whole family will enjoy!". Furthermore, when the generation AI displays the details of a "chicken and vegetable stew" recipe, it analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This stew is packed with nutrients and is healthy!". Furthermore, when the generation AI displays the details of a "spaghetti" recipe, it analyzes the user's emotions and displays comments and advice to elicit positive emotions. For example, it displays a message such as, "This spaghetti is easy to make and delicious!". This makes it possible to analyze the user's emotions when viewing a recipe and display comments and advice to elicit positive emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit inputs the name of the dish or ingredients. For example, the user can input information such as "curry rice" or "chicken, potatoes, carrots." Step 2: The generation unit generates a recipe based on the dish name or ingredients entered by the input unit. For example, the generation AI generates a recipe based on the school lunch menu based on the input information. The generation AI can generate a recipe using text generation AI (e.g., LLM) or multimodal generation AI. For example, in the case of "curry rice," the generation AI will suggest a recipe that can be recreated at home based on the curry rice recipe provided in school lunches. Step 3: The display unit displays the recipe generated by the generator. For example, a recipe generated by the generation AI may be displayed with detailed steps and a list of required ingredients. For example, it may be displayed in the following format: "Cut the chicken into bite-sized pieces, and cut the potatoes and carrots to bite size pieces. Heat oil in a pot and fry the chicken. Next, add the potatoes and carrots and fry some more. Add water and simmer, then add the curry roux and it's done."

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting a dish name or ingredients; a generation unit that generates a recipe based on the dish name or the ingredients input by the input unit; a display unit that displays the recipe generated by the generation unit. A system characterized by:

2. The input unit For the name of the dish or the ingredients entered by the user, the generation AI refers to a database of past school lunch menus and suggests the most similar recipe.

2. The system of claim 1.

3. The input unit Add a voice input function so that users can input the name of the dish and the ingredients just by speaking.

2. The system of claim 1.

4. The generation unit The recipes proposed by the generation AI are provided to the user with history or background information of past school lunch menus.

2. The system of claim 1.

5. The display unit Add cooking tips or points to the detailed display of the recipe to help users make it even more delicious.

2. The system of claim 1.

6. The input unit Analyzes the user's emotions as they type and suggests ingredients or names of dishes that elicit positive emotions 2. The system of claim 1.

7. The generation unit Adjusting the difficulty or cooking time of the recipe based on the user's feelings 2. The system of claim 1.

8. The display unit Analyze the emotions felt when the user views the recipe and display comments or advice to elicit positive emotions 2. The system of claim 1.

Images