system

Abstract

The system according to this embodiment aims to make effective use of ingredients in the refrigerator and suggest recipes that suit the user's preferences. [Solution] The system according to the embodiment comprises a reception unit, an identification unit, a generation unit, and a serving unit. The reception unit takes a photograph of the ingredients. The identification unit analyzes the photograph taken by the reception unit and identifies the ingredients. The generation unit generates a recipe based on the ingredients identified by the identification unit, taking into account the user's preferences and constraints. The serving unit provides the recipe generated by the generation unit to the user.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, the ingredients in the refrigerator have not been fully utilized effectively to propose recipes that match the user's preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize the ingredients in the refrigerator and propose recipes that match the user's preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an identification unit, a generation unit, and a serving unit. The reception unit takes a photograph of the ingredients. The identification unit analyzes the photograph taken by the reception unit and identifies the ingredients. The generation unit generates a recipe based on the ingredients identified by the identification unit, taking into account the user's preferences and constraints. The serving unit provides the recipe generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize ingredients in the refrigerator and suggest recipes that suit the user's preferences. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 recipes based on the user's preferences by utilizing ingredients in the refrigerator. The recipe suggestion system works by having the user take photos of the ingredients in the refrigerator and inputting them into the system. Next, the recipe suggestion system uses image recognition technology to identify the ingredients and generates a recipe considering the user's preferences and cooking time constraints. This recipe is tailored to the user's preferences and may suggest, for example, a light Western dish or a rich Chinese dish. This allows the user to easily make a delicious dinner without wasting ingredients in the refrigerator. For example, the user takes photos of the ingredients in the refrigerator. In this case, the user takes photos of the ingredients using a smartphone and uploads them to the system. For example, if the refrigerator contains ingredients such as 2 cherry tomatoes, 300g of ground chicken, 10 dumpling wrappers, cheese, 1 egg, and 1 bell pepper, the user takes photos of them. Next, the recipe suggestion system uses image recognition technology to identify the ingredients. The recipe suggestion system analyzes the uploaded photos and recognizes each ingredient. For example, it identifies ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, egg, and bell pepper. The system then generates recipes, taking into account the user's preferences and cooking time constraints. Users input their preferences, such as whether they prefer light or rich dishes, Western, Japanese, or Chinese cuisine, and their desired cooking time. For example, if a user prefers light Western food and wants a dish that can be cooked in under 20 minutes, a recipe matching those conditions will be generated. The generated recipes are then suggested to the user. For example, Italian-style tomato dumplings using cherry tomatoes and ground chicken, or stir-fried eggs and bell peppers might be suggested. This allows users to easily prepare a delicious dinner without wasting ingredients in their refrigerator. The recipe suggestion system enables users to easily prepare dinner using ingredients in their refrigerator, even if they are tired from work or childcare. Furthermore, since recipes are suggested according to the user's preferences, meal satisfaction is also improved. For example, Italian-style tomato dumplings might be suggested to a user who prefers light Western food, while stir-fried eggs and bell peppers might be suggested to a user who prefers rich Chinese food.This allows users to enjoy dishes tailored to their preferences. The recipe suggestion system, by providing recipes based on user preferences, helps them avoid wasting ingredients in their refrigerator and makes preparing dinner easy.

[0029] The recipe suggestion system according to this embodiment comprises a reception unit, an identification unit, a generation unit, and a serving unit. The reception unit takes photographs of ingredients. The reception unit can, for example, allow a user to take photographs of ingredients using a smartphone and upload them to the system. The reception unit can also take photographs of ingredients using a digital camera and upload them to the system. Furthermore, when taking photographs of ingredients, the reception unit can also allow the user to input the names of the ingredients. For example, the reception unit can allow a user to upload a photograph of ingredients taken with a smartphone to the system and input the names of the ingredients. The identification unit analyzes the photographs taken by the reception unit and identifies the ingredients. The identification unit identifies ingredients using, for example, image recognition technology. The identification unit analyzes the uploaded photographs and recognizes each ingredient. For example, the identification unit identifies ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. The identification unit can identify the type and quantity of ingredients using image recognition technology. For example, the identification unit can identify the number of cherry tomatoes and the quantity of ground chicken using image recognition technology. The generation unit generates recipes based on ingredients identified by the identification unit, taking into account the user's preferences and constraints. For example, the generation unit generates recipes considering the user's preferences and cooking time constraints. The generation unit allows the user to input preferences such as whether they like light or rich dishes, Western food, Japanese food, or Chinese food, and how long the cooking time should be. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit can generate recipes based on the user's preferences and constraints. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The provision unit provides the user with the recipes generated by the generation unit. For example, the provision unit may suggest the generated recipes to the user. The provision unit can provide the generated recipes to the user visually. For example, the provision unit can display the generated recipes on a smartphone or tablet screen. The provision unit can also print and provide the generated recipes.For example, the supply unit can print the generated recipe using a printer and provide it to the user. This allows the recipe suggestion system according to the embodiment to provide recipes based on the user's preferences, enabling them to easily prepare dinner without wasting ingredients in the refrigerator.

[0030] The reception desk takes photos of ingredients. For example, users can take photos of ingredients using their smartphones and upload them to the system. The reception desk can also take photos of ingredients using digital cameras and upload them to the system. Furthermore, when taking photos of ingredients, users can input the names of the ingredients. For example, users can upload photos of ingredients taken with their smartphones to the system and input the names of the ingredients. The reception desk provides an interface for quickly importing photos taken by users into the system. For example, users can easily upload photos via a smartphone app or web browser. The reception desk also automatically adjusts the resolution and format of the photos so that the system can perform optimal analysis. In addition, the reception desk supports cases where users photograph multiple ingredients at once and has a function to individually recognize each ingredient. For example, even if a user photographs all the ingredients in their refrigerator at once, the system can analyze each ingredient individually and provide accurate information. This allows the reception desk to easily provide ingredient information to the system, improving the accuracy of recipe suggestions.

[0031] The identification unit analyzes photos taken by the reception unit to identify ingredients. The identification unit identifies ingredients using, for example, image recognition technology. The identification unit analyzes uploaded photos and recognizes each ingredient. For example, the identification unit can identify ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. The identification unit can identify the type and quantity of ingredients using image recognition technology. For example, the identification unit can identify the number of cherry tomatoes or the amount of ground chicken using image recognition technology. The identification unit utilizes the latest deep learning algorithms to analyze the type, shape, color, and texture of ingredients. This allows the identification unit to recognize ingredients with high accuracy even at different angles and under different lighting conditions. Furthermore, the identification unit also has a function to evaluate the freshness and quality of ingredients. For example, it can analyze changes in the color and shape of vegetables to evaluate their freshness. The identification unit matches the name of the ingredient entered by the user with the image recognition results to provide accurate ingredient information. The identification unit also analyzes the size and area occupied by ingredients in the image to estimate the quantity of ingredients. This allows the specific unit to accurately grasp information about the ingredients the user possesses, thereby improving the accuracy of recipe suggestions.

[0032] The generation unit generates recipes based on ingredients identified by the identification unit, taking into account the user's preferences and constraints. For example, the generation unit generates recipes considering the user's preferences and cooking time constraints. The generation unit allows users to input preferences such as whether they like light or rich dishes, Western, Japanese, or Chinese cuisine, and how long they want to cook. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit can generate recipes based on the user's preferences and constraints. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit utilizes AI to learn the user's preferences and past selection history, and proposes more personalized recipes. For example, the generation unit analyzes the user's preferences based on recipes and ratings the user has previously selected and generates the optimal recipe. The generation unit can also propose nutritionally balanced recipes by considering ingredient combinations and cooking methods. Furthermore, the generation unit takes into account the user's cooking equipment and skills to generate feasible recipes. This allows the generation unit to meet diverse user needs and provide highly satisfying recipes.

[0033] The provider unit provides users with recipes generated by the generator unit. For example, the provider unit suggests generated recipes to users. The provider unit can provide users with generated recipes visually. For example, the provider unit can display generated recipes on smartphone or tablet screens. The provider unit can also provide generated recipes in print. For example, the provider unit can print generated recipes on a printer and provide them to users. The provider unit provides a visually easy-to-understand interface so that users can easily view and execute recipes. For example, it explains each step of a recipe with photos or videos to make it easier for users to understand the cooking procedure. The provider unit also displays detailed information such as ingredient lists, cooking times, and calorie information so that users can grasp the necessary information at a glance. Furthermore, the provider unit also has functions that allow users to save and share recipes. For example, users can save their favorite recipes and view them again later. They can also share recipes with friends and family via social media or email. In this way, the provider unit can enable users to make the most of the generated recipes and enhance the enjoyment of cooking.

[0034] The input section allows users to input their preferences and constraints. For example, the input section allows users to input preferences such as whether they prefer light or rich dishes, Western food, Japanese food, or Chinese food, and how long they like to cook. The input section also allows users to input constraints such as allergy information and calorie restrictions. For example, the input section allows users to input allergy information and exclude specific ingredients. The input section can also generate low-calorie recipes based on the user's calorie restrictions. In this way, more appropriate recipes can be generated by inputting the user's preferences and constraints. Some or all of the above processing in the input section may be performed using AI, for example, or not. For example, when the user inputs their preferences and constraints, the input section can use AI to analyze the user's input and suggest the most suitable recipe.

[0035] The display unit can display the generated recipe. For example, the display unit can display the generated recipe on the screen of a smartphone or tablet. The display unit can assist the user in cooking by visually providing the generated recipe. For example, the display unit can display the generated recipe on the screen, allowing the user to check the cooking procedure. The display unit can also print and provide the generated recipe. For example, the display unit can print the generated recipe using a printer and provide it to the user. This allows the display unit to assist the user in cooking by visually providing the generated recipe. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when displaying a generated recipe, the display unit can use AI to suggest a display method tailored to the user's preferences.

[0036] The analysis unit can analyze photographs of food ingredients. The analysis unit identifies food ingredients, for example, using image recognition technology. The analysis unit analyzes uploaded photographs and recognizes each food ingredient. For example, the analysis unit identifies ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. The analysis unit can identify the type and quantity of food ingredients using image recognition technology. For example, the analysis unit can identify the number of cherry tomatoes or the amount of ground chicken using image recognition technology. This improves the accuracy of food ingredient identification by analyzing photographs of food ingredients. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to improve the accuracy of food ingredient identification when analyzing photographs of food ingredients.

[0037] The generation unit can generate recipes based on user preferences and constraints. For example, the generation unit generates recipes considering user preferences and cooking time constraints. The generation unit allows users to input preferences such as whether they like light or rich dishes, Western food, Japanese food, or Chinese food, and how long they want to cook. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit can generate recipes based on user preferences and constraints. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. In this way, by generating recipes based on user preferences and constraints, the generation unit can provide the user with the most suitable recipe. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, when the user inputs preferences and constraints, the generation unit can use AI to analyze the user's input and suggest the most suitable recipe.

[0038] The service provider can provide the generated recipe to the user. For example, the service provider can suggest the generated recipe to the user. The service provider can provide the generated recipe to the user visually. For example, the service provider can display the generated recipe on the screen of a smartphone or tablet. The service provider can also provide the generated recipe in print. For example, the service provider can print the generated recipe on a printer and provide it to the user. This allows the user to easily check the recipe by providing the generated recipe to the user. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, when providing a generated recipe, the service provider can use AI to suggest a delivery method tailored to the user's preferences.

[0039] The reception desk can analyze the user's past food photography history and suggest the optimal shooting method. For example, the reception desk can suggest the optimal shooting angle based on the types and frequency of food photographed by the user in the past. The reception desk can also analyze the quality of food photographs taken by the user in the past and suggest areas for improvement. For example, the reception desk can suggest the optimal shooting environment by considering the background and lighting conditions of food photographs taken by the user in the past. In this way, the reception desk can suggest the optimal shooting method by analyzing the past shooting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the user's past food photography history, the reception desk can use AI to analyze the user's shooting history and suggest the optimal shooting method.

[0040] The reception desk can prompt users to take photos of ingredients while considering the current inventory status of their refrigerator. For example, if the refrigerator inventory is low, the reception desk can prompt the user to prioritize photographing the necessary ingredients. If the refrigerator inventory is high, the reception desk can also suggest which ingredients to photograph. For example, the reception desk can suggest the order in which to photograph ingredients based on the refrigerator inventory status. This allows users to prioritize photographing the necessary ingredients by considering the refrigerator inventory status. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the refrigerator inventory status, the reception desk can use AI to analyze inventory data and suggest the optimal shooting method.

[0041] The reception desk can prioritize photographing ingredients that are highly relevant to the user's geographical location when taking pictures of ingredients. For example, if the user lives in a specific region, the reception desk can prioritize photographing ingredients commonly used in that region. If the user is traveling, the reception desk can also prioritize photographing local ingredients. For example, if the user is in a specific season, the reception desk can prioritize photographing ingredients related to that season. In this way, by considering geographical location, the reception desk can prioritize photographing ingredients that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the user's geographical location, the reception desk can use AI to analyze the location information and suggest the optimal shooting method.

[0042] The reception desk can analyze a user's social media activity when photographing ingredients and photograph relevant ingredients. For example, the reception desk can photograph relevant ingredients based on photos of dishes shared by the user on social media. The reception desk can also analyze posts from cooking accounts that the user follows and photograph relevant ingredients. For example, the reception desk can analyze trends in cooking communities that the user participates in and photograph relevant ingredients. In this way, relevant ingredients can be photographed by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing a user's social media activity, the reception desk can use AI to analyze the content of posts and suggest the optimal photography method.

[0043] The identification unit can improve the accuracy of identification by considering the freshness and quality of the ingredients during the identification process. For example, the identification unit can determine the freshness of the ingredients through image analysis and prioritize the identification of ingredients with high freshness. The identification unit can also determine the quality of the ingredients through image analysis and prioritize the identification of ingredients with high quality. For example, the identification unit can adjust the accuracy of identification based on the freshness and quality of the ingredients. This allows for improved accuracy of identification by considering the freshness and quality of the ingredients. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can use AI to improve the accuracy of analysis when analyzing the freshness and quality of the ingredients.

[0044] The identification unit can apply different identification algorithms to each category of food ingredient during identification. For example, the identification unit can apply different identification algorithms to categories such as vegetables, meat, and fish. The identification unit can also apply different identification algorithms to processed foods and fresh foods. For example, the identification unit can apply different identification algorithms to seasonings and main ingredients. By applying different identification algorithms to each category, the accuracy of identification can be improved. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit applies different identification algorithms to each category of food ingredient, it can use AI to optimize the algorithms.

[0045] The identification unit can perform identification while considering the geographical distribution of ingredients. For example, the identification unit can prioritize identifying ingredients commonly used in the area where the user lives. If the user is traveling, the identification unit can also prioritize identifying local ingredients. For example, if the user is in a particular season, the identification unit can also prioritize identifying ingredients associated with that season. This allows for the identification of highly relevant ingredients by considering geographical distribution. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when analyzing the geographical distribution of ingredients, the identification unit can use AI to analyze the distribution data and propose the optimal identification method.

[0046] The identification unit can improve the accuracy of its identification by referring to relevant literature on the food ingredient during the identification process. For example, the identification unit can improve the accuracy of its identification by referring to academic papers related to the identification of food ingredients. The identification unit can also improve the accuracy of its identification by referring to patent documents related to the identification of food ingredients. For example, the identification unit can improve the accuracy of its identification by referring to industry reports related to the identification of food ingredients. In this way, the accuracy of identification can be improved by referring to relevant literature. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, when analyzing relevant literature on food ingredients, the identification unit can use AI to analyze the literature data and propose the optimal identification method.

[0047] The generation unit can adjust the level of detail in a recipe based on the importance of the ingredients when generating a recipe. For example, the generation unit can provide a detailed recipe that focuses on the main ingredients. The generation unit can also provide a brief explanation of auxiliary ingredients. For example, the generation unit can adjust the steps of a recipe based on the importance of the ingredients. This allows for the provision of more appropriate recipes by adjusting the level of detail based on the importance of the ingredients. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, when analyzing the importance of ingredients, the generation unit can use AI to evaluate importance and suggest the optimal recipe.

[0048] The generation unit can apply different generation algorithms depending on the category of ingredients when generating recipes. For example, the generation unit can apply different generation algorithms for each category, such as vegetables, meat, and fish. The generation unit can also apply different generation algorithms for processed foods and fresh foods. For example, the generation unit can apply different generation algorithms for seasonings and main ingredients. By applying different generation algorithms for each category, it is possible to provide more appropriate recipes. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use AI to optimize the algorithm when applying different generation algorithms for each category of ingredients.

[0049] The generation unit can determine the priority of recipes based on when ingredients are submitted during recipe generation. For example, the generation unit can provide recipes that prioritize ingredients that need to be consumed quickly. The generation unit can also provide recipes that postpone the use of ingredients that can be stored for a long time. For example, the generation unit can adjust the steps of a recipe based on when ingredients are submitted. This allows for the provision of more appropriate recipes by prioritizing recipes based on when ingredients are submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, when analyzing when ingredients are submitted, the generation unit can use AI to evaluate the submission timing and propose the optimal recipe.

[0050] The generation unit can adjust the order of ingredients in a recipe based on their relationships during recipe generation. For example, the generation unit can provide a recipe centered on the main ingredients. The generation unit can also provide recipes that complement auxiliary ingredients. For example, the generation unit can adjust the steps of a recipe based on the relationships of the ingredients. This allows for the provision of more appropriate recipes by adjusting the order of recipes based on the relationships of the ingredients. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, when analyzing the relationships between ingredients, the generation unit can use AI to evaluate the relationships and propose the optimal recipe.

[0051] The service provider can select the optimal delivery method by referring to the user's past recipe usage history when providing recipes. For example, the service provider can provide the optimal recipe based on recipes the user has used in the past. The service provider can also provide recipes by analyzing the user's preferences from their past recipe usage history. For example, the service provider can provide relevant recipes by referring to the user's past recipe usage history. In this way, the service provider can provide the optimal recipe by referring to the user's past recipe usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, when analyzing the user's past recipe usage history, the service provider can use AI to analyze the historical data and suggest the optimal recipe.

[0052] The service provider can customize the recipe based on the user's current ingredient inventory when providing recipes. For example, the service provider can provide the optimal recipe based on the ingredients the user has in their refrigerator. The service provider can also suggest missing ingredients, taking into account the user's ingredient inventory. For example, the service provider can suggest alternative ingredients based on the user's ingredient inventory. This allows for the provision of more appropriate recipes by customizing the recipe based on the current ingredient inventory. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, when analyzing the user's ingredient inventory, the service provider can use AI to analyze inventory data and suggest the optimal recipe.

[0053] The service provider can select the optimal delivery method when providing recipes, taking into account the user's geographical location. For example, if the user lives in a specific region, the service provider can provide recipes based on ingredients commonly used in that region. If the user is traveling, the service provider can also provide recipes based on local ingredients. For example, if the user is in a specific season, the service provider can provide recipes based on ingredients associated with that season. This allows the service provider to provide the most suitable recipe by considering geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, when analyzing the user's geographical location, the service provider can use AI to analyze the location information and propose the most suitable recipe delivery method.

[0054] The service provider can analyze a user's social media activity to suggest recipes. For example, the service provider can provide relevant recipes based on photos of dishes shared by the user on social media. The service provider can also analyze posts from cooking accounts that the user follows and provide relevant recipes. For example, the service provider can analyze trends in cooking communities that the user participates in and provide relevant recipes. In this way, relevant recipes can be provided by analyzing social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, when analyzing a user's social media activity, the service provider can use AI to analyze the content of posts and suggest the most suitable recipes.

[0055] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can automatically display preferences and constraints that the user has frequently entered in the past as candidates. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the input unit can predict and suggest preferences and constraints used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by referring to past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, when analyzing the user's past input history, the input unit can use AI to analyze the historical data and suggest the optimal input method.

[0056] The input unit can select the optimal input method when inputting data, taking into account the user's device information. For example, if the user is using a smartphone, the input unit can provide an input method that matches the screen size. If the user is using a tablet, the input unit can also provide an input method optimized for a larger screen. For example, if the user is using a smartwatch, the input unit can provide a concise and highly visible input method. In this way, the optimal input method can be provided by considering device information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, when analyzing the user's device information, the input unit can use AI to analyze the device data and propose the optimal input method.

[0057] The display unit can select the optimal display method by referring to the user's past viewing history when displaying information. For example, the display unit can provide the optimal display method based on recipes the user has previously viewed. The display unit can also provide a display method by analyzing the user's preferences from their past viewing history. For example, the display unit can refer to the user's past viewing history and display relevant recipes. This allows the display unit to provide the optimal display method by referring to past viewing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when analyzing the user's past viewing history, the display unit can use AI to analyze the historical data and propose the optimal display method.

[0058] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when analyzing the user's device information, the display unit can use AI to analyze device data and propose the optimal display method.

[0059] The analysis unit can improve the accuracy of its analysis by considering the freshness and quality of the ingredients during the analysis. For example, the analysis unit can determine the freshness of the ingredients using image analysis and prioritize the analysis of ingredients with high freshness. The analysis unit can also determine the quality of the ingredients using image analysis and prioritize the analysis of ingredients with high quality. For example, the analysis unit can adjust the accuracy of its analysis based on the freshness and quality of the ingredients. This allows for improved accuracy of the analysis by considering the freshness and quality of the ingredients. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to improve the accuracy of its analysis when analyzing the freshness and quality of the ingredients.

[0060] The analysis unit can perform analysis while considering the geographical distribution of ingredients. For example, the analysis unit can prioritize the analysis of ingredients commonly used in the user's area. If the user is traveling, the analysis unit can also prioritize the analysis of local ingredients. For example, if the user is in a particular season, the analysis unit can prioritize the analysis of ingredients associated with that season. By considering geographical distribution, it is possible to provide highly relevant analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when analyzing the geographical distribution of ingredients, the analysis unit can use AI to analyze the distribution data and propose the optimal analysis method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can analyze the user's past food photography history and suggest the optimal shooting method. For example, it can suggest the optimal shooting angle based on the types and frequency of food photographed by the user in the past. Furthermore, it can analyze the quality of food photographs taken by the user in the past and suggest areas for improvement. For example, it can suggest the optimal shooting environment by considering the background and lighting conditions of food photographs taken by the user in the past. In this way, by analyzing past shooting history, the optimal shooting method can be suggested. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when the reception desk analyzes the user's past food photography history, it can use AI to analyze the user's shooting history and suggest the optimal shooting method.

[0063] The reception desk can prompt users to take photos of ingredients while considering the current inventory status of their refrigerator. For example, if the refrigerator is low in inventory, it can prompt them to prioritize photographing the necessary ingredients. If the refrigerator is high in inventory, it can also suggest which ingredients to photograph. Furthermore, it can suggest the order in which to photograph the ingredients based on the refrigerator's inventory status. This allows users to prioritize photographing the necessary ingredients by considering their current inventory. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, when analyzing the refrigerator's inventory status, the reception desk can use AI to analyze the inventory data and suggest the optimal shooting method.

[0064] The reception desk can prioritize photographing ingredients that are highly relevant to the user's geographical location when taking pictures of ingredients. For example, if the user lives in a specific region, it can prioritize photographing ingredients commonly used in that region. If the user is traveling, it can also prioritize photographing local ingredients. Furthermore, if the user is in a specific season, it can prioritize photographing ingredients related to that season. In this way, by considering geographical location, it is possible to prioritize photographing ingredients that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, when analyzing the user's geographical location, the reception desk can use AI to analyze the location information and suggest the optimal shooting method.

[0065] The identification unit can improve the accuracy of identification by considering the freshness and quality of the ingredients during the identification process. For example, it can determine the freshness of the ingredients using image analysis and prioritize the identification of ingredients with high freshness. It can also determine the quality of the ingredients using image analysis and prioritize the identification of ingredients with high quality. Furthermore, it can adjust the accuracy of identification based on the freshness and quality of the ingredients. In this way, the accuracy of identification can be improved by considering the freshness and quality of the ingredients. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can use AI to improve the analysis accuracy when analyzing the freshness and quality of the ingredients.

[0066] The identification unit can apply different identification algorithms to each food category during identification. For example, different identification algorithms can be applied to categories such as vegetables, meat, and fish. Different identification algorithms can also be applied to processed foods and fresh foods. Furthermore, different identification algorithms can be applied to seasonings and main ingredients. By applying different identification algorithms to each category, the accuracy of identification can be improved. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit applies different identification algorithms to each food category, it can use AI to optimize the algorithms.

[0067] The identification unit can perform identification while considering the geographical distribution of ingredients. For example, it can prioritize identifying ingredients commonly used in the user's area. If the user is traveling, it can also prioritize identifying local ingredients. Furthermore, if the user is in a particular season, it can prioritize identifying ingredients associated with that season. In this way, by considering geographical distribution, highly relevant ingredients can be identified. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when analyzing the geographical distribution of ingredients, the identification unit can use AI to analyze the distribution data and propose the optimal identification method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk takes photos of the ingredients. Users can take photos of the ingredients using their smartphones or digital cameras and upload them to the system. Users can also enter the names of the ingredients. Step 2: The identification unit analyzes the photos taken by the reception unit to identify the ingredients. The identification unit can use image recognition technology to identify the type and quantity of ingredients. For example, it can identify ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. Step 3: The generation unit generates a recipe based on the ingredients identified by the identification unit, taking into account the user's preferences and constraints. For example, it can generate a recipe while considering the user's preferences and cooking time constraints. Step 4: The supply unit provides the user with the recipe generated by the generation unit. The supply unit can display the generated recipe on a smartphone or tablet screen, or print it out using a printer.

[0070] (Example of form 2) The recipe suggestion system according to an embodiment of the present invention is a system that suggests recipes based on the user's preferences by utilizing ingredients in the refrigerator. The recipe suggestion system works by having the user take photos of the ingredients in the refrigerator and inputting them into the system. Next, the recipe suggestion system uses image recognition technology to identify the ingredients and generates a recipe considering the user's preferences and cooking time constraints. This recipe is tailored to the user's preferences and may suggest, for example, a light Western dish or a rich Chinese dish. This allows the user to easily make a delicious dinner without wasting ingredients in the refrigerator. For example, the user takes photos of the ingredients in the refrigerator. In this case, the user takes photos of the ingredients using a smartphone and uploads them to the system. For example, if the refrigerator contains ingredients such as 2 cherry tomatoes, 300g of ground chicken, 10 dumpling wrappers, cheese, 1 egg, and 1 bell pepper, the user takes photos of them. Next, the recipe suggestion system uses image recognition technology to identify the ingredients. The recipe suggestion system analyzes the uploaded photos and recognizes each ingredient. For example, it identifies ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, egg, and bell pepper. The system then generates recipes, taking into account the user's preferences and cooking time constraints. Users input their preferences, such as whether they prefer light or rich dishes, Western, Japanese, or Chinese cuisine, and their desired cooking time. For example, if a user prefers light Western food and wants a dish that can be cooked in under 20 minutes, a recipe matching those conditions will be generated. The generated recipes are then suggested to the user. For example, Italian-style tomato dumplings using cherry tomatoes and ground chicken, or stir-fried eggs and bell peppers might be suggested. This allows users to easily prepare a delicious dinner without wasting ingredients in their refrigerator. The recipe suggestion system enables users to easily prepare dinner using ingredients in their refrigerator, even if they are tired from work or childcare. Furthermore, since recipes are suggested according to the user's preferences, meal satisfaction is also improved. For example, Italian-style tomato dumplings might be suggested to a user who prefers light Western food, while stir-fried eggs and bell peppers might be suggested to a user who prefers rich Chinese food.This allows users to enjoy dishes tailored to their preferences. The recipe suggestion system, by providing recipes based on user preferences, helps them avoid wasting ingredients in their refrigerator and makes preparing dinner easy.

[0071] The recipe suggestion system according to this embodiment comprises a reception unit, an identification unit, a generation unit, and a serving unit. The reception unit takes photographs of ingredients. The reception unit can, for example, allow a user to take photographs of ingredients using a smartphone and upload them to the system. The reception unit can also take photographs of ingredients using a digital camera and upload them to the system. Furthermore, when taking photographs of ingredients, the reception unit can also allow the user to input the names of the ingredients. For example, the reception unit can allow a user to upload a photograph of ingredients taken with a smartphone to the system and input the names of the ingredients. The identification unit analyzes the photographs taken by the reception unit and identifies the ingredients. The identification unit identifies ingredients using, for example, image recognition technology. The identification unit analyzes the uploaded photographs and recognizes each ingredient. For example, the identification unit identifies ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. The identification unit can identify the type and quantity of ingredients using image recognition technology. For example, the identification unit can identify the number of cherry tomatoes and the quantity of ground chicken using image recognition technology. The generation unit generates recipes based on ingredients identified by the identification unit, taking into account the user's preferences and constraints. For example, the generation unit generates recipes considering the user's preferences and cooking time constraints. The generation unit allows the user to input preferences such as whether they like light or rich dishes, Western food, Japanese food, or Chinese food, and how long the cooking time should be. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit can generate recipes based on the user's preferences and constraints. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The provision unit provides the user with the recipes generated by the generation unit. For example, the provision unit may suggest the generated recipes to the user. The provision unit can provide the generated recipes to the user visually. For example, the provision unit can display the generated recipes on a smartphone or tablet screen. The provision unit can also print and provide the generated recipes.For example, the supply unit can print the generated recipe using a printer and provide it to the user. This allows the recipe suggestion system according to the embodiment to provide recipes based on the user's preferences, enabling them to easily prepare dinner without wasting ingredients in the refrigerator.

[0072] The reception desk takes photos of ingredients. For example, users can take photos of ingredients using their smartphones and upload them to the system. The reception desk can also take photos of ingredients using digital cameras and upload them to the system. Furthermore, when taking photos of ingredients, users can input the names of the ingredients. For example, users can upload photos of ingredients taken with their smartphones to the system and input the names of the ingredients. The reception desk provides an interface for quickly importing photos taken by users into the system. For example, users can easily upload photos via a smartphone app or web browser. The reception desk also automatically adjusts the resolution and format of the photos so that the system can perform optimal analysis. In addition, the reception desk supports cases where users photograph multiple ingredients at once and has a function to individually recognize each ingredient. For example, even if a user photographs all the ingredients in their refrigerator at once, the system can analyze each ingredient individually and provide accurate information. This allows the reception desk to easily provide ingredient information to the system, improving the accuracy of recipe suggestions.

[0073] The identification unit analyzes photos taken by the reception unit to identify ingredients. The identification unit identifies ingredients using, for example, image recognition technology. The identification unit analyzes uploaded photos and recognizes each ingredient. For example, the identification unit can identify ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. The identification unit can identify the type and quantity of ingredients using image recognition technology. For example, the identification unit can identify the number of cherry tomatoes or the amount of ground chicken using image recognition technology. The identification unit utilizes the latest deep learning algorithms to analyze the type, shape, color, and texture of ingredients. This allows the identification unit to recognize ingredients with high accuracy even at different angles and under different lighting conditions. Furthermore, the identification unit also has a function to evaluate the freshness and quality of ingredients. For example, it can analyze changes in the color and shape of vegetables to evaluate their freshness. The identification unit matches the name of the ingredient entered by the user with the image recognition results to provide accurate ingredient information. The identification unit also analyzes the size and area occupied by ingredients in the image to estimate the quantity of ingredients. This allows the specific unit to accurately grasp information about the ingredients the user possesses, thereby improving the accuracy of recipe suggestions.

[0074] The generation unit generates recipes based on ingredients identified by the identification unit, taking into account the user's preferences and constraints. For example, the generation unit generates recipes considering the user's preferences and cooking time constraints. The generation unit allows users to input preferences such as whether they like light or rich dishes, Western, Japanese, or Chinese cuisine, and how long they want to cook. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit can generate recipes based on the user's preferences and constraints. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit utilizes AI to learn the user's preferences and past selection history, and proposes more personalized recipes. For example, the generation unit analyzes the user's preferences based on recipes and ratings the user has previously selected and generates the optimal recipe. The generation unit can also propose nutritionally balanced recipes by considering ingredient combinations and cooking methods. Furthermore, the generation unit takes into account the user's cooking equipment and skills to generate feasible recipes. This allows the generation unit to meet diverse user needs and provide highly satisfying recipes.

[0075] The provider unit provides users with recipes generated by the generator unit. For example, the provider unit suggests generated recipes to users. The provider unit can provide users with generated recipes visually. For example, the provider unit can display generated recipes on smartphone or tablet screens. The provider unit can also provide generated recipes in print. For example, the provider unit can print generated recipes on a printer and provide them to users. The provider unit provides a visually easy-to-understand interface so that users can easily view and execute recipes. For example, it explains each step of a recipe with photos or videos to make it easier for users to understand the cooking procedure. The provider unit also displays detailed information such as ingredient lists, cooking times, and calorie information so that users can grasp the necessary information at a glance. Furthermore, the provider unit also has functions that allow users to save and share recipes. For example, users can save their favorite recipes and view them again later. They can also share recipes with friends and family via social media or email. In this way, the provider unit can enable users to make the most of the generated recipes and enhance the enjoyment of cooking.

[0076] The input section allows users to input their preferences and constraints. For example, the input section allows users to input preferences such as whether they prefer light or rich dishes, Western food, Japanese food, or Chinese food, and how long they like to cook. The input section also allows users to input constraints such as allergy information and calorie restrictions. For example, the input section allows users to input allergy information and exclude specific ingredients. The input section can also generate low-calorie recipes based on the user's calorie restrictions. In this way, more appropriate recipes can be generated by inputting the user's preferences and constraints. Some or all of the above processing in the input section may be performed using AI, for example, or not. For example, when the user inputs their preferences and constraints, the input section can use AI to analyze the user's input and suggest the most suitable recipe.

[0077] The display unit can display the generated recipe. For example, the display unit can display the generated recipe on the screen of a smartphone or tablet. The display unit can assist the user in cooking by visually providing the generated recipe. For example, the display unit can display the generated recipe on the screen, allowing the user to check the cooking procedure. The display unit can also print and provide the generated recipe. For example, the display unit can print the generated recipe using a printer and provide it to the user. This allows the display unit to assist the user in cooking by visually providing the generated recipe. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when displaying a generated recipe, the display unit can use AI to suggest a display method tailored to the user's preferences.

[0078] The analysis unit can analyze photographs of food ingredients. The analysis unit identifies food ingredients, for example, using image recognition technology. The analysis unit analyzes uploaded photographs and recognizes each food ingredient. For example, the analysis unit identifies ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. The analysis unit can identify the type and quantity of food ingredients using image recognition technology. For example, the analysis unit can identify the number of cherry tomatoes or the amount of ground chicken using image recognition technology. This improves the accuracy of food ingredient identification by analyzing photographs of food ingredients. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to improve the accuracy of food ingredient identification when analyzing photographs of food ingredients.

[0079] The generation unit can generate recipes based on user preferences and constraints. For example, the generation unit generates recipes considering user preferences and cooking time constraints. The generation unit allows users to input preferences such as whether they like light or rich dishes, Western food, Japanese food, or Chinese food, and how long they want to cook. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. The generation unit can generate recipes based on user preferences and constraints. For example, if the user prefers light Western food and wants a cooking time of 20 minutes or less, the generation unit will generate a recipe that meets those conditions. In this way, by generating recipes based on user preferences and constraints, the generation unit can provide the user with the most suitable recipe. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, when the user inputs preferences and constraints, the generation unit can use AI to analyze the user's input and suggest the most suitable recipe.

[0080] The service provider can provide the generated recipe to the user. For example, the service provider can suggest the generated recipe to the user. The service provider can provide the generated recipe to the user visually. For example, the service provider can display the generated recipe on the screen of a smartphone or tablet. The service provider can also provide the generated recipe in print. For example, the service provider can print the generated recipe on a printer and provide it to the user. This allows the user to easily check the recipe by providing the generated recipe to the user. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, when providing a generated recipe, the service provider can use AI to suggest a delivery method tailored to the user's preferences.

[0081] The reception desk can estimate the user's emotions and adjust the timing of food photography based on those emotions. For example, if the user is stressed, the reception desk may prompt them to take photos of the food at a time when they can relax. If the user is in a hurry, the reception desk may also provide a simplified interface to allow for quick photography. For example, if the user is having fun, the reception desk may provide an interface that makes photography more enjoyable, like a game. By adjusting the timing of photography according to the user's emotions, food photos can be taken at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, when estimating the user's emotions, the reception desk may use AI to analyze the user's emotions and suggest the optimal timing for photography.

[0082] The reception desk can analyze the user's past food photography history and suggest the optimal shooting method. For example, the reception desk can suggest the optimal shooting angle based on the types and frequency of food photographed by the user in the past. The reception desk can also analyze the quality of food photographs taken by the user in the past and suggest areas for improvement. For example, the reception desk can suggest the optimal shooting environment by considering the background and lighting conditions of food photographs taken by the user in the past. In this way, the reception desk can suggest the optimal shooting method by analyzing the past shooting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the user's past food photography history, the reception desk can use AI to analyze the user's shooting history and suggest the optimal shooting method.

[0083] The reception desk can prompt users to take photos of ingredients while considering the current inventory status of their refrigerator. For example, if the refrigerator inventory is low, the reception desk can prompt the user to prioritize photographing the necessary ingredients. If the refrigerator inventory is high, the reception desk can also suggest which ingredients to photograph. For example, the reception desk can suggest the order in which to photograph ingredients based on the refrigerator inventory status. This allows users to prioritize photographing the necessary ingredients by considering the refrigerator inventory status. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the refrigerator inventory status, the reception desk can use AI to analyze inventory data and suggest the optimal shooting method.

[0084] The reception unit can estimate the user's emotions and determine the priority of ingredients to photograph based on the estimated emotions. For example, if the user is tired, the reception unit may prioritize photographing ingredients that are easy to cook. If the user is energetic, the reception unit may also prioritize photographing ingredients that require complex cooking. For example, if the user wants to make a specific dish, the reception unit may prioritize photographing the ingredients needed for that dish. This allows for the photographing of more appropriate ingredients by determining the priority of ingredients to photograph according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, when estimating the user's emotions, the reception unit can use AI to analyze the user's emotions and suggest the optimal photographing priority.

[0085] The reception desk can prioritize photographing ingredients that are highly relevant to the user's geographical location when taking pictures of ingredients. For example, if the user lives in a specific region, the reception desk can prioritize photographing ingredients commonly used in that region. If the user is traveling, the reception desk can also prioritize photographing local ingredients. For example, if the user is in a specific season, the reception desk can prioritize photographing ingredients related to that season. In this way, by considering geographical location, the reception desk can prioritize photographing ingredients that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the user's geographical location, the reception desk can use AI to analyze the location information and suggest the optimal shooting method.

[0086] The reception desk can analyze a user's social media activity when photographing ingredients and photograph relevant ingredients. For example, the reception desk can photograph relevant ingredients based on photos of dishes shared by the user on social media. The reception desk can also analyze posts from cooking accounts that the user follows and photograph relevant ingredients. For example, the reception desk can analyze trends in cooking communities that the user participates in and photograph relevant ingredients. In this way, relevant ingredients can be photographed by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing a user's social media activity, the reception desk can use AI to analyze the content of posts and suggest the optimal photography method.

[0087] The identification unit can estimate the user's emotions and adjust the accuracy of ingredient identification based on the estimated emotions. For example, if the user is tired, the identification unit will prioritize easily identifiable ingredients. If the user is energetic, the identification unit can also identify complex ingredients. For example, if the user wants to make a specific dish, the identification unit can increase the accuracy of identifying the ingredients needed for that dish. By adjusting the identification accuracy according to the user's emotions, more appropriate ingredients can be identified. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, or not. For example, when estimating the user's emotions, the identification unit can use AI to analyze the user's emotions and suggest the optimal identification accuracy.

[0088] The identification unit can improve the accuracy of identification by considering the freshness and quality of the ingredients during the identification process. For example, the identification unit can determine the freshness of the ingredients through image analysis and prioritize the identification of ingredients with high freshness. The identification unit can also determine the quality of the ingredients through image analysis and prioritize the identification of ingredients with high quality. For example, the identification unit can adjust the accuracy of identification based on the freshness and quality of the ingredients. This allows for improved accuracy of identification by considering the freshness and quality of the ingredients. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can use AI to improve the accuracy of analysis when analyzing the freshness and quality of the ingredients.

[0089] The identification unit can apply different identification algorithms to each category of food ingredient during identification. For example, the identification unit can apply different identification algorithms to categories such as vegetables, meat, and fish. The identification unit can also apply different identification algorithms to processed foods and fresh foods. For example, the identification unit can apply different identification algorithms to seasonings and main ingredients. By applying different identification algorithms to each category, the accuracy of identification can be improved. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit applies different identification algorithms to each category of food ingredient, it can use AI to optimize the algorithms.

[0090] The identification unit can estimate the user's emotions and adjust the display order of identified ingredients based on the estimated user emotions. For example, if the user is tired, the identification unit can display ingredients that are easy to cook at the top. If the user is energetic, the identification unit can also display ingredients that require complex cooking at the top. For example, if the user wants to make a specific dish, the identification unit can also display the ingredients needed for that dish at the top. In this way, ingredients can be displayed in a more appropriate order by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the identification unit can use AI to analyze the user's emotions and suggest the optimal display order.

[0091] The identification unit can perform identification while considering the geographical distribution of ingredients. For example, the identification unit can prioritize identifying ingredients commonly used in the area where the user lives. If the user is traveling, the identification unit can also prioritize identifying local ingredients. For example, if the user is in a particular season, the identification unit can also prioritize identifying ingredients associated with that season. This allows for the identification of highly relevant ingredients by considering geographical distribution. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when analyzing the geographical distribution of ingredients, the identification unit can use AI to analyze the distribution data and propose the optimal identification method.

[0092] The identification unit can improve the accuracy of its identification by referring to relevant literature on the food ingredient during the identification process. For example, the identification unit can improve the accuracy of its identification by referring to academic papers related to the identification of food ingredients. The identification unit can also improve the accuracy of its identification by referring to patent documents related to the identification of food ingredients. For example, the identification unit can improve the accuracy of its identification by referring to industry reports related to the identification of food ingredients. In this way, the accuracy of identification can be improved by referring to relevant literature. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, when analyzing relevant literature on food ingredients, the identification unit can use AI to analyze the literature data and propose the optimal identification method.

[0093] The generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated emotions. For example, if the user is tired, the generation unit can provide a concise and easy-to-understand recipe. If the user is energetic, the generation unit can also provide a recipe with detailed instructions. For example, if the user wants to make a specific dish, the generation unit can provide a recipe specifically for that dish. This allows for the provision of more appropriate recipes by adjusting the way the recipe is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, when estimating the user's emotions, the generation unit can use AI to analyze the user's emotions and propose the optimal way to present the recipe.

[0094] The generation unit can adjust the level of detail in a recipe based on the importance of the ingredients when generating a recipe. For example, the generation unit can provide a detailed recipe that focuses on the main ingredients. The generation unit can also provide a brief explanation of auxiliary ingredients. For example, the generation unit can adjust the steps of a recipe based on the importance of the ingredients. This allows for the provision of more appropriate recipes by adjusting the level of detail based on the importance of the ingredients. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, when analyzing the importance of ingredients, the generation unit can use AI to evaluate importance and suggest the optimal recipe.

[0095] The generation unit can apply different generation algorithms depending on the category of ingredients when generating recipes. For example, the generation unit can apply different generation algorithms for each category, such as vegetables, meat, and fish. The generation unit can also apply different generation algorithms for processed foods and fresh foods. For example, the generation unit can apply different generation algorithms for seasonings and main ingredients. By applying different generation algorithms for each category, it is possible to provide more appropriate recipes. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use AI to optimize the algorithm when applying different generation algorithms for each category of ingredients.

[0096] The generation unit can estimate the user's emotions and adjust the recipe length based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide a short, concise recipe. If the user is relaxed, the generation unit can also provide a longer recipe with detailed explanations. For example, if the user wants to cook a specific dish, the generation unit can provide a recipe specifically for that dish. This allows for the provision of more appropriate recipes by adjusting the recipe length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, when estimating the user's emotions, the generation unit can use AI to analyze the user's emotions and suggest the optimal recipe length.

[0097] The generation unit can determine the priority of recipes based on when ingredients are submitted during recipe generation. For example, the generation unit can provide recipes that prioritize ingredients that need to be consumed quickly. The generation unit can also provide recipes that postpone the use of ingredients that can be stored for a long time. For example, the generation unit can adjust the steps of a recipe based on when ingredients are submitted. This allows for the provision of more appropriate recipes by prioritizing recipes based on when ingredients are submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, when analyzing when ingredients are submitted, the generation unit can use AI to evaluate the submission timing and propose the optimal recipe.

[0098] The generation unit can adjust the order of ingredients in a recipe based on their relationships during recipe generation. For example, the generation unit can provide a recipe centered on the main ingredients. The generation unit can also provide recipes that complement auxiliary ingredients. For example, the generation unit can adjust the steps of a recipe based on the relationships of the ingredients. This allows for the provision of more appropriate recipes by adjusting the order of recipes based on the relationships of the ingredients. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, when analyzing the relationships between ingredients, the generation unit can use AI to evaluate the relationships and propose the optimal recipe.

[0099] The service provider can estimate the user's emotions and adjust the recipe delivery method based on the estimated emotions. For example, if the user is tired, the service provider can provide a simple and easy-to-understand recipe. If the user is energetic, the service provider can also provide a recipe with detailed instructions. For example, if the user wants to make a specific dish, the service provider can provide a recipe specifically for that dish. This allows for the provision of more appropriate recipes by adjusting the recipe delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, when estimating the user's emotions, the service provider can use AI to analyze the user's emotions and propose the optimal recipe delivery method.

[0100] The service provider can select the optimal delivery method by referring to the user's past recipe usage history when providing recipes. For example, the service provider can provide the optimal recipe based on recipes the user has used in the past. The service provider can also provide recipes by analyzing the user's preferences from their past recipe usage history. For example, the service provider can provide relevant recipes by referring to the user's past recipe usage history. In this way, the service provider can provide the optimal recipe by referring to the user's past recipe usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, when analyzing the user's past recipe usage history, the service provider can use AI to analyze the historical data and suggest the optimal recipe.

[0101] The service provider can customize the recipe based on the user's current ingredient inventory when providing recipes. For example, the service provider can provide the optimal recipe based on the ingredients the user has in their refrigerator. The service provider can also suggest missing ingredients, taking into account the user's ingredient inventory. For example, the service provider can suggest alternative ingredients based on the user's ingredient inventory. This allows for the provision of more appropriate recipes by customizing the recipe based on the current ingredient inventory. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, when analyzing the user's ingredient inventory, the service provider can use AI to analyze inventory data and suggest the optimal recipe.

[0102] The service provider can estimate the user's emotions and adjust the order in which recipes are presented based on the estimated emotions. For example, if the user is tired, the service provider may prioritize providing easy-to-cook recipes. If the user is energetic, the service provider may also prioritize providing recipes that require more complex cooking. For example, if the user wants to cook a specific dish, the service provider may also prioritize providing recipes specifically for that dish. By adjusting the order in which recipes are presented according to the user's emotions, more appropriate recipes can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, when estimating the user's emotions, the service provider can use AI to analyze the user's emotions and suggest the optimal order in which recipes are presented.

[0103] The service provider can select the optimal delivery method when providing recipes, taking into account the user's geographical location. For example, if the user lives in a specific region, the service provider can provide recipes based on ingredients commonly used in that region. If the user is traveling, the service provider can also provide recipes based on local ingredients. For example, if the user is in a specific season, the service provider can provide recipes based on ingredients associated with that season. This allows the service provider to provide the most suitable recipe by considering geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, when analyzing the user's geographical location, the service provider can use AI to analyze the location information and propose the most suitable recipe delivery method.

[0104] The service provider can analyze a user's social media activity to suggest recipes. For example, the service provider can provide relevant recipes based on photos of dishes shared by the user on social media. The service provider can also analyze posts from cooking accounts that the user follows and provide relevant recipes. For example, the service provider can analyze trends in cooking communities that the user participates in and provide relevant recipes. In this way, relevant recipes can be provided by analyzing social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, when analyzing a user's social media activity, the service provider can use AI to analyze the content of posts and suggest the most suitable recipes.

[0105] The input unit can estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is tired, the input unit can provide a simple input method. If the user is energetic, the input unit can also provide detailed input options. For example, if the user is in a hurry, the input unit can prioritize voice input. This allows for a more appropriate input method to be provided by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the input unit can use AI to analyze the user's emotions and suggest the optimal input method.

[0106] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can automatically display preferences and constraints that the user has frequently entered in the past as candidates. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the input unit can predict and suggest preferences and constraints used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by referring to past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, when analyzing the user's past input history, the input unit can use AI to analyze the historical data and suggest the optimal input method.

[0107] The input unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is tired, the input unit may prioritize easy input. If the user is energetic, the input unit may also prioritize detailed input. For example, if the user wants to cook a specific dish, the input unit may prioritize input related to that dish. This allows for more appropriate input content to be provided by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not. For example, when estimating the user's emotions, the input unit may use AI to analyze the user's emotions and suggest the most appropriate input content.

[0108] The input unit can select the optimal input method when inputting data, taking into account the user's device information. For example, if the user is using a smartphone, the input unit can provide an input method that matches the screen size. If the user is using a tablet, the input unit can also provide an input method optimized for a larger screen. For example, if the user is using a smartwatch, the input unit can provide a concise and highly visible input method. In this way, the optimal input method can be provided by considering device information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, when analyzing the user's device information, the input unit can use AI to analyze the device data and propose the optimal input method.

[0109] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tired, the display unit can provide a simple and highly visible display method. If the user is energetic, the display unit can also provide a display method that includes detailed information. For example, if the user wants to cook a specific dish, the display unit can provide a display method specific to that dish. In this way, by adjusting the display method according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the display unit can use AI to analyze the user's emotions and propose the optimal display method.

[0110] The display unit can select the optimal display method by referring to the user's past viewing history when displaying information. For example, the display unit can provide the optimal display method based on recipes the user has previously viewed. The display unit can also provide a display method by analyzing the user's preferences from their past viewing history. For example, the display unit can refer to the user's past viewing history and display relevant recipes. This allows the display unit to provide the optimal display method by referring to past viewing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when analyzing the user's past viewing history, the display unit can use AI to analyze the historical data and propose the optimal display method.

[0111] The display unit can estimate the user's emotions and determine the priority of the displayed content based on the estimated emotions. For example, if the user is tired, the display unit may prioritize displaying easy-to-prepare recipes. If the user is energetic, the display unit may also prioritize displaying recipes that require complex preparation. For example, if the user wants to make a specific dish, the display unit may prioritize displaying recipes specifically for that dish. This allows for more appropriate content to be displayed by prioritizing the displayed content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or not. For example, when estimating the user's emotions, the display unit can use AI to analyze the user's emotions and suggest the most appropriate displayed content.

[0112] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, when analyzing the user's device information, the display unit can use AI to analyze device data and propose the optimal display method.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is tired, the analysis unit can provide a simple analysis method. If the user is energetic, the analysis unit can also provide a detailed analysis method. For example, if the user wants to cook a specific dish, the analysis unit can provide an analysis method specific to that dish. By adjusting the analysis method according to the user's emotions, a more appropriate analysis method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the analysis unit can use AI to analyze the user's emotions and propose the optimal analysis method.

[0114] The analysis unit can improve the accuracy of its analysis by considering the freshness and quality of the ingredients during the analysis. For example, the analysis unit can determine the freshness of the ingredients using image analysis and prioritize the analysis of ingredients with high freshness. The analysis unit can also determine the quality of the ingredients using image analysis and prioritize the analysis of ingredients with high quality. For example, the analysis unit can adjust the accuracy of its analysis based on the freshness and quality of the ingredients. This allows for improved accuracy of the analysis by considering the freshness and quality of the ingredients. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to improve the accuracy of its analysis when analyzing the freshness and quality of the ingredients.

[0115] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is tired, the analysis unit can display easily understandable analysis results at the top. If the user is energetic, the analysis unit can also display detailed analysis results at the top. For example, if the user wants to cook a specific dish, the analysis unit can display analysis results related to that dish at the top. In this way, by adjusting the display order of analysis results according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the analysis unit can use AI to analyze the user's emotions and suggest the optimal display order of the analysis results.

[0116] The analysis unit can perform analysis while considering the geographical distribution of ingredients. For example, the analysis unit can prioritize the analysis of ingredients commonly used in the user's area. If the user is traveling, the analysis unit can also prioritize the analysis of local ingredients. For example, if the user is in a particular season, the analysis unit can prioritize the analysis of ingredients associated with that season. By considering geographical distribution, it is possible to provide highly relevant analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when analyzing the geographical distribution of ingredients, the analysis unit can use AI to analyze the distribution data and propose the optimal analysis method.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The reception desk can estimate the user's emotions and adjust the timing of food photography based on those emotions. For example, if the user is stressed, it can prompt them to take photos of the food at a time when they can relax. If the user is in a hurry, a simplified interface can be provided to allow for quick photography. Furthermore, if the user is having fun, an interface can be provided that makes photography feel like a game. By adjusting the timing of photography according to the user's emotions, food photos can be taken at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, when estimating the user's emotions, the reception desk can use AI to analyze the user's emotions and suggest the optimal timing for photography.

[0119] The reception desk can analyze the user's past food photography history and suggest the optimal shooting method. For example, it can suggest the optimal shooting angle based on the types and frequency of food photographed by the user in the past. Furthermore, it can analyze the quality of food photographs taken by the user in the past and suggest areas for improvement. For example, it can suggest the optimal shooting environment by considering the background and lighting conditions of food photographs taken by the user in the past. In this way, by analyzing past shooting history, the optimal shooting method can be suggested. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when the reception desk analyzes the user's past food photography history, it can use AI to analyze the user's shooting history and suggest the optimal shooting method.

[0120] The reception desk can prompt users to take photos of ingredients while considering the current inventory status of their refrigerator. For example, if the refrigerator is low in inventory, it can prompt them to prioritize photographing the necessary ingredients. If the refrigerator is high in inventory, it can also suggest which ingredients to photograph. Furthermore, it can suggest the order in which to photograph the ingredients based on the refrigerator's inventory status. This allows users to prioritize photographing the necessary ingredients by considering their current inventory. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, when analyzing the refrigerator's inventory status, the reception desk can use AI to analyze the inventory data and suggest the optimal shooting method.

[0121] The reception unit can estimate the user's emotions and determine the priority of ingredients to photograph based on the estimated emotions. For example, if the user is tired, it can prioritize photographing ingredients that are easy to cook. If the user is energetic, it can also prioritize photographing ingredients that require complex cooking. Furthermore, if the user wants to make a specific dish, it can prioritize photographing the ingredients needed for that dish. In this way, by determining the priority of ingredients to photograph according to the user's emotions, more appropriate ingredients can be photographed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the reception unit can use AI to analyze the user's emotions and suggest the optimal photographing priority.

[0122] The reception desk can prioritize photographing ingredients that are highly relevant to the user's geographical location when taking pictures of ingredients. For example, if the user lives in a specific region, it can prioritize photographing ingredients commonly used in that region. If the user is traveling, it can also prioritize photographing local ingredients. Furthermore, if the user is in a specific season, it can prioritize photographing ingredients related to that season. In this way, by considering geographical location, it is possible to prioritize photographing ingredients that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, when analyzing the user's geographical location, the reception desk can use AI to analyze the location information and suggest the optimal shooting method.

[0123] The identification unit can estimate the user's emotions and adjust the accuracy of ingredient identification based on the estimated emotions. For example, if the user is tired, it can prioritize easily identifiable ingredients. If the user is energetic, it can also identify more complex ingredients. Furthermore, if the user wants to make a specific dish, it can increase the accuracy of identifying the ingredients needed for that dish. By adjusting the identification accuracy according to the user's emotions, more appropriate ingredients can be identified. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, or not. For example, when estimating the user's emotions, the identification unit can use AI to analyze the user's emotions and propose the optimal identification accuracy.

[0124] The identification unit can improve the accuracy of identification by considering the freshness and quality of the ingredients during the identification process. For example, it can determine the freshness of the ingredients using image analysis and prioritize the identification of ingredients with high freshness. It can also determine the quality of the ingredients using image analysis and prioritize the identification of ingredients with high quality. Furthermore, it can adjust the accuracy of identification based on the freshness and quality of the ingredients. In this way, the accuracy of identification can be improved by considering the freshness and quality of the ingredients. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can use AI to improve the analysis accuracy when analyzing the freshness and quality of the ingredients.

[0125] The identification unit can apply different identification algorithms to each food category during identification. For example, different identification algorithms can be applied to categories such as vegetables, meat, and fish. Different identification algorithms can also be applied to processed foods and fresh foods. Furthermore, different identification algorithms can be applied to seasonings and main ingredients. By applying different identification algorithms to each category, the accuracy of identification can be improved. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when the identification unit applies different identification algorithms to each food category, it can use AI to optimize the algorithms.

[0126] The identification unit can estimate the user's emotions and adjust the display order of the identified ingredients based on the estimated user emotions. For example, if the user is tired, ingredients that are easy to cook can be displayed at the top. If the user is energetic, ingredients that require complex cooking can be displayed at the top. Furthermore, if the user wants to make a specific dish, the ingredients needed for that dish can be displayed at the top. In this way, ingredients can be displayed in a more appropriate order by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the identification unit can use AI to analyze the user's emotions and suggest the optimal display order.

[0127] The identification unit can perform identification while considering the geographical distribution of ingredients. For example, it can prioritize identifying ingredients commonly used in the user's area. If the user is traveling, it can also prioritize identifying local ingredients. Furthermore, if the user is in a particular season, it can prioritize identifying ingredients associated with that season. In this way, by considering geographical distribution, highly relevant ingredients can be identified. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, when analyzing the geographical distribution of ingredients, the identification unit can use AI to analyze the distribution data and propose the optimal identification method.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk takes photos of the ingredients. Users can take photos of the ingredients using their smartphones or digital cameras and upload them to the system. Users can also enter the names of the ingredients. Step 2: The identification unit analyzes the photos taken by the reception unit to identify the ingredients. The identification unit can use image recognition technology to identify the type and quantity of ingredients. For example, it can identify ingredients such as cherry tomatoes, ground chicken, dumpling wrappers, cheese, eggs, and bell peppers. Step 3: The generation unit generates a recipe based on the ingredients identified by the identification unit, taking into account the user's preferences and constraints. For example, it can generate a recipe while considering the user's preferences and cooking time constraints. Step 4: The supply unit provides the user with the recipe generated by the generation unit. The supply unit can display the generated recipe on a smartphone or tablet screen, or print it out using a printer.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, identification unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit takes a picture of the ingredients using the camera 42 of the smart device 14 and uploads it to the system by the control unit 46A. The identification unit identifies the ingredients using image recognition technology by the identification processing unit 290 of the data processing unit 12. The generation unit generates a recipe considering the user's preferences and cooking time constraints by the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the generated recipe to the user using the display 40A of the smart device 14. 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.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, identification unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit takes a picture of the ingredients using the camera 42 of the smart glasses 214 and uploads it to the system by the control unit 46A. The identification unit identifies the ingredients using image recognition technology by the identification processing unit 290 of the data processing unit 12. The generation unit generates a recipe considering the user's preferences and cooking time constraints by the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the generated recipe to the user using the display of the smart glasses 214. 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.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, identification unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit takes a picture of the ingredients using the camera 42 of the headset terminal 314 and uploads it to the system by the control unit 46A. The identification unit identifies the ingredients using image recognition technology by the identification processing unit 290 of the data processing unit 12. The generation unit generates a recipe considering the user's preferences and cooking time constraints by the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the generated recipe to the user using the display 343 of the headset terminal 314. 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.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the reception unit, identification unit, generation unit, and serving unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit takes a picture of the ingredients using the camera 42 of the robot 414 and uploads it to the system by the control unit 46A. The identification unit identifies the ingredients using image recognition technology by the identification processing unit 290 of the data processing unit 12. The generation unit generates a recipe considering the user's preferences and cooking time constraints by the identification processing unit 290 of the data processing unit 12. The serving unit visually provides the generated recipe to the user using the display of the robot 414. 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) The reception desk takes photos of the ingredients, The identification unit analyzes the photograph taken by the reception unit to identify the ingredients, A generation unit generates a recipe based on the ingredients identified by the aforementioned identification unit, taking into account the user's preferences and constraints. The system includes a supply unit that provides the user with the recipe generated by the generation unit. A system characterized by the following features. (Note 2) It features an input section for users to enter their preferences and constraints. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a display unit that shows the generated recipe. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with an analysis unit that analyzes photographs of food ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate recipes based on user preferences and constraints. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated recipe to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of food photography based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past food photography history and suggest the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When taking photos of ingredients, prompt the user to consider the current inventory in their refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of food items to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When photographing ingredients, the system prioritizes photographing ingredients that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When photographing ingredients, the system analyzes the user's social media activity and photographs related ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, The system estimates the user's emotions and adjusts the accuracy of ingredient identification based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, When identifying ingredients, the freshness and quality of the ingredients are taken into consideration to improve the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, At specific times, different specific algorithms are applied for each food category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, The system estimates the user's emotions and adjusts the display order of identified ingredients based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, When identifying ingredients, the geographical distribution of those ingredients is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying ingredients, we refer to relevant literature to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the way recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a recipe, adjust the level of detail based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating recipes, different generation algorithms are applied depending on the category of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the recipe length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating recipes, prioritize recipes based on when the ingredients are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating recipes, the order of ingredients is adjusted based on their relationships. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the recipe delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing recipes, the system will refer to the user's past recipe usage history to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing recipes, customize the content based on the user's current ingredient inventory. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which recipes are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing recipes, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing recipes, we analyze users' social media activity to suggest content to offer. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned input unit is It estimates the user's emotions and adjusts the input method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned input unit is During input, the system refers to the user's past input history to suggest the optimal input method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned input unit is When inputting data, the system selects the optimal input method considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past viewing history. The system described in Appendix 3, characterized by the features described herein. (Note 37) 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 3, characterized by the features described herein. (Note 38) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned analysis unit, During analysis, the freshness and quality of the ingredients are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned analysis unit, During the analysis, the geographical distribution of the ingredients will be taken into consideration. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0202] 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. The reception desk takes photos of the ingredients, The identification unit analyzes the photograph taken by the reception unit to identify the ingredients, A generation unit generates a recipe based on the ingredients identified by the aforementioned identification unit, taking into account the user's preferences and constraints. The system includes a supply unit that provides the user with the recipe generated by the generation unit. A system characterized by the following features.

2. It features an input section for users to enter their preferences and constraints. The system according to feature 1.

3. It includes a display unit that shows the generated recipe. The system according to feature 1.

4. It is equipped with an analysis unit that analyzes photographs of food ingredients. The system according to feature 1.

5. The generating unit is Generate recipes based on user preferences and constraints. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated recipe to the user. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of food photography based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past food photography history and suggest the optimal shooting method. The system according to feature 1.

9. The aforementioned reception unit is When taking photos of ingredients, prompt the user to consider the current inventory in their refrigerator. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of food items to photograph based on those estimated emotions. The system according to feature 1.

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