system

Abstract

The system according to this embodiment aims to easily suggest menus based on the ingredients in a refrigerator. [Solution] The system according to the embodiment comprises an analysis unit, a suggestion unit, and a display unit. The analysis unit analyzes an image of the refrigerator. The suggestion unit suggests a recipe based on the ingredients analyzed by the analysis unit. The display unit displays the ingredients that are missing from the recipe suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is troublesome to consider a menu based on the ingredients in the refrigerator and sometimes stressful.

[0005] The system according to the embodiment aims to easily propose a menu based on the ingredients in the refrigerator.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and a display unit. The analysis unit analyzes an image of the refrigerator. The proposal unit proposes a recipe based on the ingredients analyzed by the analysis unit. The display unit displays the ingredients lacking in the recipe proposed by the proposal unit.

Effects of the Invention

[0007] The system according to this embodiment can easily suggest menus based on the ingredients in the refrigerator. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The recipe suggestion system according to an embodiment of the present invention is a system that suggests multiple recipes that can be made from an image of a refrigerator, thereby reducing the stress of considering menus. This recipe suggestion system reduces the stress of the user having to think about daily menus by having the user input an image of the inside of the refrigerator into a generating AI, which analyzes the image to identify the ingredients in the refrigerator, and then suggesting multiple recipes that can be made based on the identified ingredients. For example, the user inputs an image of the inside of the refrigerator into the generating AI. For example, the user takes a picture of the inside of the refrigerator with a smartphone and sends the image to the generating AI. This image is analyzed by the generating AI. Next, the generating AI analyzes the input image and identifies the ingredients in the refrigerator. The generating AI recognizes the ingredients in the refrigerator using image analysis technology. For example, the generating AI identifies ingredients such as vegetables, meat, and seasonings from the image. The generating AI suggests multiple recipes that can be made based on the identified ingredients. The generating AI compares the identified ingredients with a database of cooking recipes and suggests recipes that can be made with the ingredients in the refrigerator. For example, if there are vegetables and meat in the refrigerator, the generating AI suggests recipes such as "vegetable-filled tacos" and "dumplings with plenty of perilla leaves." Furthermore, the generating AI also displays ingredients that are missing from the suggested recipes. For example, if the "Vegetable-filled Tacos" recipe is missing avocado and tomato, the generating AI will also display that information. This allows users to check which ingredients are missing and purchase what they need. This system reduces the stress of planning daily meals for users. Users can simply take a picture of the inside of their refrigerator, and the generating AI will suggest recipes they can make, saving them the trouble of planning meals. In addition, the AI ​​displays ingredients that are missing from the suggested recipes, making it easy to check what is needed and purchase it. For example, users can decide on their menu based on recipes suggested by the generating AI, such as "Shiso-filled Dumplings" on Monday, "Kenchin Udon" on Tuesday, and "Vegetable-filled Tacos" on Wednesday. This reduces the stress of planning daily meals and makes meal preparation easier. Moreover, the generating AI can learn from the user's cooking history and improve the accuracy of its recommendations.For example, the system can learn from the user's past cooking experiences and preferred ingredients, and then suggest more appropriate recipes based on that information. This allows for menu suggestions tailored to the user's preferences, further increasing satisfaction. In this way, by utilizing generative AI, it's possible to suggest multiple recipes that can be made from a picture of the refrigerator, reducing the stress of deciding what to cook. Users simply take a picture of the inside of their refrigerator, and the generative AI will suggest recipes they can make, saving them the trouble of thinking about daily menus. In addition, any missing ingredients in the suggested recipes are displayed, making it easy to check what's needed and purchase it. Furthermore, the generative AI can learn from the user's cooking history and improve the accuracy of its recommendations, allowing it to suggest menus tailored to the user's preferences. As a result, the recipe suggestion system can reduce the stress of deciding what to cook and make meal preparation easier for the user.

[0029] The recipe suggestion system according to this embodiment comprises an analysis unit, a suggestion unit, and a display unit. The analysis unit analyzes an image of the refrigerator. For example, the analysis unit inputs an image of the inside of the refrigerator to a generation AI, which analyzes the image to identify the ingredients in the refrigerator. The generation AI recognizes the ingredients in the refrigerator using image analysis technology. For example, the generation AI identifies ingredients such as vegetables, meat, and seasonings from the image. The suggestion unit suggests a recipe based on the ingredients analyzed by the analysis unit. For example, the suggestion unit compares the identified ingredients with a database of recipes and suggests a recipe that can be made with the ingredients in the refrigerator. For example, if there are vegetables and meat in the refrigerator, the suggestion unit suggests recipes such as "vegetable-filled tacos" or "dumplings with plenty of perilla leaves." The display unit displays the ingredients that are missing from the recipe suggested by the suggestion unit. For example, if the "vegetable-filled tacos" recipe is missing avocado and tomato, the display unit displays that information. This allows the user to check which ingredients are missing and purchase what is needed. Some or all of the above-described processes in the analysis unit, proposal unit, and display unit are performed using a generation AI. For example, the analysis unit inputs an image of a refrigerator to the generation AI, which analyzes the image to identify ingredients. The proposal unit inputs the identified ingredients to the generation AI, which then proposes a recipe. The display unit inputs any missing ingredients from the proposed recipe to the generation AI, which then displays the missing ingredients. As a result, the recipe proposal system according to this embodiment can reduce the stress of planning meals by identifying ingredients from an image of a refrigerator, proposing a recipe, and displaying any missing ingredients.

[0030] The analysis unit analyzes images of the refrigerator. For example, the analysis unit inputs an image of the inside of the refrigerator into the generating AI, which analyzes the image to identify the food items inside the refrigerator. The generating AI recognizes the food items inside the refrigerator using image analysis technology. Specifically, the generating AI uses a deep learning model to classify objects in the image and identify each food item. For example, the generating AI analyzes the pixel information in the image and extracts features such as color, shape, and texture. This allows it to recognize food items such as vegetables, meat, and condiments with high accuracy. Furthermore, by learning from past data, the generating AI has the ability to accurately identify food items even under different angles and lighting conditions. For example, even if the lighting inside the refrigerator is dim or food items are overlapping, the generating AI can capture their features and accurately recognize the food items. In addition, the generating AI can adapt to new food items and package designs by continuing to learn using regularly updated datasets. As a result, the analysis unit can quickly and accurately identify the food items inside the refrigerator and provide the information necessary for the next processing.

[0031] The suggestion department proposes recipes based on ingredients analyzed by the analysis department. For example, the suggestion department matches identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. Specifically, the suggestion department selects the optimal recipe by considering ingredient combinations, cooking methods, and the use of seasonings. For example, if there are vegetables and meat in the refrigerator, the suggestion department will propose recipes such as "vegetable-filled tacos" or "dumplings with plenty of perilla leaves." The suggestion department can also use generative AI to learn ingredient combinations and cooking methods, and customize recipes by considering the user's preferences and past selection history. For example, it will prioritize suggesting recipes that match the user's preferences based on recipes and ratings the user has previously selected. The suggestion department can also propose recipes according to the season and events. For example, it will suggest cold dishes and barbecue recipes in the summer, and warm soup and hot pot recipes in the winter. In this way, the suggestion department can provide a variety of recipes that meet the user's needs and expand the variety of menus.

[0032] The display unit shows any missing ingredients in the recipe suggested by the suggestion unit. For example, if the "Vegetable-filled Tacos" recipe is missing avocado and tomato, the display unit will show this information. Specifically, the display unit compares the suggested recipe with the list of ingredients in the refrigerator to identify the missing ingredients. The display unit visually displays a list of missing ingredients to the user, informing them of the need to purchase them. For example, the list of missing ingredients can be displayed through a smartphone or tablet application, making it easy for the user to check. The display unit can also link information from nearby supermarkets and online stores to provide information on where to buy the missing ingredients and their prices. This allows the user to purchase the missing ingredients efficiently. Furthermore, the display unit can suggest alternative ingredients, taking into account the user's purchase history and preferences. For example, if avocado is missing, it may suggest cream cheese or tofu as a substitute. This allows the user to flexibly adjust the recipe and easily enjoy cooking.

[0033] The learning unit learns the user's cooking history. For example, the learning unit learns the dishes the user has made in the past and their preferred ingredients, and improves the accuracy of recommendations based on this. The learning unit uses a generative AI to record the user's cooking history and improves the accuracy of recommendations based on this. For example, the learning unit inputs data on dishes the user has made in the past into the generative AI, and the generative AI learns that data. In this way, the learning unit can learn the user's cooking history and improve the accuracy of recommendations. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's cooking history into the generative AI, and the generative AI learns that data. In this way, the recipe suggestion system according to the embodiment can improve the accuracy of recommendations by learning the user's cooking history.

[0034] The customization unit proposes recipes tailored to the user's preferences. For example, the customization unit proposes recipes based on the user's preferred ingredients and past selection history. The customization unit uses a generative AI to propose recipes tailored to the user's preferences. For example, the customization unit inputs the user's preferred ingredients and past selection history into the generative AI, which then proposes recipes based on that input. This allows the customization unit to propose recipes tailored to the user's preferences. Some or all of the above-described processes in the customization unit are performed using the generative AI. For example, the customization unit inputs the user's preferred ingredients and past selection history into the generative AI, which then proposes recipes based on that input. This allows the recipe suggestion system according to the embodiment to improve user satisfaction by proposing recipes tailored to the user's preferences.

[0035] The seasonal suggestion unit proposes recipes appropriate for the season. For example, the seasonal suggestion unit proposes recipes appropriate for the four seasons or a specific month. The seasonal suggestion unit proposes recipes appropriate for the season using a generation AI. For example, the seasonal suggestion unit inputs recipes appropriate for the four seasons or a specific month into the generation AI, and the generation AI proposes recipes based on that input. In this way, the seasonal suggestion unit can propose recipes appropriate for the season. Some or all of the above processing in the seasonal suggestion unit is performed using a generation AI. For example, the seasonal suggestion unit inputs recipes appropriate for the four seasons or a specific month into the generation AI, and the generation AI proposes recipes based on that input. In this way, the recipe suggestion system according to the embodiment allows users to enjoy seasonal meals by suggesting recipes appropriate for the season.

[0036] The analysis unit recognizes food items in the refrigerator using image analysis technology. The analysis unit recognizes food items in the refrigerator using, for example, an image analysis algorithm. The analysis unit recognizes food items in the refrigerator using a generation AI. For example, the analysis unit inputs an image analysis algorithm into the generation AI, and the generation AI recognizes food items in the refrigerator based on it. This allows the analysis unit to recognize food items in the refrigerator using image analysis technology. Some or all of the above-described processes in the analysis unit are performed using the generation AI. For example, the analysis unit inputs an image analysis algorithm into the generation AI, and the generation AI recognizes food items in the refrigerator based on it. This allows the recipe suggestion system according to the embodiment to accurately recognize food items in the refrigerator by using image analysis technology.

[0037] The suggestion unit compares the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. For example, the suggestion unit compares the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. The suggestion unit uses a generation AI to compare the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. For example, the suggestion unit inputs the identified ingredients with a database of recipes into the generation AI, and the generation AI proposes recipes based on that. This allows the suggestion unit to compare the identified ingredients with a database of recipes and propose recipes that can be made with the ingredients in the refrigerator. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs the identified ingredients with a database of recipes into the generation AI, and the generation AI proposes recipes based on that. This allows the recipe suggestion system according to the embodiment to make efficient use of ingredients by suggesting recipes that can be made with the ingredients in the refrigerator.

[0038] The display unit displays the ingredients that are missing from the proposed recipe. The display unit displays the ingredients that are missing from the proposed recipe, for example. The display unit uses a generation AI to display the ingredients that are missing from the proposed recipe. For example, the display unit inputs the ingredients that are missing from the proposed recipe into the generation AI, and the generation AI displays the ingredients that are missing based on that input. This allows the display unit to display the ingredients that are missing from the proposed recipe. Some or all of the above processing in the display unit is performed using the generation AI. For example, the display unit inputs the ingredients that are missing from the proposed recipe into the generation AI, and the generation AI displays the ingredients that are missing based on that input. This allows the recipe suggestion system according to the embodiment to easily check the necessary ingredients by displaying the missing ingredients.

[0039] The analysis unit determines the freshness of the ingredients in the refrigerator during image analysis and adjusts the analysis results based on the freshness. For example, the analysis unit prioritizes the analysis of ingredients with high freshness and reflects this in the recipe suggestions. The analysis unit uses a generation AI to determine the freshness of the ingredients in the refrigerator during image analysis and adjusts the analysis results based on the freshness. For example, the analysis unit inputs the freshness of the ingredients in the refrigerator into the generation AI, and the generation AI adjusts the analysis results based on that input. This allows the analysis unit to determine the freshness of the ingredients in the refrigerator during image analysis and adjust the analysis results based on the freshness. Some or all of the above-described processes in the analysis unit are performed using the generation AI. For example, the analysis unit inputs the freshness of the ingredients in the refrigerator into the generation AI, and the generation AI adjusts the analysis results based on that input. This allows the recipe suggestion system according to the embodiment to provide more accurate analysis results by taking into account the freshness of the ingredients.

[0040] The analysis unit improves the accuracy of image analysis by considering the arrangement and overlap of ingredients. For example, if ingredients overlap, the analysis unit performs image processing to resolve the overlap. The analysis unit uses a generation AI to improve the accuracy of image analysis by considering the arrangement and overlap of ingredients. For example, the analysis unit inputs the arrangement and overlap of ingredients into the generation AI, and the generation AI improves the accuracy of the analysis based on that input. This allows the analysis unit to improve the accuracy of image analysis by considering the arrangement and overlap of ingredients. Some or all of the above-described processes in the analysis unit are performed using the generation AI. For example, the analysis unit inputs the arrangement and overlap of ingredients into the generation AI, and the generation AI improves the accuracy of the analysis based on that input. This allows the recipe suggestion system according to the embodiment to improve its accuracy of analysis by considering the arrangement and overlap of ingredients.

[0041] The analysis unit improves the accuracy of image analysis by considering temperature and humidity information inside the refrigerator. For example, the analysis unit determines the freshness of food based on temperature information inside the refrigerator. The analysis unit uses a generating AI to improve the accuracy of image analysis by considering temperature and humidity information inside the refrigerator. For example, the analysis unit inputs temperature and humidity information inside the refrigerator into the generating AI, and the generating AI improves the accuracy of the analysis based on that information. This allows the analysis unit to improve the accuracy of image analysis by considering temperature and humidity information inside the refrigerator. Some or all of the above processing in the analysis unit is performed using the generating AI. For example, the analysis unit inputs temperature and humidity information inside the refrigerator into the generating AI, and the generating AI improves the accuracy of the analysis based on that information. This allows the recipe suggestion system according to the embodiment to improve its analysis accuracy by considering temperature and humidity information.

[0042] The analysis unit supplements the analysis results by referring to the user's past food usage history during image analysis. For example, the analysis unit identifies the current food based on the food the user has used in the past. The analysis unit uses a generation AI to supplement the analysis results by referring to the user's past food usage history during image analysis. For example, the analysis unit inputs the user's past food usage history into the generation AI, and the generation AI supplements the analysis results based on that. This allows the analysis unit to supplement the analysis results by referring to the user's past food usage history during image analysis. Some or all of the above processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's past food usage history into the generation AI, and the generation AI supplements the analysis results based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of the analysis results by referring to past usage history.

[0043] The suggestion unit prioritizes suggesting healthy recipes when suggesting recipes, taking into account the nutritional value of the ingredients. For example, the suggestion unit prioritizes suggesting recipes that use highly nutritious ingredients. The suggestion unit uses a generation AI to prioritize suggesting healthy recipes when suggesting recipes, taking into account the nutritional value of the ingredients. For example, the suggestion unit inputs the nutritional value of the ingredients into the generation AI, and the generation AI proposes healthy recipes based on that. This allows the suggestion unit to prioritize suggesting healthy recipes when suggesting recipes, taking into account the nutritional value of the ingredients. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the nutritional value of the ingredients into the generation AI, and the generation AI proposes healthy recipes based on that. This allows the recipe suggestion system according to the embodiment to suggest healthy meals by taking into account the nutritional value of the ingredients.

[0044] The suggestion unit improves the accuracy of its suggestions by referring to the user's past cooking history when suggesting recipes. For example, the suggestion unit suggests recipes that suit the user's preferences based on dishes the user has made in the past. The suggestion unit uses a generation AI to improve the accuracy of its suggestions by referring to the user's past cooking history when suggesting recipes. For example, the suggestion unit inputs the user's past cooking history into the generation AI, and the generation AI suggests recipes based on that. This allows the suggestion unit to improve the accuracy of its suggestions by referring to the user's past cooking history when suggesting recipes. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the user's past cooking history into the generation AI, and the generation AI suggests recipes based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its suggestions by referring to past cooking history.

[0045] The suggestion unit proposes safe recipes while considering the user's allergy information. For example, the suggestion unit proposes allergen-free recipes based on the user's allergy information. The suggestion unit uses a generation AI to propose safe recipes while considering the user's allergy information. For example, the suggestion unit inputs the user's allergy information into the generation AI, and the generation AI proposes safe recipes based on that information. This allows the suggestion unit to propose safe recipes while considering the user's allergy information. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the user's allergy information into the generation AI, and the generation AI proposes safe recipes based on that information. This allows the recipe suggestion system according to the embodiment to propose safe recipes by considering allergy information.

[0046] The suggestion unit adjusts the suggested recipes considering the user's dietary restrictions when suggesting recipes. For example, the suggestion unit suggests low-calorie recipes based on the user's diet information. The suggestion unit uses a generation AI to adjust the suggested recipes considering the user's dietary restrictions when suggesting recipes. For example, the suggestion unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the suggested recipes based on that input. This allows the suggestion unit to adjust the suggested recipes considering the user's dietary restrictions when suggesting recipes. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the suggested recipes based on that input. This allows the recipe suggestion system according to the embodiment to suggest appropriate recipes by taking dietary restrictions into consideration.

[0047] The display unit adds a function to suggest substitute ingredients when a missing ingredient is displayed. For example, the display unit suggests substitute ingredients for the missing ingredient, allowing the user to select one. The display unit adds a function to suggest substitute ingredients when a missing ingredient is displayed, using a generation AI. For example, the display unit inputs substitute ingredients for the missing ingredient into the generation AI, and the generation AI suggests substitutes based on that input. This allows the display unit to add a function to suggest substitute ingredients when a missing ingredient is displayed. Some or all of the above processing in the display unit is performed using the generation AI. For example, the display unit inputs substitute ingredients for the missing ingredient into the generation AI, and the generation AI suggests substitutes based on that input. This allows the recipe suggestion system according to the embodiment to have choices for the user by suggesting substitutes.

[0048] The display unit displays information on where to purchase and the price of ingredients when missing ingredients are indicated. For example, the display unit displays the nearest place to purchase the missing ingredients. The display unit uses a generating AI to display information on where to purchase and the price of ingredients when missing ingredients are indicated. For example, the display unit inputs information on where to purchase and the price of ingredients into the generating AI, and the generating AI displays the information based on that input. This allows the display unit to display information on where to purchase and the price of ingredients when missing ingredients are indicated. Some or all of the above processing in the display unit is performed using the generating AI. For example, the display unit inputs information on where to purchase and the price of ingredients into the generating AI, and the generating AI displays the information based on that input. This allows the recipe suggestion system according to the embodiment to display information on where to purchase and the price of ingredients, enabling the user to purchase ingredients efficiently.

[0049] The display unit, when displaying missing ingredients, prioritizes displaying ingredients that the user frequently purchases by referring to the user's purchase history. For example, the display unit prioritizes displaying ingredients that the user frequently buys. The display unit uses a generation AI to prioritize displaying ingredients that the user frequently purchases by referring to the user's purchase history when displaying missing ingredients. For example, the display unit inputs the user's purchase history into the generation AI, and the generation AI displays ingredients that the user frequently purchases based on that. This allows the display unit to prioritize displaying ingredients that the user frequently purchases by referring to the user's purchase history when displaying missing ingredients. Some or all of the above processing in the display unit is performed using a generation AI. For example, the display unit inputs the user's purchase history into the generation AI, and the generation AI displays ingredients that the user frequently purchases based on that. This allows the recipe suggestion system according to the embodiment to prioritize displaying ingredients that the user frequently purchases by referring to the purchase history.

[0050] The display unit, when displaying missing ingredients, suggests the nearest place to buy them, taking into account the user's geographical location. For example, the display unit displays the nearest place to buy them based on the user's current location. The display unit uses a generating AI to suggest the nearest place to buy them, taking into account the user's geographical location when displaying missing ingredients. For example, the display unit inputs the user's geographical location into the generating AI, and the generating AI suggests the nearest place to buy them based on that. This allows the display unit to suggest the nearest place to buy them, taking into account the user's geographical location when displaying missing ingredients. Some or all of the above processing in the display unit is performed using the generating AI. For example, the display unit inputs the user's geographical location into the generating AI, and the generating AI suggests the nearest place to buy them based on that. This allows the recipe suggestion system according to the embodiment to suggest the nearest place to buy them by taking into account geographical location information.

[0051] The learning unit optimizes the learning algorithm by referring to past learning data during learning. The learning unit adjusts the learning algorithm based on past learning data, for example. The learning unit uses a generative AI to optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit inputs past learning data into the generative AI, and the generative AI optimizes the learning algorithm based on it. This allows the learning unit to optimize the learning algorithm by referring to past learning data during learning. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs past learning data into the generative AI, and the generative AI optimizes the learning algorithm based on it. This allows the recipe suggestion system according to the embodiment to optimize its learning algorithm by referring to past learning data.

[0052] The learning unit adjusts the learning content during training, taking into account the user's cooking frequency and success rate. The learning unit weights the learning data based on, for example, the user's cooking frequency. The learning unit uses a generative AI to adjust the learning content during training, taking into account the user's cooking frequency and success rate. For example, the learning unit inputs the user's cooking frequency and success rate into the generative AI, and the generative AI adjusts the learning content based on that. This allows the learning unit to adjust the learning content during training, taking into account the user's cooking frequency and success rate. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's cooking frequency and success rate into the generative AI, and the generative AI adjusts the learning content based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its learning content by taking into account cooking frequency and success rate.

[0053] The learning unit weights the learning data by referring to the user's ingredient usage history during the learning process. For example, the learning unit weights the learning data by giving more emphasis to ingredients that the user frequently uses. The learning unit uses a generative AI to weight the learning data by referring to the user's ingredient usage history during the learning process. For example, the learning unit inputs the user's ingredient usage history into the generative AI, and the generative AI weights the learning data based on that. This allows the learning unit to weight the learning data by referring to the user's ingredient usage history during the learning process. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's ingredient usage history into the generative AI, and the generative AI weights the learning data based on that. This enables the recipe suggestion system according to the embodiment to weight the learning data by referring to the ingredient usage history.

[0054] The learning unit incorporates the user's satisfaction with their meals as feedback during the learning process. The learning unit adjusts the learning data based on the user's satisfaction with their meals, for example. The learning unit uses a generative AI to incorporate the user's satisfaction with their meals as feedback during the learning process. For example, the learning unit inputs the user's satisfaction with their meals into the generative AI, which then adjusts the learning data based on that input. This allows the learning unit to incorporate the user's satisfaction with their meals as feedback during the learning process. Some or all of the above-described processes in the learning unit are performed using the generative AI. For example, the learning unit inputs the user's satisfaction with their meals into the generative AI, which then adjusts the learning data based on that input. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its learning by incorporating the user's satisfaction with their meals as feedback.

[0055] The customization unit proposes the optimal customization when customizing a recipe by referring to the user's past cooking history. For example, the customization unit proposes a customization that suits the user's preferences based on dishes the user has made in the past. The customization unit uses a generation AI to propose the optimal customization when customizing a recipe by referring to the user's past cooking history. For example, the customization unit inputs the user's past cooking history into the generation AI, and the generation AI proposes a customization based on that. This allows the customization unit to propose the optimal customization when customizing a recipe by referring to the user's past cooking history. Some or all of the above processing in the customization unit is performed using the generation AI. For example, the customization unit inputs the user's past cooking history into the generation AI, and the generation AI proposes a customization based on that. This enables the recipe suggestion system according to the embodiment to perform optimal customization by referring to past cooking history.

[0056] The customization unit adjusts the customization content when customizing a recipe, taking into account the user's food preferences and allergy information. For example, the customization unit proposes a customization that prioritizes the use of the user's favorite ingredients. The customization unit uses a generation AI to adjust the customization content when customizing a recipe, taking into account the user's food preferences and allergy information. For example, the customization unit inputs the user's preferences and allergy information into the generation AI, and the generation AI adjusts the customization content based on that. This allows the customization unit to adjust the customization content when customizing a recipe, taking into account the user's food preferences and allergy information. Some or all of the above-described processes in the customization unit are performed using the generation AI. For example, the customization unit inputs the user's preferences and allergy information into the generation AI, and the generation AI adjusts the customization content based on that. This enables the recipe suggestion system according to the embodiment to perform more appropriate customization by taking into account food preferences and allergy information.

[0057] The customization unit suggests region-specific ingredients while considering the user's geographical location information during recipe customization. For example, the customization unit suggests region-specific ingredients based on the user's current location. The customization unit uses a generation AI to suggest region-specific ingredients while considering the user's geographical location information during recipe customization. For example, the customization unit inputs the user's geographical location information into the generation AI, which then suggests region-specific ingredients based on that information. This allows the customization unit to suggest region-specific ingredients while considering the user's geographical location information during recipe customization. Some or all of the above-described processes in the customization unit are performed using the generation AI. For example, the customization unit inputs the user's geographical location information into the generation AI, which then suggests region-specific ingredients based on that information. This allows the recipe suggestion system according to the embodiment to suggest region-specific ingredients by considering geographical location information.

[0058] The customization unit adjusts the customization content while considering the user's dietary restrictions when customizing recipes. For example, the customization unit suggests a low-calorie customization based on the user's diet information. The customization unit uses a generation AI to adjust the customization content while considering the user's dietary restrictions when customizing recipes. For example, the customization unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the customization content based on that. This allows the customization unit to adjust the customization content while considering the user's dietary restrictions when customizing recipes. Some or all of the above processing in the customization unit is performed using the generation AI. For example, the customization unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the customization content based on that. This enables the recipe suggestion system according to the embodiment to perform more appropriate customization by considering dietary restrictions.

[0059] The seasonal suggestion unit adjusts the suggested recipes when proposing seasonal recipes, taking into account the freshness and nutritional value of seasonal ingredients. For example, the seasonal suggestion unit proposes recipes using fresh seasonal ingredients. The seasonal suggestion unit uses a generation AI to adjust the suggested recipes when proposing seasonal recipes, taking into account the freshness and nutritional value of seasonal ingredients. For example, the seasonal suggestion unit inputs the freshness and nutritional value of seasonal ingredients into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the seasonal suggestion unit to adjust the suggested recipes when proposing seasonal recipes, taking into account the freshness and nutritional value of seasonal ingredients. Some or all of the above processing in the seasonal suggestion unit is performed using a generation AI. For example, the seasonal suggestion unit inputs the freshness and nutritional value of seasonal ingredients into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the recipe suggestion system according to the embodiment to propose more appropriate seasonal recipes by taking into account the freshness and nutritional value of seasonal ingredients.

[0060] The seasonal suggestion unit improves the accuracy of its suggestions by referring to evaluations of past seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit suggests recipes that suit the user's preferences based on evaluations of past seasonal recipes. The seasonal suggestion unit uses a generation AI to improve the accuracy of its suggestions by referring to evaluations of past seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit inputs evaluations of past seasonal recipes into the generation AI, and the generation AI improves the accuracy of its suggestions based on that. In this way, the seasonal suggestion unit can improve the accuracy of its suggestions by referring to evaluations of past seasonal recipes when suggesting seasonal recipes. Some or all of the above processing in the seasonal suggestion unit is performed using a generation AI. For example, the seasonal suggestion unit inputs evaluations of past seasonal recipes into the generation AI, and the generation AI improves the accuracy of its suggestions based on that. In this way, the recipe suggestion system according to the embodiment can improve the accuracy of its suggestions by referring to past evaluations.

[0061] The seasonal suggestion unit, when suggesting seasonal recipes, proposes region-specific seasonal ingredients while considering the user's geographical location information. For example, the seasonal suggestion unit proposes region-specific seasonal ingredients based on the user's current location. The seasonal suggestion unit uses a generation AI to propose region-specific seasonal ingredients while considering the user's geographical location information when suggesting seasonal recipes. For example, the seasonal suggestion unit inputs the user's geographical location information into the generation AI, and the generation AI proposes region-specific seasonal ingredients based on that information. This allows the seasonal suggestion unit to propose region-specific seasonal ingredients while considering the user's geographical location information when suggesting seasonal recipes. Some or all of the above processing in the seasonal suggestion unit is performed using the generation AI. For example, the seasonal suggestion unit inputs the user's geographical location information into the generation AI, and the generation AI proposes region-specific seasonal ingredients based on that information. This allows the recipe suggestion system according to the embodiment to propose region-specific seasonal ingredients by considering geographical location information.

[0062] The seasonal suggestion unit adjusts the suggested recipes by referring to the user's past usage history of seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit suggests recipes that suit the user's preferences based on seasonal recipes the user has used in the past. The seasonal suggestion unit uses a generation AI to adjust the suggested recipes by referring to the user's past usage history of seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit inputs the user's past usage history of seasonal recipes into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the seasonal suggestion unit to adjust the suggested recipes by referring to the user's past usage history when suggesting seasonal recipes. Some or all of the above processing in the seasonal suggestion unit is performed using the generation AI. For example, the seasonal suggestion unit inputs the user's past usage history of seasonal recipes into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its suggestions by referring to past usage history.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] Recipe suggestion systems can also suggest recipes that take into account the user's dietary preferences and allergy information. For example, if a user is allergic to a specific ingredient, the system will prioritize suggesting recipes that do not contain that ingredient. Furthermore, if a user likes a particular ingredient, the system can suggest recipes that use that ingredient extensively. It can even learn from the user's past eating history to suggest recipes they might enjoy. This allows users to easily find recipes that suit their preferences and allergies, improving their satisfaction with meals.

[0065] The recipe suggestion system can also take the user's geographical location into account and suggest recipes using ingredients specific to that region. For example, if a user lives in a particular area, it can suggest recipes using ingredients commonly used in that area. It can also suggest recipes using seasonal ingredients specific to a particular region. Furthermore, if a user is traveling, it can suggest recipes using local specialties from that region. This allows users to enjoy meals that utilize local specialties.

[0066] Recipe suggestion systems can also suggest recipes that take into account the user's dietary restrictions. For example, if a user is on a diet, the system will prioritize suggesting low-calorie recipes. It can also suggest recipes that are rich in specific nutrients if the user needs to consume them. Furthermore, if a user needs to avoid certain ingredients, the system can suggest recipes that do not contain those ingredients. This makes it easy for users to find recipes that suit their dietary restrictions and maintain a healthy eating lifestyle.

[0067] The recipe suggestion system can also refer to the user's purchase history to suggest recipes using ingredients they frequently buy. For example, it can prioritize suggesting recipes that use ingredients the user often buys. It can also suggest recipes using ingredients that the user is likely to have in their refrigerator based on ingredients they have purchased in the past. Furthermore, it can adjust the order of recipe suggestions considering how often the user buys specific ingredients. This allows users to easily find recipes based on their purchase history and reduces food waste.

[0068] The recipe suggestion system can also suggest recipes that the user prefers by referring to their past cooking history. For example, it can suggest similar recipes based on dishes the user has made in the past. It can also prioritize suggesting recipes that the user has previously given high ratings to. Furthermore, it can adjust the order of recipe suggestions considering the user's cooking frequency and success rate. This allows users to easily find recipes based on their past cooking history, improving their satisfaction with meals.

[0069] The recipe suggestion system can also display the nearest shopping locations and price information, taking into account the user's geographical location. For example, it can display the nearest supermarket and ingredient price information based on the user's current location. Furthermore, if the user lives in a specific region, it can suggest places to buy ingredients commonly used in that area. It can even suggest places to buy local specialties if the user is traveling. This allows users to easily find shopping locations and price information based on their geographical location, enabling them to purchase ingredients efficiently.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The analysis unit analyzes the image of the refrigerator. The analysis unit inputs, for example, an image taken inside the refrigerator into the generating AI, which analyzes the image to identify the food items inside the refrigerator. The generating AI recognizes the food items inside the refrigerator using image analysis technology. For example, the generating AI identifies food items such as vegetables, meat, and condiments from the image. Step 2: The suggestion unit proposes recipes based on the ingredients analyzed by the analysis unit. For example, the suggestion unit matches the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. For example, if there are vegetables and meat in the refrigerator, the suggestion unit will propose recipes such as "Tacos with plenty of vegetables" or "Dumplings with plenty of perilla leaves." Step 3: The display unit shows any missing ingredients in the recipe suggested by the suggestion unit. For example, if the "Vegetable-filled Tacos" recipe is missing avocado and tomato, the display unit will show that information. This allows the user to check which ingredients are missing and purchase what they need.

[0072] (Example of form 2) The recipe suggestion system according to an embodiment of the present invention is a system that suggests multiple recipes that can be made from an image of a refrigerator, thereby reducing the stress of considering menus. This recipe suggestion system reduces the stress of the user having to think about daily menus by having the user input an image of the inside of the refrigerator into a generating AI, which analyzes the image to identify the ingredients in the refrigerator, and then suggesting multiple recipes that can be made based on the identified ingredients. For example, the user inputs an image of the inside of the refrigerator into the generating AI. For example, the user takes a picture of the inside of the refrigerator with a smartphone and sends the image to the generating AI. This image is analyzed by the generating AI. Next, the generating AI analyzes the input image and identifies the ingredients in the refrigerator. The generating AI recognizes the ingredients in the refrigerator using image analysis technology. For example, the generating AI identifies ingredients such as vegetables, meat, and seasonings from the image. The generating AI suggests multiple recipes that can be made based on the identified ingredients. The generating AI compares the identified ingredients with a database of cooking recipes and suggests recipes that can be made with the ingredients in the refrigerator. For example, if there are vegetables and meat in the refrigerator, the generating AI suggests recipes such as "vegetable-filled tacos" and "dumplings with plenty of perilla leaves." Furthermore, the generating AI also displays ingredients that are missing from the suggested recipes. For example, if the "Vegetable-filled Tacos" recipe is missing avocado and tomato, the generating AI will also display that information. This allows users to check which ingredients are missing and purchase what they need. This system reduces the stress of planning daily meals for users. Users can simply take a picture of the inside of their refrigerator, and the generating AI will suggest recipes they can make, saving them the trouble of planning meals. In addition, the AI ​​displays ingredients that are missing from the suggested recipes, making it easy to check what is needed and purchase it. For example, users can decide on their menu based on recipes suggested by the generating AI, such as "Shiso-filled Dumplings" on Monday, "Kenchin Udon" on Tuesday, and "Vegetable-filled Tacos" on Wednesday. This reduces the stress of planning daily meals and makes meal preparation easier. Moreover, the generating AI can learn from the user's cooking history and improve the accuracy of its recommendations.For example, the system can learn from the user's past cooking experiences and preferred ingredients, and then suggest more appropriate recipes based on that information. This allows for menu suggestions tailored to the user's preferences, further increasing satisfaction. In this way, by utilizing generative AI, it's possible to suggest multiple recipes that can be made from a picture of the refrigerator, reducing the stress of deciding what to cook. Users simply take a picture of the inside of their refrigerator, and the generative AI will suggest recipes they can make, saving them the trouble of thinking about daily menus. In addition, any missing ingredients in the suggested recipes are displayed, making it easy to check what's needed and purchase it. Furthermore, the generative AI can learn from the user's cooking history and improve the accuracy of its recommendations, allowing it to suggest menus tailored to the user's preferences. As a result, the recipe suggestion system can reduce the stress of deciding what to cook and make meal preparation easier for the user.

[0073] The recipe suggestion system according to this embodiment comprises an analysis unit, a suggestion unit, and a display unit. The analysis unit analyzes an image of the refrigerator. For example, the analysis unit inputs an image of the inside of the refrigerator to a generation AI, which analyzes the image to identify the ingredients in the refrigerator. The generation AI recognizes the ingredients in the refrigerator using image analysis technology. For example, the generation AI identifies ingredients such as vegetables, meat, and seasonings from the image. The suggestion unit suggests a recipe based on the ingredients analyzed by the analysis unit. For example, the suggestion unit compares the identified ingredients with a database of recipes and suggests a recipe that can be made with the ingredients in the refrigerator. For example, if there are vegetables and meat in the refrigerator, the suggestion unit suggests recipes such as "vegetable-filled tacos" or "dumplings with plenty of perilla leaves." The display unit displays the ingredients that are missing from the recipe suggested by the suggestion unit. For example, if the "vegetable-filled tacos" recipe is missing avocado and tomato, the display unit displays that information. This allows the user to check which ingredients are missing and purchase what is needed. Some or all of the above-described processes in the analysis unit, proposal unit, and display unit are performed using a generation AI. For example, the analysis unit inputs an image of a refrigerator to the generation AI, which analyzes the image to identify ingredients. The proposal unit inputs the identified ingredients to the generation AI, which then proposes a recipe. The display unit inputs any missing ingredients from the proposed recipe to the generation AI, which then displays the missing ingredients. As a result, the recipe proposal system according to this embodiment can reduce the stress of planning meals by identifying ingredients from an image of a refrigerator, proposing a recipe, and displaying any missing ingredients.

[0074] The analysis unit analyzes images of the refrigerator. For example, the analysis unit inputs an image of the inside of the refrigerator into the generating AI, which analyzes the image to identify the food items inside the refrigerator. The generating AI recognizes the food items inside the refrigerator using image analysis technology. Specifically, the generating AI uses a deep learning model to classify objects in the image and identify each food item. For example, the generating AI analyzes the pixel information in the image and extracts features such as color, shape, and texture. This allows it to recognize food items such as vegetables, meat, and condiments with high accuracy. Furthermore, by learning from past data, the generating AI has the ability to accurately identify food items even under different angles and lighting conditions. For example, even if the lighting inside the refrigerator is dim or food items are overlapping, the generating AI can capture their features and accurately recognize the food items. In addition, the generating AI can adapt to new food items and package designs by continuing to learn using regularly updated datasets. As a result, the analysis unit can quickly and accurately identify the food items inside the refrigerator and provide the information necessary for the next processing.

[0075] The suggestion department proposes recipes based on ingredients analyzed by the analysis department. For example, the suggestion department matches identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. Specifically, the suggestion department selects the optimal recipe by considering ingredient combinations, cooking methods, and the use of seasonings. For example, if there are vegetables and meat in the refrigerator, the suggestion department will propose recipes such as "vegetable-filled tacos" or "dumplings with plenty of perilla leaves." The suggestion department can also use generative AI to learn ingredient combinations and cooking methods, and customize recipes by considering the user's preferences and past selection history. For example, it will prioritize suggesting recipes that match the user's preferences based on recipes and ratings the user has previously selected. The suggestion department can also propose recipes according to the season and events. For example, it will suggest cold dishes and barbecue recipes in the summer, and warm soup and hot pot recipes in the winter. In this way, the suggestion department can provide a variety of recipes that meet the user's needs and expand the variety of menus.

[0076] The display unit shows any missing ingredients in the recipe suggested by the suggestion unit. For example, if the "Vegetable-filled Tacos" recipe is missing avocado and tomato, the display unit will show this information. Specifically, the display unit compares the suggested recipe with the list of ingredients in the refrigerator to identify the missing ingredients. The display unit visually displays a list of missing ingredients to the user, informing them of the need to purchase them. For example, the list of missing ingredients can be displayed through a smartphone or tablet application, making it easy for the user to check. The display unit can also link information from nearby supermarkets and online stores to provide information on where to buy the missing ingredients and their prices. This allows the user to purchase the missing ingredients efficiently. Furthermore, the display unit can suggest alternative ingredients, taking into account the user's purchase history and preferences. For example, if avocado is missing, it may suggest cream cheese or tofu as a substitute. This allows the user to flexibly adjust the recipe and easily enjoy cooking.

[0077] The learning unit learns the user's cooking history. For example, the learning unit learns the dishes the user has made in the past and their preferred ingredients, and improves the accuracy of recommendations based on this. The learning unit uses a generative AI to record the user's cooking history and improves the accuracy of recommendations based on this. For example, the learning unit inputs data on dishes the user has made in the past into the generative AI, and the generative AI learns that data. In this way, the learning unit can learn the user's cooking history and improve the accuracy of recommendations. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's cooking history into the generative AI, and the generative AI learns that data. In this way, the recipe suggestion system according to the embodiment can improve the accuracy of recommendations by learning the user's cooking history.

[0078] The customization unit proposes recipes tailored to the user's preferences. For example, the customization unit proposes recipes based on the user's preferred ingredients and past selection history. The customization unit uses a generative AI to propose recipes tailored to the user's preferences. For example, the customization unit inputs the user's preferred ingredients and past selection history into the generative AI, which then proposes recipes based on that input. This allows the customization unit to propose recipes tailored to the user's preferences. Some or all of the above-described processes in the customization unit are performed using the generative AI. For example, the customization unit inputs the user's preferred ingredients and past selection history into the generative AI, which then proposes recipes based on that input. This allows the recipe suggestion system according to the embodiment to improve user satisfaction by proposing recipes tailored to the user's preferences.

[0079] The seasonal suggestion unit proposes recipes appropriate for the season. For example, the seasonal suggestion unit proposes recipes appropriate for the four seasons or a specific month. The seasonal suggestion unit proposes recipes appropriate for the season using a generation AI. For example, the seasonal suggestion unit inputs recipes appropriate for the four seasons or a specific month into the generation AI, and the generation AI proposes recipes based on that input. In this way, the seasonal suggestion unit can propose recipes appropriate for the season. Some or all of the above processing in the seasonal suggestion unit is performed using a generation AI. For example, the seasonal suggestion unit inputs recipes appropriate for the four seasons or a specific month into the generation AI, and the generation AI proposes recipes based on that input. In this way, the recipe suggestion system according to the embodiment allows users to enjoy seasonal meals by suggesting recipes appropriate for the season.

[0080] The analysis unit recognizes food items in the refrigerator using image analysis technology. The analysis unit recognizes food items in the refrigerator using, for example, an image analysis algorithm. The analysis unit recognizes food items in the refrigerator using a generation AI. For example, the analysis unit inputs an image analysis algorithm into the generation AI, and the generation AI recognizes food items in the refrigerator based on it. This allows the analysis unit to recognize food items in the refrigerator using image analysis technology. Some or all of the above-described processes in the analysis unit are performed using the generation AI. For example, the analysis unit inputs an image analysis algorithm into the generation AI, and the generation AI recognizes food items in the refrigerator based on it. This allows the recipe suggestion system according to the embodiment to accurately recognize food items in the refrigerator by using image analysis technology.

[0081] The suggestion unit compares the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. For example, the suggestion unit compares the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. The suggestion unit uses a generation AI to compare the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. For example, the suggestion unit inputs the identified ingredients with a database of recipes into the generation AI, and the generation AI proposes recipes based on that. This allows the suggestion unit to compare the identified ingredients with a database of recipes and propose recipes that can be made with the ingredients in the refrigerator. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs the identified ingredients with a database of recipes into the generation AI, and the generation AI proposes recipes based on that. This allows the recipe suggestion system according to the embodiment to make efficient use of ingredients by suggesting recipes that can be made with the ingredients in the refrigerator.

[0082] The display unit displays the ingredients that are missing from the proposed recipe. The display unit displays the ingredients that are missing from the proposed recipe, for example. The display unit uses a generation AI to display the ingredients that are missing from the proposed recipe. For example, the display unit inputs the ingredients that are missing from the proposed recipe into the generation AI, and the generation AI displays the ingredients that are missing based on that input. This allows the display unit to display the ingredients that are missing from the proposed recipe. Some or all of the above processing in the display unit is performed using the generation AI. For example, the display unit inputs the ingredients that are missing from the proposed recipe into the generation AI, and the generation AI displays the ingredients that are missing based on that input. This allows the recipe suggestion system according to the embodiment to easily check the necessary ingredients by displaying the missing ingredients.

[0083] The analysis unit estimates the user's emotions and adjusts the accuracy of the image analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit improves the accuracy of the analysis and provides results quickly. The analysis unit uses a generative AI to estimate the user's emotions and adjusts the accuracy of the image analysis based on the estimated emotions. For example, the analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the accuracy of the image analysis based on that. This allows the analysis unit to estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated emotions. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the accuracy of the image analysis based on that. This allows the recipe suggestion system according to the embodiment to provide more appropriate analysis results by adjusting the accuracy of the image analysis according to the user's emotions.

[0084] The analysis unit determines the freshness of the ingredients in the refrigerator during image analysis and adjusts the analysis results based on the freshness. For example, the analysis unit prioritizes the analysis of ingredients with high freshness and reflects this in the recipe suggestions. The analysis unit uses a generation AI to determine the freshness of the ingredients in the refrigerator during image analysis and adjusts the analysis results based on the freshness. For example, the analysis unit inputs the freshness of the ingredients in the refrigerator into the generation AI, and the generation AI adjusts the analysis results based on that input. This allows the analysis unit to determine the freshness of the ingredients in the refrigerator during image analysis and adjust the analysis results based on the freshness. Some or all of the above-described processes in the analysis unit are performed using the generation AI. For example, the analysis unit inputs the freshness of the ingredients in the refrigerator into the generation AI, and the generation AI adjusts the analysis results based on that input. This allows the recipe suggestion system according to the embodiment to provide more accurate analysis results by taking into account the freshness of the ingredients.

[0085] The analysis unit improves the accuracy of image analysis by considering the arrangement and overlap of ingredients. For example, if ingredients overlap, the analysis unit performs image processing to resolve the overlap. The analysis unit uses a generation AI to improve the accuracy of image analysis by considering the arrangement and overlap of ingredients. For example, the analysis unit inputs the arrangement and overlap of ingredients into the generation AI, and the generation AI improves the accuracy of the analysis based on that input. This allows the analysis unit to improve the accuracy of image analysis by considering the arrangement and overlap of ingredients. Some or all of the above-described processes in the analysis unit are performed using the generation AI. For example, the analysis unit inputs the arrangement and overlap of ingredients into the generation AI, and the generation AI improves the accuracy of the analysis based on that input. This allows the recipe suggestion system according to the embodiment to improve its accuracy of analysis by considering the arrangement and overlap of ingredients.

[0086] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple display method. The analysis unit uses a generative AI to estimate the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the display method of the analysis results based on that input. This allows the analysis unit to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the display method of the analysis results based on that input. This allows the recipe suggestion system according to the embodiment to provide more appropriate information by adjusting the display method according to the user's emotions.

[0087] The analysis unit improves the accuracy of image analysis by considering temperature and humidity information inside the refrigerator. For example, the analysis unit determines the freshness of food based on temperature information inside the refrigerator. The analysis unit uses a generating AI to improve the accuracy of image analysis by considering temperature and humidity information inside the refrigerator. For example, the analysis unit inputs temperature and humidity information inside the refrigerator into the generating AI, and the generating AI improves the accuracy of the analysis based on that information. This allows the analysis unit to improve the accuracy of image analysis by considering temperature and humidity information inside the refrigerator. Some or all of the above processing in the analysis unit is performed using the generating AI. For example, the analysis unit inputs temperature and humidity information inside the refrigerator into the generating AI, and the generating AI improves the accuracy of the analysis based on that information. This allows the recipe suggestion system according to the embodiment to improve its analysis accuracy by considering temperature and humidity information.

[0088] The analysis unit supplements the analysis results by referring to the user's past food usage history during image analysis. For example, the analysis unit identifies the current food based on the food the user has used in the past. The analysis unit uses a generation AI to supplement the analysis results by referring to the user's past food usage history during image analysis. For example, the analysis unit inputs the user's past food usage history into the generation AI, and the generation AI supplements the analysis results based on that. This allows the analysis unit to supplement the analysis results by referring to the user's past food usage history during image analysis. Some or all of the above processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's past food usage history into the generation AI, and the generation AI supplements the analysis results based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of the analysis results by referring to past usage history.

[0089] The suggestion unit estimates the user's emotions and adjusts the way the recipe suggestion is presented based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit will provide a simple recipe suggestion. The suggestion unit uses generative AI to estimate the user's emotions and adjusts the way the recipe suggestion is presented based on the estimated emotions. For example, the suggestion unit inputs the user's emotions into the generative AI, and the generative AI adjusts the way the recipe suggestion is presented based on that input. This allows the suggestion unit to estimate the user's emotions and adjust the way the recipe suggestion is presented based on the estimated emotions. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotions into the generative AI, and the generative AI adjusts the way the recipe suggestion is presented based on that input. This enables the recipe suggestion system according to the embodiment to provide more appropriate suggestions by adjusting the way the recipe suggestion is presented according to the user's emotions.

[0090] The suggestion unit prioritizes suggesting healthy recipes when suggesting recipes, taking into account the nutritional value of the ingredients. For example, the suggestion unit prioritizes suggesting recipes that use highly nutritious ingredients. The suggestion unit uses a generation AI to prioritize suggesting healthy recipes when suggesting recipes, taking into account the nutritional value of the ingredients. For example, the suggestion unit inputs the nutritional value of the ingredients into the generation AI, and the generation AI proposes healthy recipes based on that. This allows the suggestion unit to prioritize suggesting healthy recipes when suggesting recipes, taking into account the nutritional value of the ingredients. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the nutritional value of the ingredients into the generation AI, and the generation AI proposes healthy recipes based on that. This allows the recipe suggestion system according to the embodiment to suggest healthy meals by taking into account the nutritional value of the ingredients.

[0091] The suggestion unit improves the accuracy of its suggestions by referring to the user's past cooking history when suggesting recipes. For example, the suggestion unit suggests recipes that suit the user's preferences based on dishes the user has made in the past. The suggestion unit uses a generation AI to improve the accuracy of its suggestions by referring to the user's past cooking history when suggesting recipes. For example, the suggestion unit inputs the user's past cooking history into the generation AI, and the generation AI suggests recipes based on that. This allows the suggestion unit to improve the accuracy of its suggestions by referring to the user's past cooking history when suggesting recipes. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the user's past cooking history into the generation AI, and the generation AI suggests recipes based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its suggestions by referring to past cooking history.

[0092] The suggestion unit estimates the user's emotions and determines the priority of recipe suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit will prioritize suggesting easy and simple recipes. The suggestion unit uses generative AI to estimate the user's emotions and determines the priority of recipe suggestions based on the estimated emotions. For example, the suggestion unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of recipe suggestions based on that. This allows the suggestion unit to estimate the user's emotions and determine the priority of recipe suggestions based on the estimated emotions. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of recipe suggestions based on that. This enables the recipe suggestion system according to the embodiment to make more appropriate suggestions by determining the priority of recipe suggestions according to the user's emotions.

[0093] The suggestion unit proposes safe recipes while considering the user's allergy information. For example, the suggestion unit proposes allergen-free recipes based on the user's allergy information. The suggestion unit uses a generation AI to propose safe recipes while considering the user's allergy information. For example, the suggestion unit inputs the user's allergy information into the generation AI, and the generation AI proposes safe recipes based on that information. This allows the suggestion unit to propose safe recipes while considering the user's allergy information. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the user's allergy information into the generation AI, and the generation AI proposes safe recipes based on that information. This allows the recipe suggestion system according to the embodiment to propose safe recipes by considering allergy information.

[0094] The suggestion unit adjusts the suggested recipes considering the user's dietary restrictions when suggesting recipes. For example, the suggestion unit suggests low-calorie recipes based on the user's diet information. The suggestion unit uses a generation AI to adjust the suggested recipes considering the user's dietary restrictions when suggesting recipes. For example, the suggestion unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the suggested recipes based on that input. This allows the suggestion unit to adjust the suggested recipes considering the user's dietary restrictions when suggesting recipes. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the suggested recipes based on that input. This allows the recipe suggestion system according to the embodiment to suggest appropriate recipes by taking dietary restrictions into consideration.

[0095] The display unit estimates the user's emotions and adjusts the display method for missing ingredients based on the estimated user emotions. For example, if the user is feeling stressed, the display unit provides a simpler display method. The display unit uses a generative AI to estimate the user's emotions and adjusts the display method for missing ingredients based on the estimated user emotions. For example, the display unit inputs the user's emotions into the generative AI, and the generative AI adjusts the display method for missing ingredients based on that. This allows the display unit to estimate the user's emotions and adjust the display method for missing ingredients based on the estimated user emotions. Some or all of the above processing in the display unit is performed using the generative AI. For example, the display unit inputs the user's emotions into the generative AI, and the generative AI adjusts the display method for missing ingredients based on that. This allows the recipe suggestion system according to the embodiment to provide more appropriate information by adjusting the display method according to the user's emotions.

[0096] The display unit adds a function to suggest substitute ingredients when a missing ingredient is displayed. For example, the display unit suggests substitute ingredients for the missing ingredient, allowing the user to select one. The display unit adds a function to suggest substitute ingredients when a missing ingredient is displayed, using a generation AI. For example, the display unit inputs substitute ingredients for the missing ingredient into the generation AI, and the generation AI suggests substitutes based on that input. This allows the display unit to add a function to suggest substitute ingredients when a missing ingredient is displayed. Some or all of the above processing in the display unit is performed using the generation AI. For example, the display unit inputs substitute ingredients for the missing ingredient into the generation AI, and the generation AI suggests substitutes based on that input. This allows the recipe suggestion system according to the embodiment to have choices for the user by suggesting substitutes.

[0097] The display unit displays information on where to purchase and the price of ingredients when missing ingredients are indicated. For example, the display unit displays the nearest place to purchase the missing ingredients. The display unit uses a generating AI to display information on where to purchase and the price of ingredients when missing ingredients are indicated. For example, the display unit inputs information on where to purchase and the price of ingredients into the generating AI, and the generating AI displays the information based on that input. This allows the display unit to display information on where to purchase and the price of ingredients when missing ingredients are indicated. Some or all of the above processing in the display unit is performed using the generating AI. For example, the display unit inputs information on where to purchase and the price of ingredients into the generating AI, and the generating AI displays the information based on that input. This allows the recipe suggestion system according to the embodiment to display information on where to purchase and the price of ingredients, enabling the user to purchase ingredients efficiently.

[0098] The display unit estimates the user's emotions and determines the priority of the displayed content based on the estimated emotions. For example, if the user is feeling stressed, the display unit prioritizes displaying important information. The display unit uses a generative AI to estimate the user's emotions and determines the priority of the displayed content based on the estimated emotions. For example, the display unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of the displayed content based on that input. This allows the display unit to estimate the user's emotions and determine the priority of the displayed content based on the estimated emotions. Some or all of the above processing in the display unit is performed using the generative AI. For example, the display unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of the displayed content based on that input. This allows the recipe suggestion system according to the embodiment to provide more appropriate information by determining the priority of the displayed content according to the user's emotions.

[0099] The display unit, when displaying missing ingredients, prioritizes displaying ingredients that the user frequently purchases by referring to the user's purchase history. For example, the display unit prioritizes displaying ingredients that the user frequently buys. The display unit uses a generation AI to prioritize displaying ingredients that the user frequently purchases by referring to the user's purchase history when displaying missing ingredients. For example, the display unit inputs the user's purchase history into the generation AI, and the generation AI displays ingredients that the user frequently purchases based on that. This allows the display unit to prioritize displaying ingredients that the user frequently purchases by referring to the user's purchase history when displaying missing ingredients. Some or all of the above processing in the display unit is performed using a generation AI. For example, the display unit inputs the user's purchase history into the generation AI, and the generation AI displays ingredients that the user frequently purchases based on that. This allows the recipe suggestion system according to the embodiment to prioritize displaying ingredients that the user frequently purchases by referring to the purchase history.

[0100] The display unit, when displaying missing ingredients, suggests the nearest place to buy them, taking into account the user's geographical location. For example, the display unit displays the nearest place to buy them based on the user's current location. The display unit uses a generating AI to suggest the nearest place to buy them, taking into account the user's geographical location when displaying missing ingredients. For example, the display unit inputs the user's geographical location into the generating AI, and the generating AI suggests the nearest place to buy them based on that. This allows the display unit to suggest the nearest place to buy them, taking into account the user's geographical location when displaying missing ingredients. Some or all of the above processing in the display unit is performed using the generating AI. For example, the display unit inputs the user's geographical location into the generating AI, and the generating AI suggests the nearest place to buy them based on that. This allows the recipe suggestion system according to the embodiment to suggest the nearest place to buy them by taking into account geographical location information.

[0101] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is feeling stressed, the learning unit selects simple recipes as training data. The learning unit uses generative AI to estimate the user's emotions and selects training data based on the estimated emotions. For example, the learning unit inputs the user's emotions into the generative AI, and the generative AI selects training data based on that. This allows the learning unit to estimate the user's emotions and select training data based on the estimated emotions. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's emotions into the generative AI, and the generative AI selects training data based on that. This enables the recipe suggestion system according to the embodiment to learn more appropriately by selecting training data according to the user's emotions.

[0102] The learning unit optimizes the learning algorithm by referring to past learning data during learning. The learning unit adjusts the learning algorithm based on past learning data, for example. The learning unit uses a generative AI to optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit inputs past learning data into the generative AI, and the generative AI optimizes the learning algorithm based on it. This allows the learning unit to optimize the learning algorithm by referring to past learning data during learning. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs past learning data into the generative AI, and the generative AI optimizes the learning algorithm based on it. This allows the recipe suggestion system according to the embodiment to optimize its learning algorithm by referring to past learning data.

[0103] The learning unit adjusts the learning content during training, taking into account the user's cooking frequency and success rate. The learning unit weights the learning data based on, for example, the user's cooking frequency. The learning unit uses a generative AI to adjust the learning content during training, taking into account the user's cooking frequency and success rate. For example, the learning unit inputs the user's cooking frequency and success rate into the generative AI, and the generative AI adjusts the learning content based on that. This allows the learning unit to adjust the learning content during training, taking into account the user's cooking frequency and success rate. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's cooking frequency and success rate into the generative AI, and the generative AI adjusts the learning content based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its learning content by taking into account cooking frequency and success rate.

[0104] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is feeling stressed, the learning unit sets a lower learning frequency. The learning unit uses a generative AI to estimate the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, the learning unit inputs the user's emotions into the generative AI, and the generative AI adjusts the learning frequency based on that. This allows the learning unit to estimate the user's emotions and adjust the learning frequency based on the estimated emotions. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's emotions into the generative AI, and the generative AI adjusts the learning frequency based on that. This enables the recipe suggestion system according to the embodiment to perform more appropriate learning by adjusting the learning frequency according to the user's emotions.

[0105] The learning unit weights the learning data by referring to the user's ingredient usage history during the learning process. For example, the learning unit weights the learning data by giving more emphasis to ingredients that the user frequently uses. The learning unit uses a generative AI to weight the learning data by referring to the user's ingredient usage history during the learning process. For example, the learning unit inputs the user's ingredient usage history into the generative AI, and the generative AI weights the learning data based on that. This allows the learning unit to weight the learning data by referring to the user's ingredient usage history during the learning process. Some or all of the above processing in the learning unit is performed using the generative AI. For example, the learning unit inputs the user's ingredient usage history into the generative AI, and the generative AI weights the learning data based on that. This enables the recipe suggestion system according to the embodiment to weight the learning data by referring to the ingredient usage history.

[0106] The learning unit incorporates the user's satisfaction with their meals as feedback during the learning process. The learning unit adjusts the learning data based on the user's satisfaction with their meals, for example. The learning unit uses a generative AI to incorporate the user's satisfaction with their meals as feedback during the learning process. For example, the learning unit inputs the user's satisfaction with their meals into the generative AI, which then adjusts the learning data based on that input. This allows the learning unit to incorporate the user's satisfaction with their meals as feedback during the learning process. Some or all of the above-described processes in the learning unit are performed using the generative AI. For example, the learning unit inputs the user's satisfaction with their meals into the generative AI, which then adjusts the learning data based on that input. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its learning by incorporating the user's satisfaction with their meals as feedback.

[0107] The customization unit estimates the user's emotions and adjusts the recipe customization method based on the estimated user emotions. For example, if the user is feeling stressed, the customization unit provides a simpler customization method. The customization unit uses generative AI to estimate the user's emotions and adjusts the recipe customization method based on the estimated user emotions. For example, the customization unit inputs the user's emotions into the generative AI, and the generative AI adjusts the customization method based on that. This allows the customization unit to estimate the user's emotions and adjust the recipe customization method based on the estimated user emotions. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit inputs the user's emotions into the generative AI, and the generative AI adjusts the customization method based on that. This allows the recipe suggestion system according to the embodiment to perform more appropriate customization by adjusting the customization method according to the user's emotions.

[0108] The customization unit proposes the optimal customization when customizing a recipe by referring to the user's past cooking history. For example, the customization unit proposes a customization that suits the user's preferences based on dishes the user has made in the past. The customization unit uses a generation AI to propose the optimal customization when customizing a recipe by referring to the user's past cooking history. For example, the customization unit inputs the user's past cooking history into the generation AI, and the generation AI proposes a customization based on that. This allows the customization unit to propose the optimal customization when customizing a recipe by referring to the user's past cooking history. Some or all of the above processing in the customization unit is performed using the generation AI. For example, the customization unit inputs the user's past cooking history into the generation AI, and the generation AI proposes a customization based on that. This enables the recipe suggestion system according to the embodiment to perform optimal customization by referring to past cooking history.

[0109] The customization unit adjusts the customization content when customizing a recipe, taking into account the user's food preferences and allergy information. For example, the customization unit proposes a customization that prioritizes the use of the user's favorite ingredients. The customization unit uses a generation AI to adjust the customization content when customizing a recipe, taking into account the user's food preferences and allergy information. For example, the customization unit inputs the user's preferences and allergy information into the generation AI, and the generation AI adjusts the customization content based on that. This allows the customization unit to adjust the customization content when customizing a recipe, taking into account the user's food preferences and allergy information. Some or all of the above-described processes in the customization unit are performed using the generation AI. For example, the customization unit inputs the user's preferences and allergy information into the generation AI, and the generation AI adjusts the customization content based on that. This enables the recipe suggestion system according to the embodiment to perform more appropriate customization by taking into account food preferences and allergy information.

[0110] The customization unit estimates the user's emotions and determines the priority of customizations based on the estimated emotions. For example, if the user is feeling stressed, the customization unit will prioritize suggesting simple and easy customizations. The customization unit uses generative AI to estimate the user's emotions and determines the priority of customizations based on the estimated emotions. For example, the customization unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of customizations based on that input. This allows the customization unit to estimate the user's emotions and determine the priority of customizations based on the estimated emotions. Some or all of the above-described processes in the customization unit are performed using generative AI. For example, the customization unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of customizations based on that input. This enables the recipe suggestion system according to the embodiment to perform more appropriate customizations by determining the priority of customizations according to the user's emotions.

[0111] The customization unit suggests region-specific ingredients while considering the user's geographical location information during recipe customization. For example, the customization unit suggests region-specific ingredients based on the user's current location. The customization unit uses a generation AI to suggest region-specific ingredients while considering the user's geographical location information during recipe customization. For example, the customization unit inputs the user's geographical location information into the generation AI, which then suggests region-specific ingredients based on that information. This allows the customization unit to suggest region-specific ingredients while considering the user's geographical location information during recipe customization. Some or all of the above-described processes in the customization unit are performed using the generation AI. For example, the customization unit inputs the user's geographical location information into the generation AI, which then suggests region-specific ingredients based on that information. This allows the recipe suggestion system according to the embodiment to suggest region-specific ingredients by considering geographical location information.

[0112] The customization unit adjusts the customization content while considering the user's dietary restrictions when customizing recipes. For example, the customization unit suggests a low-calorie customization based on the user's diet information. The customization unit uses a generation AI to adjust the customization content while considering the user's dietary restrictions when customizing recipes. For example, the customization unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the customization content based on that. This allows the customization unit to adjust the customization content while considering the user's dietary restrictions when customizing recipes. Some or all of the above processing in the customization unit is performed using the generation AI. For example, the customization unit inputs the user's dietary restrictions into the generation AI, and the generation AI adjusts the customization content based on that. This enables the recipe suggestion system according to the embodiment to perform more appropriate customization by considering dietary restrictions.

[0113] The seasonal suggestion unit estimates the user's emotions and adjusts the method of suggesting seasonal recipes based on the estimated emotions. For example, if the user is feeling stressed, the seasonal suggestion unit suggests a simple seasonal recipe. The seasonal suggestion unit uses generative AI to estimate the user's emotions and adjusts the method of suggesting seasonal recipes based on the estimated emotions. For example, the seasonal suggestion unit inputs the user's emotions into the generative AI, and the generative AI adjusts the method of suggesting seasonal recipes based on that input. This allows the seasonal suggestion unit to estimate the user's emotions and adjust the method of suggesting seasonal recipes based on the estimated emotions. Some or all of the above processing in the seasonal suggestion unit is performed using generative AI. For example, the seasonal suggestion unit inputs the user's emotions into the generative AI, and the generative AI adjusts the method of suggesting seasonal recipes based on that input. This allows the recipe suggestion system according to the embodiment to suggest more appropriate seasonal recipes by adjusting the suggestion method according to the user's emotions.

[0114] The seasonal suggestion unit adjusts the suggested recipes when proposing seasonal recipes, taking into account the freshness and nutritional value of seasonal ingredients. For example, the seasonal suggestion unit proposes recipes using fresh seasonal ingredients. The seasonal suggestion unit uses a generation AI to adjust the suggested recipes when proposing seasonal recipes, taking into account the freshness and nutritional value of seasonal ingredients. For example, the seasonal suggestion unit inputs the freshness and nutritional value of seasonal ingredients into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the seasonal suggestion unit to adjust the suggested recipes when proposing seasonal recipes, taking into account the freshness and nutritional value of seasonal ingredients. Some or all of the above processing in the seasonal suggestion unit is performed using a generation AI. For example, the seasonal suggestion unit inputs the freshness and nutritional value of seasonal ingredients into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the recipe suggestion system according to the embodiment to propose more appropriate seasonal recipes by taking into account the freshness and nutritional value of seasonal ingredients.

[0115] The seasonal suggestion unit improves the accuracy of its suggestions by referring to evaluations of past seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit suggests recipes that suit the user's preferences based on evaluations of past seasonal recipes. The seasonal suggestion unit uses a generation AI to improve the accuracy of its suggestions by referring to evaluations of past seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit inputs evaluations of past seasonal recipes into the generation AI, and the generation AI improves the accuracy of its suggestions based on that. In this way, the seasonal suggestion unit can improve the accuracy of its suggestions by referring to evaluations of past seasonal recipes when suggesting seasonal recipes. Some or all of the above processing in the seasonal suggestion unit is performed using a generation AI. For example, the seasonal suggestion unit inputs evaluations of past seasonal recipes into the generation AI, and the generation AI improves the accuracy of its suggestions based on that. In this way, the recipe suggestion system according to the embodiment can improve the accuracy of its suggestions by referring to past evaluations.

[0116] The seasonal suggestion unit estimates the user's emotions and determines the priority of seasonal recipes based on the estimated emotions. For example, if the user is feeling stressed, the seasonal suggestion unit will prioritize suggesting simple and easy seasonal recipes. The seasonal suggestion unit uses generative AI to estimate the user's emotions and determines the priority of seasonal recipes based on the estimated emotions. For example, the seasonal suggestion unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of seasonal recipes based on that. This allows the seasonal suggestion unit to estimate the user's emotions and determine the priority of seasonal recipes based on the estimated emotions. Some or all of the above processing in the seasonal suggestion unit is performed using generative AI. For example, the seasonal suggestion unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of seasonal recipes based on that. This allows the recipe suggestion system according to the embodiment to suggest more appropriate seasonal recipes by determining priorities according to the user's emotions.

[0117] The seasonal suggestion unit, when suggesting seasonal recipes, proposes region-specific seasonal ingredients while considering the user's geographical location information. For example, the seasonal suggestion unit proposes region-specific seasonal ingredients based on the user's current location. The seasonal suggestion unit uses a generation AI to propose region-specific seasonal ingredients while considering the user's geographical location information when suggesting seasonal recipes. For example, the seasonal suggestion unit inputs the user's geographical location information into the generation AI, and the generation AI proposes region-specific seasonal ingredients based on that information. This allows the seasonal suggestion unit to propose region-specific seasonal ingredients while considering the user's geographical location information when suggesting seasonal recipes. Some or all of the above processing in the seasonal suggestion unit is performed using the generation AI. For example, the seasonal suggestion unit inputs the user's geographical location information into the generation AI, and the generation AI proposes region-specific seasonal ingredients based on that information. This allows the recipe suggestion system according to the embodiment to propose region-specific seasonal ingredients by considering geographical location information.

[0118] The seasonal suggestion unit adjusts the suggested recipes by referring to the user's past usage history of seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit suggests recipes that suit the user's preferences based on seasonal recipes the user has used in the past. The seasonal suggestion unit uses a generation AI to adjust the suggested recipes by referring to the user's past usage history of seasonal recipes when suggesting seasonal recipes. For example, the seasonal suggestion unit inputs the user's past usage history of seasonal recipes into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the seasonal suggestion unit to adjust the suggested recipes by referring to the user's past usage history when suggesting seasonal recipes. Some or all of the above processing in the seasonal suggestion unit is performed using the generation AI. For example, the seasonal suggestion unit inputs the user's past usage history of seasonal recipes into the generation AI, and the generation AI adjusts the suggested recipes based on that. This allows the recipe suggestion system according to the embodiment to improve the accuracy of its suggestions by referring to past usage history.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] Recipe suggestion systems can also suggest recipes that take into account the user's dietary preferences and allergy information. For example, if a user is allergic to a specific ingredient, the system will prioritize suggesting recipes that do not contain that ingredient. Furthermore, if a user likes a particular ingredient, the system can suggest recipes that use that ingredient extensively. It can even learn from the user's past eating history to suggest recipes they might enjoy. This allows users to easily find recipes that suit their preferences and allergies, improving their satisfaction with meals.

[0121] The recipe suggestion system can estimate the user's emotions and adjust its recipe suggestions based on those emotions. For example, if the user is stressed, it will prioritize suggesting simple, easy recipes. Conversely, if the user is relaxed, it may suggest slightly more elaborate recipes. Furthermore, it can change the order of recipe suggestions according to the user's emotions, displaying the recipe the user needs most first. This makes it easy for users to find recipes that match their mood, making meal preparation easier.

[0122] The recipe suggestion system can also take the user's geographical location into account and suggest recipes using ingredients specific to that region. For example, if a user lives in a particular area, it can suggest recipes using ingredients commonly used in that area. It can also suggest recipes using seasonal ingredients specific to a particular region. Furthermore, if a user is traveling, it can suggest recipes using local specialties from that region. This allows users to enjoy meals that utilize local specialties.

[0123] Recipe suggestion systems can also suggest recipes that take into account the user's dietary restrictions. For example, if a user is on a diet, the system will prioritize suggesting low-calorie recipes. It can also suggest recipes that are rich in specific nutrients if the user needs to consume them. Furthermore, if a user needs to avoid certain ingredients, the system can suggest recipes that do not contain those ingredients. This makes it easy for users to find recipes that suit their dietary restrictions and maintain a healthy eating lifestyle.

[0124] The recipe suggestion system can also refer to the user's purchase history to suggest recipes using ingredients they frequently buy. For example, it can prioritize suggesting recipes that use ingredients the user often buys. It can also suggest recipes using ingredients that the user is likely to have in their refrigerator based on ingredients they have purchased in the past. Furthermore, it can adjust the order of recipe suggestions considering how often the user buys specific ingredients. This allows users to easily find recipes based on their purchase history and reduces food waste.

[0125] The recipe suggestion system can estimate the user's emotions and adjust the recipe customization based on those emotions. For example, if the user is stressed, it will suggest a simple and easy customization method. Conversely, if the user is relaxed, it may suggest a slightly more elaborate customization method. Furthermore, it can change the priority of customizations according to the user's emotions, displaying the customization method the user needs most first. This makes it easy for users to find a customization method that suits their emotions, making meal preparation easier.

[0126] The recipe suggestion system can also suggest recipes that the user prefers by referring to their past cooking history. For example, it can suggest similar recipes based on dishes the user has made in the past. It can also prioritize suggesting recipes that the user has previously given high ratings to. Furthermore, it can adjust the order of recipe suggestions considering the user's cooking frequency and success rate. This allows users to easily find recipes based on their past cooking history, improving their satisfaction with meals.

[0127] The recipe suggestion system can estimate the user's emotions and adjust how recipes are displayed based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. Conversely, if the user is relaxed, it can provide a display that includes more detailed information. Furthermore, it can change the priority of displayed content according to the user's emotions, displaying the information the user needs most first. This makes it easy for users to find a display method that suits their emotions and makes it easier to obtain information.

[0128] The recipe suggestion system can also display the nearest shopping locations and price information, taking into account the user's geographical location. For example, it can display the nearest supermarket and ingredient price information based on the user's current location. Furthermore, if the user lives in a specific region, it can suggest places to buy ingredients commonly used in that area. It can even suggest places to buy local specialties if the user is traveling. This allows users to easily find shopping locations and price information based on their geographical location, enabling them to purchase ingredients efficiently.

[0129] The recipe suggestion system can estimate the user's emotions and prioritize recipes based on those emotions. For example, if the user is stressed, it will prioritize simple, easy recipes. Conversely, if the user is relaxed, it can prioritize slightly more elaborate recipes. Furthermore, it can change the order of recipe suggestions according to the user's emotions, displaying the recipe the user needs most first. This makes it easy for users to find recipes that match their mood, making meal preparation easier.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The analysis unit analyzes the image of the refrigerator. The analysis unit inputs, for example, an image taken inside the refrigerator into the generating AI, which analyzes the image to identify the food items inside the refrigerator. The generating AI recognizes the food items inside the refrigerator using image analysis technology. For example, the generating AI identifies food items such as vegetables, meat, and condiments from the image. Step 2: The suggestion unit proposes recipes based on the ingredients analyzed by the analysis unit. For example, the suggestion unit matches the identified ingredients with a database of recipes and proposes recipes that can be made with the ingredients in the refrigerator. For example, if there are vegetables and meat in the refrigerator, the suggestion unit will propose recipes such as "Tacos with plenty of vegetables" or "Dumplings with plenty of perilla leaves." Step 3: The display unit shows any missing ingredients in the recipe suggested by the suggestion unit. For example, if the "Vegetable-filled Tacos" recipe is missing avocado and tomato, the display unit will show that information. This allows the user to check which ingredients are missing and purchase what they need.

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0135] Each of the multiple elements described above, including the analysis unit, suggestion unit, display unit, learning unit, customization unit, and seasonal suggestion unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by using the camera 42 of the smart device 14 to photograph the inside of the refrigerator and inputting the image to the generating AI. The suggestion unit suggests a recipe based on the identified ingredients by the identification processing unit 290 of the data processing unit 12. The display unit displays the suggested recipe and any missing ingredients using the display 40A of the smart device 14. The learning unit learns the user's cooking history by the identification processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The customization unit suggests a recipe tailored to the user's preferences by the control unit 46A of the smart device 14. The seasonal suggestion unit suggests a recipe appropriate for the season by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 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.

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

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the analysis unit, suggestion unit, display unit, learning unit, customization unit, and seasonal suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by using the camera 42 of the smart glasses 214 to photograph the inside of the refrigerator and inputting the image to the generating AI. The suggestion unit suggests a recipe based on the identified ingredients using the identification processing unit 290 of the data processing unit 12. The display unit displays the suggested recipe and any missing ingredients using the display of the smart glasses 214. The learning unit learns the user's cooking history using the identification processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The customization unit suggests a recipe tailored to the user's preferences using the control unit 46A of the smart glasses 214. The seasonal suggestion unit suggests a recipe appropriate for the season using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the analysis unit, suggestion unit, display unit, learning unit, customization unit, and seasonal suggestion unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by using the camera 42 of the headset terminal 314 to photograph the inside of the refrigerator and inputting the image to the generating AI. The suggestion unit suggests a recipe based on the identified ingredients using the identification processing unit 290 of the data processing unit 12. The display unit displays the suggested recipe and any missing ingredients using the display 343 of the headset terminal 314. The learning unit learns the user's cooking history using the identification processing unit 290 of the data processing unit 12 to improve the accuracy of recommendations. The customization unit suggests a recipe tailored to the user's preferences using the control unit 46A of the headset terminal 314. The seasonal suggestion unit suggests a recipe appropriate for the season using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 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.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the analysis unit, suggestion unit, display unit, learning unit, customization unit, and seasonal suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by using the camera 42 of the robot 414 to photograph the inside of the refrigerator and inputting the image to the generating AI. The suggestion unit suggests a recipe based on the identified ingredients by the identification processing unit 290 of the data processing unit 12. The display unit displays the suggested recipe and any missing ingredients using the display of the robot 414. The learning unit learns the user's cooking history by the identification processing unit 290 of the data processing unit 12 and improves the accuracy of recommendations. The customization unit suggests a recipe tailored to the user's preferences by the control unit 46A of the robot 414. The seasonal suggestion unit suggests a recipe appropriate for the season by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] 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.

[0195] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) An analysis unit that analyzes images of refrigerators, A suggestion unit proposes a recipe based on the ingredients analyzed by the aforementioned analysis unit, The system includes a display unit that shows the ingredients missing from the recipe proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) It features a learning unit that learns from the user's cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a customization section that suggests recipes tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The department has a seasonal suggestions section that proposes recipes appropriate for each season. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Recognizing food items inside a refrigerator using image analysis technology The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The system matches identified ingredients with a database of recipes and suggests recipes that can be made with the ingredients you have in your refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is Display any missing ingredients in the suggested recipe. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During image analysis, the freshness of the food in the refrigerator is determined, and the analysis results are adjusted based on the freshness. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During image analysis, the accuracy of the analysis is improved by considering the arrangement and overlap of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing images, consider temperature and humidity information inside the refrigerator to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During image analysis, the system supplements the analysis results by referencing the user's past food usage history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way recipe suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When suggesting recipes, we prioritize healthy recipes that take into account the nutritional value of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When suggesting recipes, we improve the accuracy of the suggestions by referring to the user's past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, The system estimates the user's emotions and prioritizes recipe suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When suggesting recipes, we take the user's allergy information into consideration and suggest safe recipes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When suggesting recipes, we adjust the suggestions to take into account the user's dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is The system estimates the user's emotions and adjusts how missing ingredients are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is Add a feature that suggests substitute ingredients when missing ingredients are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying missing ingredients, show information on where to purchase the ingredients and their prices. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the user's emotions and determines the priority of displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying missing ingredients, the system prioritizes showing ingredients that the user frequently purchases, referencing their purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying missing ingredients, the system will suggest the nearest place to purchase them, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned learning unit, During the learning process, the learning content is adjusted considering the user's cooking frequency and success rate. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, the training data is weighted by referencing the user's food usage history. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned learning unit, During the learning process, the user's satisfaction with their meals will be incorporated as feedback. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned customization unit is It estimates the user's emotions and adjusts how recipes are customized based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned customization unit is When customizing recipes, the system refers to the user's past cooking history to suggest the most suitable customizations. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned customization unit is When customizing recipes, the system adjusts the customization settings to take into account the user's food preferences and allergy information. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned customization unit is When customizing recipes, the system suggests regionally specific ingredients based on the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned customization unit is When customizing recipes, the customization settings are adjusted to take into account the user's dietary restrictions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned seasonal proposal department We estimate the user's emotions and adjust the way seasonal recipes are suggested based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned seasonal proposal department When suggesting seasonal recipes, we adjust the suggestions to take into account the freshness and nutritional value of seasonal ingredients. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned seasonal proposal department When suggesting seasonal recipes, we refer to the evaluations of past seasonal recipes to improve the accuracy of the suggestions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned seasonal proposal department The system estimates the user's emotions and prioritizes seasonal recipes based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned seasonal proposal department When suggesting seasonal recipes, the system takes the user's geographical location into consideration and suggests seasonal ingredients specific to their region. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned seasonal proposal department When suggesting seasonal recipes, we adjust the suggestions by referring to the user's past usage history of seasonal recipes. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes images of refrigerators, A suggestion unit proposes a recipe based on the ingredients analyzed by the aforementioned analysis unit, The system includes a display unit that shows the ingredients missing from the recipe proposed by the aforementioned proposal unit. A system characterized by the following features.

2. It features a learning unit that learns from the user's cooking history. The system according to feature 1.

3. It features a customization section that suggests recipes tailored to the user's preferences. The system according to feature 1.

4. The department has a seasonal suggestions section that proposes recipes appropriate for each season. The system according to feature 1.

5. The aforementioned analysis unit, Recognizing food items inside a refrigerator using image analysis technology The system according to feature 1.

6. The aforementioned proposal section is, The system matches identified ingredients with a database of recipes and suggests recipes that can be made with the ingredients you have in your refrigerator. The system according to feature 1.

7. The aforementioned display unit is Display any missing ingredients in the suggested recipe. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of image analysis based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, During image analysis, the freshness of the food in the refrigerator is determined, and the analysis results are adjusted based on the freshness. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing images, the accuracy of the analysis is improved by considering the arrangement and overlap of ingredients. The system according to feature 1.

Images