System

Abstract

An object of a system according to an embodiment is to grasp the contents of a refrigerator or a freezer and propose an optimal recipe to a user.SOLUTION: A system according to an embodiment includes a camera, a generation AI, and a notifying unit. The camera monitors a situation in each room of the refrigerator or the freezer. The generation AI inputs conditions of a dish that the user wants to make. The notifying unit notifies the user of the recipe and the necessary ingredients proposed by the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to grasp the contents of a refrigerator or freezer and suggest appropriate recipes.

[0005] The system according to the embodiment aims to grasp the contents of the refrigerator and freezer and suggest optimal recipes to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a generation AI, and a notification unit. The camera monitors the status of each compartment of the refrigerator and freezer. The generation AI inputs the conditions for the dish the user wants to make. The notification unit notifies the user of the recipe suggested by the generation AI and the necessary ingredients. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the contents of the refrigerator and freezer and suggest optimal recipes to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recipe suggestion system according to an embodiment of the present invention is a system that monitors the status of each compartment of a refrigerator or freezer, and suggests specific recipes and necessary ingredients when a user inputs the conditions for the dish they want to make. This allows the recipe suggestion system to suggest recipes for dishes that are easy to make or that can be made with ingredients already on hand, while keeping track of the contents of the refrigerator or freezer.

[0029] A recipe suggestion system according to an embodiment includes a camera, a generation AI, and a notification unit. The camera monitors the status of each compartment of a refrigerator or freezer. For example, the camera periodically takes images to check what is stored on the shelves and in the drawers of the refrigerator and sends the captured images to the cloud. The camera can also monitor the temperature and humidity inside the refrigerator. For example, the camera may have a built-in temperature sensor and humidity sensor, which sends the captured images to the cloud. The generation AI receives input from a user about the recipe they want to make. For example, when the user inputs criteria such as "something that's easy to make" or "something that can be made with what's already on hand," the generation AI searches for and suggests recipes that meet those criteria. The generation AI generates recipes based on the user's criteria using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the criteria entered by the user and suggests optimal recipes. The notification unit notifies the user of the recipes suggested by the generation AI and the necessary ingredients. For example, the notification unit may send a notification to the user via a smartphone app. The notification unit may also send notifications via email or SMS. For example, information about recipes and ingredients is sent based on the notification method set by the user. As a result, the recipe suggestion system according to the embodiment allows the user to get suggestions for recipes that are easy to make or that can be made with ingredients that are already on hand while keeping track of the contents of the refrigerator or freezer.

[0030] The camera can periodically take images to check what is stored on the shelves and in the drawers of the refrigerator and send the data to the cloud. For example, the camera can scan handwritten answers and save them as image data. It can then use OCR technology to convert the image data into text data. The camera can also take photos of handwritten answers using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The camera can also write handwritten answers with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. This allows the status inside the refrigerator to be monitored regularly.

[0031] The generation AI can search for and suggest recipes such as easy-to-make pasta dishes based on conditions entered by the user. For example, when a user enters a condition such as "easy-to-make pasta dishes," the generation AI searches for and suggests recipes that meet those conditions. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a recipe based on the user's conditions. The generation AI can also use a multimodal generation AI to generate a recipe based on the user's conditions. For example, the generation AI analyzes the conditions entered by the user and suggests the optimal recipe. This allows it to suggest recipes that meet the user's conditions.

[0032] The generating AI can list the necessary ingredients based on the proposed recipe and analyze the camera image data to confirm whether the necessary ingredients are available. The generating AI, for example, lists the necessary ingredients based on the proposed recipe. For example, the generating AI lists the ingredients needed for the recipe and provides that list to the user. The generating AI also analyzes the camera image data to confirm whether the necessary ingredients are available. For example, the generating AI uses image recognition technology to analyze the type and amount of food in the refrigerator and confirm whether the necessary ingredients are available. This makes it possible to confirm whether the necessary ingredients are available.

[0033] The generation AI can search for reliable external recipe sites and photos of finished dishes and attach them to the suggested recipes. The generation AI, for example, searches for reliable external recipe sites and attaches them to the suggested recipes. For example, the generation AI attaches a link to an external recipe site to the suggested recipe. The generation AI also searches for photos of finished dishes and attaches them to the suggested recipes. For example, the generation AI attaches photos to make it easier to visually imagine the finished dish. This makes it easier to visually imagine the finished dish.

[0034] The generation AI can analyze user feedback and reflect it in the next recipe suggestions. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language regarding the amount and taste. The generation AI analyzes this feedback and reflects it in the next recipe suggestions. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest a recipe with adjusted saltiness the next time. This makes it possible to suggest recipes that reflect user feedback.

[0035] The generative AI can suggest different variations of the same recipe based on user feedback. For example, after a user actually cooks a dish, the generative AI inputs feedback in natural language about the amount and taste. The generative AI analyzes this feedback and suggests different variations of the same recipe. For example, if a user inputs feedback such as "I wish it was a little saltier," the generative AI will suggest a variation with adjusted saltiness the next time it suggests a recipe. This makes it possible to suggest recipe variations that suit the user's preferences.

[0036] The generation AI can make suggestions to adjust the difficulty and cooking time of a recipe based on user feedback. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste. The generation AI analyzes this feedback and makes suggestions to adjust the difficulty and cooking time of the recipe. For example, if a user inputs feedback such as "the cooking time was too long," the generation AI will suggest a recipe with a shorter cooking time the next time it makes a suggestion. This makes it possible to adjust the difficulty and cooking time of a recipe based on user feedback.

[0037] Based on user feedback, the generation AI can suggest recipe improvements that can be shared with other users. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste of the dish. The generation AI analyzes this feedback and suggests recipe improvements that can be shared with other users. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest an improved recipe that adjusts the saltiness the next time it makes a suggestion. This allows the generation AI to suggest recipe improvements that can be shared with other users.

[0038] The generation AI can learn the user's past cooking history and suggest recipes that take into account preferences and allergy information. The generation AI can, for example, learn the user's past cooking history and suggest recipes that take into account preferences and allergy information. For example, the generation AI can suggest optimal recipes based on the user's past recipe data. The generation AI can also consider the user's allergy information and suggest recipes that exclude ingredients that may cause allergies. This makes it possible to suggest recipes that take into account the user's preferences and allergy information.

[0039] Generative AI can suggest recipes according to the season and weather, and provide dishes that allow you to enjoy the feeling of the season. Generative AI can, for example, analyze seasonal and weather data and suggest recipes accordingly. For example, generative AI can suggest recipes that use seasonal ingredients. Generative AI can also suggest recipes that are according to the weather. For example, it can suggest cold dishes in the hot summer and warm dishes in the cold winter. This makes it possible to provide dishes that allow you to enjoy the feeling of the season.

[0040] The generation AI can take into account the user's mealtimes and schedule and suggest recipes at the optimal time. For example, the generation AI can analyze the user's mealtimes and schedule and suggest recipes accordingly. For example, it can suggest easy breakfast recipes for busy mornings. The generation AI can also suggest recipes at the optimal time based on the user's schedule. For example, it can suggest dinner recipes to coincide with the time the user gets home from work. This allows it to suggest recipes at the optimal time, taking into account the user's mealtimes and schedule.

[0041] Generative AI can suggest recipes based on the user's health condition and diet goals, supporting health management. For example, generative AI can analyze the user's health condition and diet goals and suggest recipes accordingly. For example, it can suggest recipes that take calorie restrictions and nutritional balance into consideration. Generative AI can also suggest recipes that are high in specific nutrients based on the user's health condition. For example, it can suggest recipes that use ingredients rich in vitamin C. This allows it to suggest recipes based on the user's health condition and diet goals, supporting health management.

[0042] The generative AI can learn how often foods are used in the refrigerator and prioritize checking the inventory of frequently used ingredients. For example, the generative AI can learn how often foods are used in the refrigerator and prioritize checking the inventory of frequently used ingredients. For example, the generative AI can identify frequently used ingredients based on past usage data and prioritize checking their inventory. The generative AI can also analyze how often foods are used in the refrigerator and list the ingredients that are used most frequently. This allows the generative AI to prioritize checking the inventory of frequently used ingredients.

[0043] The generation AI can refer to the user's purchase history and predict and notify the user of the stock of materials purchased in the past. The generation AI can, for example, refer to the user's purchase history and predict the stock of materials purchased in the past. For example, the generation AI makes an inventory prediction based on past purchase data and notifies the user of the results. The generation AI also analyzes the user's purchase history and prioritizes checking the stock of materials purchased frequently. This allows it to predict and notify the user of the stock of materials purchased in the past.

[0044] The generation AI can link with online shopping sites based on the suggested recipes and automatically order missing ingredients. The generation AI can, for example, link with online shopping sites and automatically add the required ingredients to the cart based on the suggested recipes. The generation AI can also automatically confirm the order based on the user's settings. This allows missing ingredients to be ordered automatically.

[0045] The generating AI can refer to inventory information of supermarkets near the user and guide the user to purchase the missing ingredients at the nearest store. For example, the generating AI can refer to inventory information of supermarkets near the user and guide the user to stores where the missing ingredients can be purchased. For example, the generating AI can obtain supermarket inventory data in real time and notify the user. The generating AI can also identify the nearest store based on the user's location information and guide the user to purchase the missing ingredients at that store. This allows the user to be guided to purchase the missing ingredients at the nearest store.

[0046] The generation AI can analyze ratings and reviews on external recipe sites and prioritize highly reliable recipes. The generation AI can, for example, analyze ratings and reviews on external recipe sites and prioritize highly reliable recipes. For example, the generation AI evaluates the reliability of a recipe based on the rating score and number of reviews. The generation AI can also analyze expert ratings and user reviews to identify highly reliable recipes. This allows the generation AI to prioritize highly reliable recipes.

[0047] The generative AI can learn from a user's past recipe browsing history and suggest recipe sites and photos that match their preferences. The generative AI can, for example, learn from a user's past recipe browsing history and suggest recipe sites and photos that match their preferences. For example, the generative AI can suggest the optimal recipe site based on past browsing data. The generative AI can also suggest visually appealing photos based on the user's preferences. This makes it possible to suggest recipe sites and photos that match the user's preferences.

[0048] The generative AI can suggest video content from external recipe sites and provide recipes that are visually easy to understand. For example, the generative AI can analyze video content from external recipe sites and suggest recipes that are visually easy to understand. For example, the generative AI can evaluate the reliability of a recipe based on the number of views and ratings of the video. The generative AI can also suggest videos of cooking steps and videos of the finished dish. This makes it possible to provide recipes that are visually easy to understand.

[0049] The generation AI can link with the user's social media account and suggest recipes shared by friends and followers. The generation AI can link with the user's social media account and suggest recipes shared by friends and followers. For example, the generation AI can analyze social media posting data and identify shared recipes. The generation AI can also prioritize suggesting recipes shared by friends and followers based on the user's social media account. This allows the generation AI to suggest recipes shared by friends and followers.

[0050] The generative AI can analyze user feedback and automatically learn how to improve the recipe and reflect this in its next suggestions. For example, after a user actually cooks a dish, the generative AI inputs feedback in natural language about the amount and taste. The generative AI analyzes this feedback and automatically learns how to improve the recipe. For example, if a user inputs feedback such as "I wish it was a little saltier," the generative AI will suggest a recipe with adjusted saltiness the next time it makes a suggestion. This allows the AI ​​to learn how to improve the recipe based on user feedback and reflect this in its next suggestions.

[0051] The generative AI can suggest different variations of the same recipe based on user feedback. For example, after a user actually cooks a dish, the generative AI inputs feedback in natural language about the amount and taste. The generative AI analyzes this feedback and suggests different variations of the same recipe. For example, if a user inputs feedback such as "I wish it was a little saltier," the generative AI will suggest a variation with adjusted saltiness the next time it suggests a recipe. This makes it possible to suggest different variations of the same recipe based on user feedback.

[0052] Based on user feedback, the generation AI can suggest recipe improvements that can be shared with other users. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste of the dish. The generation AI analyzes this feedback and suggests recipe improvements that can be shared with other users. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest an improved recipe that adjusts the saltiness the next time it makes a suggestion. This allows the generation AI to suggest recipe improvements that can be shared with other users based on user feedback.

[0053] The generation AI can make suggestions to adjust the difficulty and cooking time of a recipe based on user feedback. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste. The generation AI analyzes this feedback and makes suggestions to adjust the difficulty and cooking time of the recipe. For example, if a user inputs feedback such as "the cooking time was too long," the generation AI will suggest a recipe with a shorter cooking time the next time it makes a suggestion. This makes it possible to make suggestions to adjust the difficulty and cooking time of a recipe based on user feedback.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The recipe suggestion system can further include a voice recognition unit. The voice recognition unit analyzes the cooking conditions input verbally by the user and sends them to the generation AI. For example, if the user verbally instructs the system to make an "easy pasta dish," the voice recognition unit converts the instruction into text data and sends it to the generation AI. This allows the user to input cooking conditions without using their hands. The voice recognition unit also improves convenience by allowing the user to give voice instructions even if their hands are dirty while cooking.

[0056] The recipe suggestion system may further include a nutritional analysis unit. The nutritional analysis unit analyzes the nutritional value of the suggested recipe and provides it to the user. For example, it analyzes the amount of calories, protein, fat, and carbohydrates in the suggested recipe and notifies the user. The nutritional analysis unit can also suggest nutritionally balanced recipes based on the user's health condition and diet goals. This allows the user to enjoy cooking while managing their health.

[0057] The recipe suggestion system can further include an ingredient shelf life management unit. The ingredient shelf life management unit monitors the expiration dates of ingredients in the refrigerator and suggests recipes that prioritize ingredients that are approaching their expiration date. For example, it can list ingredients that are close to their expiration date and have the generation AI suggest recipes that use those ingredients. The ingredient shelf life management unit can also notify the user of ingredients that have passed their expiration date. This reduces ingredient waste and allows for more efficient cooking.

[0058] The recipe suggestion system can also learn the user's eating history and suggest repeats of dishes they have made in the past. For example, it can save data on dishes the user has made in the past and suggest dishes that were particularly popular. Also, if a user frequently makes a particular dish, it can suggest variations of that dish. This allows users to repeatedly enjoy their favorite dishes.

[0059] The recipe suggestion system can also suggest recipes that utilize local specialties. For example, the generation AI can suggest recipes that use local specialties from the area where the user lives. It can also suggest recipes that utilize local seasonal specialties. This allows users to enjoy cooking while using local ingredients.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The camera monitors the status of each compartment of the refrigerator or freezer. For example, the camera periodically takes pictures to check what is stored on the shelves and in the drawers inside the refrigerator and sends the data to the cloud. The camera can also monitor the temperature and humidity inside the refrigerator. For example, it has built-in temperature and humidity sensors and sends this data to the cloud. Step 2: The generation AI inputs the conditions for the dish the user wants to make. For example, if the user inputs conditions such as "something that can be made easily" or "something that can be made with what is already on hand," the generation AI searches for and suggests recipes that meet those conditions. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate a recipe based on the user's conditions. For example, the generation AI analyzes the conditions entered by the user and suggests the optimal recipe. Step 3: The notification unit notifies the user of the recipe and necessary ingredients suggested by the generation AI. For example, the notification unit sends a notification to the user through a smartphone app. The notification unit can also send notifications via email or SMS. For example, the notification unit sends recipe and ingredient information based on the notification method set by the user.

[0062] (Example 2) The recipe suggestion system according to an embodiment of the present invention is a system that monitors the status of each compartment of a refrigerator or freezer, and suggests specific recipes and necessary ingredients when a user inputs the conditions for the dish they want to make. This allows the recipe suggestion system to suggest recipes for dishes that are easy to make or that can be made with ingredients already on hand, while keeping track of the contents of the refrigerator or freezer.

[0063] A recipe suggestion system according to an embodiment includes a camera, a generation AI, and a notification unit. The camera monitors the status of each compartment of a refrigerator or freezer. For example, the camera periodically takes images to check what is stored on the shelves and in the drawers of the refrigerator and sends the captured images to the cloud. The camera can also monitor the temperature and humidity inside the refrigerator. For example, the camera may have a built-in temperature sensor and humidity sensor, which sends the captured images to the cloud. The generation AI receives input from a user about the recipe they want to make. For example, when the user inputs criteria such as "something that's easy to make" or "something that can be made with what's already on hand," the generation AI searches for and suggests recipes that meet those criteria. The generation AI generates recipes based on the user's criteria using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the criteria entered by the user and suggests optimal recipes. The notification unit notifies the user of the recipes suggested by the generation AI and the necessary ingredients. For example, the notification unit may send a notification to the user via a smartphone app. The notification unit may also send notifications via email or SMS. For example, information about recipes and ingredients is sent based on the notification method set by the user. As a result, the recipe suggestion system according to the embodiment allows the user to get suggestions for recipes that are easy to make or that can be made with ingredients that are already on hand while keeping track of the contents of the refrigerator or freezer.

[0064] The camera can periodically take images to check what is stored on the shelves and in the drawers of the refrigerator and send the data to the cloud. For example, the camera can scan handwritten answers and save them as image data. It can then use OCR technology to convert the image data into text data. The camera can also take photos of handwritten answers using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The camera can also write handwritten answers with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. This allows the status inside the refrigerator to be monitored regularly.

[0065] The generation AI can search for and suggest recipes such as easy-to-make pasta dishes based on conditions entered by the user. For example, when a user enters a condition such as "easy-to-make pasta dishes," the generation AI searches for and suggests recipes that meet those conditions. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a recipe based on the user's conditions. The generation AI can also use a multimodal generation AI to generate a recipe based on the user's conditions. For example, the generation AI analyzes the conditions entered by the user and suggests the optimal recipe. This allows it to suggest recipes that meet the user's conditions.

[0066] The generating AI can list the necessary ingredients based on the proposed recipe and analyze the camera image data to confirm whether the necessary ingredients are available. The generating AI, for example, lists the necessary ingredients based on the proposed recipe. For example, the generating AI lists the ingredients needed for the recipe and provides that list to the user. The generating AI also analyzes the camera image data to confirm whether the necessary ingredients are available. For example, the generating AI uses image recognition technology to analyze the type and amount of food in the refrigerator and confirm whether the necessary ingredients are available. This makes it possible to confirm whether the necessary ingredients are available.

[0067] The generation AI can search for reliable external recipe sites and photos of finished dishes and attach them to the suggested recipes. The generation AI, for example, searches for reliable external recipe sites and attaches them to the suggested recipes. For example, the generation AI attaches a link to an external recipe site to the suggested recipe. The generation AI also searches for photos of finished dishes and attaches them to the suggested recipes. For example, the generation AI attaches photos to make it easier to visually imagine the finished dish. This makes it easier to visually imagine the finished dish.

[0068] The generation AI can analyze user feedback and reflect it in the next recipe suggestions. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language regarding the amount and taste. The generation AI analyzes this feedback and reflects it in the next recipe suggestions. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest a recipe with adjusted saltiness the next time. This makes it possible to suggest recipes that reflect user feedback.

[0069] The generative AI can suggest different variations of the same recipe based on user feedback. For example, after a user actually cooks a dish, the generative AI inputs feedback in natural language about the amount and taste. The generative AI analyzes this feedback and suggests different variations of the same recipe. For example, if a user inputs feedback such as "I wish it was a little saltier," the generative AI will suggest a variation with adjusted saltiness the next time it suggests a recipe. This makes it possible to suggest recipe variations that suit the user's preferences.

[0070] The generation AI can make suggestions to adjust the difficulty and cooking time of a recipe based on user feedback. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste. The generation AI analyzes this feedback and makes suggestions to adjust the difficulty and cooking time of the recipe. For example, if a user inputs feedback such as "the cooking time was too long," the generation AI will suggest a recipe with a shorter cooking time the next time it makes a suggestion. This makes it possible to adjust the difficulty and cooking time of a recipe based on user feedback.

[0071] Based on user feedback, the generation AI can suggest recipe improvements that can be shared with other users. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste of the dish. The generation AI analyzes this feedback and suggests recipe improvements that can be shared with other users. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest an improved recipe that adjusts the saltiness the next time it makes a suggestion. This allows the generation AI to suggest recipe improvements that can be shared with other users.

[0072] The generation AI uses the emotion estimation function to analyze the emotions expressed when the user provides feedback, and can improve the recipe to elicit positive emotions. For example, after the user actually cooks a dish, the generation AI inputs feedback in natural language regarding the amount and taste of the dish. The generation AI analyzes this feedback and uses the emotion estimation function to analyze the user's emotions. For example, if the user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest a recipe with adjusted saltiness the next time it suggests a recipe. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotions. This allows the generation AI to analyze the emotions expressed when the user provides feedback, and improve the recipe to elicit positive emotions.

[0073] The generation AI uses its emotion estimation function to analyze the user's emotions when providing feedback and can suggest recipe improvements that elicit positive emotions to other users. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language regarding the amount and taste of the dish. The generation AI analyzes this feedback and uses the emotion estimation function to analyze the user's emotions. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest an improved recipe with adjusted saltiness the next time it makes a suggestion. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotions. This allows the generation AI to analyze the user's emotions when providing feedback and suggest recipe improvements that elicit positive emotions to other users.

[0074] The generation AI can learn the user's past cooking history and suggest recipes that take into account preferences and allergy information. The generation AI can, for example, learn the user's past cooking history and suggest recipes that take into account preferences and allergy information. For example, the generation AI can suggest optimal recipes based on the user's past recipe data. The generation AI can also consider the user's allergy information and suggest recipes that exclude ingredients that may cause allergies. This makes it possible to suggest recipes that take into account the user's preferences and allergy information.

[0075] Generative AI can suggest recipes according to the season and weather, and provide dishes that allow you to enjoy the feeling of the season. Generative AI can, for example, analyze seasonal and weather data and suggest recipes accordingly. For example, generative AI can suggest recipes that use seasonal ingredients. Generative AI can also suggest recipes that are according to the weather. For example, it can suggest cold dishes in the hot summer and warm dishes in the cold winter. This makes it possible to provide dishes that allow you to enjoy the feeling of the season.

[0076] The generation AI can use its emotion estimation function to suggest recipes that match the user's mood, aiming to reduce stress and provide a relaxing effect. For example, the generation AI can use the emotion estimation function to analyze the user's mood in real time and suggest recipes based on the results. For example, it can suggest recipes that are suitable for when the user wants to relax. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotions. This makes it possible to suggest recipes that match the user's mood, aiming to reduce stress and provide a relaxing effect.

[0077] The generation AI can take into account the user's mealtimes and schedule and suggest recipes at the optimal time. For example, the generation AI can analyze the user's mealtimes and schedule and suggest recipes accordingly. For example, it can suggest easy breakfast recipes for busy mornings. The generation AI can also suggest recipes at the optimal time based on the user's schedule. For example, it can suggest dinner recipes to coincide with the time the user gets home from work. This allows it to suggest recipes at the optimal time, taking into account the user's mealtimes and schedule.

[0078] Generative AI can suggest recipes based on the user's health condition and diet goals, supporting health management. For example, generative AI can analyze the user's health condition and diet goals and suggest recipes accordingly. For example, it can suggest recipes that take calorie restrictions and nutritional balance into consideration. Generative AI can also suggest recipes that are high in specific nutrients based on the user's health condition. For example, it can suggest recipes that use ingredients rich in vitamin C. This allows it to suggest recipes based on the user's health condition and diet goals, supporting health management.

[0079] Generative AI can use emotion estimation to suggest recipes based on the user's emotions, providing a positive dining experience. For example, generative AI can use emotion estimation to analyze the user's emotions in real time and suggest recipes based on the results. For example, it can suggest recipes suitable for when the user wants to relax. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotions. This allows it to suggest recipes based on the user's emotions, providing a positive dining experience.

[0080] The generative AI can learn how often foods are used in the refrigerator and prioritize checking the inventory of frequently used ingredients. For example, the generative AI can learn how often foods are used in the refrigerator and prioritize checking the inventory of frequently used ingredients. For example, the generative AI can identify frequently used ingredients based on past usage data and prioritize checking their inventory. The generative AI can also analyze how often foods are used in the refrigerator and list the ingredients that are used most frequently. This allows the generative AI to prioritize checking the inventory of frequently used ingredients.

[0081] The generation AI can refer to the user's purchase history and predict and notify the user of the stock of materials purchased in the past. The generation AI can, for example, refer to the user's purchase history and predict the stock of materials purchased in the past. For example, the generation AI makes an inventory prediction based on past purchase data and notifies the user of the results. The generation AI also analyzes the user's purchase history and prioritizes checking the stock of materials purchased frequently. This allows it to predict and notify the user of the stock of materials purchased in the past.

[0082] The generation AI can use the emotion estimation function to analyze the emotions of the user when they receive a notification of a missing material and optimize the notification method. For example, the generation AI uses the emotion estimation function to analyze the emotions of the user when they receive a notification of a missing material in real time. For example, it can use facial recognition technology to estimate the user's emotions and store them in a database. The generation AI can also optimize the notification method based on the user's emotions. For example, it can adjust the timing and format of the notification to notify the user in the most optimal way. This makes it possible to optimize the notification method based on the user's emotions.

[0083] The generation AI can link with online shopping sites based on the suggested recipes and automatically order missing ingredients. The generation AI can, for example, link with online shopping sites and automatically add the required ingredients to the cart based on the suggested recipes. The generation AI can also automatically confirm the order based on the user's settings. This allows missing ingredients to be ordered automatically.

[0084] The generating AI can refer to inventory information of supermarkets near the user and guide the user to purchase the missing ingredients at the nearest store. For example, the generating AI can refer to inventory information of supermarkets near the user and guide the user to stores where the missing ingredients can be purchased. For example, the generating AI can obtain supermarket inventory data in real time and notify the user. The generating AI can also identify the nearest store based on the user's location information and guide the user to purchase the missing ingredients at that store. This allows the user to be guided to purchase the missing ingredients at the nearest store.

[0085] The generation AI can use its emotion estimation function to analyze the emotions of users when they receive a notification of a shortage of materials and suggest notification content that elicits positive emotions. For example, the generation AI uses its emotion estimation function to analyze the emotions of users when they receive a notification of a shortage of materials in real time. For example, it can use facial recognition technology to estimate the user's emotions and store them in a database. The generation AI also suggests notification content that elicits positive emotions based on the user's emotions. For example, it can include encouraging messages or sharing of success stories in the notification content. This makes it possible to suggest notification content that elicits positive emotions.

[0086] The generation AI can analyze ratings and reviews on external recipe sites and prioritize highly reliable recipes. The generation AI can, for example, analyze ratings and reviews on external recipe sites and prioritize highly reliable recipes. For example, the generation AI evaluates the reliability of a recipe based on the rating score and number of reviews. The generation AI can also analyze expert ratings and user reviews to identify highly reliable recipes. This allows the generation AI to prioritize highly reliable recipes.

[0087] The generative AI can learn from a user's past recipe browsing history and suggest recipe sites and photos that match their preferences. The generative AI can, for example, learn from a user's past recipe browsing history and suggest recipe sites and photos that match their preferences. For example, the generative AI can suggest the optimal recipe site based on past browsing data. The generative AI can also suggest visually appealing photos based on the user's preferences. This makes it possible to suggest recipe sites and photos that match the user's preferences.

[0088] Generative AI can use its emotion estimation function to analyze the emotions felt when a user views a recipe or photo, and suggest recipes that elicit positive emotions. For example, generative AI can use its emotion estimation function to analyze the emotions felt when a user views a recipe or photo in real time. For example, it can use facial recognition technology to estimate the user's emotions and store them in a database. Generative AI can also suggest recipes that elicit positive emotions based on the user's emotions. For example, it can suggest dishes that look beautiful or are easy to make. This makes it possible to suggest recipes that elicit positive emotions.

[0089] The generative AI can suggest video content from external recipe sites and provide recipes that are visually easy to understand. For example, the generative AI can analyze video content from external recipe sites and suggest recipes that are visually easy to understand. For example, the generative AI can evaluate the reliability of a recipe based on the number of views and ratings of the video. The generative AI can also suggest videos of cooking steps and videos of the finished dish. This makes it possible to provide recipes that are visually easy to understand.

[0090] The generation AI can link with the user's social media account and suggest recipes shared by friends and followers. The generation AI can link with the user's social media account and suggest recipes shared by friends and followers. For example, the generation AI can analyze social media posting data and identify shared recipes. The generation AI can also prioritize suggesting recipes shared by friends and followers based on the user's social media account. This allows the generation AI to suggest recipes shared by friends and followers.

[0091] The generative AI can analyze user feedback and automatically learn how to improve the recipe and reflect this in its next suggestions. For example, after a user actually cooks a dish, the generative AI inputs feedback in natural language about the amount and taste. The generative AI analyzes this feedback and automatically learns how to improve the recipe. For example, if a user inputs feedback such as "I wish it was a little saltier," the generative AI will suggest a recipe with adjusted saltiness the next time it makes a suggestion. This allows the AI ​​to learn how to improve the recipe based on user feedback and reflect this in its next suggestions.

[0092] The generative AI can suggest different variations of the same recipe based on user feedback. For example, after a user actually cooks a dish, the generative AI inputs feedback in natural language about the amount and taste. The generative AI analyzes this feedback and suggests different variations of the same recipe. For example, if a user inputs feedback such as "I wish it was a little saltier," the generative AI will suggest a variation with adjusted saltiness the next time it suggests a recipe. This makes it possible to suggest different variations of the same recipe based on user feedback.

[0093] The generation AI uses the emotion estimation function to analyze the emotions expressed when the user provides feedback, and can improve the recipe to elicit positive emotions. For example, after the user actually cooks a dish, the generation AI inputs feedback in natural language regarding the amount and taste of the dish. The generation AI analyzes this feedback and uses the emotion estimation function to analyze the user's emotions. For example, if the user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest a recipe with adjusted saltiness the next time it suggests a recipe. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotions. This allows the generation AI to analyze the emotions expressed when the user provides feedback, and improve the recipe to elicit positive emotions.

[0094] Based on user feedback, the generation AI can suggest recipe improvements that can be shared with other users. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste of the dish. The generation AI analyzes this feedback and suggests recipe improvements that can be shared with other users. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest an improved recipe that adjusts the saltiness the next time it makes a suggestion. This allows the generation AI to suggest recipe improvements that can be shared with other users based on user feedback.

[0095] The generation AI can make suggestions to adjust the difficulty and cooking time of a recipe based on user feedback. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language about the amount and taste. The generation AI analyzes this feedback and makes suggestions to adjust the difficulty and cooking time of the recipe. For example, if a user inputs feedback such as "the cooking time was too long," the generation AI will suggest a recipe with a shorter cooking time the next time it makes a suggestion. This makes it possible to make suggestions to adjust the difficulty and cooking time of a recipe based on user feedback.

[0096] The generation AI uses its emotion estimation function to analyze the user's emotions when providing feedback and can suggest recipe improvements that elicit positive emotions to other users. For example, after a user actually cooks a dish, the generation AI inputs feedback in natural language regarding the amount and taste of the dish. The generation AI analyzes this feedback and uses the emotion estimation function to analyze the user's emotions. For example, if a user inputs feedback such as "I wish it was a little saltier," the generation AI will suggest an improved recipe with adjusted saltiness the next time it makes a suggestion. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotions. This allows the generation AI to analyze the user's emotions when providing feedback and suggest recipe improvements that elicit positive emotions to other users.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The recipe suggestion system can further include a voice recognition unit. The voice recognition unit analyzes the cooking conditions input verbally by the user and sends them to the generation AI. For example, if the user verbally instructs the system to make an "easy pasta dish," the voice recognition unit converts the instruction into text data and sends it to the generation AI. This allows the user to input cooking conditions without using their hands. The voice recognition unit also improves convenience by allowing the user to give voice instructions even if their hands are dirty while cooking.

[0099] The recipe suggestion system may further include a nutritional analysis unit. The nutritional analysis unit analyzes the nutritional value of the suggested recipe and provides it to the user. For example, it analyzes the amount of calories, protein, fat, and carbohydrates in the suggested recipe and notifies the user. The nutritional analysis unit can also suggest nutritionally balanced recipes based on the user's health condition and diet goals. This allows the user to enjoy cooking while managing their health.

[0100] The recipe suggestion system can further include an ingredient shelf life management unit. The ingredient shelf life management unit monitors the expiration dates of ingredients in the refrigerator and suggests recipes that prioritize ingredients that are approaching their expiration date. For example, it can list ingredients that are close to their expiration date and have the generation AI suggest recipes that use those ingredients. The ingredient shelf life management unit can also notify the user of ingredients that have passed their expiration date. This reduces ingredient waste and allows for more efficient cooking.

[0101] The recipe suggestion system can also learn the user's eating history and suggest repeats of dishes they have made in the past. For example, it can save data on dishes the user has made in the past and suggest dishes that were particularly popular. Also, if a user frequently makes a particular dish, it can suggest variations of that dish. This allows users to repeatedly enjoy their favorite dishes.

[0102] The recipe suggestion system can also suggest recipes that utilize local specialties. For example, the generation AI can suggest recipes that use local specialties from the area where the user lives. It can also suggest recipes that utilize local seasonal specialties. This allows users to enjoy cooking while using local ingredients.

[0103] The generative AI can estimate the user's emotions and, based on the estimated emotions, suggest recipes suitable for when the user wants to relax. For example, if the user is feeling stressed, it can suggest dishes that use herbs with a relaxing effect or dishes that are easy to make. Also, if the user wants to feel energized, it can suggest dishes that will replenish energy. In this way, it is possible to suggest recipes that correspond to the user's emotions and provide a positive dining experience.

[0104] Generative AI can estimate a user's emotions and, based on the estimated emotions, suggest recipes that the user will enjoy. For example, when a user is in a happy mood, it can suggest dishes that look visually appealing or that can be enjoyed with family and friends. When a user is feeling down, it can also suggest sweet desserts to lift their spirits or dishes that are easy to make. This makes it possible to suggest recipes that correspond to the user's emotions and increase the enjoyment of eating.

[0105] The generative AI can estimate the user's emotions and, based on the estimated emotions, suggest recipes that the user can enjoy eating healthy. For example, if the user is concerned about their health, it can suggest low-calorie, nutritionally balanced dishes. Also, if the user is on a diet, it can suggest recipes that take calorie restrictions into consideration. In this way, it can suggest healthy recipes that correspond to the user's emotions and support health management.

[0106] Generative AI can estimate a user's emotions and, based on the estimated emotions, suggest recipes that the user can enjoy on special occasions. For example, if a user is celebrating a special occasion such as a birthday or anniversary, it can suggest luxurious dishes and desserts that are suitable for that occasion. It can also suggest recipes for dishes that the user needs to prepare before the special occasion. This makes it possible to suggest recipes for special occasions that correspond to the user's emotions, providing a memorable dining experience.

[0107] Generative AI can estimate a user's emotions and, based on those inferred emotions, suggest recipes that will motivate the user to try new dishes. For example, if a user is tired of the same old meals, it can suggest recipes that incorporate new ingredients and cooking methods. Also, if a user has lost interest in cooking, it can suggest easy and fun recipes. In this way, it can suggest new recipes that correspond to the user's emotions and rekindle their interest in cooking.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The camera monitors the status of each compartment of the refrigerator or freezer. For example, the camera periodically takes pictures to check what is stored on the shelves and in the drawers inside the refrigerator and sends the data to the cloud. The camera can also monitor the temperature and humidity inside the refrigerator. For example, it has built-in temperature and humidity sensors and sends this data to the cloud. Step 2: The generation AI inputs the conditions for the dish the user wants to make. For example, if the user inputs conditions such as "something that can be made easily" or "something that can be made with what is already on hand," the generation AI searches for and suggests recipes that meet those conditions. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate a recipe based on the user's conditions. For example, the generation AI analyzes the conditions entered by the user and suggests the optimal recipe. Step 3: The notification unit notifies the user of the recipe and necessary ingredients suggested by the generation AI. For example, the notification unit sends a notification to the user through a smartphone app. The notification unit can also send notifications via email or SMS. For example, the notification unit sends recipe and ingredient information based on the notification method set by the user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Cameras that monitor the conditions inside each refrigerator and freezer, A generation AI that allows users to input the conditions for the dish they want to make, and A notification unit that notifies the user of the recipes and necessary ingredients suggested by the generation AI. A system characterized by:

2. The camera is To check what's in the shelves and drawers of your refrigerator, take regular pictures and send the data to the cloud.

2. The system of claim 1.

3. The generated AI is It learns the user's cooking history and suggests recipes that take into account their preferences and allergies.

2. The system of claim 1.

4. The generated AI is The system learns the frequency of use of food in the refrigerator and prioritizes checking the inventory of frequently used ingredients.

2. The system of claim 1.

5. The generated AI is Analyzes ratings and reviews on external recipe sites and prioritizes highly reliable recipes.

2. The system of claim 1.

6. The generated AI is Based on user feedback, we improve the recipe to elicit positive emotions.

2. The system of claim 1.

7. The generated AI is The app suggests recipes that match the user's mood, aiming to reduce stress and promote relaxation.

2. The system of claim 1.

8. The generated AI is Analyzing the emotions of the user when receiving the notification of the shortage of materials and optimizing the notification method 2. The system of claim 1.

Images