system

Abstract

The system according to this embodiment aims to devise and recommend recipes by efficiently utilizing ingredients in a refrigerator. [Solution] The system according to the embodiment comprises a shooting unit, a storage unit, a reception unit, a design unit, and a recommendation unit. The shooting unit photographs the food items in the refrigerator. The storage unit stores information about the food items photographed by the shooting unit. The reception unit receives requests from users. The design unit devises recipes based on the requests received by the reception unit. The recommendation unit recommends the recipes devised by the design 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently utilize the ingredients in the refrigerator to devise recipes.

[0005] The system according to the embodiment aims to efficiently utilize the ingredients in the refrigerator to devise and recommend recipes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, a storage unit, a reception unit, a design unit, and a recommendation unit. The shooting unit photographs the food items in the refrigerator. The storage unit stores information about the food items photographed by the shooting unit. The reception unit receives requests from users. The design unit devises recipes based on the requests received by the reception unit. The recommendation unit recommends the recipes devised by the design unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently utilize ingredients in a refrigerator to devise and recommend recipes. [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 manages communication between multiple computers. Examples of communication standards applicable 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 photographs and memorizes ingredients in a refrigerator using an AI camera, and then, based on a theme chosen via Chat, requests a menu recipe, and the system devises and recommends a recipe using the ingredients in the refrigerator. The recipe suggestion system photographs the ingredients in the refrigerator using an AI camera and memorizes that information. Next, the user chooses a theme via Chat and requests a menu recipe. Based on this request, the AI ​​devises a recipe using the ingredients in the refrigerator and recommends it to the user. This mechanism also contributes to reducing food waste. For example, the recipe suggestion system photographs the ingredients in the refrigerator using an AI camera. At this time, the AI ​​camera recognizes the type and quantity of ingredients and memorizes that information. For example, it can memorize detailed information about vegetables, meat, seasonings, etc., in the refrigerator. Next, the recipe suggestion system receives a request from the user who chooses a theme via Chat and requests a menu recipe. For example, the user enters a theme such as "healthy dinner" or "lunch for children." This request is sent to the AI. Based on the request, the AI ​​devises a recipe using the ingredients in the refrigerator. For example, the system can create a recipe for a healthy dinner using vegetables and meat from the refrigerator. The AI ​​considers ingredient combinations and cooking methods to generate the optimal recipe. The generated recipe is then recommended to the user. For instance, detailed recipe information and cooking instructions might be displayed via chat. This allows users to cook efficiently without wasting ingredients in their refrigerator. This system also contributes to reducing food waste. By effectively utilizing ingredients in the refrigerator, it reduces food waste and creates environmentally friendly meals. For example, suggesting recipes using leftover vegetables in the refrigerator can prevent food waste. Thus, the recipe suggestion system can efficiently utilize ingredients in the refrigerator and provide users with the most suitable recipes.

[0029] The recipe suggestion system according to this embodiment comprises a shooting unit, a storage unit, a reception unit, a design unit, and a recommendation unit. The shooting unit photographs the ingredients in the refrigerator. The shooting unit recognizes the type and quantity of ingredients using, for example, an AI camera and stores that information. For example, the shooting unit can store detailed information about vegetables, meat, seasonings, etc., in the refrigerator. The storage unit stores information about the ingredients photographed by the shooting unit. The storage unit can store information about ingredients in, for example, a database and retrieve it as needed. For example, the storage unit can store information such as the type and quantity of ingredients and their shelf life. The reception unit receives requests from users. For example, the reception unit can receive requests from users via Chat, where they can choose a theme and request a menu recipe. For example, the reception unit can receive requests for themes such as "healthy dinner" or "lunch for children." The design unit designs a recipe based on the request received by the reception unit. For example, the design unit uses AI to design a recipe using the ingredients in the refrigerator. For example, the design unit can devise a recipe for a healthy dinner using vegetables and meat found in the refrigerator. The recommendation unit recommends the recipe devised by the design unit to the user. The recommendation unit can, for example, display the recipe details and cooking instructions on Chat. For example, the recommendation unit can recommend the generated recipe to the user, enabling them to cook efficiently without wasting ingredients in the refrigerator. In this way, the recipe suggestion system according to this embodiment can efficiently utilize the ingredients in the refrigerator and provide the user with the most suitable recipe.

[0030] The camera unit photographs the food items inside the refrigerator. For example, it uses an AI camera to recognize the type and quantity of food items and stores that information. Specifically, the AI ​​camera utilizes image recognition technology to automatically identify the food items inside the refrigerator. For instance, the camera categorizes items into vegetables, fruits, meats, seafood, dairy products, and condiments, and understands the quantity and condition of each item. Furthermore, the AI ​​camera can also recognize the freshness and expiration date of the food items. This allows users to constantly monitor the condition of the food items in their refrigerator, reducing waste. The camera unit periodically scans the refrigerator, updating newly added and consumed items in real time. For example, it automatically scans each time the refrigerator door is opened and closed, recording the latest information. Additionally, by positioning multiple cameras, the camera unit can gain a detailed overall view of the refrigerator's interior. This allows for accurate acquisition of food information from every corner of the refrigerator. Furthermore, the camera unit can also receive manual input of food information from users, enabling it to provide even more accurate data.

[0031] The memory unit stores information about ingredients photographed by the camera unit. For example, the memory unit can store ingredient information in a database and retrieve it as needed. Specifically, the memory unit stores detailed information such as ingredient type, quantity, storage period, freshness, and expiration date in the database. The database is equipped with an efficient search algorithm and is designed to allow users to quickly obtain specific ingredient information. For example, if a user wants to check the remaining amount of tomatoes or the expiration date of milk, the memory unit can immediately provide the relevant information. Furthermore, the memory unit also records the consumption and purchase history of ingredients, allowing for analysis of the user's consumption patterns. This enables users to efficiently manage ingredients and plan purchases based on past consumption data. Additionally, the memory unit utilizes cloud storage to enable data backup and data sharing across multiple devices. This allows users to access ingredient information from devices such as smartphones and tablets, improving convenience.

[0032] The reception desk receives requests from users. For example, users can request menu recipes by selecting a theme via chat. Specifically, users input requests to the chatbot through a smartphone or tablet application. For example, by entering a theme such as "healthy dinner" or "kids' lunch," the reception desk will accept the request. Furthermore, the reception desk can learn the user's past request history and preferences to provide more personalized suggestions. For example, if a user has frequently requested "low-calorie" or "gluten-free" in the past, the reception desk will take that information into consideration and suggest more suitable recipes. The reception desk also supports voice input, allowing users to make requests by voice. This makes it easy to make requests even when cooking or when hands are busy. In addition, the reception desk can pre-register users' allergy information and food preferences, and filter requests based on this information.

[0033] The Recipe Development Department develops recipes based on requests received by the Reception Department. For example, the Recipe Development Department can use AI to develop recipes using ingredients found in the refrigerator. Specifically, the Recipe Development Department uses an AI algorithm to match information about the ingredients in the refrigerator with the user's request and generate the optimal recipe. For example, it can develop a recipe for a healthy dinner using vegetables and meat found in the refrigerator. The AI ​​collects information from past recipe databases and online recipe sites to generate the recipe that best suits the user's request. Furthermore, the Recipe Development Department can select ingredients that should be used preferentially, taking into account their expiration dates and freshness. This minimizes food waste. The Recipe Development Department can also propose balanced recipes, taking into account the user's health information, such as nutritional balance and calorie restrictions. For example, it can suggest low-calorie recipes for users on a diet and high-protein recipes for users aiming to build muscle. In addition, the Recipe Development Department can propose easy and quick recipes, taking into account the user's cooking skills and time constraints.

[0034] The recommendation team recommends recipes created by the development team to users. For example, the recommendation team can display recipe details and cooking instructions via chat. Specifically, the recommendation team recommends generated recipes to users, enabling them to cook efficiently without wasting ingredients in their refrigerators. The recommendation team sends notifications to users' smartphones and tablets, displaying detailed recipe information and cooking instructions. For example, it provides detailed information such as ingredients, quantities, cooking steps, and cooking time. The recommendation team can also provide step-by-step guides and video tutorials to support users during cooking. This allows users to confidently proceed with cooking, even if it's their first time. Furthermore, the recommendation team can collect user feedback to improve recipes and suggest new ones. For example, users can rate recipes and leave comments, allowing the recommendation team to personalize future suggestions based on that information. The recommendation team can also monitor users' ingredient consumption and automatically generate shopping lists for the next trip. This allows users to manage ingredients efficiently and reduce waste.

[0035] The camera unit can recognize the type and quantity of ingredients. For example, the camera unit can recognize the type and quantity of ingredients using an AI camera. For example, the camera unit can recognize detailed information about vegetables, meat, and seasonings in a refrigerator. The camera unit can also recognize the type and quantity of ingredients using an image analysis algorithm. For example, the camera unit can recognize the type and quantity of ingredients using an image analysis algorithm and store that information. Furthermore, the camera unit can also recognize the quantity of ingredients using a weight sensor. For example, the camera unit can recognize the quantity of ingredients using a weight sensor and store that information. This improves the accuracy of recipes by accurately recognizing the type and quantity of ingredients. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input image data captured by an AI camera into a generating AI and have the generating AI perform the recognition of the type and quantity of ingredients.

[0036] The design unit can generate recipes by considering ingredient combinations and cooking methods. The design unit can generate recipes by considering ingredient combinations and cooking methods, for example, using AI. For example, the design unit can devise a recipe for a healthy dinner using vegetables and meat that are in the refrigerator. The design unit can also generate the optimal recipe by considering ingredient combinations and cooking methods. For example, the design unit can generate the optimal recipe by considering ingredient combinations and cooking methods. Furthermore, the design unit can learn past recipe data and user preferences and generate recipes based on that. For example, the design unit can learn past recipe data and user preferences and generate the optimal recipe based on that. This allows for the generation of the optimal recipe by considering ingredient combinations and cooking methods. Some or all of the above processing in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data on ingredient combinations and cooking methods into a generation AI and have the generation AI generate the optimal recipe.

[0037] The recommendation unit can display recipe details and cooking instructions on the chat. For example, the recommendation unit can recommend generated recipes to users, enabling them to cook efficiently without wasting ingredients in their refrigerators. The recommendation unit can also display recipe details and cooking instructions in text format. For example, the recommendation unit can display recipe details and cooking instructions in text format, making them easy for users to check. Furthermore, the recommendation unit can display recipe details and cooking instructions with images. For example, the recommendation unit can display recipe details and cooking instructions with images, making them easier for users to understand visually. This allows users to easily check recipe details and cooking instructions. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input generated recipe data into a generating AI and have the generating AI display recipe details and cooking instructions.

[0038] The design unit can learn past recipe data and user preferences. The design unit can learn past recipe data and user preferences using, for example, AI. For example, the design unit can learn past recipe data and user preferences and generate the optimal recipe based on that. The design unit can also learn past recipe data and user preferences using machine learning algorithms. For example, the design unit can learn past recipe data and user preferences using machine learning algorithms and generate the optimal recipe based on that. Furthermore, the design unit can learn user feedback data and generate recipes based on that. For example, the design unit can learn user feedback data and generate the optimal recipe based on that. This allows for the provision of more personalized recipes by learning past data and user preferences. Some or all of the above processing in the design unit may be performed using, for example, AI, or not using AI. For example, the design unit can input past recipe data and user preference data into a generating AI and have the generating AI generate the optimal recipe.

[0039] The recommendation function can suggest recipes using leftover ingredients in the refrigerator. For example, the recommendation function can suggest recipes using leftover vegetables in the refrigerator, thus preventing food waste. The recommendation function can also suggest recipes that prioritize the use of ingredients nearing their expiration date, thus reducing food waste. Furthermore, the recommendation function can suggest recipes that utilize infrequently used ingredients, promoting the efficient use of ingredients. This reduces food loss by effectively utilizing leftover ingredients in the refrigerator. Some or all of the above processes in the recommendation function may be performed using AI, for example, or without AI. For example, the recommendation function can input data on leftover ingredients in the refrigerator into a generating AI and have the generating AI suggest the optimal recipe.

[0040] The photography unit can determine the freshness of ingredients and prioritize photographing ingredients that are losing their freshness. The photography unit can, for example, use AI to determine the freshness of ingredients and prioritize photographing ingredients that are losing their freshness. For example, the photography unit can use AI to analyze the color and shape of ingredients and identify ingredients that are losing their freshness. The photography unit can also prioritize photographing ingredients that are losing their freshness and notify the user. For example, the photography unit can prioritize photographing ingredients that are losing their freshness and notify the user. Furthermore, the photography unit can record information about ingredients that are losing their freshness and use it later to develop recipes. For example, the photography unit can record information about ingredients that are losing their freshness and use it later to develop recipes. This reduces food waste by prioritizing the photography of ingredients that are losing their freshness. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input ingredient freshness data into a generating AI and have the generating AI identify ingredients that are losing their freshness.

[0041] The imaging unit can analyze the shape and color of ingredients in detail and record their condition. For example, the imaging unit can use AI to analyze the shape and color of ingredients in detail and record their condition. For example, the imaging unit can use AI to 3D scan the shape of ingredients and record detailed data. The imaging unit can also analyze the color of ingredients and evaluate their freshness and quality. For example, the imaging unit can analyze the color of ingredients and evaluate their freshness and quality. Furthermore, the imaging unit can periodically record the condition of ingredients and track changes. For example, the imaging unit can periodically record the condition of ingredients and track changes. This makes ingredient management easier by recording the condition of ingredients in detail. Some or all of the above processes in the imaging unit may be performed using AI, or not. For example, the imaging unit can input data on the shape and color of ingredients into a generating AI and have the generating AI perform the analysis of the ingredient's condition.

[0042] The photography unit can efficiently photograph food items by taking into account their placement within the refrigerator. For example, the photography unit can use AI to efficiently photograph food items by taking into account their placement within the refrigerator. For example, the photography unit can use AI to analyze the placement information within the refrigerator and plan an efficient shooting route. The photography unit can also photograph all food items in the shortest possible time based on their placement information. For example, the photography unit can photograph all food items in the shortest possible time based on their placement information. Furthermore, the photography unit can update the placement information within the refrigerator and use it for the next shoot. For example, the photography unit can update the placement information within the refrigerator and use it for the next shoot. This makes efficient photography possible by taking into account the placement information within the refrigerator. Some or all of the above processes in the photography unit may be performed using AI, or not. For example, the photography unit can input data on the placement information within the refrigerator into a generating AI and have the generating AI plan an efficient shooting route.

[0043] The camera unit can adjust the frequency of shooting by referring to the refrigerator's opening and closing history when photographing food items. For example, the camera unit can use AI to adjust the frequency of shooting by referring to the refrigerator's opening and closing history when photographing food items. For example, the camera unit can use AI to analyze the refrigerator's opening and closing history and increase the shooting frequency if it is opened and closed frequently. The camera unit can also set a lower shooting frequency if the opening and closing history is infrequent. For example, the camera unit can set a lower shooting frequency if the opening and closing history is infrequent. Furthermore, the camera unit can estimate the freshness of the food items based on the opening and closing history and adjust the timing of the shooting. For example, the camera unit can estimate the freshness of the food items based on the opening and closing history and adjust the timing of the shooting. This allows for setting an appropriate shooting frequency by referring to the refrigerator's opening and closing history. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the refrigerator opening and closing history data into a generating AI and have the generating AI adjust the shooting frequency.

[0044] The memory unit can record the shelf life of ingredients and prioritize storing ingredients with longer shelf lives. For example, the memory unit can use AI to record the shelf life of ingredients and prioritize storing ingredients with longer shelf lives. For example, the memory unit can use AI to analyze the shelf life of ingredients and prioritize storing ingredients that can be stored for a long time. The memory unit can also choose not to record detailed information about ingredients with short shelf lives. For example, the memory unit can choose not to record detailed information about ingredients with short shelf lives. Furthermore, the memory unit can periodically update the information about ingredients with long shelf lives. For example, the memory unit can periodically update the information about ingredients with long shelf lives. This makes it easier to manage ingredients by prioritizing the storage of ingredients with long shelf lives. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data on the shelf life of ingredients into a generating AI and have the generating AI store information about ingredients with long shelf lives.

[0045] The memory unit can record nutritional information of ingredients and store information while considering nutritional balance. For example, the memory unit can use AI to record nutritional information of ingredients and store information while considering nutritional balance. For example, the memory unit can use AI to analyze the nutritional information of ingredients and store information on well-balanced ingredients. The memory unit can also prioritize the storage of ingredients with high nutritional value. For example, the memory unit can prioritize the storage of ingredients with high nutritional value. Furthermore, the memory unit can store information while considering combinations of ingredients based on nutritional information. For example, the memory unit can store information while considering combinations of ingredients based on nutritional information. This allows for the storage of information while considering nutritional balance, thereby supporting a healthy diet. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data on the nutritional information of ingredients into a generating AI and have the generating AI perform storage while considering nutritional balance.

[0046] The memory unit can adjust the frequency of memory storage by considering temperature changes inside the refrigerator when storing food items. For example, the memory unit can use AI to adjust the frequency of memory storage by considering temperature changes inside the refrigerator when storing food items. For example, the memory unit can use AI to analyze temperature changes inside the refrigerator and increase the memory frequency if the temperature changes frequently. The memory unit can also set a lower memory frequency if there are few temperature changes. For example, the memory unit can set a lower memory frequency if there are few temperature changes. Furthermore, the memory unit can estimate the freshness of the food items based on temperature changes and adjust the timing of memory storage. For example, the memory unit can estimate the freshness of the food items based on temperature changes and adjust the timing of memory storage. This allows for setting an appropriate memory frequency by considering temperature changes inside the refrigerator. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data on temperature changes inside the refrigerator into a generating AI and have the generating AI adjust the memory frequency.

[0047] The memory unit can improve the accuracy of its memory by referring to the user's purchase history when memorizing ingredients. For example, the memory unit can use AI to improve the accuracy of its memory by referring to the user's purchase history when memorizing ingredients. For example, the memory unit can use AI to analyze the user's purchase history and prioritize memorizing ingredients that are frequently purchased. The memory unit can also predict the frequency of use of ingredients based on the purchase history and improve the accuracy of its memory. For example, the memory unit can predict the frequency of use of ingredients based on the purchase history and improve the accuracy of its memory. Furthermore, the memory unit can supplement detailed information about ingredients by referring to the purchase history. For example, the memory unit can supplement detailed information about ingredients by referring to the purchase history. This improves the accuracy of memory by referring to the user's purchase history. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data from the user's purchase history into a generating AI and have the generating AI perform the improvement of memory accuracy.

[0048] The reception unit can select the optimal reception method by referring to the user's past request history when receiving a request. For example, the reception unit can use AI to select the optimal reception method by referring to the user's past request history when receiving a request. For example, the reception unit can use AI to analyze the user's past request history and automatically display frequently requested themes as candidates. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can prioritize suggesting reception methods that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used during specific time periods based on the user's past request history. For example, the reception unit can predict and suggest themes to be used during specific time periods based on the user's past request history. In this way, the reception unit can provide the optimal reception method by referring to the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data from the user's past request history into a generating AI and have the generating AI select the optimal reception method.

[0049] The reception unit can filter requests based on the user's current health status. For example, the reception unit can use AI to filter requests based on the user's current health status. For example, the reception unit can use AI to analyze the user's health status and prioritize requests that are health-conscious. The reception unit can also suggest less burdensome requests if the user's health status is poor. For example, the reception unit can suggest less burdensome requests if the user's health status is poor. Furthermore, the reception unit can filter requests based on the user's health status and suggest the most suitable options. For example, the reception unit can filter requests based on the user's health status and suggest the most suitable options. This allows the reception unit to provide the most suitable requests based on the user's health status. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user health data into a generating AI and have the generating AI perform the filtering of requests.

[0050] The reception unit can select the optimal reception method when receiving a request, taking into account the user's device information. For example, the reception unit can use AI to select the optimal reception method when receiving a request, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that is adapted to the screen size. Also, if the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. For example, if the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. For example, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. This allows the reception unit to provide the optimal reception method according to the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user device information data into a generating AI and have the generating AI select the optimal reception method.

[0051] The reception unit can adjust the timing of a request by referring to the user's schedule information when it receives a request. For example, the reception unit can use AI to adjust the timing of a request by referring to the user's schedule information when it receives a request. For example, the reception unit can use AI to analyze the user's schedule information and accept the request at the optimal time. The reception unit can also determine the priority of requests based on the schedule information. For example, the reception unit can determine the priority of requests based on the schedule information. Furthermore, the reception unit can adjust the timing of requests by referring to the schedule information. For example, the reception unit can adjust the timing of requests by referring to the schedule information. This allows the reception unit to provide the optimal request timing according to the user's schedule. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input user schedule information data into a generating AI and have the generating AI perform the adjustment of the request timing.

[0052] The design unit can generate balanced recipes by considering the nutritional value of ingredients when designing recipes. For example, the design unit can use AI to generate balanced recipes by considering the nutritional value of ingredients when designing recipes. For example, the design unit can use AI to analyze the nutritional value of ingredients and design recipes that provide balanced meals. The design unit can also design recipes that prioritize the use of ingredients with high nutritional value. For example, the design unit can design recipes that prioritize the use of ingredients with high nutritional value. Furthermore, the design unit can design recipes that combine multiple ingredients while considering nutritional balance. For example, the design unit can design recipes that combine multiple ingredients while considering nutritional balance. This allows for the provision of recipes that consider nutritional balance. Some or all of the above-described processes in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data on the nutritional value of ingredients into a generating AI and have the generating AI execute the generation of balanced recipes.

[0053] The design unit can generate the optimal recipe by referring to the user's past cooking history when designing a recipe. For example, the design unit can use AI to generate the optimal recipe by referring to the user's past cooking history when designing a recipe. For example, the design unit can use AI to analyze the user's past cooking history and design a recipe that suits their preferences. The design unit can also suggest new recipes based on data of dishes made in the past. For example, the design unit can suggest new recipes based on data of dishes made in the past. Furthermore, the design unit can also design similar recipes by referring to the user's cooking history. For example, the design unit can design similar recipes by referring to the user's cooking history. This allows the design unit to provide the optimal recipe based on the user's past cooking history. Some or all of the above processes in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data of the user's past cooking history into a generating AI and have the generating AI perform the generation of the optimal recipe.

[0054] The design unit can generate recipes while considering the user's geographical food culture. For example, the design unit can use AI to generate recipes while considering the user's geographical food culture. For example, the design unit can use AI to consider the food culture of the area where the user lives and devise recipes using local ingredients. The design unit can also suggest traditional dishes based on geographical food culture. For example, the design unit can suggest traditional dishes based on geographical food culture. Furthermore, the design unit can devise recipes that reflect the food culture of the user's region. For example, the design unit can devise recipes that reflect the food culture of the user's region. This allows the design unit to provide optimal recipes based on the user's geographical food culture. Some or all of the above processing in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data on the user's geographical food culture into a generating AI and have the generating AI execute the generation of optimal recipes.

[0055] The design unit can generate safe recipes by referring to the user's allergy information when designing recipes. For example, the design unit can use AI to generate safe recipes by referring to the user's allergy information when designing recipes. For example, the design unit can use AI to analyze the user's allergy information and design recipes that do not contain allergens. The design unit can also suggest recipes using alternative ingredients based on the allergy information. For example, the design unit can suggest recipes using alternative ingredients based on the allergy information. Furthermore, the design unit can generate safe recipes by taking the user's allergy information into consideration. For example, the design unit can generate safe recipes by taking the user's allergy information into consideration. This allows for the provision of safe recipes based on the user's allergy information. Some or all of the above processing in the design unit may be performed using AI, for example, or without using AI. For example, the design unit can input the user's allergy information data into a generating AI and have the generating AI perform the generation of safe recipes.

[0056] The recommendation unit can display the most suitable recipe by referring to the user's past recommendation history when recommending recipes. For example, the recommendation unit can use AI to refer to the user's past recommendation history when recommending recipes and display the most suitable recipe. For example, the recommendation unit can use AI to analyze the user's past recommendation history and display recipes that match their preferences. The recommendation unit can also suggest new recipes based on data of previously recommended recipes. For example, the recommendation unit can suggest new recipes based on data of previously recommended recipes. Furthermore, the recommendation unit can also display similar recipes by referring to the user's recommendation history. For example, the recommendation unit can display similar recipes by referring to the user's recommendation history. This allows the recommendation unit to provide the most suitable recipe based on the user's past recommendation history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation section can input data from the user's past recommendation history into a generating AI, which can then display the most suitable recipe.

[0057] The recommendation unit can suggest recipes while considering the user's current ingredient inventory. For example, the recommendation unit can use AI to suggest recipes while considering the user's current ingredient inventory. For example, the recommendation unit can use AI to analyze the current ingredient inventory and suggest recipes that make effective use of it. The recommendation unit can also devise recipes that use up ingredients without waste, based on the ingredient inventory. For example, the recommendation unit can devise recipes that use up ingredients without waste, based on the ingredient inventory. Furthermore, the recommendation unit can suggest recipes that prioritize the use of available ingredients. For example, the recommendation unit can suggest recipes that prioritize the use of available ingredients. This allows the recommendation unit to provide the optimal recipe based on the user's current ingredient inventory. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input data on the user's current ingredient inventory into a generating AI and have the generating AI suggest the optimal recipe.

[0058] The recommendation unit can select the optimal display method when recommending recipes, taking into account the user's device information. For example, the recommendation unit can use AI to select the optimal display method when recommending recipes, taking into account the user's device information. For example, if the user is using a smartphone, the recommendation unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the recommendation unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the recommendation unit can provide a concise and highly visible display method. This allows the recommendation unit to provide the optimal recipe display method based on the user's device information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0059] The recommendation unit can analyze the user's social media activity and suggest relevant recipes when recommending recipes. For example, the recommendation unit can use AI to analyze the user's social media activity and suggest relevant recipes when recommending recipes. For example, the recommendation unit can use AI to analyze the user's social media activity and suggest recipes that the user might be interested in. The recommendation unit can also suggest popular recipes that are being shared on social media. For example, the recommendation unit can suggest popular recipes that are being shared on social media. Furthermore, the recommendation unit can suggest recipes that are being enjoyed by the user's followers. For example, the recommendation unit can suggest recipes that are being enjoyed by the user's followers. This allows the recommendation unit to provide optimal recipes based on the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input data on the user's social media activity into a generating AI and have the generating AI suggest relevant recipes.

[0060] The recommendation unit can suggest the most suitable recipe when recommending recipes, taking into account the user's past recipe ratings. For example, the recommendation unit can use AI to suggest the most suitable recipe when recommending recipes, taking into account the user's past recipe ratings. For example, the recommendation unit can suggest new recipes based on recipes that the user has previously given high ratings to. The recommendation unit can also analyze past recipe ratings and suggest recipes that match the user's preferences. For example, the recommendation unit can analyze past recipe ratings and suggest recipes that match the user's preferences. Furthermore, the recommendation unit can suggest similar recipes by referring to the user's rating history. For example, the recommendation unit can suggest similar recipes by referring to the user's rating history. This allows the recommendation unit to provide the most suitable recipe based on the user's past recipe ratings. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input data on the user's past recipe ratings into a generating AI and have the generating AI suggest the most suitable recipe.

[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 recipe suggestion system can also include a nutritional analysis unit that analyzes the nutritional value of ingredients. This unit can, for example, use AI to analyze the nutritional value of ingredients and suggest recipes tailored to the user's health condition. For instance, if the user is on a diet, the unit can suggest recipes using low-calorie ingredients. Similarly, if the user is aiming to build muscle, the unit can suggest recipes using high-protein ingredients. Furthermore, if the user wants to consume a specific nutrient, the unit can suggest recipes using ingredients rich in that nutrient. This allows the system to provide optimal recipes tailored to the user's health condition and goals.

[0063] The recipe suggestion system can also include an allergy management unit that manages the user's food allergy information. The allergy management unit can, for example, use AI to manage the user's allergy information and suggest recipes that do not contain allergens. For instance, if a user has a nut allergy, the allergy management unit can suggest recipes that do not contain nuts. Similarly, if a user has a dairy allergy, the allergy management unit can suggest recipes using dairy-free alternative ingredients. Furthermore, based on the user's allergy information, the allergy management unit can suggest safe recipes that avoid allergens. This allows for the provision of safe recipes based on the user's allergy information.

[0064] The recipe suggestion system can also include a preference learning unit that learns the user's food preferences. This unit can, for example, use AI to learn the user's food preferences and suggest recipes based on that. For instance, if the user prefers a particular ingredient, the preference learning unit can prioritize suggesting recipes using that ingredient. Furthermore, if the user prefers a specific cooking method, the preference learning unit can suggest recipes using that method. In addition, the preference learning unit can suggest recipes that match the user's preferences based on their past recipe selection history. This allows the system to provide the most suitable recipes tailored to the user's preferences.

[0065] The recipe suggestion system can also include a meal history management unit that manages the user's eating history. This unit can, for example, use AI to manage the user's eating history and suggest recipes based on that history. For instance, it can record recipes the user has eaten in the past and suggest recipes to avoid repeating the same recipes. Furthermore, if the user frequently uses a particular ingredient, the unit can suggest new recipes using that ingredient. In addition, based on the user's eating history, the unit can suggest recipes that provide a balanced diet. This allows the system to provide optimal recipes based on the user's eating history.

[0066] The recipe suggestion system can also include an expiration date management unit that manages the expiration dates of the user's ingredients. This unit can, for example, use AI to manage ingredient expiration dates and suggest recipes that prioritize the use of ingredients nearing their expiration date. For instance, it can monitor the expiration dates of ingredients in the refrigerator and suggest recipes using ingredients that are nearing their expiration date. Furthermore, it can notify users of expired ingredients, ensuring that ingredients are used efficiently. Additionally, by prioritizing the use of ingredients nearing their expiration date, the unit can reduce food waste. This allows the system to provide optimal recipes based on ingredient expiration dates.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The camera unit photographs the food items inside the refrigerator. For example, it uses an AI camera to recognize the type and quantity of food items and stores that information. The camera unit can store detailed information about vegetables, meat, condiments, etc., inside the refrigerator. Step 2: The memory unit stores information about the food items photographed by the camera unit. For example, it can store information about the food items in a database and retrieve it as needed. The memory unit can store information such as the type and quantity of food items and their shelf life. Step 3: The reception desk receives requests from users. For example, users can request menu recipes by choosing a theme via Chat. The reception desk can input themes such as "healthy dinner" or "kids' lunch." Step 4: The design department devises recipes based on requests received by the reception department. For example, they might use AI to create recipes using ingredients found in a refrigerator. The design department could create a healthy dinner recipe using vegetables and meat found in a refrigerator. Step 5: The recommendation unit recommends recipes created by the design unit to the user. For example, it can display recipe details and cooking instructions on the chat. The recommendation unit recommends the generated recipes to the user, enabling them to cook efficiently without wasting ingredients in their refrigerator.

[0069] (Example of form 2) The recipe suggestion system according to an embodiment of the present invention is a system that photographs and memorizes ingredients in a refrigerator using an AI camera, and then, based on a theme chosen via Chat, requests a menu recipe, and the system devises and recommends a recipe using the ingredients in the refrigerator. The recipe suggestion system photographs the ingredients in the refrigerator using an AI camera and memorizes that information. Next, the user chooses a theme via Chat and requests a menu recipe. Based on this request, the AI ​​devises a recipe using the ingredients in the refrigerator and recommends it to the user. This mechanism also contributes to reducing food waste. For example, the recipe suggestion system photographs the ingredients in the refrigerator using an AI camera. At this time, the AI ​​camera recognizes the type and quantity of ingredients and memorizes that information. For example, it can memorize detailed information about vegetables, meat, seasonings, etc., in the refrigerator. Next, the recipe suggestion system receives a request from the user who chooses a theme via Chat and requests a menu recipe. For example, the user enters a theme such as "healthy dinner" or "lunch for children." This request is sent to the AI. Based on the request, the AI ​​devises a recipe using the ingredients in the refrigerator. For example, the system can create a recipe for a healthy dinner using vegetables and meat from the refrigerator. The AI ​​considers ingredient combinations and cooking methods to generate the optimal recipe. The generated recipe is then recommended to the user. For instance, detailed recipe information and cooking instructions might be displayed via chat. This allows users to cook efficiently without wasting ingredients in their refrigerator. This system also contributes to reducing food waste. By effectively utilizing ingredients in the refrigerator, it reduces food waste and creates environmentally friendly meals. For example, suggesting recipes using leftover vegetables in the refrigerator can prevent food waste. Thus, the recipe suggestion system can efficiently utilize ingredients in the refrigerator and provide users with the most suitable recipes.

[0070] The recipe suggestion system according to this embodiment comprises a shooting unit, a storage unit, a reception unit, a design unit, and a recommendation unit. The shooting unit photographs the ingredients in the refrigerator. The shooting unit recognizes the type and quantity of ingredients using, for example, an AI camera and stores that information. For example, the shooting unit can store detailed information about vegetables, meat, seasonings, etc., in the refrigerator. The storage unit stores information about the ingredients photographed by the shooting unit. The storage unit can store information about ingredients in, for example, a database and retrieve it as needed. For example, the storage unit can store information such as the type and quantity of ingredients and their shelf life. The reception unit receives requests from users. For example, the reception unit can receive requests from users via Chat, where they can choose a theme and request a menu recipe. For example, the reception unit can receive requests for themes such as "healthy dinner" or "lunch for children." The design unit designs a recipe based on the request received by the reception unit. For example, the design unit uses AI to design a recipe using the ingredients in the refrigerator. For example, the design unit can devise a recipe for a healthy dinner using vegetables and meat found in the refrigerator. The recommendation unit recommends the recipe devised by the design unit to the user. The recommendation unit can, for example, display the recipe details and cooking instructions on Chat. For example, the recommendation unit can recommend the generated recipe to the user, enabling them to cook efficiently without wasting ingredients in the refrigerator. In this way, the recipe suggestion system according to this embodiment can efficiently utilize the ingredients in the refrigerator and provide the user with the most suitable recipe.

[0071] The camera unit photographs the food items inside the refrigerator. For example, it uses an AI camera to recognize the type and quantity of food items and stores that information. Specifically, the AI ​​camera utilizes image recognition technology to automatically identify the food items inside the refrigerator. For instance, the camera categorizes items into vegetables, fruits, meats, seafood, dairy products, and condiments, and understands the quantity and condition of each item. Furthermore, the AI ​​camera can also recognize the freshness and expiration date of the food items. This allows users to constantly monitor the condition of the food items in their refrigerator, reducing waste. The camera unit periodically scans the refrigerator, updating newly added and consumed items in real time. For example, it automatically scans each time the refrigerator door is opened and closed, recording the latest information. Additionally, by positioning multiple cameras, the camera unit can gain a detailed overall view of the refrigerator's interior. This allows for accurate acquisition of food information from every corner of the refrigerator. Furthermore, the camera unit can also receive manual input of food information from users, enabling it to provide even more accurate data.

[0072] The memory unit stores information about ingredients photographed by the camera unit. For example, the memory unit can store ingredient information in a database and retrieve it as needed. Specifically, the memory unit stores detailed information such as ingredient type, quantity, storage period, freshness, and expiration date in the database. The database is equipped with an efficient search algorithm and is designed to allow users to quickly obtain specific ingredient information. For example, if a user wants to check the remaining amount of tomatoes or the expiration date of milk, the memory unit can immediately provide the relevant information. Furthermore, the memory unit also records the consumption and purchase history of ingredients, allowing for analysis of the user's consumption patterns. This enables users to efficiently manage ingredients and plan purchases based on past consumption data. Additionally, the memory unit utilizes cloud storage to enable data backup and data sharing across multiple devices. This allows users to access ingredient information from devices such as smartphones and tablets, improving convenience.

[0073] The reception desk receives requests from users. For example, users can request menu recipes by selecting a theme via chat. Specifically, users input requests to the chatbot through a smartphone or tablet application. For example, by entering a theme such as "healthy dinner" or "kids' lunch," the reception desk will accept the request. Furthermore, the reception desk can learn the user's past request history and preferences to provide more personalized suggestions. For example, if a user has frequently requested "low-calorie" or "gluten-free" in the past, the reception desk will take that information into consideration and suggest more suitable recipes. The reception desk also supports voice input, allowing users to make requests by voice. This makes it easy to make requests even when cooking or when hands are busy. In addition, the reception desk can pre-register users' allergy information and food preferences, and filter requests based on this information.

[0074] The Recipe Development Department develops recipes based on requests received by the Reception Department. For example, the Recipe Development Department can use AI to develop recipes using ingredients found in the refrigerator. Specifically, the Recipe Development Department uses an AI algorithm to match information about the ingredients in the refrigerator with the user's request and generate the optimal recipe. For example, it can develop a recipe for a healthy dinner using vegetables and meat found in the refrigerator. The AI ​​collects information from past recipe databases and online recipe sites to generate the recipe that best suits the user's request. Furthermore, the Recipe Development Department can select ingredients that should be used preferentially, taking into account their expiration dates and freshness. This minimizes food waste. The Recipe Development Department can also propose balanced recipes, taking into account the user's health information, such as nutritional balance and calorie restrictions. For example, it can suggest low-calorie recipes for users on a diet and high-protein recipes for users aiming to build muscle. In addition, the Recipe Development Department can propose easy and quick recipes, taking into account the user's cooking skills and time constraints.

[0075] The recommendation team recommends recipes created by the development team to users. For example, the recommendation team can display recipe details and cooking instructions via chat. Specifically, the recommendation team recommends generated recipes to users, enabling them to cook efficiently without wasting ingredients in their refrigerators. The recommendation team sends notifications to users' smartphones and tablets, displaying detailed recipe information and cooking instructions. For example, it provides detailed information such as ingredients, quantities, cooking steps, and cooking time. The recommendation team can also provide step-by-step guides and video tutorials to support users during cooking. This allows users to confidently proceed with cooking, even if it's their first time. Furthermore, the recommendation team can collect user feedback to improve recipes and suggest new ones. For example, users can rate recipes and leave comments, allowing the recommendation team to personalize future suggestions based on that information. The recommendation team can also monitor users' ingredient consumption and automatically generate shopping lists for the next trip. This allows users to manage ingredients efficiently and reduce waste.

[0076] The camera unit can recognize the type and quantity of ingredients. For example, the camera unit can recognize the type and quantity of ingredients using an AI camera. For example, the camera unit can recognize detailed information about vegetables, meat, and seasonings in a refrigerator. The camera unit can also recognize the type and quantity of ingredients using an image analysis algorithm. For example, the camera unit can recognize the type and quantity of ingredients using an image analysis algorithm and store that information. Furthermore, the camera unit can also recognize the quantity of ingredients using a weight sensor. For example, the camera unit can recognize the quantity of ingredients using a weight sensor and store that information. This improves the accuracy of recipes by accurately recognizing the type and quantity of ingredients. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input image data captured by an AI camera into a generating AI and have the generating AI perform the recognition of the type and quantity of ingredients.

[0077] The design unit can generate recipes by considering ingredient combinations and cooking methods. The design unit can generate recipes by considering ingredient combinations and cooking methods, for example, using AI. For example, the design unit can devise a recipe for a healthy dinner using vegetables and meat that are in the refrigerator. The design unit can also generate the optimal recipe by considering ingredient combinations and cooking methods. For example, the design unit can generate the optimal recipe by considering ingredient combinations and cooking methods. Furthermore, the design unit can learn past recipe data and user preferences and generate recipes based on that. For example, the design unit can learn past recipe data and user preferences and generate the optimal recipe based on that. This allows for the generation of the optimal recipe by considering ingredient combinations and cooking methods. Some or all of the above processing in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data on ingredient combinations and cooking methods into a generation AI and have the generation AI generate the optimal recipe.

[0078] The recommendation unit can display recipe details and cooking instructions on the chat. For example, the recommendation unit can recommend generated recipes to users, enabling them to cook efficiently without wasting ingredients in their refrigerators. The recommendation unit can also display recipe details and cooking instructions in text format. For example, the recommendation unit can display recipe details and cooking instructions in text format, making them easy for users to check. Furthermore, the recommendation unit can display recipe details and cooking instructions with images. For example, the recommendation unit can display recipe details and cooking instructions with images, making them easier for users to understand visually. This allows users to easily check recipe details and cooking instructions. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input generated recipe data into a generating AI and have the generating AI display recipe details and cooking instructions.

[0079] The design unit can learn past recipe data and user preferences. The design unit can learn past recipe data and user preferences using, for example, AI. For example, the design unit can learn past recipe data and user preferences and generate the optimal recipe based on that. The design unit can also learn past recipe data and user preferences using machine learning algorithms. For example, the design unit can learn past recipe data and user preferences using machine learning algorithms and generate the optimal recipe based on that. Furthermore, the design unit can learn user feedback data and generate recipes based on that. For example, the design unit can learn user feedback data and generate the optimal recipe based on that. This allows for the provision of more personalized recipes by learning past data and user preferences. Some or all of the above processing in the design unit may be performed using, for example, AI, or not using AI. For example, the design unit can input past recipe data and user preference data into a generating AI and have the generating AI generate the optimal recipe.

[0080] The recommendation function can suggest recipes using leftover ingredients in the refrigerator. For example, the recommendation function can suggest recipes using leftover vegetables in the refrigerator, thus preventing food waste. The recommendation function can also suggest recipes that prioritize the use of ingredients nearing their expiration date, thus reducing food waste. Furthermore, the recommendation function can suggest recipes that utilize infrequently used ingredients, promoting the efficient use of ingredients. This reduces food loss by effectively utilizing leftover ingredients in the refrigerator. Some or all of the above processes in the recommendation function may be performed using AI, for example, or without AI. For example, the recommendation function can input data on leftover ingredients in the refrigerator into a generating AI and have the generating AI suggest the optimal recipe.

[0081] The camera unit 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 AI ​​can automatically photograph the food, saving the user time. Furthermore, if the user is relaxed, the AI ​​can prompt the user to photograph the food, giving the user the opportunity to take the photos themselves. Additionally, if the user is in a hurry, the AI ​​can quickly photograph the food, reducing the time required for photography. This reduces the user's burden by adjusting the timing of photography according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input user emotion data into a generating AI and have the generating AI adjust the timing of food photography.

[0082] The photography unit can determine the freshness of ingredients and prioritize photographing ingredients that are losing their freshness. The photography unit can, for example, use AI to determine the freshness of ingredients and prioritize photographing ingredients that are losing their freshness. For example, the photography unit can use AI to analyze the color and shape of ingredients and identify ingredients that are losing their freshness. The photography unit can also prioritize photographing ingredients that are losing their freshness and notify the user. For example, the photography unit can prioritize photographing ingredients that are losing their freshness and notify the user. Furthermore, the photography unit can record information about ingredients that are losing their freshness and use it later to develop recipes. For example, the photography unit can record information about ingredients that are losing their freshness and use it later to develop recipes. This reduces food waste by prioritizing the photography of ingredients that are losing their freshness. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input ingredient freshness data into a generating AI and have the generating AI identify ingredients that are losing their freshness.

[0083] The imaging unit can analyze the shape and color of ingredients in detail and record their condition. For example, the imaging unit can use AI to analyze the shape and color of ingredients in detail and record their condition. For example, the imaging unit can use AI to 3D scan the shape of ingredients and record detailed data. The imaging unit can also analyze the color of ingredients and evaluate their freshness and quality. For example, the imaging unit can analyze the color of ingredients and evaluate their freshness and quality. Furthermore, the imaging unit can periodically record the condition of ingredients and track changes. For example, the imaging unit can periodically record the condition of ingredients and track changes. This makes ingredient management easier by recording the condition of ingredients in detail. Some or all of the above processes in the imaging unit may be performed using AI, or not. For example, the imaging unit can input data on the shape and color of ingredients into a generating AI and have the generating AI perform the analysis of the ingredient's condition.

[0084] The camera unit can estimate the user's emotions and determine the priority of ingredients to photograph based on those emotions. For example, if the user is stressed, the AI ​​can prioritize photographing important ingredients. If the user is relaxed, the AI ​​can photograph all ingredients equally. Furthermore, if the user is in a hurry, the AI ​​can prioritize photographing ingredients that are likely to be used immediately. This enables efficient photography by prioritizing ingredients 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input user emotion data into a generating AI and have the generating AI determine the priority of the ingredients to be photographed.

[0085] The photography unit can efficiently photograph food items by taking into account their placement within the refrigerator. For example, the photography unit can use AI to efficiently photograph food items by taking into account their placement within the refrigerator. For example, the photography unit can use AI to analyze the placement information within the refrigerator and plan an efficient shooting route. The photography unit can also photograph all food items in the shortest possible time based on their placement information. For example, the photography unit can photograph all food items in the shortest possible time based on their placement information. Furthermore, the photography unit can update the placement information within the refrigerator and use it for the next shoot. For example, the photography unit can update the placement information within the refrigerator and use it for the next shoot. This makes efficient photography possible by taking into account the placement information within the refrigerator. Some or all of the above processes in the photography unit may be performed using AI, or not. For example, the photography unit can input data on the placement information within the refrigerator into a generating AI and have the generating AI plan an efficient shooting route.

[0086] The camera unit can adjust the frequency of shooting by referring to the refrigerator's opening and closing history when photographing food items. For example, the camera unit can use AI to adjust the frequency of shooting by referring to the refrigerator's opening and closing history when photographing food items. For example, the camera unit can use AI to analyze the refrigerator's opening and closing history and increase the shooting frequency if it is opened and closed frequently. The camera unit can also set a lower shooting frequency if the opening and closing history is infrequent. For example, the camera unit can set a lower shooting frequency if the opening and closing history is infrequent. Furthermore, the camera unit can estimate the freshness of the food items based on the opening and closing history and adjust the timing of the shooting. For example, the camera unit can estimate the freshness of the food items based on the opening and closing history and adjust the timing of the shooting. This allows for setting an appropriate shooting frequency by referring to the refrigerator's opening and closing history. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the refrigerator opening and closing history data into a generating AI and have the generating AI adjust the shooting frequency.

[0087] The memory unit can estimate the user's emotions and adjust the level of detail of the food information it stores based on the estimated emotions. For example, the memory unit can use AI to estimate the user's emotions and adjust the level of detail of the food information it stores based on the estimated emotions. For example, if the user is stressed, the memory unit can store only basic food information. Also, if the user is relaxed, the memory unit can store detailed food information. For example, if the user is relaxed, the memory unit can store detailed food information. Furthermore, if the user is in a hurry, the memory unit can store only the minimum necessary food information. For example, if the user is in a hurry, the memory unit can store only the minimum necessary food information. This allows for efficient information management by adjusting the level of detail of the food information stored according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input user emotion data into the generating AI and have the generating AI adjust the level of detail in the food information to be stored.

[0088] The memory unit can record the shelf life of ingredients and prioritize storing ingredients with longer shelf lives. For example, the memory unit can use AI to record the shelf life of ingredients and prioritize storing ingredients with longer shelf lives. For example, the memory unit can use AI to analyze the shelf life of ingredients and prioritize storing ingredients that can be stored for a long time. The memory unit can also choose not to record detailed information about ingredients with short shelf lives. For example, the memory unit can choose not to record detailed information about ingredients with short shelf lives. Furthermore, the memory unit can periodically update the information about ingredients with long shelf lives. For example, the memory unit can periodically update the information about ingredients with long shelf lives. This makes it easier to manage ingredients by prioritizing the storage of ingredients with long shelf lives. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data on the shelf life of ingredients into a generating AI and have the generating AI store information about ingredients with long shelf lives.

[0089] The memory unit can record nutritional information of ingredients and store information while considering nutritional balance. For example, the memory unit can use AI to record nutritional information of ingredients and store information while considering nutritional balance. For example, the memory unit can use AI to analyze the nutritional information of ingredients and store information on well-balanced ingredients. The memory unit can also prioritize the storage of ingredients with high nutritional value. For example, the memory unit can prioritize the storage of ingredients with high nutritional value. Furthermore, the memory unit can store information while considering combinations of ingredients based on nutritional information. For example, the memory unit can store information while considering combinations of ingredients based on nutritional information. This allows for the storage of information while considering nutritional balance, thereby supporting a healthy diet. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data on the nutritional information of ingredients into a generating AI and have the generating AI perform storage while considering nutritional balance.

[0090] The memory unit can estimate the user's emotions and determine the priority of food information to remember based on the estimated emotions. For example, the memory unit can use AI to estimate the user's emotions and determine the priority of food information to remember based on the estimated emotions. For instance, if the user is stressed, the memory unit can prioritize remembering important food information. Conversely, if the user is relaxed, the memory unit can remember all food information equally. Furthermore, if the user is in a hurry, the memory unit can prioritize remembering food information that is likely to be used immediately. This allows for efficient information management by prioritizing food information 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 includes, 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 memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input user emotion data into a generating AI and have the generating AI determine the priority of food ingredient information to be stored.

[0091] The memory unit can adjust the frequency of memory storage by considering temperature changes inside the refrigerator when storing food items. For example, the memory unit can use AI to adjust the frequency of memory storage by considering temperature changes inside the refrigerator when storing food items. For example, the memory unit can use AI to analyze temperature changes inside the refrigerator and increase the memory frequency if the temperature changes frequently. The memory unit can also set a lower memory frequency if there are few temperature changes. For example, the memory unit can set a lower memory frequency if there are few temperature changes. Furthermore, the memory unit can estimate the freshness of the food items based on temperature changes and adjust the timing of memory storage. For example, the memory unit can estimate the freshness of the food items based on temperature changes and adjust the timing of memory storage. This allows for setting an appropriate memory frequency by considering temperature changes inside the refrigerator. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data on temperature changes inside the refrigerator into a generating AI and have the generating AI adjust the memory frequency.

[0092] The memory unit can improve the accuracy of its memory by referring to the user's purchase history when memorizing ingredients. For example, the memory unit can use AI to improve the accuracy of its memory by referring to the user's purchase history when memorizing ingredients. For example, the memory unit can use AI to analyze the user's purchase history and prioritize memorizing ingredients that are frequently purchased. The memory unit can also predict the frequency of use of ingredients based on the purchase history and improve the accuracy of its memory. For example, the memory unit can predict the frequency of use of ingredients based on the purchase history and improve the accuracy of its memory. Furthermore, the memory unit can supplement detailed information about ingredients by referring to the purchase history. For example, the memory unit can supplement detailed information about ingredients by referring to the purchase history. This improves the accuracy of memory by referring to the user's purchase history. Some or all of the above processing in the memory unit may be performed using AI, for example, or without AI. For example, the memory unit can input data from the user's purchase history into a generating AI and have the generating AI perform the improvement of memory accuracy.

[0093] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, the reception desk can use AI to estimate the user's emotions and adjust the request processing method based on the estimated emotions. For instance, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. Alternatively, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and process the request quickly. This reduces the user's burden by adjusting the request processing method according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI adjust the request reception method.

[0094] The reception unit can select the optimal reception method by referring to the user's past request history when receiving a request. For example, the reception unit can use AI to select the optimal reception method by referring to the user's past request history when receiving a request. For example, the reception unit can use AI to analyze the user's past request history and automatically display frequently requested themes as candidates. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can prioritize suggesting reception methods that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used during specific time periods based on the user's past request history. For example, the reception unit can predict and suggest themes to be used during specific time periods based on the user's past request history. In this way, the reception unit can provide the optimal reception method by referring to the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data from the user's past request history into a generating AI and have the generating AI select the optimal reception method.

[0095] The reception unit can filter requests based on the user's current health status. For example, the reception unit can use AI to filter requests based on the user's current health status. For example, the reception unit can use AI to analyze the user's health status and prioritize requests that are health-conscious. The reception unit can also suggest less burdensome requests if the user's health status is poor. For example, the reception unit can suggest less burdensome requests if the user's health status is poor. Furthermore, the reception unit can filter requests based on the user's health status and suggest the most suitable options. For example, the reception unit can filter requests based on the user's health status and suggest the most suitable options. This allows the reception unit to provide the most suitable requests based on the user's health status. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user health data into a generating AI and have the generating AI perform the filtering of requests.

[0096] The reception desk can estimate the user's emotions and prioritize requests based on those emotions. For example, the reception desk can use AI to estimate the user's emotions and prioritize requests based on those emotions. For example, if the user is stressed, the reception desk can prioritize important requests. If the user is relaxed, the reception desk can also accept all requests equally. For example, if the user is relaxed, the reception desk can accept all requests equally. Furthermore, if the user is in a hurry, the reception desk can prioritize requests that require a quick response. For example, if the user is in a hurry, the reception desk can prioritize requests that require a quick response. This enables efficient request processing by prioritizing requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​determine the priority of requests.

[0097] The reception unit can select the optimal reception method when receiving a request, taking into account the user's device information. For example, the reception unit can use AI to select the optimal reception method when receiving a request, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that is adapted to the screen size. Also, if the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. For example, if the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. For example, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. This allows the reception unit to provide the optimal reception method according to the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user device information data into a generating AI and have the generating AI select the optimal reception method.

[0098] The reception unit can adjust the timing of a request by referring to the user's schedule information when it receives a request. For example, the reception unit can use AI to adjust the timing of a request by referring to the user's schedule information when it receives a request. For example, the reception unit can use AI to analyze the user's schedule information and accept the request at the optimal time. The reception unit can also determine the priority of requests based on the schedule information. For example, the reception unit can determine the priority of requests based on the schedule information. Furthermore, the reception unit can adjust the timing of requests by referring to the schedule information. For example, the reception unit can adjust the timing of requests by referring to the schedule information. This allows the reception unit to provide the optimal request timing according to the user's schedule. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input user schedule information data into a generating AI and have the generating AI perform the adjustment of the request timing.

[0099] The design unit can estimate the user's emotions and adjust the recipe design method based on the estimated emotions. For example, the design unit can use AI to estimate the user's emotions and adjust the recipe design method based on the estimated emotions. For example, if the user is stressed, the design unit can design a simple and easy recipe. If the user is relaxed, the design unit can design a recipe that includes detailed cooking instructions. For example, if the user is relaxed, the design unit can design a recipe that includes detailed cooking instructions. Furthermore, if the user is in a hurry, the design unit can design a recipe that can be cooked in a short amount of time. For example, if the user is in a hurry, the design unit can design a recipe that can be cooked in a short amount of time. This allows for the provision of an optimal recipe design method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the design unit may be performed using AI, for example, or without AI. For example, the design department can input user emotion data into the generating AI and have the generating AI adjust the recipe design method.

[0100] The design unit can generate balanced recipes by considering the nutritional value of ingredients when designing recipes. For example, the design unit can use AI to generate balanced recipes by considering the nutritional value of ingredients when designing recipes. For example, the design unit can use AI to analyze the nutritional value of ingredients and design recipes that provide balanced meals. The design unit can also design recipes that prioritize the use of ingredients with high nutritional value. For example, the design unit can design recipes that prioritize the use of ingredients with high nutritional value. Furthermore, the design unit can design recipes that combine multiple ingredients while considering nutritional balance. For example, the design unit can design recipes that combine multiple ingredients while considering nutritional balance. This allows for the provision of recipes that consider nutritional balance. Some or all of the above-described processes in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data on the nutritional value of ingredients into a generating AI and have the generating AI execute the generation of balanced recipes.

[0101] The design unit can generate the optimal recipe by referring to the user's past cooking history when designing a recipe. For example, the design unit can use AI to generate the optimal recipe by referring to the user's past cooking history when designing a recipe. For example, the design unit can use AI to analyze the user's past cooking history and design a recipe that suits their preferences. The design unit can also suggest new recipes based on data of dishes made in the past. For example, the design unit can suggest new recipes based on data of dishes made in the past. Furthermore, the design unit can also design similar recipes by referring to the user's cooking history. For example, the design unit can design similar recipes by referring to the user's cooking history. This allows the design unit to provide the optimal recipe based on the user's past cooking history. Some or all of the above processes in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data of the user's past cooking history into a generating AI and have the generating AI perform the generation of the optimal recipe.

[0102] The design unit can estimate the user's emotions and determine the priority of recipes based on those emotions. For example, the design unit can use AI to estimate the user's emotions and determine the priority of recipes based on those emotions. For example, if the user is stressed, the design unit can prioritize suggesting easy and simple recipes. If the user is relaxed, the design unit can prioritize suggesting recipes that include detailed cooking instructions. For example, if the user is relaxed, the design unit can prioritize suggesting recipes that include detailed cooking instructions. Furthermore, if the user is in a hurry, the design unit can prioritize suggesting recipes that can be cooked in a short amount of time. For example, if the user is in a hurry, the design unit can prioritize suggesting recipes that can be cooked in a short amount of time. This allows for the provision of optimal recipe priorities tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input user emotion data into a generating AI and have the generating AI determine the priority of recipes.

[0103] The design unit can generate recipes while considering the user's geographical food culture. For example, the design unit can use AI to generate recipes while considering the user's geographical food culture. For example, the design unit can use AI to consider the food culture of the area where the user lives and devise recipes using local ingredients. The design unit can also suggest traditional dishes based on geographical food culture. For example, the design unit can suggest traditional dishes based on geographical food culture. Furthermore, the design unit can devise recipes that reflect the food culture of the user's region. For example, the design unit can devise recipes that reflect the food culture of the user's region. This allows the design unit to provide optimal recipes based on the user's geographical food culture. Some or all of the above processing in the design unit may be performed using AI, for example, or without AI. For example, the design unit can input data on the user's geographical food culture into a generating AI and have the generating AI execute the generation of optimal recipes.

[0104] The design unit can generate safe recipes by referring to the user's allergy information when designing recipes. For example, the design unit can use AI to generate safe recipes by referring to the user's allergy information when designing recipes. For example, the design unit can use AI to analyze the user's allergy information and design recipes that do not contain allergens. The design unit can also suggest recipes using alternative ingredients based on the allergy information. For example, the design unit can suggest recipes using alternative ingredients based on the allergy information. Furthermore, the design unit can generate safe recipes by taking the user's allergy information into consideration. For example, the design unit can generate safe recipes by taking the user's allergy information into consideration. This allows for the provision of safe recipes based on the user's allergy information. Some or all of the above processing in the design unit may be performed using AI, for example, or without using AI. For example, the design unit can input the user's allergy information data into a generating AI and have the generating AI perform the generation of safe recipes.

[0105] The recommendation system can estimate the user's emotions and adjust how recipes are displayed based on those emotions. For example, the recommendation system can use AI to estimate the user's emotions and adjust how recipes are displayed based on those emotions. For instance, if the user is stressed, the recommendation system can provide a simple and easy-to-read display. Similarly, if the user is relaxed, the recommendation system can provide a display with more detailed information. Furthermore, if the user is in a hurry, the recommendation system can provide a concise display. This allows the system to provide the optimal recipe display method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section may input user sentiment data into a generating AI and have the generating AI adjust how recipes are displayed.

[0106] The recommendation unit can display the most suitable recipe by referring to the user's past recommendation history when recommending recipes. For example, the recommendation unit can use AI to refer to the user's past recommendation history when recommending recipes and display the most suitable recipe. For example, the recommendation unit can use AI to analyze the user's past recommendation history and display recipes that match their preferences. The recommendation unit can also suggest new recipes based on data of previously recommended recipes. For example, the recommendation unit can suggest new recipes based on data of previously recommended recipes. Furthermore, the recommendation unit can display similar recipes by referring to the user's recommendation history. For example, the recommendation unit can display similar recipes by referring to the user's recommendation history. This allows the recommendation unit to provide the most suitable recipe based on the user's past recommendation history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation section can input data from the user's past recommendation history into a generating AI, which can then display the most suitable recipe.

[0107] The recommendation unit can suggest recipes while considering the user's current ingredient inventory. For example, the recommendation unit can use AI to suggest recipes while considering the user's current ingredient inventory. For example, the recommendation unit can use AI to analyze the current ingredient inventory and suggest recipes that make effective use of it. The recommendation unit can also devise recipes that use up ingredients without waste, based on the ingredient inventory. For example, the recommendation unit can devise recipes that use up ingredients without waste, based on the ingredient inventory. Furthermore, the recommendation unit can suggest recipes that prioritize the use of available ingredients. For example, the recommendation unit can suggest recipes that prioritize the use of available ingredients. This allows the recommendation unit to provide the optimal recipe based on the user's current ingredient inventory. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input data on the user's current ingredient inventory into a generating AI and have the generating AI suggest the optimal recipe.

[0108] The recommendation system can estimate the user's emotions and prioritize recipes based on those emotions. For example, it can use AI to estimate the user's emotions and prioritize recipes based on those emotions. For instance, if the user is stressed, the recommendation system can prioritize simple, easy-to-prepare recipes. Similarly, if the user is relaxed, it can prioritize recipes with detailed cooking instructions. Furthermore, if the user is in a hurry, it can prioritize recipes that can be prepared quickly. This allows the system to provide an optimal recipe priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user sentiment data into a generating AI and have the generating AI determine the priority of recipes.

[0109] The recommendation unit can select the optimal display method when recommending recipes, taking into account the user's device information. For example, the recommendation unit can use AI to select the optimal display method when recommending recipes, taking into account the user's device information. For example, if the user is using a smartphone, the recommendation unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the recommendation unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the recommendation unit can provide a concise and highly visible display method. This allows the recommendation unit to provide the optimal recipe display method based on the user's device information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0110] The recommendation unit can analyze the user's social media activity and suggest relevant recipes when recommending recipes. For example, the recommendation unit can use AI to analyze the user's social media activity and suggest relevant recipes when recommending recipes. For example, the recommendation unit can use AI to analyze the user's social media activity and suggest recipes that the user might be interested in. The recommendation unit can also suggest popular recipes that are being shared on social media. For example, the recommendation unit can suggest popular recipes that are being shared on social media. Furthermore, the recommendation unit can suggest recipes that are being enjoyed by the user's followers. For example, the recommendation unit can suggest recipes that are being enjoyed by the user's followers. This allows the recommendation unit to provide optimal recipes based on the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input data on the user's social media activity into a generating AI and have the generating AI suggest relevant recipes.

[0111] The recommendation unit can suggest the most suitable recipe when recommending recipes, taking into account the user's past recipe ratings. For example, the recommendation unit can use AI to suggest the most suitable recipe when recommending recipes, taking into account the user's past recipe ratings. For example, the recommendation unit can suggest new recipes based on recipes that the user has previously given high ratings to. The recommendation unit can also analyze past recipe ratings and suggest recipes that match the user's preferences. For example, the recommendation unit can analyze past recipe ratings and suggest recipes that match the user's preferences. Furthermore, the recommendation unit can suggest similar recipes by referring to the user's rating history. For example, the recommendation unit can suggest similar recipes by referring to the user's rating history. This allows the recommendation unit to provide the most suitable recipe based on the user's past recipe ratings. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input data on the user's past recipe ratings into a generating AI and have the generating AI suggest the most suitable recipe.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The recipe suggestion system can also include a nutritional analysis unit that analyzes the nutritional value of ingredients. This unit can, for example, use AI to analyze the nutritional value of ingredients and suggest recipes tailored to the user's health condition. For instance, if the user is on a diet, the unit can suggest recipes using low-calorie ingredients. Similarly, if the user is aiming to build muscle, the unit can suggest recipes using high-protein ingredients. Furthermore, if the user wants to consume a specific nutrient, the unit can suggest recipes using ingredients rich in that nutrient. This allows the system to provide optimal recipes tailored to the user's health condition and goals.

[0114] The recipe suggestion system can also include an allergy management unit that manages the user's food allergy information. The allergy management unit can, for example, use AI to manage the user's allergy information and suggest recipes that do not contain allergens. For instance, if a user has a nut allergy, the allergy management unit can suggest recipes that do not contain nuts. Similarly, if a user has a dairy allergy, the allergy management unit can suggest recipes using dairy-free alternative ingredients. Furthermore, based on the user's allergy information, the allergy management unit can suggest safe recipes that avoid allergens. This allows for the provision of safe recipes based on the user's allergy information.

[0115] The recipe suggestion system can also include a preference learning unit that learns the user's food preferences. This unit can, for example, use AI to learn the user's food preferences and suggest recipes based on that. For instance, if the user prefers a particular ingredient, the preference learning unit can prioritize suggesting recipes using that ingredient. Furthermore, if the user prefers a specific cooking method, the preference learning unit can suggest recipes using that method. In addition, the preference learning unit can suggest recipes that match the user's preferences based on their past recipe selection history. This allows the system to provide the most suitable recipes tailored to the user's preferences.

[0116] The recipe suggestion system can also include a meal history management unit that manages the user's eating history. This unit can, for example, use AI to manage the user's eating history and suggest recipes based on that history. For instance, it can record recipes the user has eaten in the past and suggest recipes to avoid repeating the same recipes. Furthermore, if the user frequently uses a particular ingredient, the unit can suggest new recipes using that ingredient. In addition, based on the user's eating history, the unit can suggest recipes that provide a balanced diet. This allows the system to provide optimal recipes based on the user's eating history.

[0117] The recipe suggestion system can also include an expiration date management unit that manages the expiration dates of the user's ingredients. This unit can, for example, use AI to manage ingredient expiration dates and suggest recipes that prioritize the use of ingredients nearing their expiration date. For instance, it can monitor the expiration dates of ingredients in the refrigerator and suggest recipes using ingredients that are nearing their expiration date. Furthermore, it can notify users of expired ingredients, ensuring that ingredients are used efficiently. Additionally, by prioritizing the use of ingredients nearing their expiration date, the unit can reduce food waste. This allows the system to provide optimal recipes based on ingredient expiration dates.

[0118] The recipe suggestion system can also estimate the user's emotions and adjust the difficulty of the recipes based on those emotions. For example, if the user is stressed, the system can suggest easy, low-effort recipes. If the user is relaxed, the system can suggest recipes with detailed cooking instructions. Furthermore, if the user is in a hurry, the system can suggest recipes that can be prepared in a short amount of time. This allows the system to provide the optimal recipe tailored to the user's emotions.

[0119] The recipe suggestion system can further estimate the user's emotions and adjust how recipes are displayed based on those emotions. For example, if the user is stressed, the system can provide a simple and easy-to-read display. If the user is relaxed, the system can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the system can provide a concise display. This allows the system to provide the optimal recipe display method tailored to the user's emotions.

[0120] The recipe suggestion system can further estimate the user's emotions and prioritize recipes based on those emotions. For example, if the user is stressed, the system can prioritize simple, easy-to-prepare recipes. If the user is relaxed, the system can prioritize recipes with detailed cooking instructions. Furthermore, if the user is in a hurry, the system can prioritize recipes that can be prepared quickly. This allows the system to provide the optimal recipe priority based on the user's emotions.

[0121] The recipe suggestion system can also estimate the user's emotions and adjust how requests are processed based on those emotions. For example, if the user is stressed, the system can provide a simple interface and minimize the input steps. If the user is relaxed, the system can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the system can prioritize voice input and process requests quickly. This allows the system to provide the most appropriate request processing method based on the user's emotions.

[0122] The recipe suggestion system can further estimate the user's emotions and adjust its recipe development method based on those emotions. For example, if the user is stressed, the system can create a simple, easy-to-prepare recipe. If the user is relaxed, the system can create a recipe with detailed cooking instructions. Furthermore, if the user is in a hurry, the system can create a recipe that can be prepared in a short amount of time. This allows the system to provide the optimal recipe development method tailored to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The camera unit photographs the food items inside the refrigerator. For example, it uses an AI camera to recognize the type and quantity of food items and stores that information. The camera unit can store detailed information about vegetables, meat, condiments, etc., inside the refrigerator. Step 2: The memory unit stores information about the food items photographed by the camera unit. For example, it can store information about the food items in a database and retrieve it as needed. The memory unit can store information such as the type and quantity of food items and their shelf life. Step 3: The reception desk receives requests from users. For example, users can request menu recipes by choosing a theme via Chat. The reception desk can input themes such as "healthy dinner" or "kids' lunch." Step 4: The design department creates recipes based on requests received by the reception department. For example, they might use AI to create recipes using ingredients found in a refrigerator. The design department could create a healthy dinner recipe using vegetables and meat found in a refrigerator. Step 5: The recommendation unit recommends recipes created by the design unit to the user. For example, it can display recipe details and cooking instructions on the chat. The recommendation unit recommends the generated recipes to the user, enabling them to cook efficiently without wasting ingredients in their refrigerator.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the shooting unit, storage unit, reception unit, design unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart device 14 to photograph ingredients and the control unit 46A recognizes the type and quantity of ingredients. The storage unit stores information about the ingredients in the database 24 of the data processing unit 12. The reception unit receives requests from users through the reception device 38 of the smart device 14. The design unit uses the identification processing unit 290 of the data processing unit 12 to design a recipe based on the request. The recommendation unit recommends recipes to the user through the output device 40 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.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the shooting unit, storage unit, reception unit, design unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart glasses 214 to photograph ingredients and the control unit 46A recognizes the type and quantity of ingredients. The storage unit stores information about the ingredients in the database 24 of the data processing unit 12. The reception unit receives requests from the user through the microphone 238 of the smart glasses 214. The design unit uses the identification processing unit 290 of the data processing unit 12 to design a recipe based on the request. The recommendation unit recommends the recipe to the user through the speaker 240 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.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the shooting unit, storage unit, reception unit, design unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the headset terminal 314 to photograph ingredients and the control unit 46A recognizes the type and quantity of ingredients. The storage unit stores information about the ingredients in the database 24 of the data processing unit 12. The reception unit receives requests from the user through the microphone 238 of the headset terminal 314. The design unit uses the identification processing unit 290 of the data processing unit 12 to design a recipe based on the request. The recommendation unit recommends recipes to the user through 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the shooting unit, memory unit, reception unit, design unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the robot 414 to photograph ingredients and the control unit 46A recognizes the type and quantity of ingredients. The memory unit stores information about the ingredients in the database 24 of the data processing unit 12. The reception unit receives requests from the user through the microphone 238 of the robot 414. The design unit uses the identification processing unit 290 of the data processing unit 12 to design a recipe based on the request. The recommendation unit recommends a recipe to the user through the speaker 240 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The photography team takes pictures of the food inside the refrigerator, A storage unit that stores information about the food items photographed by the aforementioned photography unit, A reception desk that receives requests from users, A design department which devises recipes based on requests received by the aforementioned reception department, The system includes a recommendation unit that recommends recipes devised by the aforementioned invention unit to the user. A system characterized by the following features. (Note 2) The aforementioned imaging unit is Recognize the type and quantity of ingredients The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned invention is Recipes are generated considering the combination of ingredients and cooking methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, Display recipe details and cooking instructions on the chat. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned invention is Learns from past recipe data and user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recommendation unit is, I'll suggest recipes using leftover ingredients in your refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging 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 imaging unit is The system assesses the freshness of ingredients and prioritizes photographing ingredients that are past their prime. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is The shape and color of the ingredients are analyzed in detail, and their condition is recorded. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging 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 imaging unit is When photographing ingredients, take into account their arrangement inside the refrigerator to ensure efficient shooting. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is When photographing food ingredients, refer to the refrigerator's opening and closing history to adjust the frequency of photography. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned storage unit is The system estimates the user's emotions and adjusts the level of detail in the ingredient information stored based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned storage unit is Record the shelf life of ingredients and prioritize remembering ingredients with longer shelf lives. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned storage unit is Record the nutritional value information of ingredients and remember them while considering nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned storage unit is The system estimates the user's emotions and determines the priority of food information to remember based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned storage unit is When memorizing food items, the frequency of memorization is adjusted to take into account temperature changes inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned storage unit is When memorizing ingredients, the system improves accuracy by referencing the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are processed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is When a request is received, the system will refer to the user's past request history to select the most suitable processing method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is When a request is received, the request content is filtered based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is It estimates the user's emotions and prioritizes requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is When a request is received, the system selects the most appropriate processing method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is When a request is received, the system adjusts the timing of the request by referring to the user's schedule information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned invention is The system estimates the user's emotions and adjusts the recipe development method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned invention is When developing recipes, we create balanced recipes that take into account the nutritional value of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned invention is When creating recipes, the system generates the optimal recipe by referring to the user's past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned invention is It estimates the user's emotions and prioritizes recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned invention is When creating recipes, the system generates recipes that take into account the user's geographical location and food culture. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned invention is When developing recipes, the system references the user's allergy information to generate safe recipes. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recommendation unit is, It estimates the user's emotions and adjusts how recipes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recommendation unit is, When recommending recipes, the system refers to the user's past recommendation history to display the most suitable recipes. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recommendation unit is, When recommending recipes, the system takes into account the user's current ingredient inventory to suggest recipes. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recommendation unit is, It estimates the user's emotions and prioritizes recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recommendation unit is, When recommending recipes, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The recommendation unit is, When recommending recipes, the system analyzes the user's social media activity to suggest relevant recipes. The system described in Appendix 1, characterized by the features described herein. (Note 37) The recommendation unit is, When recommending recipes, the system takes into account the user's past recipe ratings to suggest the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 photography team takes pictures of the food inside the refrigerator, A storage unit that stores information about the food items photographed by the aforementioned photography unit, A reception desk that receives requests from users, The design department devises recipes based on requests received by the aforementioned reception department, The system includes a recommendation unit that recommends recipes devised by the aforementioned invention unit to the user. A system characterized by the following features.

2. The aforementioned imaging unit is Recognize the type and quantity of ingredients The system according to feature 1.

3. The aforementioned invention is Recipes are generated considering the combination of ingredients and cooking methods. The system according to feature 1.

4. The recommendation unit is, Display recipe details and cooking instructions on the chat. The system according to feature 1.

5. The aforementioned invention is Learns from past recipe data and user preferences. The system according to feature 1.

6. The recommendation unit is, I'll suggest recipes using leftover ingredients in your refrigerator. The system according to feature 1.

7. The aforementioned imaging 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 imaging unit is The system assesses the freshness of ingredients and prioritizes photographing ingredients that are past their prime. The system according to feature 1.

9. The aforementioned imaging unit is The shape and color of the ingredients are analyzed in detail, and their condition is recorded. The system according to feature 1.

10. The aforementioned imaging 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.

Images